An electrical system evaluation method, device and equipment based on a causal inference model

Abstract

This application relates to the fields of power technology and artificial intelligence technology. It discloses a method, apparatus, and equipment for evaluating temporary power supply systems based on a causal reasoning model. The method includes: inputting the current fusion features of the current temporary power supply system into a trained causal reasoning model; generating the current risk level, current abnormal parameter information, and current causal chain path of the current temporary power supply system through the trained causal reasoning model; obtaining the handling suggestions corresponding to the current risk level; obtaining the deviation value between the environmental parameters of the current temporary power supply system and the standard environmental parameters; when the deviation value is greater than a preset value, multiplying a preset current threshold by a reduction coefficient to generate a reduced current threshold; and writing the current risk level, current abnormal parameter information, current causal chain path, reduced current threshold, and handling suggestions into a preset evaluation template to generate an evaluation report for the current temporary power supply system. This application is beneficial for improving the efficiency of obtaining evaluation reports for temporary power supply systems.

Description

Technical Field

[0001] This application relates to the fields of power technology and artificial intelligence technology, and in particular to a method, apparatus and equipment for evaluating temporary power systems based on a causal reasoning model. Background Technology

[0002] Temporary power systems, also known as temporary power supply systems, are power supply systems built to meet temporary power needs. Temporary power systems utilize modular components for rapid deployment, significantly shortening the time from planning to commissioning, making them particularly suitable for tight construction schedules or emergency rescue scenarios.

[0003] However, the current process of obtaining assessment reports for temporary power systems is cumbersome, hindering the efficiency of report acquisition. This is because the assessment reports for temporary power systems currently rely on manual operation. This manual operation requires manually using various testing tools to collect the electrical parameters of the temporary power system item by item, and also manually comparing equipment parameters. The entire process is not only time-consuming and lengthy but also easily affected by human intervention, thus hindering the efficiency of report acquisition. Summary of the Invention

[0004] This application provides a method, apparatus, and device for evaluating temporary power supply systems based on a causal reasoning model, in order to solve the technical problem that the current process of obtaining evaluation reports for temporary power supply systems is cumbersome and not conducive to improving the efficiency of obtaining evaluation reports.

[0005] In a first aspect, embodiments of this application provide a method for evaluating a temporary power supply system based on a causal reasoning model, applied to electronic devices. The method for evaluating a temporary power supply system includes: Acquire multimodal data of the current temporary power system, which includes the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system. A feature extraction model was used to extract features from the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system, respectively, and the features of the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system were obtained. The current fusion characteristics of the current temporary power system are obtained by splicing together the characteristics of the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system. The current fusion features of the current temporary power system are input into the trained causal reasoning model, and the current risk level, current abnormal parameter information and current causal chain path of the current temporary power system are generated through the trained causal reasoning model. Obtain the handling suggestions corresponding to the current risk level, obtain the deviation value between the environmental parameters of the current temporary power system and the standard environmental parameters, and when the deviation value is greater than the preset value, multiply the preset current threshold by the reduction coefficient to generate the reduced current threshold. Write the current risk level, current abnormal parameter information, current causal chain path, reduced current threshold and handling suggestions into the preset assessment template to generate the assessment report of the current temporary power system.

[0006] In one possible implementation of the first aspect, before acquiring the multimodal data of the current temporary power system, which includes the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system, the temporary power system evaluation method includes: Acquire multimodal data of the preset temporary power system, which includes the electrical parameters, environmental parameters, and equipment aging parameters of the preset temporary power system. A feature extraction model was used to extract features from the electrical parameters, environmental parameters, and equipment aging parameters of the preset temporary power system, respectively, and the features of the electrical parameters, environmental parameters, and equipment aging parameters of the preset temporary power system were obtained. The characteristics of the electrical parameters, environmental parameters, and equipment aging parameters of the preset temporary power system are spliced ​​together to obtain the preset fusion characteristics of the preset temporary power system. The preset fusion characteristics, the actual risk level, the actual abnormal parameter information, and the actual causal chain path of the preset temporary power system are combined into a sample. Different samples are used to form a training set, and the causal inference model is trained using the training set. The trained causal inference model is then saved.

