Historical statistical data-based state division and probability evaluation method and system for security and stability control device

Abstract

The invention discloses a state division and probability evaluation method and system for a safety and stability control device based on historical statistical data, and the method comprises the steps: dividing the operation states of the safety and stability control device based on the operation principle and historical statistical data of the safety and stability control device, and determining a transfer method of each operation state; the operation state comprises a normal operation state, a dominant defect state, a hidden fault state, a refusal operation state and a maloperation state; analyzing transition probabilities among the operation states, and constructing a Markov model of the safety and stability control device; based on historical data, calculating each parameter in the Markov model, and evaluating the probability that the safety and stability control device is in each operation state; according to the invention, a safety and stability control device fault probability evaluation model with clear physical significance and a parameter calculation method are constructed, and reliability evaluation of the safety and stability control device and the stability control system can be supported.

Description

Technical Field

[0001] This invention belongs to the field of power grid safety and stability control technology, and in particular relates to a method and system for state division and probability assessment of safety and stability control devices based on historical statistical data. Background Technology

[0002] With the advancement of new power systems, stability control systems are exhibiting characteristics of large scale (increased number of controlled objects and expanded control range) and strong coupling (between multiple stability control systems, between stability control systems and external systems such as DC control and protection systems, and between the dynamic behavior of stability control systems and the dynamic characteristics of the primary power grid). The reliable operation of stability control systems faces severe challenges. Furthermore, due to functional defects in the stability control devices themselves and secondary circuit defects, there have been instances of incorrect device operation, exposing existing problems in the production, commissioning, maintenance, and operation and management of stability control systems. Reliability assessment of stability control systems is a crucial technical means to ensure their operational reliability. Through multi-dimensional quantitative assessment, weaknesses in the stability control system can be accurately identified, thereby improving the operational reliability of safety stability control devices and systems. Summary of the Invention

[0003] Purpose of the invention: The purpose of this invention is to provide a method and system for classifying and probabilistically evaluating the state of a safety and stability control device based on historical statistical data, which can improve the operational reliability of the safety and stability control device.

[0004] Technical solution: The present invention provides a method for classifying and probabilistically evaluating the state of a safety and stability control device based on historical statistical data, comprising:

[0005] Step 1: Based on the operating principle and historical statistical data of the safety and stability control device, classify the operating states of the safety and stability control device and determine the transition method for each operating state;

[0006] The operating states include normal operating state, explicit defect state, latent fault state, refusal to operate state, and malfunction state; the latent fault states include software latent fault states, design latent fault states, and operation and maintenance latent fault states.

[0007] Step 2: Analyze the transition probabilities between different operating states and construct a Markov model for the safe and stable control device;

[0008] Step 3: Based on historical data, calculate the parameters in the Markov model and assess the probability of the safety and stability control device being in each operating state.

[0009] Furthermore, the explicit defect state described in step 1 refers to the defect state that the safety and stability control device can detect through detection; the detection includes self-testing, alarming, and inspection by maintenance personnel.

[0010] The latent fault state is a defect state that cannot be detected by testing;

[0011] The aforementioned latent software fault state refers to the latent fault state of the safety and stability control device caused by software problems.

[0012] The aforementioned design-induced latent fault state refers to the latent fault state of the safety and stability control device caused by design problems.

[0013] The aforementioned latent fault states in operation and maintenance refer to latent fault states caused by misoperation, accidental contact, or incorrect debugging by operation and maintenance personnel.

[0014] Furthermore, the method for determining the transition of each operating state in step 1 includes:

[0015] When the safety and stability control device is in normal operation, if a failure of the safety and stability control device is detected, the safety and stability control device will switch to an explicit defect state.

[0016] When the safety and stability control device fails and is not detected, the safety and stability control device will switch to a latent fault state.

[0017] When the safety and stability control device is in a state of obvious defect, it can be transferred to a normal operating state through defect repair;

[0018] When the safety and stability control device is in a latent fault state, it is transferred to an explicit defect state through test verification. If the failure to operate or maloperation triggering conditions are met at the same time, the safety and stability control device will trigger failure to operate or maloperation and transfer to a failure to operate state or maloperation state.

[0019] When the safety and stability control device is in a non-operational or malfunctioning state, it is converted to a normal operating state through fault repair.

[0020] Further, step 2 includes:

[0021] Let L x This represents the defect rate of a safety and stability control device that transitions from a normal operating state to a manifest defect state, based on historical data. The defect rate is taken as the corresponding state transition probability.

