JP Cabinet Fault Warning Method, Device, Equipment and Storage Medium

Abstract

The present application discloses a JP cabinet fault warning method, device, equipment and storage medium, belonging to the technical field of JP cabinet fault warning. According to the digital twin model of the JP cabinet pre-operated locally and the adaptive power distribution strategy preset for the target JP cabinet, the present application predicts the target operating parameters of the target JP cabinet; obtains the real-time operating parameters of the target JP cabinet, and compares and analyzes the real-time operating parameters and the target operating parameters; according to the result of the comparison and analysis, determines whether there is a fault in the target JP cabinet. If there is a fault, corresponding alarm information is output according to the result of the comparison and analysis, so as to realize rapid and accurate prediction of the adaptive load change of the JP cabinet, and determine whether there is a fault according to the prediction result, ensuring accurate monitoring of the operation process of the JP cabinet.

Description

Technical Field

[0001] This application relates to the technical field of JP cabinet fault warning, and particularly relates to a JP cabinet fault warning method, device, equipment and storage medium. Background Art

[0002] As a low-voltage power distribution device frequently used in a power distribution scenario, the JP cabinet (Junction and Protection Cabinet) usually has real-time load adjustment due to power distribution requirements in actual applications. For example, the power distribution load is adjusted according to the peak-valley electricity price, and the factory equipment or medical equipment is supplied according to the actual power demand. However, there are indeed some potential safety hazards in this process.

[0003] For example, due to the frequent adjustment of the power distribution load of the JP cabinet, the heat generation of its internal electrical components may cause the cabinet temperature to be too high under high load conditions, thus affecting the normal operation of electrical equipment. If there are problems such as poor electrical connection and aging of insulating materials inside the JP cabinet, it may lead to a short circuit and then cause a fire.

[0004] In actual applications, relevant personnel will be configured to perform regular operation and maintenance on the JP cabinet. However, the same person needs to manage a relatively large number of JP cabinets at the same time, and since the specific actual load of the JP cabinet often changes at different time periods, it results in the relevant personnel being unable to detect the abnormal risks during the operation of the JP in a timely manner, thus causing economic losses and other situations to the JP cabinet. Summary of the Invention

[0005] The main purpose of this application is to provide a JP cabinet fault warning method, device, equipment and storage medium, aiming to solve the technical problem of being unable to detect the operation risks of the JP cabinet in a timely manner and prompt the relevant operation and maintenance personnel.

[0006] To achieve the above purpose, this application provides a JP cabinet fault warning method, and the JP cabinet fault warning method includes the following steps:

[0007] Predict the target operation parameters of the target JP cabinet according to the JP cabinet digital twin model pre-run locally and the adaptive power distribution strategy preset for the target JP cabinet;

[0008] Obtain the real-time operation parameters of the target JP cabinet, and compare and analyze the real-time operation parameters with the target operation parameters;

[0009] According to the result of the comparison and analysis, determine whether there is a fault in the target JP cabinet. If so, output the corresponding alarm information according to the result of the comparison and analysis.

[0010] In one embodiment, the step of predicting the target operating parameters of the target JP cabinet according to the adaptive power distribution strategy preset for the digital twin model of the JP cabinet pre-run locally and the target JP cabinet includes:

[0011] Wherein, the digital twin model of the JP cabinet includes first simulated load data when other JP cabinets adjacent to the target JP cabinet are operating, second simulated load data when the superior substation of the target JP cabinet is operating, and historical load data of the target JP cabinet;

[0012] According to the first simulated load data, the second simulated load data, the historical load data, and the adaptive power distribution strategy preset for the target JP cabinet, predict the load change trend and the adaptive load optimization result of the target JP cabinet;

[0013] According to the change trend and the adaptive load optimization result, determine the target operating parameters of the target JP cabinet.

[0014] In one embodiment, before the step of predicting the target operating parameters of the target JP cabinet according to the adaptive power distribution strategy preset for the digital twin model of the JP cabinet pre-run locally and the target JP cabinet, the method further includes:

[0015] Determine the power grid topological structure where the target JP cabinet is located, and determine the load data of other JP cabinets adjacent to the target JP cabinet and the superior substation in the power grid topological structure, and obtain the historical operating status of the target JP cabinet,

[0016] Wherein, the historical operating status at least includes the factory hardware configuration parameters of the target JP cabinet, and the load change data of the target JP cabinet based on minute-level, hour-level, and seasonal;

[0017] According to the power grid topological structure, generate virtual load data of the JP cabinet in the target scenario, wherein the target scenario at least includes a scenario where the temperature and humidity of the JP cabinet operating environment are greater than the preset standard value and a scenario where there are partial equipment failures in the power grid topological structure;

[0018] According to the power grid topological structure, the load change data, the virtual load data, and the historical operating status, construct the digital twin model of the target JP cabinet.

[0019] In one embodiment, after the step of constructing the digital twin model of the target JP cabinet according to the power grid topological structure, the load change data, the virtual load data, and the historical operating status, the method further includes:

[0020] Obtain user-defined state space parameters and action space parameters based on the power grid topology structure;

[0021] Construct a deep learning policy engine according to the state space parameters and action space parameters;

[0022] Train the deep learning policy engine through the digital twin model of the JP cabinet to obtain an adaptive power distribution strategy.

[0023] In one embodiment, the step of obtaining the real-time operation parameters of the target JP cabinet and comparing and analyzing the real-time operation parameters with the target operation parameters includes:

[0024] Obtain the real-time operation parameters of the JP cabinet;

[0025] Generate a defined range for determining abnormal operation parameters according to the target operation parameters;

[0026] Determine the abnormal operation parameters in the real-time operation parameters according to the defined range, and compare and analyze the abnormal operation parameters with the target operation parameters.

[0027] In one embodiment, the step of comparing and analyzing the abnormal operation parameters with the target operation parameters includes:

[0028] Conduct a multi-dimensional comprehensive evaluation of the abnormal operation parameters according to the target operation parameters to obtain an evaluation result;

[0029] Retrieve the historical operation record of the target JP cabinet, and compare and analyze the evaluation result with the historical operation record.

