Electrical Appliance Temperature Monitoring Method, Device, Equipment and Storage Medium

Abstract

The present invention relates to the technical field of electrical appliance monitoring, and particularly relates to a method, device, equipment and storage medium for monitoring the temperature of an electrical appliance. The method includes the following steps: obtaining electrical appliance structure data; performing topological modeling on the electrical appliance structure data to generate electrical appliance operation circuit topological modeling data; constructing a circuit connection route for the electrical appliance structure data based on the electrical appliance operation circuit topological modeling data to generate electrical appliance operation circuit connection route data; and dividing the electrical appliance structure data through the electrical appliance operation circuit connection route data to obtain electrical appliance operation structure data. Through the topological modeling of the electrical appliance structure, multi-dimensional sensor deployment, heat flow simulation and intelligent temperature threshold analysis, the present invention realizes more accurate, comprehensive and intelligent electrical appliance temperature monitoring, and solves the defects of insufficient accuracy, incomplete local monitoring and non-intelligent abnormal temperature monitoring in the traditional technology.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical appliance monitoring, and in particular, to a method, device, equipment and storage medium for monitoring the temperature of electrical appliances. Background Art

[0002] Initially, temperature monitoring mainly relied on simple mechanical thermometers or thermocouple sensors. Although these technologies could provide basic temperature measurements, they had disadvantages such as slow response, low accuracy, and inability to provide real-time feedback on temperature changes. With the development of electronic technology, thermocouples and resistance temperature sensors have gradually been widely used in the temperature monitoring of electrical appliances, especially with good stability and sensitivity in high-temperature environments. With the rise of digital technology, intelligent temperature sensors began to emerge, capable of providing digital signal outputs for easy integration with control systems. During this period, temperature monitoring systems gradually developed towards automation and remote operation, and temperature data could be monitored and fed back in real time through wireless transmission technology, greatly improving the safety and intelligence level of electrical appliances. The rise of the Internet of Things (IoT) technology has further enhanced the electrical appliance temperature monitoring system. Through networked transmission, users can view the temperature data of devices in real time anywhere and perform predictive analysis through intelligent algorithms to identify potential fault risks in advance. However, temperature monitoring in the prior art often fails to accurately capture the hot spots in electrical circuits, resulting in the inability to detect potential overheating problems in a timely manner. At the same time, the complex heat flow relationship between the internal heat sources of electrical appliances and the external air flow is usually ignored, leading to low accuracy in monitoring the pipeline temperature of the electrical operation circuit. Summary of the Invention

[0003] Based on this, it is necessary to provide a method, device, equipment and storage medium for monitoring the temperature of electrical appliances to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for monitoring the temperature of an electrical appliance, the method includes the following steps:

[0005] Step S1: Obtain electrical appliance structure data; perform topological modeling on the electrical appliance structure data to generate electrical appliance operation circuit topological modeling data; construct a circuit connection route for the electrical appliance structure data based on the electrical appliance operation circuit topological modeling data to generate electrical appliance operation circuit connection route data; divide the electrical appliance structure data through the electrical appliance operation circuit connection route data to obtain electrical appliance operation structure data;

[0006] Step S2: Deploy multi-dimensional sensors on the electrical circuit pipelines of the electrical appliance using the electrical appliance operation structure data to obtain multi-dimensional sensor deployment data; construct a temperature field calculation model based on the multi-dimensional sensor deployment data to obtain the electrical appliance operation temperature field data, and use the electrical appliance operation temperature field data to extract the hot spot areas of the electrical appliance circuit, generating the electrical appliance circuit operation hot spot areas; extract the electrical circuit pipeline material characteristics of the electrical appliance operation structure data, and perform electrical circuit operation heat source mapping on the electrical appliance circuit operation hot spot areas based on the electrical circuit pipeline material characteristics, generating the internal temperature mapping data of the electrical pipeline.

[0007] Step S3: Perform air flow calculation based on the multi-dimensional sensor deployment data to obtain the air flow data during circuit operation; perform air heat flow simulation on the air flow data during circuit operation based on the internal temperature mapping data of the electrical pipeline, generating the air heat flow simulation data during circuit operation; predict the heat flow flow path for the air heat flow simulation data during circuit operation, generating the heat flow flow prediction path data; perform electrical circuit heat conduction mapping on the electrical appliance circuit operation hot spot areas using the heat flow flow prediction path data, generating the external temperature mapping data of the electrical pipeline.

[0008] Step S4: Perform actual heat analysis on the electrical circuit pipeline based on the internal temperature mapping data of the electrical pipeline and the external temperature mapping data of the electrical pipeline, generating the actual heat-receiving temperature of the electrical circuit pipeline; analyze the heat-receiving temperature threshold of the electrical circuit pipeline based on the electrical circuit pipeline material characteristics, and use the actual heat-receiving temperature of the electrical circuit pipeline to monitor the abnormal temperature of the electrical circuit pipeline for the heat-receiving temperature threshold, so as to perform the abnormal temperature monitoring operation of the electrical circuit pipeline.

[0009] Through topological modeling of the electrical appliance structure data, constructing the circuit connection route, and dividing the electrical appliance structure, this step can accurately reconstruct the structure of the internal circuit of the electrical appliance, provide complete electrical appliance operation circuit data, lay a solid foundation for subsequent temperature monitoring and heat source identification, and thus improve the accuracy and comprehensiveness in the monitoring process. Deploying multi-dimensional sensors using the electrical appliance operation structure data can accurately arrange sensors at key positions of the electrical appliance circuit to obtain more accurate temperature field data. Extracting the hot spot area of the electrical appliance circuit through the temperature field calculation model and performing heat source mapping in combination with the characteristics of the circuit pipeline material helps to accurately identify the high-temperature area and the heat source distribution, so as to discover the overheating risk existing in the electrical appliance in advance. Through air flow calculation and heat flow simulation, the behavior of air flow and heat flow inside the electrical appliance can be simulated, and an accurate heat flow path prediction can be generated. This not only improves the accuracy of temperature monitoring, but also takes into account the influence of air flow on heat conduction, effectively evaluates the heat transfer conditions inside and outside the electrical appliance, and avoids temperature deviation caused by air flow changes. By combining the internal and external temperature mapping data for heat analysis, the actual heat reception situation of the electrical appliance circuit pipeline can be evaluated more accurately. Based on the pipeline material characteristics and temperature threshold analysis for abnormal temperature monitoring, early fault warning can be realized, equipment damage or safety hazards caused by overheating can be avoided, and at the same time, the reliability and safety of the electrical appliance operation can be improved. Therefore, through the topological modeling of the electrical appliance structure, multi-dimensional sensor deployment, heat flow simulation, and intelligent temperature threshold analysis of the present invention, more accurate, comprehensive, and intelligent electrical appliance temperature monitoring is realized, and the defects of insufficient accuracy, incomplete local monitoring, and non-intelligent abnormal temperature monitoring in the traditional technology are solved.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain the electrical appliance structure data;

[0012] Step S12: Conduct circuit topology analysis on the electrical appliance structure data to generate electrical appliance circuit topology data; perform topological modeling on the electrical appliance structure data according to the electrical appliance circuit topology data to generate electrical appliance operation circuit topological modeling data;

[0013] Step S13: Based on the electrical appliance operation circuit topological modeling data, construct the circuit connection route for the electrical appliance structure data to generate electrical appliance operation circuit connection route data;

[0014] Step S14: Divide the electrical appliance structure data through the electrical appliance operation circuit connection route data to obtain the electrical appliance operation structure data.

[0015] Through circuit topology analysis, the present invention can clearly understand the connection relationships of various circuit components inside the electrical appliance, thereby effectively identifying potential circuit design problems or conflicts, and providing data support for subsequent design optimization. Through the topological modeling of the electrical appliance structure and circuit, the comprehensive integration of the structure and circuit can be achieved, ensuring the smooth operation of the electrical appliance in actual applications and avoiding the mutual influence or constraint problems between the circuit and the structure. According to the circuit topology modeling data of the electrical appliance operation, an accurate circuit connection route is constructed, which helps to optimize the electrical wiring layout and improve the working efficiency and reliability of the electrical appliance. By dividing the electrical appliance structure, the functions and functional areas of each component of the electrical appliance can be clarified, enhancing the maintainability and scalability of the electrical appliance.

[0016] Preferably, step S2 includes the following steps:

[0017] Step S21: Deploy multi-dimensional sensors on the circuit pipelines of the electrical appliance using the electrical appliance operation structure data to obtain multi-dimensional sensor deployment data, where the multi-dimensional sensor deployment data includes MEMS micro temperature sensor deployment data, circuit operation sensor deployment data, and ultrasonic flow field sensor deployment data;

[0018] Step S22: Screen the power density distribution data of the circuit operation sensor deployment data to obtain the electrical appliance operation power density distribution data; based on the electrical appliance operation power density distribution data, construct a temperature field calculation model to obtain the electrical appliance operation temperature field data;

[0019] Step S23: Collect the circuit current distribution data of the circuit operation sensor deployment data to obtain the electrical appliance operation circuit distribution data; extract the hot spot areas from the electrical appliance operation temperature field data according to the electrical appliance operation circuit distribution data to generate the electrical appliance circuit operation hot spot areas;

[0020] Step S24: Extract the electrical appliance circuit pipeline material characteristics of the electrical appliance operation structure data, and map the electrical appliance circuit operation heat sources to the electrical appliance circuit operation hot spot areas based on the electrical appliance circuit pipeline material characteristics to generate the internal temperature mapping data of the electrical appliance pipeline.

[0021] By deploying a variety of sensors (MEMS micro temperature sensors, circuit operation sensors, and ultrasonic flow field sensors), the present invention can comprehensively monitor the operating status of electrical appliances, covering multiple dimensions such as temperature, current, and flow field, ensuring the accuracy and integrity of data collection. By screening the data of the circuit operation sensors, the power density distribution data of the electrical appliances is obtained, and then a temperature field calculation model is established based on these data. This process can accurately predict the temperature distribution inside the electrical appliances, timely identify high-temperature areas, and help prevent failures caused by overheating. By collecting the current distribution of the circuit operation data, the hot spots in the circuit can be accurately located, providing data support for the optimized design of the electrical appliances. This can effectively avoid failures caused by local overload or poor heat dissipation, and improve the operating safety and stability of the electrical appliances. Based on the material characteristics of the electrical circuit pipelines of the electrical appliances, heat source mapping is carried out, and combined with the temperature field data, the temperature mapping data inside the pipelines is generated. In this way, the temperature of each part of the pipeline can be monitored in real time, ensuring that the heat is effectively dissipated and preventing risks such as local overheating or fire.

