Safety assessment method and device for medium-voltage switch cabinet handcart interlocking

Abstract

The invention relates to the technical field of switch device safety assessment, and provides a safety assessment method and device for medium-voltage switch cabinet handcart interlocking, and the method comprises the steps: obtaining real-time operation data; setting a potential fault mode of handcart interlocking, and establishing a fault analysis tree model; the method comprises the following steps: evaluating a safety risk level, setting a grading reminding mechanism, triggering a safety reminding signal, and continuously optimizing according to a response condition, thereby solving the technical problems of lack of systematic recognition capability for a complex fault mode, difficulty in accurately evaluating a potential fault and a risk thereof, and insufficient accuracy and adaptability of risk evaluation. Classification and clustering analysis based on historical fault data are realized, a complex fault mode and potential risks thereof can be systematically identified in combination with a fault analysis tree model, and a safety risk level of current handcart interlocking can be accurately evaluated in combination with real-time operation data and historical fault data. And the potential fault mode is continuously updated and the fault analysis tree model is optimized, so that the adaptability of safety assessment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of safety assessment of switchgear, and particularly to a safety assessment method and device for the handcart interlock of medium-voltage switchgear cabinets. Background Art

[0002] As a key device in the power system, medium-voltage switchgear cabinets are widely used in the distribution networks of industries, commercial facilities and public facilities. Their safety and reliability are directly related to the stable operation of the power system. The handcart interlock is an important part of medium-voltage switchgear cabinets, mainly used to ensure the normal mechanical and electrical interlock functions of the handcart during operation, and prevent equipment damage or personal safety accidents caused by misoperation. With the continuous expansion of the scale of the power system and the increasing demand for intelligence, the limitations of traditional handcart interlock safety assessment methods have gradually emerged.

[0003] Currently, the safety assessment of the handcart interlock of medium-voltage switchgear cabinets mainly relies on regular inspections and repairs after faults, lacking comprehensive monitoring and analysis of real-time operation data. Existing fault diagnosis methods are mostly based on single data sources or simple threshold judgments, and it is difficult to systematically identify complex fault modes and their potential risks. In addition, static fault analysis models are usually adopted, which cannot be dynamically optimized according to the actual operation conditions and historical data, resulting in insufficient accuracy and adaptability of risk assessment. Although sensors and data analysis means have begun to be introduced, there are still obvious shortcomings in fault mode recognition, risk level classification and dynamic optimization.

[0004] In summary, there are technical problems in the prior art, such as the lack of systematic identification ability for complex fault modes, the difficulty in accurately assessing potential faults and their risks, resulting in insufficient accuracy and adaptability of risk assessment. Summary of the Invention

[0005] This application provides a safety assessment method and device for the handcart interlock of medium-voltage switchgear cabinets, aiming to solve the technical problems in the prior art, such as the lack of systematic identification ability for complex fault modes, the difficulty in accurately assessing potential faults and their risks, resulting in insufficient accuracy and adaptability of risk assessment.

[0006] In view of the above problems, the embodiments of this application provide a safety assessment method and device for the handcart interlock of medium-voltage switchgear cabinets.

[0007] In the first aspect disclosed in this application, a safety assessment method for the handcart interlock of a medium-voltage switchgear is provided. The method includes: obtaining real-time operation data of the handcart interlock of the medium-voltage switchgear, where the real-time operation data includes the handcart position and the interlock status; setting potential fault modes of the handcart interlock through historical fault data, evaluating the safety risks of the handcart interlock, and establishing a fault analysis tree model; based on the real-time operation data, evaluating the safety risk level of the current handcart interlock of the medium-voltage switchgear according to the fault analysis tree model; setting a hierarchical reminder mechanism through the safety risk level and triggering a safety reminder signal; at the same time, collecting the response situation of the safety reminder signal and continuously optimizing the fault analysis tree model.

[0008] In another aspect disclosed in this application, a safety assessment device for the handcart interlock of a medium-voltage switchgear is provided. The device includes: a data acquisition module for obtaining real-time operation data of the handcart interlock of the medium-voltage switchgear, where the real-time operation data includes the handcart position and the interlock status; a risk assessment module for setting potential fault modes of the handcart interlock through historical fault data, evaluating the safety risks of the handcart interlock, and establishing a fault analysis tree model; a fault analysis module for evaluating the safety risk level of the current handcart interlock of the medium-voltage switchgear based on the real-time operation data according to the fault analysis tree model; a signal triggering module for setting a hierarchical reminder mechanism through the safety risk level and triggering a safety reminder signal; an optimization module for collecting the response situation of the safety reminder signal at the same time and continuously optimizing the fault analysis tree model.