[0007] In one possible implementation of the first aspect, the step of inputting the current fusion features of the current power supply system into the trained causal inference model, and generating the current risk level, current abnormal parameter information, and current causal chain path of the current power supply system through the trained causal inference model, includes: Obtain the model file and load the trained causal inference model using the model file; The current fusion characteristics of the current temporary power system are input into the trained causal reasoning model, and the current risk level, current abnormal parameter information and current causal chain path of the current temporary power system are generated through the trained causal reasoning model.

[0008] In one possible implementation of the first aspect, the step of obtaining the handling suggestions corresponding to the current risk level involves obtaining the deviation value between the environmental parameters of the current temporary power system and the standard environmental parameters. When the deviation value is greater than a preset value, a preset current threshold is multiplied by a reduction coefficient to generate a reduced current threshold. The current risk level, current abnormal parameter information, current causal chain path, reduced current threshold, and handling suggestions are written into a preset evaluation template to generate an evaluation report of the current temporary power system, including: Access the rule base, obtain the handling suggestions corresponding to the current risk level from the rule base, obtain the current temporary power system's ambient temperature and humidity from the current temporary power system's environmental parameters, and obtain the standard temperature and humidity from the standard environmental parameters; Obtain the first difference between the current ambient temperature of the temporary power system and the standard temperature, obtain the second difference between the current ambient humidity of the temporary power system and the standard ambient humidity, and select the maximum value between the first difference and the second difference as the deviation value between the current environmental parameters of the temporary power system and the standard environmental parameters; When the deviation value is greater than the preset value, the preset current threshold is multiplied by the reduction factor to generate the reduced current threshold. The current risk level, current abnormal parameter information, current causal chain path, reduced current threshold and disposal suggestions are written into the preset evaluation template to generate the current temporary power system evaluation report.

[0009] In one possible implementation of the first aspect, after obtaining the handling recommendations corresponding to the current risk level, obtaining the deviation value between the environmental parameters of the current temporary power system and the standard environmental parameters, and when the deviation value is greater than a preset value, multiplying the preset current threshold by a reduction coefficient to generate a reduced current threshold, and writing the current risk level, current abnormal parameter information, current causal chain path, reduced current threshold, and handling recommendations into a preset evaluation template to generate an evaluation report for the current temporary power system, the temporary power system evaluation method includes: Using 5G communication, the current temporary power system assessment report is uploaded to the cloud platform.

[0010] In one possible implementation of the first aspect, the electrical parameters of the preset temporary power system include the three-phase current imbalance of the preset temporary power system, the harmonic distortion rate of the preset temporary power system, and the power factor of the preset temporary power system. The environmental parameters of the preset temporary power system include the ambient temperature and humidity of the preset temporary power system. The equipment aging parameters of the preset temporary power system include the operating time of the electrical equipment in the preset temporary power system, the cumulative temperature rise of the electrical equipment in the preset temporary power system during operation, and the number of times the electrical equipment in the preset temporary power system has failed within a statistical period.

[0011] In one possible implementation of the first aspect, the electrical parameters of the current temporary power system include the three-phase current imbalance of the current temporary power system, the harmonic distortion rate of the current temporary power system, and the power factor of the current temporary power system. The environmental parameters of the current temporary power system include the ambient temperature and humidity of the current temporary power system; The current temporary power system equipment aging parameters include the operating time of the current temporary power system electrical equipment, the cumulative temperature rise of the current temporary power system electrical equipment during operation, and the number of times the current temporary power system electrical equipment has failed within the statistical period.

[0012] In one possible implementation of the first aspect, the causal inference model includes the causal Bayesian network model and the causal forest model.

[0013] Secondly, embodiments of this application provide a power-on system evaluation device based on a causal reasoning model, applied to electronic devices, comprising: The acquisition module is used to acquire the multimodal data of the current temporary power system. The multimodal data of the current temporary power system includes the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system. The extraction module is used to extract features from the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system using a feature extraction model, respectively, to obtain the features of the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system. The fusion module is used to combine the characteristics of the electrical parameters of the current temporary power system, the characteristics of the environmental parameters of the current temporary power system, and the characteristics of the equipment aging parameters of the current temporary power system to obtain the current fusion characteristics of the current temporary power system. The generation module is used to input the current fusion features of the current temporary power system into the trained causal inference model, and generate the current risk level, current abnormal parameter information and current causal chain path of the current temporary power system through the trained causal inference model. The assessment module is used to obtain the handling suggestions corresponding to the current risk level, obtain the deviation value between the environmental parameters of the current temporary power system and the standard environmental parameters, and when the deviation value is greater than the preset value, multiply the preset current threshold by the reduction coefficient to generate the reduced current threshold. The current risk level, current abnormal parameter information, current causal chain path, reduced current threshold and handling suggestions are written into the preset assessment template to generate the assessment report of the current temporary power system.