[0022] Let L1, L2, and L3 represent the failure rates of a safety and stability control device transitioning from a normal operating state to a latent failure state, namely, the software latent failure state, the design latent failure state, and the operation and maintenance latent failure state, respectively, based on historical data. Considering that the statistical data of latent failures cannot completely cover all latent failure scenarios, a latent failure coefficient C1 is preset according to the requirements. Then, the corresponding state transition probabilities are L1 / C1, L2 / C1, and L3 / C1.

[0023] When the safety and stability control device is in a latent fault state, it is converted to an explicit defect state through experimental verification. Considering the current experimental verification level, an experimental verification level coefficient is set.

[0024] Establish the state transition matrix for the safety and stability control device:

[0025]

[0026] Among them, L g To ensure the safety and stability of the control device, when it is in a latent fault state, the failure rate of entering a non-operation or maloperation state is determined by meeting the trigger conditions for non-operation and maloperation. C3 represents the percentage of non-operation instances out of the total number of non-operation and maloperation instances. M1 represents the fault repair rate of the safety and stability control device, which is converted to normal operation through fault repair; M2 represents the defect repair rate of the safety and stability control device, which is converted to normal operation through defect repair; C4 represents the percentage of failures caused by operation and maintenance when there are obvious defects.

[0027] Let the stationary state probability matrix of the safety and stability control device be p = [p0 p1 p2 p3 p4 p5 p6]. The Markov state-space equations are established as follows:

[0028] PT=0

[0029]

[0030] Among them, p0, p1, p2, p3, p4, p5 and p6 represent the probabilities of the safety and stability control device being in normal operation, explicit defect, software hidden fault, design hidden fault, operation and maintenance hidden fault, refusal to operate and malfunction, respectively.

[0031] Based on the same inventive concept, the present invention also provides a system for classifying and probabilistically evaluating the state of a safety and stability control device based on historical statistical data, comprising:

[0032] The segmentation module is used to segment the operating states of the safety and stability control device based on the operating principles and historical statistical data of the safety and stability control device, and to determine the transition method for each operating state.

[0033] The operating states include normal operating state, explicit defect state, latent fault state, refusal to operate state, and malfunction state; the latent fault states include software latent fault states, design latent fault states, and operation and maintenance latent fault states.

[0034] The modeling module is used to analyze the transition probabilities between various operating states and to construct a Markov model of the safe and stable control device.

[0035] The evaluation module is used to calculate the parameters in the Markov model based on historical data and to evaluate the probability of the safety and stability control device being in each operating state.

[0036] Furthermore, the explicit defect states described in the classification module are defect states that the safety and stability control device can detect through detection; the detection includes self-testing, alarms, and inspections by maintenance personnel.

[0037] The latent fault state is a defect state that cannot be detected by testing;

[0038] The aforementioned latent software fault state refers to the latent fault state of the safety and stability control device caused by software problems.

[0039] The aforementioned design-induced latent fault state refers to the latent fault state of the safety and stability control device caused by design problems.

[0040] The aforementioned latent fault states in operation and maintenance refer to latent fault states caused by misoperation, accidental contact, or incorrect debugging by operation and maintenance personnel.

[0041] Furthermore, the method for determining the transition of each operating state described in the partitioning module includes:

[0042] When the safety and stability control device is in normal operation, if a failure of the safety and stability control device is detected, the safety and stability control device will switch to an explicit defect state.

[0043] When the safety and stability control device fails and is not detected, the safety and stability control device will switch to a latent fault state.

[0044] When the safety and stability control device is in a state of obvious defect, it can be transferred to a normal operating state through defect repair;

[0045] When the safety and stability control device is in a latent fault state, it is transferred to an explicit defect state through test verification. If the failure to operate or maloperation triggering conditions are met at the same time, the safety and stability control device will trigger failure to operate or maloperation and transfer to a failure to operate state or maloperation state.

[0046] When the safety and stability control device is in a non-operational or malfunctioning state, it is converted to a normal operating state through fault repair.

[0047] Furthermore, the modeling module includes:

[0048] Let L x This represents the defect rate of a safety and stability control device that transitions from a normal operating state to a manifest defect state, based on historical data. The defect rate is taken as the corresponding state transition probability.

