Measurement and control system fault diagnosis method and system based on accident tree and storage medium

Abstract

The invention discloses a measurement and control system fault diagnosis method and system based on an accident tree and a storage medium. The method comprises the following steps: configuring a corresponding interpretation condition according to a measurement data type of a measurement and control system; an accident tree is constructed according to the data circulation process of the measurement and control system, and a diagnosis scheme is formulated; acquiring real-time data of the measurement and control system, and performing interpretation according to the preset interpretation condition; according to abnormal conditions identified through data interpretation and in combination with a preset diagnosis scheme, an actual fault is confirmed; performing relation matching according to the fault type of the actual fault and the fault phenomenon in the accident tree, and confirming a corresponding fault label to perform reasoning diagnosis; according to the method, the fault factors with significant influence can be quickly identified by combining data interpretation and the accident tree, so that the system failure source is traced.

Description

Technical Field

[0001] The present invention relates to the technical field of fault diagnosis, and more particularly to a fault diagnosis method, system and storage medium for a measurement and control system based on an accident tree. Background Art

[0002] At present, the measurement and control information system is one of the core systems of the aircraft test mission. It can provide real-time connection, optimal integration and command display of various test information data such as telemetry and external measurement. It belongs to the pipeline architecture style. In addition, the aircraft test mission has the characteristics of short time cycle and strong timeliness. Once a fault occurs in a certain link, if the fault cannot be identified and restored in a very short time, it will directly affect the progress of the entire test process, which may directly lead to the failure of the test mission and cause heavy losses.

[0003] Therefore, how to improve the response speed of fault identification and recovery is a technical problem that technical personnel in this field need to solve urgently. Summary of the invention

[0004] In view of this, the present invention provides a fault diagnosis method for a measurement and control system based on an accident tree, which can combine data interpretation and the accident tree to quickly identify fault factors with significant impact, thereby tracing the root cause of system failure.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] A fault diagnosis method for a measurement and control system based on an accident tree comprises the following steps:

[0007] Configure corresponding interpretation conditions according to the measurement data type of the measurement and control system;

[0008] Construct an accident tree according to the data flow process of the measurement and control system, and formulate a diagnosis plan;

[0009] Acquire the real-time data of the measurement and control system, and interpret it according to the preset interpretation conditions;

[0010] The abnormal conditions identified through data interpretation are combined with the preset diagnostic scheme to confirm the actual fault;

[0011] According to the relationship between the fault type of the actual fault and the fault phenomenon in the fault tree, the corresponding fault label is confirmed for inference diagnosis.

[0012] Preferably, the inference diagnosis includes calculating a minimum cut set and a minimum path set, comparing the sizes of the minimum cut set and the minimum path set, and selecting the smallest size for fault state determination.

[0013] Preferably, the judgment conditions include:

[0014] The data dependency condition, the value range judgment condition, the time domain judgment condition and the state are one or more of the judgment conditions.

[0015] Preferably, the various judgment conditions specifically include:

[0016] The data dependency condition includes confirming the one-way flow dependency relationship of the measurement and control information, obtaining the correlation between the preceding data and the following data, and judging the abnormality of the data generation behavior according to the correlation;

[0017] The value range judgment conditions include confirming the reasonable range of various measurement and control data;

[0018] The time domain judgment conditions include confirming the delay thresholds of various types of data;

[0019] The status bit judgment condition includes constructing a status bit inspection rule by combing and classifying the data status bits in the data transmission protocol.

[0020] Preferably, the step of constructing the accident tree includes:

[0021] The first measurement and control data is taken as the top event, the abnormal situation of the first measurement and control data is taken as the first-level event, and the abnormality of the preceding data of the first measurement and control data is taken as the second-level event. The second-level event is further decomposed into different abnormal situations until the decomposition of the most original data is completed to obtain the fault tree.

[0022] Preferably, the steps also include: setting a reasoning credibility threshold, and when the output reasoning diagnosis result does not meet the credibility threshold, the result is deemed to be unreliable and further verification is performed.

