Circuit breaker operation state evaluation method and device based on data driving, storage medium and computer equipment

Abstract

The invention discloses a circuit breaker operation state evaluation method and device based on data driving, a storage medium and computer equipment. The method comprises the steps of collecting multi-dimensional operation data of a circuit breaker to be subjected to operation state evaluation; for each dimension in the multi-dimensional operation data, constructing a feature data sequence corresponding to the dimension based on the operation data of the dimension; respectively calling a feature evaluation function corresponding to each dimension, and for the feature data sequence under each dimension, calculating a feature evaluation result under the dimension through the feature evaluation function under the corresponding dimension; obtaining an operation environment condition and / or an operation condition of the circuit breaker, and dynamically determining a dynamic weight coefficient corresponding to each characteristic evaluation result based on the operation environment condition and / or the operation condition; and based on the dynamic weight coefficient corresponding to each characteristic evaluation result, carrying out weighted addition on the characteristic evaluation results under each dimension to obtain a health index of the circuit breaker, and according to the health index, evaluating the operation state corresponding to the circuit breaker.

Description

Data-driven method and apparatus for evaluating the operational status of circuit breakers, storage media, and computer equipment. Technical Field

[0001] This application relates to the field of power equipment monitoring and intelligent early warning technology, and in particular to a data-driven method and device for evaluating the operating status of circuit breakers, a storage medium, and a computer device. Background Technology

[0002] With the rapid development of the national economy and the acceleration of urbanization, society's reliance on electricity continues to increase. As the core link in energy transmission, the safe, stable, and efficient operation of the power system is crucial to ensuring the normal functioning of the social economy. Within the power system, circuit breakers, as important switching devices that realize circuit opening and closing and protect equipment from overload and short-circuit damage, directly affect the safety and stability of the entire power grid. Circuit breakers not only undertake the tasks of opening and closing under normal operating conditions, but also need to quickly interrupt current in abnormal situations such as short circuits or overcurrents to prevent the escalation of accidents. Therefore, the health status assessment of circuit breakers is of great significance for preventing systemic power outages and reducing economic losses.

[0003] Currently, traditional circuit breaker operation and maintenance methods mainly include two types. The first is the post-fault maintenance mode: maintenance is only carried out after the circuit breaker exhibits a significant fault or malfunctions. While this method saves short-term costs, it easily leads to unplanned power outages, cascading failures, and significant economic losses. The second is the periodic maintenance mode: periodic maintenance is performed based on the circuit breaker's service life or frequency of use. While this method can prevent sudden failures to some extent, it presents a contradiction between "over-maintenance" and "under-maintenance." Over-maintenance wastes manpower and resources, while under-maintenance can easily lead to the neglect of potential risks. Summary of the Invention

[0004] In view of this, this application provides a data-driven method and apparatus for evaluating the operating status of circuit breakers, as well as a storage medium and computer equipment. By collecting and deeply analyzing multi-dimensional operating data of circuit breakers, it can comprehensively and meticulously extract various information during the circuit breaker's operation, improving the accuracy and comprehensiveness of the evaluation. Furthermore, the determination of dynamic weighting coefficients fully considers the circuit breaker's operating environment and operating conditions, enabling the evaluation results to be dynamically adjusted according to actual conditions, better reflecting the actual operating status and enhancing the reliability of the evaluation results. This adaptive mechanism significantly improves the reliability of circuit breaker status evaluation under different operating environments and conditions, avoiding the misjudgment problems caused by traditional fixed weights, and enhancing the ability to identify early fault characteristics through multi-dimensional data fusion. This can effectively reduce the fault incidence rate and improve the operational stability and reliability of the power system.

[0005] According to one aspect of this application, a data-driven method for evaluating the operating status of a circuit breaker is provided, comprising: collecting multi-dimensional operating data of the circuit breaker to be evaluated; for each dimension of the multi-dimensional operating data, constructing a feature data sequence corresponding to the dimension based on the operating data of the dimension; respectively calling the feature evaluation function corresponding to each dimension, and calculating the feature evaluation result of the dimension for the feature data sequence under each dimension through the feature evaluation function under the corresponding dimension; obtaining the operating environment conditions and / or operating conditions of the circuit breaker, and dynamically determining the dynamic weight coefficients corresponding to each feature evaluation result based on the operating environment conditions and / or operating conditions; weighting and summing the feature evaluation results under each dimension based on the dynamic weight coefficients corresponding to each feature evaluation result to obtain the health index of the circuit breaker, and evaluating the operating status of the circuit breaker according to the health index.

[0006] According to another aspect of this application, a data-driven circuit breaker operation status assessment device is provided, comprising: a data acquisition module for acquiring multi-dimensional operation data of a circuit breaker to be assessed for operation status; a data sequence construction module for constructing a feature data sequence corresponding to each dimension based on the operation data of the dimension in the multi-dimensional operation data; a feature assessment module for calling the feature assessment function corresponding to each dimension respectively, and calculating the feature assessment result of the dimension for the feature data sequence under each dimension through the feature assessment function under the corresponding dimension; a weight coefficient determination module for obtaining the operating environment conditions and / or operating conditions of the circuit breaker, and dynamically determining the dynamic weight coefficients corresponding to each feature assessment result based on the operating environment conditions and / or operating conditions; and a status assessment module for weighted summing of the feature assessment results under each dimension based on the dynamic weight coefficients corresponding to each feature assessment result to obtain the health index of the circuit breaker, and assessing the operation status of the circuit breaker based on the health index.

[0007] According to another aspect of this application, a storage medium is provided that stores a computer program thereon, which, when executed by a processor, implements the above-described data-driven circuit breaker operating status evaluation method.

[0008] According to another aspect of this application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the program to implement the above-described data-driven circuit breaker operating status assessment method.

[0009] By employing the above technical solution, this application provides a data-driven method and apparatus for evaluating the operating status of circuit breakers, including a storage medium and a computer device. First, multi-dimensional operating data of the circuit breaker to be evaluated is collected. Next, for each dimension of operating data, a feature data sequence can be organized and constructed according to certain rules or time order. Further, based on the feature evaluation functions of different dimensions, the feature evaluation results for each dimension are calculated using the feature data sequence. The operating environment conditions and operating status of the circuit breaker are obtained, and dynamic weighting coefficients corresponding to each feature evaluation result are dynamically determined based on this information. After determining the dynamic weighting coefficients for each feature evaluation result, these dynamic weighting coefficients are multiplied by the corresponding feature evaluation result, and then all weighted results are summed to obtain a comprehensive health index. Finally, based on the pre-set correspondence between the health index and the operating status, the operating status of the circuit breaker can be evaluated. This application's embodiment, by collecting and deeply analyzing multi-dimensional operating data of the circuit breaker, can comprehensively and meticulously mine various information during the circuit breaker's operation, improving the accuracy and comprehensiveness of the evaluation. Furthermore, the determination of dynamic weighting coefficients fully considers the operating environment and conditions of the circuit breakers, enabling the operational status assessment results to be dynamically adjusted according to actual conditions, thus better reflecting the actual operating status and enhancing the reliability of the assessment results. This adaptive mechanism significantly improves the reliability of circuit breaker status assessment under different operating environments and conditions. It avoids the misjudgment problems caused by traditional fixed weights and enhances the ability to identify early fault characteristics through multi-dimensional data fusion, effectively reducing the fault incidence rate and improving the operational stability and reliability of the power system.