[0049] Let L1, L2, and L3 represent the failure rates of a safety and stability control device transitioning from a normal operating state to a latent failure state, namely, the software latent failure state, the design latent failure state, and the operation and maintenance latent failure state, respectively, based on historical data. Considering that the statistical data of latent failures cannot completely cover all latent failure scenarios, a latent failure coefficient C1 is preset according to the requirements. Then, the corresponding state transition probabilities are L1 / C1, L2 / C1, and L3 / C1.

[0050] When the safety and stability control device is in a latent fault state, it is converted to an explicit defect state through experimental verification. Considering the current experimental verification level, an experimental verification level coefficient is set.

[0051] Establish the state transition matrix for the safety and stability control device:

[0052]

[0053] Among them, L g To ensure the safety and stability of the control device, when it is in a latent fault state, the failure rate of entering a non-operation or maloperation state is determined by meeting the trigger conditions for non-operation and maloperation. C3 represents the percentage of non-operation instances out of the total number of non-operation and maloperation instances. M1 represents the fault repair rate of the safety and stability control device, which is converted to normal operation through fault repair; M2 represents the defect repair rate of the safety and stability control device, which is converted to normal operation through defect repair; C4 represents the percentage of failures caused by operation and maintenance when there are obvious defects.

[0054] Let the stationary state probability matrix of the safety and stability control device be p = [p0 p1 p2 p3 p4 p5 p6]. The Markov state-space equations are established as follows:

[0055] PT=0

[0056]

[0057] Among them, p0, p1, p2, p3, p4, p5 and p6 represent the probabilities of the safety and stability control device being in normal operation, explicit defect, software hidden fault, design hidden fault, operation and maintenance hidden fault, refusal to operate and malfunction, respectively.

[0058] Based on the same inventive concept, the present invention also provides a computing device, comprising: one or more processors, one or more memories, and one or more programs, wherein the programs are stored in the memory and configured to be executed by the processor, and when the programs are loaded onto the processor, they implement the steps of the security and stability control device state division and probability evaluation method based on historical statistical data as described in any of the preceding claims.

[0059] Based on the same inventive concept, the present invention also provides a storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the steps of the method for classifying and probabilistically evaluating the state of a safety and stability control device based on historical statistical data as described in any of the preceding claims.

[0060] Beneficial effects: Compared with the prior art, this invention, based on the statistical analysis of historical data of the safety and stability control device and combined with the operating mechanism of the safety and stability control device, rationally divides the state of the safety and stability control device, clarifies the physical meaning of each state and state transition, provides data sources and parameter calculation principles, and provides a standardized process for probability assessment of the safety and stability control device, which greatly improves the scientificity of the state division of the safety and stability control device and the operability of probability assessment. Attached Figure Description

[0061] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0062] Figure 2 This is a schematic diagram illustrating the state division of the safety and stability control device according to an embodiment of the present invention;

[0063] Figure 3 This is a Markov state space diagram according to an embodiment of the present invention. Detailed Implementation

[0064] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0065] As attached Figure 1 As shown, the method for classifying and probabilistically evaluating the state of a safety and stability control device based on historical statistical data in this embodiment includes:

[0066] Step 1: Based on the operating principle and historical statistical data of the safety and stability control device, classify the operating states of the safety and stability control device and determine the transition method for each operating state;

[0067] The operating states include normal operating state, explicit defect state, latent fault state, refusal to operate state, and malfunction state; the latent fault states include software latent fault states, design latent fault states, and operation and maintenance latent fault states.

[0068] Step 2: Analyze the transition probabilities between different operating states and construct a Markov model for the safe and stable control device;

[0069] Step 3: Based on historical data, calculate the parameters in the Markov model and assess the probability of the safety and stability control device being in each operating state.

[0070] Specifically, step 1, based on the operating principle and historical statistical data of the safety and stability control device, divides the typical operating states of the safety and stability control device and clarifies the physical meaning between each state transition;

[0071] in accordance with Figure 2 The operating states of safety and stability control devices are classified. Typical operating states of safety and stability control devices can be divided into normal operation state, explicit defect state, latent fault state, failure to operate state, and malfunction state. Explicit defect state refers to a defect state that the safety and stability control device can detect through self-inspection or alarm methods, or a defect state that maintenance personnel can promptly detect through routine inspections. Latent fault state refers to a defect state that is difficult to detect promptly through device self-inspection or maintenance personnel routine inspections. Latent fault states of safety and stability control devices are triggered under certain conditions, causing the safety and stability control device to enter a failure to operate or malfunction state. Based on statistical analysis of historical operation and experimental data, and according to the causes of latent faults, latent fault states can be further subdivided, including but not limited to the following states: software latent fault states (such as software defects, unreasonable criteria, etc.), design latent fault states (such as unreasonable secondary circuit design, etc.), and maintenance latent fault states (such as misoperation, etc.).

