Bus duct control method with self-detection function, medium and equipment

Abstract

The invention discloses a bus duct control method with a self-detection function, a medium and equipment, and relates to the technical field of cable installation, and the method comprises the steps: constructing a double-bus power supply architecture when a first bus duct and a second bus duct supply power to load equipment at the same time; and performing self-detection according to the double-bus power supply architecture, and determining a fault prediction result. And performing fault risk coupling analysis according to the fault prediction result, and determining a first bus duct fault coefficient, a second bus duct fault coefficient and a load equipment fault coefficient. And judging whether the load equipment fault coefficient is smaller than a load equipment fault threshold. If yes, power supply distribution optimization adjustment is carried out according to the first bus duct fault coefficient and the second bus duct fault coefficient, and a bus duct control optimization strategy is obtained. And controlling the double-bus power supply architecture according to the bus duct control optimization strategy. The technical problems that in the prior art, a double-bus power supply framework is inaccurate in fault early warning and unreasonable in power supply distribution, and the stability and the power supply efficiency of a system are affected are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of cable installation, and in particular to a bus duct control method, medium and equipment with self-detection. Background Art

[0002] In power systems, especially in large industrial and building facilities, a dual-busbar power supply architecture is often used to provide a reliable power supply. This architecture connects two busbars (a primary bus duct and a secondary bus duct) in parallel and simultaneously supplies power to the load equipment, thereby providing redundant power. If one busbar fails, the other busbar continues to supply power, ensuring stable and continuous power supply. However, as the load equipment and bus duct age, the risk of various failures increases. Traditional power supply system monitoring mostly relies on the detection and assessment of each component individually, which cannot effectively predict the mutual impact of failures and the overall system risk.

[0003] The dual-bus power supply architecture in the existing technology has technical problems such as inaccurate fault warning and unreasonable power distribution, which affect the stability and power supply efficiency of the system. Summary of the Invention

[0004] This application solves the technical problems of inaccurate fault warnings and unreasonable power distribution in the existing dual-bus power supply architecture, which affect the stability and power supply efficiency of the system, by providing a bus duct control method, device, and storage medium based on self-detection. By real-time monitoring of the operating status of the dual-bus power supply architecture and combining the fault prediction results of the first bus duct, the second bus duct, and the load equipment, fault risk coupling analysis and power supply distribution optimization and adjustment are performed, thereby improving the stability and power supply efficiency of the power system, reducing the risk of faults, and ensuring that the power supply system can respond and recover quickly in the event of a fault.

[0005] In view of the above problems, the present application provides a bus duct control method, medium and device with self-detection.

[0006] A first aspect of an embodiment of the present application provides a bus duct control method with self-detection, the method comprising: constructing a dual bus duct power supply architecture when a first bus duct and a second bus duct supply power to a load device at the same time; performing self-detection according to the dual bus duct power supply architecture to determine a first bus duct fault prediction result, a second bus duct fault prediction result, and a load device fault prediction result; performing a fault risk coupling analysis based on the first bus duct fault prediction result, the second bus duct fault prediction result, and the load device fault prediction result to determine a first bus duct fault coefficient, a second bus duct fault coefficient, and a load device fault coefficient; determining whether the load device failure coefficient is less than a load device failure threshold; if the load device failure coefficient is less than the load device failure threshold, performing power supply distribution optimization adjustment on the dual bus duct power supply architecture according to the first bus duct fault coefficient and the second bus duct fault coefficient to obtain a bus duct control optimization strategy; and controlling the dual bus duct power supply architecture according to the bus duct control optimization strategy.

[0007] In the implementation method, a fault risk coupling analysis is performed based on the first bus duct fault prediction result, the second bus duct fault prediction result and the load equipment fault prediction result, including: predicting the fault propagation impact of the first bus duct based on the second bus duct fault prediction result to determine the first fault propagation influence coefficient; predicting the fault propagation impact of the first bus duct based on the load equipment fault prediction result to obtain the second fault propagation influence coefficient; performing weighted calculation based on the first fault propagation influence coefficient and the second fault propagation influence coefficient to obtain the passive risk coefficient of the first bus duct; using the bus duct fault risk coefficient in the first bus duct fault prediction result as the first bus duct body risk coefficient, combined with the first bus duct passive risk coefficient, to calculate the first bus duct fault coefficient.