[0009] Preferably, multi-source monitoring devices are deployed on the handcart interlock unit of the medium-voltage switchgear. The multi-source monitoring devices include a position sensor and a status sensor; based on the multi-source monitoring devices, the Modbus industrial communication protocol is used to connect to a cloud database, and the fault case storage section of the cloud database is used to store historical fault data.

[0010] Preferably, the historical fault data is classified to determine the fault types, where the fault types include mechanical jamming, abnormal electrical signals, and interlock failure; clustering analysis is performed according to the fault types to determine the high-fault regions; the high-fault regions are associated with potential fault modes to draw a fault mode distribution map.

[0011] Preferably, based on the fault types, the fault manifestation forms and detection conditions corresponding to each type of fault are defined; according to the fault manifestation forms and detection conditions corresponding to each type of fault, safety risk thresholds are set, the logical relationships between various types of faults are analyzed, and a fault analysis tree model is set.

[0012] Preferably, the fault analysis tree model includes a root node, intermediate nodes, and leaf nodes. Among them, the root node refers to the failure of the handcart interlock. The intermediate nodes are associated and mapped with the fault mode distribution diagram, and the leaf nodes are associated with the fault manifestation forms. According to the historical fault data, determine the historical occurrence frequency corresponding to each type of fault. Through the historical occurrence frequency corresponding to each type of fault, combined with the fault mode distribution diagram, adjust the weight distribution of the fault analysis tree model.

[0013] Preferably, through the hierarchical reminder mechanism, record the triggering time, type, and response situation of the safety reminder signal. Based on the triggering time, type, and response situation of the safety reminder signal, evaluate the response efficiency and processing effect. According to the response efficiency and processing effect, dynamically optimize the fault analysis tree model and update the safety risk threshold.

[0014] Preferably, set up a fault case storage section, which is used to store the fault type, fault manifestation form, and detection conditions corresponding to each fault event. Based on the fault case storage section, conduct data mining and analysis under the limitation of a preset time period and update the potential fault mode.

[0015] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0016] By obtaining the real-time operation data of the handcart interlock of the medium-voltage switchgear, the real-time operation data includes the handcart position and the interlock status; setting the potential fault mode of the handcart interlock through historical fault data and evaluating the safety risk of the handcart interlock, establishing a fault analysis tree model; based on the real-time operation data, evaluating the safety risk level of the current handcart interlock of the medium-voltage switchgear according to the fault analysis tree model; through the safety risk level, setting up a hierarchical reminder mechanism and triggering a safety reminder signal; at the same time, collecting the response situation of the safety reminder signal and continuously optimizing the fault analysis tree model, realizing the classification and clustering analysis based on historical fault data. Combining with the fault analysis tree model, it can systematically identify complex fault modes and their potential risks. Combining real-time operation data and historical fault data, it can accurately evaluate the safety risk level of the current handcart interlock, continuously update the potential fault mode and optimize the fault analysis tree model, and improve the adaptability of safety assessment.

[0017] The above description is only an overview of the technical solutions of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the following specifically illustrates the embodiments of this application. Brief Description of the Drawings

[0018] Figure 1 This application embodiment provides a possible flow schematic diagram of a safety assessment method for the handcart interlock of a medium-voltage switchgear.

[0019] Figure 2 This application embodiment provides a possible flow schematic diagram of updating the safety risk threshold in the safety assessment method for the handcart interlock of a medium-voltage switchgear.

[0020] Figure 3 This application embodiment provides a possible structural schematic diagram of a safety assessment device for the handcart interlock of a medium-voltage switchgear.

[0021] Explanation of reference numerals: data acquisition module 11, risk assessment module 12, fault analysis module 13, signal trigger module 14, optimization module 15. Detailed implementation manners

[0022] The following describes exemplary embodiments of the present invention with reference to the accompanying drawings. Various details of the embodiments of the present invention are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description below omits the description of well-known functions and structures.

[0023] Embodiment 1

[0024] As Figure 1 shown, this application embodiment provides a safety assessment method for the handcart interlock of a medium-voltage switchgear, wherein the method includes:

[0025] S100: Obtain the real-time operation data of the handcart interlock of the medium-voltage switchgear, where the real-time operation data includes the handcart position and the interlock status; S200: Set the potential fault modes of the handcart interlock through historical fault data, evaluate the safety risk of the handcart interlock, and establish a fault analysis tree model.

