Power distribution cabinet operation monitoring method and apparatus, device, and storage medium

Abstract

A power distribution cabinet operation monitoring method and apparatus, a device, and a storage medium, relating to the technical field of power distribution cabinet monitoring. The power distribution cabinet operation monitoring method comprises: performing long time-series continuous monitoring on a target power distribution cabinet according to a preset electrical parameter set, to obtain a plurality of monitored electrical parameter sets; traversing the plurality of monitored electrical parameter sets for comprehensive power distribution cabinet operation fluctuation analysis, and determining an operation fluctuation factor; performing temperature long time-series monitoring on a plurality of key devices of the target power distribution cabinet to obtain a plurality of monitored temperature sets; performing analysis on the basis of the plurality of monitored temperature sets, and on the basis of temperature fluctuation conditions of different key devices, determining temperature field dynamic monitoring schemes for the plurality of key devices; matching an electrical parameter monitoring period of the target power distribution cabinet on the basis of the operation fluctuation factor; and performing operation monitoring on the target power distribution cabinet on the basis of the electrical parameter monitoring period and the temperature field dynamic monitoring schemes.

Description

A method, device, equipment and storage medium for monitoring the operation of a power distribution cabinet. Technical Field

[0001] This application relates to the field of power distribution cabinet monitoring technology, specifically to a method, device, equipment and storage medium for monitoring the operation of power distribution cabinets. Background Technology

[0002] In modern power systems, distribution cabinets are key components that play an important role in distributing and controlling electrical energy. Their stable operation directly affects the quality of power supply and the safety of the power grid.

[0003] Current methods for monitoring the operation of power distribution cabinets mainly rely on regular manual inspections combined with simple automated monitoring tools. While these methods can ensure the basic operation of equipment to a certain extent, they have significant limitations. Firstly, these monitoring methods focus on instantaneous values ​​or short-term trends of electrical parameters, with monitoring cycles typically at fixed intervals. They neglect the potential for in-depth analysis of long-term time-series data, making it difficult to dynamically adjust according to actual operating conditions. This results in low sensitivity to operational fluctuations and an inability to provide early warnings of potential risks. Secondly, in terms of temperature monitoring, static standards are generally used, failing to fully consider the operating characteristics and temperature distribution differences of critical equipment. This makes it easy to overlook abnormal temperature signals, increasing the probability of sudden equipment failures. Summary of the Invention

[0004] This application provides a method, device, equipment, and storage medium for monitoring the operation of a power distribution cabinet. It solves the technical problems that existing power distribution cabinet operation monitoring technologies mostly adopt static schemes and short-term monitoring, while lacking personalized monitoring of key equipment, resulting in low monitoring accuracy and difficulty in timely early warning of equipment failures. It achieves the technical effect of improving the accuracy of power distribution cabinet operation monitoring, thereby improving the stability and safety of power distribution cabinet operation.

[0005] In view of the above problems, this application provides a method for monitoring the operation of a power distribution cabinet. The method includes: performing long-term continuous monitoring of a target power distribution cabinet according to a preset set of electrical parameters to obtain multiple sets of monitored electrical parameters; traversing the multiple sets of monitored electrical parameters to perform comprehensive analysis of the operational fluctuations of the power distribution cabinet and determine an operational fluctuation factor; extracting multiple key devices of the target power distribution cabinet and performing long-term temperature monitoring of the multiple key devices to obtain multiple sets of monitored temperatures; analyzing the multiple sets of monitored temperatures and determining a dynamic temperature field monitoring scheme for the multiple key devices based on the temperature fluctuations of different key devices; matching the electrical parameter monitoring cycle of the target power distribution cabinet based on the operational fluctuation factor; and monitoring the operation of the target power distribution cabinet according to the electrical parameter monitoring cycle and the dynamic temperature field monitoring scheme.

[0006] On the other hand, this application also provides a power distribution cabinet operation monitoring device, the device comprising: a continuous monitoring module, which is used to perform long-term continuous monitoring of a target power distribution cabinet according to a preset set of electrical parameters to obtain multiple sets of monitored electrical parameters; a fluctuation analysis module, which is used to traverse the multiple sets of monitored electrical parameters to perform comprehensive analysis of the operation fluctuation of the power distribution cabinet and determine the operation fluctuation factor; a temperature monitoring module, which is used to extract multiple key devices of the target power distribution cabinet and perform long-term temperature monitoring of the multiple key devices to obtain multiple sets of monitored temperatures; a monitoring scheme determination module, which is used to analyze the multiple sets of monitored temperatures and determine a dynamic monitoring scheme for the temperature field of the multiple key devices according to the temperature fluctuation of different key devices; a monitoring cycle matching module, which is used to match the electrical parameter monitoring cycle of the target power distribution cabinet based on the operation fluctuation factor; and an operation monitoring module, which is used to perform operation monitoring of the target power distribution cabinet according to the electrical parameter monitoring cycle and the dynamic temperature field monitoring scheme.

[0007] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the power distribution cabinet operation monitoring method described in any of the preceding claims.