[0008] In the implementation method, a weighted calculation is performed based on the first influence coefficient of fault propagation and the second influence coefficient of fault propagation to obtain the passive risk coefficient of the first bus duct, including: predicting the fault propagation probability of the first bus duct based on the fault prediction result of the second bus duct to obtain the first probability coefficient of fault propagation; predicting the fault propagation probability of the first bus duct based on the load equipment fault prediction result to obtain the second probability coefficient of fault propagation; performing a proportion calculation based on the first probability coefficient of fault propagation and the second probability coefficient of fault propagation to obtain the first weight coefficient of fault propagation and the second weight coefficient of fault propagation; performing a weighted calculation on the first influence coefficient of fault propagation and the second influence coefficient of fault propagation based on the first weight coefficient of fault propagation and the second weight coefficient of fault propagation to obtain the passive risk coefficient of the first bus duct.

[0009] In the implementation method, if the load device failure coefficient is less than the load device failure threshold, the power supply distribution of the dual-bus power supply architecture is optimized and adjusted according to the first bus duct failure coefficient and the second bus duct failure coefficient, and the bus duct control optimization strategy is obtained, including: judging whether the first bus duct failure coefficient and the second bus duct failure coefficient are less than the bus duct failure threshold; if the first bus duct failure coefficient is less than the bus duct failure threshold, and the second bus duct failure coefficient is less than the bus duct failure threshold, the power supply distribution of the dual-bus power supply architecture is adjusted according to the first bus duct failure coefficient and the second bus duct failure coefficient, and a dual-bus power supply adjustment space is established; power loss is minimized according to the dual-bus power supply adjustment space to generate the bus duct control optimization strategy.

[0010] In the implementation method, whether the first bus duct fault coefficient and the second bus duct fault coefficient are less than the bus duct fault threshold is determined, including: if the first bus duct fault coefficient is less than the bus duct fault threshold, and the second bus duct fault coefficient is greater than or equal to the bus duct fault threshold, the load equipment is independently powered according to the first bus duct, and the bus duct control optimization strategy is generated; if the first bus duct fault coefficient is greater than or equal to the bus duct fault threshold, and the second bus duct fault coefficient is less than the bus duct fault threshold, the load equipment is independently powered according to the second bus duct, and the bus duct control optimization strategy is generated; if the first bus duct fault coefficient is greater than or equal to the bus duct fault threshold, and the second bus duct fault coefficient is greater than or equal to the bus duct fault threshold, the standby bus duct is activated, the load equipment is powered according to the standby bus duct, and the bus duct control optimization strategy is generated.

[0011] In the implementation method, self-detection is performed based on the dual-bus power supply architecture to determine the first bus duct fault prediction result, the second bus duct fault prediction result and the load equipment fault prediction result, including: real-time monitoring of the dual-bus power supply architecture to obtain the first bus duct monitoring data, the second bus duct monitoring data and the load equipment monitoring data; performing fault prediction fusion on the first bus duct based on the first bus duct monitoring data to obtain the first bus duct fault prediction result; performing fault prediction fusion on the second bus duct based on the second bus duct monitoring data to obtain the second bus duct fault prediction result; performing fault prediction fusion on the load equipment based on the load equipment monitoring data to generate the load equipment fault prediction result.

[0012] In the implementation method, fault prediction fusion is performed on the first bus duct according to the first bus duct monitoring data to obtain the first bus duct fault prediction result, including: supervised learning is performed on P learners according to the bus duct monitoring sample set and the bus duct fault sample set corresponding to the first bus duct to generate P bus duct fault predictors, where P is a positive integer greater than 1; the first bus duct monitoring data is input into the P bus duct fault predictors to obtain P bus duct fault prediction results; and the P bus duct fault prediction results are integrated to generate the first bus duct fault prediction result.

[0013] In the implementation method, determining whether the load device failure coefficient is less than the load device failure threshold includes: if the load device failure coefficient is greater than or equal to the load device failure threshold, generating a bus duct protection signal; activating the bus duct protection device according to the bus duct protection signal to perform bus duct protection on the dual bus power supply architecture.

[0014] According to a second aspect of the embodiments of the present application, the present application provides a computer-readable storage medium storing a computer program for executing the bus duct control method with self-detection provided by the present application.

[0015] According to a third aspect of the embodiments of the present application, the present application provides an electronic device comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is used to execute the bus duct control method with self-detection provided in the present application.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages: when the first bus duct and the second bus duct supply power to the load device at the same time, a dual-bus power supply architecture is constructed; self-detection is performed based on the dual-bus power supply architecture to determine the first bus duct fault prediction result, the second bus duct fault prediction result, and the load device fault prediction result; a fault risk coupling analysis is performed based on the first bus duct fault prediction result, the second bus duct fault prediction result, and the load device fault prediction result to determine the first bus duct fault coefficient, the second bus duct fault coefficient, and the load device fault coefficient; it is determined whether the load device fault coefficient is less than the load device fault threshold; if the load device fault coefficient is less than the load device fault threshold, the power supply distribution of the dual-bus power supply architecture is optimized according to the first bus duct fault coefficient and the second bus duct fault coefficient to obtain a bus duct control optimization strategy; the dual-bus power supply architecture is controlled according to the bus duct control optimization strategy. This solves the technical problems in the prior art of dual-bus power supply architecture, such as inaccurate fault warnings and unreasonable power distribution, which affect system stability and power supply efficiency. By real-time monitoring of the operating status of the dual-busbar power supply architecture and combining the fault prediction results of the first busbar, the second busbar, and the load equipment, fault risk coupling analysis and power distribution optimization adjustment are carried out, thereby improving the stability and power supply efficiency of the power system, reducing the risk of faults, and ensuring that the power supply system can respond and recover quickly in the event of a fault. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A flow chart of a bus duct control method with self-detection provided in an embodiment of the present application;

