CN119617731A

CN119617731A - Control method of compressor system, compressor system and air conditioner

Info

Publication number: CN119617731A
Application number: CN202411989572.7A
Authority: CN
Inventors: 张婉婷; 马壮壮; 李百宇
Original assignee: Gree Electric Appliances Inc of Zhuhai
Current assignee: Gree Electric Appliances Inc of Zhuhai
Priority date: 2024-12-31
Filing date: 2024-12-31
Publication date: 2025-03-14

Abstract

The invention provides a control method of a compressor system, the compressor system and an air conditioner, and the control method of the compressor system detects the change of power demand, the target compressor is intelligently determined and started in combination with the current power demand data and the power range data for each compressor. And then, after the target compressor runs for a period of time, acquiring real-time running data of the target compressor, and adjusting the target compressor according to a preset control strategy to realize load balance among the compressors. The method dynamically optimizes the running state of the compressor by comprehensively utilizing real-time and historical running data, avoids excessive use and frequent start and stop of certain compressors, and remarkably reduces mechanical abrasion and energy consumption, thereby effectively prolonging the service life of the whole compressor system.

Description

Control method of compressor system, compressor system and air conditioner

Technical Field

The invention relates to the technical field of compressors, in particular to a control method of a compressor system, the compressor system and an air conditioner.

Background

A multi-compressor system is a working system composed of two or more compressors, which can provide a desired compression power or gas flow rate through the cooperative operation of the compressors. The multi-pressure compressor system can be applied to the fields of refrigeration, air conditioning, industrial gas supply and the like.

In the multi-compressor system, there is a technical problem that the compressor control is not adequate, which in turn leads to a reduction in the service life of the compressor system.

Disclosure of Invention

The invention aims to overcome the technical defects and provide a control method of a compressor system, the compressor system and an air conditioner, so as to solve the technical problems that in the related art, in a multi-compressor system, the compressor is not controlled properly, and the service life of the compressor system is reduced.

In order to achieve the technical purpose, the invention adopts the following technical scheme:

in a first aspect, the present invention provides a control method of a compressor system, the compressor system including at least two compressors, the method comprising:

Determining current power demand data of the compressor system and power range data of each compressor in the compressor system after detecting that the power demand changes;

Determining a target compressor based on the power demand data and the power range data, wherein the target compressor is a compressor which needs to be in a starting state;

After the target compressor runs for a plurality of times, at least acquiring real-time running data of the target compressor;

And performing adjustment on at least the target compressor based on at least the real-time operation data of the target compressor and a preset control strategy to balance the load of each compressor in the compressor system under the condition of meeting the current power demand.

In a second aspect, the present invention provides a compressor system comprising at least two compressors and a controller for performing the above method.

In a third aspect, the present invention provides an air conditioner comprising a controller for performing the above method or the above compressor system.

The beneficial effects are that:

The control method of the compressor system provided by the invention intelligently determines and starts the target compressor by combining the current power demand data and the power range data of each compressor after detecting the power demand change. And then, after the target compressor runs for a period of time, acquiring real-time running data of the target compressor, and adjusting the target compressor according to a preset control strategy to realize load balance among the compressors. The method dynamically optimizes the running state of the compressor by comprehensively utilizing real-time and historical running data, avoids excessive use and frequent start and stop of certain compressors, and remarkably reduces mechanical abrasion and energy consumption, thereby effectively prolonging the service life of the whole compressor system. Meanwhile, the reasonable distribution of the load improves the operation efficiency and stability of the compressor system, and solves the technical problem that the service life of the compressor is reduced due to improper control in the existing multi-compressor system. The method not only improves the overall reliability of the compressor system, but also reduces the maintenance cost, and ensures the efficient and durable operation of the compressor system under different operation environments.

Drawings

FIG. 1 is a flow chart of a method for controlling a compressor system according to an embodiment of the present invention;

FIG. 2 is a flow chart of a method for controlling a compressor system according to an embodiment of the present invention;

FIG. 3 is a flow chart of a method for controlling a compressor system according to an embodiment of the present invention;

FIG. 4 is a block diagram of a compressor system provided by an embodiment of the present invention;

FIG. 5 is a schematic diagram of the operating power ranges of two compressors provided by an embodiment of the present invention;

Fig. 6 is a block diagram of an electronic device employed by an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.

In the related art, the multi-compressor system is widely used in industrial refrigeration, commercial air conditioning, and other scenes where efficient cooling is required. Such systems typically consist of a plurality of compressors that work cooperatively, in parallel or in series, equipped with a controller and various sensors for monitoring the operating state and environmental conditions of the system. And the controller executes start-stop and load distribution of the compressor according to the information fed back by the sensor so as to meet different power requirements.

However, the existing multi-compressor control methods often adopt a fixed start-stop strategy or a simple load distribution algorithm, and the methods have a plurality of defects in practical application. First, the fixed start-stop strategy cannot flexibly cope with dynamically changing power demands, resulting in a portion of compressors being in high load operation for long periods of time, while other compressors are in idle states for long periods of time. The unbalanced load distribution reduces the overall operation efficiency of the system, accelerates the abrasion of the compressor caused by frequent start and stop, and shortens the service life of the compressor. In addition, the simple load distribution algorithm lacks comprehensive evaluation on the health state of the compressors, and cannot dynamically adjust the working modes of the compressors according to the running conditions of the compressors, so that the fault risk and the maintenance cost of the system are further increased.

Therefore, there is a need for an intelligent multi-compressor control method that can comprehensively evaluate the operational health and current power requirements of each compressor based on real-time and historical operational data, optimizing the selection of target compressors and load distribution strategies. By the technical means, reasonable distribution of the compressor load can be realized, and excessive use of certain compressors is avoided, so that the service life of the whole compressor system is prolonged, and the running efficiency and reliability of the system are improved.

The embodiment provides a control method of a compressor system, wherein the compressor system comprises at least two compressors, an execution subject of the method can be a controller of the compressor system, and the method can comprise the following steps:

and step S12, after detecting that the power demand changes, determining current power demand data of the compressor system and power range data of each compressor in the compressor system.

In this embodiment, the detection of the change in the power demand may be that the controller of the compressor system receives an instruction sent by the user side, for example, a start-up instruction or a temperature adjustment instruction, where the temperature adjustment instruction may be to raise the temperature or lower the temperature, and so on.

In this embodiment, the detection of the change in the power demand may also be that the controller detects a change in an environmental parameter, so that the controller is triggered to autonomously determine the current power demand data of the compressor system. For example, the controller monitors the ambient temperature in real time through a temperature sensor built into the compressor system. When the ambient temperature exceeds or falls below a preset threshold, a change in power demand is detected, thus triggering a controller to determine current power demand data for the compressor system. The environmental parameter may also be humidity changes, changes in air pressure, etc.