[0023] A fault diagnosis system for a measurement and control system based on an accident tree, comprising a measurement and control system and a fault diagnosis system, wherein the fault diagnosis system is connected to the measurement and control system in communication, and is used to obtain the operation status of the measurement and control system and monitor the occurrence of actual faults in real time according to pre-defined judgment conditions, accident trees and diagnosis schemes, and perform reasoning diagnosis;

[0024] The measurement and control system is an optical measurement system, which includes a data acquisition subsystem, an information collection and distribution subsystem, and a real-time processing subsystem;

[0025] The data acquisition subsystem uses an optical lens to capture the target and packages the lens information into raw measurement data and sends it to the information collection and distribution subsystem;

[0026] The information collection and distribution subsystem performs protocol conversion on the raw measurement data to obtain internal optical measurement data and sends the internal optical measurement data to the real-time processing subsystem;

[0027] The real-time processing subsystem receives the optical measurement data from multiple measurement stations and performs intersection calculation to obtain optical intersection track data.

[0028] Preferably, the fault diagnosis system includes a first configuration module, a second configuration module, a third configuration module, a real-time monitoring module and a reasoning diagnosis module;

[0029] The first configuration module is used to configure data interpretation conditions according to data types; the data types include intersection track data, internal optical strategy data and raw measurement data;

[0030] The second configuration module is used to construct an accident tree; in the accident tree, the top event is the intersection track data, the next level event is the real-time processing subsystem abnormality and the internal optical measurement data abnormality; the next level event is the information aggregation and distribution subsystem abnormality and the original measurement data abnormality; the next level event is the optical lens abnormality;

[0031] The third configuration module is used to configure the diagnosis scheme;

[0032] The real-time monitoring module is used to obtain real-time measurement and control data and perform real-time interpretation according to the interpretation conditions configured by the first configuration module, identify abnormal conditions, and perform preliminary diagnosis according to the diagnostic scheme configured by the third module to confirm the fault phenomenon;

[0033] The inference diagnosis module is used to perform inference diagnosis according to the fault phenomenon.

[0034] Preferably, it also includes a display module for displaying the diagnostic process of the inference diagnosis.

[0035] A computer-readable storage medium stores a computer program, and when the computer program is executed, the fault diagnosis method described above is implemented.

[0036] It can be seen from the above technical solution that, compared with the prior art, the present invention discloses a fault diagnosis method, system and storage medium for a measurement and control system based on an accident tree, which can, according to preset configurations, enable instant interpretation of strong real-time data based on rules, realize rapid and automatic identification of fault phenomena in the measurement and control information system, and compress the fault discovery time to milliseconds to increase the rate of fault discovery, effectively reducing the risks of untimely fault discovery and long fault phenomenon confirmation time caused by human factors.

[0037] Accurate fault location: It can achieve rapid diagnosis and maintain consistent probabilistic reasoning. It can be used to select search channels for rules to a certain extent and improve diagnostic efficiency. As long as the corresponding fault tree is given, diagnosis can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0039] Figure 1 A schematic diagram of a fault diagnosis method for a measurement and control system based on an accident tree is provided in an embodiment of the present invention.

[0040] Figure 2 A schematic diagram of the structure of a fault diagnosis system for a measurement and control system based on an accident tree is provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] Example 1

[0043] like Figure 1 The embodiment of the present invention discloses a fault diagnosis method for a measurement and control system based on an accident tree, comprising the following steps:

[0044] S1: Configure corresponding interpretation conditions according to the measurement data type of the measurement and control system.

[0045] S2: Construct an accident tree according to the data flow process of the measurement and control system, and formulate a diagnosis plan.

[0046] S3: Acquire the real-time data of the measurement and control system, and interpret it according to the preset interpretation conditions.

[0047] S4: Confirm the actual fault by identifying abnormal conditions through data interpretation and combining them with the preset diagnostic solution.