[0030] In one embodiment, the step of conducting a multi-dimensional comprehensive evaluation of the abnormal operation parameters according to the target operation parameters to obtain an evaluation result includes:

[0031] Determine the multi-dimensional influence relationship between the target operation parameters and the abnormal operation parameters at the same moment according to the preset influence factors;

[0032] Conduct a multi-dimensional comprehensive evaluation of the abnormal operation parameters according to the multi-dimensional influence relationship to obtain an evaluation result.

[0033] In addition, to achieve the above object, the present application also provides a JP cabinet fault warning device, and the JP cabinet fault warning device includes:

[0034] A prediction module, configured to predict the target operation parameters of the target JP cabinet according to the digital twin model of the JP cabinet pre-run locally and the adaptive power distribution strategy preset for the target JP cabinet;

[0035] An acquisition module, configured to acquire real-time operation parameters of the target JP cabinet, and perform comparative analysis on the real-time operation parameters and the target operation parameters;

[0036] A determination module, configured to determine whether there is a fault in the target JP cabinet according to the result of the comparative analysis, and if so, output corresponding alarm information according to the result of the comparative analysis.

[0037] In addition, to achieve the above object, the present application further provides a JP cabinet fault warning device, where the JP cabinet fault warning device includes: a memory, a processor, and a JP cabinet fault warning program stored on the memory and executable on the processor, and the JP cabinet fault warning program is configured to implement the steps of the JP cabinet fault warning method as described above. In addition, to achieve the above object, the present application further provides a computer-readable storage medium, on which a JP cabinet fault warning program is stored, and the JP cabinet fault warning program, when executed by a processor, implements the steps of the JP cabinet fault warning method as described above.

[0038] In addition, to achieve the above object, the present invention further provides a computer program product, where the computer program product includes a JP cabinet fault warning program, and the JP cabinet fault warning program, when executed by a processor, implements the steps of the JP cabinet fault warning method as described above.

[0039] One or more technical solutions proposed in this application have at least the following technical effects: By predicting the target operating parameters of the target JP cabinet according to the digital twin model of the JP cabinet pre-run locally and the adaptive power distribution strategy preset for the target JP cabinet; obtaining the real-time operating parameters of the target JP cabinet, and comparing and analyzing the real-time operating parameters and the target operating parameters; according to the results of the comparison and analysis, determining whether there is a fault in the target JP cabinet, and if so, outputting corresponding alarm information according to the results of the comparison and analysis, that is, through the digital twin model of the JP cabinet preset locally and the adaptive power distribution strategy corresponding to the target JJP cabinet, simulating the adaptive adjustment process of the operating parameters of the JP cabinet in the actual application of the adaptive power distribution process, and predicting the target operating parameters of the target JP cabinet in the actual adaptive power distribution adjustment process through the simulation method of this digital twin model. After predicting the target operating parameters, the real-time operating parameters of the JP cabinet can be obtained, and the real-time operating parameters can be compared and analyzed with the target operating parameters, so as to determine whether the actual operating parameters of the target JP cabinet are abnormal, and thereby determine whether the target JP cabinet has achieved the actual adaptive power distribution effect in the adaptive power distribution process, and reversely deduce whether the target JP cabinet has a fault. When there is a fault, corresponding alarm information is output according to the results of the comparison and analysis, thereby realizing the quick detection of whether there is a fault in the operating state of the target JP cabinet when the load changes. Therefore, through the above solution, the operating risk of the JP cabinet can be detected in time and relevant maintenance personnel can be prompted. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application.

[0041] In order to more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0042] Figure 1 It is a schematic flowchart provided for Embodiment 1 of the JP cabinet fault warning method of this application;

[0043] Figure 2 It is a schematic flowchart provided for Embodiment 2 of the JP cabinet fault warning method of this application;

[0044] Figure 3 It is a schematic module structure diagram of the JP cabinet fault warning device for the embodiments of this application;

[0045] Figure 4This is a schematic diagram of the device structure of the hardware operating environment involved in the JP cabinet fault warning method in the embodiments of the present application. The implementation, functional features, and advantages of the purpose of the present application will be further described in conjunction with the embodiments with reference to the accompanying drawings. Specific Embodiments

[0046] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] Refer to Figure 1 , Figure 1 This is a schematic flowchart of the first embodiment of the JP cabinet fault warning method of the present application.

[0048] In the first embodiment, the JP cabinet fault warning method includes the following steps:

[0049] S10. Predict the target operating parameters of the target JP cabinet according to the JP cabinet digital twin model pre-run locally and the adaptive power distribution strategy preset for the target JP cabinet.

[0050] It should be noted first that the execution subject of this embodiment is the central server, and the central server is used as the data connection between the cloud and multiple terminal JP cabinets. The central server can receive the data of each JP cabinet and perform functions such as collecting, processing, and analyzing the data of each JP cabinet in the central server.

[0051] Among them, a corresponding JP cabinet digital twin model is set on the central server side. Through this JP cabinet digital twin model, the operating effects of multiple JP cabinets (including the same-level and upper and lower power distribution structures) in the same power grid topology can be simulated, and according to the operating effects, the operating parameters of different JP cabinets can be predicted, etc.

[0052] In addition, the central server can be used as the processing end for predicting the operating state and fault warning of the JP cabinet. Relevant operation and maintenance personnel can directly monitor on the central server side, avoiding the need for relevant operation and maintenance personnel to go to the field for multi-faceted monitoring. With the JP cabinet digital twin model, accurate online analysis can be carried out to quickly determine whether there is a fault in the JP cabinet, etc.

[0053] It is understandable that the digital twin model of the JP cabinet refers to an actual digital derivative model that can simulate the JP cabinet whose operating status needs to be monitored. The digital twin model of the JP cabinet includes the actual power grid topology, the upper and lower level power distribution structures, the connection status of each JP cabinet in the power grid, the actual hardware configuration parameters of each JP cabinet, the actual maximum load parameters of each JP cabinet in the power grid, etc. It should be noted that the target JP cabinet refers to the JP cabinet that needs to be monitored currently. The JP cabinet refers to any JP cabinet in the digital twin model of the JP cabinet (the actual power grid topology that needs to be monitored). Each JP cabinet in the power grid topology that needs to be monitored is configured with a corresponding adaptive power distribution strategy. Among them, the adaptive power distribution strategy can adaptively control the circuit connection effect according to the actual power load demand, and adjust the power load configuration by adaptively controlling the closing and opening effect of the circuit breaker.