In this embodiment, the detection of the change in the power demand may also be based on time and operation mode by the controller. For example, the compressor system may automatically adjust the operating state of the compressor according to a predetermined schedule. For example, the number of compressors operated is reduced at night when the load is low, thereby saving energy. For example, the controller automatically detects and adjusts the power demand according to different modes of operation (energy saving mode, efficient mode).

In this embodiment, the step of determining current power demand data of the compressor system may be:

firstly, a controller responds to a temperature adjustment instruction or a starting instruction sent by a user side to detect the current environment temperature, wherein the temperature adjustment instruction or the starting instruction carries a target temperature.

The controller then calculates the power demand data based on the ambient temperature and the target temperature.

In this embodiment, the step of determining the current power demand data of the compressor system may also be:

First, when it is detected that the ambient temperature exceeds or falls below a preset threshold, the controller determines that the power demand has changed.

The controller then recalculates the current power demand data based on the magnitude of the change in ambient temperature.

In this embodiment, the step of determining the current power demand data of the compressor system may be that the controller automatically adjusts the operation state of the compressor according to a preset schedule. For example, at night when the load is low, the number of compressors operated is reduced to save energy. The controller calculates corresponding power demand data based on the expected loads for the different time periods.

It will be appreciated that in this embodiment, the detection of a change in power demand is a trigger condition that, once satisfied, triggers the controller to determine the current power demand data of the compressor system, which is a quantization process.

Specifically, detecting that the power demand changes is a starting point of the entire control method, and is also a precondition for execution of the subsequent steps. This detection process may be accomplished by a variety of means. For example, the controller may be triggered by a user command, and when the controller receives a command sent by the user side, such as a power-on command or a temperature adjustment command (including raising the temperature or lowering the temperature), the power requirement is considered to be changed. For example, the controller monitors environmental parameters in real time through sensors (temperature sensor, humidity sensor, air pressure sensor) built in the compressor system. And when the parameters such as the ambient temperature, the humidity or the air pressure exceed or are lower than a preset threshold value, determining that the power demand is changed. Upon detecting that a trigger condition is met that the power demand has changed, the controller will initiate a process of determining current power demand data. This process is a quantization process. For example, the controller first responds to the instruction of the user side, detects the current ambient temperature, and calculates the required power demand data in combination with the target temperature set by the user. For example, under the condition of changing environmental parameters, the controller can comprehensively analyze the current data of environmental temperature, humidity, air pressure and the like, and quantify the current power demand based on a preset algorithm or formula.

In this embodiment, the step of determining the power range data of each compressor in the compressor system may be:

And the controller reads the upper power limit value and the lower power limit value of each compressor in the compressor system from a preset database.

It will be appreciated that the upper and lower power values are the power range data.

In this embodiment, the step of determining the power range data of each compressor in the compressor system may be that the controller continuously monitors the operation parameters (pressure, temperature, flow rate and energy consumption) of the compressors, and determines the power upper limit value and the power lower limit value according to the operation parameters.

In this embodiment, the power demand data may be expressed as cooling or heating power required to meet environmental conditions set by a user or a change in load of the compressor system.

In this embodiment, the power range data may be used to represent the operating capacity of each compressor, including its upper and lower power limits. The upper power limit is expressed as the maximum power output that each compressor can achieve, ensuring that the compressors are not damaged by overload. The lower power limit is expressed as the lowest power at which each compressor can maintain stable operation, avoiding frequent start-up and shut-down of the compressor under low load.

In this embodiment, the compressor system may include two compressors, or may include three or even more compressors. The compressors may be connected in parallel or in series.

In this embodiment, the compressor system may be a two-machine parallel compressor system.

In the present embodiment, the compressor system may be a multi-compressor parallel compressor system (a number of three compressors or more).

In the present embodiment, the compressor system may be a multistage tandem compressor system.

In this embodiment, the compressor system may be a parallel-serial hybrid compressor system.

In the present embodiment, the compressor system may be a partitioned compressor system or a multi-stage compressor system.

And step S14, determining a target compressor based on the power demand data and the power range data, wherein the target compressor is a compressor which needs to be in a starting state.

In this embodiment, the determined action may be expressed as selecting one compressor from the compressor system as a target compressor and starting the compressor.

It will be appreciated that if the selected compressor is already in the start-up state, the start-up control is not required, and the start-up state is maintained. Accordingly, when the selected compressor is in the off state, the controller issues a control command thereto to start it.

Specifically, in this embodiment, the step of determining the target compressor based on the power demand data and the power range data may be:

firstly, the controller generates a plurality of operation power intervals based on the power range data, wherein at least one compressor corresponds to one operation power interval.

In this embodiment, the operation power interval may be represented as an interval for dividing different power demands generated from power range data (upper power limit value and lower power limit value) of each compressor in the compressor system. These intervals reflect the ability of the compressor system to match and meet load demands under different power demand conditions. Each operating power interval may correspond to one or more compressors, depending on the overlapping power ranges of the compressors. The function of this interval division is to provide a clear load distribution basis for the controller to dynamically select the most appropriate compressor combination for operation at different power demands.

Therefore, it can be understood that after the operation power interval is divided, the controller can clearly determine which compressors should bear a certain power demand, so as to avoid the excessive concentration of the load on a certain compressor and balance the service life of the equipment. And secondly, the controller can be used for quickly matching the power requirement and the compressor combination based on the operation power interval division, so that complex calculation and logic judgment are reduced, and the response speed of the compressor system is improved.

The controller then determines a target operating power interval from the number of operating power intervals based on the power demand data.

Finally, the controller selects at least one compressor from the target operating power interval as the target compressor and starts the target compressor.

In one possible embodiment, the compressor system may include two compressors, wherein the power range data for one compressor M1 may be (a, c) and the power range data for the other compressor M2 may be (b, d), wherein a < b < c < d. Based on the two power range data, the following operating power intervals (a, b), (b, c), (c, d) can be determined. The operating power interval at (a, b) may correspond to the M1 compressor, the operating power interval at (b, c) may correspond to the M1 compressor and the M2 compressor, and the operating power interval at (c, d) may correspond to the M2 compressor.

In one possible embodiment, the compressor system may include three compressors, M1, M2, M3, with the power range data for each compressor as follows:

The power range data of the compressor M1 is (a, c) the power range is from a=5 kW to c=15 kW;

The power range data for compressor M2 is (b, d) power ranges from b=10 kW to d=25 kW;

the power range data for compressor M3 is (e, f) power ranges from e=20 kW to f=35 kW.

The controller first lists all the division points according to the power range data:

{a=5,b=10,c=15,d=25,e=20,f=35}。

the controller then orders the segmentation points from small to large and de-weights:

{5,10,15,20,25,35}。

next, the controller determines each continuous power interval as an operation power interval based on the dividing point:

1. intervals (5, 10);

2. Intervals (10, 15);

3. intervals (15, 20);

4. intervals (20, 25);

5. intervals (25, 35).

Finally, the controller combines the power range data of each compressor to determine the corresponding compressor of each operation power interval:

1. Interval (5, 10):

Only the power range (5, 15) of the compressor M1 covers this interval. Therefore, the section (5, 10) corresponds to the compressor M1.