[0014] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the temporary power system evaluation method described in the first aspect above.

[0015] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the temporary power system evaluation method described in the first aspect.

[0016] Fifthly, embodiments of this application provide a computer program product that, when run on an electronic device, causes the electronic device to execute the temporary power system evaluation method described in the first aspect.

[0017] The beneficial effects of the embodiments of this application are as follows: Firstly, the system obtains the handling recommendations corresponding to the current risk level, obtains the deviation value between the environmental parameters of the current temporary power system and the standard environmental parameters, and when the deviation value is greater than the preset value, multiplies the preset current threshold by a reduction coefficient to generate a reduced current threshold. The system writes the current risk level, current abnormal parameter information, current causal chain path, reduced current threshold and handling recommendations into a preset assessment template to generate an assessment report for the current temporary power system. Since the assessment report for the current temporary power system does not require manual operation, the time for obtaining the assessment report for the current temporary power system is reduced, which is conducive to improving the efficiency of obtaining the assessment report for the current temporary power system. Secondly, since the generation of the current temporary power system assessment report is not affected by human intervention, it helps to improve the reliability of the current temporary power system assessment report. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is an application scenario diagram of the temporary power system evaluation method provided in the embodiments of this application; Figure 2 This is a flowchart illustrating the temporary power system evaluation method provided in the embodiments of this application; Figure 3 A flowchart illustrating the training process of the causal reasoning model provided in this application embodiment; Figure 4A schematic block diagram of a temporary power system evaluation device provided in the embodiments of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0021] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0022] It should be understood that in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance. The terms "comprising," "including," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.

[0023] Furthermore, the technical solutions of the various embodiments can be combined with each other, but only if they are based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0024] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0025] The temporary power system evaluation method provided in this application can be applied to electronic devices, including but not limited to smart distribution boxes, edge servers, mobile phones, and tablets. This application does not impose any restrictions on the specific type of electronic device.

[0026] Please see Figure 1 , Figure 1The application scenario diagram of the temporary power system evaluation method provided in the embodiments of this application is described in detail below: Electronic devices are connected to data acquisition devices to acquire multimodal data of the current temporary power system. The multimodal data of the current temporary power system includes the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system.

[0027] In this embodiment of the application, multimodal data of the current temporary power system is obtained through data acquisition equipment, which can fully realize the actual operating status of the current temporary power system, avoid ignoring key fault causes such as environmental interference and equipment aging due to reliance on single-dimensional data, and reduce the probability of missed or misjudged risks.

[0028] Please see Figure 2 , Figure 2 This is a flowchart illustrating the temporary power system evaluation method provided in this application embodiment, which can be applied to electronic devices.

[0029] like Figure 2 As shown, the temporary power system evaluation method provided in this application includes the following steps, detailed below: S201, Obtain the multimodal data of the current temporary power system. The multimodal data of the current temporary power system includes the electrical parameters of the current temporary power system, the environmental parameters of the current temporary power system, and the equipment aging parameters of the current temporary power system. Among them, the electrical parameters of the current temporary power system include the three-phase current imbalance of the current temporary power system, the harmonic distortion rate of the current temporary power system, and the power factor of the current temporary power system; The environmental parameters of the current temporary power system include the ambient temperature and humidity of the current temporary power system; The current temporary power system equipment aging parameters include the operating time of the current temporary power system electrical equipment, the cumulative temperature rise of the current temporary power system electrical equipment during operation, and the number of times the current temporary power system electrical equipment has failed within the statistical period.

