CN115329856A - Fault warning method and system applied to integrated test module of integrated bay equipment - Google Patents

Abstract

The invention discloses a fault early warning method applied to an integrated spacer equipment comprehensive test module, which comprises the following steps: acquiring index data corresponding to each detection item according to a preset corresponding relation between the detection item and the index parameter and the variable type; calculating index prediction values excluding noise for each index data corresponding to each detection item; determining an index data range corresponding to the index type according to the index predicted value and a predetermined noise value; matching the index data with the index data range of the corresponding index type, and if the index data range exceeds the corresponding index data range, outputting a fault early warning signal of the corresponding index type under the corresponding detection item of the corresponding equipment; the method and the system can improve the reliable fault early warning under the requirement of multi-index monitoring of the integrated interval equipment, and reduce the labor cost.

Description

Fault early warning method and system applied to integrated test module of interval equipment

technical field

The invention relates to the technical field of electronic equipment system testing in the electric power field, in particular to a fault early warning method and system applied to an integrated spacer equipment comprehensive testing module.

Background technique

A set of integrated interval equipment comprehensive test module needs to test multiple test items, each test item covers a variety of indicators, and the combination of multiple indicators can complete the comprehensive test of a transformer. However, due to the large amount of test content, fine detection and identification are required for each test, which requires a lot of manpower, resulting in high test costs and high work intensity for testers.

At the same time, during the testing process of electronic equipment systems in the power field, there are potential safety hazards due to the high voltage and extra high voltage involved. In the traditional electronic equipment system testing process, the testing process is monitored manually, which relies heavily on experience, has great potential safety hazards, and is extremely inefficient.

Contents of the invention

The purpose of the present invention is to provide a fault early warning method and system applied to the integrated test module of the integrated partition equipment, to improve the reliable fault early warning under the multi-index monitoring requirements of the integrated partition equipment, and to reduce labor costs. The technical scheme adopted in the present invention is as follows.

On the one hand, the present invention provides a fault early warning method applied to the comprehensive test module of integrated spacer equipment, including:

Collect the index data corresponding to each inspection item according to the corresponding relationship between the preset inspection items and the index parameters and variable types;

For each index data corresponding to each test item, calculate the predicted value of the index except the noise;

Determine the index data range corresponding to the index type according to the index prediction value and the predetermined noise value;

Matching the index data with the index data range of the corresponding index type, if exceeding the corresponding index data range, outputting a failure warning signal of the corresponding index type under the corresponding detection item of the corresponding equipment;

Wherein, the determination method of described noise value comprises:

For the same index type of the same inspection item, obtain historical index data collected from multiple similar equipment, and obtain a horizontal comparison data set;

和下限

According to the horizontal comparison data set, the noise upper limit of the indicator data under this indicator type is obtained by analysis

and lower limit

其中，

为实测指标数据的指标预测值。According to the predicted value of the index and the predetermined noise value, the range of the index data corresponding to the index type is determined as

in,

is the index prediction value of the measured index data.

和下限

包括：Optionally, according to the horizontal comparison data set, analyze and obtain the upper limit of noise of the indicator data under this indicator type

and lower limit

include:

For each indicator data a(t) in the horizontal comparison data set, the trend item trend(t), cycle item cycle(t) and season item season(t) of each indicator data are respectively obtained;

Calculate the noise item noise(t) of each index data according to the following formula:

a(t)=trend(t)+cycle(t)+season(t)+noise(t)

和下限

For the noise items of all index data in the horizontal comparison data set, determine the specified proportional probability distribution range within the probability distribution range, obtain the upper bound and lower bound of the range, and obtain the noise upper bound of the corresponding index type

and lower limit

Optionally, the determination of the probability distribution range of the noise items of all index data in the horizontal comparison data set includes:

Assuming that the noise distribution of the index data is a normal distribution, the probability distribution function p(t) is obtained by fitting the index data to a normal distribution, and the probability distribution range of 90% of the normal distribution is taken to obtain the index corresponding to the index data Type upper and lower bounds on the noise term.