[0008] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps in the above-described method for monitoring the operation of a power distribution cabinet.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] Long-term continuous monitoring of the target distribution cabinet is performed according to a preset set of electrical parameters to obtain multiple sets of monitored electrical parameters, providing data support for subsequent fluctuation analysis. A comprehensive analysis of the distribution cabinet's operational fluctuations is conducted by traversing these multiple sets of monitored electrical parameters, quantifying fluctuation characteristics, and determining operational fluctuation factors to identify fluctuations and operational characteristics of the distribution cabinet during long-term operation and to identify potential anomalies. Multiple key devices of the target distribution cabinet are extracted, and their temperatures are monitored over a long period to obtain multiple sets of monitored temperatures. Based on the analysis of these multiple sets of monitored temperatures, a dynamic temperature field monitoring scheme is developed for the key devices according to their temperature fluctuations, more accurately capturing potential faults caused by temperature changes. The monitoring cycle of the target distribution cabinet's electrical parameters is matched based on the operational fluctuation factors, allowing the monitoring cycle to be flexibly adjusted according to the actual situation of the equipment, enhancing the timeliness of monitoring. Comprehensive operational monitoring of the target distribution cabinet is then performed according to the electrical parameter monitoring cycle and the dynamic temperature field monitoring scheme.

[0011] In summary, this application achieves more intelligent and comprehensive real-time monitoring of distribution cabinets through the aforementioned steps. By conducting long-term monitoring and fluctuation analysis of electrical parameters and the temperature of key equipment, it determines the monitoring cycle and dynamic temperature field monitoring scheme, thereby optimizing monitoring resources and ensuring that key equipment is always within the key monitoring range, avoiding monitoring blind spots. This technical solution significantly improves the flexibility and accuracy of distribution cabinet operation monitoring, enhances the stability and safety of distribution cabinet operation, and thus strengthens the reliability and stability of the power system.

[0012] 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

[0013] Figure 1 is a flowchart illustrating a power distribution cabinet operation monitoring method provided in an embodiment of this application;

[0014] Figure 2 is a flowchart illustrating the process of determining the operation fluctuation factor in a power distribution cabinet operation monitoring method provided in an embodiment of this application;

[0015] Figure 3 is a schematic diagram of the structure of a power distribution cabinet operation monitoring device provided in an embodiment of this application;

[0016] Figure 4 is a schematic diagram of the structure of a computer device provided in an embodiment of this application.

[0017] Explanation of reference numerals in the attached diagram: Continuous monitoring module 10, fluctuation analysis module 20, temperature monitoring module 30, monitoring scheme determination module 40, monitoring cycle matching module 50, operation monitoring module 60, bus 300, receiver 301, processor 302, transmitter 303, memory 304, bus interface 305. Detailed Implementation

[0018] This application provides a method, device, equipment, and storage medium for monitoring the operation of power distribution cabinets. It solves the technical problems of existing power distribution cabinet operation monitoring technologies, which mostly adopt static schemes and short-term monitoring, and lack personalized monitoring of key equipment, resulting in low monitoring accuracy and difficulty in timely early warning of equipment failures. It achieves the technical effect of improving the accuracy of power distribution cabinet operation monitoring, thereby improving the stability and safety of power distribution cabinet operation.

[0019] Example 1, as shown in Figure 1, provides a method for monitoring the operation of a power distribution cabinet, the method including:

[0020] Step S1: Perform long-term continuous monitoring of the target distribution cabinet according to the preset electrical parameter set to obtain multiple monitoring electrical parameter sets.

[0021] Specifically, the preset electrical parameter set refers to a set of electrical parameters set before monitoring, typically including voltage, current, power, and power factor. These parameters reflect the electrical operating status of the distribution cabinet. The monitoring electrical parameter set refers to the set of electrical data recorded and generated during continuous monitoring.

[0022] First, select a set of electrical parameters from the key electrical status indicators in the distribution cabinet as a preset set of electrical parameters. Then, collect real-time data through sensors installed in the distribution cabinet, such as voltage sensors and current transformers. Record the collected data continuously over a long period of time through a data acquisition device, and summarize it to generate multiple sets of monitoring electrical parameters, providing basic data support for subsequent fluctuation analysis.

[0023] Step S2: Traverse the multiple sets of monitored electrical parameters to perform a comprehensive analysis of the distribution cabinet's operational fluctuations and determine the operational fluctuation factor.

[0024] Specifically, the operational fluctuation factor refers to a key indicator derived from data analysis that reflects the operational fluctuation of the distribution cabinet. It can be a drastic change or abnormal pattern of certain electrical parameters.

[0025] The system iterates through the entire set of electrical parameters, using data processing tools or software (such as the Pandas library in Python) to analyze the parameter data at each time point. Through data comparison and trend analysis, it identifies abnormal operational fluctuations in each electrical parameter, such as significant fluctuations in current or voltage instability during certain periods, and generates corresponding operational fluctuation factors. For example, a sudden 10% increase in current in the distribution cabinet over a period of time would be calculated as an abnormal operational fluctuation factor.

[0026] Step S3: Extract multiple key devices of the target power distribution cabinet, perform long-term temperature monitoring on the multiple key devices, and obtain multiple monitoring temperature sets.

[0027] Specifically, key equipment refers to the core components in a distribution cabinet, including circuit breakers, contactors, transformers, busbars, and cable connectors. The temperature monitoring dataset is a collection of temperature data from these key equipment at different points in time.

[0028] Temperature sensors are installed on key equipment in the distribution cabinet to continuously monitor the temperature of each key device. The temperature data of each key device is then aggregated to form a monitoring temperature set. The temperature sensors can be thermocouples or infrared thermometers. For example, circuit breakers are one of the components prone to overheating. Temperature sensors are installed around them, and their temperature values ​​are recorded hourly using a data logging device, forming a set containing temperature data of the circuit breaker at different time points.