[0026] Specifically, real-time operation data refers to the data collected in real time by monitoring devices such as sensors during the operation of the medium-voltage switchgear handcart interlocking device, including the position information of the handcart (such as the handcart is in the test position, working position or intermediate position) and the interlocking status (such as whether the mechanical interlock is normal, whether the electrical interlock is conducting, etc.). These data can reflect the working conditions of the handcart interlocking device at the current moment. Among them, the handcart usually refers to a movable part or component, including a circuit breaker, disconnector or other key equipment; the potential failure mode refers to the possible failure types and forms analyzed and predicted based on historical failure data. Historical failure data refers to the failure records that have occurred during the operation of the equipment, including information such as the time, location and manifestation form of the failure. Through these data, possible failure modes can be deduced, providing a basis for subsequent risk assessment; the fault analysis tree model is an analysis tool based on logical relationships, used to systematically analyze the causal relationships and propagation paths of faults, and display the root nodes of faults (such as handcart interlock failure), intermediate nodes (such as failure modes) and leaf nodes (such as failure manifestation forms) in a tree structure. Through this structure, the root causes and influencing factors of faults can be clearly analyzed.

[0027] First, obtain real-time operation data through multi-source monitoring devices (such as position sensors, status sensors, etc.) deployed on the medium-voltage switchgear handcart interlocking unit. These data can reflect the current state of the handcart interlocking device in real time; then, use historical failure data for classification and clustering analysis to determine high-fault areas and potential failure modes. On this basis, combined with the failure type and historical occurrence frequency, establish a fault analysis tree model. This model analyzes the correlation between faults through logical relationships, can systematically identify the root causes of faults, and provide targeted risk assessment. In the whole solution, this process provides basic data and an analysis framework for subsequent safety risk level assessment and hierarchical reminder mechanism, and is a key link to achieve intelligent safety assessment.

[0028] S300: Based on the real-time operation data, evaluate the safety risk level of the current medium-voltage switchgear handcart interlock according to the fault analysis tree model; S400: Set a hierarchical reminder mechanism through the safety risk level and trigger a safety reminder signal; S500: At the same time, collect the response of the safety reminder signal and continuously optimize the fault analysis tree model.

[0029] Specifically, the safety risk level refers to the result of quantitatively evaluating the safety of the current state of the handcart interlock device based on real-time operation data and the fault analysis tree model. It is usually divided into different levels such as low risk, medium risk, and high risk, reflecting the degree of safety threats that the equipment may face currently. The grading reminder mechanism is a system that triggers different levels of reminder signals according to the safety risk level. For example, low risk may only trigger a prompt message, medium risk triggers a warning signal, and high risk triggers an emergency alarm. This mechanism can take corresponding measures according to the severity of the risk. The safety reminder signal refers to the alarm or prompt message sent to the operator or maintenance personnel when the system detects potential risks, which is used to remind the relevant personnel to take measures in a timely manner. The response situation refers to the processing result of the operator or maintenance personnel to the safety reminder signal, including information such as response time, measures taken, and whether the problem is successfully solved. These data are used to evaluate the effectiveness of the reminder mechanism and provide feedback for model optimization.

[0030] First, based on the real-time operation data and combined with the fault analysis tree model, conduct a safety risk assessment on the current state of the handcart interlock device. Through model analysis, determine the safety risk level of the current handcart interlock (such as low risk, medium risk, or high risk). Then, trigger the corresponding level of safety reminder signal according to the evaluation result to remind the operator or maintenance personnel to take measures in a timely manner. At the same time, collect the response situation of the safety reminder signal, including information such as response time, processing measures, and processing effects. These feedback data are used to dynamically optimize the fault analysis tree model, adjust the thresholds and weight distributions of risk assessment, so as to improve the accuracy and adaptability of the model. In the whole solution, this process realizes the closed-loop management from risk assessment to the reminder mechanism, ensures the intelligence and dynamic optimization ability of the system, and improves the safety management level of the handcart interlock of medium-voltage switchgear.

[0031] Furthermore, the method of the present application includes:

[0032] Deploy multi-source monitoring devices on the handcart interlock unit of the medium-voltage switchgear. The multi-source monitoring devices include position sensors and status sensors; based on the multi-source monitoring devices, use the Modbus industrial communication protocol to connect to the cloud database, and the fault case storage segment of the cloud database is used to store historical fault data.