Optionally, for each index data a(t) in the horizontal comparison data set, the trend item trend(t), cycle item cycle(t) and season item season(t) of each index data are obtained respectively, including:

Pass the indicator data a(t) through a low-pass filter to obtain the trend item trend(t);

Pass the indicator data a(t) through a band-pass filter with a lower frequency to obtain the cycle item cycle(t);

Pass the indicator data a(t) through a band-pass filter with a higher frequency to obtain the seasonal item season(t).

The so-called seasonal data here is a proper term in the discipline of statistics and signal processing, and does not refer to the meaning expressed by the word season in the usual sense.

Optionally, for each index data corresponding to each detection item, calculating the predicted value of the index other than the noise includes:

Calculate the trend item trend(t), cycle item cycle(t) and season item season(t) of the indicator data;

The calculated trend item, cycle item and season item data are superimposed to obtain the predicted value of the indicator.

In the second aspect, the present invention provides a fault early warning device applied to the comprehensive test module of integrated spacer equipment, including:

The index data acquisition module is configured to collect index data corresponding to each inspection item according to the preset correspondence between inspection items, index parameters, and variable types;

The index prediction value calculation module is configured to calculate the index prediction value except noise for each index data corresponding to each detection item;

The index data range determination module is configured to determine the index data range corresponding to the index type according to the index prediction value and the predetermined noise value;

The fault early warning judgment module is configured to match the index data with the index data range of the corresponding index type, and if it exceeds the corresponding index data range, output a fault early warning signal of the corresponding index type under the corresponding detection item of the corresponding equipment;

Wherein, the method for determining the noise value by the index data range determination module includes:

For the same index type of the same inspection item, obtain historical index data collected from multiple similar equipment, and obtain a horizontal comparison data set;

和下限

According to the horizontal comparison data set, the noise upper limit of the indicator data under this indicator type is obtained by analysis

and lower limit

其中，

为实测指标数据的指标预测值。According to the predicted value of the index and the predetermined noise value, the range of the index data corresponding to the index type is determined as

in,

is the index prediction value of the measured index data.

和下限

包括：Optionally, according to the horizontal comparison data set, analyze and obtain the upper limit of noise of the indicator data under this indicator type

and lower limit

include:

For each indicator data a(t) in the horizontal comparison data set, the trend item trend(t), cycle item cycle(t) and season item season(t) of each indicator data are respectively obtained;

Calculate the noise item noise(t) of each index data according to the following formula:

a(t)=trend(t)+cycle(t)+season(t)+noise(t)

和下限

For the noise items of all index data in the horizontal comparison data set, determine the specified proportional probability distribution range within the probability distribution range, obtain the upper bound and lower bound of the range, and obtain the noise upper bound of the corresponding index type

and lower limit

Optionally, the index prediction value calculation module, for each index data corresponding to each detection item, calculates the index prediction value except noise includes:

Calculate the trend item trend(t), cycle item cycle(t) and season item season(t) of the indicator data;

Superimpose the calculated trend item, cycle item and season item data to obtain the predicted value of the indicator;

Among them: the trend item trend(t), the cycle item cycle(t) and the seasonal item season(t) are calculated for the indicator data, including:

Pass the indicator data a(t) through a low-pass filter to obtain the trend item trend(t);

Pass the indicator data a(t) through a band-pass filter with a lower frequency to obtain the cycle item cycle(t);

Pass the indicator data a(t) through a band-pass filter with a higher frequency to obtain the seasonal item season(t).

In a third aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it can realize the failure of the integrated spacer equipment comprehensive test module as described in the first aspect. Early warning method.