[0029] Step S4: Analyze the multiple monitoring temperature sets and determine the dynamic monitoring scheme for the temperature field of the multiple key devices according to the temperature fluctuation of different key devices.

[0030] Specifically, the dynamic temperature monitoring scheme is a monitoring strategy developed based on the temperature change characteristics of key equipment. Data analysis tools (such as Excel and the Pandas library in Python) are used to process the temperature data from the multiple monitoring temperature sets obtained in step S3 to identify trends and patterns in temperature changes. For example, circuit breakers may experience temperature increases during high-load periods, while transformers may experience a slow temperature decrease after the load decreases. Based on the temperature data analysis results for each key piece of equipment, corresponding dynamic monitoring strategies are developed for the characteristics of different equipment. For example, for equipment with drastic temperature fluctuations, such as circuit breakers, the monitoring frequency can be increased to once per minute to ensure that temperature changes are captured in real time. For equipment with slower temperature changes, the monitoring interval can be extended.

[0031] By analyzing temperature fluctuations, corresponding monitoring schemes can be developed based on the needs of different equipment. The frequency and focus of temperature monitoring can be dynamically adjusted, allowing equipment to receive more intensive attention during high-risk periods. This enables timely responses to abnormal temperatures and reduces the risk of equipment overheating or malfunction.

[0032] Step S5: Match the electrical parameter monitoring cycle of the target distribution cabinet based on the operating fluctuation factor.

[0033] Specifically, the monitoring cycle refers to the time interval for monitoring the electrical parameters of the distribution cabinet, i.e., how often data is collected and analyzed. By analyzing the fluctuation factors of the distribution cabinet's operation, the monitoring cycle of electrical parameters can be adjusted. When the fluctuation factor is large, the monitoring cycle should be shortened and the collection frequency increased to ensure that detailed changes during fluctuations are captured in real time. Conversely, when the fluctuation factor is small, indicating stable equipment operation, the monitoring cycle can be extended to reduce unnecessary data collection and thus save system resources. For example, if the analysis shows that the fluctuation factor is high during high-load periods, the monitoring cycle can be shortened, changing from hourly data collection to every 15 minutes. During stable operating periods, the monitoring cycle can be relatively extended. This dynamic adjustment of the monitoring cycle ensures that more monitoring data is obtained at critical moments, thereby enabling faster detection of anomalies.

[0034] Step S6: Monitor the operation of the target distribution cabinet according to the electrical parameter monitoring cycle and the temperature field dynamic monitoring scheme.

[0035] Specifically, the real-time data acquisition system of the distribution cabinet monitors the electrical parameters and temperatures of key equipment in real time according to the aforementioned determined electrical parameter monitoring cycle and temperature field dynamic monitoring scheme to ensure the safe and stable operation of the distribution cabinet. The electrical parameter monitoring cycle is dynamically adjusted based on operational fluctuation factors to ensure that abnormal electrical parameters are addressed promptly. The temperature field monitoring scheme performs intensive monitoring based on equipment temperature fluctuations, allocating more monitoring resources to abnormal equipment. For example, electrical parameter data is collected frequently during high-load periods, and more intensive temperature monitoring is performed on equipment prone to overheating. Alarms are issued when electrical parameter fluctuations or critical equipment temperatures are abnormal, prompting maintenance personnel to conduct inspections.

[0036] By combining the monitoring cycle of electrical parameters with the dynamic monitoring scheme of temperature field for the operation monitoring of the distribution cabinet, not only is the monitoring accuracy improved, but faults can also be prevented in a timely manner, ensuring the safety and reliability of the distribution cabinet operation.

[0037] Furthermore, the preset electrical parameter set mentioned in step S1 of this application embodiment includes voltage, current, power, power factor, and frequency.

[0038] Specifically, sensors and monitoring devices installed on the distribution cabinet collect real-time data on voltage, current, power, power factor, and frequency. For example, voltage sensors measure voltage values ​​in the circuit, current transformers measure current values, power meters calculate real-time power and power factor, and frequency meters measure the frequency of current or voltage. Continuous monitoring of these parameters over a long period accumulates a large amount of data, providing a basis for subsequent analysis of the distribution cabinet's operational status.

[0039] Furthermore, as shown in Figure 2, step S2 of this embodiment further includes:

[0040] The variance of the fluctuations of the multiple sets of monitored electrical parameters is calculated to obtain multiple fluctuation coefficients. The sum of the multiple fluctuation coefficients is divided by the sum of the multiple fluctuation coefficients, and the ratio is multiplied by a preset search step size to obtain multiple parameter search step sizes. Based on the multiple parameter search step sizes, the multiple sets of monitored electrical parameters are filtered for dense distribution to obtain multiple densely distributed parameter sets. The variance of the fluctuations of the multiple densely distributed parameter sets is calculated, and the mean of the calculation results is calculated to obtain the operating fluctuation factor.

[0041] Specifically, for each set of monitored electrical parameters obtained in step S1, the squared mean of the deviations of each data point in the set from its average value is calculated to obtain the variance of fluctuation. The variance of fluctuation reflects the degree of fluctuation of each set of electrical parameters over time; the larger the variance of fluctuation, the greater the fluctuation amplitude of the data. The obtained variance of fluctuation is recorded as the fluctuation coefficient corresponding to each set of monitored data.