[0010] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0011] The accompanying drawings, which are included to provide a further understanding of this application and constitute a part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 shows a flowchart of a data-driven circuit breaker operation status assessment method provided by an embodiment of this application; Figure 2 shows a flowchart of another data-driven circuit breaker operation status assessment method provided by an embodiment of this application; Figure 3 shows a flowchart of a method for preprocessing collected multi-dimensional operation data through a data preprocessing module provided by an embodiment of this application; Figure 4 shows a flowchart of the triggering process of a fault prediction module and an intelligent early warning module provided by an embodiment of this application; Figure 5 shows a flowchart of the generation process of a target decision scheme provided by an embodiment of this application; Figure 6 shows a structural schematic diagram of a data-driven circuit breaker operation status assessment device provided by an embodiment of this application; Figure 7 shows a structural schematic diagram of a computer device provided by an embodiment of this application. Detailed Implementation

[0012] The present application will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present application can be combined with each other.

[0013] This embodiment provides a data-driven method for evaluating the operating status of circuit breakers, as shown in Figure 1. The method includes: step 101, collecting multi-dimensional operating data of the circuit breaker to be evaluated.

[0014] Step 102: For each dimension in the multidimensional operational data, construct the feature data sequence corresponding to the dimension based on the operational data of the dimension.

[0015] Step 103: Call the feature evaluation function corresponding to each dimension respectively. For the feature data sequence under each dimension, calculate the feature evaluation result under the corresponding dimension through the feature evaluation function.

[0016] Step 104: Obtain the operating environment conditions and / or operating conditions of the circuit breaker, and dynamically determine the dynamic weight coefficients corresponding to the evaluation results of each feature based on the operating environment conditions and / or operating conditions.

[0017] Step 105: Based on the dynamic weight coefficients corresponding to the evaluation results of each feature, the evaluation results of each dimension are weighted and summed to obtain the health index of the circuit breaker, and the operating status of the circuit breaker is evaluated according to the health index.

[0018] This application provides a data-driven method for evaluating the operational status of circuit breakers. First, multi-dimensional operational data of the circuit breaker to be evaluated can be collected. This multi-dimensional operational data can cover various dimensions of operational data, including electrical parameters (such as current and voltage), mechanical parameters (such as the number of operations and vibration), temperature parameters, and historical operational data. By comprehensively collecting this multi-dimensional operational data, rich raw information can be provided for subsequent circuit breaker operational status evaluation, ensuring that the operational status of the circuit breaker can be characterized from multiple perspectives.

[0019] After acquiring multidimensional operational data, the data for each dimension can be organized and structured into a feature data sequence according to certain rules or chronological order. For example, for the current dimension, the current values ​​collected at different times can be arranged in chronological order to form a current feature data sequence. This transforms the raw operational data of the circuit breaker into a more ordered and easier-to-analyze form, highlighting the changes in each dimension of operational data over time.

[0020] Because operational data from different dimensions possess different characteristics and influence the circuit breaker's operating status in different ways, specialized feature evaluation functions can be pre-defined for each dimension. These functions, constructed based on statistical methods, machine learning algorithms, or other mathematical models, can extract key information reflecting the circuit breaker's operating status in that dimension based on the characteristics of the feature data sequence, and transform it into specific evaluation results. For example, for the dimension of operation frequency, the feature evaluation function can calculate an evaluation value representing the degree of influence of the operational data in that dimension on the circuit breaker's operating status based on the cumulative number of operations and a comparison with the normal operating range.

[0021] The operating environment conditions (such as ambient temperature and humidity) and operating conditions (such as load size and number of operations) of a circuit breaker can affect its operating status to varying degrees. Therefore, this information can be acquired, and the dynamic weighting coefficients corresponding to the evaluation results of each feature can be dynamically determined based on the actual situation. For example, when the ambient temperature rises, the weighting coefficient for the electrical temperature rise feature automatically decreases; while when the number of operations approaches the design life, the weighting coefficient for the mechanical vibration index automatically increases. This method achieves an "environmentally adaptive" health assessment mechanism, ensuring that the health index reflects the true state trend of the circuit breaker.

[0022] After determining the dynamic weighting coefficients for each feature evaluation result, these coefficients are multiplied by the corresponding feature evaluation result, and then all weighted results are summed to obtain a comprehensive health index. This health index integrates the circuit breaker's operating status information across various dimensions, taking into account the differences in importance of different dimensions under different operating environment conditions and working conditions. Finally, based on the pre-defined correspondence between the health index and operating status, the circuit breaker's operating status can be evaluated, determining whether it is in a normal, slightly abnormal, or severely faulty state. In actual operation, the health index can also be calculated periodically and health curves plotted to quantify and visualize the circuit breaker's operating status and trends.

[0023] In one specific embodiment, the health index of the circuit breaker can be calculated based on the following formula:

[0024] ;in, It is the health index of the circuit breaker at time t. These are the dynamic weighting coefficients associated with the nth feature data sequence. It is the feature evaluation function associated with the nth feature data sequence. This represents the nth feature data sequence.

[0025] By applying the technical solution of this embodiment, multi-dimensional operational data of the circuit breaker to be evaluated is first collected. Then, for each dimension of operational data, a feature data sequence can be organized and constructed according to certain rules or time order. Further, based on the feature evaluation functions of different dimensions, the feature evaluation results for each dimension are calculated based on the feature data sequence. The operating environment conditions and operating status of the circuit breaker are obtained, and the dynamic weight coefficients corresponding to each feature evaluation result are dynamically determined based on this information. After determining the dynamic weight coefficients of each feature evaluation result, these dynamic weight coefficients are multiplied by the corresponding feature evaluation result, and then all weighted results are summed to obtain a comprehensive health index. Finally, based on the pre-set correspondence between the health index and the operating status, the operating status of the circuit breaker can be evaluated. This embodiment of the application, by collecting and deeply analyzing multi-dimensional operational data of the circuit breaker, can comprehensively and meticulously mine various information during the circuit breaker's operation, improving the accuracy and comprehensiveness of the evaluation. Furthermore, the determination of dynamic weighting coefficients fully considers the operating environment and conditions of the circuit breakers, enabling the operational status assessment results to be dynamically adjusted according to actual conditions, thus better reflecting the actual operating status and enhancing the reliability of the assessment results. This adaptive mechanism significantly improves the reliability of circuit breaker status assessment under different operating environments and conditions. It avoids the misjudgment problems caused by traditional fixed weights and enhances the ability to identify early fault characteristics through multi-dimensional data fusion, effectively reducing the fault incidence rate and improving the operational stability and reliability of the power system.

[0026] Optionally, in this embodiment of the application, the method further includes: responding to a fault prediction trigger signal, acquiring multi-dimensional feature data of the circuit breaker at the current moment, and constructing a current feature vector of the circuit breaker based on the multi-dimensional feature data; determining the target model of the circuit breaker, and acquiring historical fault data of all circuit breakers of the target model, and based on the historical fault data, identifying a first probability of all circuit breakers of the target model failing, and a second probability of the current feature vector appearing under the premise of a failure; acquiring historical operating data of all circuit breakers of the target model, and identifying a third probability of all circuit breakers of the target model appearing the current feature vector based on the historical operating data; calculating the product of the first probability and the second probability, and calculating the ratio of the product to the third probability, and using the ratio as the fault probability prediction value of the circuit breaker.