[0022] Preferably, step S24 includes the following steps:

[0023] Step S241: Extract the material characteristics of the electrical circuit pipelines of the electrical appliance operation structure data to obtain the material characteristics and geometric shapes of the circuit pipelines; calculate the material characteristics and geometric shapes of the circuit pipelines according to Ohm's law to obtain the circuit pipeline resistance data;

[0024] Step S242: Calculate the power dissipation of the circuit pipelines through the circuit pipeline resistance data and the electrical appliance operation circuit distribution data to obtain the circuit pipeline power dissipation density data; use the circuit pipeline power dissipation density data to analyze the pipeline heat source intensity of the electrical appliance circuit operation hot spot area to generate the pipeline heat source intensity data;

[0025] Step S243: Based on the pipeline heat source intensity data, perform heat source distribution mapping on the electrical appliance circuit operation hot spot area to generate pipeline heat source distribution mapping data; through the pipeline heat source distribution mapping data, perform temperature field simulation inside the pipeline of the electrical appliance circuit operation hot spot area, so as to obtain the temperature mapping data inside the electrical appliance pipeline.

[0026] The present invention can accurately predict the resistance characteristics of electrical circuit pipelines by extracting the material characteristics and geometric shapes of the electrical circuit pipelines and calculating the resistance according to Ohm's law. This provides a reliable basis for subsequent power dissipation analysis and ensures that the electrical performance of the electrical circuit is accurately mastered. Based on the resistance data of the circuit pipelines and the distribution data of the electrical operation circuits, the power dissipation density data of the circuit pipelines can be calculated, and the heat source intensity of the pipelines can be further analyzed. This enables the timely discovery of the areas that cause overheating during the operation of the electrical appliance, optimizes the heat dissipation design, and prevents the decline or damage of the electrical performance of the electrical appliance due to overheating. Using the heat source intensity data of the pipelines, a heat source distribution mapping is performed on the hot spots of the electrical circuit operation to generate accurate heat source distribution data. This process helps to identify the key heat source areas in the electrical appliance system, and through temperature field simulation, the temperature changes inside the pipeline can be predicted, providing decision-making support for the thermal management of the electrical appliance. Based on the above temperature mapping data, the heat dissipation system design of the electrical appliance can be optimized, the radiator or heat-conducting material can be reasonably configured, and the stability and service life of the electrical appliance can be improved. Through refined heat source and temperature field simulation, faults caused by overheating or uneven heat distribution can be effectively prevented.

[0027] Preferably, step S3 includes the following steps:

[0028] Step S31: Screen the air velocity data from the ultrasonic flow field sensor deployment data to obtain the air velocity data of the circuit operation; perform air flow calculation on the air velocity data of the circuit operation based on the Navier-Stokes equation to obtain the air flow data of the circuit operation;

[0029] Step S32: Perform air heat flow simulation on the air flow data of the circuit operation based on the internal temperature mapping data of the electrical pipelines to generate the air heat flow simulation data of the circuit operation;

[0030] Step S33: Construct a heat flow prediction model for the circuit operation according to the air heat flow simulation data of the circuit operation, and predict the heat flow path of the air heat flow simulation data of the circuit operation according to the heat flow prediction model of the circuit operation to generate the heat flow prediction path data;

[0031] Step S34: Use the heat flow prediction path data to perform electrical circuit heat conduction mapping on the hot spots of the electrical circuit operation to generate the external temperature mapping data of the electrical pipelines.

[0032] Through screening air velocity data and using the Navier-Stokes equation for air flow calculation, the present invention can accurately simulate the air flow inside the electrical appliance. This process helps analyze the air flow characteristics inside the electrical appliance, thereby better understanding the performance of the heat dissipation and air circulation systems. Based on the temperature mapping data inside the electrical appliance pipeline, a heat flow simulation is carried out for the air flow during circuit operation. This process can simulate the heat propagation path in the air, accurately predict the internal heat distribution and flow trend of the electrical appliance, and provide a scientific basis for heat dissipation design and optimization. According to the air heat flow simulation data during circuit operation, a heat flow prediction model is constructed and the heat flow path is predicted. Through this process, potential heat accumulation areas or places with poor air flow can be identified, optimized in a timely manner, and the risks brought by heat concentration can be reduced. Using the heat flow prediction path data, a heat conduction mapping is carried out for the hot spot areas during the operation of the electrical appliance circuit to generate the temperature mapping data outside the pipeline, which provides guidance for the external heat dissipation and thermal management of the electrical appliance, optimizes the external temperature distribution of the electrical appliance, and reduces the losses or safety hazards caused by heat leakage. Through these detailed heat flow simulation and prediction steps, the thermal management system of the electrical appliance can be comprehensively improved, not only optimizing the air flow and heat dissipation effect, but also reducing the risks caused by uneven or excessive heat sources, thereby improving the operating efficiency of the electrical appliance, extending its service life and reducing the failure rate.

[0033] Preferably, step S32 includes the following steps:

[0034] Step S321: Extract temperature field data based on the temperature mapping data inside the electrical appliance pipeline to obtain temperature field data; divide the air flow data during circuit operation for the electrical appliance circuit to obtain air flow region data;

[0035] Step S322: Model the air flow path according to the air flow region data to obtain air flow path data;

[0036] Step S323: Construct a heat flow transfer model for the air flow path data through the temperature field data to obtain heat flow transfer data;

[0037] Step S324: Perform air heat flow simulation calculation on the air flow path data using the heat flow transfer data to obtain the air heat flow simulation data during circuit operation.

[0038] The present invention can accurately identify the temperature distribution and air flow conditions in different regions inside an electrical appliance by extracting the temperature field data inside the electrical pipeline and dividing the air flow during circuit operation into regions. This helps to comprehensively understand the thermal distribution inside the electrical appliance, timely detect potential overheating regions, and provide detailed basis for heat dissipation design. By performing path modeling on the air flow regions, air flow path data is obtained, which helps to construct an accurate air flow model, predict the dynamic changes of air flow, and thus optimize the air flow circulation inside the electrical appliance, improving the heat dissipation effect and air flow efficiency. Based on the temperature field data and air flow path data, a heat flow transfer model is constructed. This model can accurately describe the heat transfer relationship between the temperature and air flow inside the electrical appliance, provide a theoretical basis for the thermal management system of the electrical appliance, and optimize the distribution and transfer of thermal energy. Through heat flow simulation calculation of the air flow path using the heat flow transfer data, air thermal flow simulation data inside the electrical appliance is obtained. This process helps to simulate the heat propagation path inside the electrical appliance, optimize the design of the air flow and heat dissipation system, ensure that all components of the electrical appliance can be evenly cooled, and avoid local overheating problems. These steps provide comprehensive support for the thermal management system of the electrical appliance through detailed heat flow and air flow analysis. By optimizing the air flow and heat flow distribution, the heat dissipation capacity of the electrical appliance is significantly improved, thereby reducing the overheating risk and enhancing the stability, efficiency, and service life of the electrical appliance.

[0039] Preferably, step S33 includes the following steps:

[0040] Step S331: Extract heat flow characteristics from the air thermal flow simulation data during circuit operation to obtain heat flow characteristic extraction data, where heat flow characteristic extraction includes temperature distribution extraction and heat flow density extraction;

[0041] Step S332: Divide the heat flow characteristic extraction data into a dataset to generate a model training set and a model test set; use the support vector machine algorithm to train the model training set to generate a pre-model for predicting the heat flow during circuit operation; perform model optimization iteration on the pre-model for predicting the heat flow during circuit operation through the model test set to generate a model for predicting the heat flow during circuit operation;

[0042] Step S333: Predict the heat flow distribution of the air thermal flow simulation data during circuit operation according to the model for predicting the heat flow during circuit operation to generate heat flow distribution prediction data;

[0043] Step S334: Use the heat flow distribution prediction data to evolve the heat flow path of the air thermal flow simulation data during circuit operation to generate heat flow flow prediction path data.

[0044] The present invention extracts heat flow characteristics from the simulated data of the air heat flow during the operation of the circuit to obtain temperature distribution and heat flux density data. This process can accurately identify the heat distribution characteristics inside the electrical appliance, providing detailed preliminary data for subsequent heat flow prediction, which helps to understand the heat behavior of the electrical appliance under different operating conditions. The heat flow characteristic extraction data is divided into a model training set and a test set, and the support vector machine algorithm is used for training. This method enables the heat flow prediction model for circuit operation to learn patterns from a large amount of data and achieve efficient heat flow prediction. The heat flow prediction pre-model for circuit operation is optimized and iterated through the model test set, so that the final heat flow prediction model can more accurately predict the heat flow changes of the electrical appliance under different conditions. This optimization process helps to improve the robustness of the model and ensure the accuracy and reliability of the prediction results. Using the optimized heat flow prediction model, the distribution of the air heat flow during circuit operation can be predicted to generate heat flow distribution prediction data, which enables a comprehensive prediction of the heat distribution of the electrical appliance and corresponding heat management adjustments. According to the heat flow distribution prediction data, the evolution and prediction of the heat flow path are carried out to obtain the heat flow movement prediction path data. This process can simulate the evolution process of the heat flow and predict how heat propagates inside the electrical appliance system, providing decision-making support for heat dissipation design and optimization. Through these steps, more accurate prediction and control means can be provided for the heat management system of the electrical appliance, helping to identify and solve potential overheating problems and ensuring the stable operation of the electrical appliance under high-load operation. In addition, the optimized heat flow path evolution helps to evenly distribute the heat inside the electrical appliance, avoiding local overheating or uneven cooling.

[0045] In this specification, an electrical appliance temperature monitoring device is provided for performing the above-mentioned electrical appliance temperature monitoring method. The electrical appliance temperature monitoring system includes:

[0046] An operating structure screening module, configured to obtain electrical appliance structure data; perform topological modeling on the electrical appliance structure data to generate electrical appliance operation circuit topological modeling data; construct a circuit connection route for the electrical appliance structure data based on the electrical appliance operation circuit topological modeling data to generate electrical appliance operation circuit connection route data; divide the electrical appliance structure data through the electrical appliance operation circuit connection route data to obtain electrical appliance operation structure data;

[0047] The internal temperature monitoring module is used to deploy multi-dimensional sensors for the electrical circuit pipelines of the electrical appliance by using the electrical appliance operation structure data to obtain multi-dimensional sensor deployment data; construct a temperature field calculation model according to the multi-dimensional sensor deployment data, so as to obtain the electrical appliance operation temperature field data, and extract the hot spot areas of the electrical appliance circuit by using the electrical appliance operation temperature field data to generate the hot spot areas of the electrical appliance circuit operation; extract the material characteristics of the electrical circuit pipelines of the electrical appliance operation structure data, and perform electrical appliance circuit operation heat source mapping on the hot spot areas of the electrical appliance circuit operation based on the material characteristics of the electrical circuit pipelines to generate the internal temperature mapping data of the electrical pipelines.

