CN112526885A - Equipment guarantee oriented autonomous decision making system - Google Patents
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Abstract
The invention discloses an equipment guarantee oriented autonomous decision making system, and belongs to the technical field of autonomous decision making. The autonomic decision making system comprises: the system comprises a data perception module, an equipment level decision module, a system level decision module and a user service module, wherein the data perception module is in data connection with the equipment level decision module, the equipment level decision module is in data interconnection with the system level decision module, and the system level decision module is in data interconnection with the user service module. The invention provides an autonomous decision making system, which autonomously feeds back and controls the information of the outside and the equipment, thereby improving the equipment guarantee capability; the invention optimizes the equipment guarantee effect by forming a control mechanism integrating global decision and local processing on the equipment guarantee task; the invention provides a system-level decision module structure using a D-S evidence theory, so that the system has the capability of processing multi-source and uncertain information.
Description
Technical Field
The invention relates to an equipment guarantee oriented autonomous decision making system, and belongs to the technical field of autonomous decision making.
Background
The equipment guarantee refers to protection of various important resources of the equipment system, and the scope of the protection includes various guarantee attributes such as resource effectiveness, reliability and safety. The aim is to maintain the comprehensive efficiency of the target resource by planning and implementing the guarantee of the target resource.
In the prior art, equipment guarantee decision is mainly made by adopting an evaluation and analysis method. The representative equipment protection method mainly comprises the following steps: the patent "hierarchical analysis and evaluation system based on information entropy (CN111126801A) for equipment guarantee capability" proposes a hierarchical analysis and evaluation system based on information entropy for equipment guarantee capability, which obtains the credibility of test data by adding data quantification information, assigns corresponding uncertainty weight to the test data, and adds data quantification information of an analytic hierarchy process, thereby effectively improving the evaluation accuracy; the patent "a method and system for matching equipment guarantee data based on multistage similarity calculation" (CN110795607A) "proposes a method and system for matching equipment guarantee data based on multistage similarity calculation, and the patent claims that the matching relation of each equipment guarantee data item in each service system is established by performing literal similarity calculation, semantic similarity calculation, value range similarity calculation and manual identification on the preprocessed equipment guarantee data to be matched; the patent "equipment guarantee characteristic evaluation method based on space mission" (CN109885933A) proposes to establish an equipment guarantee characteristic evaluation model of a basic mission first, and then to evaluate the equipment guarantee characteristic of the whole mission by using the established evaluation model.
Therefore, the existing equipment securing method has the following disadvantages:
1. the method is limited to a stage of evaluation analysis and matching perception, and autonomous decision making cannot be carried out on the evaluation and perception results;
2. the method is limited to a step-by-step and step-by-step processing mode, and a control mechanism integrating global decision and local processing cannot be formed on equipment guarantee tasks;
3. data evaluation is limited to known deterministic models and fails to feed back multi-source, uncertain information.
Disclosure of Invention
The invention aims to provide an equipment guarantee-oriented autonomous decision making system to solve the problems of the existing equipment guarantee method.
An equipment assurance oriented autonomic decision making system, the autonomic decision making system comprising: the system comprises a data perception module, an equipment level decision module, a system level decision module and a user service module, wherein the data perception module is in data connection with the equipment level decision module, the equipment level decision module is in data interconnection with the system level decision module, and the system level decision module is in data interconnection with the user service module.
Further, the data perception module further comprises a sensor sub-module and an agent sub-module, wherein,
the sensor submodule is used for acquiring equipment key data, the equipment key data comprise core component information and control information, and the core component information comprises engine information, radar system information and life support system information;
and the agent sub-module is used for forwarding and transmitting the equipment key data of the sensor sub-module to the equipment level decision module and carrying out priority scheduling.
Further, the device-level decision module includes a device local feedback control unit, and the device local feedback control unit is configured to receive the equipment key data, execute a control process without autonomous learning according to the equipment key data, process local device data information, and execute a corresponding control action.
Further, the local feedback control unit of the device comprises a sensor, a decision maker and an actuator, wherein the sensor, the decision maker and the actuator are sequentially connected to form a circular feedback control chain,
the sensor is used for receiving the equipment key data forwarded by the agent sub-module and performing de-duplication, de-jitter and structuring processing on the data;
the decision maker is used for making a decision according to the existing control rule without autonomous learning based on the data processing result of the sensor;
and the executor is used for guarding and executing the decision instruction generated by the decision maker.