[0027] In this embodiment, the fault prediction trigger signal can be generated by a preset time period, a specific operating event, or manual operation. Upon receiving the signal, multi-dimensional feature data of the circuit breaker at the current moment can be immediately collected from the relevant monitoring equipment or data storage. This feature data can cover multiple aspects, such as electrical parameters (e.g., instantaneous values ​​and rates of change of voltage and current), mechanical parameters (e.g., operating time of the operating mechanism, wear degree of contacts), and temperature parameters (e.g., temperature values ​​of key parts). After acquiring this multi-dimensional feature data, it can be integrated into a current feature vector according to certain rules and formats. This feature vector is a digital representation of the circuit breaker's operating state at the current moment, comprehensively reflecting the circuit breaker's feature information in multiple dimensions, providing basic data for subsequent fault probability prediction.

[0028] Next, the specific target model of the circuit breaker for operational status assessment can be identified. Different models of circuit breakers differ in design, structure, and performance, and their fault occurrence patterns and characteristics may also differ. After determining the target model, historical fault data for all circuit breakers of that model can be collected from a database or historical records. This historical fault data contains various fault scenarios that occurred with this model of circuit breaker over a past period, including fault type, occurrence time, and operating parameters at the time of the fault. Through statistical analysis of this historical fault data, the first probability of a fault occurring with this model of circuit breaker can be calculated, i.e., the overall likelihood of a fault occurring with this model of circuit breaker. Simultaneously, given that a fault has been confirmed, the proportion of the current feature vector (i.e., a vector similar to the circuit breaker's characteristics at the current moment) appearing in these fault cases can be further analyzed to obtain the second probability. The second probability reflects the likelihood of a fault occurring with this model of circuit breaker under the operating state represented by the current feature vector.

[0029] In addition to historical fault data, historical operating data for all circuit breakers of the target model can also be obtained. This historical operating data records various parameters and status information of the circuit breaker throughout its entire (normal + abnormal) operation, reflecting its performance under different operating environments and conditions. By mining and analyzing this historical operating data, the frequency of the current feature vector (a vector with the same or similar characteristics to the circuit breaker at the current moment) appearing in all operating records of this model of circuit breaker is statistically analyzed, thus obtaining a third probability. This third probability represents the likelihood of this current characteristic state occurring throughout the entire operation of this model of circuit breaker.

[0030] After obtaining the first probability (the overall probability of a circuit breaker of this type failing), the second probability (the probability of the current feature vector appearing under the premise of a failure), and the third probability (the probability of the current feature vector appearing during the entire operation), the calculation is performed according to the principle of Bayes' theorem. First, the product of the first and second probabilities is calculated. This product represents an intermediate probability of a circuit breaker of this type failing after considering the correlation between the current feature vector and the failure. Then, this product is divided by the third probability, and the result is the predicted failure probability value of the circuit breaker at the current moment. This predicted failure probability value comprehensively considers factors such as the overall failure occurrence of the circuit breaker of this type, the correlation between the current feature vector and the failure, and the frequency of the current feature vector appearing during the entire operation. It can more accurately reflect the likelihood of the circuit breaker failing at the current moment, providing an important reference for maintenance personnel to take measures in advance and prevent failures.

[0031] Optionally, after "using the ratio as the predicted fault probability value of the circuit breaker", the method further includes: calculating a comprehensive early warning level index based on the following formula: ;in, Indicates the comprehensive early warning level indicators, and These represent the weighting factors for the health index and the predicted failure probability, respectively. This indicates the health index. The predicted fault probability value is represented; if the comprehensive early warning level index is greater than the preset alarm threshold, an early warning message is generated and sent to the target terminal.

[0032] In this embodiment, after calculating the circuit breaker failure probability prediction value, a comprehensive early warning level index can be further calculated to more comprehensively assess the circuit breaker's operational risk. This index comprehensively considers the previously calculated health index and failure probability prediction value. The health index reflects the overall health status of the circuit breaker across multiple dimensions, while the failure probability prediction value focuses on the likelihood of the circuit breaker failing at the current moment. Weighting factors are used to adjust the relative importance of the health index and failure probability prediction value in the comprehensive early warning level index. By multiplying the health index and failure probability prediction value by their respective weighting factors and then summing them, the comprehensive early warning level index can be obtained. This index integrates the circuit breaker's current overall health status and potential failure risk information, providing a more scientific and comprehensive basis for subsequent early warning decisions.

[0033] After calculating the comprehensive early warning level index, it can be compared with a preset alarm threshold. The preset alarm threshold can be set comprehensively based on factors such as the circuit breaker's operating characteristics, historical data, and actual operation and maintenance needs, representing a critical value for the circuit breaker's operational risk. When the comprehensive early warning level index exceeds the preset alarm threshold, it indicates that the circuit breaker currently has a high operational risk, and may be about to fail or is already in an abnormal operating state. At this time, an early warning message can be automatically generated. The early warning message can include relevant information such as the circuit breaker's specific location, current health index, predicted failure probability value, and comprehensive early warning level index, so that operation and maintenance personnel can quickly understand the circuit breaker's status. After generating the early warning message, it can be sent to the target terminal. The target terminal can be the operation and maintenance personnel's mobile phone or computer terminal, or the large screen of the power system's monitoring center, etc. By sending the early warning message to the target terminal in a timely manner, it can be ensured that operation and maintenance personnel obtain the abnormal status of the circuit breaker as soon as possible, and thus quickly take corresponding measures, such as arranging maintenance and adjusting operating parameters, to avoid the occurrence of failure or mitigate the impact of failure, and ensure the stable operation of the power system.

[0034] In a specific embodiment, the comprehensive early warning level index ranges from [0,1]. Based on the magnitude of the comprehensive early warning level index, the early warning level can be divided into three levels: (1) 0.0-0.4: normal state, only daily monitoring is required; (2) 0.4-0.7: slight equipment abnormality, maintenance or adjustment is recommended; (3) 0.7-1.0: serious risk, which will trigger an alarm, generate early warning information and recommend shutdown for maintenance. At this time, the preset alarm threshold is 0.7.

[0035] In this embodiment, optionally, the preset alarm threshold is dynamically adjustable; the method further includes: monitoring at least one dynamic factor affecting the operating status of the circuit breaker, wherein the dynamic factor includes real-time load rate, ambient temperature, and cumulative operating years; determining a corresponding threshold adjustment amount according to the current value or range of the at least one dynamic factor through a predefined adjustment rule; and calibrating the preset alarm threshold in real-time or periodically according to the threshold adjustment amount; wherein the predefined adjustment rule includes: when the dynamic factor includes real-time load rate, if the real-time load rate is higher than the rated load within a preset time interval, determining the threshold adjustment amount to be negative; and / or, when the dynamic factor includes ambient temperature, if the ambient temperature is higher than the standard operating temperature, determining the threshold adjustment amount to be negative; and / or, when the dynamic factor includes cumulative operating years, if the cumulative operating years exceed a preset number of years, determining the threshold adjustment amount to be negative.

[0036] In this embodiment, the preset alarm threshold is dynamically adjustable, allowing for more accurate assessment of the circuit breaker's risk status under different operating conditions. In actual operation, the environment and operating conditions of the circuit breaker are constantly changing. A fixed alarm threshold is difficult to adapt to these dynamic changes, potentially leading to untimely or false alarms. Therefore, setting the preset alarm threshold to be dynamically adjustable allows for flexible adjustments based on the actual operating status of the circuit breaker, improving the accuracy and effectiveness of early warnings.