2. Interval (10, 15):

The power range (5, 15) of the compressor M1 covers this interval, as does the power range (10, 25) of the compressor M2. Therefore, the sections (10, 15) correspond to the compressors M1 and M2.

3. Interval (15, 20):

The power range (10, 25) of the compressor M2 covers this interval, corresponding to the compressor M2.

4. Interval (20, 25):

the power range (10, 25) of the compressor M2 covers this interval and the power range (20,35) of the compressor M3 covers this interval. Corresponding to compressor M1 and compressor M2.

5. Interval (25, 35):

the power range (20,35) of the compressor M3 covers this interval. Corresponding to the compressor M3.

And S16, at least acquiring real-time operation data of the target compressor after the target compressor is operated for a plurality of times.

In this embodiment, the several times may be thirty minutes, one hour, two hours, or the like.

In this embodiment, after the target compressor is operated for several times, real-time operation data of the target compressor may be acquired.

In this embodiment, after the target compressor is operated for several times, real-time operation data and history operation data of the target compressor may be acquired.

In this embodiment, after the target compressor is operated for a plurality of times, real-time operation data and historical operation data of the target compressor may be acquired, and historical operation data of a non-target compressor may also be acquired.

It will be appreciated that the non-target compressor is indicative of a compressor that is not currently being started.

In this embodiment, the real-time operation data may include a current operation duration, a current operation temperature, or a current operation frequency.

Specifically, the current operation duration may be expressed as a duration that has been continuously operated since the start of the compressor, for judging its load condition and whether adjustment (switching or down-conversion) is required.

The current operating temperature may be indicative of a current operating temperature of the compressor. If the temperature exceeds the threshold, a down-conversion or switching operation may be performed.

The current operating frequency may represent a current operating load of the target compressor, and by the operating frequency, it may be determined whether it is in a high-efficiency operating state.

In this embodiment, the historical operating data may include a historical operating time period, a historical average operating temperature, a historical average operating frequency, or a record of occurrence of a fault.

Specifically, the historical operation duration can represent accumulated operation duration, can reflect the service life and the wear degree of the compressor, and can provide basis for health evaluation.

The historical average operating temperature, which may represent a long-term operating temperature average, may be used to evaluate the heat rejection of the compressor and whether it is in a high temperature state for a long period of time.

The historical average operating frequency may reflect an average load level of the compressor.

Fault occurrence records, which may record historical fault conditions of the compressor, may include fault type, frequency of occurrence, and the like.

More specifically, the real-time operating data may be used to assess the instant health status of the target compressor, while the historical data may be used to comprehensively assess whether the target compressor needs to adjust operating status (down-conversion, switching). Therefore, by combining real-time operation data and historical operation data of the target compressor and the non-target compressor, the controller can dynamically distribute loads among the compressors according to factors such as health, load balancing requirements, operation life and the like.

In this embodiment, the controller may send an instruction to a monitoring sensor preset in the compressor system to obtain the real-time operation data.

In particular, a time recording module or an operating state sensor may be provided in the compressor system in advance, which may record the continuous operation time of the compressor from the start-up to the present. A temperature sensor (thermocouple or thermistor) may be provided in the compressor system in advance, which can monitor the temperature of the critical components of the compressor (compressor shell, discharge port, suction port) in real time. A frequency detection module may be provided in the compressor system in advance, which may detect the motor operating frequency of the compressor to reflect the current operating load and regulation status. The frequency detection module can be a frequency sensing module built in the frequency converter, can be a Hall effect sensor, can be a current sensor, and can indirectly calculate the frequency by monitoring the running current of the motor.

The controller can trigger the sensor to collect real-time data by sending a request signal to the sensor. The sensor feeds back the collected operation data to the controller in the form of signals. The controller digitizes the acquired analog signal (e.g., via an AD converter). These data may be stored in the controller internal memory or uploaded to a cloud database for later analysis.

In this embodiment, the controller may read the historical operating data from a preset database.

In particular, the database may be a local database. In particular, the database may be stored in an internal memory of the controller or in a local storage device (e.g., SD card, embedded memory chip) directly connected to the controller.

The database may be a cloud database, in other words, the historical operating data may be stored on a remote server or a cloud platform, and communicate with the controller through a network interface.

In this embodiment, the database may be a relational database or a non-relational database.

In this embodiment, the controller may directly read the data in the memory through an embedded interface (such as SPI, I2C). For example, the controller may query historical operating data by compressor ID and timestamp index.

In this embodiment, the controller may also communicate with the cloud database through a network protocol (HTTP, MQTT, MODBUS TCP), send a query request, and receive returned historical operation data.

It can be appreciated that after the target compressor is operated for a period of time, the controller collects real-time operation data of the target compressor through a sensor built in the compressor system, and further obtains historical operation data of the target compressor and other historical operation data of the non-started (non-target compressor) according to specific requirements. The acquisition of the data can provide accurate running state information and health evaluation basis for the execution of the subsequent control strategy.

And step S18, at least adjusting the target compressor at least based on the real-time operation data of the target compressor and a preset control strategy so as to balance the load of each compressor in the compressor system under the condition of meeting the current power demand.

In this embodiment, the controller may perform adjustment on the target compressor based on the real-time operation data of the target compressor and a preset control strategy, so as to balance the load of each compressor in the compressor system in case of meeting the current power demand.

Specifically, the controller may determine whether the target compressor needs to be adjusted by monitoring the operation state (e.g., current temperature, frequency, duration, etc.) of the target compressor in real time, and comparing the operation state with a preset control strategy.

More specifically, the controller may reduce the operating frequency of a certain one of the target compressors, even if it is shut down, when the operating temperature of the target compressor exceeds a safety threshold. Accordingly, the controller may increase the operating frequency of the target compressor whose operating temperature does not exceed the safety threshold to balance the load of each compressor in the compressor system while meeting the current power demand.

More specifically, in this case, the preset control policy may be a rule base. The rule base may include a plurality of sets of predefined rules that set a safety threshold based on the operating parameters of the compressor. For example:

safety threshold of temperature-if the operating temperature is >80 ℃, then the frequency-down is triggered.

Safety threshold for frequency if the operating frequency is near 90% of maximum frequency, an alarm or limit is triggered.

Run time safety threshold, if the continuous run time is >2 hours, the run frequency is triggered to be reduced or a cooling measure is performed.

The rule base may be defined in terms of "condition-actions" and may be determined using a rule engine.

In this embodiment, the controller may also perform adjustment on the target compressor and the non-target compressor based on the real-time operation data of the target compressor and a preset control policy, so as to balance the load of each compressor in the compressor system when the current power requirement is satisfied.

Specifically, the controller may perform adjustments to both the target compressor and the non-target compressor by monitoring the operating state (e.g., current temperature, frequency, duration, etc.) of the target compressor in real time, as compared to a preset control strategy.