[0048] The external temperature monitoring module is used to calculate the air flow according to the multi-dimensional sensor deployment data to obtain the air flow data of the circuit operation; perform air heat flow simulation on the air flow data of the circuit operation based on the internal temperature mapping data of the electrical pipelines to generate the air heat flow simulation data of the circuit operation; predict the heat flow flow path for the air heat flow simulation data of the circuit operation to generate the heat flow flow prediction path data; perform electrical appliance circuit heat conduction mapping on the hot spot areas of the electrical appliance circuit operation by using the heat flow flow prediction path data to generate the external temperature mapping data of the electrical pipelines.

[0049] The actual heat analysis module is used to perform actual heat analysis on the electrical circuit pipelines according to the internal temperature mapping data of the electrical pipelines and the external temperature mapping data of the electrical pipelines to generate the actual heat-receiving temperature of the electrical circuit pipelines; analyze the heat-receiving temperature threshold of the electrical circuit pipelines based on the material characteristics of the electrical circuit pipelines, and perform abnormal temperature monitoring of the electrical circuit pipelines by using the actual heat-receiving temperature of the electrical circuit pipelines to execute the abnormal temperature monitoring operation of the electrical circuit pipelines.

[0050] The present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above-mentioned electrical appliance temperature monitoring method is implemented.

[0051] The present invention also provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned electrical appliance temperature monitoring method is implemented.

[0052] The beneficial effects of the present invention are as follows: By performing topological modeling on the electrical appliance structure data, the system can accurately construct the topological structure and connection routes of the electrical appliance operation circuit, ensuring the correct connection and operation path of the electrical appliance circuit, thereby optimizing the design and performance of the electrical appliance. By dividing the structure based on the electrical appliance circuit connection route data, scientific division of each component and functional module of the electrical appliance can be achieved, which is helpful for subsequent operations such as temperature monitoring and thermal management, improving the working efficiency and safety of the electrical appliance. Through the deployment of multi-dimensional sensors, the system can accurately monitor the temperature of the electrical appliance circuit and generate electrical appliance operation temperature field data, helping to identify hot spots in the electrical appliance circuit and ensuring that the electrical appliance equipment operates within a safe temperature range. According to the material characteristics of the electrical appliance circuit pipeline, the system can perform heat source mapping on the hot spots of the electrical appliance circuit, accurately draw the temperature distribution inside the pipeline, and give early warnings of potential heat damage areas to avoid overheating damage. By calculating the air flow data of the electrical appliance circuit and performing air heat flow simulation in combination with the internal temperature mapping data, the heat flow path inside the electrical appliance can be effectively simulated, providing a basis for heat dissipation optimization. Through heat flow path prediction, the system can foresee the heat propagation path, accurately generate the temperature mapping data outside the electrical pipeline, provide guidance for the heat dissipation design of the electrical appliance circuit and external temperature monitoring, and reduce the potential impact of excessive temperature on the electrical appliance. By combining the internal and external temperature mapping data for actual heat reception analysis, the heat influence on the electrical circuit pipeline during operation can be accurately evaluated, ensuring that the electrical appliance equipment is not damaged by overheating. The system can set reasonable temperature thresholds based on the material characteristics of the electrical appliance circuit pipeline, monitor the temperature changes in real time, and give timely warnings when abnormal temperatures occur, preventing safety hazards such as overheating, short circuit, or fire in the electrical appliance equipment and ensuring the long-term safe operation of the electrical appliance. Therefore, through the topological modeling of the electrical appliance structure, multi-dimensional sensor deployment, heat flow simulation, and intelligent temperature threshold analysis, the present invention realizes more accurate, comprehensive, and intelligent electrical appliance temperature monitoring, solving the defects of insufficient accuracy, incomplete local monitoring, and non-intelligent abnormal temperature monitoring in traditional technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 FIG. is a schematic diagram of the step flow of a method for monitoring the temperature of an electrical appliance;

[0054] Figure 2 is Figure 1 a detailed implementation step flow diagram of step S2 in;

[0055] Figure 3 is Figure 1 a detailed implementation step flow diagram of step S3 in;

[0056] The realization, functional characteristics, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] The technical method of the present invention patent will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work belong to the scope of protection of the present invention.

[0058] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0059] It should be understood that although terms such as "first" and "second" may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed associated items.

[0060] To achieve the above object, please refer to Figures 1 to 3 , an electrical appliance temperature monitoring method, the method comprising the following steps:

[0061] Step S1: Obtain electrical appliance structure data; perform topological modeling on the electrical appliance structure data to generate electrical appliance operation circuit topological modeling data; construct a circuit connection route for the electrical appliance structure data based on the electrical appliance operation circuit topological modeling data to generate electrical appliance operation circuit connection route data; divide the electrical appliance structure data through the electrical appliance operation circuit connection route data to obtain electrical appliance operation structure data;

[0062] Step S2: Deploy multi-dimensional sensors on the circuit pipelines of the electrical appliance using the electrical appliance operation structure data to obtain multi-dimensional sensor deployment data; construct a temperature field calculation model based on the multi-dimensional sensor deployment data to obtain electrical appliance operation temperature field data, and use the electrical appliance operation temperature field data to extract hot spot areas of the electrical appliance circuit to generate electrical appliance circuit operation hot spot areas; extract the electrical appliance circuit pipeline material characteristics of the electrical appliance operation structure data, and perform electrical appliance circuit operation heat source mapping on the electrical appliance circuit operation hot spot areas based on the electrical appliance circuit pipeline material characteristics to generate electrical appliance pipeline internal temperature mapping data;

[0063] Step S3: Perform air flow calculation based on the multi-dimensional sensor deployment data to obtain the air flow data during circuit operation; perform air heat flow simulation on the air flow data during circuit operation based on the internal temperature mapping data of the electrical appliance pipeline to generate the air heat flow simulation data during circuit operation; perform prediction on the heat flow path of the air heat flow simulation data during circuit operation to generate the predicted heat flow path data; use the predicted heat flow path data to perform electrical circuit heat conduction mapping on the hot spot area during the operation of the electrical appliance circuit to generate the external temperature mapping data of the electrical pipeline.

[0064] Step S4: Perform actual heat analysis on the electrical circuit pipeline based on the internal temperature mapping data of the electrical appliance pipeline and the external temperature mapping data of the electrical pipeline to generate the actual heat temperature of the electrical circuit pipeline; analyze the heat temperature threshold of the electrical circuit pipeline based on the material characteristics of the electrical circuit pipeline, and use the actual heat temperature of the electrical circuit pipeline to monitor the abnormal temperature of the heat temperature threshold for the electrical circuit pipeline to perform the abnormal temperature monitoring operation of the electrical circuit pipeline.

[0065] In the present invention, by performing topological modeling on the electrical appliance structure data, constructing the circuit connection route, and dividing the electrical appliance structure, this step can accurately reconstruct the structure of the internal circuit of the electrical appliance, provide complete electrical appliance operation circuit data, lay a solid foundation for subsequent temperature monitoring and heat source identification, thereby improving the accuracy and comprehensiveness during the monitoring process. Deploying multi-dimensional sensors using the electrical appliance operation structure data can accurately arrange sensors at key positions of the electrical appliance circuit to obtain more accurate temperature field data. Extracting the hot spot area of the electrical appliance circuit through the temperature field calculation model and performing heat source mapping in combination with the material characteristics of the circuit pipeline helps to accurately identify the high-temperature area and the heat source distribution, thereby discovering in advance the overheating risk existing in the electrical appliance. Through air flow calculation and heat flow simulation, the behavior of air flow and heat flow inside the electrical appliance can be simulated, and an accurate heat flow path prediction can be generated. This not only improves the accuracy of temperature monitoring but also takes into account the influence of air flow on heat conduction, effectively evaluates the heat transfer situation inside and outside the electrical appliance, and avoids temperature deviation caused by air flow changes. By performing heat analysis by combining the internal and external temperature mapping data, the actual heat situation of the electrical circuit pipeline can be evaluated more accurately. Performing abnormal temperature monitoring based on the pipeline material characteristics and temperature threshold analysis can achieve early fault warning, avoid equipment damage or safety hazards caused by overheating, and improve the reliability and safety of the electrical appliance operation. Therefore, through the topological modeling of the electrical appliance structure, multi-dimensional sensor deployment, heat flow simulation, and intelligent temperature threshold analysis, the present invention realizes more accurate, comprehensive, and intelligent electrical appliance temperature monitoring, and solves the defects of insufficient accuracy, incomplete local monitoring, and non-intelligent abnormal temperature monitoring in the traditional technology.

[0066] In the embodiment of the present invention, with reference to Figure 1As described, it is a schematic diagram of the step process of a method for monitoring the temperature of an electrical appliance according to the present invention. In this example, the method for monitoring the temperature of an electrical appliance includes the following steps:

[0067] Step S1: Obtain electrical appliance structure data; perform topological modeling on the electrical appliance structure data to generate electrical appliance operation circuit topological modeling data; construct a circuit connection route for the electrical appliance structure data based on the electrical appliance operation circuit topological modeling data to generate electrical appliance operation circuit connection route data; divide the electrical appliance structure data through the electrical appliance operation circuit connection route data to obtain electrical appliance operation structure data;

[0068] In an embodiment of the present invention, the physical structure data of the electrical appliance is obtained through scanning techniques (such as 3D laser scanning or CT scanning). These data can include information such as the external form of the electrical appliance, the internal circuit layout, and the component positions. For complex electrical appliances, CAD (Computer-Aided Design) data can be used for supplementation to ensure the integrity of the structure data. Using the obtained electrical appliance structure data, a topological modeling method (such as a graph theory-based modeling method) is used to model the internal circuit of the electrical appliance. By mathematically describing each component and its connection method in the internal circuit of the electrical appliance, electrical appliance operation circuit topological structure data is generated, which is represented as a directed graph or undirected graph model containing each electrical component and connection line of the electrical appliance. According to the electrical appliance operation circuit topological modeling data, an algorithm (such as depth-first search or breadth-first search) is used to construct the circuit connection route inside the electrical appliance. Through these algorithms, the electrical connection relationship between each component in the circuit is identified, and then the electrical appliance circuit connection route data is generated. These data specifically describe how each electrical component is connected through the circuit lines and can be further used to guide the actual sensor layout. Based on the previously generated circuit connection route data, the structure of the electrical appliance is divided. The implementation process of this step involves dividing the electrical appliance into different functional blocks or modules, such as a power module, a control module, a load module, etc., to ensure that the electrical connections within each module can be made through appropriate lines. Finally, the entire structure of the electrical appliance is divided into different regions, and electrical appliance operation structure data is formed. These data will assist in subsequent heat flow simulation, temperature field analysis, etc.