In a fourth aspect, the present invention provides a fault early warning system applied to an integrated compartment equipment comprehensive test module. The fault early warning system includes a hardware layer, a data layer, an application layer, and a main control layer, wherein the data layer, the application layer and the main control layer are arranged on a main control circuit board, and the hardware layer is configured in the test circuit of each detection circuit board, and is used to collect and obtain specified index parameters and variables corresponding to the test circuit; the hardware layer will The collected index parameters and digital data of variables are output to the data layer; the data layer saves the data input by the hardware layer; the application layer executes the method described in the first aspect, and the data layer The saved data is processed, the index data range corresponding to the index data is calculated, and whether the index data exceeds the index data range is judged, and if it exceeds, the fault warning signal of the corresponding index type under the corresponding detection item corresponding to the corresponding equipment is output; The above hardware layer, data layer, and application layer are managed.

Optionally, the main control layer has a user interface through which the user registers corresponding user information, detects task information, sets the data content to be collected, the initial index data range, and the history of required participation in the calculation of the index data range The length of the event, the deviation range of the predicted value, and the internal communication method are set, and the final fault warning signal is obtained through the user interface.

Optionally, the hardware layer is provided with several data probes, and each data probe is configured in the test circuit of each detection circuit board for collecting corresponding index parameters and variables. The data probe is an ADC at the hardware level, and converts the hardware data of the test circuit of each circuit board of the integrated interval device comprehensive test module into digital data and obtains it; at the software level, it is a data bypass, The main index parameters and variables in the software running on the test circuit of each circuit board of the integrated spacer equipment comprehensive test module are collected.

Optionally, the data layer includes a data redundancy protection module, a persistent database, and a data communication module; the data redundancy protection module is connected to all data probe signals, and receives indicator parameters and output from all data probes Variable data, and multiple acquisitions and cross-checks are performed on the index parameters and variable data to obtain accurate index parameters and variable data; the data redundancy protection module communicates with the persistent database, and the data The redundancy protection module stores the index parameters and variable data after the cross-check into the persistent database; the data redundancy protection module is also connected to the application layer through communication, and stores the index parameters after the cross-check and variable data are sent to the application layer; the data communication module is used for the communication between the data layer and the hardware layer, the application layer, and the main control layer, as well as the communication between the data redundancy protection module and the persistent database communication. The cross checking mechanism can guarantee the absolute accuracy of the main index parameters and variables collected by the data probe and the digital data.

Optionally, the persistent database stores the indicator parameters and variable data cross-checked by the data redundancy protection module in the form of data snapshots.

Optionally, after a fixed time interval, the persistent database acquires and saves the cross-checked index parameters and variable data output by the data redundancy protection module.

Beneficial effect

The present invention uses the traditional time-domain signal processing model to model the collection index data of the integrated interval equipment comprehensive test module to analyze the predicted value of the actual measurement data and the noise range of each index type in the historical horizontal comparison data, so as to determine each The target detection range of the index data in the actual test, and judge whether the measured index data exceeds the standard according to the target detection range, and output the corresponding fault warning signal when the data exceeds the standard, to remind the user of the power equipment corresponding to the corresponding index data, such as transformers, etc. question. The fault early warning method of the present invention can improve reliable analysis and early warning of index data in a multi-device multi-indicator monitoring environment, improve the efficiency of early warning analysis, and reduce labor cost input.

Description of drawings

Fig. 1 is the schematic diagram of the structure of the fault warning system applied to the comprehensive test module of the integrated spacer of the present invention;

Fig. 2 is a schematic diagram of the implementation flow of the fault early warning method of the present invention.

Detailed ways

It will be further described below in conjunction with the accompanying drawings and specific embodiments.

Example 1

This embodiment introduces a fault early warning method applied to the integrated test module of the integrated compartment equipment, including:

Collect the index data corresponding to each inspection item according to the corresponding relationship between the preset inspection items and the index parameters and variable types;

For each index data corresponding to each test item, calculate the predicted value of the index except the noise;

Determine the index data range corresponding to the index type according to the index prediction value and the predetermined noise value;

The index data is matched with the index data range of the corresponding index type, and if it exceeds the corresponding index data range, a fault warning signal of the corresponding index type under the corresponding detection item of the corresponding equipment is output.