[0042] To ensure comparability between different fluctuation coefficients, each fluctuation coefficient is divided by the sum of all fluctuation coefficients to obtain the proportion of each parameter relative to the overall fluctuation. These proportions are then multiplied by a preset search step size to obtain the parameter search step size for each parameter. The step size determines the accuracy when filtering densely distributed parameters. A smaller step size allows for finer filtering, thus more accurately reflecting operational fluctuations within specific time periods. The preset search step size is a predefined value used to determine the search accuracy during the filtering process for densely distributed parameters.

[0043] The corresponding set of monitored electrical parameters is divided according to the parameter search step size of each parameter. The number of data points in each interval is counted, and the interval with the densest data points is selected to form a densely distributed parameter set. These sets can be regarded as sets of electrical parameters under relatively stable operating conditions, which can represent the operating fluctuations of the distribution cabinet in a short period of time. The above parameter dense distribution screening operation is performed on each set of monitored electrical parameters to obtain multiple densely distributed parameter sets. The variance of the fluctuation of all densely distributed parameter sets is calculated, which is the squared mean of the deviations of each data point in the set from its mean. Then, the average of all variances of fluctuation is calculated and denoted as the operating fluctuation factor. This factor provides a quantitative basis for dynamically adjusting the monitoring cycle of the distribution cabinet to ensure that the fluctuations of electrical parameters can be captured in a timely manner within the monitoring cycle, thereby more accurately monitoring the operating status of the distribution cabinet.

[0044] Furthermore, based on the multiple parameter search step sizes, the multiple sets of monitored electrical parameters are filtered for dense distribution to obtain multiple densely distributed parameter sets, which also includes:

[0045] Determine the mean values ​​of multiple electrical parameters in the multiple sets of monitored electrical parameters, and use these mean values ​​as multiple initial screening centers. Using the multiple parameter search step sizes as radii, determine multiple initial densely distributed parameter sets, where each initial densely distributed parameter is a monitored electrical parameter whose difference from the mean value of an electrical parameter is within the corresponding parameter search step size. Count multiple initial screening quantities for the multiple initial densely distributed parameter sets. Iterate through the multiple initial screening centers in the multiple sets of monitored electrical parameters based on the multiple parameter search step sizes to determine multiple iterative screening centers and multiple iterative densely distributed parameter sets. Count multiple iterative screening quantities for the multiple iterative densely distributed parameter sets, and compare the magnitudes of the multiple iterative screening quantities with the multiple initial screening quantities to determine whether to update the multiple initial screening centers. After multiple updates until screening stops, the multiple iterative densely distributed parameter sets corresponding to the maximum screening quantity are taken as multiple densely distributed parameter sets.

[0046] Furthermore, when the difference between the multiple iterative screening amounts and the multiple initial screening amounts is less than or equal to a preset screening amount difference, the screening process stops.

[0047] Specifically, the parameter dense distribution screening process first calculates the mean values ​​of electrical parameters corresponding to each set of monitored electrical parameters, including the mean values ​​of voltage, current, power, power factor, and frequency. Using the mean value of each electrical parameter as the initial center point, and with the corresponding search step size as the radius, the monitored electrical parameter set is screened. Values ​​of monitored electrical parameters whose difference from the mean value is less than the corresponding search step size are selected. These selected data are then aggregated into the corresponding initial dense distribution parameter set. For example, taking a current parameter set as an example, with a mean current of 50A and a search step size of 5A, all current values ​​in the range of 45A to 55A are selected as the initial dense distribution current parameters.

[0048] The number of parameters in each initial densely distributed parameter set is counted and denoted as the initial screening quantity. This initial screening quantity reflects the distribution density of each parameter near its mean and is used to assess the density of parameters captured during the initial screening. Next, the initial screening center is moved by a distance equal to the parameter search step size, and multiple initial screening centers are iterated. During the iteration process, for each new center position, the densely distributed parameter set is redefined according to the same steps described above.

[0049] After each iteration, the number of data points in each new densely distributed parameter set is counted and denoted as the iteration screening quantity. The iteration screening quantity is compared with the initial screening quantity. If the iteration screening quantity is greater than the initial screening quantity, the initial screening center is updated to the current iteration center, and the iteration screening quantity becomes the new initial screening quantity; otherwise, no update is performed.

[0050] After each iteration, the difference between the iterative screening quantity and the initial screening quantity is calculated. When the difference between the iterative screening quantity and the initial screening quantity is less than or equal to the preset screening quantity difference, it indicates that the change in the screening quantity has stabilized, meaning the screening process has converged, and the screening process can be stopped. The iterative densely distributed parameter set corresponding to the maximum screening quantity is taken as the final densely distributed parameter set. These sets contain the regions with the densest parameter values, corresponding to key characteristics or potential problems in the operation of the distribution cabinet.

[0051] For example, for the electrical parameter set of voltage monitoring in a power distribution cabinet, the average voltage is first calculated to be 120V, and the parameter search step size is 2V. Using 120V as the starting center, an initial dense distribution set is obtained, containing 100 data points, i.e., the initial screening quantity is 100. The iteration begins, the center is moved to 122V, and it is found to contain 110 data points, so the center is updated to 122V, and the new initial screening quantity is 110. Iteration continues to 124V, at which point there are 105 data points, less than the new initial screening quantity, but the stopping condition (the preset screening quantity difference is 5) is not met, so iterative screening continues. When the center moves to 126V, the screening quantity is 104, less than the new initial screening quantity and the difference is less than or equal to the preset screening quantity difference of 5, so screening stops. Finally, the dense distribution set (110 data points) at the 122V center position is determined as the dense distribution parameter set.