[0069] Step S2: Deploy multi-dimensional sensors for the circuit pipelines of the electrical appliance using the electrical appliance operation structure data to obtain multi-dimensional sensor deployment data; construct a temperature field calculation model based on the multi-dimensional sensor deployment data to obtain electrical appliance operation temperature field data, and use the electrical appliance operation temperature field data to extract hot spot regions of the electrical appliance circuit to generate electrical appliance circuit operation hot spot regions; extract the electrical appliance circuit pipeline material characteristics of the electrical appliance operation structure data, and perform electrical appliance circuit operation heat source mapping on the electrical appliance circuit operation hot spot regions based on the electrical appliance circuit pipeline material characteristics to generate electrical appliance pipeline internal temperature mapping data;

[0070] In the embodiments of the present invention, by using the electrical appliance operation structure data obtained in step S1, multi-dimensional sensors are arranged for the internal circuit pipelines of the electrical appliance. The types of sensors may include temperature sensors, humidity sensors, pressure sensors, etc. The arrangement positions of the sensors should cover all key areas of the electrical appliance, especially areas with intensive current or high heat generation, such as near the power supply module, power conversion module, and radiator. The deployment process can be implemented in the following ways: According to the three-dimensional structure data of the electrical appliance, virtual sensor deployment is carried out using three-dimensional modeling software (such as SolidWorks, AutoCAD) to ensure the rationality of the sensor positions. Sensor deployment optimization algorithms (such as genetic algorithms, simulated annealing algorithms, etc.) are used to improve the monitoring coverage and accuracy. The arranged sensors collect parameters such as temperature, pressure, and humidity in real time to generate multi-dimensional sensor deployment data. Based on the multi-dimensional sensor deployment data, a temperature field calculation model inside the electrical appliance is constructed through numerical simulation methods (such as the finite element method, computational fluid dynamics CFD model, etc.). This model will consider the internal heat sources of the electrical appliance, sensor data, and the heat dissipation characteristics of electrical components to simulate the temperature distribution of each part of the electrical appliance. The implementation steps are as follows: Input parameters such as the internal structure of the electrical appliance, sensor data, heat source distribution, and ambient temperature. Use simulation software (such as COMSOL, ANSYS, etc.) to calculate the temperature field to obtain the temperature distribution data inside the electrical appliance. Through the temperature field data, the hot spots in the electrical appliance circuit are extracted using the threshold method or clustering analysis method. The hot spot area refers to the high-temperature area where the temperature exceeds the preset threshold or causes equipment failure. The specific method is as follows: Set the temperature threshold according to the normal operating temperature range of the electrical appliance. The area exceeding this threshold is the hot spot area. Use heat map analysis methods (such as hot zone identification algorithms, regional clustering algorithms, etc.) to extract the hot spot areas from the temperature field data. According to the operation structure data of the electrical appliance, the material characteristics of the electrical appliance circuit pipelines are extracted, such as thermal conductivity, resistivity, thermal expansion coefficient, etc. Different materials have different effects on heat conduction, so it is necessary to accurately extract the material parameters: Obtain the material information of the circuit pipelines (such as copper, aluminum, iron, etc.) from the CAD drawings or material databases and accurately model according to the actual situation. Use the existing material property databases to obtain parameters such as the heat conduction performance and thermal stability of different materials. Combining the extracted material characteristics of the electrical appliance circuit pipelines and the temperature field data, heat source mapping is performed on the hot spot areas. This step can be implemented in the following ways: Identify the main heat sources in the hot spot areas through the temperature distribution data. Based on the pipeline material characteristics (such as thermal conductivity) and the temperature distribution in the hot spot areas, heat source mapping is carried out to generate a heat source distribution map inside the electrical appliance circuit. Through the heat source mapping data, further understand the heat conduction paths in different areas and their effects on the performance of the electrical appliance. Finally, based on the temperature field and heat source mapping data, temperature mapping data inside the electrical appliance pipelines is generated.

[0071] Step S3: Perform air flow calculation based on the multi-dimensional sensor deployment data to obtain the air flow data during circuit operation; perform air heat flow simulation on the air flow data during circuit operation based on the internal temperature mapping data of the electrical pipelines to generate the air heat flow simulation data during circuit operation; predict the heat flow path for the air heat flow simulation data during circuit operation to generate the heat flow prediction path data; use the heat flow prediction path data to perform electrical circuit heat conduction mapping on the hot spot areas during electrical circuit operation to generate the external temperature mapping data of the electrical pipelines.

[0072] In the embodiments of the present invention, by calculating the air flow inside the electrical appliance based on the multi-dimensional sensor deployment data, the air flow data during the circuit operation is obtained. Air flow is crucial for the heat dissipation effect of the electrical appliance. Therefore, accurately calculating the air flow is an important part of temperature monitoring. The implementation method is as follows: Data is collected by wind speed, pressure, and temperature sensors deployed inside the electrical appliance. This data includes air flow velocity, flow direction, temperature distribution, etc. Computational Fluid Dynamics (CFD) software (such as ANSYS Fluent, OpenFOAM, etc.) is used to perform numerical simulation of the air flow inside the electrical appliance in combination with the sensor data, and calculate the flow path, velocity field, and temperature field of the air inside the electrical appliance. The air flow calculation model needs to input information such as the internal structure of the electrical appliance, fan configuration, positions of air inlets and outlets, etc. Based on the temperature mapping data inside the electrical appliance pipeline and the calculated air flow data, air heat flow simulation is carried out to generate air heat flow simulation data inside the electrical appliance. The calculation of air heat flow helps to understand how the heat inside the electrical appliance is transferred to the air and affects the entire heat dissipation process. The implementation method is as follows: The heat transfer equation is introduced in the CFD simulation, considering the coupling effect of air flow and heat conduction. The model will calculate the heat exchange process between the air flow and the electrical appliance pipeline. Simulation software (such as ANSYS Fluent, COMSOL Multiphysics, etc.) is used to simulate the air heat flow inside the electrical appliance, simulate how the heat is dissipated through the air flow, generate heat flow distribution data, and the obtained air heat flow simulation data includes information such as the intensity, direction, and temperature distribution of the heat flow. By analyzing the air heat flow simulation data, the flow path of the heat flow is predicted to more accurately understand the trend of heat propagation and its impact on other components of the electrical appliance. The specific implementation steps are as follows: Based on the heat flow simulation results, a path tracking algorithm (such as Lagrangian path tracking or streamline analysis method) is used to predict the flow path of the heat flow. Identify the main channels of heat transfer and analyze different paths to predict how the heat will be distributed to other components of the electrical appliance, especially the hot spots. Using the predicted heat flow path data, thermal conduction mapping is performed on the hot spots in the electrical appliance circuit operation to generate temperature mapping data outside the electrical pipeline. This step helps to comprehensively understand how the heat is transferred from the inside to the outside, evaluate the heat dissipation effect, and potential high-temperature areas. The implementation method is as follows: A thermal conduction model is established outside the electrical appliance circuit, considering factors such as the material, thermal conductivity, and heat dissipation surface area of the electrical appliance shell, and simulating how the internal heat is dissipated through the outside of the electrical pipeline. According to the heat flow path data, the thermal conduction model is used to simulate the external temperature of the electrical appliance to generate a temperature distribution map of the outside of the electrical appliance, showing the temperature changes outside the electrical pipeline. Through the temperature mapping data, the temperature conditions of each area outside the electrical appliance are analyzed, especially the areas outside associated with the internal hot spots. Finally, combining the internal temperature mapping, air flow simulation, heat flow path prediction, and external temperature conduction data, a comprehensive temperature mapping result is generated.

[0073] Step S4: Perform an actual heat analysis on the electrical circuit pipeline based on the internal temperature mapping data and external temperature mapping data of the electrical pipeline to generate the actual heat temperature of the electrical circuit pipeline; analyze the heat temperature threshold of the electrical circuit pipeline based on the material characteristics of the electrical circuit pipeline, and use the actual heat temperature of the electrical circuit pipeline to monitor the abnormal temperature of the heat temperature threshold to perform the abnormal temperature monitoring operation of the electrical circuit pipeline.

[0074] In the embodiments of the present invention, by analyzing the actual heat absorption of electrical circuit pipelines based on the internal temperature mapping data and external temperature mapping data of electrical pipelines, the actual heat absorption temperature of electrical circuit pipelines is generated. This process helps to accurately evaluate the heat absorption of electrical circuits under different working conditions. The implementation method is as follows: Obtain the internal temperature mapping data of electrical pipelines and the external temperature mapping data of electrical pipelines as the basis for actual heat absorption analysis. Calculate the heat absorption of electrical pipelines under the influence of internal temperature and external environment through heat conduction analysis. Combine the geometric shape, material properties, and working conditions of the pipelines to evaluate the heat absorption distribution of the pipelines. Use thermal analysis software (such as ANSYS, COMSOL, etc.) for simulation to calculate the actual heat absorption temperature of electrical pipelines under different conditions and obtain the temperature change data of the pipelines during operation. Based on the material characteristics of electrical circuit pipelines, analyze the heat absorption temperature threshold of electrical circuit pipelines. Different pipeline materials have different temperature resistance characteristics. Therefore, reasonable temperature thresholds need to be set for pipelines of different materials. The implementation steps are as follows: According to the material characteristics of electrical circuit pipelines (such as copper, aluminum, steel, etc.), extract physical properties such as thermal conductivity, melting point, and temperature resistance range of the pipelines. Set the temperature threshold for each material based on material characteristics through experimental data or standard materials. The temperature threshold indicates whether the pipeline material will be damaged or deformed under high-temperature conditions. Set a heat absorption temperature threshold, which represents the highest temperature that the pipeline material can withstand. This temperature threshold is adjusted according to factors such as the working temperature range of the material and the actual application environment. According to the actual heat absorption temperature of electrical circuit pipelines and the set temperature threshold, monitor the abnormal temperature of electrical circuit pipelines and detect in a timely manner whether the electrical circuit pipelines exceed the set temperature range. The implementation steps are as follows: Use the deployed multi-dimensional sensors to continuously monitor the actual heat absorption temperature of electrical circuit pipelines. Compare the real-time obtained actual heat absorption temperature with the set temperature threshold. When the actual heat absorption temperature exceeds the preset threshold, trigger an abnormal alarm. If the actual heat absorption temperature of the electrical circuit pipeline exceeds the temperature threshold of the material, it is judged as an abnormal temperature state, and the system immediately issues an alarm and records the abnormal data for subsequent analysis. When an abnormal temperature is detected, corresponding processing and responses are carried out to ensure the safety of electrical equipment. The implementation method is as follows: When an abnormal temperature is monitored, the system automatically triggers an alarm to notify relevant personnel for timely processing. An automatic control system can be set up to automatically adjust the load of electrical equipment or cut off the power supply to prevent equipment damage caused by excessive temperature. The system records the temperature abnormal data and analyzes the occurrence time, duration, and cause of the abnormal temperature to provide preventive measures and maintenance suggestions.