[0048] S5: Match the fault type of the actual fault with the fault phenomenon in the fault tree, confirm the corresponding fault label for inference diagnosis. Specifically, through quantitative and qualitative analysis, calculate the minimum cut set and minimum path set of the fault tree, compare the set size, and select the minimum size for fault state judgment.

[0049] In this implementation, S1 and S2 are the configuration process before the test task, and S3-S5 are the test process after the configuration is completed. During the test process, the actual fault can be confirmed and reasoning diagnosis can be performed based on the pre-configured judgment conditions, fault tree and diagnosis scheme.

[0050] In order to facilitate the understanding of this solution, the technical terms involved in this implementation are explained accordingly:

[0051] a) Measurement and control information system: The measurement and control information system is one of the core systems of the aircraft test mission, which can provide real-time access, optimal integration and command display capabilities for various test information data such as telemetry and external measurement. The measurement and control information system is mainly composed of subsystems such as information collection and distribution, real-time processing, rapid judgment, command display, data storage management, simulation and replay.

[0052] b) Quantitative analysis: used to evaluate the probability of occurrence of top events and the importance of bottom events. Based on the probability of occurrence of bottom events and the relationship between logic gates, the comprehensive probability of top events can be calculated. For "AND gates", the probability of top events is the product of all bottom event probabilities. For "OR gates", the probability of top events is the sum of all bottom event probabilities minus the intersection probability.

[0053] c) Qualitative analysis: Aims to identify the minimum combination of bottom events that lead to the top event (minimum cut set). For simple fault trees, directly expand their structure functions and obtain the minimum cut set through Boolean algebra simplification. For complex fault trees, use recursive decomposition to determine the minimum cut set.

[0054] d) Minimum cut sets: Each minimum cut set represents a possibility of the top event. Mastering the minimum cut set actually means mastering the various possibilities of the top event. This helps us master the occurrence rules of the accident tree and provide a basis for accident investigation and analysis and accident prevention. Through the minimum cut set, we can find out the loopholes in the system and formulate corresponding preventive measures to comprehensively control the occurrence of accidents, thereby improving the safety of the system. If there are several minimum cut sets in the accident tree, there is a concentrated possibility of the top event. The more minimum cut sets there are, the more possibilities of accidents, and the more dangerous the system is.

[0055] e) Minimum path set: If all the basic events in a minimum path set do not occur, the top event will not occur. By mastering the minimum path set, we can know which basic events must be controlled to prevent the top event from happening in order to prevent the accident from happening, and we can know how many solutions there are to control system accidents. The more minimum path sets in the accident tree, the safer the system.

[0056] In order to further implement the above technical solution, the configuration of the judgment conditions in S1 includes:

[0057] (1) Data dependency conditions

[0058] Considering the pipeline architecture of the measurement and control system, the test information is a one-way flow, and the dependency relationship between data can be confirmed according to the flow order. In the judgment process, it can be judged whether the post-data is generated in dependence on the pre-data, or whether the corresponding post-data is generated based on the pre-data.

[0059] (2) Value range judgment conditions

[0060] The test information data is mainly the spatial data formed by measuring the aircraft. It can confirm the theoretical indicators of various measurement elements in the space based on the test objectives, so as to establish the value range interpretation rules and set the maximum and minimum theoretical values ​​of the measurement data. In the interpretation process, it is mainly used to determine whether the measurement data is within the theoretical range.

[0061] (3) Time domain interpretation conditions

[0062] Since the measurement and control information system is a strong real-time system, when the data delay exceeds a certain range, it will no longer be used as valid data for data processing. Therefore, the data deviation allowable threshold is set by constructing the time domain interpretation rule to check whether the data delay meets the system requirements.

[0063] (4) Status bit reading conditions

[0064] By sorting out and classifying the data status bits in the data transmission protocol, configuring the normal and abnormal values ​​of various status bits, and building status bit inspection conditions, it is mainly used to judge the status bits carried by the data itself.