[0054] Among them, the adaptive power distribution strategy mainly includes a cost control strategy for controlling electricity costs, reducing the maximum load of the JP cabinet, and ensuring the service life of the JP cabinet in this embodiment. This strategy can be adaptively regulated by the microprocessor built in each JP cabinet, and the adaptive power distribution strategy will fine-tune its specific control time, control logic, etc. according to the historical operation requirements in the actual power grid topology. For example, according to the peak and valley values of the electricity price, during a fixed time period when the electricity price is at the peak, the maximum power load is reduced.

[0055] It is understandable that through the digital twin model of the JP cabinet and the adaptive power distribution strategy of the target JP cabinet, the adaptive power distribution adjustment plan of the target JP cabinet and the actual load and operation parameters of the target JP cabinet after actual adaptive power distribution adjustment can be simulated in different time periods, and based on this simulation plan, the target operation parameters of the target JP cabinet in the current time period can be predicted.

[0056] It is understandable that the target operation parameters refer to the target reasonable values of the load and operation parameter changes when the target JP cabinet operates according to the adaptive power distribution strategy. For example, when the electricity cost is the lowest in the current time period, according to the adaptive power distribution strategy, the target JP cabinet will tend to increase the power load to provide a high-power distribution output. At this time, the corresponding target operation parameters of the high load can be obtained through simulation.

[0057] In this embodiment, the step of predicting the target operation parameters of the target JP cabinet according to the digital twin model of the JP cabinet pre-run locally and the adaptive power distribution strategy preset for the target JP cabinet includes:

[0058] Among them, the digital twin model of the JP cabinet includes the first simulated load data during the operation of other JP cabinets adjacent to the target JP cabinet, the second simulated load data during the operation of the superior substation of the target JP cabinet, and the historical load data of the target JP cabinet; according to the first simulated load data, the second simulated load data, the historical load data, and the adaptive power distribution strategy preset for the target JP cabinet, predict the load change trend and the adaptive load optimization result of the target JP cabinet; according to the change trend and the adaptive load optimization result, determine the target operating parameters of the target JP cabinet.

[0059] It should be noted first that in the actual application process of the JP cabinet, in addition to the adaptive adjustment according to the set control conditions such as the adaptive adjustment time or the specific electricity price, etc., it is also necessary to consider the actual dynamic balance adjustment of the JP cabinet in the power grid topology structure. For example, considering the global power change, after the load capacity of any one of the JP cabinets fails, the load originally borne by it needs to be borne by other adjacent JP cabinets, so that the load dynamic change effect is generated on other adjacent JP cabinets. Therefore, when simulating the target operating parameters of the target JP cabinet, it is necessary to consider not only the hardware parameters during the operation of the target JP cabinet itself and the optimization effect of the adaptive power distribution strategy, but also the global application effect of the target JP cabinet corresponding to the power grid topology structure.

[0060] Therefore, in the digital twin model of the JP cabinet, the simulated load data of each power node will be taken into account. When determining the target JP cabinet, the first simulated load data of the JP cabinets adjacent to the target JP cabinet and the second simulated load data of the superior substation of the target JP cabinet will be included in the scope of simulation prediction. Combining the three types of load data of the historical load data of the target JP cabinet, predict the load change trend and the adaptive load optimization result during the actual operation of the target JP cabinet, and thereby determine the target operating parameters of the target JP cabinet.

[0061] Among them, the load change trend refers to the load change trend of the target JP cabinet in the global load change of the power grid topology structure. For example, when the global load change trend decreases as a whole, the load of the target JP cabinet will also decrease. The adaptive load optimization result refers to the optimization result of the adaptive control of the load of the target JP cabinet according to the adaptive power distribution strategy. For example, adjusting the number of power distribution output branches of the JP cabinet, etc.

[0062] In this embodiment, before the step of predicting the target operating parameters of the target JP cabinet according to the digital twin model of the JP cabinet pre-run locally and the adaptive power distribution strategy preset for the target JP cabinet, the method further includes:

[0063] Determine the power grid topology where the target JP cabinet is located, and determine the load data of other JP cabinets adjacent to the target JP cabinet and the superior substation in the power grid topology, and obtain the historical operating status of the target JP cabinet, where the historical operating status at least includes the factory hardware configuration parameters of the target JP cabinet, and the load change data of the target JP cabinet based on minute-level, hour-level, and seasonal levels; generate virtual load data of the JP cabinet in the target scenario according to the power grid topology, where the target scenario at least includes a scenario where the temperature and humidity of the JP cabinet operating environment are greater than the preset standard value and a scenario where there are partial equipment failures in the power grid topology; construct a digital twin model of the target JP cabinet according to the power grid topology, the load change data, the virtual load data, and the historical operating status.

[0064] It should be noted that the digital twin model of the JP cabinet includes multiple JP cabinets with power connection relationships and power distribution nodes such as the superior substation in the same power grid topology, and when simulating the power load change of any JP inside it, the global change of the power environment where the JP cabinet is located needs to be considered. Therefore, when constructing this digital twin model, not only the historical operating status of the target JP cabinet needs to be obtained to construct the digital model of the target JP cabinet, but also the load data of power nodes such as the superior substation in the power grid topology where it has a power connection relationship with the JP cabinet needs to be obtained, and the power grid topology where the target JP cabinet is located needs to be obtained to limit the distribution of global power nodes during the construction of the digital model, which is beneficial to the accuracy of subsequent model simulation.

[0065] It should be noted that the historical operating status in this embodiment includes the factory hardware configuration parameters of the target JP cabinet and the load change data of the target JP cabinet at different time periods. In order to ensure the accuracy of the data, the load change data is classified into three types of data: minute-level, hour-level, and seasonal-level. Thus, the load change data of the target JP cabinet is quantitatively classified at different time levels from different time dimensions, which helps to accurately capture the situation where the JP cabinet adjusts its power load according to the adaptive power distribution strategy.