[0075] Preferably, step S1 includes the following steps:

[0076] Step S11: Obtain electrical structure data;

[0077] Step S12: Perform circuit topology analysis on the electrical appliance structure data to generate electrical appliance circuit topology data; perform topology modeling on the electrical appliance structure data according to the electrical appliance circuit topology data to generate electrical appliance operating circuit topology modeling data;

[0078] Step S13: Based on the electrical appliance operating circuit topology modeling data, construct the circuit connection routes for the electrical appliance structure data to generate electrical appliance operating circuit connection route data;

[0079] Step S14: Divide the electrical appliance structure data through the electrical appliance operating circuit connection route data to obtain the electrical appliance operating structure data.

[0080] In the embodiment of the present invention, by using technologies such as 3D scanners, CAD models, or electrical appliance schematic diagrams, three-dimensional geometric structure data of the electrical appliance is collected, including the dimensions, positions, shapes, and material properties of components. The acquired data is converted into a format suitable for subsequent analysis and processing, such as STL, OBJ formats, or the electrical properties of the electrical appliance are imported into the model through a dedicated tool. Redundant information is removed, and missing structure data is supplemented to ensure the integrity and accuracy of the data. Using circuit analysis software (such as SPICE) or custom algorithms, analyze the electrical connection relationships between electrical components (such as resistors, capacitors, inductors, semiconductor components, etc.) within the electrical appliance. Based on the geometric information of the electrical appliance structure, identify the electrical connections between electrical components (such as wire connections, solder joints, etc.). At the same time, analyze the functional division and connection rules of the circuit to obtain the topological structure of the circuit. Convert the topological relationship into a structured data form (such as a graph database or adjacency matrix), including circuit components and their connection methods, and label the electrical characteristics (voltage, current, impedance, etc.) of each component. Combining the electrical appliance operating environment and the functional requirements of the circuit, based on the circuit topology data, create a circuit model adapted to the operating conditions, considering factors such as current load, operating frequency, and heat dissipation requirements. Optimize the circuit connection routes through algorithms (such as the shortest path algorithm, network flow algorithm) to ensure the stability and efficiency of current flow, and avoid interference and power loss. According to the circuit design requirements, generate the actual physical routes of the circuit connections (including wire routing, PCB board design, etc.), and record information such as connection points, wire materials, and path lengths. According to the circuit connection routes, divide the structure modules of the electrical appliance (such as power supply module, signal processing module, output module, etc.), and each module has clear electrical functions and connection methods internally. By dividing the structural units, it is possible to better perform fault diagnosis, function optimization, and post-maintenance. The independence of each module improves the detachable and expandable nature of the electrical appliance, generating operating structure data reflecting the internal structure, functional division, and circuit connections of the electrical appliance, including key indicators such as the working state and power consumption of each module.

[0081] As an example of the present invention, refer to Figure 2As shown, in this example, step S2 includes:

[0082] Step S21: Deploy multi-dimensional sensors on the circuit pipelines of the electrical appliance using the electrical appliance operation structure data to obtain multi-dimensional sensor deployment data, where the multi-dimensional sensor deployment data includes MEMS micro temperature sensor deployment data, circuit operation sensor deployment data, and ultrasonic flow field sensor deployment data;

[0083] Step S22: Screen the power density distribution data from the circuit operation sensor deployment data to obtain the electrical appliance operation power density distribution data; construct a temperature field calculation model based on the electrical appliance operation power density distribution data to obtain the electrical appliance operation temperature field data;

[0084] Step S23: Collect the circuit current distribution data from the circuit operation sensor deployment data to obtain the electrical appliance operation circuit distribution data; extract the hot spot areas from the electrical appliance operation temperature field data according to the electrical appliance operation circuit distribution data to generate the electrical appliance circuit operation hot spot areas;

[0085] Step S24: Extract the electrical appliance circuit pipeline material characteristics of the electrical appliance operation structure data, and map the electrical appliance circuit operation heat sources to the electrical appliance circuit operation hot spot areas based on the electrical appliance circuit pipeline material characteristics to generate the internal temperature mapping data of the electrical appliance pipeline.

[0086] In the embodiment of the present invention, MEMS micro temperature sensors are deployed at key positions of the electrical appliance circuit pipelines (such as the power supply end, near the heating element, current-intensive area, etc.). The sensors are small and sensitive, and can perform high-precision temperature monitoring without disturbing the normal operation of the electrical appliance. Current sensors, voltage sensors, etc. are selected and deployed on the core current path of the circuit to monitor the changes in current and voltage, capture the electrical signals generated during the operation of the electrical appliance, and evaluate the working state of the electrical appliance. Ultrasonic sensors are deployed in areas where fluid flow needs to be monitored (such as coolant, air flow, etc.), and ultrasonic technology is used to monitor parameters such as the speed, temperature, and density of the fluid. Especially in the electrical appliance cooling system, accurate flow field data can be obtained. According to the structural characteristics of the electrical appliance and the layout of the circuit pipelines, the deployment positions of the sensors are reasonably planned to ensure that all functional areas of the electrical appliance are covered and comprehensive data on the operating state can be effectively obtained. When deploying, factors such as electrical isolation, mechanical stability, and sensor-circuit compatibility need to be considered. Current, voltage, and other data are obtained through circuit operation sensors, and combined with the working principle of the electrical appliance, the power density of the current and voltage (i.e., the spatial distribution of power) is calculated. Filtering and data analysis algorithms (such as Kalman filtering, least squares method, etc.) are applied to process the data collected by the sensors to eliminate noise and obtain accurate power density distribution data. According to the power density calculation formula: ; where \(P(x, y, z)\) is the power density, and \(I(x, y, z)\) and \(V(x, y, z)\) are the values of current and voltage at the spatial position \((x, y, z)\) respectively. Based on this data, the power consumption density in different regions of the circuit is determined, and the high-power density regions during circuit operation are identified. Based on the power density distribution data, a temperature field calculation model can be established through the heat conduction equation (such as Fourier's heat conduction equation). In the model, the heat source position is related to the power density distribution, and the law of temperature field variation with time and space is calculated. The temperature field calculation formula: ; where is the temperature distribution in space and time, is the thermal diffusivity, is the heat source term related to the power density and the heat source. The current sensors deployed in the electrical circuit will measure the current distribution in real time. Through multi-point acquisition, the current intensity data on each current path is obtained. An array of sensors (such as an array of Hall effect sensors) is applied to detect the current density at different positions. Through mathematical modeling or graphical methods, the current distribution in each region is visualized to obtain the spatial data of the current flow inside the electrical appliance. Based on the electrical appliance operation current distribution data, using the relationship between the heat flux density and the current, the current-intensive regions are identified, and these regions usually correspond to the hot spots of the electrical appliance. Clustering algorithms (such as K-means clustering, DBSCAN, etc.) are used to extract the hot spot regions in the temperature field, and through the correlation between the current density and the temperature, the heat source points in the circuit are found. The high-temperature regions or overheated regions in the electrical appliance are marked for further design optimization or fault detection. Using the material information in the electrical appliance structure data (such as the material of the wire, the material of the circuit board, etc.), the physical properties such as thermal conductivity, specific heat capacity, and density of each material are extracted. A material library is created, which contains the material information of common electrical components and circuit pipelines, as the basis for subsequent heat source mapping. Based on the material characteristics of the circuit and the temperature field data, the hot spot regions of the electrical appliance circuit operation are matched with the material characteristics of the electrical appliance circuit pipelines. The heat conduction formula is used to combine the current density with the thermal characteristics of the material to generate the heat source mapping data of the electrical appliance circuit operation, revealing the process of heat transfer from the region where the current flows to the electrical appliance pipelines. According to the thermal conductivity, heat convection and other characteristics of the material, the temperature distribution of each pipeline part in the electrical appliance circuit is simulated. The finally obtained temperature mapping data inside the electrical appliance pipelines shows the temperature conditions of each pipeline region, for further heat dissipation design, fault warning or performance optimization.

[0087] Preferably, step S24 includes the following steps:

[0088] Step S241: Extract the material characteristics of the electrical appliance circuit pipelines from the electrical appliance operation structure data to obtain the material characteristics and geometric shapes of the circuit pipelines; calculate the material characteristics and geometric shapes of the circuit pipelines according to Ohm's law to obtain the circuit pipeline resistance data;

[0089] Step S242: Calculate the power dissipation of the circuit pipeline through the circuit pipeline resistance data and the electrical appliance operation circuit distribution data to obtain the circuit pipeline power dissipation density data; use the circuit pipeline power dissipation density data to analyze the pipeline heat source intensity of the hot spot area of the electrical appliance circuit operation, and generate the pipeline heat source intensity data;

[0090] Step S243: Based on the pipeline heat source intensity data, map the heat source distribution of the hot spot area of the electrical appliance circuit operation to generate the pipeline heat source distribution mapping data; through the pipeline heat source distribution mapping data, simulate the temperature field inside the pipeline of the hot spot area of the electrical appliance circuit operation, so as to obtain the temperature mapping data inside the electrical appliance pipeline.

[0091] In the embodiment of the present invention, by extracting the material type (such as copper, aluminum, metal alloy, etc.) and physical properties (such as thermal conductivity, specific heat capacity, density, resistivity, etc.) of the circuit pipeline from the electrical appliance structure data, these material characteristics are usually extracted through the data in the CAD model or the physical parameter library. According to the design drawings or 3D models of the electrical appliance, extract the geometric features of the circuit pipeline, including the diameter, length, surface area, etc. of the pipeline. When considering the circuit pipeline routing and layout, combined with the connection between specific electrical components, determine the geometric structure of the circuit pipeline and generate the 3D geometric data of the pipeline. According to Ohm's law, the resistance calculation formula is: ; where is the resistance of the circuit pipeline, is the resistivity of the material, is the length of the circuit pipeline, is the cross-sectional area of the pipeline. The resistance data of each section of the pipeline is calculated through the geometric shape and material characteristics of the pipeline. Based on the relationship between current and resistance, the power dissipation in the circuit pipeline can be calculated using Joule's law:

[0092] ; where is the power dissipation, is the current, is the resistance of the circuit pipeline. Using the current data in the electrical appliance operation circuit distribution data and combining with the resistance data of each pipeline, the power dissipation of the circuit pipeline is calculated. The power density is the power dissipation per unit volume or per unit surface. The power dissipation density can be calculated by combining the power dissipation with the geometric area or volume of the pipeline. The formula for calculating the power dissipation density is: Power dissipation density = Power ÷ Volume of the circuit pipeline. Using the power dissipation density data of the circuit pipeline, the hot spot areas of the electrical appliance circuit operation are analyzed. The higher the power dissipation, the greater the heat source intensity. According to the power density of different regions, the pipeline heat source intensity data is generated to identify the regions with higher heat source intensity in the circuit pipeline. Using the pipeline heat source intensity data, the visualization of the heat source distribution is carried out in the three-dimensional space of the electrical appliance. The regions with greater heat source intensity will be displayed as hot spot areas in the mapping. Through the spatial distribution of the heat source intensity, the heat source distribution map of the electrical appliance circuit pipeline is generated, which can help designers understand the concentrated distribution areas of heat in the electrical appliance. The heat conduction equation is used to simulate the temperature field inside the electrical appliance circuit pipeline. It is assumed that the electrical appliance pipeline is a three-dimensional heat conduction medium, and the heat source comes from the power dissipation generated when the current flows through. Through this model, the change of the temperature inside the pipeline over time can be simulated, and the temperature distribution can be analyzed. The simulation results will provide the temperature distribution data of different regions inside the pipeline. By simulating the temperature field inside the pipeline, the region with the highest temperature, that is, the heat source region, can be obtained. According to the simulation data, the temperature mapping of the inside of the pipeline can be generated to show the temperature distribution.