[0019] Figure 2 This is a schematic diagram of the structure of an electronic device provided in this application.

[0020] Description of reference numerals: processor 21 , memory 22 , input device 23 , output device 24 . DETAILED DESCRIPTION

[0021] This application provides a bus duct control method, medium and equipment with self-detection, which is used to solve the technical problems in the existing technology of dual-bus power supply architecture, such as inaccurate fault warning, unreasonable power supply distribution, and impact on system stability and power supply efficiency.

[0022] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of this application.

[0023] Example 1, as Figure 1 As shown, the present application provides a bus duct control method with self-detection, the method comprising: constructing a dual bus duct power supply architecture when the first bus duct and the second bus duct supply power to the load equipment at the same time; performing self-detection according to the dual bus duct power supply architecture to determine the first bus duct fault prediction result, the second bus duct fault prediction result and the load equipment fault prediction result; performing fault risk coupling analysis according to the first bus duct fault prediction result, the second bus duct fault prediction result and the load equipment fault prediction result to determine the first bus duct fault coefficient, the second bus duct fault coefficient and the load equipment fault coefficient.

[0024] In a power system, a dual-busbar power supply architecture is formed by connecting two busbars, a first bus duct and a second bus duct, in parallel and simultaneously supplying power to load devices. A bus duct refers to an electrical conductor installation system typically used to transmit power within buildings or industrial facilities. The dual-busbar power supply architecture provides redundancy, ensuring that if one busbar fails, the other busbar continues to provide power, ensuring power supply stability. After the dual-busbar power supply architecture is established, a self-detection program is automatically initiated to perform fault self-detection on the dual-busbar power supply architecture based on current operating parameters, determining the first bus duct fault prediction result, the second bus duct fault prediction result, and the load device fault prediction result. By analyzing the monitoring data of the first bus duct and the second bus duct, the system uses a fault prediction model to predict the possible fault types and probability of the bus duct faults. Real-time monitoring of load device operating data analyzes the operating status of the load device and predicts its potential failure risk. The monitoring data includes, but is not limited to, parameters such as current, voltage, temperature, and load. Furthermore, a fault risk coupling analysis is performed based on the first bus duct fault prediction result, the second bus duct fault prediction result and the load equipment fault prediction result to determine the first bus duct fault coefficient, the second bus duct fault coefficient and the load equipment failure coefficient.

[0025] The method provided in an embodiment of the present application also includes: predicting the fault propagation impact of the first bus duct according to the second bus duct fault prediction result, and determining a first fault propagation influence coefficient; predicting the fault propagation impact of the first bus duct according to the load equipment fault prediction result, and obtaining a second fault propagation influence coefficient; performing weighted calculation based on the first fault propagation influence coefficient and the second fault propagation influence coefficient to obtain a passive risk coefficient of the first bus duct; using the bus duct fault risk coefficient in the first bus duct fault prediction result as the first bus duct body risk coefficient, combined with the first bus duct passive risk coefficient, to calculate the first bus duct fault coefficient.

[0026] A coupled fault risk analysis is performed based on the first busbar trunking fault prediction result, the second busbar trunking fault prediction result, and the load device fault prediction result, including: predicting the impact of fault propagation on the first busbar trunking based on the second busbar trunking fault prediction result, obtaining the frequency of impacts on the first busbar trunking after the occurrence of the second busbar trunking fault prediction result, as recorded in historical operating environment records. The impacts include changes in operating parameters of the first busbar trunking due to the occurrence of the second busbar trunking fault prediction result during operation of the first busbar trunking, such as significant changes in current increase, voltage instability, and other operating parameters. Significant changes can be determined based on a preset threshold; if the threshold is exceeded, it is considered a significant change. A first fault propagation impact coefficient is obtained by calculating the ratio of the frequency of impacts on the first busbar trunking to the frequency of occurrences of the second busbar trunking fault prediction result in the historical operating environment records. A second fault propagation impact coefficient is obtained by predicting the impact of fault propagation on the first busbar trunking based on the load device fault prediction result. Specifically, a second fault propagation impact coefficient is obtained by calculating the ratio of the frequency of impacts on the first busbar trunking after the occurrence of the load device fault prediction result in the historical operating environment records to the frequency of occurrences of the load device fault prediction result in the historical operating environment records. Furthermore, a weighted calculation is performed based on the first fault propagation influence coefficient and the second fault propagation influence coefficient to obtain the passive risk coefficient of the first bus duct. Finally, the bus duct fault risk coefficient within the first bus duct fault prediction result is used as the first bus duct body risk coefficient. Combined with the first bus duct passive risk coefficient, the first bus duct fault coefficient is calculated. The first bus duct fault coefficient is equal to the first bus duct body risk coefficient plus the first bus duct passive risk coefficient. Furthermore, the second bus duct fault coefficient and the load equipment fault coefficient are obtained in the same manner as the first bus duct passive risk coefficient.