[0033] Specifically, the multi-source monitoring device refers to a variety of sensor devices installed on the handcart interlock unit of the medium-voltage switchgear, which is used to collect the operation data of the handcart interlock device in real time. These devices include position sensors (used to detect the real-time position of the handcart, such as the test position, working position or intermediate position) and status sensors (used to detect the electrical and mechanical status of the interlock device, such as whether the interlock is normal, whether the signal is conducted, etc.). The Modbus industrial communication protocol is a communication protocol widely used in the field of industrial automation, which is used to realize data transmission and communication between devices. It supports a variety of physical media, such as serial communication and Ethernet, and can efficiently transmit the collected real-time data to the cloud database. The cloud database is a database system based on cloud computing technology, which is used to store and manage a large amount of data. The fault case storage segment of the cloud database is specifically used to store historical fault data, including information such as the time, location, manifestation form, and processing result of the fault occurrence. These data provide a basis for subsequent fault mode analysis and risk assessment.

[0034] First, deploy multi-source monitoring devices on the handcart interlock unit of the medium-voltage switchgear, including position sensors and status sensors, which are used to collect the operation data of the handcart interlock device in real time. These sensors can monitor the position and interlock status of the handcart in real time to ensure the accuracy and real-time nature of the data. Subsequently, through the Modbus industrial communication protocol, the collected real-time data is transmitted to the cloud database. The fault case storage segment in the cloud database is used to store historical fault data. After classification and clustering analysis of these data, it provides an important basis for the identification of fault modes and risk assessment. In the whole solution, this process realizes the efficient collection and storage of data, provides strong data support for subsequent intelligent analysis and dynamic optimization, and is the basic link for realizing accurate safety assessment.

[0035] Furthermore, by using the historical fault data, potential fault modes of the handcart interlock are set. The method of this application further includes:

[0036] Classify based on the historical fault data to determine the fault types, and the fault types include mechanical jamming, abnormal electrical signals, and interlock failure; perform clustering analysis according to the fault types to determine the high-fault-occurrence areas; associate the high-fault-occurrence areas with potential fault modes to draw up a fault mode distribution map.

[0037] Specifically, classification refers to grouping historical fault data according to certain rules and standards to better understand and analyze the characteristics of faults. The basis for classification is the manifestation form and root cause of faults. Faults are classified into types such as mechanical jamming, abnormal electrical signals, and interlock failures. Cluster analysis is a data mining technique used to divide objects in a data set into multiple classes or clusters, so that objects within the same cluster have high similarity, while objects between different clusters have large differences. Cluster analysis is used to determine high-fault-occurrence areas, that is, which parts are more likely to have faults. High-fault-occurrence areas refer to the parts or links where faults frequently occur determined through cluster analysis. These areas may be weak links in equipment design or vulnerable parts caused by the operating environment. The fault mode distribution map is a visualization tool used to show the correlation between high-fault-occurrence areas and potential fault modes, helping technicians intuitively understand the distribution of faults and possible propagation paths.

[0038] First, classify the historical fault data, dividing faults into types such as mechanical jamming, abnormal electrical signals, and interlock failures. This classification method helps to clarify the root causes and manifestation forms of different faults, providing a basis for further analysis. Subsequently, determine the high-fault-occurrence areas through cluster analysis, that is, which parts are more likely to have faults, quickly locate the weak links of the equipment, and provide a basis for preventive maintenance. Finally, associate the high-fault-occurrence areas with potential fault modes and draw up a fault mode distribution map. This distribution map can intuitively show the distribution of faults and possible propagation paths, providing important support for subsequent risk assessment and the establishment of a fault analysis tree model. In the whole solution, this process realizes mining fault rules from historical data, provides a systematic analysis basis for intelligent safety assessment, and improves the accuracy of fault prediction and risk assessment.

[0039] Furthermore, to evaluate the safety risks of the breaker interlock and establish a fault analysis tree model, the method of this application includes:

[0040] Based on the fault types, define the fault manifestation forms and detection conditions corresponding to each type of fault; according to the fault manifestation forms and detection conditions corresponding to each type of fault, set safety risk thresholds, and analyze the logical relationships between various types of faults to set up a fault analysis tree model.

[0041] Specifically, the fault manifestation refers to the specific abnormal state or phenomenon exhibited by the equipment when a fault occurs. For example, mechanical jamming may cause the trolley to fail to move normally, abnormal electrical signals may cause the sensor to output incorrect signals, and interlock failure may cause the safety mechanism to fail to start properly. These manifestations are important bases for fault diagnosis; the detection conditions refer to the specific parameters or state conditions used to determine whether a fault has occurred. For example, when the trolley position sensor detects that the trolley position does not match the expectation, it may trigger the detection condition for mechanical jamming; when the electrical signal exceeds the preset range, it may trigger the detection condition for abnormal electrical signals. The safety risk threshold refers to the risk level threshold set according to the severity and potential hazards of the fault. When the detected fault manifestation or detection condition reaches or exceeds these thresholds, it is determined that the current equipment is in a certain safety risk level (such as low risk, medium risk, or high risk). The fault analysis tree model is a logical relationship-based analysis tool used to systematically analyze the causal relationships and propagation paths of faults. It shows the root nodes (such as trolley interlock failure), intermediate nodes (such as fault modes), and leaf nodes (such as fault manifestations) of the fault in a tree structure. Through this structure, the root causes and influencing factors of the fault can be clearly analyzed.