[0093] Preferably, step S24 includes the following steps:

[0094] Step S241: Extract the material characteristics of the electrical appliance circuit pipeline from the electrical appliance operation structure data to obtain the material characteristics and geometric shape of the circuit pipeline; calculate the material characteristics and geometric shape of the circuit pipeline according to Ohm's law to obtain the circuit pipeline resistance data;

[0095] Step S242: Calculate the power dissipation of the circuit pipeline through the circuit pipeline resistance data and the electrical appliance operation circuit distribution data to obtain the power dissipation density data of the circuit pipeline; use the power dissipation density data of the circuit pipeline to analyze the pipeline heat source intensity of the hot spot areas of the electrical appliance circuit operation and generate the pipeline heat source intensity data;

[0096] Step S243: Based on the pipeline heat source intensity data, perform heat source distribution mapping on the hot spot areas of the electrical appliance circuit operation to generate the pipeline heat source distribution mapping data; through the pipeline heat source distribution mapping data, simulate the temperature field inside the pipeline of the hot spot areas of the electrical appliance circuit operation, so as to obtain the temperature mapping data inside the electrical appliance pipeline.

[0097] In the embodiments of the present invention, the basic material characteristics of electrical circuit pipelines are extracted from electrical circuit diagrams or circuit simulation data. For example, the conductor materials of cables (such as copper, aluminum, etc.), insulation materials, outer sheath materials, etc. The electrical conductivity, thermal conductivity, etc. of these materials are key data for subsequent analysis. The geometric shape characteristics of the circuit pipelines are extracted, including the cross-sectional dimensions, length, bending angle, etc. of the pipelines. These geometric parameters directly affect the resistance and heat loss when current passes through the pipelines. According to Ohm's law, the resistance of the circuit pipeline can be calculated by the following formula: ; where is the resistance of the circuit pipeline, is the resistivity of the material, is the length of the circuit pipeline, is the cross-sectional area of the pipeline. For cables with complex shapes, integral calculations need to be performed according to the cross-sectional geometry, or the finite element analysis method is used to more accurately simulate the current distribution. The resistance calculation results of the pipelines are summarized to generate the resistance data of different circuit pipelines, providing basic data for subsequent power dissipation calculations. When the current in the circuit passes through the resistance, power dissipation will occur. The power dissipation density can be calculated by the following formula: ; where is the power dissipation, is the current, is the resistance of the circuit pipeline. According to the circuit distribution data, analyze the current distribution in each circuit pipeline, and then calculate the power dissipation density of each pipeline. Based on the power dissipation density of the circuit pipeline, calculate the heat source intensity of each pipeline. The heat source intensity is closely related to the power dissipation density. The greater the power dissipation, the more heat is generated. The heat flux density model can be used to analyze the spatial distribution of the heat source intensity and predict the temperature rise of each part of the electrical circuit. For each circuit pipeline, pipeline heat source intensity data is generated based on the power dissipation density. According to the power dissipation data of different circuit regions, calculate the heat source intensity of the corresponding regions, and then provide preliminary data for temperature field simulation. Using the heat source intensity data, based on the geometric shape and current distribution of the pipeline, generate pipeline heat source distribution mapping data. This mapping data visualizes the heat source distribution in each region of the electrical circuit, helping to identify hot spots. Adopt a heat source model (such as the heat conduction equation), combine the heat source intensity with the geometric structure of the circuit pipeline, and simulate the heat distribution of different pipeline segments. Based on the heat source distribution mapping data, further use the heat conduction model to simulate the temperature field inside the electrical circuit pipeline. This step considers factors such as temperature changes caused by heat sources (power dissipation), external environmental temperature, material thermal conductivity, and heat convection effects. Visualize the simulation results of the temperature field inside the pipeline as temperature mapping data, showing the temperature distribution of each part inside the electrical circuit pipeline. The temperature distribution can be visualized through 3D modeling, heat maps, or thermodynamic simulation tools to help designers identify hot spots and conduct heat dissipation design.

[0098] As an example of the present invention, refer to Figure 3 shown. In this example, step S3 includes:

[0099] Step S31: Screen the air velocity data from the ultrasonic flow field sensor deployment data to obtain the air velocity data for circuit operation; perform air flow calculation on the air velocity data for circuit operation based on the Navier-Stokes equation to obtain the air flow data for circuit operation;

[0100] Step S32: Perform air heat flow simulation on the air flow data for circuit operation based on the internal temperature mapping data of the electrical pipeline to generate the air heat flow simulation data for circuit operation;

[0101] Step S33: Construct a heat flow prediction model for circuit operation according to the air heat flow simulation data for circuit operation, and perform heat flow path prediction on the air heat flow simulation data for circuit operation according to the heat flow prediction model for circuit operation to generate the heat flow path prediction data;

[0102] Step S34: Use the heat flow path prediction data to perform electrical circuit heat conduction mapping on the hot spots of the electrical circuit operation to generate the external temperature mapping data of the electrical pipeline.

[0103] In the embodiment of the present invention, by deploying ultrasonic flow field sensors in different areas of the electrical equipment, these sensors can monitor the air flow velocity in real time and calculate the air flow velocity through the echo reflection time. The sensors should be arranged in the key areas of the electrical circuit, such as the entrances and exits of the circuit pipelines, near the heat sources, etc., to obtain comprehensive air flow velocity data. The flow velocity data obtained by the ultrasonic sensors is screened using data preprocessing algorithms to remove noise and outliers. Common screening techniques include moving average filtering, Kalman filtering, etc. Focus on the impact of the air flow velocity on the electrical components, and screen out the air flow velocity data of the areas that have a greater impact on the electrical heat management. Organize the screened flow velocity data into tables or time series data for subsequent air flow calculation and heat flow simulation. The Navier-Stokes equation is the basic equation describing the motion of viscous fluids (such as air). For incompressible fluids, its expression is: ; where is the fluid density, is the velocity field, is the pressure field, is the fluid viscosity, is the body force (such as gravity, thermal force generated by electronic devices, etc.). Numerical calculations are performed using the above equations to calculate the air flow within the circuit region. Based on the flow velocity data, the air flow is simulated using computational fluid dynamics (CFD) software. The air flow velocity in the electrical circuit is dynamically calculated in space and time to obtain the air flow data within the entire circuit region, including information such as the flow velocity field and streamline distribution. Through CFD simulation and the solution of the Navier-Stokes equations, air flow data during the operation of the electrical circuit is generated, including the flow velocity field, turbulence characteristics, and pressure distribution in each region. Using the internal temperature mapping data of the electrical pipeline as input and combining with the air flow data (such as the velocity field, turbulence characteristics), air heat flow simulation is carried out. The influence of the temperature field on the air flow is described through a heat convection model to calculate the heat transfer in the electrical circuit. A common heat flow model is: ; where is the thermal diffusivity, is the temperature field, Take the heat source. Conduct a coupled analysis of the heat source inside the electrical appliance (such as the heat generated by current passing through a resistor) and the fluid (air), and simulate the interaction between the heat flow and the air flow. Through CFD simulation, calculate the distribution of the heat flow and predict how the air flow carries heat within the circuit area. Based on the simulation results, generate the air heat flow simulation data within the circuit area, which includes information such as heat flux density, temperature field distribution, and the flow path of the air carrying heat. Combining the heat flow simulation data obtained from the simulation, use machine learning or deep learning algorithms (such as regression models, neural networks, etc.) to construct a heat flow prediction model. This model takes the input air flow characteristics and temperature field data as inputs and predicts the heat flow distribution and hot spot areas of the electrical appliance circuit. Use a large amount of historical data (such as the heat flow data under different operating states of the electrical appliance circuit) to train the heat flow prediction model and optimize the model parameters to enable it to accurately predict the heat flow distribution under future operating states. Use the trained heat flow prediction model to analyze the current air flow data and temperature data of the electrical appliance circuit and predict the flow path of the heat flow in the electrical appliance circuit. This path prediction includes the flow path of heat from the heat source area to the cooling area, and predicts the heat conduction channels and air flow distribution in each area. Convert the heat flow path simulation results into path data, record the flow route of the heat flow, hot spot areas, and the temperature distribution of the air flow. These data can provide decision-making support for the thermal management optimization of electrical appliances. Based on the heat flow prediction path data, use the heat conduction model to simulate the propagation of heat outside the electrical appliance circuit pipeline. Conduct a heat conduction simulation on the outside of the electrical appliance circuit, calculate the temperature change outside the electrical appliance, and generate a temperature distribution map. This mapping can display the external surface temperature and potential overheating areas, assisting in heat dissipation design and optimization. Convert the heat conduction simulation results into temperature mapping data to show the temperature field outside the electrical appliance circuit, helping to evaluate the heat dissipation effect and hot spot areas. According to the external temperature mapping data, optimize the external heat dissipation design of the electrical appliance, select appropriate heat dissipation materials or external heat dissipation devices (such as fans, heat sinks, etc.) to reduce the temperature of the electrical appliance and prevent overheating.

[0104] Preferably, step S32 includes the following steps:

[0105] Step S321: Extract temperature field data based on the internal temperature mapping data of the electrical appliance pipeline to obtain temperature field data; divide the air flow area of the electrical appliance circuit for the air flow data during circuit operation to obtain air flow area data;

[0106] Step S322: Model the air flow path according to the air flow area data to obtain air flow path data;

[0107] Step S323: Construct a heat flow transfer model for the air flow path data through the temperature field data to obtain heat flow transfer data;

[0108] Step S324: Use the heat flow transfer data to perform air heat flow simulation calculations on the air flow path data, thereby obtaining the air heat flow simulation data for circuit operation.