More specifically, for example, when the operating temperatures in all of the target compressors exceed a safety threshold, then the controller may choose to start the non-target compressors to balance the load of each compressor in the compressor system if the current power demand is met.

More specifically, in this case, the preset control strategy may be to add a priority decision algorithm to determine the order of target compressor adjustments and non-target compressor activations based on the rule base described above. For example, the targeted and non-targeted compressors may be assigned start-stop weights by a priority decision algorithm. The target compressor adjusts priority and the non-target compressor starts as required.

In this embodiment, the controller may also perform adjustment on the target compressor based on the real-time operation data, the historical operation data, and the preset control policy of the target compressor, so as to balance the load of each compressor in the compressor system when the current power requirement is satisfied.

Specifically, the controller may not only monitor the operation state (e.g., current temperature, frequency, duration, etc.) of the target compressor in real time, but also extract historical operation data of the target compressor, and perform adjustment on the target compressor in comparison with a preset control strategy.

More specifically, for example, when the operating temperature of a certain one of the target compressors exceeds a safety threshold, the controller may decrease the operating frequency of the target compressor, even stop. Accordingly, the controller evaluates the health of the target compressors whose operating temperatures do not exceed the safety threshold based on the historical operating data, and then increases the operating frequency of the target compressors whose operating temperatures that are highest in health do not exceed the safety threshold to balance the load of each compressor in the compressor system under the condition of meeting the current power demand.

In this embodiment, the controller may also perform adjustment on the target compressor and the non-target compressor based on the real-time operation data, the historical operation data, and the preset control policy of the target compressor, so as to balance the load of each compressor in the compressor system when the current power requirement is satisfied.

Specifically, the controller may not only monitor the operating state (e.g., current temperature, frequency, duration, etc.) of the target compressor in real time, but also extract historical operating data of the target compressor, and perform adjustments for both the target compressor and the non-target compressor in comparison to a preset control strategy.

More specifically, for example, when the operating temperature of a certain one of the target compressors exceeds a safety threshold, the controller may decrease the operating frequency of the target compressor, even stop. Accordingly, the controller evaluates the health degree of the target compressor with the operation temperature not exceeding the safety threshold based on the historical operation data, and then improves the operation frequency of the target compressor with the highest health degree (or the operation temperature meeting the health degree threshold) with the operation temperature not exceeding the safety threshold.

In this embodiment, the controller may also perform adjustment on the target compressor and the non-target compressor based on the real-time operation data and the historical operation data of the target compressor, the historical operation data of the non-target compressor, and a preset control policy, so as to balance the load of each compressor in the compressor system under the condition of meeting the current power demand.

Specifically, the controller may not only monitor the operation state (e.g., the current temperature, frequency, duration, etc.) of the target compressor in real time, but may also extract historical operation data of the target compressor, and may also extract historical operation data of the non-target compressor, and perform adjustment on both the target compressor and the non-target compressor in comparison with a preset control strategy.

More specifically, for example, when the operating temperature of a certain one of the target compressors exceeds a safety threshold, the controller may decrease the operating frequency of the target compressor, even stop. Accordingly, the controller evaluates the health degree of the target compressor whose operation temperature does not exceed the safety threshold based on the historical operation data, and then increases the operation frequency of the target compressor whose operation temperature is highest (that is, satisfies the health degree threshold) and does not exceed the safety threshold, and after the operation frequency of the corresponding target compressor is increased, under the condition that the current power requirement still cannot be met, the controller can evaluate the health degree of the non-target compressor based on the historical operation data of the non-target compressor, and continuously start the non-target compressor with the highest health degree (or can meet the health degree threshold value), so that the load of each compressor in the compressor system is balanced under the condition that the current power requirement is met.

The control method of the compressor system provided in this embodiment intelligently determines and starts the target compressor by combining current power demand data and power range data of each compressor after detecting the power demand change. And then, after the target compressor runs for a period of time, acquiring real-time running data of the target compressor, and adjusting the target compressor according to a preset control strategy to realize load balance among the compressors. The method dynamically optimizes the running state of the compressor by comprehensively utilizing real-time and historical running data, avoids excessive use and frequent start and stop of certain compressors, and remarkably reduces mechanical abrasion and energy consumption, thereby effectively prolonging the service life of the whole compressor system. Meanwhile, the reasonable distribution of the load improves the running efficiency and stability of the system, and solves the technical problem that the service life of the compressor is reduced due to improper control in the existing multi-compressor system. The method not only improves the overall reliability of the compressor system, but also reduces the maintenance cost, and ensures the efficient and durable operation of the compressor system under different operation environments.

In some embodiments, the step of determining current power demand data for the compressor system after detecting a change in power demand comprises:

Step S122, detecting the current environment temperature in response to a temperature adjustment instruction or a starting instruction sent by a user side, wherein the temperature adjustment instruction or the starting instruction carries a target temperature.

Step S124, calculating the power demand data based on the ambient temperature and the target temperature.

In this embodiment, the controller client calculates the power demand data based on the ambient temperature and the target temperature by:

first, the controller calculates a difference between the ambient temperature and the target temperature.

Then, the controller determines the required power demand data according to the temperature difference through a preset power demand mapping relation table (or a power calculation formula).

In this embodiment, the current ambient temperature is detected by responding to a temperature adjustment instruction or a startup instruction sent by the user side, and the power demand data of the compressor system is calculated in combination with the target temperature carried in the instruction. The method can realize dynamic adjustment and accurate calculation of the power demand, and effectively meets the requirements of users on the instantaneity and the accuracy of temperature adjustment. Meanwhile, the calculation mode of combining the environment temperature and the target temperature can ensure that the calculation of the power demand data is more fit with the actual load demand, so that the operation efficiency of the compressor is optimized, the situation of excessive or insufficient power is avoided, and the energy-saving effect and the operation stability of the system are improved. In addition, the response mechanism further enhances the user interaction capability of the compressor system and improves the overall use experience.

In some embodiments, the step of determining power range data for each compressor in the compressor system comprises:

And step S126, reading the upper power limit value and the lower power limit value of each compressor in the compressor system from a preset database.

In this embodiment, the database may be a local database. In particular, the database may be stored in an internal memory of the controller or in a local storage device (e.g., SD card, embedded memory chip) directly connected to the controller.

In this embodiment, the database may be a cloud database, in other words, the historical operation data may be stored on a remote server or a cloud platform, and communicate with the controller through a network interface.

In the present embodiment, by reading the power upper limit value and the power lower limit value of each compressor in the compressor system from a preset database, the power range data of each compressor can be quickly and accurately determined. The method not only simplifies the power range acquisition process, but also avoids errors possibly caused by complex real-time calculation or manual input. By storing the power upper limit value and the power lower limit value in the database in advance, the compressor system can be quickly matched with corresponding power range data according to different models or running states, so that the instantaneity and the accuracy of the control strategy are improved. In addition, the realization mode provides stable basic data for dynamic load distribution of the multi-compressor system, ensures that the system can reasonably distribute loads under different power demands, further improves the operation efficiency and prolongs the service life of the compressors.