[0027] The method provided in an embodiment of the present application also includes: predicting the fault propagation probability of the first bus duct according to the fault prediction result of the second bus duct to obtain a first probability coefficient of fault propagation; predicting the fault propagation probability of the first bus duct according to the fault prediction result of the load equipment to obtain a second probability coefficient of fault propagation; performing a proportion calculation based on the first probability coefficient of fault propagation and the second probability coefficient of fault propagation to obtain a first weight coefficient of fault propagation and a second weight coefficient of fault propagation; performing a weighted calculation on the first influence coefficient of fault propagation and the second influence coefficient of fault propagation based on the first weight coefficient of fault propagation and the second weight coefficient of fault propagation to obtain a passive risk coefficient of the first bus duct.

[0028] A fault propagation probability prediction is performed on the first busbar trunk based on the second busbar trunk fault prediction result. The frequency of faults caused by the second busbar trunk fault prediction result affecting the first busbar trunk is obtained from historical operating environment records. A first fault propagation probability coefficient is obtained based on the ratio of the simultaneous fault frequency to the frequency of occurrence of the second busbar trunk fault prediction result. A fault propagation probability prediction is performed on the first busbar trunk based on the load device fault prediction result. The frequency of faults caused by the load device fault prediction result affecting the first busbar trunk is obtained from historical operating environment records. A second fault propagation probability coefficient is obtained based on the ratio of the simultaneous load device fault frequency to the frequency of occurrence of the load device fault prediction result. Furthermore, a weighting calculation is performed based on the first and second fault propagation probability coefficients. The first weighting calculation is performed by dividing the sum of the first and second probability coefficients by the first probability coefficient. The second weighting calculation is performed by dividing the sum of the first and second probability coefficients by the second probability coefficient. Finally, according to the first fault propagation weight coefficient and the second fault propagation weight coefficient, the first fault propagation influence coefficient and the second fault propagation influence coefficient are weightedly calculated to obtain the first bus duct passive risk coefficient.

[0029] The method provided in an embodiment of the present application also includes: real-time monitoring of the dual-bus power supply architecture to obtain first bus duct monitoring data, second bus duct monitoring data and load equipment monitoring data; performing fault prediction fusion on the first bus duct based on the first bus duct monitoring data to obtain the first bus duct fault prediction result; performing fault prediction fusion on the second bus duct based on the second bus duct monitoring data to obtain the second bus duct fault prediction result; performing fault prediction fusion on the load equipment based on the load equipment monitoring data to generate the load equipment fault prediction result.

[0030] A self-test is performed based on the dual-busbar power supply architecture to determine first busbar duct fault prediction results, second busbar duct fault prediction results, and load device fault prediction results. This includes: using monitoring sensors and equipment deployed in the dual-busbar architecture to collect real-time operating data of the first busbar duct, second busbar duct, and load devices, obtaining real-time operating parameters such as voltage, current, temperature, and load rate, and obtaining first busbar duct monitoring data, second busbar duct monitoring data, and load device monitoring data, respectively. The first busbar duct monitoring data includes real-time current, voltage, and temperature rise data for the first busbar duct. The second busbar duct monitoring data includes real-time current, voltage, and temperature rise data for the second busbar duct. The load device monitoring data includes characteristic parameters of the load device's operating current and load rate. Subsequently, a fault prediction fusion is performed on the first busbar duct based on the first busbar duct monitoring data to obtain the first busbar duct fault prediction result. A fault prediction fusion is performed on the second busbar duct based on the second busbar duct monitoring data to obtain the second busbar duct fault prediction result. A fault prediction fusion is performed on the load device based on the load device monitoring data to generate the load device fault prediction result.