[0052] Through the above iterative process, the region with dense parameter distribution can be located more accurately, providing more accurate data support for subsequent calculation of operational fluctuation factors, thereby improving the sensitivity and accuracy of the monitoring system.

[0053] Furthermore, step S4 in this embodiment of the application also includes:

[0054] Fluctuation analysis is performed on the multiple monitoring temperature sets to determine multiple temperature fluctuation coefficients; the multiple monitoring temperature sets are then filtered according to the reference temperature tolerance range to extract multiple out-of-range monitoring temperature sets; multiple temperature anomaly coefficients of the multiple key equipment are determined based on the multiple out-of-range monitoring temperature sets; and the dynamic monitoring scheme for the temperature field is determined based on the multiple temperature fluctuation coefficients and the multiple temperature anomaly coefficients.

[0055] Specifically, the temperature fluctuation coefficient is a coefficient calculated through fluctuation analysis to measure the degree of temperature fluctuation in critical equipment. A larger fluctuation coefficient indicates more drastic temperature changes. The reference temperature tolerance range is the allowable temperature fluctuation range during normal operation of the equipment, set according to the performance characteristics of each critical component in the distribution cabinet. The out-of-range monitoring temperature set refers to the data set of equipment temperatures exceeding the reference temperature tolerance range, indicating that these temperature values ​​are abnormal or require special attention. The temperature anomaly coefficient is a coefficient calculated based on the out-of-range data to measure the severity of temperature anomalies. A higher anomaly coefficient indicates more pronounced or frequent temperature anomalies in the equipment.

[0056] Fluctuation analysis was performed on the monitoring temperature sets of each key device to calculate the temperature fluctuation coefficient for each device. The calculation process is similar to the electrical parameter fluctuation coefficient in step S2. Based on the established baseline temperature tolerance range, the monitoring temperature sets of each key device were filtered, and data exceeding the baseline temperature tolerance range were extracted and summarized into an over-range monitoring temperature set.

[0057] Data from each out-of-range monitoring temperature set is analyzed to quantify the frequency and extent to which equipment temperatures exceed the baseline temperature range, and the temperature anomaly coefficient for each key piece of equipment is calculated. The data analysis process can use the anomaly frequency ratio or the integral of the abnormal temperature difference in each out-of-range monitoring temperature set as the temperature anomaly coefficient. The anomaly frequency ratio is the ratio of the number of times equipment temperature data exceeds the baseline temperature range (upper and lower limits) to the total number of monitoring data points. A higher anomaly frequency ratio indicates that the equipment is frequently in an abnormal temperature state. The integral of the abnormal temperature difference is the sum of the temperature differences when equipment temperature data exceeds the baseline temperature range. A larger integral value indicates a more severe deviation in abnormal temperature, and the equipment is at risk of overheating or overcooling. Based on the calculated temperature fluctuation coefficient and temperature anomaly coefficient for each key piece of equipment, a corresponding dynamic temperature field monitoring scheme is developed for each key piece of equipment.

[0058] By following all the steps above, the temperature monitoring strategy can be dynamically adjusted based on the actual operating conditions of key equipment, thereby improving the targeting and effectiveness of operation monitoring.

[0059] Furthermore, determining the dynamic temperature field monitoring scheme based on the plurality of temperature fluctuation coefficients and the plurality of temperature anomaly coefficients also includes:

[0060] Multiple critical abnormal devices are identified based on the multiple temperature fluctuation coefficients and the multiple temperature anomaly coefficients; an anomaly ratio is obtained based on the multiple critical abnormal devices and the number of the multiple critical devices; dynamic monitoring constraints for the critical abnormal devices are determined based on the anomaly ratio, wherein the dynamic monitoring constraints for the critical abnormal devices are that the number of critical abnormal devices must be greater than or equal to the anomaly ratio during each dynamic monitoring; the multiple critical devices are randomly extracted according to a preset number of synchronous temperature monitoring devices to obtain multiple sets of first dynamic monitoring critical devices; it is determined whether the multiple sets of first dynamic monitoring critical devices satisfy the dynamic monitoring constraints for the critical abnormal devices; if so, the first dynamic monitoring critical devices are used as the dynamic monitoring scheme for the temperature field.

[0061] Specifically, dynamic monitoring constraints are limitations set during dynamic monitoring to ensure that a certain number of critical devices with abnormalities are included in each dynamic monitoring session. The number of synchronous temperature monitoring devices refers to the number of devices simultaneously performing temperature monitoring during each dynamic monitoring process.

[0062] Based on the temperature fluctuation coefficient and temperature anomaly coefficient, critical equipment that exceeds preset thresholds or exhibits abnormal behavior is identified and marked as abnormal critical equipment. The ratio of the number of abnormal critical equipment to the total number of critical equipment is calculated and denoted as the anomaly ratio, which reflects the proportion of abnormal equipment in the distribution cabinet.

[0063] During each dynamic monitoring session, the number of abnormal critical devices must reach or exceed the abnormality ratio. This condition is set as a constraint for dynamic monitoring of abnormal critical devices. This condition is set to ensure that monitoring resources are preferentially allocated to abnormal devices, thereby improving the efficiency and targeting of operational monitoring.