Referring to Fig. 2, the implementation of the fault warning function in this embodiment needs to determine the noise range of the historical detection data of the corresponding index type before judging the range of the measured value index. Specifically, taking the transformer index data as an example, the analysis process is as follows.

After collecting the index data, first calculate the index prediction value of the index data, according to the following modeling formula for the index data:

a(t)=trend(t)+cycle(t)+season(t)+noise(t)

For each indicator data corresponding to each detection item, calculate the trend item trend(t), cycle item cycle(t) and season item season(t) of the indicator data; superimpose the calculated trend item, cycle item and season item data , to get the predicted value of the index except the noise item

The trend item trend(t), the cycle item cycle(t) and the seasonal item season(t) of the above calculation indicator data include: passing the indicator data a(t) through a low-pass filter to obtain the trend item trend(t); Pass the index data a(t) through a band-pass filter with a lower frequency to obtain the cycle item cycle(t); pass the index data a(t) through a band-pass filter with a higher frequency to obtain the seasonal item season(t ).

To determine the range of indicator data, it is also necessary to determine the range of noise. The methods for determining the range of noise values include:

For the same index type of the same inspection item, obtain historical index data collected from multiple similar equipment, and obtain a horizontal comparison data set;

For each indicator data a(t) in the horizontal comparison data set, the trend item trend(t), cycle item cycle(t) and season item season(t) of each indicator data are respectively obtained;

Calculate the noise item noise(t) of each indicator data according to the previous indicator data model formula:

和下限

For the noise items of all index data in the horizontal comparison data set, determine the specified proportional probability distribution range within the probability distribution range, obtain the upper bound and lower bound of the range, and obtain the noise upper bound of the corresponding index type

and lower limit

Assuming that the noise distribution of the index data is a normal distribution, the probability distribution function p(t) is obtained by fitting the index data to a normal distribution, and the probability distribution range of 90% of the normal distribution is taken to obtain the index corresponding to the index data The upper and lower bounds of the noise item of the type, so far the corresponding index data range of the index type can be determined as

When the index data exceeds the range of an index data, a corresponding fault alarm signal is output.

Example 2

Based on the same inventive concept as Embodiment 1, this embodiment introduces a fault early warning device applied to the integrated test module of the integrated spacer, including:

The index data acquisition module is configured to collect index data corresponding to each inspection item according to the preset correspondence between inspection items, index parameters, and variable types;

The index prediction value calculation module is configured to calculate the index prediction value except noise for each index data corresponding to each detection item;

The index data range determination module is configured to determine the index data range corresponding to the index type according to the index prediction value and the predetermined noise value;

The fault early warning judgment module is configured to match the index data with the index data range of the corresponding index type, and if it exceeds the corresponding index data range, output a fault early warning signal of the corresponding index type under the corresponding detection item of the corresponding equipment;

Wherein, the method for determining the noise value by the index data range determination module includes:

For the same index type of the same inspection item, obtain historical index data collected from multiple similar equipment, and obtain a horizontal comparison data set;

和下限

According to the horizontal comparison data set, the noise upper limit of the indicator data under this indicator type is obtained by analysis

and lower limit

其中，

为实测指标数据的指标预测值。According to the predicted value of the index and the predetermined noise value, the range of the index data corresponding to the index type is determined as

in,

is the index prediction value of the measured index data.

For the implementation of the specific functions of the above functional modules, refer to the corresponding content in Embodiment 1, and particularly point out the following content.