[0031] The method provided in an embodiment of the present application also includes: performing supervised learning on P learners based on the bus duct monitoring sample set and the bus duct fault sample set corresponding to the first bus duct to generate P bus duct fault predictors, where P is a positive integer greater than 1; inputting the first bus duct monitoring data into the P bus duct fault predictors to obtain P bus duct fault prediction results; and fusing the P bus duct fault prediction results to generate the first bus duct fault prediction result.

[0032] Fault prediction fusion is performed on the first busbar trunk based on the first busbar trunk monitoring data to obtain the first busbar trunk fault prediction result. This includes: performing supervised learning on P learners based on the busbar trunk monitoring sample set and busbar trunk fault sample set corresponding to the first busbar trunk to generate P busbar trunk fault predictors, where P is a positive integer greater than 1. The busbar trunk monitoring sample set is a data set accumulated through long-term monitoring under the same normal operating conditions as the first busbar trunk, covering various state parameters, including current, voltage, and temperature data. The busbar trunk fault sample set is operational data that may cause busbar trunk faults, as well as the corresponding fault type and inherent risk factor, i.e., the failure probability. Supervised learning is performed on the P learners based on the busbar trunk monitoring sample set and busbar trunk fault sample set. The learners are based on a neural network model or other machine learning model, and the P learners can be constructed using the same model or different models. Supervised learning is performed on the P learners until the output of each learner meets a preset accuracy requirement, completing the training and obtaining P busbar trunk fault predictors, where P is a positive integer greater than 1. Subsequently, the first busbar duct monitoring data is input into the P busbar duct fault predictors, and the model prediction output results of the P busbar duct fault predictors are obtained respectively, thereby obtaining P busbar duct fault prediction results, which include the predicted specific fault type and the busbar duct body risk coefficient. Finally, the P busbar duct fault prediction results are fused. When fusing the P busbar duct fault prediction results, the most frequently occurring fault type and the corresponding busbar duct body risk coefficient are extracted, and all busbar duct body risk coefficients of this fault type are weighted and summed. The weight is set according to the accuracy of the corresponding fault predictor during the training phase test, and the higher the accuracy, the higher the corresponding weight. Based on the weighted calculation result, the first busbar duct fault prediction result is generated, which includes the fault type and the corresponding busbar duct body risk coefficient. The method for obtaining the second busbar duct fault prediction result is the same as the method for obtaining the first busbar duct fault prediction result.

[0033] The method for obtaining load device fault prediction results is consistent with that for the first busbar trunking fault prediction results, differing only in the training data and input data. The load device fault predictor is trained based on the corresponding load device monitoring sample set and load device fault sample set. The load device monitoring sample set contains a data set accumulated through long-term monitoring under the same normal operating conditions as the load device, covering various state parameters, including current, voltage, and temperature data. The load device fault sample set contains records of operating data that may cause load device failure, as well as the corresponding fault type and inherent risk factor (i.e., failure probability) for the operating data. The load device fault predictor is trained based on the load device monitoring sample set and the load device fault sample set. A trained load device fault predictor is obtained. Finally, the load device monitoring data is input into the load device fault predictor to perform fault prediction fusion, using the same method as for the first busbar trunking fault prediction fusion, thereby generating a load device fault prediction result. The load device fault prediction result includes the load device fault type and inherent risk factor.

[0034] Determine whether the load device failure coefficient is less than the load device failure threshold; if the load device failure coefficient is less than the load device failure threshold, optimize the power supply distribution of the dual-bus power supply architecture according to the first bus duct failure coefficient and the second bus duct failure coefficient to obtain a bus duct control optimization strategy; control the dual-bus power supply architecture according to the bus duct control optimization strategy.

[0035] A determination is made as to whether the load device failure coefficient is less than a load device failure threshold, where the load device failure threshold is a preset maximum allowable threshold for the load device failure coefficient. If the load device failure coefficient is less than the load device failure threshold, indicating that the load device failure risk is within an acceptable range, the dual-busbar power supply architecture is optimized for power distribution based on the first busbar failure coefficient and the second busbar failure coefficient to obtain a busbar control optimization strategy. The dual-busbar power supply architecture is controlled according to the busbar control optimization strategy. If the load device failure coefficient is greater than the threshold, protective measures are required. This solves the technical problem in the prior art of dual-busbar power supply architectures, which suffer from inaccurate fault warnings and irrational power distribution, impacting system stability and efficiency. By real-time monitoring of the operating status of the dual-busbar power supply architecture and combining fault prediction results for the first busbar, the second busbar, and the load devices, a coupled fault risk analysis and optimized power distribution are performed, thereby improving the stability and efficiency of the power system, reducing the risk of failures, and ensuring rapid response and recovery of the power supply system in the event of a failure.