[0030] S202, using a feature extraction model, features are extracted from the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system, respectively, to obtain the features of the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system; S203, the characteristics of the electrical parameters of the current temporary power system, the characteristics of the environmental parameters of the current temporary power system, and the characteristics of the equipment aging parameters of the current temporary power system are spliced ​​together to obtain the current fusion characteristics of the current temporary power system; Among them, the current integration characteristics of the current temporary power system is a comprehensive set of characteristics formed by integrating the characteristics of the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system. Its core advantage is that it breaks the limitations of a single data dimension and provides a more comprehensive basis for risk assessment.

[0031] S204, Input the current fusion features of the current temporary power system into the trained causal reasoning model, and generate the current risk level, current abnormal parameter information and current causal chain path of the current temporary power system through the trained causal reasoning model; The step of inputting the current fusion features of the current power supply system into the trained causal inference model, and generating the current risk level, current abnormal parameter information, and current causal chain path of the current power supply system through the trained causal inference model, includes: Obtain the model file and load the trained causal inference model using the model file; The current fusion characteristics of the current temporary power system are input into the trained causal reasoning model, and the current risk level, current abnormal parameter information and current causal chain path of the current temporary power system are generated through the trained causal reasoning model.

[0032] The current causal chain path provides a traceable logical basis for handling the current problems of the temporary power system.

[0033] S205, obtain the handling suggestions corresponding to the current risk level, obtain the deviation value between the environmental parameters of the current temporary power system and the standard environmental parameters, when the deviation value is greater than the preset value, multiply the preset current threshold by the reduction coefficient to generate the reduced current threshold, write the current risk level, current abnormal parameter information, current causal chain path, reduced current threshold and handling suggestions into the preset assessment template, and generate the assessment report of the current temporary power system.

[0034] The system incorporates the current risk level, current abnormal parameter information, current causal chain path, reduced current threshold, and handling suggestions into a preset assessment template to generate an assessment report for the current temporary power system. This report provides closed-loop guidance for the safe operation and maintenance and risk management of the current temporary power system, accurately locating the root cause of the fault and providing actionable handling suggestions.

[0035] For ease of explanation, the following example is provided: For example, the current temporary power system is the temporary power system for the steel bar processing area of ​​the construction site. The load of the steel bar processing area of ​​the construction site fluctuates greatly and the environment is hot and humid, so the distribution cabinet is prone to insulation deterioration.

[0036] Once the temporary power system detects an anomaly and generates an assessment report, this report, which contains multi-dimensional information, can directly guide on-site operation and maintenance work.

[0037] The current risk level in the assessment report allows maintenance personnel to determine the urgency level immediately. If it is marked as a level 1 risk, it means that the system needs to be shut down immediately, rather than being inspected routinely. Abnormal parameter information can clearly point out the core problem. For example, it can pinpoint the key anomaly of non-compliance with insulation resistance, rather than blindly checking all parameters. The cause-and-effect chain path can trace the origin and development of the fault, clearly presenting the complete logic that high temperature and humidity in the environment lead to a decrease in insulation resistance, and the decrease in insulation resistance leads to the risk of short circuit. This helps maintenance personnel to find the root cause of the problem, rather than just staying at the superficial understanding of parameters exceeding the standard. The lowered current threshold can adapt to the harsh on-site environment, avoid missed detections caused by using the standard threshold, and make subsequent risk monitoring more in line with actual working conditions. The proposed solutions can provide specific operational directions, such as replacing aging-resistant insulating components or reducing the area's load limit. Maintenance personnel can follow the steps without additional analysis.

[0038] The process involves obtaining the handling recommendations corresponding to the current risk level, acquiring the deviation value between the environmental parameters of the current temporary power system and the standard environmental parameters, and when the deviation value is greater than a preset value, multiplying the preset current threshold by a reduction coefficient to generate a reduced current threshold. The current risk level, current abnormal parameter information, current causal chain path, reduced current threshold, and handling recommendations are then written into a preset assessment template to generate an assessment report for the current temporary power system, including: Access the rule base, obtain the handling suggestions corresponding to the current risk level from the rule base, obtain the current temporary power system's ambient temperature and humidity from the current temporary power system's environmental parameters, and obtain the standard temperature and humidity from the standard environmental parameters; Obtain the first difference between the current ambient temperature of the temporary power system and the standard temperature, obtain the second difference between the current ambient humidity of the temporary power system and the standard ambient humidity, and select the maximum value between the first difference and the second difference as the deviation value between the current environmental parameters of the temporary power system and the standard environmental parameters; When the deviation value is greater than the preset value, the preset current threshold is multiplied by the reduction factor to generate the reduced current threshold. The current risk level, current abnormal parameter information, current causal chain path, reduced current threshold and disposal suggestions are written into the preset evaluation template to generate the current temporary power system evaluation report.