[0066] In addition, after obtaining the actual operation data, in order to supplement the extreme negative data of the model, virtual load data in the corresponding target scenario is generated according to the power grid topology. This virtual load data actually refers to the fault data when there are equipment failures or abnormal loads of equipment in the power grid topology. The target scenario at least includes a scenario where the temperature and humidity of the JP cabinet operating environment are greater than the preset standard value (in this scenario, the load of the JP cabinet increases but its distributed power output decreases), and when there are partial equipment failures, in order to balance such failures, the load of adjacent JP cabinets near the faulty equipment increases.

[0067] Further, after determining the power grid topology structure, load change data, virtual load data, and historical operation status, the digital twin model of the target JP cabinet can be constructed, and at the same time, the connection conditions between the target JP cabinet and adjacent JP cabinets and the corresponding power nodes of the superior substation in the power grid topology structure where the target JP cabinet is located can be shown. In this embodiment, after the step of constructing the digital twin model of the target JP cabinet according to the power grid topology structure, the load change data, the virtual load data, and the historical operation status, the method further includes:

[0068] Obtain user-defined state space parameters and action space parameters based on the power grid topology structure; construct a deep learning policy engine according to the state space parameters and action space parameters; and train the deep learning policy engine through the digital twin model of the JP cabinet to obtain an adaptive power distribution strategy.

[0069] It can be understood that the adaptive power distribution strategy refers to configuring corresponding automatic control adjustment strategies in the JP cabinet according to the actual needs specified by the user. In the actual operation state, electrical components in the JP, such as circuit breakers, are controlled to control the power distribution effect of the JP cabinet. And this adaptive power distribution strategy will generate corresponding control tendencies according to the actual needs of the user. For example, to control the power consumption cost, when the electricity price is high, reduce the power usage load, and when the electricity price is low, increase the power usage load, or to ensure the service life of the JP cabinet, balance the power load conditions of multiple JP cabinets from the global structure of the power grid topology, and reduce the maximum power load of a certain JP cabinet.

[0070] Therefore, when formulating the adaptive power distribution strategy, the corresponding state space parameters and action space parameters can be constructed by means of deep reinforcement learning, and according to the actual needs of the user, the corresponding reward function can be set, and the digital twin model of the JP cabinet can be used for cooperative training to achieve the effect of quickly converging to obtain the adaptive power distribution strategy.

[0071] It should be noted that in this embodiment, the reward function for controlling the power cost is mainly used as an example for elaboration. Specifically, the state space parameters corresponding to the deep reinforcement learning decision engine in this embodiment include, but are not limited to: the real-time load of the JP cabinet, the electricity price, the weather, the equipment status (such as the health of the capacitor), the user priority label (specifically referring to ensuring the usage priority of any power branch, etc.), and the action space parameters corresponding to this learning engine include, but are not limited to: branch switch control, reactive power compensation switching, energy storage charge and discharge instructions, and demand response signals.

[0072] Among them, the reward functions that can be set in this embodiment include but are not limited to: controlling the electricity cost, equipment loss cost, and carbon emission cost from an economic perspective; controlling the voltage deviation penalty and overload risk penalty from a safety perspective, and controlling the weighted deduction of the power supply interruption duration for users (such as factories) from a comfort perspective.

[0073] Among them, the training mechanism of this embodiment can be: offline pre-training: based on historical data + digital twin simulation environment to accelerate policy convergence, or online fine-tuning: adapting to real-time scenarios through transfer learning and updating the policy network every 24 hours. It should be noted that the dynamic scheduling algorithm corresponding to the user's custom priority label mainly refers to elastic weight allocation: for example, critical loads (hospitals, data centers) are assigned fixed weights, and the weights of ordinary loads (charging piles, advertising light boxes) are dynamically adjusted according to the electricity price and grid status. Example: when the grid is in an emergency, the power of the charging pile is automatically reduced (the weight drops from 1.0 to 0.3) to give priority to ensuring the power supply to the operating room.

[0074] In addition, this embodiment can set game theory optimization: model the multi-JP cabinet collaboration problem as a non-cooperative game, with each JP cabinet as an agent, and solve the global optimal strategy through Nash equilibrium.

[0075] S20. Obtain the real-time operation parameters of the target JP cabinet, and compare and analyze the real-time operation parameters with the target operation parameters;

[0076] S30. Determine whether there is a fault in the target JP cabinet according to the result of the comparison and analysis. If so, output the corresponding alarm information according to the result of the comparison and analysis.

[0077] It can be understood that the target operation parameters refer to the operation parameters of the target JP cabinet in the simulated state. However, in the actual power grid topology, when the ambient temperature and humidity change, or there is a power output or distribution fault in any JP cabinet or other substations in the power grid topology, the operation parameters will change. Therefore, when obtaining the target operation parameters through simulation, it is necessary to obtain the real-time operation parameters of the target JP cabinet, compare and analyze the real-time operation parameters with the target operation parameters, and corresponding analysis results can be obtained to determine whether there is an operation fault, etc. When it is determined that there is an operation fault, the corresponding alarm information can be output according to the result of the comparison and analysis. For example, if the load data in the real-time operation parameters of the current JP cabinet in the result of the comparison and analysis is much smaller than the target operation parameters, it can be determined that the JP cabinet has not implemented the adjustment mechanism in the adaptive power distribution strategy. Since the alarm information can be output according to the load situation, relevant personnel can be reminded that there is a problem with the load capacity of the JP cabinet.