[0072] Safety and stability control devices are typically in normal operating condition. Considering their self-testing capabilities, if a failure is detected, the device enters a manifest defect state; if a failure goes undetected, it enters a latent fault state. The state transition probability is negatively correlated with the device's design level. When in a manifest defect state, the device can be restored to normal operation through defect repair, with the state transition probability positively correlated with the defect repair procedures. Alternatively, maintenance issues may lead to a latent fault state, with the state transition probability negatively correlated with the maintenance level. When in a latent fault state, the device can be restored to a manifest defect state through testing and verification, with the state transition probability positively correlated with the testing and verification level. If a power grid fault occurs simultaneously, and the device fails to operate or operates malfunctions, it enters a fault state. When in a fault state, the device must be repaired promptly to restore normal operation, with the state transition probability related to the fault repair management procedures.

[0073] In step 2, the transition probabilities between each state are analyzed, based on... Figure 3 Construct a Markov model for a safe and stable control device;

[0074] L x This represents the defect rate at which a safe and stable control device transitions from a normal operating state to a state with obvious defects, based on statistical data of obvious defects in historical data. The defect rate is taken as the corresponding state transition probability.

[0075] L1, L2, and L3 represent the failure rates of a safety and stability control device transitioning from a normal operating state to a latent failure state, namely, software failure state, design failure state, and operation and maintenance failure state, respectively, based on the latent failure statistics data obtained from historical data. Considering that the latent failure statistics data cannot completely cover all latent failure scenarios, let C1 be the proportion of detected latent failures to total latent failures. Then, the corresponding state transition probabilities are L1 / C1, L2 / C1, and L3 / C1.

[0076] When the safety and stability control device is in a latent fault state, it can be transformed into an explicit defect state through test verification and other methods. Considering the current stability control test verification level (Y out of X incorrect actions occur during test verification), the test verification level coefficient C2 is set to Y / X.

[0077] When the safety and stability control device is in a latent fault state, it may trigger a corresponding primary power grid fault, causing it to switch to a non-operation state or a maloperation state. Let the fault occurrence rate that triggers the safety and stability control device fault be L. g (Of the X incorrect action events, Z events occur during the operation of the device on site, and the coefficient is Z / X). The proportion of the number of refusals to action to the sum of the number of refusals to action and the number of erroneous actions is C3.

[0078] M1 represents the fault repair rate of the safety and stability control device, and its value is determined according to the management procedures.

[0079] M2 represents the defect repair rate of the safety and stability control device, and its value is determined according to the management procedures.

[0080] C4 represents the proportion of explicit operational defects to explicit defects, which is negatively correlated with the level of operational maintenance and is determined based on historical statistical data.

[0081] Based on the above parameters, establish the state transition matrix of the safety and stability control device.

[0082]

[0083] The stationary state probability matrix p = [p0 p1 p2 p3 p4 p5 p6] of the safety and stability control device represents the probability of the device being in normal operation, explicit defect, software fault, design fault, maintenance fault, no-operation, and malfunction, respectively. The Markov state-space equations are established as follows:

[0084] PT=0

[0085]

[0086] Step 3: Based on Table 1, construct a historical data statistics table for the safety and stability control device, clarify the source of statistical data and calculation principles, calculate the key parameters in the Markov model, and thus assess the probability of the safety and stability control device being in each state.

[0087] Table 1 Key parameters in the Markov model

[0088]

[0089]

[0090] Assuming the historical statistical data of a certain area's safety and stability control device are shown in Table 2, the probability of the safety and stability control device in different states can be obtained according to the method proposed in this invention.

[0091] Table 2 Historical Statistical Data and Evaluation Results

[0092]

[0093]

[0094] Based on the same inventive concept, this embodiment also provides a system for classifying and probabilistically evaluating the state of a safety and stability control device based on historical statistical data, including:

[0095] The segmentation module is used to segment the operating states of the safety and stability control device based on the operating principles and historical statistical data of the safety and stability control device, and to determine the transition method for each operating state.