[0064] Based on the preset number of synchronous temperature monitoring devices, devices are randomly selected from all key devices for monitoring, forming the first dynamic monitoring key device set. It is then determined whether the number of abnormal key devices among the randomly selected devices reaches or exceeds the abnormality ratio. If not, random selection is repeated. If the conditions are met, the set of devices that meets the criteria is implemented as the dynamic temperature field monitoring scheme.

[0065] For example, a power distribution cabinet has 50 critical devices. After temperature fluctuation and anomaly analysis, 10 devices are marked as abnormal critical devices, with an anomaly rate of (10 / 50) × 100% = 20%. The set number of devices for synchronous monitoring is 10, and the dynamic monitoring constraint is that at least 20% of the devices are abnormal critical devices. First, 10 devices are randomly selected from the 50 for monitoring. Of the first 10 selected devices, 3 are abnormal critical devices, with an anomaly rate of 30%, exceeding the 20% anomaly rate. Therefore, the selected devices meet the dynamic monitoring constraint. These 10 devices are then used as the final set of dynamic monitoring critical devices, and their temperature field is monitored in subsequent monitoring cycles.

[0066] By formulating a dynamic temperature field monitoring plan through the above steps, while balancing resources, we can ensure that critical equipment with abnormalities accounts for a sufficient proportion of the monitoring, thereby increasing attention to abnormal equipment, reducing the risk of missed detection, and making operation monitoring more efficient and reliable.

[0067] In summary, the power distribution cabinet operation monitoring method provided in this application has the following technical effects:

[0068] Long-term continuous monitoring of the target distribution cabinet is performed according to a preset set of electrical parameters to obtain multiple sets of monitored electrical parameters, providing data support for subsequent fluctuation analysis. A comprehensive analysis of the distribution cabinet's operational fluctuations is conducted by traversing these multiple sets of monitored electrical parameters. By calculating fluctuation variance and filtering dense distributions, fluctuation characteristics are quantified, and operational fluctuation factors are determined to identify fluctuations and operational characteristics of the distribution cabinet during long-term operation, identify potential anomalies, and improve sensitivity to equipment operational status fluctuations. Multiple key devices of the target distribution cabinet are extracted, and their temperatures are monitored over a long period to obtain multiple sets of monitored temperatures. Based on the analysis of these multiple sets of monitored temperatures, and according to the temperature fluctuations of different key devices, fluctuation analysis and anomaly screening are used to extract devices with abnormal temperatures. Temperature fluctuation coefficients and anomaly coefficients are calculated, and temperature anomaly analysis is performed on the devices to develop a dynamic temperature field monitoring scheme for key devices. This dynamic temperature field monitoring scheme allows resources to be concentrated on monitoring abnormal devices, while reducing the monitoring burden on normal devices, more accurately capturing potential faults caused by temperature changes, and improving the efficiency and accuracy of operational monitoring. The monitoring cycle of the electrical parameters of the target distribution cabinet is matched with the operational fluctuation factor, allowing the monitoring cycle to be flexibly adjusted according to the actual situation of the equipment, thus optimizing monitoring resources. Comprehensive operational monitoring of the target distribution cabinet is then performed based on the electrical parameter monitoring cycle and the dynamic temperature field monitoring scheme.

[0069] Overall, the embodiments of this application achieve more intelligent and comprehensive real-time monitoring of distribution cabinets. By conducting long-term monitoring and fluctuation analysis of electrical parameters and the temperature of key equipment, the monitoring cycle and dynamic temperature field monitoring scheme are determined, thereby optimizing monitoring resources and ensuring that key equipment is always within the key monitoring range, avoiding monitoring blind spots. This method significantly improves the flexibility and accuracy of distribution cabinet operation monitoring, enhances the stability and safety of distribution cabinet operation, and thus strengthens the reliability and stability of the power system.

[0070] Example 2, as shown in Figure 3, provides a power distribution cabinet operation monitoring device, the device comprising:

[0071] The continuous monitoring module 10 is used to perform long-term continuous monitoring of the target distribution cabinet according to a preset set of electrical parameters, and obtain multiple sets of monitoring electrical parameters.

[0072] The fluctuation analysis module 20 is used to traverse the multiple sets of monitored electrical parameters to perform comprehensive analysis of the operation fluctuation of the distribution cabinet and determine the operation fluctuation factor.

[0073] Temperature monitoring module 30 is used to extract multiple key devices of the target power distribution cabinet, perform long-term temperature monitoring on the multiple key devices, and obtain multiple sets of monitoring temperatures.

[0074] The monitoring scheme determination module 40 is used to analyze the multiple monitoring temperature sets and determine the dynamic monitoring scheme of the temperature field of the multiple key equipment according to the temperature fluctuation of different key equipment.

[0075] The monitoring cycle matching module 50 is used to match the monitoring cycle of the electrical parameters of the target distribution cabinet based on the operating fluctuation factor.

[0076] The operation monitoring module 60 is used to monitor the operation of the target distribution cabinet according to the electrical parameter monitoring cycle and the temperature field dynamic monitoring scheme.

[0077] Furthermore, the preset electrical parameter set in the continuous monitoring module 10 of this application embodiment includes voltage, current, power, power factor, and frequency.