[0036] The method provided in an embodiment of the present application also includes: judging whether the first bus duct fault coefficient and the second bus duct fault coefficient are less than the bus duct fault threshold; if the first bus duct fault coefficient is less than the bus duct fault threshold, and the second bus duct fault coefficient is less than the bus duct fault threshold, adjusting the power supply distribution of the dual bus power supply architecture according to the first bus duct fault coefficient and the second bus duct fault coefficient, and establishing a dual bus power supply adjustment space; optimizing the power supply loss minimization according to the dual bus power supply adjustment space, and generating the bus duct control optimization strategy.

[0037] Obtaining a busbar control optimization strategy includes: determining whether the load device failure coefficient is less than a load device failure threshold, and further determining whether the first busbar failure coefficient and the second busbar failure coefficient are less than a busbar failure threshold, where the busbar failure threshold is a preset maximum allowable busbar failure threshold. If the first busbar failure coefficient is less than the busbar failure threshold, and the second busbar failure coefficient is less than the busbar failure threshold, then the failure coefficients of both busbars are less than the preset threshold, indicating that the system's power supply capacity is stable and suitable for power distribution optimization. For example, the failure coefficient of the first busbar is 0.2, the failure coefficient of the second busbar is 0.25, and the busbar failure threshold is 0.3. Since the failure coefficients of both busbars are less than the threshold, the system can continue to optimize power distribution. Power distribution is then adjusted for the dual-busbar power supply architecture based on the first and second busbar failure coefficients. The adjustment objective is to dynamically allocate power based on the failure coefficient of each busbar to minimize power loss and maintain efficient and stable system operation. A dual-bus power supply regulation space is established, comprising the current and voltage adjustment ranges of the second and first bus ducts. Expected currents and voltages are randomly selected within the dual-bus power supply regulation space, ensuring that the selected expected current and voltage parameters meet the load requirements. Subsequently, average operating index parameters for the second and first bus ducts under the corresponding expected current and expected voltage operating conditions are extracted based on historical data, including temperature, load, and other data. Corresponding fault prediction results are obtained based on the average operating index parameters, and an expected fault coefficient is determined. The expected fault coefficients for the second and first bus ducts are summed to obtain the expected power loss. Multiple iterations of optimization are performed within the dual-bus power supply regulation space to obtain the expected current, expected voltage, and the expected power loss. After the number of iterations is reached, the expected current and expected voltage for the second and first bus ducts with the minimum expected power loss are obtained as the bus duct control optimization strategy.

[0038] The method provided in an embodiment of the present application also includes: if the first bus duct fault coefficient is less than the bus duct fault threshold, and the second bus duct fault coefficient is greater than or equal to the bus duct fault threshold, the load equipment is independently powered according to the first bus duct, and the bus duct control optimization strategy is generated; if the first bus duct fault coefficient is greater than or equal to the bus duct fault threshold, and the second bus duct fault coefficient is less than the bus duct fault threshold, the load equipment is independently powered according to the second bus duct, and the bus duct control optimization strategy is generated; if the first bus duct fault coefficient is greater than or equal to the bus duct fault threshold, and the second bus duct fault coefficient is greater than or equal to the bus duct fault threshold, the standby bus duct is activated, the load equipment is powered according to the standby bus duct, and the bus duct control optimization strategy is generated.

[0039] If the first busbar duct fault coefficient is less than the busbar duct fault threshold, and the second busbar duct fault coefficient is greater than or equal to the busbar duct fault threshold, only the first busbar duct can be adjusted for power supply. The first busbar duct is used to independently adjust the power supply to the load devices, so that the first busbar duct fault coefficient is less than the busbar duct fault threshold, and the maximum expected power supply current and expected voltage are used as the busbar duct control optimization strategy. The busbar duct control optimization strategy is obtained. If the first busbar duct fault coefficient is greater than or equal to the busbar duct fault threshold, and the second busbar duct fault coefficient is less than the busbar duct fault threshold, only the second busbar duct can be adjusted for power supply. The second busbar duct is used to independently adjust the power supply to the load devices, so that the second busbar duct fault coefficient is less than the busbar duct fault threshold, and the maximum expected power supply current and expected voltage are used as the busbar duct control optimization strategy. If the first busbar duct fault coefficient is greater than or equal to the busbar duct fault threshold, and the second busbar duct fault coefficient is greater than or equal to the busbar duct fault threshold, both the first and second busbar ducts are at high risk of failure and can no longer provide power. The standby bus duct is activated, and the power supply to the load equipment is adjusted according to the standby bus duct. The expected power supply current and expected voltage with a fault coefficient of the standby bus duct being less than the maximum fault threshold of the bus duct are used as the bus duct control optimization strategy to generate the bus duct control optimization strategy.