In some embodiments, the step of determining a target compressor based on the power demand data and the power range data comprises:

and step S142, generating a plurality of operation power intervals based on the power range data, wherein at least one compressor corresponds to the operation power intervals.

In this embodiment, the controller may generate a number of operation power intervals based on the power range data by:

First, the controller collects the upper and lower power limits of all compressors and sums them into a set.

Then, the upper and lower power limit sets of the controller are de-duplicated and are ordered from small to large to generate an ordered partition point set.

Then, the controller constructs continuous power intervals based on the ordered partition point set, and sequentially traverses each operation power interval to judge which power ranges of the compressors overlap with the interval.

And finally, the controller generates a final mapping table for all the operation power intervals and the corresponding compressors.

And step S144, determining a target operation power interval from the operation power intervals based on the power demand data.

In this embodiment, the controller may traverse all the operation power intervals one by one according to the current power demand data, and determine whether the current power demand data falls into the corresponding operation power interval, so as to determine the target operation power interval.

Step S146, selecting at least one compressor from the target operation power interval as the target compressor and starting the target compressor.

In the present embodiment, one compressor may be randomly selected from the target operation power section as the target compressor and started.

In this embodiment, the historical operation data of each compressor in the target operation power interval may be further acquired, and then the health degree of the corresponding compressor may be estimated based on the historical operation data. And finally, selecting the compressor with the highest health degree or meeting the health degree threshold as a target compressor, wherein the target compressor can be one compressor or a plurality of compressors.

In the embodiment, a plurality of operation power intervals are generated based on the power range data, the target operation power interval is determined by combining the power demand data, and the most suitable compressor is further selected from the target operation power intervals and started to serve as the target compressor, so that the accuracy and the intellectualization of the load distribution of the compressor system are effectively realized. The method can ensure that the operation of the compressors is more fit with the actual power requirement, and avoid the condition of overload operation or resource waste of a single compressor. In addition, the division of the operation power interval enables the system to quickly match the power requirement with the compressor capacity, and the redundancy of the operation power is reduced by optimizing the compressors, so that the energy utilization efficiency is improved, the overall energy consumption is reduced, the loads of the compressors are balanced, and the service life of the equipment is prolonged.

In some embodiments, the step of selecting at least one compressor from the target operating power interval as the target compressor comprises:

step S1462, obtaining historical operation data of each compressor in a target operation power interval, wherein the historical operation data comprises historical operation duration, historical average operation temperature, historical average operation frequency or fault occurrence records.

And step S1464, evaluating the health degree of each compressor in the target operation power interval according to the historical operation data.

In this embodiment, the controller may input the historical operation data into a preset health evaluation model, and evaluate health of each compressor in the target operation power interval.

In a specific embodiment, the health assessment model may be a weighted scoring model for quantifying the impact of different historical data on health, for example, it may be:

H=100-(w₁·S_Operation+w₂·S_{Temperature (temperature)}+w₃·S_{Frequency of}+w₄·S_{Failure of})

In the formula, H is expressed as a health score, the health score ranges from 0 to 100, the higher the value is the healthier the compressor is, if the health score H < H _{Threshold value} (e.g., H _{Threshold value} is 50), the compressor is deemed unsuitable for operation. w ₁-w₄ is a weight parameter, each weight reflects the influence degree of the corresponding index on the health degree, and the weight value satisfies:

S _Operation is represented as a quantization score of the historical operating time period, S _{Temperature (temperature)} is represented as a quantization score of the historical average operating temperature, S _{Frequency of} is represented as a quantization score of the historical average operating frequency, and S _{Failure of} is represented as a quantization score of the fault occurrence record.

In a specific embodiment, the quantified score of the historical operating time duration may be an impact of the operating time duration on health. It will be appreciated that the length of operation reflects the cumulative extent of use of the compressor, and that the longer the operation, the higher the wear of the internal components and the lower the health. Each compressor has a design life value, for example 10000 hours, which means that the operation of the device is relatively reliable during this time frame. The controller records the accumulated operating time of each compressor. The impact on health may be reflected by the proportion of the cumulative operating time to the design life, with the negative impact on health increasing as the cumulative operating time approaches the upper limit of the design life. For example, if the operation duration is 50% of the design life, the effect on the health is moderate, and if the operation duration is 80% of the design life, the effect on the health is large. The effect of the run length is a gradual decrease process, i.e., the health decreases gradually as the cumulative run time increases.

In a specific embodiment, the quantification score for the historical average operating temperature may be the impact of average temperature on health. It will be appreciated that the effect of temperature on health is related to the operating thermal load of the device. The performance of the internal components (such as electric insulation materials, lubricating oil and the like) of the equipment can be obviously affected under long-term high-temperature operation, so that the equipment aging is accelerated, and the health degree is reduced. Each compressor has a safe normal operating temperature range, which can be defined by the manufacturer. For example, 60 ℃ may be an upper limit value. The controller calculates the long-term average operating temperature of the device, as compared to the safe temperature range. If the average temperature exceeds the upper normal operating temperature limit, health may be negatively affected, with higher temperatures having greater impact. The controller will translate this excess into a decrease in health with a proportional relationship based on the degree of excess temperature (i.e., the difference in average temperature over normal operating temperature) if the temperature is only slightly outside of the normal range (e.g., 65 ℃ over 60 ℃) with less impact on health. If the temperature is significantly outside the normal range (e.g., 75 ℃ over 60 ℃), the impact on health is significant. The effect of temperature is progressively increasing and the controller will accumulate each degree of temperature difference outside the temperature range as a reduced value for health.

In a specific embodiment, the quantification score for the historical average operating frequency may be the impact of the average frequency on health. It will be appreciated that frequency is a direct reflection of the device's workload, the higher the operating frequency, the heavier the load, and the greater the impact on the health of the device. Long term high frequency operation can accelerate wear of internal components such as bearings, rotational speed of the motor, and vibrating parts. Each compressor has a nominal operating frequency, for example 50Hz, which is the recommended operating frequency for the plant at the design load conditions. The controller calculates a long-term average operating frequency of the device. By comparing the average operating frequency of the device with the nominal operating frequency, the impact on the health is assessed, i.e. less impact on health when the average frequency is close to the nominal operating frequency. When the average frequency is higher than the nominal operating frequency, the negative impact on health increases gradually. The higher the frequency exceeded, the more the health declines. The controller may use a scaling relationship to translate the excess frequency to a decrease in health, for example, 10% over the nominal frequency has a minor impact on health. Exceeding 20% or more of the rated frequency, the influence on the health degree is remarkable.