[0037] Specifically, to achieve dynamic adjustment of preset alarm thresholds, dynamic factors that significantly affect the circuit breaker's operating status can be monitored in real time or periodically. These dynamic factors can include real-time load rate, ambient temperature, and cumulative operating years. Real-time load rate reflects the current electrical load on the circuit breaker; an excessively high load rate may accelerate the aging and damage of the circuit breaker. Ambient temperature affects the electrical and mechanical performance of the circuit breaker; high temperatures may lead to decreased insulation performance and component expansion and deformation. Cumulative operating years reflect the length of time the circuit breaker has been in use; as the usage time increases, the various components of the circuit breaker will gradually wear down, and the probability of failure will increase accordingly. By monitoring these dynamic factors, changes in the circuit breaker's operating environment and working conditions can be promptly understood.

[0038] After obtaining the current value or range of dynamic factors, the corresponding threshold adjustment amount can be determined according to predefined adjustment rules. These predefined adjustment rules can be formulated based on extensive experimental data, historical experience, and the operating characteristics of the circuit breaker, clearly defining the direction and extent of the influence of different dynamic factors on the alarm threshold under different conditions. After determining the threshold adjustment amount, the preset alarm threshold can be calibrated based on this amount. Calibration can be real-time, meaning the alarm threshold is adjusted immediately once a change in the dynamic factor is detected and the threshold adjustment amount is determined; or it can be periodic, adjusting the alarm threshold at certain time intervals (such as hourly or daily). Through real-time or periodic calibration, the preset alarm threshold can always adapt to the actual operating state of the circuit breaker, ensuring timely early warning when abnormal conditions occur in the circuit breaker and guaranteeing the safe and stable operation of the power system.

[0039] In one specific embodiment, the predefined adjustment rule is configured as follows: when the real-time load rate remains higher than the rated load within a preset time interval, it indicates that the circuit breaker is operating under high load for an extended period. This puts significant stress on the various components of the circuit breaker, accelerating their aging and damage, thereby increasing the probability of failure. Therefore, to detect potential fault risks more promptly, the preset alarm threshold can be appropriately lowered, i.e., the threshold adjustment amount can be set to a negative value, so that an early warning can be issued when the circuit breaker's condition shows even slight abnormalities.

[0040] Ambient temperature has a significant impact on the performance of circuit breakers. When the ambient temperature exceeds the standard operating temperature, the insulation material of the circuit breaker may age faster, its electrical performance may deteriorate, and mechanical components may deform or jam due to thermal expansion. All of these factors can increase the likelihood of circuit breaker failure. Therefore, to adapt to the operational risks in high-temperature environments, the preset alarm threshold can be lowered, and the threshold adjustment amount can be set to a negative value to detect abnormal conditions of the circuit breaker earlier.

[0041] As circuit breakers age, the wear and tear on their components gradually intensifies, increasing the probability of failure. In this situation, even if the overall warning level of the circuit breaker does not reach the originally set alarm threshold, the actual risk of failure is already high. Therefore, to more accurately reflect the actual risk status of the circuit breaker, the preset alarm threshold can be appropriately lowered, i.e., the threshold adjustment can be set to a negative value. This allows for timely warnings and prompt scheduling of inspections and maintenance.

[0042] Optionally, after "using the ratio as the predicted fault probability value of the circuit breaker", the method further includes: if the health index is less than a first preset threshold, or the predicted fault probability value is greater than a second preset threshold, then multiple candidate decision schemes are obtained, and the multiple candidate decision schemes are evaluated based on a preset multi-objective optimization decision model. The multiple candidate decision schemes include immediate shutdown for maintenance, planned maintenance, and continued operation with enhanced monitoring. The multi-objective optimization decision model is a model that comprehensively considers the maintenance cost of the candidate decision schemes, the fault risk of the circuit breaker, and the operating benefits of the circuit breaker. The candidate decision scheme that minimizes the output value of the multi-objective optimization decision model is selected as the target decision scheme.

[0043] In this embodiment, after calculating the circuit breaker health index and fault probability prediction value, a judgment can be made. The health index reflects the overall health status of the circuit breaker. When it is less than a first preset threshold, it indicates that the circuit breaker's health status is poor and there may be potential fault hazards. The fault probability prediction value directly reflects the probability of the circuit breaker failing in the future. When it is greater than a second preset threshold, it indicates a high fault risk. Here, the first and second preset thresholds can be determined according to actual needs. Once either of these two situations occurs, it indicates that there is a problem with the current operating state of the circuit breaker, and timely measures need to be taken. At this time, multiple candidate decision schemes can be obtained. These schemes are possible handling methods for the current state of the circuit breaker, providing multiple options for subsequent decision-making.

[0044] The pre-defined multi-objective optimization decision-making model is an evaluation tool that comprehensively considers multiple key factors. When evaluating multiple candidate decision schemes, this model can comprehensively consider the maintenance cost, circuit breaker failure risk, and circuit breaker operational benefits of each candidate scheme. Maintenance costs can include the input of manpower, materials, and time; failure risk involves the probability of circuit breaker failure and the potential losses after the implementation of the candidate decision scheme; operational benefits focus on the impact of the candidate decision scheme on the normal operation of the circuit breaker and the stability of the power system. Through this comprehensive evaluation, the advantages and disadvantages of various candidate decision schemes can be compared more scientifically and objectively, providing a basis for selecting the optimal scheme.

[0045] In a specific embodiment, multiple candidate decision-making schemes may include immediate shutdown for maintenance, planned maintenance, and continued operation with enhanced monitoring. Immediate shutdown for maintenance is a relatively aggressive approach, enabling a comprehensive inspection and repair of the circuit breaker immediately, completely eliminating potential fault hazards. However, this immediate shutdown may impact the power supply stability of the power system, and the maintenance cost is relatively high. Planned maintenance, on the other hand, involves performing maintenance work according to a specific plan and schedule without affecting the normal operation of the circuit breaker. This can reduce the risk of faults to some extent and has a smaller impact on operational efficiency, but it may not be able to handle unexpected problems in a timely manner. Continued operation with enhanced monitoring involves close monitoring of the circuit breaker without immediate maintenance measures to detect fault signs and take appropriate action in a timely manner. This approach is less costly, but the risk of faults is relatively high.

[0046] After evaluating each candidate decision scheme, the multi-objective optimization decision model yields an output value that comprehensively reflects the performance of each candidate scheme in terms of maintenance cost, failure risk, and operational efficiency. Subsequently, the candidate decision scheme with the smallest output value can be selected, that is, the candidate decision scheme with the best overall performance after comprehensively considering all factors. This candidate decision scheme can minimize maintenance costs and failure risks while ensuring the operational efficiency of the circuit breaker, while meeting the requirements for safe operation. This provides maintenance personnel with a scientific and reasonable basis for decision-making, ensuring the safe, stable, and efficient operation of the circuit breaker.

[0047] Optionally, after the step of "if the health index is less than a first preset threshold, or the fault probability prediction value is greater than a second preset threshold", the method further includes: when the fault probability prediction value is greater than the first preset fault probability threshold and the maintenance cost of the circuit breaker is less than a preset maintenance cost threshold, immediate shutdown for maintenance is taken as the target decision scheme; when the fault probability prediction value is less than the second preset fault probability threshold, but the operating efficiency of the circuit breaker decreases and the rate of change of operating efficiency is greater than a preset rate of change, planned maintenance is taken as the target decision scheme; when the fault probability prediction value is less than or equal to the first preset fault probability threshold and greater than or equal to the second preset fault probability threshold, continued operation and enhanced monitoring are taken as the target decision scheme.