[0039] The temporary power system assessment method includes the following steps: First, obtaining the handling recommendations corresponding to the current risk level; second, obtaining the deviation value between the environmental parameters of the current temporary power system and the standard environmental parameters; third, when the deviation value is greater than a preset value, multiplying the preset current threshold by a reduction coefficient to generate a reduced current threshold; and fourth, writing the current risk level, current abnormal parameter information, current causal chain path, reduced current threshold, and handling recommendations into a preset assessment template to generate an assessment report for the current temporary power system. Using 5G communication, the current temporary power system assessment report is uploaded to the cloud platform. When the cloud platform detects that the current risk level exceeds the standard, it triggers a dual early warning mechanism. It issues a visual early warning signal on the cloud interface, notifying monitoring personnel with eye-catching color markings and sound prompts. At the same time, it sends an early warning command to the local early warning device, triggering the local sound and light alarm unit, which reminds on-site staff of potential safety hazards in the temporary power system through devices such as buzzers and warning lights.

[0040] In step 315, simultaneously with the triggering of the warning, the cloud platform pushes the current abnormal parameter information and the current causal chain path generated by the causal reasoning model to the staff. The current causal chain path identifies the complete path from the root cause to the final abnormal state, providing staff with accurate fault location and troubleshooting guidance, greatly simplifying fault diagnosis time, and enabling staff to directly address the root cause of the problem and carry out targeted treatment of the current temporary power system.

[0041] Among them, causal inference models include causal Bayesian network models and causal forest models.

[0042] The beneficial effects of the embodiments of this application are as follows: Firstly, the system obtains the handling recommendations corresponding to the current risk level, obtains the deviation value between the environmental parameters of the current temporary power system and the standard environmental parameters, and when the deviation value is greater than the preset value, multiplies the preset current threshold by a reduction coefficient to generate a reduced current threshold. The system writes the current risk level, current abnormal parameter information, current causal chain path, reduced current threshold and handling recommendations into a preset assessment template to generate an assessment report for the current temporary power system. Since the assessment report for the current temporary power system does not require manual operation, the time for obtaining the assessment report for the current temporary power system is reduced, which is conducive to improving the efficiency of obtaining the assessment report for the current temporary power system. Secondly, since the generation of the current temporary power system assessment report is not affected by human intervention, it helps to improve the reliability of the current temporary power system assessment report.

[0043] Please see Figure 3 , Figure 3 The training flowchart of the causal reasoning model provided in the embodiments of this application is described in detail below: S301, acquire the multimodal data of the preset temporary power system. The multimodal data of the preset temporary power system includes the electrical parameters of the preset temporary power system, the environmental parameters of the preset temporary power system, and the equipment aging parameters of the preset temporary power system. The electrical parameters of the pre-set temporary power system include the three-phase current imbalance of the pre-set temporary power system, the harmonic distortion rate of the pre-set temporary power system, and the power factor of the pre-set temporary power system. The environmental parameters of the preset temporary power system include the ambient temperature and humidity of the preset temporary power system. The equipment aging parameters of the preset temporary power system include the operating time of the electrical equipment in the preset temporary power system, the cumulative temperature rise of the electrical equipment in the preset temporary power system during operation, and the number of times the electrical equipment in the preset temporary power system has failed within a statistical period.

[0044] S302, using a feature extraction model, features are extracted from the electrical parameters, environmental parameters, and equipment aging parameters of the preset temporary power system, respectively, to obtain the features of the electrical parameters, environmental parameters, and equipment aging parameters of the preset temporary power system. S303, the characteristics of the electrical parameters of the preset temporary power system, the characteristics of the environmental parameters of the preset temporary power system, and the characteristics of the equipment aging parameters of the preset temporary power system are spliced ​​together to obtain the preset fusion characteristics of the preset temporary power system. The preset fusion characteristics, the actual risk level of the preset temporary power system, the actual abnormal parameter information of the preset temporary power system, and the actual causal chain path of the preset temporary power system are combined into a sample. Different samples are combined into a training set. The causal reasoning model is trained using the training set, and the trained causal reasoning model is saved.