In a specific embodiment, the quantitative score of the fault occurrence record may be the impact of the fault occurrence record on health. It will be appreciated that fault logging is an important assessment dimension of the health of a device, as the frequency and severity of faults directly reflect the current status of the device and potential reliability issues. The fault record for each compressor may include the number of faults and the type of fault (e.g., light fault, heavy fault). The increase of the number of faults can reduce the health degree of the equipment, and the controller can evaluate according to the accumulation of the number of faults, wherein the more the number of faults is, the more the health degree is obviously reduced. The severity of the fault type may further exacerbate the health impact, with minor faults (e.g., short time overload alarms) having less impact on health, but frequent occurrences may accumulate impact. The impact of severe faults (e.g., excessive temperatures resulting in downtime) on health is significant. The controller may assign impact weights to different types of faults, e.g., each time a light fault occurs, the impact on health is a small value. The impact on health is a large value for each occurrence of a critical fault. Finally, the controller can calculate the overall influence of the faults on the health degree according to the comprehensive influence of the times and the types of the faults.

In this embodiment, the health degree evaluation model may be a machine learning model, and it may be understood that the health degree evaluation model may be constructed by using a machine learning technology, and the health degree evaluation model is trained according to historical operation data, so as to predict the health degree of the compressor. Specifically, the health assessment model may be a regression model (e.g., a linear regression model or a random forest regression model), and the health assessment model may also be a classification model (e.g., a logistic regression model, a support vector machine model).

In this embodiment, the health evaluation model may be a neural network model, a fuzzy logic model, or the like.

In this embodiment, the health evaluation model may also be a physical model based on state estimation. For example, the physical characteristics of the compressor may be utilized to build a health model that estimates the health of the device from the operational data.

Step S1466, selecting the compressor meeting the health threshold in the target operation power interval as the target compressor.

In this embodiment, the health threshold may be a fixed value for uniformly and standardizing the operation state of the compressor. For example, the health threshold is set to 50 (split fully to 100). Only compressors with a health score greater than or equal to 50 can be selected as target compressors.

In this embodiment, the health degree threshold may be a threshold that can be dynamically adjusted. In other words, the health threshold may be dynamically adjusted based on the operational requirements of the compressor system or the environmental conditions. For example, during peak load periods (when compressor demand is large), the threshold may be suitably lowered, e.g., from 50 to 40, to increase the number of available compressors to meet the power demand. During periods of low load (when compressor demand is small), the threshold may be raised, for example, from 50 to 60, to preferentially protect the better-health compressor.

In this embodiment, by acquiring historical operation data (historical operation duration, average operation temperature, average operation frequency, and failure occurrence record) of each compressor in the target operation power interval, the controller can comprehensively evaluate the health degree of each compressor, and select a compressor satisfying the health degree threshold as the target compressor to start operation based on the health degree. The method fully utilizes the historical operation data of the compressor, ensures accurate judgment of the state of the compressor, and preferentially selects the compressor with higher health degree to operate, thereby effectively balancing the service life of the compressor and avoiding the compressor with lower health degree or fault risk from participating in high-load operation. The method not only improves the reliability and safety of the operation of the compressor system, but also realizes reasonable scheduling and optimal utilization of the compressor resources, further prolongs the service life of the whole compressor system, and reduces the maintenance cost.

In some embodiments, the step of performing an adjustment on at least the target compressor based on at least real-time operating data of the target compressor and a preset control strategy includes:

And S182, based on the real-time operation data of the target compressor, the health degree and the preset control strategy, performing adjustment on the target compressor and the non-target compressor in the target operation power interval.

In this embodiment, the controller may first determine whether real-time operation data (e.g., a current operation duration, a current operation temperature, or a current operation frequency) of the target compressor exceeds a corresponding safety threshold.

If the corresponding safety threshold is not exceeded, then a judgment is made after a number of times.

If at least one target compressor is present, the corresponding safety threshold is exceeded. It will be appreciated that the exceeding of the respective safety threshold may indicate that the real-time operational data of at least one of the above-mentioned classes of real-time operational data exceeds the respective safety threshold. The first control strategy and the second control strategy are executed.

Specifically, in a specific embodiment, the first control strategy may be:

closing a target compressor with real-time operation data exceeding a safety threshold;

and reducing the target compressor operating frequency of the real-time operating data exceeding the safety threshold.

The second control strategy may be:

and starting non-target compressors meeting the health threshold in the target running power interval.

Further, in a specific embodiment, the first control strategy may further include:

the output power of the target compressor for which the real-time operation data exceeds the safety threshold is reduced, for example, the operation power of the target compressor is reduced to a preset minimum power value.

And switching the target compressor with the real-time operation data exceeding the safety threshold value into a rest mode, temporarily stopping for a period of time, and restarting after the equipment is cooled or the load is recovered.

A cooling system (e.g., a fan or water cooling device) of the target compressor is started with real-time operating data exceeding a safety threshold, and the cooling is assisted to delay shutdown.

Further, in a specific embodiment, the second control strategy may further include:

After starting a non-target compressor meeting a health threshold in a target operation power interval, dynamically increasing the operation frequency of the non-target compressor to quickly compensate the power requirement;

If the health of the non-target compressor meets the minimum threshold, but the state is close to the health boundary (for example, slightly higher than the health threshold), the non-target compressor can be started gradually, namely, the non-target compressor is operated in a low power mode first, whether real-time operation data of the non-target compressor is stable or not is observed, and if the real-time operation data of the non-target compressor is stable, the power is gradually increased;

If a plurality of non-target compressors with higher health degree exist in the target operation power interval, a plurality of compressors can be started simultaneously and loads are distributed among the compressors, so that high-load operation of a single compressor is avoided;

When non-target compressors are selected, the compressor with the highest health may be selected preferentially to take on high load operation, while the compressor with a slightly lower health takes on lower load.

After the non-target compressor is started, its health status can be monitored in real time. If the health is significantly reduced due to frequent operation, it is switched to a standby state and other compressors are started to share the load.

In the present embodiment, by performing adjustment on both the target compressor and the non-target compressor in the target operation power interval based on the real-time operation data of the target compressor, the health degree, and the preset control policy, efficient load allocation and dynamic optimization of the compressor system can be achieved. The method not only performs fine regulation (such as frequency reduction, shutdown or frequency lifting) on the target compressor, but also comprehensively considers the health state and real-time requirement of the non-target compressor, and performs start-stop or frequency adjustment on the non-target compressor when necessary. Therefore, the system can further balance the load of each compressor while meeting the current power demand, avoid the overload operation of a single compressor and prolong the whole service life of equipment. In addition, the method can intelligently adjust the operation strategy by comprehensively considering the real-time operation data and the historical health, reduce the fault risk, improve the reliability and stability of the operation of the compressor system, and simultaneously realize the maximization of the energy utilization efficiency.