[0048] In this embodiment, the predicted fault probability value is used to measure the likelihood of the circuit breaker failing in the future. When it exceeds a first preset fault probability threshold, it indicates a high risk of fault occurrence, and measures can be taken as soon as possible to prevent the fault from happening. Meanwhile, maintenance cost is also an important factor in the decision-making process, and the preset maintenance cost threshold is an economically critical value. When the maintenance cost of the circuit breaker is less than this threshold, it means that immediate shutdown and maintenance are economically feasible, and will not place an excessive burden on the power system due to excessive costs. In this case, choosing immediate shutdown and maintenance as the target decision-making scheme allows for timely and comprehensive inspection and repair of the circuit breaker, eliminating potential fault hazards and ensuring the stable operation of the power system.

[0049] If the predicted fault probability is less than the second preset fault probability threshold, it indicates that the risk of circuit breaker failure is relatively low. However, if the circuit breaker's operating efficiency decreases and the rate of change in operating efficiency exceeds the preset rate of change, it indicates that the circuit breaker's performance is gradually declining. Although the current fault risk is not high, if not addressed promptly, it may lead to more serious problems in the future. Planned maintenance, without affecting the normal operation of the circuit breaker, involves carrying out maintenance work according to a certain plan and schedule. This includes necessary inspections, adjustments, and repairs to restore its operating efficiency and prevent further development of the fault. This approach considers both the current operating status of the circuit breaker and the power supply stability of the power system, making it a more balanced decision-making solution.

[0050] When the predicted fault probability is less than or equal to the first preset fault probability threshold and greater than or equal to the second preset fault probability threshold, the circuit breaker's fault probability is within an intermediate range. It is neither at a high-risk level requiring immediate shutdown for maintenance, nor so low as to be completely negligible. The option of continuing operation with enhanced monitoring allows the circuit breaker to continue serving the power system within a controllable range, maintaining normal power supply. Furthermore, enhanced monitoring enables real-time monitoring of the circuit breaker's operating status, allowing for timely detection of any abnormal changes. If an upward trend in the fault probability or other abnormalities are detected, corresponding measures can be taken quickly, such as adjusting operating parameters or arranging temporary maintenance. This approach avoids losses from unnecessary shutdowns for maintenance while ensuring timely response to increased fault risk, guaranteeing the safe operation of the circuit breaker. The aforementioned first and second preset fault probability thresholds can be determined based on actual needs.

[0051] Optionally, after step 101, the method further includes: for each dimension of the running data, decomposing the running data into wavelet coefficients of different frequency bands, applying a threshold processing function to the wavelet coefficients, filtering the wavelet coefficients of different frequency bands through the threshold processing function to obtain retained wavelet coefficients, performing inverse wavelet transform on the retained wavelet coefficients, and reconstructing the denoised running data; performing missing data verification on the denoised running data, and when missing data is confirmed to exist, completing the missing data based on an interpolation algorithm to obtain completed running data; performing normalization processing on the completed running data to obtain the normalization processing result under the dimension, and constructing the feature data sequence of the dimension based on the normalization processing result.

[0052] In this embodiment, multi-dimensional operational data can be preprocessed. First, noise interference in the circuit breaker's multi-dimensional operational data can be removed. Specifically, wavelet transform can be used to decompose the operational data of each dimension into different frequency bands, each corresponding to components within a different frequency range. The wavelet coefficients of different frequency bands reflect the characteristic information of that band. Subsequently, a threshold processing function is applied to the obtained wavelet coefficients to filter them according to set rules, removing those representing noise components and retaining those representing valid signals. Next, inverse wavelet transform is used to recombine the retained wavelet coefficients after filtering, reconstructing the denoised operational data. The data processed in this way more accurately reflects the actual operating state of the circuit breaker and reduces the interference of noise on subsequent analysis and evaluation.

[0053] Secondly, during actual data acquisition, due to various reasons (such as sensor failure, communication interruption, etc.), the acquired operational data may be incomplete. Missing data will affect the accurate assessment of the circuit breaker's operating status, therefore, it can be addressed. Specifically, the denoised operational data undergoes a missing data verification process to check for any missing data points. If missing data is confirmed, interpolation algorithms can be used to fill in the missing parts. Interpolation algorithms can estimate the values ​​of missing data points based on known patterns and trends between data points, thus obtaining a complete data sequence—the completed operational data. This ensures data integrity and provides a reliable foundation for subsequent analysis.

[0054] Finally, normalization is used to unify the completed operational data from different dimensions into a specific range (such as [0,1] or [-1,1]), eliminating the influence of units and numerical ranges and making the data comparable. Through normalization, the data in each dimension can be presented in a standardized form, facilitating subsequent feature extraction and the construction of feature data sequences. Constructing a feature data sequence for that dimension based on the normalization results can more accurately reflect the characteristics and patterns of the operational data in that dimension, providing effective feature input for subsequent evaluation of the circuit breaker's operating status.

[0055] Furthermore, as a refinement and extension of the specific implementation of the above embodiments, to fully illustrate the specific implementation process of this embodiment, another data-driven circuit breaker operation status assessment method is provided, as shown in Figure 2. This method includes: First, the data acquisition module collects multi-dimensional operation data of the circuit breaker to be assessed. Specifically, the operation status of the circuit breaker can be monitored in real time using various sensors, and the collected operation data is transmitted to the central processing unit (CPU). Specifically, the data can be initially compressed, encrypted, and feature extracted by the edge computing unit before being uploaded to the central data processing platform. These sensors are installed in key parts of the circuit breaker, and the monitored content includes, but is not limited to: electrical parameters, including current, voltage, power (active and reactive), power factor, frequency, etc. These data can reflect the electrical load status of the circuit breaker.

[0056] Mechanical parameters, including vibration signals, contact pressure, closing speed, contact stroke, number of operations, and wear, can reveal the mechanical health status of the circuit breaker.

[0057] Environmental parameters, such as temperature, humidity, air pressure, and concentration of external pollutants, have a significant impact on the long-term operation of circuit breakers.

[0058] Historical operating data: This includes the circuit breaker's historical maintenance records, number of operations, outage records, etc. This data is crucial for the health assessment of the circuit breaker.

[0059] The second step is to preprocess the collected multidimensional operating data through the data preprocessing module, as shown in Figure 3, including: (1) Denoising: Wavelet threshold denoising technology is used to automatically select appropriate denoising parameters based on the characteristics of different dimensions of operating data, eliminate high-frequency noise, and retain the transient change characteristics of the circuit breaker. The denoised operating data can clearly reflect the actual operating status of the circuit breaker, which is helpful for feature extraction and operating status evaluation.

[0060] (2) Interpolation and Filling in Missing Values: During the transmission of multidimensional data, some sampling points may be missing due to network fluctuations or equipment failures. In this case, the missing data can be filled in using Lagrange interpolation or Kalman filtering to ensure the continuity and integrity of the data in each dimension.