[0096] The operating states include normal operating state, explicit defect state, latent fault state, refusal to operate state, and malfunction state; the latent fault states include software latent fault states, design latent fault states, and operation and maintenance latent fault states.

[0097] The modeling module is used to analyze the transition probabilities between various operating states and to construct a Markov model of the safe and stable control device.

[0098] The evaluation module is used to calculate the parameters in the Markov model based on historical data and to evaluate the probability of the safety and stability control device being in each operating state.

[0099] Furthermore, the explicit defect states described in the classification module are defect states that the safety and stability control device can detect through detection; the detection includes self-testing, alarms, and inspections by maintenance personnel.

[0100] The latent fault state is a defect state that cannot be detected by testing;

[0101] The aforementioned latent software fault state refers to the latent fault state of the safety and stability control device caused by software problems.

[0102] The aforementioned design-induced latent fault state refers to the latent fault state of the safety and stability control device caused by design problems.

[0103] The aforementioned latent fault states in operation and maintenance refer to latent fault states caused by misoperation, accidental contact, or incorrect debugging by operation and maintenance personnel.

[0104] Furthermore, the method for determining the transition of each operating state described in the partitioning module includes:

[0105] When the safety and stability control device is in normal operation, if a failure of the safety and stability control device is detected, the safety and stability control device will switch to an explicit defect state.

[0106] When the safety and stability control device fails and is not detected, the safety and stability control device will switch to a latent fault state.

[0107] When the safety and stability control device is in a state of obvious defect, it can be transferred to a normal operating state through defect repair;

[0108] When the safety and stability control device is in a latent fault state, it is transferred to an explicit defect state through test verification. If the failure to operate or maloperation triggering conditions are met at the same time, the safety and stability control device will trigger failure to operate or maloperation and transfer to a failure to operate state or maloperation state.

[0109] When the safety and stability control device is in a non-operational or malfunctioning state, it is converted to a normal operating state through fault repair.

[0110] Furthermore, the modeling module includes:

[0111] Let L x This represents the defect rate of a safety and stability control device that transitions from a normal operating state to a manifest defect state, based on historical data. The defect rate is taken as the corresponding state transition probability.

[0112] Let L1, L2, and L3 represent the failure rates of a safety and stability control device transitioning from a normal operating state to a latent failure state, namely, the software latent failure state, the design latent failure state, and the operation and maintenance latent failure state, respectively, based on historical data. Considering that the statistical data of latent failures cannot completely cover all latent failure scenarios, a latent failure coefficient C1 is preset according to the requirements. Then, the corresponding state transition probabilities are L1 / C1, L2 / C1, and L3 / C1.

[0113] When the safety and stability control device is in a latent fault state, it is converted to an explicit defect state through experimental verification. Considering the current experimental verification level, an experimental verification level coefficient is set.

[0114] Establish the state transition matrix for the safety and stability control device:

[0115]

[0116] Among them, L g To ensure the safety and stability of the control device, when it is in a latent fault state, the failure rate of entering a non-operation or maloperation state is determined by meeting the trigger conditions for non-operation and maloperation. C3 represents the percentage of non-operation instances out of the total number of non-operation and maloperation instances. M1 represents the fault repair rate of the safety and stability control device, which is converted to normal operation through fault repair; M2 represents the defect repair rate of the safety and stability control device, which is converted to normal operation through defect repair; C4 represents the percentage of failures caused by operation and maintenance when there are obvious defects.

[0117] Let the stationary state probability matrix of the safety and stability control device be p = [p0 p1 p2 p3 p4 p5 p6]. The Markov state-space equations are established as follows:

[0118] PT=0

[0119]

[0120] Among them, p0, p1, p2, p3, p4, p5 and p6 represent the probabilities of the safety and stability control device being in normal operation, explicit defect, software hidden fault, design hidden fault, operation and maintenance hidden fault, refusal to operate and malfunction, respectively.

[0121] Based on the same inventive concept, this embodiment also provides a computing device, including: one or more processors, one or more memories, and one or more programs, wherein the programs are stored in the memory and configured to be executed by the processor, and when the programs are loaded onto the processor, they implement the steps of the security and stability control device state division and probability evaluation method based on historical statistical data as described in any of the preceding claims.

[0122] Based on the same inventive concept, this embodiment also provides a storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the steps of the security and stability control device state division and probability evaluation method based on historical statistical data as described in any of the preceding claims.