[0045] It should be noted that the training process of the causal inference model is executed before acquiring the multimodal data of the current temporary power system, which includes the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system.

[0046] In this embodiment, the trained causal reasoning model is saved, and the trained causal reasoning model can be directly called for real-time reasoning, without having to repeat the training every time the trained causal reasoning model is started, which greatly shortens the evaluation time of the current power supply system.

[0047] For the temporary power system evaluation method described in the above embodiments, please refer to [link / reference]. Figure 4 , Figure 4 This is a schematic block diagram of the temporary power system evaluation device provided in the embodiments of this application. Figure 4 The temporary power system evaluation device 400 shown can be applied to, for example... Figure 1The application scenario diagram shows electronic devices. The following section uses electronic devices as an example to illustrate this. Figure 4 The temporary power system evaluation device 400 shown will be described in detail. The temporary power system evaluation device 400 may include an acquisition module 401, an extraction module 402, a fusion module 403, a generation module 404, and an evaluation module 405.

[0048] The acquisition module 401 is used to acquire the multimodal data of the current temporary power system. The multimodal data of the current temporary power system includes the electrical parameters of the current temporary power system, the environmental parameters of the current temporary power system, and the equipment aging parameters of the current temporary power system. The extraction module 402 is used to extract features from the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system using a feature extraction model, respectively, to obtain the features of the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system. The fusion module 403 is used to splice together the characteristics of the electrical parameters of the current temporary power system, the characteristics of the environmental parameters of the current temporary power system, and the characteristics of the equipment aging parameters of the current temporary power system to obtain the current fusion characteristics of the current temporary power system. The generation module 404 is used to input the current fusion features of the current temporary power system into the trained causal reasoning model, and generate the current risk level, current abnormal parameter information and current causal chain path of the current temporary power system through the trained causal reasoning model. The assessment module 405 is used to obtain the handling suggestions corresponding to the current risk level, obtain the deviation value between the environmental parameters of the current temporary power system and the standard environmental parameters, and when the deviation value is greater than the preset value, multiply the preset current threshold by the reduction coefficient to generate the reduced current threshold. The current risk level, current abnormal parameter information, current causal chain path, reduced current threshold and handling suggestions are written into the preset assessment template to generate the assessment report of the current temporary power system.

[0049] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0050] The beneficial effects of the embodiments of this application are as follows: Firstly, the system obtains the handling recommendations corresponding to the current risk level, obtains the deviation value between the environmental parameters of the current temporary power system and the standard environmental parameters, and when the deviation value is greater than the preset value, multiplies the preset current threshold by a reduction coefficient to generate a reduced current threshold. The system writes the current risk level, current abnormal parameter information, current causal chain path, reduced current threshold and handling recommendations into a preset assessment template to generate an assessment report for the current temporary power system. Since the assessment report for the current temporary power system does not require manual operation, the time for obtaining the assessment report for the current temporary power system is reduced, which is conducive to improving the efficiency of obtaining the assessment report for the current temporary power system. Secondly, since the generation of the current temporary power system assessment report is not affected by human intervention, it helps to improve the reliability of the current temporary power system assessment report.

[0051] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0052] like Figure 5 As shown, Figure 5 The electronic device 2 includes: at least one processor 20, a memory 21, and a computer program 22 stored in the memory 21 and executable on the at least one processor 20, wherein the processor 20 executes the computer program 22 to implement the steps in any of the above method embodiments.

[0053] The electronic device 2 may include, but is not limited to, a processor 20 and a memory 21. Those skilled in the art will understand that... Figure 5 This is merely an example of electronic device 2 and does not constitute a limitation on electronic device 2. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0054] The processor 20 is used to run a computer program 22 stored in the memory 21, and performs the following steps when executing the computer program 22: Acquire multimodal data of the current temporary power system, which includes the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system. A feature extraction model was used to extract features from the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system, respectively, and the features of the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system were obtained. The current fusion characteristics of the current temporary power system are obtained by splicing together the characteristics of the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system. The current fusion features of the current temporary power system are input into the trained causal reasoning model, and the current risk level, current abnormal parameter information and current causal chain path of the current temporary power system are generated through the trained causal reasoning model. Obtain the handling suggestions corresponding to the current risk level, obtain the deviation value between the environmental parameters of the current temporary power system and the standard environmental parameters, and when the deviation value is greater than the preset value, multiply the preset current threshold by the reduction coefficient to generate the reduced current threshold. Write the current risk level, current abnormal parameter information, current causal chain path, reduced current threshold and handling suggestions into the preset assessment template to generate the assessment report of the current temporary power system.