[0065] In order to further implement the above technical solution, the construction of the data in S2 includes:

[0066] According to the data information categories, data flow relationships and data interpretation rules in the measurement and control information system, the possible fault phenomena and the factors that may cause the fault phenomena are predicted, and the measurement and control information system fault tree is designed.

[0067] The accident tree is a tree-like model of the fault subject, main function and fault. The accident tree is an inverted tree structure. The basic events of the accident tree are very important. They may affect the intermediate events or top events of the next layer of structure. There is a causal relationship between them. We use logic gates to represent their relationship.

[0068] Specifically, various types of data are set as the top events of the fault tree, and data not being generated as expected, abnormal value range interpretation, abnormal time domain interpretation, abnormal status bit interpretation, etc. are set as secondary events, and subsystem abnormalities, abnormal front data, etc. are set as third-level events. In this way, the fault is decomposed level by level and finally decomposed to specific fault points, such as the working status of key functions, the status of a certain type of data, etc., as the basic events of the fault tree.

[0069] Furthermore, the configuration of the diagnostic scheme in S2 includes: configuring the corresponding judgment results under various fault phenomena.

[0070] According to the data information category of the measurement and control information system, the applicable interpretation rules, and the relationship with the fault tree, the settings are made to build a process from data reception to data interpretation and then to fault tree diagnosis. The fault phenomenon identification rules are set (for example, how many consecutive abnormalities are considered to be a fault phenomenon) to form a data diagnosis plan. In the process of fault diagnosis, the human participation in the test task is reduced and the degree of automation of fault diagnosis is increased.

[0071] In order to further implement the above technical solution, S3 interprets real-time data including:

[0072] Data generation and interpretation: According to the data dependency inspection rules, determine whether the preceding data meets the rules. If the rules are met, further determine whether the following data is generated as expected. If not, it is determined to be abnormal.

[0073] Data quality interpretation: According to the data value range interpretation rules, the measurement data is checked to see if it is within the theoretical range. If it exceeds the theoretical range, it is judged as abnormal;

[0074] Data delay interpretation: According to the data time domain interpretation rules, the data time code is extracted and compared with the time server. If it exceeds the predetermined range, it is judged as abnormal;

[0075] Data status interpretation: According to the data status bit interpretation rules, extract the data status bits, match them with the rules, and judge whether the data status is abnormal.

[0076] When the data is judged to be abnormal and meets the fault conditions identified by the preset diagnostic scheme, inference diagnosis is performed.

[0077] Furthermore, the steps of reasoning diagnosis in S5 include:

[0078] When an abnormality is found in the data interpretation, an alarm will be triggered. Common alarm information includes accidents, abnormalities, notifications, etc. The alarm information is displayed in a visual way. The alarm information includes fault name, fault category, occurrence time, etc. At the same time, the fault phenomenon is used as the top event to match the accident tree. The fault phenomenon (a certain data abnormality) is used as the top event, and the factors that cause the abnormality are combined as basic events to build a temporary accident tree and accident labels, and start the accident tree diagnosis. Each type of data anomaly can have a corresponding node in the accident tree. According to the current phenomenon, the accident tree is qualitatively analyzed, and the accident tree is simplified by Boolean algebra to obtain the minimum cut set or minimum path set of the accident tree, calculate the structural importance of each basic event, and draw a qualitative analysis conclusion. By analyzing the structure of the accident tree, the minimum cut set and minimum path set of the accident tree are obtained, so as to obtain the logical relationship between the basic event and the top event; the quantitative analysis of the accident tree is mainly based on the probability of occurrence of each basic event that causes the accident (according to the accident rate or error rate), using the logical relationship of the accident tree, the probability of occurrence of each basic event is combined, and the probability of occurrence of the top event of the accident tree, as well as the probability importance and critical importance of each basic event, is calculated. The influence of each minimum cut set on the occurrence of the top event is obtained. Then, by comparing the scale (number) of the minimum cut set and the minimum path set, a small set is selected, and the state judgment is performed in combination with the probability importance and critical importance of each basic event. When making a judgment, the basic events in the set are first extracted and deduplicated to further reduce the scale of calculation and interpretation, and then the basic events are interpreted one by one. There are three ways to interpret. One is to automatically interpret the status data corresponding to the basic events based on the interpretation rules; the other is to obtain the software health status through the API interface and perform health checks on the key functions of the software; the last one is that the corresponding status cannot be automatically obtained from the system, and the results need to be output and then manually assisted in interpretation. After the basic event interpretation is completed, it is reorganized into the minimum cut set or minimum path set, and the priority is sorted according to the number of fault events, the number of manual interpretations, the probability of basic event failure, etc., and output to the front desk for users to troubleshoot.