In some embodiments, the real-time operational data includes a current operational time length, a current operational temperature, or a current operational frequency;

the step of executing adjustment on the target compressor and the non-target compressor in the target operation power interval based on the real-time operation data of the target compressor, the health degree and the preset control strategy includes:

step S1822, judging whether a target compressor with real-time operation data exceeding a safety threshold exists in the target compressors, wherein the real-time operation data exceeding the safety threshold is indicative of at least one type of real-time operation data exceeding the corresponding safety threshold;

Step S1824, executing a preset first control strategy and a preset second control strategy when the target compressor with the real-time operation data exceeding the safety threshold exists, wherein the first control strategy at least comprises a control strategy for closing the target compressor with the real-time operation data exceeding the safety threshold or reducing the operation frequency of the target compressor with the real-time operation data exceeding the safety threshold, and the second control strategy at least comprises a control strategy for starting a non-target compressor meeting the health threshold in the target operation power interval.

In this embodiment, the controller can actively take adjustment measures before the apparatus reaches the dangerous operation state by monitoring the operation data (including the operation time length, the operation temperature, the operation frequency, etc.) of the target compressor in real time and judging whether there is a situation that the safety threshold is exceeded. If the real-time operation data of the target compressor exceeds the safety threshold, a preset first control strategy (such as closing the target compressor or reducing the operation frequency thereof) is executed, so that the target compressor is effectively prevented from being failed or damaged due to overload or overheat operation. Meanwhile, by executing a preset second control strategy (such as starting a non-target compressor meeting a health degree threshold in a target operation power interval), the load can be rapidly distributed on the premise of maintaining the current power demand, and the operation continuity and stability of the compressor system are ensured. The method comprehensively considers real-time operation data, the safety threshold value, the health degree and the control strategy, effectively avoids excessive loss of a single compressor, improves the reliability and the service life of the system, realizes dynamic optimization distribution of loads, and remarkably enhances the operation safety and the operation efficiency of the compressor system.

In some embodiments, the step of obtaining at least real-time operation data of the target compressor includes:

real-time operating data of a target compressor is obtained and historical operating data of at least some of the compressors in the compressor system is obtained.

In this embodiment, the historical operation data of a part of the compressors in the compressor system may be obtained, and the historical operation data of the part of the compressors may or may not include the historical operation data of the target compressors.

In this embodiment, historical operation data of all compressors in the compressor system may be obtained.

After the step of obtaining real-time operating data of the target compressor and obtaining historical operating data of at least some of the compressors in the compressor system, the method further comprises:

And step S110, inputting the real-time operation data of the target compressor and/or the historical operation data of at least part of the compressors into a preset fault risk identification model to identify, and judging whether the compressors in the compressor system have fault risks or not.

In this embodiment, the real-time operation data of the target compressor may be input to a preset fault risk identification model to identify, and whether the fault risk exists in the compressor of the compressor system may be determined. For example, it is determined whether the current target compressor is at risk of failure.

In particular, in this case, the preset fault risk recognition model may be a rule-based monitoring model or a statistical analysis model. A preset rule base may be used, for example, if the temperature exceeds 70 ℃ and lasts for 5 minutes, a fault early warning is triggered, and if the frequency exceeds 120% of the rated frequency, it is judged that an overload risk exists.

In this embodiment, the real-time operation data of the target compressor and the historical operation data of the target compressor may be input to a preset fault risk identification model to identify, and whether the fault risk exists in the compressor of the compressor system may be determined. For example, it is determined whether the current target compressor is at risk of failure.

In particular, in this case, the preset fault risk recognition model may be a time series analysis model or a hybrid model based on rules and machine learning. For example, dynamic security thresholds may be constructed in combination with real-time operational data and historical operational data. A supervised learning model (random forest or logistic regression) may be trained to predict the risk of failure of the target compressor. Historical operating data may be used as features (e.g., cumulative time length, historical average temperature, historical load frequency) and real-time operating data as inputs.

In this embodiment, the real-time operation data of the target compressor, the historical operation data of the target compressor, and the historical operation data of a part of non-target compressors may be input to a preset fault risk identification model to identify, and whether the fault risk exists in the compressors in the compressor system may be determined. For example, it is determined whether the current target compressor and some non-target compressors are at risk of failure.

Specifically, in this case, the preset fault risk recognition model may be a health evaluation model or a cluster analysis model. More specifically, historical data of non-target compressors may be utilized to construct a compressor system reference (e.g., average operating temperature and frequency of healthy compressors in a compressor system). If the real-time data of the target compressor deviates from the reference value of the compressor system more, the fault risk of the target compressor is judged to be increased.

More specifically, an unsupervised learning method (e.g., K-means clustering) may also be employed to cluster historical operating data of the compressor. The real-time operation data of the target compressor can be compared with clusters to which the target compressor belongs in history, and if the target compressor deviates from the cluster characteristics, fault early warning is triggered.

In this embodiment, the real-time operation data of the target compressor, the historical operation data of the target compressor, and the historical operation data of all non-target compressors may be input to a preset fault risk identification model to identify, and whether the fault risk exists in the compressors in the compressor system may be determined.

In particular, in this case, the preset fault risk recognition model may be a prediction model based on deep learning. The multi-dimensional time series data of the target compressor and the non-target compressor can be analyzed using a Recurrent Neural Network (RNN) or a long short-term memory network (LSTM) to capture potential failure risk trends. For example, complex nonlinear failure modes can be identified using extensive training of historical operational data and real-time input of real-time operational data.

In the embodiment, by acquiring real-time operation data of the target compressor and historical operation data of at least part of compressors in the compressor system and inputting the data into a preset fault risk identification model for identification, intelligent prediction and quick response of potential fault risks of the compressor system can be realized. By combining real-time operation data (such as current temperature, frequency, duration and the like) with historical operation data (such as accumulated operation duration, historical average temperature, fault records and the like), the fault risk identification model can comprehensively analyze the operation state of the compressor and capture early characteristics or abnormal trends of faults. By timely identifying and early warning the compressors possibly having fault risks, the method can effectively reduce the influence of sudden faults on the system operation, reduce the occurrence rate of unplanned shutdown of equipment, prolong the overall service life of the compressors, and remarkably improve the reliability and safety of the system operation. In addition, the intelligent risk assessment process realizes the transition from passive maintenance to active prevention, and provides powerful technical support for efficient operation of the compressor system.

In a specific and possible embodiment, a compressor system is provided, as shown in fig. 4, which may include:

the health detection module 1 and the health detection module 2 are respectively used for monitoring the operation state data of the two compressors (M1 and M2) in real time, and can comprise historical operation time, temperature, load, current, voltage, vibration and the like.

And the sampling circuit can receive the monitoring data from the health detection module 1 and the health detection module 2 and perform signal sampling and digital processing.

The main control chip is a core control unit, receives the data transmitted by the sampling circuit, and executes data analysis, power demand calculation, a control method of the compressor system and the like.

And the control circuit is used for controlling the start and stop, the lifting frequency and other operation parameters of the two compressors (M1 and M2) according to the control instruction generated by the main control chip.

As shown in fig. 5, the power range of M1 is (0, power 1). The power range of M2 is (power 2, maximum power). There is an overlap region (power 1 to power 2) between the operating power intervals of M1 and M2. Therefore, when the power demand of the compressor system is small (less than power 1), the low-power compressor M1 can be preferentially started, and the idle waste of the high-power compressor can be avoided. When the power demand is in the overlapping interval (power 1 to power 2), the main control chip selects one compressor with lighter load according to the historical operation data (such as the operation time length) of the two compressors. When the power demand is large (exceeding power 2), both compressors may be operated simultaneously to meet the load demand.