[0109] In the embodiment of the present invention, by obtaining the temperature mapping data inside the electrical pipeline, which can be obtained through temperature sensors or thermal imaging technology and covers the temperature distribution of each component in the electrical circuit. Apply data processing techniques (such as image processing, interpolation method, or data fitting, etc.) to extract the temperature field data from the temperature mapping data. The temperature field data refers to the temperature distribution at each point inside the electrical circuit, including the temperature intensity of each region. Use the circuit operation air flow data obtained in step S31, which describes the air flow velocity, air flow direction, etc. in the electrical circuit area. According to factors such as air flow velocity, flow direction, and temperature gradient, divide the air flow area inside the electrical circuit through clustering analysis (such as K-means or DBSCAN algorithms). After division, divide the air flow area of the electrical circuit into multiple sub-regions (for example, hot spots, cooling areas, slow-flow areas, etc.) to obtain the air flow area data. Based on the air flow area data, model the air flow path through a fluid mechanics model (such as the Navier-Stokes equation, particle tracking method, etc.). The purpose of modeling is to predict the air flow path, flow direction, and the time and space distribution of the air flow passing through inside the electrical circuit. By simulating the movement trajectory of particles in the air flow, calculate the air flow path, and then obtain the air flow path. In the air flow area, according to the flow velocity data, use the streamline method to draw the air flow path. The streamline shows the direction of the air flow and its distribution characteristics. Through calculation and simulation, generate the path data of the air flow in the electrical circuit, including the flow direction, velocity of the air flow, and the path of heat transfer. The inputs are the temperature field data and the air flow path data. The temperature field data describes the temperature distribution in each region of the electrical circuit, while the air flow path data describes the air flow route. Use the classical convective heat transfer equation: ; where is the heat transfer rate, is the convective heat transfer coefficient, is the surface area, is the surface temperature, is the ambient temperature. By combining the air flow path and the temperature gradient, a heat transfer model is constructed to describe how heat flows from the heat source area to the cooling area through air flow. Considering the heat diffusion characteristics of air and pipeline materials, the heat transfer in the electrical circuit is not only through air flow but also involves heat diffusion. Through the constructed heat transfer model, calculate how heat is transferred in the electrical circuit and obtain the heat transfer data. The heat transfer data includes: the heat transfer rate of each area, the heat flow path and heat flux density, and the temperature change at each point. The input includes temperature field data, air flow path data, and heat transfer data. This data will provide the temperature change, air flow path, and heat transfer method inside the electrical circuit. Use CFD (Computational Fluid Dynamics) software (such as ANSYS Fluent, OpenFOAM, etc.) to simulate and calculate the air heat flow in the electrical circuit. During the simulation process, solve the fluid dynamics equations (including the Navier-Stokes equations) and heat transfer equations by numerical methods to calculate the heat transfer process in the air flow. During the simulation process, consider the coupling effect of air flow and heat flow, that is, the air flow not only carries heat but also changes the properties of the air flow (such as flow velocity, flow direction, etc.) due to the existence of heat flow. Simulate the interaction between air flow and temperature change in the electrical circuit to obtain the air heat flow distribution and temperature distribution inside the electrical circuit. According to the CFD simulation results, generate the air heat flow simulation data of the electrical circuit.

[0110] Preferably, step S33 includes the following steps:

[0111] Step S331: Extract the heat flow characteristics from the air heat flow simulation data of the circuit operation to obtain the heat flow characteristic extraction data, where the heat flow characteristic extraction includes temperature distribution extraction and heat flux density extraction;

[0112] Step S332: Divide the heat flow characteristic extraction data into a data set to generate a model training set and a model test set; use the support vector machine algorithm to train the model training set to generate a pre-model for predicting the heat flow of the circuit operation; optimize and iterate the pre-model for predicting the heat flow of the circuit operation through the model test set to generate a prediction model for the heat flow of the circuit operation;

[0113] Step S333: Predict the heat flow distribution of the air heat flow simulation data of the circuit operation according to the prediction model of the heat flow of the circuit operation to generate the heat flow distribution prediction data;

[0114] Step S334: Use the heat flow distribution prediction data to evolve the heat flow path of the air heat flow simulation data of the circuit operation to generate the predicted heat flow path data.

[0115] In an embodiment of the present invention, the air heat flow simulation data of the circuit operation obtained from step S32 is used as input. This data includes information such as the temperature distribution, air flow velocity, heat flux density, etc. in each area of the electrical circuit. Numerical analysis methods (such as temperature field gradient calculation, interpolation techniques, etc.) are used to extract the temperature distribution data in each area of the circuit. This data can be represented as the spatial distribution of temperature (for example, the temperature at each point in a two-dimensional or three-dimensional coordinate system). By analyzing the change trend of temperature, the heat source and the cooling area are judged, so as to obtain the change pattern of temperature in the circuit. Based on the temperature changes in different areas of the circuit, the heat flux density is calculated using the heat flux density equation. The heat flux density is given by the following formula: ; where is the thermal conductivity of the material, is the temperature gradient. By analyzing the simulation data, the heat flux density data is extracted to describe the heat flux transfer intensity in each region of the circuit. Through the above method, the obtained heat flux characteristic data includes temperature distribution and heat flux density, providing data support for subsequent model training. According to the heat flux characteristics, data is extracted to divide the dataset. Usually, the dataset is divided into a training set and a test set, with a general ratio of 70% for the training set and 30% for the test set, or 80% for the training set and 20% for the test set. The training set is used to train the model and contains known inputs (such as the internal temperature distribution of the circuit, air flow path, etc.) and target outputs (such as heat flux density, heat flux path, etc.). The test set is used to verify the accuracy of the model and ensure the generalization ability of the model. The support vector machine (SVM) algorithm is used to train the training set. SVM is a commonly used classification and regression analysis method that realizes data classification by finding the optimal hyperplane or optimizes the prediction function in regression problems. According to the characteristics of the data, an appropriate kernel function (such as linear kernel, radial basis kernel, etc.) is selected. Training is carried out by maximizing the margin to optimize the parameters of the support vector machine model so that it can accurately predict the heat flux distribution during circuit operation. The SVM model is verified through the test set, the prediction error of the model is calculated, and further parameter adjustment and optimization are carried out. According to the optimization goal, multiple model iterations are performed. The optimization process usually includes adjusting the penalty parameter (such as C) of SVM and the parameters of the kernel function (such as γ). The optimized model is the heat flux prediction model for circuit operation (heat flux prediction pre-model for circuit operation). The trained heat flux prediction model for circuit operation is used to predict the air heat flux simulation data during circuit operation. According to the changes in the circuit operation environment (such as temperature distribution, air flow path, material properties, etc.), the trained support vector machine model is used to predict the heat flux distribution in the circuit. The generated prediction data includes: predicting the heat flux density and flow direction in different regions of the electrical circuit. By analyzing the prediction results, the heat source regions and cooling regions in the electrical circuit can be identified, and the design can be further optimized to generate the heat flux distribution prediction data for circuit operation, describing the heat flux intensity and distribution in each region of the circuit. According to the heat flux distribution prediction data generated in step S333, the flow path of the heat flux is further predicted. The evolution of the heat flux path is to speculate the conduction path of heat from the source region to the cooling region by analyzing the predicted heat flux distribution data. Numerical methods (such as streamline analysis, particle tracking, etc.) are used to simulate the heat propagation process in the circuit. Considering the mutual influence of the temperature field and the air flow path, the evolution path of the heat flux is calculated by combining the heat flux transfer equation. Through the heat flux path evolution process, the flow paths of the heat flux in the electrical circuit are obtained, and these paths show how heat flows from the high-temperature region (heat source) to the low-temperature region (cooling region).

[0116] In this specification, an electrical appliance temperature monitoring device is provided for performing the above-mentioned electrical appliance temperature monitoring method. The electrical appliance temperature monitoring system includes:

[0117] An operating structure screening module, configured to obtain electrical appliance structure data; perform topological modeling on the electrical appliance structure data to generate electrical appliance operating circuit topological modeling data; construct a circuit connection route for the electrical appliance structure data based on the electrical appliance operating circuit topological modeling data to generate electrical appliance operating circuit connection route data; divide the electrical appliance structure data through the electrical appliance operating circuit connection route data to obtain electrical appliance operating structure data;

[0118] An internal temperature monitoring module, configured to deploy multi-dimensional sensors for the electrical circuit pipelines of the electrical appliance by using the electrical appliance operating structure data to obtain multi-dimensional sensor deployment data; construct a temperature field calculation model according to the multi-dimensional sensor deployment data to obtain electrical appliance operating temperature field data, and extract hot spot areas of the electrical appliance circuit by using the electrical appliance operating temperature field data to generate hot spot areas of the electrical appliance circuit operation; extract the material characteristics of the electrical circuit pipelines of the electrical appliance operating structure data, and perform heat source mapping of the electrical appliance circuit operation on the hot spot areas of the electrical appliance circuit operation based on the material characteristics of the electrical circuit pipelines to generate internal temperature mapping data of the electrical pipelines;

[0119] An external temperature monitoring module, configured to perform air flow calculation according to the multi-dimensional sensor deployment data to obtain air flow data of the circuit operation; perform air heat flow simulation on the air flow data of the circuit operation based on the internal temperature mapping data of the electrical pipelines to generate air heat flow simulation data of the circuit operation; predict the heat flow flow path for the air heat flow simulation data of the circuit operation to generate heat flow flow prediction path data; perform heat conduction mapping of the electrical appliance circuit operation on the hot spot areas of the electrical appliance circuit operation by using the heat flow flow prediction path data to generate external temperature mapping data of the electrical pipelines;

[0120] An actual heat analysis module, configured to perform actual heat analysis on the electrical circuit pipelines according to the internal temperature mapping data of the electrical pipelines and the external temperature mapping data of the electrical pipelines to generate the actual heated temperature of the electrical circuit pipelines; analyze the heated temperature threshold of the electrical circuit pipelines based on the material characteristics of the electrical circuit pipelines, and perform abnormal temperature monitoring of the electrical circuit pipelines by using the actual heated temperature of the electrical circuit pipelines on the heated temperature threshold to perform the abnormal temperature monitoring operation of the electrical circuit pipelines.

[0121] The present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above-mentioned electrical appliance temperature monitoring method is implemented.

[0122] The present invention also provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned electrical appliance temperature monitoring method is implemented.