[0078] In this embodiment, the target operating parameters of the target JP cabinet are predicted according to the digital twin model of the JP cabinet pre-run locally and the adaptive power distribution strategy preset for the target JP cabinet; the real-time operating parameters of the target JP cabinet are obtained, and the real-time operating parameters and the target operating parameters are compared and analyzed; according to the results of the comparison and analysis, it is determined whether there is a fault in the target JP cabinet. If so, corresponding alarm information is output according to the results of the comparison and analysis. That is, through the digital twin model of the JP cabinet preset locally and the adaptive power distribution strategy preset for the target JJP cabinet, the adaptive adjustment process of the operating parameters of the JP cabinet in the actual application of the adaptive power distribution process is simulated. And through the simulation method of this digital twin model, the target operating parameters of the target JP cabinet in the actual adaptive power distribution adjustment process are predicted. After predicting the target operating parameters, the real-time operating parameters of the JP cabinet can be obtained, and the real-time operating parameters can be compared and analyzed with the target operating parameters, so as to determine whether there is an abnormality in the actual operating parameters of the target JP cabinet, and thus determine whether the target JP cabinet has achieved the actual adaptive power distribution effect in the adaptive power distribution process, and reversely deduce whether there is a fault in the target JP cabinet. When there is a fault, corresponding alarm information is output according to the results of the comparison and analysis, thus realizing the rapid detection of whether there is a fault in the operating state of the target JP cabinet when the load changes. Therefore, through the above solution, the operation risk of the JP cabinet can be timely detected and relevant maintenance personnel can be prompted. As Figure 2 shown, based on the first embodiment, the second embodiment of the JP cabinet fault warning method of the present application is proposed. In this embodiment, step S20 specifically includes:

[0079] S21, obtaining the real-time operating parameters of the JP cabinet;

[0080] It can be understood that the real-time operating parameters mainly include multi-dimensional index parameters during the operation of the JP cabinet. For example, the input and output power during the operation of the JP cabinet, the on-off situation of the JP cabinet relay, the connection situation of the JP cabinet grounding line, the temperature and humidity of the JP cabinet, and other parameters.

[0081] Among them, the real-time operating parameters can be obtained by means of sensors or detectors set in the target JP cabinet.

[0082] S22, generating a defined range for judging abnormal operating parameters according to the target operating parameters;

[0083] It should be noted that each parameter in the target operating parameters is used as standard value data, including but not limited to the standard values of temperature and humidity during the operation of the JP cabinet obtained by prediction, the input and output power during the operation of the JP cabinet, the standard value of the grounding current, etc. Based on this, it is possible to determine whether there is an abnormal situation in the operation parameters during the current operation of the JP cabinet by comparing the collected real-time operation parameters with the target operation parameters. That is, when there is a mismatch between the numerical values of the corresponding parameters in the real-time operation parameters and the standard values of the corresponding parameters in the target operation parameters, it is determined that an abnormality has occurred.

[0084] Among them, specific matching rules can be set. According to the target operation parameters, a defined range for judging whether there are abnormal operation parameters is set. For example, the value judgment of the defined range is set at percentage values such as 5% and 10% of the upper and lower fluctuations of the target operation parameters to determine the range value centered on the standard value. When the collected data falls within this range value, it is determined that the current operation is normal. If it exceeds this range value, it is determined that the current operation is abnormal.

[0085] Among them, when it is determined that there is an abnormality, taking the abnormal temperature value, normal humidity value, and normal voltage value as examples, the temperature value is designated as an abnormal operation parameter.

[0086] S23. According to the defined range, determine the abnormal operation parameters in the real-time operation parameters, and compare and analyze the abnormal operation parameters with the target operation parameters.

[0087] Among them, it should be further noted that the abnormal operation parameter also includes the parameter deviation value between the temperature value and the standard temperature value during the operation of the JP cabinet (the parameter deviation value is obtained by normalizing the result obtained by subtracting the temperature value from the standard value. Since the units of the operation parameters are different, after obtaining different abnormal operation parameters, further normalization processing is required to calculate their parameter deviation values), and the number of abnormalities of the temperature value on the same day. For example, the detection component collects the temperature value of the JP cabinet during operation every hour, and at least 24 temperature values are generated in a day. If the temperature values during 3 collections all exceed the range of the standard value, the number of abnormalities is recorded as 3 times. At the same time, the information of the number of abnormalities also includes the time corresponding to the number of abnormalities generated.

[0088] In this embodiment, the step of comparing and analyzing the abnormal operation parameters with the target operation parameters includes:

[0089] According to the target operation parameters, conduct a multi-dimensional comprehensive evaluation of the abnormal operation parameters to obtain an evaluation result; retrieve the historical operation record of the target JP cabinet, and compare and analyze the evaluation result with the historical operation record.

[0090] It is understandable that after determining the target operating parameters and abnormal operating parameters, it is necessary to conduct a multi-dimensional comprehensive judgment on the two. For example, considering the influence of temperature and humidity changes on the line power of the JP cabinet, or considering the influence of abnormal voltage values of multiple lines on the overall power of the JP cabinet, etc.

[0091] It should be noted that the evaluation result mainly evaluates the corresponding influence between multi-dimensional operating parameters and related state parameters, as well as the data on the functions that the JP cabinet can achieve under abnormal operating parameters.

[0092] It should be noted that in this embodiment, the historical operation record refers to the record when the JP cabinet operates naturally without operation risks during the previous operation process.

[0093] In addition, it should be noted that during the process of comparing the evaluation result with the historical operation record, it includes the comparison of two aspects of data. On the one hand, it is to compare each operating parameter with the related state parameter. For example, directly compare the temperature values. On the other hand, it is to compare the cumulative value of the influence values of the multi-dimensional comprehensive evaluation, and through the comprehensive comparison of the two aspects, determine whether there is a risk for this JP cabinet.

[0094] Among them, in this embodiment, it is necessary to take into account the comparison results of both aspects, and a corresponding comparison formula needs to be designed. Specifically, it is aX + bY, where X is the deviation of the parameter value comparison result, Y is the deviation between the cumulative values of the influence values, and a and b are coefficients whose sum is 1, and a is greater than b.

[0095] It is understandable that in this embodiment, two comparison schemes will be adopted, and the results of the two comparison schemes will be compared. According to the above formula, the calculation of the comparison result is mainly to compare the evaluation result, the received original data, and the historical operation record.

[0096] Therefore, when comparing, corresponding synchronous comparison rules are also set, that is, preset synchronous comparison rules. The preset synchronous comparison rules include two points. On the one hand, the time is the same, and on the other hand, the season is the same. Since the temperature and humidity changes in the JP cabinet operating parameters will change with time and season, and at the same time, the operating loads of the JP cabinet at different times and different seasons are different, therefore, when comparing, taking the current time to be compared as 12 noon in summer as an example, it is necessary to extract the data with the same season and 12 noon from the historical operation record as the comparison data.