[0040] The method provided in an embodiment of the present application also includes: if the load device failure coefficient is greater than or equal to the load device failure threshold, generating a bus duct protection signal; activating the bus duct protection device according to the bus duct protection signal to perform bus duct protection on the dual bus power supply architecture.

[0041] Determining whether the load device failure coefficient is less than a load device failure threshold includes: if the load device failure coefficient is greater than or equal to the load device failure threshold, indicating that the load device failure coefficient is high and the load device may fail at any time, generating a bus duct protection signal; activating a bus duct protection device based on the bus duct protection signal to perform bus duct protection on the dual-bus power supply architecture, the bus duct protection device being configured to directly cut off power supply to the first bus duct and the second bus duct.

[0042] The technical solution provided by the embodiment of the present invention is to construct a dual-bus power supply architecture when the first bus duct and the second bus duct supply power to the load device at the same time; perform self-detection based on the dual-bus power supply architecture to determine the first bus duct fault prediction result, the second bus duct fault prediction result, and the load device fault prediction result; perform fault risk coupling analysis based on the first bus duct fault prediction result, the second bus duct fault prediction result, and the load device fault prediction result to determine the first bus duct fault coefficient, the second bus duct fault coefficient, and the load device fault coefficient; determine whether the load device fault coefficient is less than the load device fault threshold; if the load device fault coefficient is less than the load device fault threshold, optimize the power distribution of the dual-bus power supply architecture based on the first bus duct fault coefficient and the second bus duct fault coefficient to obtain a bus duct control optimization strategy; and control the dual-bus power supply architecture based on the bus duct control optimization strategy. This solves the technical problems in the prior art of the dual-bus power supply architecture, such as inaccurate fault warnings, unreasonable power distribution, and impact on system stability and power supply efficiency. By real-time monitoring of the operating status of the dual-busbar power supply architecture and combining the fault prediction results of the first busbar, the second busbar, and the load equipment, fault risk coupling analysis and power distribution optimization adjustment are carried out, thereby improving the stability and power supply efficiency of the power system, reducing the risk of faults, and ensuring that the power supply system can respond and recover quickly in the event of a fault.

[0043] In a second embodiment, based on the same inventive concept as the bus duct control method with self-detection in the aforementioned embodiment, this embodiment provides a computer-readable storage medium that can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the bus duct control method with self-detection in the embodiment of this application. The processor executes the software programs, instructions, and modules stored in the memory to execute various functional applications and data processing of the computer device, thereby implementing the aforementioned bus duct control method with self-detection.

[0044] Embodiment 3, based on the same inventive concept as the bus duct control method with self-detection in the above embodiment, this embodiment provides an electronic device, Figure 2This is a structural diagram of an electronic device provided in accordance with a third embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 2 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention. Figure 2 As shown, the electronic device includes a processor 21, a memory 22, an input device 23 and an output device 24; the number of processors 21 in the electronic device can be one or more. Figure 2 Taking a processor 21 as an example, the processor 21, memory 22, input device 23 and output device 24 in the electronic device can be connected through a bus or other means. Figure 2 The bus connection is taken as an example.

[0045] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0046] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0047] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A bus duct control method with self-detection, characterized in that: The method comprises: When the first bus duct and the second bus duct supply power to the load equipment at the same time, a dual bus power supply architecture is constructed; Performing self-test according to the dual-bus power supply architecture to determine a first bus duct fault prediction result, a second bus duct fault prediction result, and a load device fault prediction result; Perform a fault risk coupling analysis based on the first bus duct fault prediction result, the second bus duct fault prediction result, and the load device fault prediction result to determine the first bus duct fault coefficient, the second bus duct fault coefficient, and the load device failure coefficient; Determining whether the load device failure coefficient is less than a load device failure threshold; If the load device failure coefficient is less than the load device failure threshold, the power supply distribution of the dual-bus power supply architecture is optimized and adjusted according to the first bus duct failure coefficient and the second bus duct failure coefficient to obtain a bus duct control optimization strategy; The dual-bus power supply architecture is controlled according to the bus duct control optimization strategy.

2. The bus duct control method with self-detection according to claim 1, characterized in that: Performing a fault risk coupling analysis based on the first bus duct fault prediction result, the second bus duct fault prediction result, and the load device fault prediction result, including: Predicting the impact of fault propagation on the first bus duct according to the second bus duct fault prediction result, and determining a first fault propagation impact coefficient; Perform fault propagation impact prediction on the first bus duct according to the load equipment fault prediction result to obtain a second fault propagation impact coefficient; Performing weighted calculation based on the first fault propagation influence coefficient and the second fault propagation influence coefficient to obtain a passive risk coefficient of the first bus duct; The first bus duct fault coefficient is calculated by taking the bus duct fault risk coefficient in the first bus duct fault prediction result as the first bus duct body risk coefficient and combining it with the first bus duct passive risk coefficient.