[0042] First, based on the fault types (such as mechanical jamming, abnormal electrical signals, interlock failure), define the fault manifestations and detection conditions corresponding to each type of fault. For example, for mechanical jamming, the fault manifestations may include the trolley being unable to move or having restricted movement, and the detection condition may be that the position detected by the position sensor does not match the expectation. By clarifying these manifestations and detection conditions, clear judgment bases can be provided for rapid fault diagnosis. Subsequently, according to the manifestations and detection conditions of each type of fault, set the safety risk threshold. For example, when it is detected that the trolley position is abnormal and the duration exceeds a certain threshold, the system may determine it as a high-risk state. At the same time, analyze the logical relationships between various types of faults and set up the fault analysis tree model. For example, mechanical jamming may cause abnormal electrical signals, and abnormal electrical signals may further cause interlock failure. Through the fault analysis tree model, the root causes and propagation paths of faults can be systematically identified, providing a logical framework for risk assessment, realizing a systematic analysis from fault manifestations to risk assessment, providing a scientific basis for subsequent safety risk level assessment and classification reminder mechanisms, and improving the accuracy and systematicness of fault diagnosis and risk assessment.

[0043] Furthermore, the method of the present application further includes:

[0044] The described fault analysis tree model includes a root node, intermediate nodes, and leaf nodes. Among them, the root node refers to the failure of the handcart interlock. The intermediate nodes are associated and mapped with the fault mode distribution diagram, and the leaf nodes are associated with the fault manifestation forms. According to the historical fault data, determine the historical occurrence frequency corresponding to each type of fault. Through the historical occurrence frequency corresponding to each type of fault, combined with the fault mode distribution diagram, adjust the weight distribution of the fault analysis tree model.

[0045] Specifically, the root node is the starting point of the fault analysis tree model, representing the ultimate goal or result of the entire fault analysis. The root node is the failure of the handcart interlock, that is, the handcart interlock device cannot work properly, which may lead to equipment damage or safety accidents. The intermediate nodes are the levels connecting the root node and the leaf nodes in the fault analysis tree model, representing the intermediate states or fault modes of the fault. They are associated and mapped with the fault mode distribution diagram, reflecting the propagation path and intermediate links of the fault. The leaf nodes are the ends of the fault analysis tree model, representing specific fault manifestation forms, such as mechanical jamming, abnormal electrical signals, etc. These nodes are directly associated with the actually detected fault phenomena. The historical occurrence frequency refers to the ratio of the number of occurrences of each fault type in the past to the total number of faults. By statistically analyzing the historical fault data, the occurrence probability of each fault type can be determined. The weight distribution adjustment refers to the importance assignment of the nodes in the fault analysis tree model according to the historical occurrence frequency. The fault modes with higher occurrence frequencies will be assigned higher weights.

[0046] Clarify the structure of the fault analysis tree model, including the root node, intermediate nodes, and leaf nodes. The root node is the failure of the handcart interlock, and the intermediate nodes are associated and mapped with the fault mode distribution diagram, reflecting the propagation path and intermediate links of the fault; the leaf nodes are associated with specific fault manifestation forms. This structured design makes the fault analysis more systematic and logical. Subsequently, by statistically analyzing the historical fault data, determine the historical occurrence frequency corresponding to each type of fault. For example, the occurrence frequency of the mechanical jamming fault may be 30%, the abnormal electrical signal is 40%, and the interlock failure is 30%. Finally, according to the historical occurrence frequency, combined with the fault mode distribution diagram, adjust the weight distribution of the fault analysis tree model. For example, if the historical occurrence frequency of a certain fault mode is relatively high, it indicates that it is more likely to occur in actual operation, so a higher weight is assigned in the model. This weight adjustment makes the fault analysis tree model more in line with the actual operation situation and can more accurately evaluate the current equipment's safety risk level. In the whole solution, this process optimizes the accuracy of the fault analysis tree model through the weight adjustment driven by historical data, enabling it to better reflect the severity and priority of the fault, thereby enhancing the system's intelligent level and the adaptability of risk assessment.