[0097] Among them, the time information corresponding to the evaluation result includes both date information and time information. The date information can lock the specific season, and the time information can find the comparison data at the precise time.

[0098] It can be understood that, according to the above, in this embodiment, the comparison results of both aspects need to be taken into account, and a corresponding comparison formula needs to be designed, specifically, aX+bY, X is the parameter deviation value of the parameter value comparison result (the sum of the differences between multiple data and the corresponding standard values), Y is the deviation between the cumulative values of the influence values (the cumulative value of a single influence value), a and b are coefficients whose sum is 1, and a is greater than b.

[0099] Specifically, the comparison result can be divided into two results. The first result is that the data deviation value between the comparison data corresponding to the evaluation result and the historical operation data is greater than the preset deviation value, and the second result is that the deviation value between the comparison data corresponding to the evaluation result and the historical operation data is less than the preset deviation value.

[0100] Among them, the preset deviation value can be a value preset by relevant personnel. The value needs to be set according to the power parameters, temperature and humidity standards, etc. of the actual JP cabinet application. In this embodiment, it can be set to 5.

[0101] In this embodiment, the step of performing a multi-dimensional comprehensive evaluation on the abnormal operating parameters according to the target operating parameters to obtain the evaluation results includes:

[0102] According to preset influencing factors, a multi-dimensional influencing relationship between the target operating parameters and the abnormal operating parameters at the same time is determined; according to the multi-dimensional influencing relationship, a multi-dimensional comprehensive evaluation is performed on the abnormal operating parameters to obtain an evaluation result.

[0103] It should be noted that the target operating parameters include at least temperature and humidity parameters, power parameters and line conduction parameters, etc. These parameters are mainly the parameters of the JP cabinet during normal operation. After comparative analysis, some parameters in the real-time operating parameters of the JP cabinet will be identified as abnormal operating parameters when the JP cabinet is in an abnormal state.

[0104] In this embodiment, it is necessary to first determine the impact between abnormal operating parameters based on preset impact factors, and assign an impact factor between each two data (for example, the impact between temperature and power, or the situation where temperature increase affects the normal operation of certain electrical components), and based on the impact factor, comprehensively determine the impact value between each two data. The larger the impact value, the greater the abnormal situation.

[0105] The preset influencing factor is a factor of the degree of influence between different parameters pre-specified by relevant personnel.

[0106] The calculation scheme of the influence value is that the working state of the JP cabinet under certain conditions is predetermined by relevant personnel. For example, at a fixed temperature, the output power of the JP cabinet is a corresponding fixed value, and the proportional relationship between the two (the proportional relationship is the influence factor) is fixed. If there is a difference between the proportional relationship between the two parameters actually obtained and the influence factor, then the difference is the influence value.

[0107] Furthermore, after calculating the impact value between every two parameters, the impact values can be accumulated to obtain the final multi-dimensional comprehensive evaluation result. The larger the accumulated value of the impact value, the higher the operational risk represented by the evaluation result.

[0108] This embodiment obtains the real-time operating parameters of the JP cabinet; generates a defined range for determining abnormal operating parameters based on the target operating parameters; determines the abnormal operating parameters in the real-time operating parameters based on the defined range, and compares and analyzes the abnormal operating parameters and the target operating parameters. After determining the parameter range actually used for evaluation, the defined range and the real-time operating parameters can be further compared to ensure the accuracy of the subsequent warning effect.

[0109] In addition, the present application also provides a JP cabinet fault warning device, referring to Figure 4 , the JP cabinet fault warning device comprises:

[0110] A prediction module 10 is used to predict the target operating parameters of the target JP cabinet according to the locally pre-running digital twin model of the JP cabinet and the adaptive power distribution strategy preset and configured for the target JP cabinet;

[0111] An acquisition module 20 is used to acquire the real-time operating parameters of the target JP cabinet, and compare and analyze the real-time operating parameters with the target operating parameters;

[0112] The determination module 30 is used to determine whether the target JP cabinet has a fault according to the result of the comparative analysis, and if so, output corresponding alarm information according to the result of the comparative analysis.

[0113] In this embodiment, the target operating parameters of the target JP cabinet are predicted according to the digital twin model of the JP cabinet pre-operated locally and the adaptive power distribution strategy preset for the target JP cabinet; the real-time operating parameters of the target JP cabinet are obtained, and the real-time operating parameters and the target operating parameters are compared and analyzed; according to the result of the comparison and analysis, it is determined whether there is a fault in the target JP cabinet. If so, corresponding alarm information is output according to the result of the comparison and analysis. That is, through the digital twin model of the JP cabinet preset locally and the adaptive power distribution strategy preset for the target JJP cabinet, the adaptive adjustment process of the operating parameters of the JP cabinet in the actual application of the adaptive power distribution process is simulated. And through the simulation method of this digital twin model, the target operating parameters of the target JP cabinet in the actual adaptive power distribution adjustment process are predicted. After predicting the target operating parameters, the real-time operating parameters of the JP cabinet can be obtained, and the real-time operating parameters can be compared and analyzed with the target operating parameters, so as to determine whether there is an abnormality in the actual operating parameters of the target JP cabinet, and thereby determine whether the target JP cabinet has achieved the actual adaptive power distribution effect in the adaptive power distribution process, and reversely deduce whether there is a fault in the target JP cabinet. When there is a fault, corresponding alarm information is output according to the result of the comparison and analysis. Furthermore, the operation state of the target JP cabinet when the load changes is quickly detected for faults. Therefore, through the above solution, the operation risk of the JP cabinet can be timely detected and relevant maintenance personnel can be prompted.

[0114] It should be noted that each module in the above device can be used to implement each step in the above method and achieve the corresponding technical effects, which will not be elaborated in this embodiment. Refer to Figure 4 , Figure 4 which is a schematic structural diagram of the device of the hardware operating environment involved in the solution of the embodiment of the present application.