[0061] (3) Data normalization and standardization In order to eliminate the differences in the dimensions of data from different sensors, all the completed operational data are standardized. The Z-score standardization method can be used to make the mean of each feature 0 and the standard deviation 1, thereby eliminating the scale difference between data and facilitating subsequent analysis and processing.

[0062] The third step is to extract features from the normalized operating data. By extracting effective features from multidimensional operating data, the potential fault modes and abnormal states of the circuit breaker can be identified efficiently and accurately. The main steps include: (1) Electrical signal feature extraction: Fourier transform the operating data such as current and voltage to extract harmonic components in order to identify abnormal phenomena caused by circuit breaker faults or load fluctuations; at the same time, autocorrelation analysis is used to extract the fluctuation trend features of electrical signals.

[0063] (2) Mechanical signal feature extraction: Wavelet transform is performed on the vibration data of the circuit breaker contacts to extract the spectral features of the vibration. The fluctuation characteristics of the signal are analyzed by statistical measures such as kurtosis and skewness to capture the mechanical impact signal generated by the circuit breaker fault.

[0064] (3) Environmental signal feature extraction: By analyzing the time series of environmental parameters such as temperature, humidity, and air pressure, the sliding window averaging method is used to extract the trend features of temperature and humidity changes in order to identify the impact of environmental factors on the circuit breaker's operating status.

[0065] (4) Historical operation data feature extraction: Using historical operation and maintenance records and fault data, the aging indicators and fault modes of circuit breakers are extracted to provide historical basis for operation status assessment and fault prediction.

[0066] For each dimension, a feature data sequence can be constructed based on the extracted features, generating a multi-dimensional feature data sequence as input for subsequent operational status assessment and fault prediction.

[0067] The fourth step involves evaluating the circuit breaker's operating status using a status assessment module, based on the feature data sequences for each dimension. Specifically, the extracted multidimensional feature data sequences are used to calculate the circuit breaker's health index, reflecting its operating status.

[0068] (1) Multidimensional fusion regression model: This application can adopt a multidimensional fusion model based on linear regression and nonlinear regression to weight and merge the feature evaluation results corresponding to the electrical dimension feature data sequence, mechanical dimension feature data sequence, environmental dimension feature data sequence and historical operation dimension feature data sequence to calculate the health index of the circuit breaker. Here, the feature evaluation results can be obtained by processing according to the feature evaluation function of each dimension.

[0069] (2) Health Index: This index can be a value between 0 and 1. The smaller the value, the worse the health status of the circuit breaker. This index can be used to quickly assess the overall operating status of the circuit breaker.

[0070] Specifically, the health index can be calculated based on a multi-layered analysis structure of regression fusion: the bottom layer uses a physical mechanism model to characterize the coupling relationship between electrical parameters, environmental parameters, mechanical parameters and historical operating data; the middle layer uses a neural network regression and multi-dimensional linear fusion algorithm to fit nonlinear features; and the top layer uses fuzzy decision rules to output the health index in stages, forming a quantitative assessment result of the operating status.

[0071] The fifth step involves executing the fault prediction phase through the fault prediction module. This phase analyzes the multi-dimensional characteristic data of the current circuit breaker to assess the probability of future faults and provide early warnings.

[0072] Specifically, based on the changing trends of the predicted failure probability, potential failure warnings can be issued several hours or even days in advance, providing maintenance personnel with ample time to perform maintenance or replacement.

[0073] Here, if the health indicators (health index) meet the standards and the fault prediction indicators (fault probability prediction values) meet the standards, the circuit breaker can continue to operate; otherwise, step six can be executed.

[0074] The sixth step involves executing the intelligent early warning phase through the intelligent early warning module. During this phase, a comprehensive early warning level index is calculated based on the real-time calculated health index and predicted fault probability. This comprehensive early warning level index is then compared with a dynamically preset alarm threshold to determine whether the circuit breaker is in a potential fault risk state. This "probability-health dual-drive" early warning mechanism effectively overcomes the shortcomings of a single threshold model, improving early warning sensitivity and accuracy.

[0075] (1) Dynamically preset alarm threshold The embodiments of this application use an adaptive algorithm to dynamically adjust the preset alarm threshold according to dynamic factors such as circuit breaker load changes, ambient temperature, and humidity, which significantly reduces the false alarm rate and the missed alarm rate.

[0076] (2) Warning Judgment: When the comprehensive warning level index exceeds the dynamic preset alarm threshold, a warning signal is triggered, and maintenance personnel are prompted to perform inspection or shutdown operations. Specifically, warning information can be sent via voice, SMS, email, etc.

[0077] Step 7: Execute the intelligent decision-making stage through the intelligent decision-making module. The intelligent decision-making stage can automatically generate the optimal decision, that is, determine whether to shut down, repair, or continue operation, so as to reduce the economic losses caused by circuit breaker failure and unplanned shutdown. This stage mainly relies on a multi-objective optimization decision-making model to weigh different decision objectives and ensure the stable operation of the system. The following are the specific operation steps of this stage: (1) Multi-objective optimization decision-making model: In this embodiment of the application, a multi-objective optimization decision-making model is used to make intelligent decisions on the operating status of the circuit breaker in order to achieve a balance between different objectives. Specific decision objectives include: a. Minimize maintenance costs: Considering the economic costs of maintenance operations (such as shutdown and repair).

[0078] b. Minimize failure risk: Avoid significant losses due to failures as much as possible.

[0079] c. Maximize availability (operational efficiency): Keep circuit breakers in normal operating condition and extend their service life as much as possible.

[0080] The optimization objective function can be set as follows: ;in: : and candidate decision schemes Related maintenance costs; : Circuit breaker failure risk; The operational efficiency of circuit breakers; Weighting coefficient.

[0081] The decision-making process is based on this optimization objective function. A weighted approach is used to combine the influence of each objective to calculate the optimal decision. The decision results are as follows: a. Planned maintenance (Level 1 warning): Low risk, high benefit; b. Standby monitoring (Level 2 warning): Critical risk, requires continuous monitoring; c. Shutdown maintenance (Level 3 warning): High risk, immediate maintenance required.

[0082] (2) Risk Assessment and Decision Implementation In the process of calculating the objective function, the following three main assessment indicators can be used to determine whether to shut down the circuit breaker: a. Fault Risk Assessment: Calculate the probability of the circuit breaker failing at the current moment using the aforementioned fault probability prediction value. If the probability exceeds the set threshold, it means that the circuit breaker has a high risk and needs to be shut down for inspection and maintenance.

[0083] b. Economic cost assessment: based on the maintenance cost function Assess the economic cost of downtime maintenance. This function considers multiple factors such as maintenance time, labor costs, and material costs. When a circuit breaker is in poor operating condition but the risk of failure is low, the operating time can be extended to avoid frequent downtime, based on a trade-off between economic costs and maintenance benefits.

[0084] c. Equipment Availability Assessment: The availability U of a circuit breaker represents the effective operating time of the circuit breaker under the current decision. By balancing maintenance time and circuit breaker availability, intelligent decisions can be made regarding whether to shut down the circuit breaker based on its service life and operating cycle.

[0085] (3) Decision execution and feedback mechanism: Based on the optimization decision results, perform one of the following operations: Continue to operate: If the calculation results show that the current circuit breaker operation has low risk, low economic cost and high availability, the circuit breaker can continue to operate normally and the current operating status can be fed back to the database as a reference for future decisions.