[0055] The processor 20 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors, field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0056] In some embodiments, the memory 21 may be an internal storage unit of the electronic device 2, such as a hard disk or memory of the electronic device 2. In other embodiments, the memory 21 may be an external storage device of the electronic device 2, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the electronic device 2.

[0057] Furthermore, the memory 21 may include both internal storage units and external storage devices of the electronic device 2. The memory 21 is used to store the operating system, applications, boot loader, data, and other programs, such as the program code of the computer program. The memory 21 can also be used to temporarily store data that has been output or will be output.

[0058] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0059] Since the computer program stored in the computer-readable storage medium can execute any of the causal reasoning model-based temporary power system evaluation methods provided in the embodiments of this application, the computer-readable storage medium can achieve the beneficial effects that any of the causal reasoning model-based temporary power system evaluation methods provided in the embodiments of this application can achieve, as detailed in the preceding embodiments, and will not be repeated here.

[0060] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0061] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for evaluating a transient electrical system based on a causal reasoning model, characterized in that, The temporary power system evaluation method, applied to electronic devices, includes: Acquire multimodal data of the current temporary power system, which includes the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system. A feature extraction model was used to extract features from the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system, respectively, and the features of the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system were obtained. The current fusion characteristics of the current temporary power system are obtained by splicing together the characteristics of the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system. The current fusion features of the current temporary power system are input into the trained causal reasoning model, and the current risk level, current abnormal parameter information and current causal chain path of the current temporary power system are generated through the trained causal reasoning model. Obtain the handling suggestions corresponding to the current risk level, obtain the deviation value between the environmental parameters of the current temporary power system and the standard environmental parameters, and when the deviation value is greater than the preset value, multiply the preset current threshold by the reduction coefficient to generate the reduced current threshold. Write the current risk level, current abnormal parameter information, current causal chain path, reduced current threshold and handling suggestions into the preset assessment template to generate the assessment report of the current temporary power system.

2. The temporary power system evaluation method according to claim 1, characterized in that, Before acquiring the multimodal data of the current temporary power system, which includes the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system, the temporary power system evaluation method includes: Acquire multimodal data of the preset temporary power system, which includes the electrical parameters, environmental parameters, and equipment aging parameters of the preset temporary power system. A feature extraction model was used to extract features from the electrical parameters, environmental parameters, and equipment aging parameters of the preset temporary power system, respectively, and the features of the electrical parameters, environmental parameters, and equipment aging parameters of the preset temporary power system were obtained. The characteristics of the electrical parameters, environmental parameters, and equipment aging parameters of the preset temporary power system are spliced ​​together to obtain the preset fusion characteristics of the preset temporary power system. The preset fusion characteristics, the actual risk level, the actual abnormal parameter information, and the actual causal chain path of the preset temporary power system are combined into a sample. Different samples are used to form a training set, and the causal inference model is trained using the training set. The trained causal inference model is then saved.

3. The temporary power system evaluation method according to claim 1, characterized in that, The step of inputting the current fusion features of the current power supply system into the trained causal inference model, and generating the current risk level, current abnormal parameter information, and current causal chain path of the current power supply system through the trained causal inference model includes: Obtain the model file and load the trained causal inference model using the model file; The current fusion characteristics of the current temporary power system are input into the trained causal reasoning model, and the current risk level, current abnormal parameter information and current causal chain path of the current temporary power system are generated through the trained causal reasoning model.