3. The bus duct control method with self-detection according to claim 2, characterized in that: A weighted calculation is performed based on the first fault propagation influence coefficient and the second fault propagation influence coefficient to obtain a passive risk coefficient of the first bus duct, including: Perform fault propagation probability prediction on the first bus duct according to the second bus duct fault prediction result to obtain a first fault propagation probability coefficient; Performing a fault propagation probability prediction on the first bus duct according to the load equipment fault prediction result to obtain a second fault propagation probability coefficient; Performing a proportion calculation based on the first fault propagation probability coefficient and the second fault propagation probability coefficient to obtain a first fault propagation weight coefficient and a second fault propagation weight coefficient; The first fault propagation influence coefficient and the second fault propagation influence coefficient are weightedly calculated according to the first fault propagation weight coefficient and the second fault propagation weight coefficient to obtain the first bus duct passive risk coefficient.

4. The bus duct control method with self-detection according to claim 1, characterized in that: If the load device failure coefficient is less than the load device failure threshold, the power supply distribution of the dual-bus power supply architecture is optimized and adjusted according to the first bus duct failure coefficient and the second bus duct failure coefficient to obtain a bus duct control optimization strategy, including: Determining whether the first bus duct fault coefficient and the second bus duct fault coefficient are less than a bus duct fault threshold; If the first bus duct fault coefficient is less than the bus duct fault threshold, and the second bus duct fault coefficient is less than the bus duct fault threshold, the power supply distribution of the dual-bus power supply architecture is adjusted according to the first bus duct fault coefficient and the second bus duct fault coefficient to establish a dual-bus power supply adjustment space; The power supply loss is minimized according to the dual-bus power supply adjustment space to generate the bus duct control optimization strategy.

5. The bus duct control method with self-detection according to claim 4, characterized in that: Determining whether the first bus duct fault coefficient and the second bus duct fault coefficient are less than a bus duct fault threshold includes: If the first bus duct fault coefficient is less than the bus duct fault threshold, and the second bus duct fault coefficient is greater than or equal to the bus duct fault threshold, independently adjusting the power supply to the load device according to the first bus duct, and generating the bus duct control optimization strategy; If the first bus duct fault coefficient is greater than or equal to the bus duct fault threshold, and the second bus duct fault coefficient is less than the bus duct fault threshold, independently adjusting the power supply to the load device according to the second bus duct, and generating the bus duct control optimization strategy; If the first bus duct failure coefficient is greater than or equal to the bus duct failure threshold, and the second bus duct failure coefficient is greater than or equal to the bus duct failure threshold, activate the standby bus duct, adjust the power supply to the load equipment according to the standby bus duct, and generate the bus duct control optimization strategy.

6. The bus duct control method with self-detection according to claim 1, characterized in that: Performing self-test according to the dual-bus power supply architecture to determine a first bus duct fault prediction result, a second bus duct fault prediction result, and a load device fault prediction result, including: Real-time monitoring of the dual-bus power supply architecture to obtain first bus duct monitoring data, second bus duct monitoring data, and load device monitoring data; Performing fault prediction fusion on the first bus duct according to the first bus duct monitoring data to obtain a fault prediction result of the first bus duct; Performing fault prediction fusion on the second bus duct according to the second bus duct monitoring data to obtain a fault prediction result of the second bus duct; Fault prediction fusion is performed on the load device according to the load device monitoring data to generate the load device fault prediction result.

7. The bus duct control method with self-detection according to claim 6, characterized in that: Performing fault prediction fusion on the first bus duct according to the first bus duct monitoring data to obtain a first bus duct fault prediction result includes: Performing supervised learning on P learners based on the bus duct monitoring sample set and the bus duct fault sample set corresponding to the first bus duct to generate P bus duct fault predictors, where P is a positive integer greater than 1; Inputting the first bus duct monitoring data into the P bus duct fault predictors to obtain P bus duct fault prediction results; The P bus duct fault prediction results are integrated to generate the first bus duct fault prediction result.

8. The bus duct control method with self-detection according to claim 1, characterized in that: Determining whether the load device failure coefficient is less than a load device failure threshold includes: If the load device failure coefficient is greater than or equal to the load device failure threshold, generating a bus duct protection signal; The bus duct protection device is activated according to the bus duct protection signal to perform bus duct protection on the dual-bus power supply architecture.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the bus duct control method with self-detection as described in any one of claims 1 to 8 is implemented.

10. An electronic device, characterized in that: The electronic device comprises: a memory for storing executable instructions; The processor is configured to implement the bus duct control method with self-detection according to any one of claims 1 to 8 when executing the executable instructions stored in the memory.

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