[0115] As Figure 4 shown, the device may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard). Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0116] Those skilled in the art can understand that Figure 4 the structure shown in

[0117] does not constitute a limitation on the device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Figure 4 As shown in Figure 4 the memory 1005, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a JP cabinet fault warning program. In the device shown in

[0118] the network interface 1004 is mainly used for data communication with an external network; the user interface 1003 is mainly used for receiving user input instructions; the device calls the JP cabinet fault warning program stored in the memory 1005 through the processor 1001, and performs the following operations:

[0119] Predict the target operating parameters of the target JP cabinet according to the adaptive power distribution strategy preset for the digital twin model of the JP cabinet pre-run locally and the target JP cabinet;

[0120] Obtain the real-time operating parameters of the target JP cabinet, and compare and analyze the real-time operating parameters and the target operating parameters;

[0121] According to the result of the comparison and analysis, determine whether there is a fault in the target JP cabinet. If so, output the corresponding alarm information according to the result of the comparison and analysis.

[0122] Further, the processor 1001 can call the JP cabinet fault warning program stored in the memory 1005 and also perform the following operations:

[0123] The digital twin model of the JP cabinet includes first simulated load data when other JP cabinets adjacent to the target JP cabinet are operating, second simulated load data when the upper-level substation of the target JP cabinet is operating, and historical load data of the target JP cabinet;

[0124] Predict the load change trend and the adaptive load optimization result of the target JP cabinet according to the first simulated load data, the second simulated load data, the historical load data, and the adaptive power distribution strategy preset for the target JP cabinet;

[0125] According to the change trend and the adaptive load optimization result, determine the target operating parameters of the target JP cabinet.

[0126] Determine the power grid topological structure where the target JP cabinet is located, and determine the load data of other JP cabinets adjacent to the target JP cabinet and the superior substation in the power grid topological structure, and obtain the historical operating status of the target JP cabinet.

[0127] Wherein, the historical operating status at least includes the factory hardware configuration parameters of the target JP cabinet, and the load change data of the target JP cabinet based on minute-level, hour-level, and seasonal levels.

[0128] Generate virtual load data of the JP cabinet in a target scenario according to the power grid topological structure, wherein the target scenario at least includes a scenario where the temperature and humidity of the JP cabinet operating environment are greater than a preset standard value and a scenario where there are partial equipment failures in the power grid topological structure.

[0129] Construct a digital twin model of the target JP cabinet according to the power grid topological structure, the load change data, the virtual load data, and the historical operating status.

[0130] Further, the processor 1001 can call the JP cabinet fault warning program stored in the memory 1005 and also perform the following operations:

[0131] Obtain user-defined state space parameters and action space parameters based on the power grid topological structure.

[0132] Construct a deep learning policy engine according to the state space parameters and the action space parameters.

[0133] Train the deep learning policy engine through the digital twin model of the JP cabinet to obtain an adaptive power distribution policy.

[0134] Further, the processor 1001 can call the JP cabinet fault warning program stored in the memory 1005 and also perform the following operations:

[0135] Obtain the real-time operating parameters of the JP cabinet.

[0136] Generate a defined range for determining abnormal operating parameters according to the target operating parameters.

[0137] Determine the abnormal operating parameters in the real-time operating parameters according to the defined range, and perform a comparative analysis on the abnormal operating parameters and the target operating parameters.

[0138] Further, the processor 1001 can call the JP cabinet fault warning program stored in the memory 1005 and also perform the following operations:

[0139] Perform a multi-dimensional comprehensive evaluation on the abnormal operating parameters according to the target operating parameters to obtain an evaluation result.

[0140] Retrieve the historical operation records of the target JP cabinet, and compare and analyze the evaluation results and the historical operation records.

[0141] Further, the processor 1001 may call the JP cabinet fault warning program stored in the memory 1005 and further perform the following operations:

[0142] Determine the multi-dimensional influence relationship between the target operation parameters and the abnormal operation parameters at the same moment according to the preset influence factors; according to the multi-dimensional influence relationship, conduct a multi-dimensional comprehensive evaluation of the abnormal operation parameters to obtain an evaluation result.

[0143] It should be understood that each part disclosed in this application can be implemented by hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0144] As described above, only the specific implementation manners of this application are provided, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application shall be subject to the protection scope of the claims.

[0145] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the JP cabinet fault warning method in the above embodiments.

[0146] The computer-readable storage medium provided by this application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or component. The program code contained on the computer-readable storage medium can be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0147] The above computer-readable storage medium can be included in the JP cabinet fault warning device; it can also exist separately without being assembled into the JP cabinet fault warning device.

[0148] The above computer-readable storage medium carries one or more programs. When the one or more programs are executed by the JP cabinet fault warning device, the JP cabinet fault warning device is caused to: predict the target operating parameters of the target JP cabinet according to the adaptive power distribution strategy preset for the JP cabinet digital twin model pre-run locally and the target JP cabinet;

[0149] Obtain the real-time operating parameters of the target JP cabinet, and compare and analyze the real-time operating parameters and the target operating parameters;

[0150] According to the result of the comparison and analysis, determine whether there is a fault in the target JP cabinet. If so, output corresponding alarm information according to the result of the comparison and analysis.

[0151] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN: Local Area Network) or a wide area network (WAN: Wide Area Network), or it can be connected to an external computer (for example, by connecting through an Internet service provider via the Internet).

[0152] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0153] The modules described in the embodiments of this application can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation on the unit itself in some cases.

[0154] The readable storage medium provided by this application is a computer-readable storage medium. The computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for performing the above-mentioned JP cabinet fault warning method, and can solve the technical problem of JP cabinet fault warning. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by this application are the same as those of the JP cabinet fault warning method provided in the above embodiments, and will not be elaborated here.

[0155] The present application also provides a computer program product, including a computer program which, when executed by a processor, implements the steps of the JP cabinet fault warning method as described above.

[0156] The computer program product provided by the present application can solve the technical problem of JP cabinet fault warning. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as those of the JP cabinet fault warning method provided by the above embodiments, and will not be elaborated here.

[0157] The above are only partial embodiments of the present application, and do not limit the patent scope of the present application accordingly. Any equivalent structural transformation made by using the specification and drawings of the present application under the technical concept of the present application, or any direct / indirect application in other related technical fields, is included in the patent protection scope of the present application.

[0158] It should be noted that, in this article, the term "including", "comprising" or any other variant thereof is intended to cover a non-exclusive inclusion, such that a process, method, article or system including a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or system. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of another identical element in the process, method, article or system including that element.