4. The temporary power system evaluation method according to claim 1, characterized in that, The process involves obtaining the handling recommendations corresponding to the current risk level, acquiring the deviation value between the environmental parameters of the current temporary power system and the standard environmental parameters, and when the deviation value is greater than a preset value, multiplying the preset current threshold by a reduction coefficient to generate a reduced current threshold. The current risk level, current abnormal parameter information, current causal chain path, reduced current threshold, and handling recommendations are then written into a preset assessment template to generate an assessment report for the current temporary power system, including: Access the rule base, obtain the handling suggestions corresponding to the current risk level from the rule base, obtain the current temporary power system's ambient temperature and humidity from the current temporary power system's environmental parameters, and obtain the standard temperature and humidity from the standard environmental parameters; Obtain the first difference between the current ambient temperature of the temporary power system and the standard temperature, obtain the second difference between the current ambient humidity of the temporary power system and the standard ambient humidity, and select the maximum value between the first difference and the second difference as the deviation value between the current environmental parameters of the temporary power system and the standard environmental parameters; When the deviation value is greater than the preset value, the preset current threshold is multiplied by the reduction factor to generate the reduced current threshold. The current risk level, current abnormal parameter information, current causal chain path, reduced current threshold and disposal suggestions are written into the preset assessment template to generate the current temporary power system assessment report.

5. The temporary power system evaluation method according to claim 1, characterized in that, After obtaining the handling recommendations corresponding to the current risk level, obtaining the deviation value between the environmental parameters of the current temporary power system and the standard environmental parameters, and when the deviation value is greater than a preset value, multiplying the preset current threshold by a reduction coefficient to generate a reduced current threshold, and writing the current risk level, current abnormal parameter information, current causal chain path, reduced current threshold, and handling recommendations into a preset evaluation template to generate an evaluation report for the current temporary power system, the temporary power system evaluation method includes: Using 5G communication, the current temporary power system assessment report is uploaded to the cloud platform.

6. The temporary power system evaluation method according to claim 2, characterized in that, The electrical parameters of the pre-set temporary power system include the three-phase current imbalance of the pre-set temporary power system, the harmonic distortion rate of the pre-set temporary power system, and the power factor of the pre-set temporary power system. The environmental parameters of the preset temporary power system include the ambient temperature and humidity of the preset temporary power system. The equipment aging parameters of the preset temporary power system include the operating time of the electrical equipment in the preset temporary power system, the cumulative temperature rise of the electrical equipment in the preset temporary power system during operation, and the number of times the electrical equipment in the preset temporary power system has failed within a statistical period.

7. The temporary power system evaluation method according to claim 1, characterized in that, The electrical parameters of the current temporary power system include the three-phase current imbalance, the harmonic distortion rate, and the power factor. The environmental parameters of the current temporary power system include the ambient temperature and humidity of the current temporary power system; The current temporary power system equipment aging parameters include the operating time of the current temporary power system electrical equipment, the cumulative temperature rise of the current temporary power system electrical equipment during operation, and the number of times the current temporary power system electrical equipment has failed within the statistical period.

8. The temporary power system evaluation method according to claim 1, characterized in that, Causal inference models include causal Bayesian network models and causal forest models.

9. A temporary power system evaluation device based on a causal reasoning model, characterized in that, Applied to electronic devices, including: The acquisition module is used to acquire the multimodal data of the current temporary power system. The multimodal data of the current temporary power system includes the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system. The extraction module is used to extract features from the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system using a feature extraction model, respectively, to obtain the features of the electrical parameters, environmental parameters, and equipment aging parameters of the current temporary power system. The fusion module is used to combine the characteristics of the electrical parameters of the current temporary power system, the characteristics of the environmental parameters of the current temporary power system, and the characteristics of the equipment aging parameters of the current temporary power system to obtain the current fusion characteristics of the current temporary power system. The generation module is used to input the current fusion features of the current temporary power system into the trained causal inference model, and generate the current risk level, current abnormal parameter information and current causal chain path of the current temporary power system through the trained causal inference model. The assessment module is used to obtain the handling suggestions corresponding to the current risk level, obtain the deviation value between the environmental parameters of the current temporary power system and the standard environmental parameters, and when the deviation value is greater than the preset value, multiply the preset current threshold by the reduction coefficient to generate the reduced current threshold. The current risk level, current abnormal parameter information, current causal chain path, reduced current threshold and handling suggestions are written into the preset assessment template to generate the assessment report of the current temporary power system.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the temporary power system evaluation method as described in any one of claims 1 to 8.

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