The control method of the compressor system may include:

When the unit is electrified, which compressor is started according to the power required by the unit is selected. If the power required by the unit is smaller than power 1, M1 is started preferentially, if the power required by the unit is larger than power 1 and smaller than power 2, the historical operation time of the two compressors is compared, the compressors with short time are started preferentially, and when the power is larger than power 2, the two compressors are started simultaneously, so that only one compressor is prevented from running under high load, abrasion is caused to the compressors, and the service life of the compressors is shortened. In the running process of the compressor, the health detection module also detects the temperature, current, voltage and other data of the compressor in real time, predicts the fault risk, and timely early warns the compressor with the fault risk to stop and maintain. When the temperature exceeds D1 when the compressor is operated, it is necessary to reduce the operating temperature of the compressor itself in order to prevent the compressor from being damaged. When two compressors run at the same time, the compressor with higher temperature carries out frequency reduction, and the compressor with lower temperature carries out frequency increase, thus relieving the damage to the compressor caused by the overhigh temperature of the compressor.

The health detection module can collect data such as running time, temperature, load, current, voltage, vibration and the like of the compressor in real time, then perform data processing, remove abnormal values and missing values, and extract characteristics such as average temperature, maximum load, current fluctuation and the like. And then, estimating the health state of the compressor by using a fault prediction algorithm, and judging whether the compressor has potential faults or not. If a potential failure is predicted, an alarm is sent to inform maintenance personnel, and the working state of the compressor is automatically adjusted, such as load reduction and shutdown maintenance. If there is no potential failure, the data continues to be collected. And recording maintenance information during maintenance, and recovering normal operation of the compressor after the maintenance is completed.

According to an embodiment of the invention, a compressor system is provided, which comprises at least two compressors and a controller, wherein the controller is used for executing the control method of the compressor system.

According to an embodiment of the invention, an air conditioner is provided, which is characterized by comprising a controller, wherein the controller is used for executing the control method of the compressor system or the compressor system.

According to an embodiment of the present invention, an electronic device is provided, please refer to fig. 6. The electronic device in this embodiment may include one or more processors, network interfaces, memory, non-volatile storage, and one or more application programs, where the one or more application programs may be stored in the non-volatile storage and configured to be executed by the one or more processors, the one or more program(s) configured to perform the methods described in the foregoing method embodiments.

According to an embodiment of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a computer, causes the computer to perform the method described in any of the above embodiments.

There is also provided, in accordance with an embodiment of the present invention, a computer program product comprising instructions which, when executed by a computer, cause the computer to perform a method according to any of the above embodiments.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Alternatively, specific examples in this embodiment may refer to examples described in the foregoing embodiments, and this embodiment is not described herein.

The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.

In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.

The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application, which are intended to be comprehended within the scope of the present application.

Claims

1. A method for controlling a compressor system, wherein the compressor system comprises at least two compressors; the method comprises:

After detecting a change in power demand, determining current power demand data of the compressor system and power range data of each compressor in the compressor system;

Determine a target compressor based on the power demand data and the power range data; wherein the target compressor is a compressor that needs to be in a startup state;

After the target compressor has been running for a certain period of time, at least real-time operation data of the target compressor is obtained;

At least based on the real-time operating data of the target compressor and a preset control strategy, at least the target compressor is adjusted to balance the loads of the compressors in the compressor system while meeting the current power demand.

2. The method according to claim 1, characterized in that the step of determining the current power demand data of the compressor system after detecting a change in the power demand comprises:

In response to a temperature adjustment instruction or a power-on instruction issued by a user side, detecting the current ambient temperature; wherein the temperature adjustment instruction or the power-on instruction carries a target temperature;

The power demand data is calculated based on the ambient temperature and the target temperature.

3. The method according to claim 1, characterized in that the step of determining the power range data of each compressor in the compressor system comprises:

The upper power limit value and the lower power limit value of each compressor in the compressor system are read from a preset database.

4. The method according to claim 1, characterized in that the step of determining the target compressor based on the power demand data and the power range data comprises:

Based on the power range data, a plurality of operating power intervals are generated; wherein the operating power intervals correspond to at least one compressor;

determining a target operating power interval from the plurality of operating power intervals based on the power demand data;

At least one compressor is selected from the target operating power interval as the target compressor and the target compressor is started.

5. The method according to claim 4, characterized in that the step of selecting at least one compressor from the target operating power range as the target compressor comprises:

Obtaining historical operating data of each compressor in the target operating power range; wherein the historical operating data includes historical operating time, historical average operating temperature, historical average operating frequency or fault occurrence record;

evaluating the health of each compressor in the target operating power range according to the historical operating data;

A compressor that meets a health threshold in the target operating power range is selected as the target compressor.

6. The method according to claim 5, characterized in that the step of at least adjusting the target compressor based on the real-time operating data of the target compressor and a preset control strategy comprises:

Based on the real-time operating data of the target compressor, the health level and the preset control strategy, adjustments are performed on both the target compressor and the non-target compressors in the target operating power range.

7. The method according to claim 6, characterized in that the real-time operation data includes current operation time, current operation temperature or current operation frequency;

The step of adjusting the target compressor and the non-target compressors in the target operating power range based on the real-time operating data of the target compressor, the health level and the preset control strategy includes:

Determine whether there is a target compressor among the target compressors whose real-time operation data exceeds a safety threshold; wherein the real-time operation data exceeding the safety threshold means that at least one type of real-time operation data exceeds a corresponding safety threshold;

In the event that there is a target compressor whose real-time operating data exceeds the safety threshold, a preset first control strategy and a preset second control strategy are executed; wherein the first control strategy at least includes a control strategy for shutting down the target compressor whose real-time operating data exceeds the safety threshold or reducing the operating frequency of the target compressor whose real-time operating data exceeds the safety threshold; and the second control strategy at least includes starting a non-target compressor that meets the health threshold in the target operating power range.

8. The method according to claim 1, characterized in that the step of at least acquiring the real-time operating data of the target compressor comprises:

Acquire real-time operating data of a target compressor and acquire historical operating data of at least some compressors in the compressor system;

After the step of acquiring the real-time operating data of the target compressor and acquiring the historical operating data of at least part of the compressors in the compressor system, the method further includes:

The real-time operating data of the target compressor and/or the historical operating data of at least part of the compressor are input into a preset fault risk identification model for identification to determine whether there is a fault risk for the compressor in the compressor system.

9. A compressor system, characterized in that it comprises: at least two compressors and a controller, wherein the controller is used to execute the method described in any one of claims 1-8.

10. An air conditioner, characterized by comprising: a controller, wherein the controller is used to execute the method according to any one of claims 1 to 8 or the compressor system according to claim 9.