和下限

包括：When determining the noise value, the index data range determination module analyzes and obtains the noise upper limit of the index data under the index type according to the horizontal comparison data set

and lower limit

include:

For each indicator data a(t) in the horizontal comparison data set, the trend item trend(t), cycle item cycle(t) and season item season(t) of each indicator data are respectively obtained;

Calculate the noise item noise(t) of each index data according to the following formula:

a(t)=trend(t)+cycle(t)+season(t)+noise(t)

和下限

For the noise items of all index data in the horizontal comparison data set, determine the specified proportional probability distribution range within the probability distribution range, obtain the upper bound and lower bound of the range, and obtain the noise upper bound of the corresponding index type

and lower limit

The index prediction value calculation module calculates the index prediction values other than noise for each index data corresponding to each detection item, including:

Calculate the trend item trend(t), cycle item cycle(t) and season item season(t) of the indicator data;

Superimpose the calculated trend item, cycle item and season item data to obtain the predicted value of the indicator;

Among them: the trend item trend(t), the cycle item cycle(t) and the seasonal item season(t) are calculated for the indicator data, including:

Pass the indicator data a(t) through a low-pass filter to remove the high-frequency components in the data, so as to obtain the trend data of the data; through a high-frequency band-pass filter, remove the low-frequency trend information and Seasonal item information, so as to obtain the periodic data of the data; through a low-frequency band-pass filter, remove the low-frequency trend information and high-frequency periodic information of the data center, thereby obtaining the seasonal data of the data.

Example 3

This embodiment introduces a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the fault early warning method applied to the integrated test module of the integrated partition device as described in Embodiment 1 is implemented.

Example 4

This embodiment introduces a fault early warning system applied to the integrated spacer equipment comprehensive test module, see Figure 1, the solitary flight early warning system runs in the test circuit board corresponding to each detection item in the integrated spacer equipment comprehensive test module, the The hardware of the test result rough inspection system includes a main control circuit board. The system architecture is divided into four levels, including a hardware layer, a data layer, an application layer and a main control layer, wherein the data layer, the application layer and the main control layer are arranged on the main control circuit board,

The hardware layer is composed of the data probes in this system. On the hardware level, it is mainly ADC, which converts the hardware data in the circuit into digital data and obtains it. At the software level, it is mainly data bypass, and the main index parameters and variables in the software algorithm running in the tester are collected. The data collected by the data probe is output to the data redundancy protection module.

The data layer mainly includes three modules, data redundancy protection module, persistent database, and data communication module. Its main function is to ensure the normal and stable data collection work, and at the same time save the historical data of the tests run in the past, so as to provide historical data support for the test scope of this test.

Data redundancy protection module: During the data acquisition process, due to the noise in the hardware circuit, there may be undiscovered bugs in the software, so a data redundancy protection module is proposed. For the required collection, multiple collections and cross-checks are taken to ensure the absolute accuracy of the collected data. The cross-checked data in the data redundancy protection module is 1) stored in a persistent database. 2) Output to the algorithm module.

Persistent database: The persistent database holds the historical data of all past run tests. The data is saved in the form of a data snapshot, and after a fixed time interval, the cross-checked data output by the data redundancy protection module is obtained and saved.

Data communication module: ensure the smooth and normal operation of the data communication between the various modules in the system.

The application layer implements the algorithm logic described in Embodiment 1, analyzes and judges whether the collected index data exceeds the corresponding range, and outputs the corresponding fault early warning signal if it exceeds the corresponding range.

The main control layer also has a user interface through which the user registers the corresponding user information, detects the task information, sets the data content to be collected, the initial index data range, the length of historical events required to participate in the calculation of the index data range, and the predicted value Deviation range, and set the internal communication method, and obtain the final fault early warning signal through the user interface.

Those skilled in the art should understand that the embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present application is described with reference to flowcharts and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each procedure and/or block in the flowchart and/or block diagram, and a combination of procedures and/or blocks in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions may be provided to a general purpose computer, special purpose computer, embedded processor, or processor of other programmable data processing equipment to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing equipment produce a An apparatus for realizing the functions specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions The device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby The instructions provide steps for implementing the functions specified in the flow chart or blocks of the flowchart and/or the block or blocks of the block diagrams.