[0086] Shutdown and Maintenance: If the fault risk is too high or the circuit breaker is in a severely compromised condition, a shutdown order can be issued, instructing maintenance personnel to immediately perform maintenance on the circuit breaker. In this case, a shutdown maintenance report can be automatically generated according to a pre-set fault handling procedure, and relevant technical personnel will be notified.

[0087] Standby status: Under certain special circumstances (such as unstable load, critical health status of circuit breaker, etc.), the circuit breaker status can be set to "standby" for temporary status monitoring and adjustment, and a decision on whether to shut down will be made after further judgment.

[0088] In a specific embodiment, as shown in Figure 4, the fault prediction module can also be triggered by the health status assessment result of the circuit breaker. First, the health status of the circuit breaker can be assessed based on the multidimensional feature data sequence to obtain the circuit breaker's health index S(t). Then, the relationship between the health index S(t) and a specific threshold is compared. If S(t) ≥ the threshold, it indicates that the circuit breaker is in a healthy state and no further operation is required. If S(t) < the threshold, it indicates that the circuit breaker is in a risky state. At this time, a fault prediction trigger signal can be generated to trigger the fault prediction module to calculate the fault probability prediction value. Then, through the intelligent early warning module, based on S(t) and the fault probability prediction value, a comprehensive early warning level index is calculated to determine whether further early warning is needed.

[0089] In another specific embodiment, as shown in Figure 5, the intelligent decision-making module can be triggered not only when the health index is less than a first preset threshold or the predicted fault probability is greater than a second preset threshold, but also based on the intelligent early warning module. Specifically, the health index and the predicted fault probability can be input into the intelligent early warning module, which calculates a comprehensive early warning level index. Further, the early warning level is determined based on the calculated comprehensive early warning level index. Different comprehensive early warning level indices correspond to different early warning levels, including attention-level, normal, and severe levels. For example, a comprehensive early warning level index of 0.0-0.4 indicates a normal early warning level; 0.4-0.7 indicates a attention-level early warning level; and 0.7-1.0 indicates a severe level. For attention-level early warnings, enhanced monitoring of the circuit breaker can continue; for normal early warnings, the circuit breaker can continue operating; and for severe early warnings, the intelligent decision-making module can be further triggered to select the target decision from multiple candidate decision schemes.

[0090] Furthermore, as a specific implementation of the method in Figure 1, this application embodiment provides a data-driven circuit breaker operation status assessment device, as shown in Figure 6. The device includes: a data acquisition module for acquiring multi-dimensional operation data of the circuit breaker to be assessed; a data sequence construction module for constructing a feature data sequence corresponding to each dimension based on the operation data of that dimension; a feature assessment module for calling the feature assessment function corresponding to each dimension, and calculating the feature assessment result for each dimension's feature data sequence using the corresponding feature assessment function; a weight coefficient determination module for obtaining the circuit breaker's operating environment conditions and / or operating conditions, and dynamically determining the dynamic weight coefficients corresponding to each feature assessment result based on the operating environment conditions and / or operating conditions; and a status assessment module for weighted summation of the feature assessment results for each dimension based on the dynamic weight coefficients corresponding to each feature assessment result to obtain the circuit breaker's health index, and assessing the circuit breaker's corresponding operation status based on the health index.

[0091] Optionally, the device further includes a fault prediction module; the fault prediction module is configured to: in response to a fault prediction trigger signal, acquire multi-dimensional feature data of the circuit breaker at the current moment, and construct a current feature vector of the circuit breaker based on the multi-dimensional feature data; determine the target model of the circuit breaker, and acquire historical fault data of all circuit breakers of the target model, and based on the historical fault data, identify a first probability of all circuit breakers of the target model failing, and a second probability of the current feature vector appearing under the premise of a failure; acquire historical operating data of all circuit breakers of the target model, and identify a third probability of all circuit breakers of the target model appearing the current feature vector based on the historical operating data; calculate the product of the first probability and the second probability, and calculate the ratio of the product to the third probability, and use the ratio as the fault probability prediction value of the circuit breaker.

[0092] Optionally, the device further includes an intelligent early warning module; the intelligent early warning module is used to: after using the ratio as the predicted failure probability value of the circuit breaker, calculate a comprehensive early warning level index based on the following formula: ;in, Indicates the comprehensive early warning level indicators, and These represent the weighting factors for the health index and the predicted failure probability, respectively. This indicates the health index. The predicted fault probability value is represented; if the comprehensive early warning level index is greater than the preset alarm threshold, an early warning message is generated and sent to the target terminal.

[0093] Optionally, the preset alarm threshold is dynamically adjustable; the device further includes a threshold adjustment module; the threshold adjustment module is used to: monitor at least one dynamic factor affecting the operating status of the circuit breaker, wherein the dynamic factor includes real-time load rate, ambient temperature, and cumulative operating years; determine the corresponding threshold adjustment amount according to the current value or range of the at least one dynamic factor through a predefined adjustment rule; and calibrate the preset alarm threshold in real time or periodically according to the threshold adjustment amount; wherein the predefined adjustment rule includes: when the dynamic factor includes real-time load rate, if the real-time load rate is higher than the rated load within a preset time interval, the threshold adjustment amount is determined to be negative; and / or, when the dynamic factor includes ambient temperature, if the ambient temperature is higher than the standard operating temperature, the threshold adjustment amount is determined to be negative; and / or, when the dynamic factor includes cumulative operating years, if the cumulative operating years exceed a preset number of years, the threshold adjustment amount is determined to be negative.

[0094] Optionally, the device further includes an intelligent decision-making module; the intelligent decision-making module is configured to: after using the ratio as the predicted fault probability value of the circuit breaker, if the health index is less than a first preset threshold, or the predicted fault probability value is greater than a second preset threshold, obtain multiple candidate decision schemes, evaluate the multiple candidate decision schemes based on a preset multi-objective optimization decision model, wherein the multiple candidate decision schemes include immediate shutdown for maintenance, planned maintenance, and continued operation with enhanced monitoring, and the multi-objective optimization decision model is a model that comprehensively considers the maintenance cost of the candidate decision schemes, the fault risk of the circuit breaker, and the operating benefits of the circuit breaker; and select the candidate decision scheme that minimizes the output value of the multi-objective optimization decision model as the target decision scheme.

[0095] Optionally, the intelligent decision-making module is further configured to: if the health index is less than a first preset threshold, or the predicted fault probability is greater than a second preset threshold, and the predicted fault probability is greater than the first preset fault probability threshold and the maintenance cost of the circuit breaker is less than a preset maintenance cost threshold, immediately shut down for maintenance as the target decision scheme; if the predicted fault probability is less than the second preset fault probability threshold, but the operating efficiency of the circuit breaker decreases and the rate of change of operating efficiency is greater than a preset rate of change, planned maintenance as the target decision scheme; and if the predicted fault probability is less than or equal to the first preset fault probability threshold and greater than or equal to the second preset fault probability threshold, continue operation and strengthen monitoring as the target decision scheme.

[0096] Optionally, the device further includes a data preprocessing module; the data preprocessing module is used for: after collecting multi-dimensional operating data of the circuit breaker to be evaluated for operating status, decomposing the operating data into wavelet coefficients of different frequency bands for each dimension of operating data, applying a threshold processing function to the wavelet coefficients, filtering the wavelet coefficients of different frequency bands through the threshold processing function to obtain retained wavelet coefficients, performing inverse wavelet transform on the retained wavelet coefficients to reconstruct denoised operating data; performing missing data verification on the denoised operating data, and when missing data is confirmed to exist, completing the missing data based on an interpolation algorithm to obtain completed operating data; performing normalization processing on the completed operating data to obtain the normalization processing result under the dimension, and constructing a feature data sequence of the dimension based on the normalization processing result.