Claims

1. A method for state classification and probability assessment of a safety and stability control device based on historical statistical data, characterized in that, include: Step 1: Based on the operating principle and historical statistical data of the safety and stability control device, classify the operating states of the safety and stability control device and determine the transition method for each operating state; The operating states include normal operating state, explicit defect state, latent fault state, refusal to operate state, and malfunction state; the latent fault states include software latent fault states, design latent fault states, and operation and maintenance latent fault states. Step 2: Analyze the transition probabilities between different operating states and construct a Markov model for the safe and stable control device; Step 3: Based on historical data, calculate the parameters in the Markov model and assess the probability of the safety and stability control device being in each operating state.

2. The method for state division and probability assessment of a safety and stability control device based on historical statistical data as described in claim 1, characterized in that, The explicit defect state described in step 1 refers to the defect state that the safety and stability control device can detect through detection; the detection includes self-test, alarm, and inspection by maintenance personnel. The latent fault state is a defect state that cannot be detected by testing; The aforementioned latent software fault state refers to the latent fault state of the safety and stability control device caused by software problems. The aforementioned design-induced latent fault state refers to the latent fault state of the safety and stability control device caused by design problems. The aforementioned latent fault states in operation and maintenance refer to latent fault states caused by misoperation, accidental contact, or incorrect debugging by operation and maintenance personnel.

3. The method for state division and probability assessment of a safety and stability control device based on historical statistical data as described in claim 2, characterized in that, The method for determining the transition of each operating state as described in step 1 includes: When the safety and stability control device is in normal operation, if a failure of the safety and stability control device is detected, the safety and stability control device will switch to an explicit defect state. When the safety and stability control device fails and is not detected, the safety and stability control device will switch to a latent fault state. When the safety and stability control device is in a state of obvious defect, it can be transferred to a normal operating state through defect repair; When the safety and stability control device is in a latent fault state, it is transferred to an explicit defect state through test verification. If the failure to operate or maloperation triggering conditions are met at the same time, the safety and stability control device will trigger failure to operate or maloperation and transfer to a failure to operate state or maloperation state. When the safety and stability control device is in a non-operational or malfunctioning state, it is converted to a normal operating state through fault repair.

4. The method for state division and probability assessment of a safety and stability control device based on historical statistical data as described in claim 1, characterized in that, Step 2 includes: Let L x This represents the defect rate of a safety and stability control device that transitions from a normal operating state to a manifest defect state, based on historical data. The defect rate is taken as the corresponding state transition probability. Let L1, L2, and L3 represent the failure rates of a safety and stability control device transitioning from a normal operating state to a latent failure state, namely, the software latent failure state, the design latent failure state, and the operation and maintenance latent failure state, respectively, based on historical data. Considering that the statistical data of latent failures cannot completely cover all latent failure scenarios, a latent failure coefficient C1 is preset according to the requirements. Then, the corresponding state transition probabilities are L1 / C1, L2 / C1, and L3 / C1. When the safety and stability control device is in a latent fault state, it is converted to an explicit defect state through experimental verification. Considering the current experimental verification level, an experimental verification level coefficient is set. Establish the state transition matrix for the safety and stability control device: Among them, L g To ensure the safety and stability of the control device, when it is in a latent fault state, the failure rate of entering a non-operation or maloperation state is determined by meeting the trigger conditions for non-operation and maloperation. C3 represents the percentage of non-operation instances out of the total number of non-operation and maloperation instances. M1 represents the fault repair rate of the safety and stability control device, which is converted to normal operation through fault repair; M2 represents the defect repair rate of the safety and stability control device, which is converted to normal operation through defect repair; C4 represents the percentage of failures caused by operation and maintenance when there are obvious defects. Let the stationary state probability matrix of the safety and stability control device be p = [p0 p1 p2 p3 p4 p5 p6]. The Markov state-space equations are established as follows: PT=0 Among them, p0, p1, p2, p3, p4, p5 and p6 represent the probabilities of the safety and stability control device being in normal operation, explicit defect, software hidden fault, design hidden fault, operation and maintenance hidden fault, refusal to operate and malfunction, respectively.