[0078] Furthermore, in this embodiment of the application, the fluctuation analysis module 20 is also used to perform the following steps:

[0079] The variance of the fluctuations of the multiple sets of monitored electrical parameters is calculated to obtain multiple fluctuation coefficients. The sum of the multiple fluctuation coefficients is divided by the sum of the multiple fluctuation coefficients, and the ratio is multiplied by a preset search step size to obtain multiple parameter search step sizes. Based on the multiple parameter search step sizes, the multiple sets of monitored electrical parameters are filtered for dense distribution to obtain multiple densely distributed parameter sets. The variance of the fluctuations of the multiple densely distributed parameter sets is calculated, and the mean of the calculation results is calculated to obtain the operating fluctuation factor.

[0080] Furthermore, in this embodiment of the application, the fluctuation analysis module 20 is also used to perform the following steps:

[0081] Determine the mean values ​​of multiple electrical parameters in the multiple sets of monitored electrical parameters, and use these mean values ​​as multiple initial screening centers. Using the multiple parameter search step sizes as radii, determine multiple initial densely distributed parameter sets, where each initial densely distributed parameter is a monitored electrical parameter whose difference from the mean value of an electrical parameter is within the corresponding parameter search step size. Count multiple initial screening quantities for the multiple initial densely distributed parameter sets. Iterate through the multiple initial screening centers in the multiple sets of monitored electrical parameters based on the multiple parameter search step sizes to determine multiple iterative screening centers and multiple iterative densely distributed parameter sets. Count multiple iterative screening quantities for the multiple iterative densely distributed parameter sets, and compare the magnitudes of the multiple iterative screening quantities with the multiple initial screening quantities to determine whether to update the multiple initial screening centers. After multiple updates until screening stops, the multiple iterative densely distributed parameter sets corresponding to the maximum screening quantity are taken as multiple densely distributed parameter sets.

[0082] Furthermore, in this embodiment of the application, the fluctuation analysis module 20 is also used to perform the following steps:

[0083] When the difference between the multiple iterative screening amounts and the multiple initial screening amounts is less than or equal to the preset screening amount difference, the screening stops.

[0084] Furthermore, in this embodiment of the application, the monitoring scheme determination module 40 is also used to perform the following steps:

[0085] Fluctuation analysis is performed on the multiple monitoring temperature sets to determine multiple temperature fluctuation coefficients; the multiple monitoring temperature sets are then filtered according to the reference temperature tolerance range to extract multiple out-of-range monitoring temperature sets; multiple temperature anomaly coefficients of the multiple key equipment are determined based on the multiple out-of-range monitoring temperature sets; and the dynamic monitoring scheme for the temperature field is determined based on the multiple temperature fluctuation coefficients and the multiple temperature anomaly coefficients.

[0086] Furthermore, in this embodiment of the application, the monitoring scheme determination module 40 is also used to perform the following steps:

[0087] Multiple critical abnormal devices are identified based on the multiple temperature fluctuation coefficients and the multiple temperature anomaly coefficients; an anomaly ratio is obtained based on the multiple critical abnormal devices and the number of the multiple critical devices; dynamic monitoring constraints for the critical abnormal devices are determined based on the anomaly ratio, wherein the dynamic monitoring constraints for the critical abnormal devices are that the number of critical abnormal devices must be greater than or equal to the anomaly ratio during each dynamic monitoring; the multiple critical devices are randomly extracted according to a preset number of synchronous temperature monitoring devices to obtain multiple sets of first dynamic monitoring critical devices; it is determined whether the multiple sets of first dynamic monitoring critical devices satisfy the dynamic monitoring constraints for the critical abnormal devices; if so, the first dynamic monitoring critical devices are used as the dynamic monitoring scheme for the temperature field.

[0088] Through the foregoing detailed description of a method for monitoring the operation of a power distribution cabinet, those skilled in the art can clearly understand that the power distribution cabinet operation monitoring device in this embodiment corresponds to the method disclosed in Embodiment 2, and has corresponding functional modules and beneficial effects. For relevant details, please refer to the description in the method section.

[0089] Example 3: Based on the same inventive concept as the distribution cabinet operation monitoring method in Example 1 above, as shown in FIG4, this application also provides a computer device, including: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of any of the methods described in Example 1 above.

[0090] In Figure 3, the bus architecture is represented by bus 300. Bus 300 may include any number of interconnected buses and bridges, connecting various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 can be used to store data used by processor 302 during operation.

[0091] In addition, this application also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the various processes of the above-described embodiment of the power distribution cabinet operation monitoring method and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0092] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring the operation of a power distribution cabinet, characterized in that, The method includes: The target distribution cabinet is continuously monitored over a long period of time according to a preset set of electrical parameters to obtain multiple sets of monitored electrical parameters. By traversing the multiple sets of monitored electrical parameters, a comprehensive analysis of the distribution cabinet's operational fluctuations is performed to determine the operational fluctuation factors. Extract multiple key devices from the target power distribution cabinet, perform long-term temperature monitoring on the multiple key devices, and obtain multiple sets of monitored temperatures; Based on the analysis of the multiple monitoring temperature sets, and according to the temperature fluctuation of different key equipment, a dynamic monitoring scheme for the temperature field of the multiple key equipment is determined. The monitoring cycle of the electrical parameters of the target distribution cabinet is matched based on the operational fluctuation factor; The target distribution cabinet is monitored for operation according to the electrical parameter monitoring cycle and the temperature field dynamic monitoring scheme.

2. The method for monitoring the operation of a power distribution cabinet as described in claim 1, characterized in that, The preset set of electrical parameters includes voltage, current, power, power factor, and frequency.