[0097] It should be noted that other corresponding descriptions of the functional units involved in the data-driven circuit breaker operation status evaluation device provided in this application embodiment can be found in the corresponding descriptions in Figures 1 to 5, and will not be repeated here.

[0098] This application also provides a computer device, specifically a personal computer, server, network device, etc., as shown in Figure 7. This computer device includes a bus, processor, memory, and communication interface, and may also include input / output interfaces and a display device. The processor of this computer device provides computing and control capabilities. The memory of this computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of this computer device stores location information. The network interface of this computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the various method embodiments.

[0099] Those skilled in the art will understand that the structure shown in Figure 7 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0100] In one embodiment, a computer-readable storage medium is provided, which may be non-volatile or volatile, having stored thereon a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0101] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0102] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0103] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0104] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0105] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A data-driven method for evaluating the operating status of circuit breakers, characterized in that, include: Collect multidimensional operational data of the circuit breaker to be evaluated for operational status; for each dimension of the multidimensional operational data, construct a feature data sequence corresponding to the dimension based on the operational data of the dimension; The feature evaluation function corresponding to each dimension is called respectively. For the feature data sequence under each dimension, the feature evaluation result under the corresponding dimension is calculated through the feature evaluation function under the corresponding dimension. The operating environment conditions and / or operating conditions of the circuit breaker are obtained. Based on the operating environment conditions and / or operating conditions, the dynamic weight coefficients corresponding to each feature evaluation result are dynamically determined. Based on the dynamic weight coefficients corresponding to each feature evaluation result, the feature evaluation results under each dimension are weighted and summed to obtain the health index of the circuit breaker. The operating status of the circuit breaker is evaluated according to the health index.

2. The method according to claim 1, characterized in that, The method further includes: responding to a fault prediction trigger signal, acquiring multi-dimensional feature data of the circuit breaker at the current moment, and constructing a current feature vector of the circuit breaker based on the multi-dimensional feature data; determining the target model of the circuit breaker, and acquiring historical fault data of all circuit breakers of the target model, and based on the historical fault data, identifying a first probability of all circuit breakers of the target model failing, and a second probability of the current feature vector appearing under the premise of a failure; acquiring historical operating data of all circuit breakers of the target model, and identifying a third probability of all circuit breakers of the target model appearing the current feature vector based on the historical operating data; calculating the product of the first probability and the second probability, and calculating the ratio of the product to the third probability, and using the ratio as the fault probability prediction value of the circuit breaker.

3. The method according to claim 2, characterized in that, After using the ratio as the predicted fault probability value of the circuit breaker, the method further includes: calculating a comprehensive early warning level index based on the following formula: ;in, Indicates the comprehensive early warning level indicators, and These represent the weighting factors for the health index and the predicted failure probability, respectively. This indicates the health index. The predicted fault probability value is represented; if the comprehensive early warning level index is greater than the preset alarm threshold, an early warning message is generated and sent to the target terminal.

4. The method according to claim 3, characterized in that, The preset alarm threshold is dynamically adjustable; the method further includes: monitoring at least one dynamic factor affecting the operating status of the circuit breaker, wherein the dynamic factor includes real-time load rate, ambient temperature, and cumulative operating years; determining the corresponding threshold adjustment amount according to the current value or range of the at least one dynamic factor through predefined adjustment rules; and calibrating the preset alarm threshold in real time or periodically according to the threshold adjustment amount; wherein the predefined adjustment rules include: when the dynamic factor includes real-time load rate, if the real-time load rate is higher than the rated load within a preset time interval, determining the threshold adjustment amount to be negative; and / or, when the dynamic factor includes ambient temperature, if the ambient temperature is higher than the standard operating temperature, determining the threshold adjustment amount to be negative; and / or, when the dynamic factor includes cumulative operating years, if the cumulative operating years exceed a preset number of years, determining the threshold adjustment amount to be negative.

5. The method according to claim 2, characterized in that, After using the ratio as the predicted fault probability value of the circuit breaker, the method further includes: if the health index is less than a first preset threshold, or the predicted fault probability value is greater than a second preset threshold, then obtaining multiple candidate decision schemes, evaluating the multiple candidate decision schemes based on a preset multi-objective optimization decision model, wherein the multiple candidate decision schemes include immediate shutdown for maintenance, planned maintenance, and continued operation with enhanced monitoring, and the multi-objective optimization decision model is a model that comprehensively considers the maintenance cost of the candidate decision schemes, the fault risk of the circuit breaker, and the operating benefits of the circuit breaker; and selecting the candidate decision scheme that minimizes the output value of the multi-objective optimization decision model as the target decision scheme.

6. The method according to claim 5, characterized in that, If the health index is less than a first preset threshold, or the predicted fault probability is greater than a second preset threshold, the method further includes: when the predicted fault probability is greater than the first preset fault probability threshold and the maintenance cost of the circuit breaker is less than a preset maintenance cost threshold, immediately shutting down for maintenance is taken as the target decision plan; when the predicted fault probability is less than the second preset fault probability threshold, but the operating efficiency of the circuit breaker decreases and the rate of change of operating efficiency is greater than a preset rate of change, planned maintenance is taken as the target decision plan; when the predicted fault probability is less than or equal to the first preset fault probability threshold and greater than or equal to the second preset fault probability threshold, continuing operation and strengthening monitoring is taken as the target decision plan.

7. The method according to claim 1, characterized in that, After collecting multi-dimensional operational data of the circuit breaker to be evaluated for operational status, the method further includes: for each dimension of operational data, decomposing the operational data into wavelet coefficients of different frequency bands, applying a threshold processing function to the wavelet coefficients, filtering the wavelet coefficients of different frequency bands through the threshold processing function to obtain retained wavelet coefficients, performing inverse wavelet transform on the retained wavelet coefficients, and reconstructing denoised operational data; performing missing data verification on the denoised operational data, and when missing data is confirmed, completing the missing data based on an interpolation algorithm to obtain completed operational data; performing normalization processing on the completed operational data to obtain the normalization processing result for the dimension, and constructing a feature data sequence for the dimension based on the normalization processing result.

8. A data-driven circuit breaker operation status assessment device, characterized in that, include: The data acquisition module is used to collect multi-dimensional operating data of circuit breakers that are to be evaluated for operational status. The data sequence construction module is used to construct a feature data sequence corresponding to each dimension based on the operational data of that dimension in the multidimensional operational data; the feature evaluation module is used to call the feature evaluation function corresponding to each dimension respectively, and calculate the feature evaluation result of that dimension for the feature data sequence under each dimension through the feature evaluation function under the corresponding dimension. The weight coefficient determination module is used to obtain the operating environment conditions and / or operating conditions of the circuit breaker, and dynamically determine the dynamic weight coefficients corresponding to the evaluation results of each feature based on the operating environment conditions and / or operating conditions; the status evaluation module is used to weight and sum the feature evaluation results under each dimension based on the dynamic weight coefficients corresponding to the evaluation results of each feature to obtain the health index of the circuit breaker, and evaluate the operating status of the circuit breaker based on the health index.

9. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

10. A computer device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 7.

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