The embodiments of the present invention have been described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific embodiments, which are only illustrative, rather than restrictive, and those of ordinary skill in the art will Under the enlightenment of the present invention, many forms can also be made without departing from the gist of the present invention and the protection scope of the claims, and these all belong to the protection of the present invention.

Claims (10)

1. A method for early warning of faults applied to an integrated compartment equipment comprehensive test module, characterized in that it comprises: Collect the index data corresponding to each inspection item according to the corresponding relationship between the preset inspection items and the index parameters and variable types; For each index data corresponding to each test item, calculate the predicted value of the index except the noise; Determine the index data range corresponding to the index type according to the index prediction value and the predetermined noise value; Matching the index data with the index data range of the corresponding index type, if exceeding the corresponding index data range, outputting a failure warning signal of the corresponding index type under the corresponding detection item of the corresponding equipment; Wherein, the determination method of described noise value comprises: For the same index type of the same inspection item, obtain historical index data collected from multiple similar equipment, and obtain a horizontal comparison data set; According to the horizontal comparison data set, the noise upper limit of the indicator data under this indicator type is obtained by analysis

and lower limit

According to the predicted value of the index and the predetermined noise value, the range of the index data corresponding to the index type is determined as

in,

is the index prediction value of the measured index data. 2. The method according to claim 1, characterized in that, according to the horizontal comparison data set, the upper limit of the noise of the index data under the index type is analyzed and obtained

and lower limit

include: For each indicator data a(t) in the horizontal comparison data set, the trend item trend(t), cycle item cycle(t) and season item season(t) of each indicator data are respectively obtained; Calculate the noise item noise(t) of each index data according to the following formula: a(t)=trend(t)+cycle(t)+season(t)+noise(t) For the noise items of all index data in the horizontal comparison data set, determine the specified proportional probability distribution range within the probability distribution range, obtain the upper bound and lower bound of the range, and obtain the noise upper bound of the corresponding index type

and lower limit

3. The method according to claim 2, wherein, for the noise item of all index data in the horizontal comparison data set, determining its probability distribution range includes: Assuming that the noise distribution of the index data is a normal distribution, the probability distribution function p(t) is obtained by fitting the index data to a normal distribution, and the probability distribution range of 90% of the normal distribution is taken to obtain the index corresponding to the index data Type upper and lower bounds on the noise term. 4. The method according to claim 2, characterized in that, for each index data a(t) in the horizontal comparison data set, respectively obtain the trend item trend (t) and the cycle item cucle (t) of each index data and the season term season(t), including: Pass the indicator data a(t) through a low-pass filter to obtain the trend item trend(t); Pass the indicator data a(t) through a band-pass filter with a lower frequency to obtain the cycle item cycle(t); Pass the indicator data a(t) through a band-pass filter with a higher frequency to obtain the seasonal item season(t). 5. The method according to claim 4, wherein, for each index data corresponding to each detection item, calculating an index prediction value other than noise includes: Calculate the trend item trend(t), cycle item cycle(t) and season item season(t) of the indicator data; The calculated trend item, cycle item and season item data are superimposed to obtain the predicted value of the indicator. 6. A fault early warning device applied to an integrated interval equipment comprehensive test module, characterized in that it includes: The index data acquisition module is configured to collect index data corresponding to each inspection item according to the preset correspondence between inspection items, index parameters, and variable types; The index prediction value calculation module is configured to calculate the index prediction value except noise for each index data corresponding to each detection item; The index data range determination module is configured to determine the index data range corresponding to the index type according to the index prediction value and the predetermined noise value; The fault early warning judgment module is configured to match the index data with the index data range of the corresponding index type, and if it exceeds the corresponding index data range, output a fault early warning signal of the corresponding index type under the corresponding detection item of the corresponding equipment; Wherein, the method for determining the noise value by the index data range determination module includes: For the same index type of the same inspection item, obtain historical index data collected from multiple similar equipment, and obtain a horizontal comparison data set; According to the horizontal comparison data set, the noise upper limit of the indicator data under this indicator type is obtained by analysis