[0079] Among them, the probability importance is obtained by taking the partial derivative of the probability of occurrence of the basic probability event, and the critical importance is the probability importance of each basic event combined with the criticality of its own probability of occurrence in all events.

[0080] In order to further implement the above technical solution, during the diagnosis process, one or more thresholds are set to determine the credibility of the diagnosis result. When the diagnosis result exceeds a certain threshold, the result is considered to be credible; otherwise, the result is considered to be unreliable or needs further verification.

[0081] During the diagnosis process, different rules are given different weights to indicate their application to the inference results. The final diagnosis result will comprehensively consider the weights of all rules and their corresponding conclusions, thereby obtaining a weighted credibility.

[0082] Example 2

[0083] like Figure 2 Based on the same inventive concept, an embodiment of the present invention discloses a fault diagnosis system for a measurement and control system based on an accident tree, including two parts: a measurement and control system and a diagnosis system.

[0084] Specifically, the measurement and control system is an optical measurement system, which includes an optical lens, an information collection and distribution subsystem, and a real-time processing subsystem. After the optical lens captures the target, it packages the lens information into raw measurement data and sends it to the information collection and distribution subsystem. The fault diagnosis system is connected to the measurement and control system to obtain the operation status of the measurement and control system and monitor the occurrence of actual faults in real time according to the pre-defined judgment conditions, fault trees, and diagnostic schemes, and perform reasoning diagnosis. The information collection and distribution subsystem performs protocol conversion on the raw measurement data, obtains internal optical measurement data, and sends it to the real-time processing subsystem. The real-time processing subsystem receives the optical measurement data from multiple measurement stations and performs intersection calculation to obtain optical intersection track data.

[0085] The diagnosis system includes: a first configuration module, a second configuration module, a third configuration module, a real-time supervision module and a reasoning diagnosis module;

[0086] The first configuration module is used to configure data interpretation conditions according to data types; data types include intersection track data, internal optical strategy data and raw measurement data; the second configuration module is used to construct an accident tree; in the accident tree, the top event is the intersection track data, the next level event is the real-time processing subsystem abnormality and the internal optical measurement data abnormality; the next level event is the information aggregation and distribution subsystem abnormality and the raw measurement data abnormality; the next level event is the optical lens abnormality; the third configuration module is used to configure the diagnosis scheme; the real-time supervision module is used to obtain real-time measurement and control data and perform real-time interpretation according to the interpretation conditions configured by the first configuration module, identify abnormal conditions, and perform preliminary diagnosis according to the diagnosis scheme configured by the third module to confirm the fault phenomenon; the reasoning diagnosis module is used to perform reasoning diagnosis based on the fault phenomenon.

[0087] Among them, data anomalies at all levels can be further decomposed into abnormalities corresponding to data interpretation conditions, such as data exceeding the theoretical range, delay exceeding the threshold, and data status abnormality. Subsystem anomalies can be further decomposed into software downtime, time anomaly, and lack of optical intersection plan.

[0088] In this embodiment, the data dependency relationship is: optical raw measurement data>internal optical measurement data>optical intersection track data.