[0047] Furthermore, as Figure 2As shown, based on the security risk level, the response situation of the security reminder signal is collected to continuously optimize the fault analysis tree model. The method of this application further includes:

[0048] Through the hierarchical reminder mechanism, record the triggering time, type, and response situation of the security reminder signal; based on the triggering time, type, and response situation of the security reminder signal, evaluate the response efficiency and processing effect; according to the response efficiency and processing effect, dynamically optimize the fault analysis tree model and update the security risk threshold.

[0049] Specifically, the hierarchical reminder mechanism refers to a system that triggers different levels of reminder signals according to the security risk level. For example, low risk may trigger a prompt message, medium risk triggers a warning signal, and high risk triggers an emergency alarm. This mechanism can take corresponding measures according to the severity of the risk. The triggering time refers to the specific time point when the security reminder signal is activated, which is used to record the time information of the fault occurrence. The response situation refers to the processing result of the operator or maintenance personnel on the security reminder signal, including information such as the response time, measures taken, and whether the problem is successfully solved. These data are used to evaluate the effectiveness of the reminder mechanism and provide feedback for model optimization. The response efficiency refers to the time interval from the triggering of the security reminder signal to the operator's response and taking measures, which reflects the response speed of the system and the response ability of the operator. The processing effect refers to the processing result of the operator on the reminder signal, including whether the problem is successfully solved and whether further fault development is avoided. Dynamic optimization refers to the real-time adjustment and improvement of the fault analysis tree model according to the actual operation data and feedback information to improve the accuracy and adaptability of the model. The security risk threshold refers to the risk level threshold set according to the severity of the fault. When the detected fault manifestation or detection condition reaches or exceeds these thresholds, it is determined that the current device is in a certain security risk level.

[0050] Record the triggering time, type, and response status of safety reminder signals through a hierarchical reminder mechanism. This information provides basic data for subsequent evaluations. Subsequently, based on these recorded data, evaluate the response efficiency and handling effect. For example, if the response time for a certain reminder signal is short and the handling effect is good, it indicates that the current reminder mechanism is effective; conversely, if the response time is too long or the handling effect is poor, it is necessary to optimize the reminder mechanism or related processes. Finally, based on the response efficiency and handling effect, dynamically optimize the fault analysis tree model and update the safety risk threshold. For example, if a certain fault type has not been effectively handled after multiple reminders, it may be necessary to adjust the risk threshold for this fault type or optimize the weights of relevant nodes in the fault analysis tree to more accurately reflect its risk level, achieving a closed-loop management from reminder signal triggering to response evaluation and then to model optimization. By dynamically optimizing the fault analysis tree model and safety risk threshold, the system can continuously adjust its own performance according to the actual operating conditions and the feedback of operators, thereby improving the system's intelligence level and adaptability to potential risks.

[0051] Furthermore, the method of the present application includes:

[0052] Set up a fault case storage segment, which is used to store the fault type, fault manifestation form, and detection conditions corresponding to each fault event; based on the fault case storage segment, conduct data mining and analysis under the limitation of a preset time period, and update the potential fault mode.

[0053] Specifically, the fault case storage segment refers to an area in the database specifically used to store information related to fault events. This information includes fault types, fault manifestation forms, detection conditions, etc. It provides a basis for the management and analysis of fault data. Data mining and analysis refer to the process of extracting valuable information and patterns from a large amount of data through specific algorithms and technologies. Data mining is used to extract potential fault rules and trends from the fault case storage segment. The preset time period refers to the time range set during the data mining process, which is used to limit the time span of the analysis. For example, it can be set to conduct data mining and analysis once a month, a quarter, or a year. The potential fault mode refers to predicting the possible fault types and forms by analyzing historical fault data and current operating data. It is an important part of the fault analysis tree model and is used to identify potential risks in advance.

[0054] A fault case storage segment is set up to store the detailed information of each fault event, including the fault type, fault manifestation, and detection conditions. These data provide a basis for subsequent fault analysis and risk assessment. Subsequently, within a preset time period (such as monthly or quarterly), data mining and analysis are performed on the data in the fault case storage segment. By analyzing these data, the laws and trends of faults can be discovered, thereby updating the potential fault modes. For example, if a certain fault type occurs multiple times within a certain period, it may indicate that this fault mode has a relatively high occurrence probability, and its weight needs to be increased or relevant detection conditions need to be adjusted in the fault analysis tree model. In this way, the potential fault modes can be dynamically adjusted according to the latest operation data, ensuring that the fault analysis tree model always reflects the actual operation status of the equipment, realizing a closed-loop management from fault data collection to potential fault mode update, providing data support for the dynamic optimization of the fault analysis tree model, and improving the intelligent level of the system and the prediction ability of potential faults.