and lower limit

According to the predicted value of the index and the predetermined noise value, the range of the index data corresponding to the index type is determined as

in,

is the index prediction value of the measured index data. 7. The fault early warning device applied to the integrated compartment equipment comprehensive test module according to claim 6, characterized in that, according to the horizontal comparison data set, the analysis obtains the noise upper limit of the index data under the index type

and lower limit

include: For each indicator data a(t) in the horizontal comparison data set, the trend item trend(t), cycle item cycle(t) and season item season(t) of each indicator data are respectively obtained; Calculate the noise item noise(t) of each index data according to the following formula: a(t)=trend(t)+cycle(t)+season(t)+noise(t) For the noise items of all index data in the horizontal comparison data set, determine the specified proportional probability distribution range within the probability distribution range, obtain the upper bound and lower bound of the range, and obtain the noise upper bound of the corresponding index type

and lower limit

The index prediction value calculation module calculates the index prediction values other than noise for each index data corresponding to each detection item, including: Calculate the trend item trend(t), cycle item cycle(t) and season item season(t) of the indicator data; Superimpose the calculated trend item, cycle item and season item data to obtain the predicted value of the indicator; Among them: the trend item trend(t), the cycle item cycle(t) and the seasonal item season(t) are calculated for the indicator data, including: Pass the indicator data a(t) through a low-pass filter to obtain the trend item trend(t); Pass the indicator data a(t) through a band-pass filter with a lower frequency to obtain the cycle item cycle(t); Pass the indicator data a(t) through a band-pass filter with a higher frequency to obtain the seasonal item season(t). 8. A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the fault warning method applied to the comprehensive test module of the integrated spacer device as claimed in claim 1-5 is realized. 9. A fault early warning system applied to an integrated compartment equipment comprehensive test module, characterized in that the fault early warning system includes a hardware layer, a data layer, an application layer and a master control layer, wherein the data layer, the application layer and the The main control layer is arranged on a main control circuit board, and the hardware layer is configured in the test circuit of each detection circuit board, and is used to collect and acquire specified index parameters and variables corresponding to the test circuit; The collected index parameters and digital data of variables are output to the data layer; the data layer saves the data input by the hardware layer; the application layer executes the method described in the first aspect, and saves the data layer process the data, calculate the index data range corresponding to the index data, judge whether the index data exceeds the index data range, and if it exceeds, output the fault warning signal of the corresponding index type under the corresponding detection item of the corresponding equipment; The hardware layer, the data layer, and the application layer are managed. 10. The failure early warning system applied to the integrated compartment equipment comprehensive test module according to claim 9, wherein the main control layer has a user interface, and the user registers corresponding user information through the user interface, and the detection task Information, set the data content to be collected, the initial index data range, the length of historical events required to participate in the calculation of the index data range, the deviation range of the predicted value, and set the internal communication method, and obtain the final fault warning signal through the user interface; The hardware layer is provided with several data probes, and each data probe is configured in the test circuit of each detection circuit board for collecting corresponding index parameters and variables; The data layer includes a data redundancy protection module, a persistent database and a data communication module; the data redundancy protection module is connected to all data probe signals, receives indicator parameters and variable data output from all data probes, and The index parameters and variable data are collected multiple times and cross-checked to obtain accurate index parameters and variable data; the data redundancy protection module communicates with the persistent database, and the data redundancy protection module Store the index parameters and variable data after the cross-check in the persistent database; the data redundancy protection module is also connected to the application layer through communication, and transmit the index parameters and variable data after the cross-check to the application layer; the data communication module is used for the communication between the data layer and the hardware layer, the application layer, the main control layer and the communication between the data redundancy protection module and the persistent database; The persistent database stores the index parameters and variable data cross-checked by the data redundancy protection module in the form of data snapshots. After a fixed time interval, the persistent database acquires and saves the cross-checked index parameters and variable data output by the data redundancy protection module.

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