[0089] The range judgment conditions include the theoretical ranges of the pitch angle and azimuth angle of the optical measuring device, which can be calculated based on the theoretical track of the detection target and the location of the optical measuring device.

[0090] The time domain judgment conditions include the time delay threshold. For example, when the optical measurement data frequency is 20Hz, the time system interruption method is used to ensure a frame of 50ms. Under the strong real-time requirements of the measurement and control information system, a delay exceeding 100ms will have a negative impact on the system data processing, so the delay threshold can be set to 100ms.

[0091] The status bit judgment conditions include the tracking status of the optical measurement device and the validity of the miss amount.

[0092] In order to further implement the above technical solutions, a data diagnosis plan was formulated. Taking the optical rendezvous track data as an example, according to the data transmission protocol, the data type coding, task coding, measurement data extraction rules, applicable interpretation rules, the number of consecutive interpretation anomalies to be identified as fault phenomena (this time set to 10 times), applicable fault trees and other information of the optical rendezvous track data were set.

[0093] In this embodiment, real-time data collection and interpretation includes:

[0094] According to the data dependency inspection rules, after the internal optical measurement data is collected, it is determined whether the subsequent optical intersection track data is generated. If it is not generated, it is directly determined as a fault phenomenon; if it is generated, according to the data diagnosis plan, the optical intersection track data is collected, the measurement element is extracted, and the rules are interpreted. If it is judged to be abnormal based on the interpretation rules, such as the measurement element exceeds the theoretical range, the delay exceeds 100ms, etc., and it occurs more than 10 times in a row, it is determined to be a fault phenomenon.

[0095] In this embodiment, when the optical intersection track data is identified to be abnormal, an alarm is sent to the front desk, and the alarm content is as follows:

[0096] Data abnormality alarm:

[0097] Data name: Optical rendezvous track data.

[0098] Fault type: delay exceeds 100ms.

[0099] Time of occurrence: XXXX / XX / XX:XX:XX.XXX.

[0100] Current status: Abnormal.

[0101] Fault phenomenon: Yes / No.

[0102] The alarm information is updated in real time according to the background interpretation results. When the anomaly is identified as a fault phenomenon, the corresponding fault tree is automatically called for diagnosis according to the data diagnosis plan, the minimum cut set and the minimum path set are calculated, the complexity of different diagnostic methods is compared, and the simplest reasoning method is adopted. According to the simple fault tree constructed in step 2, the internal optical measurement data real-time processing subsystem is checked, and the fault reasoning diagnosis process is displayed in real time on the foreground. To determine whether the internal optical measurement data is abnormal, real-time judgment can be performed based on the internal optical measurement data interpretation rules; to determine whether the real-time processing subsystem is abnormal, reasoning diagnosis can be performed through the following aspects: software downtime: determine whether the service is running normally through the real-time processing subsystem health detection interface; time anomaly: obtain the server time of the real-time processing subsystem and compare it with the time server. Missing optical intersection plan: retrieve whether the optical intersection plan exists according to the data category.

[0103] Finally, based on the fault tree diagnosis results, fault handling solutions are recommended. For example, if the real-time processing subsystem is down, a quick restart method is prompted; if the time is abnormal, a quick time calibration method and command are prompted.

[0104] Example 3

[0105] Based on the same inventive concept, an embodiment of the present invention discloses a computer-readable storage medium, in which a computer program is stored. When the program is executed, the diagnostic method in Embodiment 1 is implemented.

[0106] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0107] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A fault diagnosis method for a measurement and control system based on an accident tree, characterized in that: The following steps are involved: Configure corresponding interpretation conditions according to the measurement data type of the measurement and control system; Construct an accident tree according to the data flow process of the measurement and control system, and formulate a diagnosis plan; Acquire the real-time data of the measurement and control system, and interpret it according to the preset interpretation conditions; The abnormal conditions identified through data interpretation are combined with the preset diagnostic scheme to confirm the actual fault; According to the relationship between the fault type of the actual fault and the fault phenomenon in the fault tree, the corresponding fault label is confirmed for inference diagnosis.