Further, the system level decision module is configured to process data that is thrown by the device local feedback control unit and cannot be processed by a control rule without autonomous learning, and includes a self-management loop, an autonomous decision rule base, and an equipment assurance rule based on a D-S evidence theory, where,
the self-management ring comprises a monitoring submodule, a decision submodule and a resource management submodule, wherein the monitoring submodule is used for monitoring data which cannot be processed and is thrown out by the equipment local feedback control unit; the decision submodule is used for carrying out system level decision according to the autonomous decision rule base and the equipment guarantee rule based on the D-S evidence theory; the resource management submodule is used for updating the autonomous decision rule base and the equipment guarantee rule based on the D-S evidence theory in an autonomous learning manner in real time;
the autonomous decision rule base is used for storing reasoning basis and decision rule required by system level decision and providing basic rule support for the decision submodule;
the equipment assurance rule based on the D-S evidence theory is used for processing multi-source and uncertain equipment assurance information from the equipment-level decision module.
Further, the equipment guarantee rule based on the D-S evidence theory is a theory established on a non-empty finite field theta, wherein theta is called an identification framework and represents a finite number of system states { theta [ theta ])1，θ2，…，θnH and the system state assumes HiFor a subset of Θ, an element of the power set P (Θ) of Θ, D-S evidence theory needs to define a certain pairEvidence supports a probability function, i.e., a belief assignment function, of a system state as follows:
wherein m is an objective function, and the function is mapping from a power set of theta to a [0,1] interval; a is the equipment event, m (A) represents the credibility of the equipment event in the system,
in addition, the equipment safeguard rule based on the D-S evidence theory also follows the Dempster rule, which is formally defined as follows:
assuming that m1 and m2 are the confidence distribution functions of the two events, the combination of the two events m1 and m2 yields a combined evidence with a confidence distribution function of:
where K is a normalization factor, B, C denotes a device event other than a, and K ═ ΣB∩C≠Am1(B)m2(C) As the degree of conflict between different device events.
Further, the user service module comprises a plurality of service interfaces, and the service interfaces are used for processing the feedback result from the system level decision module and sending the feedback result to the corresponding user service end, and the user service end completes the corresponding task.
The main advantages of the invention are:
(1) by the aid of the autonomous decision making system, autonomous feedback and control are performed on external and equipment self information, and equipment guarantee capacity is improved;
(2) the equipment guarantee effect is optimized by forming a control mechanism integrating global decision and local processing on the equipment guarantee task;
(3) a system level decision module structure using D-S evidence theory is provided, so that the system has the capability of processing multi-source and uncertain information.
Drawings
FIG. 1 is a block diagram of an autonomous decision-making system for equipment assurance according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an equipment assurance oriented autonomic decision making system includes: the system comprises a data perception module, an equipment level decision module, a system level decision module and a user service module, wherein the data perception module is in data connection with the equipment level decision module, the equipment level decision module is in data interconnection with the system level decision module, and the system level decision module is in data interconnection with the user service module.
The data perception module further comprises a sensor sub-module and an agent sub-module, wherein,
the sensor submodule is used for acquiring equipment key data, the equipment key data comprise core component information and control information, and the core component information comprises engine information, radar system information and life support system information;
and the agent sub-module is used for forwarding and transmitting the equipment key data of the sensor sub-module to the equipment level decision module and carrying out priority scheduling.
The equipment-level decision module comprises an equipment local feedback control unit, and the equipment local feedback control unit is used for receiving the equipment key data, executing a control process without autonomous learning according to the equipment key data, processing local equipment data information and executing a corresponding control action.
The local feedback control unit of the equipment comprises a sensor, a decision maker and an actuator, wherein the sensor, the decision maker and the actuator are sequentially connected to form a circulating feedback control chain,
the sensor is used for receiving the equipment key data forwarded by the agent sub-module and performing de-duplication, de-jitter and structuring processing on the data;
the decision maker is used for making a decision according to the existing control rule without autonomous learning based on the data processing result of the sensor;
and the executor is used for guarding and executing the decision instruction generated by the decision maker.
The system level decision module is used for processing data which is thrown out by the local feedback control unit of the equipment and can not be processed by the control rule without autonomous learning, and comprises a self-management ring, an autonomous decision rule base and an equipment guarantee rule based on a D-S evidence theory, wherein,
the self-management ring comprises a monitoring submodule, a decision submodule and a resource management submodule, wherein the monitoring submodule is used for monitoring data which cannot be processed and is thrown out by the equipment local feedback control unit; the decision submodule is used for carrying out system level decision according to the autonomous decision rule base and the equipment guarantee rule based on the D-S evidence theory; the resource management submodule is used for updating the autonomous decision rule base and the equipment guarantee rule based on the D-S evidence theory in an autonomous learning manner in real time;
the autonomous decision rule base is used for storing reasoning basis and decision rule required by system level decision and providing basic rule support for the decision submodule;
the equipment assurance rule based on the D-S evidence theory is used for processing multi-source and uncertain equipment assurance information from the equipment-level decision module.