[0159] The serial numbers of the above embodiments of the present application are only for description and do not represent the superiority or inferiority of the embodiments.

[0160] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence, or the part that makes a contribution to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium as described above (such as ROM / RAM, magnetic disk, optical disc), and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present application.

[0161] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application accordingly. Any equivalent structure or equivalent process transformation made by using the specification and drawings of the present application, or any direct or indirect application in other related technical fields, is similarly included in the patent protection scope of the present application.

Claims

1. A JP cabinet fault warning method, characterized in that, The JP cabinet fault warning method includes the following steps: predicting the target operating parameters of the target JP cabinet according to the digital twin model of the JP cabinet pre-operated locally and the adaptive power distribution strategy preset for the target JP cabinet; the step of predicting the target operating parameters of the target JP cabinet according to the digital twin model of the JP cabinet pre-operated locally and the adaptive power distribution strategy preset for the target JP cabinet includes: wherein, the digital twin model of the JP cabinet includes the first simulated load data when other JP cabinets adjacent to the target JP cabinet are operating, the second simulated load data when the superior substation of the target JP cabinet is operating, and the historical load data of the target JP cabinet; predicting the load change trend and the adaptive load optimization result of the target JP cabinet according to the first simulated load data, the second simulated load data, the historical load data, and the adaptive power distribution strategy preset for the target JP cabinet; determining the target operating parameters of the target JP cabinet according to the change trend and the adaptive load optimization result; obtaining the real-time operating parameters of the target JP cabinet, and comparing and analyzing the real-time operating parameters and the target operating parameters; determining whether there is a fault in the target JP cabinet according to the result of the comparison and analysis, and if so, outputting corresponding alarm information according to the result of the comparison and analysis.

2. The method according to claim 1, wherein Before the step of predicting the target operating parameters of the target JP cabinet according to the digital twin model of the JP cabinet pre-operated locally and the adaptive power distribution strategy preset for the target JP cabinet, the method further includes: determining the power grid topology structure where the target JP cabinet is located, and determining the load data of other JP cabinets adjacent to the target JP cabinet and the superior substation of the target JP cabinet in the power grid topology structure, and obtaining the historical operating status of the target JP cabinet, wherein the historical operating status at least includes the factory hardware configuration parameters of the target JP cabinet and the load change data of the target JP cabinet based on minute-level, hour-level, and seasonal; generating virtual load data of the JP cabinet in the target scenario according to the power grid topology structure, wherein the target scenario at least includes the scenario where the temperature and humidity of the JP cabinet operating environment are greater than the preset standard value and the scenario where there are partial equipment failures in the power grid topology structure; constructing a digital twin model of the target JP cabinet according to the power grid topology structure, the load change data, the virtual load data, and the historical operating status.

3. The method according to claim 2, wherein After the step of constructing a digital twin model of the target JP cabinet according to the power grid topology structure, the load change data, the virtual load data, and the historical operating status, the method further includes: obtaining user-defined state space parameters and action space parameters based on the power grid topology structure; constructing a deep learning strategy engine according to the state space parameters and the action space parameters; training the deep learning strategy engine through the digital twin model of the JP cabinet to obtain an adaptive power distribution strategy.

4. The method according to claim 1, characterized in that, The step of obtaining the real-time operation parameters of the target JP cabinet and comparing and analyzing the real-time operation parameters with the target operation parameters includes: obtaining the real-time operation parameters of the JP cabinet; generating a defined range for determining abnormal operation parameters according to the target operation parameters; determining the abnormal operation parameters in the real-time operation parameters according to the defined range, and comparing and analyzing the abnormal operation parameters with the target operation parameters.

5. The method according to claim 4, wherein The step of comparing and analyzing the abnormal operation parameters with the target operation parameters includes: performing a multi-dimensional comprehensive evaluation on the abnormal operation parameters according to the target operation parameters to obtain an evaluation result; retrieving the historical operation record of the target JP cabinet, and comparing and analyzing the evaluation result with the historical operation record.

6. The method according to claim 5, wherein The step of performing a multi-dimensional comprehensive evaluation on the abnormal operation parameters according to the target operation parameters to obtain an evaluation result includes: determining the multi-dimensional influence relationship between the target operation parameters and the abnormal operation parameters at the same moment according to a preset influence factor; performing a multi-dimensional comprehensive evaluation on the abnormal operation parameters according to the multi-dimensional influence relationship to obtain an evaluation result.

7. A JP cabinet fault warning device, characterized in that, The JP cabinet fault warning device includes: a prediction module, configured to predict the target operation parameters of the target JP cabinet according to the JP cabinet digital twin model pre-run locally and the adaptive power distribution strategy preset for the target JP cabinet; wherein, the JP cabinet digital twin model includes first simulated load data when other JP cabinets adjacent to the target JP cabinet are operating, second simulated load data when the upstream substation of the target JP cabinet is operating, and the historical load data of the target JP cabinet; the prediction module is further configured to predict the load change trend and the adaptive load optimization result of the target JP cabinet according to the first simulated load data, the second simulated load data, the historical load data, and the adaptive power distribution strategy preset for the target JP cabinet; determining the target operation parameters of the target JP cabinet according to the change trend and the adaptive load optimization result; an acquisition module, configured to acquire the real-time operation parameters of the target JP cabinet, and compare and analyze the real-time operation parameters with the target operation parameters; a determination module, configured to determine whether there is a fault in the target JP cabinet according to the result of the comparison and analysis, and if so, output corresponding alarm information according to the result of the comparison and analysis.

8. A JP cabinet fault warning device, characterized in that, The JP cabinet fault warning device includes: a memory, a processor, and a JP cabinet fault warning program stored on the memory and executable on the processor, and the JP cabinet fault warning program is configured to implement the steps of the JP cabinet fault warning method according to any one of claims 1 to 6.

9. A storage medium, characterized in that, A program for implementing the JP cabinet fault warning method is stored on a storage medium, and the program for implementing the JP cabinet fault warning method is executed by a processor to implement the steps of the JP cabinet fault warning method according to any one of claims 1 to 6.

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