3. The method for monitoring the operation of a power distribution cabinet as described in claim 1, characterized in that, A comprehensive analysis of the distribution cabinet's operational fluctuations is performed by traversing the multiple sets of monitored electrical parameters to determine the operational fluctuation factors, including: The fluctuation variance of the multiple sets of monitored electrical parameters is calculated respectively to obtain multiple fluctuation coefficients; Each of the multiple fluctuation coefficients is divided by the sum of the multiple fluctuation coefficients, and the ratio is multiplied by a preset search step size to obtain multiple parameter search step sizes; Based on the multiple parameter search step sizes, the multiple sets of monitored electrical parameters are filtered for dense distribution to obtain multiple densely distributed parameter sets. The variance of the fluctuations of the multiple densely distributed parameter sets is calculated, and the mean of the calculation results is calculated to obtain the operating fluctuation factor.

4. The method for monitoring the operation of a power distribution cabinet as described in claim 3, characterized in that, Based on the multiple parameter search step sizes, the multiple sets of monitored electrical parameters are filtered for dense parameter distribution to obtain multiple densely distributed parameter sets, including: Determine the mean values ​​of multiple electrical parameters in the multiple sets of monitored electrical parameters, use the mean values ​​of multiple electrical parameters as multiple initial screening centers, and use the multiple parameter search step sizes as radii to determine multiple initial densely distributed parameter sets, wherein the initial densely distributed parameters are the monitored electrical parameters whose difference from the mean value of an electrical parameter is within the corresponding parameter search step size; Statistically analyze the multiple initial screening quantities of the multiple initial dense distribution parameter sets; Based on the search step size of the multiple parameters, the multiple initial screening centers are iterated in the multiple sets of monitoring electrical parameters to determine multiple screening iteration centers and multiple sets of densely distributed iteration parameters. Statistically count multiple iterative screening quantities of multiple iterative dense distribution parameter sets, and compare the magnitudes of the multiple iterative screening quantities with the multiple initial screening quantities to determine whether to update the multiple initial screening centers. After multiple updates, until screening stops, the multiple iterative dense distribution parameter sets corresponding to the maximum screening quantity are taken as multiple dense distribution parameter sets.

5. The method for monitoring the operation of a power distribution cabinet as described in claim 4, characterized in that, When the difference between the multiple iterative screening amounts and the multiple initial screening amounts is less than or equal to the preset screening amount difference, the screening stops.

6. The method for monitoring the operation of a power distribution cabinet as described in claim 1, characterized in that, Based on the analysis of the multiple monitored temperature sets, and according to the temperature fluctuations of different key equipment, a dynamic monitoring scheme for the temperature field of the multiple key equipment is determined, including: Fluctuation analysis was performed on the multiple monitored temperature sets to determine multiple temperature fluctuation coefficients; The multiple monitoring temperature sets are filtered according to the reference temperature tolerance range to extract multiple monitoring temperature sets beyond the tolerance range; Based on the multiple sets of ultra-range monitored temperatures, multiple temperature anomaly coefficients of the multiple key devices are determined. The temperature field dynamic monitoring scheme is determined based on the multiple temperature fluctuation coefficients and the multiple temperature anomaly coefficients.

7. The method for monitoring the operation of a power distribution cabinet as described in claim 6, characterized in that, The temperature field dynamic monitoring scheme is determined based on the plurality of temperature fluctuation coefficients and the plurality of temperature anomaly coefficients, including: Based on the multiple temperature fluctuation coefficients and the multiple temperature anomaly coefficients, multiple critical abnormal equipment were identified. Based on the plurality of abnormal critical devices and the number of the plurality of critical devices, the abnormality ratio is obtained, and the dynamic monitoring constraints of the abnormal critical devices are determined based on the abnormality ratio. The dynamic monitoring constraints of the abnormal critical devices are that the number of abnormal critical devices must be greater than or equal to the abnormality ratio in each dynamic monitoring. Randomly extract the multiple key devices according to the preset number of synchronous temperature monitoring devices to obtain multiple sets of first dynamic monitoring key devices; Determine whether the set of multiple first dynamic monitoring key devices satisfies the dynamic monitoring constraints of the abnormal key devices. If so, the first dynamic monitoring key devices are used as the temperature field dynamic monitoring scheme.

8. A power distribution cabinet operation monitoring device, characterized in that, The device is used to perform a power distribution cabinet operation monitoring method according to any one of claims 1-7, including: A continuous monitoring module is used to perform long-term continuous monitoring of the target distribution cabinet according to a preset set of electrical parameters, and obtain multiple sets of monitored electrical parameters. The fluctuation analysis module is used to traverse the multiple sets of monitored electrical parameters to perform a comprehensive analysis of the operating fluctuations of the distribution cabinet and determine the operating fluctuation factors. A temperature monitoring module is used to extract multiple key devices of the target power distribution cabinet, perform long-term temperature monitoring on the multiple key devices, and obtain multiple sets of monitored temperatures. The monitoring scheme determination module is used to analyze the multiple monitoring temperature sets and determine the dynamic monitoring scheme of the temperature field of the multiple key equipment according to the temperature fluctuation of different key equipment. A monitoring cycle matching module is used to match the monitoring cycle of the electrical parameters of the target power distribution cabinet based on the operating fluctuation factor. The operation monitoring module is used to monitor the operation of the target distribution cabinet according to the electrical parameter monitoring cycle and the temperature field dynamic monitoring scheme.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the power distribution cabinet operation monitoring method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of a power distribution cabinet operation monitoring method as described in any one of claims 1-7.

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