[0055] In summary, the safety assessment method and device for the handcart interlock of medium-voltage switchgear provided by the embodiments of the present application have the following technical effects:

[0056] By obtaining the real-time operation data of the handcart interlock of the medium-voltage switchgear, the real-time operation data including the handcart position and interlock status; setting the potential fault modes of the handcart interlock through historical fault data and evaluating the safety risks of the handcart interlock, and establishing a fault analysis tree model; based on the real-time operation data, evaluating the safety risk level of the current handcart interlock of the medium-voltage switchgear according to the fault analysis tree model; setting a hierarchical reminder mechanism through the safety risk level and triggering a safety reminder signal; at the same time, collecting the response situation of the safety reminder signal and continuously optimizing the fault analysis tree model. The present application provides a safety assessment method and device for the handcart interlock of medium-voltage switchgear. Based on the classification and clustering analysis of historical fault data and combined with the fault analysis tree model, it can systematically identify complex fault modes and their potential risks. Combining real-time operation data and historical fault data, it can accurately evaluate the safety risk level of the current handcart interlock, continuously update the potential fault modes and optimize the fault analysis tree model, and improve the adaptability of safety assessment.

[0057] Embodiment 2

[0058] Based on the same inventive concept as the safety assessment method for the handcart interlock of medium-voltage switchgear in the foregoing embodiments, as Figure 3 shown, the embodiments of the present application provide a safety assessment device for the handcart interlock of medium-voltage switchgear, wherein the device includes:

[0059] The data acquisition module 11 is used to acquire the real-time operation data of the medium-voltage switchgear trolley interlock, and the real-time operation data includes the trolley position and the interlock status;

[0060] The risk assessment module 12 is used to set the potential fault modes of the trolley interlock through historical fault data, evaluate the safety risks of the trolley interlock, and establish a fault analysis tree model;

[0061] The fault analysis module 13 is used to evaluate the safety risk level of the current medium-voltage switchgear trolley interlock based on the real-time operation data according to the fault analysis tree model;

[0062] The signal triggering module 14 is used to set a hierarchical reminder mechanism through the safety risk level and trigger a safety reminder signal;

[0063] The optimization module 15 is used to collect the response conditions of the safety reminder signal and continuously optimize the fault analysis tree model at the same time.

[0064] Furthermore, the device includes:

[0065] Deploy multi-source monitoring devices on the trolley interlock unit of the medium-voltage switchgear. The multi-source monitoring devices include position sensors and status sensors; based on the multi-source monitoring devices, use the Modbus industrial communication protocol to connect to the cloud database, and the fault case storage section of the cloud database is used to store historical fault data.

[0066] Furthermore, by using historical fault data to set the potential fault modes of the trolley interlock, the device further includes:

[0067] Classify based on the historical fault data to determine the fault types, and the fault types include mechanical jamming, abnormal electrical signals, and interlock failure; perform cluster analysis according to the fault types to determine the high-fault regions; associate the high-fault regions with the potential fault modes to draw a fault mode distribution map.

[0068] Furthermore, to evaluate the safety risks of the trolley interlock and establish a fault analysis tree model, the device includes:

[0069] Based on the fault types, define the fault manifestation forms and detection conditions corresponding to each type of fault; according to the fault manifestation forms and detection conditions corresponding to each type of fault, set the safety risk thresholds, analyze the logical relationships between various faults, and set the fault analysis tree model.

[0070] Furthermore, the device includes:

[0071] The fault analysis tree model includes a root node, intermediate nodes, and leaf nodes. Among them, the root node refers to the failure of the handcart interlock. The intermediate nodes are associated and mapped with the fault mode distribution diagram, and the leaf nodes are associated with the fault manifestation forms. According to the historical fault data, determine the historical occurrence frequency corresponding to each type of fault. Through the historical occurrence frequency corresponding to each type of fault, combined with the fault mode distribution diagram, adjust the weight distribution of the fault analysis tree model.

[0072] Further, through the safety risk level, collect the response situation of the safety reminder signal, and continuously optimize the fault analysis tree model. The device further includes:

[0073] Through the hierarchical reminder mechanism, record the trigger time, type, and response situation of the safety reminder signal; based on the trigger time, type, and response situation of the safety reminder signal, evaluate the response efficiency and processing effect; according to the response efficiency and processing effect, dynamically optimize the fault analysis tree model, and update the safety risk threshold.

[0074] Further, the device includes:

[0075] Set a fault case storage segment, which is used to store the fault type, fault manifestation form, and detection conditions corresponding to each fault event. Based on the fault case storage segment, perform data mining and analysis under the limitation of a preset time period, and update the potential fault mode.