5. A system for classifying and probabilistically evaluating the state of a safety and stability control device based on historical statistical data, characterized in that, include: The segmentation module is used to segment the operating states of the safety and stability control device based on the operating principles and historical statistical data of the safety and stability control device, and to determine the transition method for each operating state. The operating states include normal operating state, explicit defect state, latent fault state, refusal to operate state, and malfunction state; the latent fault states include software latent fault states, design latent fault states, and operation and maintenance latent fault states. The modeling module is used to analyze the transition probabilities between various operating states and to construct a Markov model of the safe and stable control device. The evaluation module is used to calculate the parameters in the Markov model based on historical data and to evaluate the probability of the safety and stability control device being in each operating state.

6. The safety and stability control device state division and probability assessment system based on historical statistical data according to claim 5, characterized in that, The explicit defect states described in the classification module are defect states that the safety and stability control device can detect through detection; the detection includes self-testing, alarms, and inspections by maintenance personnel; The latent fault state is a defect state that cannot be detected by testing; The aforementioned latent software fault state refers to the latent fault state of the safety and stability control device caused by software problems. The aforementioned design-induced latent fault state refers to the latent fault state of the safety and stability control device caused by design problems. The aforementioned latent fault states in operation and maintenance refer to latent fault states caused by misoperation, accidental contact, or incorrect debugging by operation and maintenance personnel.

7. The safety and stability control device state division and probability assessment system based on historical statistical data according to claim 5, characterized in that, The method for determining the transitions between various operating states as described in the partitioning module includes: When the safety and stability control device is in normal operation, if a failure of the safety and stability control device is detected, the safety and stability control device will switch to an explicit defect state. When the safety and stability control device fails and is not detected, the safety and stability control device will switch to a latent fault state. When the safety and stability control device is in a state of obvious defect, it can be transferred to a normal operating state through defect repair; When the safety and stability control device is in a latent fault state, it is transferred to an explicit defect state through test verification. If the failure to operate or maloperation triggering conditions are met at the same time, the safety and stability control device will trigger failure to operate or maloperation and transfer to a failure to operate state or maloperation state. When the safety and stability control device is in a non-operational or malfunctioning state, it is converted to a normal operating state through fault repair.

8. The safety and stability control device state division and probability assessment system based on historical statistical data according to claim 5, characterized in that, The modeling module includes: Let L x This represents the defect rate of a safety and stability control device that transitions from a normal operating state to a manifest defect state, based on historical data. The defect rate is taken as the corresponding state transition probability. Let L1, L2, and L3 represent the failure rates of a safety and stability control device transitioning from a normal operating state to a latent failure state, namely, the software latent failure state, the design latent failure state, and the operation and maintenance latent failure state, respectively, based on historical data. Considering that the statistical data of latent failures cannot completely cover all latent failure scenarios, a latent failure coefficient C1 is preset according to the requirements. Then, the corresponding state transition probabilities are L1 / C1, L2 / C1, and L3 / C1. When the safety and stability control device is in a latent fault state, it is converted to an explicit defect state through experimental verification. Considering the current experimental verification level, an experimental verification level coefficient is set. Establish the state transition matrix for the safety and stability control device: Among them, L g To ensure the safety and stability of the control device, when it is in a latent fault state, the failure rate of entering a non-operation or maloperation state is determined by meeting the trigger conditions for non-operation and maloperation. C3 represents the percentage of non-operation instances out of the total number of non-operation and maloperation instances. M1 represents the fault repair rate of the safety and stability control device, which is converted to normal operation through fault repair; M2 represents the defect repair rate of the safety and stability control device, which is converted to normal operation through defect repair; C4 represents the percentage of failures caused by operation and maintenance when there are obvious defects. Let the stationary state probability matrix of the safety and stability control device be p = [p0 p1 p2 p3 p4 p5 p6]. The Markov state-space equations are established as follows: PT=0 Among them, p0, p1, p2, p3, p4, p5 and p6 represent the probabilities of the safety and stability control device being in normal operation, explicit defect, software hidden fault, design hidden fault, operation and maintenance hidden fault, refusal to operate and malfunction, respectively.

9. A computing device, characterized in that, include: One or more processors, one or more memories, and one or more programs, said programs being stored in the memory and configured to be executed by the processor, said programs being loaded onto the processor to implement the steps of the method for classifying and probabilistically evaluating the state of a safety and stability control device based on historical statistical data as described in any one of claims 1 to 4.

10. A storage medium, characterized in that, The storage medium stores a computer program, which includes program instructions that, when executed by a processor, cause the processor to perform the steps of the method for classifying and probabilistically evaluating the state of a safety and stability control device based on historical statistical data as described in any one of claims 1 to 4.

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