The equipment guarantee rule based on the D-S evidence theory is a theory established on a non-empty finite field theta, wherein theta is called an identification framework and represents a finite number of system states { theta [ theta ])1，θ2，…，θnH and the system state assumes HiD-S evidence is a subset of Θ, an element of the power set P (Θ) of ΘTheory needs to define a probability function, i.e., a belief allocation function, that supports a system state for some evidence, as follows:
wherein m is an objective function, and the function is mapping from a power set of theta to a [0,1] interval; a is the equipment event, m (A) represents the credibility of the equipment event in the system,
in addition, the equipment safeguard rule based on the D-S evidence theory also follows the Dempster rule, which is formally defined as follows:
assuming that m1 and m2 are the confidence distribution functions of the two events, the combination of the two events m1 and m2 yields a combined evidence with a confidence distribution function of:
where K is a normalization factor, B, C denotes a device event other than a, and K ═ ΣB∩C≠Am1(B)m2(C) As the degree of conflict between different device events.
The user service module comprises a plurality of service interfaces, the service interfaces are used for processing feedback results from the system level decision module and sending the feedback results to corresponding user service terminals, and the user service terminals complete corresponding tasks.
Claims (7)
1. An equipment assurance oriented autonomic decision making system, comprising: the system comprises a data perception module, an equipment level decision module, a system level decision module and a user service module, wherein the data perception module is in data connection with the equipment level decision module, the equipment level decision module is in data interconnection with the system level decision module, and the system level decision module is in data interconnection with the user service module.
2. The equipment assurance-oriented autonomic decision making system of claim 1, wherein the data awareness module further comprises a sensor sub-module and an agent sub-module, wherein,
the sensor submodule is used for acquiring equipment key data, the equipment key data comprise core component information and control information, and the core component information comprises engine information, radar system information and life support system information;
and the agent sub-module is used for forwarding and transmitting the equipment key data of the sensor sub-module to the equipment level decision module and carrying out priority scheduling.
3. The equipment assurance-oriented autonomic decision making system of claim 1, wherein the device-level decision making module comprises a device local feedback control unit configured to receive the equipment critical data and to perform a control process without autonomic learning based on the equipment critical data, process local device data information and perform corresponding control actions.
4. The equipment-oriented guarantee autonomous decision system of claim 3, wherein the equipment local feedback control unit comprises a sensor, a decision maker and an actuator, the sensor, the decision maker and the actuator are connected in sequence to form a cyclic feedback control chain, wherein,
the sensor is used for receiving the equipment key data forwarded by the agent sub-module and performing de-duplication, de-jitter and structuring processing on the data;
the decision maker is used for making a decision according to the existing control rule without autonomous learning based on the data processing result of the sensor;
and the executor is used for guarding and executing the decision instruction generated by the decision maker.
5. The system of claim 1, wherein the system level decision module is configured to process data thrown by the device local feedback control unit and unable to be processed by control rules without autonomous learning, and comprises a self-management loop, an autonomous decision rule base, and D-S evidence theory-based equipment assurance rules, wherein,
the self-management ring comprises a monitoring submodule, a decision submodule and a resource management submodule, wherein the monitoring submodule is used for monitoring data which cannot be processed and is thrown out by the equipment local feedback control unit; the decision submodule is used for carrying out system level decision according to the autonomous decision rule base and the equipment guarantee rule based on the D-S evidence theory; the resource management submodule is used for updating the autonomous decision rule base and the equipment guarantee rule based on the D-S evidence theory in an autonomous learning manner in real time;
the autonomous decision rule base is used for storing reasoning basis and decision rule required by system level decision and providing basic rule support for the decision submodule;
the equipment assurance rule based on the D-S evidence theory is used for processing multi-source and uncertain equipment assurance information from the equipment-level decision module.
6. The equipment assurance-oriented autonomic decision making system according to claim 5, wherein the D-S evidence theory is an equipment assurance rule based on a non-empty finite field theta, wherein theta is called an identification framework and represents a finite number of system states { theta }1，θ2，...，θnH and the system state assumes HiFor a subset of Θ, an element of the power set P (Θ) of Θ, D-S evidence theory needs to define a probability function, i.e. a belief distribution function, that supports a systematic state for some evidence, as follows:
wherein m is an objective function, and the function is mapping from a power set of theta to a [0,1] interval; a is the equipment event, m (A) represents the credibility of the equipment event in the system,
in addition, the equipment safeguard rule based on the D-S evidence theory also follows the Dempster rule, which is formally defined as follows:
assuming that m1 and m2 are the confidence distribution functions of the two events, the combination of the two events m1 and m2 yields a combined evidence with a confidence distribution function of:
where K is a normalization factor, B, C denotes a device event other than a, and K ═ ΣB∩C≠Am1(B)m2(C) As the degree of conflict between different device events.
7. The equipment assurance-oriented autonomic decision making system according to claim 1, wherein the customer service module comprises a plurality of service interfaces, the plurality of service interfaces are configured to process the feedback result from the system level decision making module and send the feedback result to a corresponding customer service, and the customer service completes a corresponding task.
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