2. A fault diagnosis method for a measurement and control system based on an accident tree according to claim 1, characterized in that: The inference diagnosis includes calculating the minimum cut set and the minimum path set, comparing the scales of the minimum cut set and the minimum path set, and selecting the minimum scale for fault state determination.

3. A fault diagnosis method for a measurement and control system based on an accident tree according to claim 1, characterized in that: The judgment conditions include: The data dependency condition, the value range judgment condition, the time domain judgment condition and the state are one or more of the judgment conditions.

4. A fault diagnosis method for a measurement and control system based on an accident tree according to claim 3, characterized in that: The various judgment conditions specifically include: The data dependency condition includes confirming the one-way flow dependency relationship of the measurement and control information, obtaining the correlation between the preceding data and the following data, and judging the abnormality of the data generation behavior according to the correlation; The value range judgment conditions include confirming the reasonable range of the corresponding measurement elements of various measurement and control data; The time domain judgment conditions include confirming the delay thresholds of various types of data; The status bit judgment condition includes constructing a status bit inspection rule by combing and classifying the data status bits in the data transmission protocol.

5. A fault diagnosis method for a measurement and control system based on an accident tree according to claim 3 or 4, characterized in that: The steps of constructing the accident tree include: The first measurement and control data is taken as the top event, the abnormal situation of the first measurement and control data is taken as the first-level event, and the abnormality of the preceding data of the first measurement and control data is taken as the second-level event. The second-level event is further decomposed into different abnormal situations until the decomposition of the most original data is completed to obtain the fault tree.

6. A fault diagnosis method for a measurement and control system based on an accident tree according to claim 1, characterized in that: The steps also include: setting a reasoning credibility threshold, and when the output reasoning diagnosis result does not meet the credibility threshold, the result is deemed unreliable and further verification is performed.

7. A fault diagnosis system for a measurement and control system based on an accident tree, characterized in that: The fault diagnosis method according to any one of claims 1 to 6 is adopted, wherein the measurement and control system is an optical measurement system, and the optical measurement system includes a data acquisition subsystem, an information collection and distribution subsystem, and a real-time processing subsystem; The data acquisition subsystem uses an optical lens to capture the target and packages the lens information into raw measurement data and sends it to the information collection and distribution subsystem; The information collection and distribution subsystem performs protocol conversion on the raw measurement data to obtain internal optical measurement data and sends the internal optical measurement data to the real-time processing subsystem; The real-time processing subsystem receives the optical measurement data from multiple measurement stations and performs intersection calculation to obtain optical intersection track data.

8. A fault diagnosis system for a measurement and control system based on an accident tree according to claim 7, characterized in that: The fault diagnosis system includes a first configuration module, a second configuration module, a third configuration module, a real-time supervision module and a reasoning diagnosis module; The first configuration module is used to configure data interpretation conditions according to data types; the data types include intersection track data, internal optical strategy data and raw measurement data; The second configuration module is used to construct an accident tree; In the accident tree, the top event is the intersection track data, the next level event is the real-time processing subsystem abnormality and the internal optical measurement data abnormality; the next level event is the information aggregation and distribution subsystem abnormality and the original measurement data abnormality; the next level event is the optical lens abnormality; The third configuration module is used to configure the diagnosis scheme; The real-time monitoring module is used to obtain real-time measurement and control data and perform real-time interpretation according to the interpretation conditions configured by the first configuration module, identify abnormal conditions, and perform preliminary diagnosis according to the diagnostic scheme configured by the third module to confirm the fault phenomenon; The inference diagnosis module is used to perform inference diagnosis according to the fault phenomenon.

9. A fault diagnosis system for a measurement and control system based on an accident tree according to claim 8, characterized in that: It also includes a display module for displaying the diagnostic process of the inference diagnosis.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed, the fault diagnosis method according to any one of claims 1 to 6 is implemented.

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