[0076] Any step of the above-mentioned method can be stored as computer instructions or programs in an unrestricted computer memory and can be called and recognized by an unrestricted computer processor to implement any method in the embodiments of the present application. There is no redundant limitation here.

[0077] Further, the first or second mentioned above may not only represent an order relationship, but may also represent a specific concept, and / or refer to the fact that multiple elements can be selected individually or in whole. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and variations.

Claims

1. A safety assessment method for medium voltage switchgear trolley interlocking, characterized in that: The method comprises: Acquire real-time operation data of the trolley interlock of the medium-voltage switch cabinet, wherein the real-time operation data includes the trolley position and interlock status; Through historical fault data, the potential failure modes of the trolley interlock are set, the safety risks of the trolley interlock are evaluated, and a fault analysis tree model is established; Based on the real-time operation data and the fault analysis tree model, the current safety risk level of the medium voltage switch cabinet trolley interlock is evaluated; According to the security risk level, a hierarchical reminder mechanism is set up and a security reminder signal is triggered; At the same time, the response status of the safety reminder signal is collected, and the fault analysis tree model is continuously optimized.

2. The method according to claim 1, characterized in that The method comprises: Deploy a multi-source monitoring device on the trolley interlocking unit of the medium voltage switch cabinet, wherein the multi-source monitoring device includes a position sensor and a state sensor; Based on the multi-source monitoring device, the Modbus industrial communication protocol is adopted to connect to the cloud database, and the fault case storage segment of the cloud database is used to store historical fault data.

3. The method according to claim 2, characterized in that By using historical fault data, a potential fault mode of the trolley interlock is set. The method further comprises: Classify the historical fault data to determine the fault type, where the fault type includes mechanical jamming, electrical signal abnormality, and interlock failure; Perform cluster analysis based on the fault types to determine high-fault-incidence areas; The high-failure-prone areas are associated with potential failure modes, and a failure mode distribution map is drawn up.

4. The method according to claim 3, characterized in that The safety risk of trolley interlock is evaluated and a fault analysis tree model is established. The method includes: Based on the fault types, define the fault manifestation and detection conditions corresponding to each type of fault; According to the fault manifestation and detection conditions corresponding to each type of fault, the safety risk threshold is set, and the logical relationship between various types of faults is analyzed to set up a fault analysis tree model.

5. The method according to claim 4, characterized in that The fault analysis tree model includes a root node, an intermediate node and a leaf node, wherein the root node refers to the trolley interlock failure, the intermediate node is associated with the fault mode distribution diagram, and the leaf node is associated with the fault manifestation form; Determine the historical occurrence frequency of each type of fault according to the historical fault data; The weight distribution of the fault analysis tree model is adjusted by combining the historical occurrence frequency corresponding to each type of fault with the fault mode distribution diagram.

6. The method according to claim 5, characterized in that By using the safety risk level, collecting the response of the safety reminder signal, and continuously optimizing the fault analysis tree model, the method further includes: Through the hierarchical reminder mechanism, the triggering time, type and response of the safety reminder signal are recorded; Evaluate the response efficiency and processing effect based on the triggering time, type and response of the safety reminder signal; According to the response efficiency and processing effect, the fault analysis tree model is dynamically optimized, and the safety risk threshold is updated.

7. The method according to claim 1, characterized in that The method comprises: Setting a fault case storage segment, wherein the fault case storage segment is used to store the fault type, fault manifestation form, and detection conditions corresponding to each fault event; Based on the fault case storage segment, data mining and analysis are performed within the limits of a preset time period, and the potential fault mode is updated.

8. Safety assessment device for medium voltage switchgear trolley interlocking, characterized in that: The method for implementing the safety assessment of the medium voltage switch cabinet trolley interlocking according to any one of claims 1 to 7 comprises: A data acquisition module is used to acquire real-time operation data of the trolley interlock of the medium-voltage switch cabinet, wherein the real-time operation data includes the trolley position and interlocking state; The risk assessment module is used to set the potential failure mode of the trolley interlock through historical failure data, evaluate the safety risk of the trolley interlock, and establish a fault analysis tree model; A fault analysis module, used to evaluate the safety risk level of the current medium voltage switch cabinet trolley interlock based on the real-time operation data and the fault analysis tree model; A signal triggering module, used to set a graded reminder mechanism according to the security risk level and trigger a security reminder signal; The optimization module is used to simultaneously collect the response status of the safety reminder signal and continuously optimize the fault analysis tree model.

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