[0123] The beneficial effects of the present invention are as follows: By performing topological modeling on the electrical appliance structure data, the system can accurately construct the topological structure and connection routes of the electrical appliance operation circuit, ensuring the correct connection and operation path of the electrical appliance circuit, thereby optimizing the design and performance of the electrical appliance. By dividing the structure based on the electrical appliance circuit connection route data, scientific division of each component and functional module of the electrical appliance can be achieved, which helps with subsequent operations such as temperature monitoring and thermal management, improving the working efficiency and safety of the electrical appliance. Through the deployment of multi-dimensional sensors, the system can accurately monitor the temperature of the electrical appliance circuit and generate electrical appliance operation temperature field data, helping to identify hot spots in the electrical appliance circuit and ensuring that the electrical appliance equipment operates within a safe temperature range. According to the material characteristics of the electrical appliance circuit pipeline, the system can perform heat source mapping on the hot spots of the electrical appliance circuit, accurately draw the temperature distribution inside the pipeline, and give early warnings of potential heat damage areas to avoid overheating damage. By calculating the air flow data of the electrical appliance circuit and conducting air heat flow simulation in combination with the internal temperature mapping data, the heat flow path inside the electrical appliance can be effectively simulated, providing a basis for heat dissipation optimization. Through heat flow path prediction, the system can foresee the heat propagation path, accurately generate the temperature mapping data outside the electrical pipeline, provide guidance for the heat dissipation design of the electrical appliance circuit and external temperature monitoring, and reduce the potential impact of excessive temperature on the electrical appliance. By combining the internal and external temperature mapping data for actual heat reception analysis, the heat impact on the electrical circuit pipeline during operation can be accurately evaluated, ensuring that the electrical appliance equipment is not damaged by overheating. The system can set reasonable temperature thresholds based on the material characteristics of the electrical appliance circuit pipeline, monitor the temperature changes in real time, and give timely warnings when abnormal temperatures occur, preventing safety hazards such as overheating, short circuit, or fire in the electrical appliance equipment and ensuring the long-term safe operation of the electrical appliance. Therefore, through topological modeling of the electrical appliance structure, multi-dimensional sensor deployment, heat flow simulation, and intelligent temperature threshold analysis, the present invention realizes more accurate, comprehensive, and intelligent electrical appliance temperature monitoring, and solves the defects of insufficient accuracy, incomplete local monitoring, and unintelligent abnormal temperature monitoring in traditional technologies.

[0124] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to embrace all changes that fall within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0125] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring the temperature of an electrical appliance, characterized in that: The following steps are involved: Step S1: Acquire electrical appliance structure data; perform topological modeling on the electrical appliance structure data to generate electrical appliance operation circuit topological modeling data; construct a circuit connection route for the electrical appliance structure data based on the electrical appliance operation circuit topological modeling data to generate electrical appliance operation circuit connection route data; perform electrical appliance structure division on the electrical appliance structure data using the electrical appliance operation circuit connection route data to obtain electrical appliance operation structure data; Step S2: using the electrical operation structure data to perform multi-dimensional sensor deployment on the electrical circuit pipeline to obtain multi-dimensional sensor deployment data; constructing a temperature field calculation model based on the multi-dimensional sensor deployment data to obtain the electrical operation temperature field data, and using the electrical operation temperature field data to extract hot spots of the electrical circuit to generate electrical circuit operation hot spots; extracting the electrical circuit pipeline material characteristics of the electrical operation structure data, and performing electrical circuit operation heat source mapping on the electrical circuit operation hot spots based on the electrical circuit pipeline material characteristics to generate electrical circuit internal temperature mapping data; Step S3: performing air flow calculation according to the multi-dimensional sensor deployment data to obtain circuit operation air flow data; performing air heat flow simulation on the circuit operation air flow data based on the internal temperature mapping data of the electrical pipeline to generate circuit operation air heat flow simulation data; performing heat flow path prediction on the circuit operation air heat flow simulation data to generate heat flow prediction path data; performing electrical circuit heat conduction mapping on the electrical circuit operation hot spot area using the heat flow prediction path data to generate electrical pipeline external temperature mapping data; Step S4: performing actual heat analysis on the electrical circuit pipeline according to the internal temperature mapping data of the electrical circuit pipeline and the external temperature mapping data of the electrical circuit pipeline to generate the actual heating temperature of the electrical circuit pipeline; The heating temperature threshold of the electrical circuit pipeline is analyzed based on the material characteristics of the electrical circuit pipeline, and the actual heating temperature of the electrical circuit pipeline is used to monitor the abnormal temperature of the electrical circuit pipeline, so as to perform abnormal temperature monitoring operations on the electrical circuit pipeline.

2. The electrical appliance temperature monitoring method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire electrical appliance structure data; Step S12: performing circuit topology analysis on the electrical appliance structure data to generate electrical appliance circuit topology data; performing topology modeling on the electrical appliance structure data according to the electrical appliance circuit topology data to generate electrical appliance operation circuit topology modeling data; Step S13: constructing a circuit connection route for the electrical appliance structure data based on the electrical appliance operation circuit topology modeling data to generate electrical appliance operation circuit connection route data; Step S14: dividing the electrical appliance structure data into electrical appliance structure data according to the electrical appliance operation circuit connection route data, thereby obtaining the electrical appliance operation structure data.

3. The electrical appliance temperature monitoring method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Perform multi-dimensional sensor deployment on the circuit pipeline of the electrical appliance using the electrical appliance operation structure data to obtain multi-dimensional sensor deployment data, wherein the multi-dimensional sensor deployment data includes MEMS micro temperature sensor deployment data, circuit operation sensor deployment data, and ultrasonic flow field sensor deployment data; Step S22: Screening the power density distribution data of the circuit operation sensor deployment data to obtain the electrical appliance operation power density distribution data; constructing a temperature field calculation model based on the electrical appliance operation power density distribution data to obtain the electrical appliance operation temperature field data; Step S23: collecting circuit current distribution data on the circuit operation sensor deployment data to obtain electrical appliance operation circuit distribution data; extracting hotspot areas from the electrical appliance operation temperature field data based on the electrical appliance operation circuit distribution data to generate electrical appliance circuit operation hotspot areas; Step S24: extracting the electrical circuit pipeline material characteristics of the electrical operation structure data, and performing electrical circuit operation heat source mapping on the electrical circuit operation hotspot area based on the electrical circuit pipeline material characteristics to generate electrical pipeline internal temperature mapping data.

4. The electrical appliance temperature monitoring method according to claim 3, characterized in that: Step S24 includes the following steps: Step S241: extracting the material characteristics of the electrical circuit pipeline of the electrical operation structure data to obtain the material characteristics and geometric shape of the circuit pipeline; performing calculations based on the material characteristics and geometric shape of the circuit pipeline to obtain the circuit pipeline resistance data; Step S242: Calculate the power dissipation of the circuit pipelines through the circuit pipeline resistance data and the electrical equipment operation circuit distribution data to obtain the circuit pipeline power dissipation density data; perform pipeline heat source intensity analysis on the electrical equipment circuit operation hotspot area using the circuit pipeline power dissipation density data to generate pipeline heat source intensity data; Step S243: Based on the pipeline heat source intensity data, heat source distribution mapping is performed on the hot spot area of ​​the electrical circuit operation to generate pipeline heat source distribution mapping data; the temperature field inside the pipeline is simulated for the hot spot area of ​​the electrical circuit operation through the pipeline heat source distribution mapping data, thereby obtaining the internal temperature mapping data of the electrical pipeline.

5. The electrical appliance temperature monitoring method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: screening the air flow velocity data of the ultrasonic flow field sensor deployment data to obtain circuit operation air flow velocity data; performing air flow calculation on the circuit operation air flow velocity data based on the Navier-Stokes equation to obtain circuit operation air flow data; Step S32: performing air heat flow simulation on the circuit operation air flow data based on the internal temperature mapping data of the electrical pipeline to generate circuit operation air heat flow simulation data; Step S33: constructing a circuit operation heat flow prediction model according to the circuit operation air heat flow simulation data, and performing heat flow path prediction on the circuit operation air heat flow simulation data according to the circuit operation heat flow prediction model to generate heat flow prediction path data; Step S34: using the heat flow prediction path data to perform electrical circuit heat conduction mapping on the hot spots of the electrical circuit operation, and generating electrical pipeline external temperature mapping data.

6. The method for monitoring the temperature of an electrical appliance according to claim 5, characterized in that: Step S32 includes the following steps: Step S321: extracting temperature field data based on the temperature mapping data inside the electrical pipeline, thereby obtaining temperature field data; dividing the circuit operation air flow data into air flow areas of the electrical circuit, thereby obtaining air flow area data; Step S322: Modeling the air flow path according to the air flow area data, thereby obtaining air flow path data; Step S323: constructing a heat flow transfer model for the air flow path data through the temperature field data, thereby obtaining heat flow transfer data; Step S324: using the heat flow transfer data to perform air heat flow simulation calculation on the air flow path data, thereby obtaining circuit operation air heat flow simulation data.

7. The method for monitoring the temperature of an electrical appliance according to claim 5, characterized in that: Step S33 includes the following steps: Step S331: extracting heat flow characteristics from circuit running air heat flow simulation data to obtain heat flow characteristic extraction data, wherein the heat flow characteristic extraction includes temperature distribution extraction and heat flux density extraction; Step S332: dividing the heat flow feature extraction data into data sets to generate a model training set and a model test set; using a support vector machine algorithm to perform model training on the model training set to generate a circuit operation heat flow prediction pre-model; performing model optimization iteration on the circuit operation heat flow prediction pre-model through the model test set, thereby generating a circuit operation heat flow prediction model; Step S333: performing heat flow distribution prediction on the circuit operation air heat flow simulation data according to the circuit operation heat flow prediction model to generate heat flow distribution prediction data; Step S334: using the heat flow distribution prediction data to perform heat flow path evolution on the circuit operation air heat flow simulation data, and generating heat flow prediction path data.

8. An electrical appliance temperature monitoring device, characterized in that: Used to perform the electrical appliance temperature monitoring method as claimed in claim 1, the electrical appliance temperature monitoring device comprises: The operation structure screening module is used to obtain electrical appliance structure data; perform topological modeling on the electrical appliance structure data to generate electrical appliance operation circuit topological modeling data; construct a circuit connection route for the electrical appliance structure data based on the electrical appliance operation circuit topological modeling data to generate electrical appliance operation circuit connection route data; perform electrical appliance structure division on the electrical appliance structure data through the electrical appliance operation circuit connection route data to obtain the electrical appliance operation structure data; The internal temperature monitoring module is used to perform multi-dimensional sensor deployment on the circuit pipeline of the electrical appliance using the electrical appliance operation structure data to obtain multi-dimensional sensor deployment data; construct a temperature field calculation model based on the multi-dimensional sensor deployment data to obtain the electrical appliance operation temperature field data, and use the electrical appliance operation temperature field data to extract the hot spot area of ​​the electrical appliance circuit to generate the electrical appliance circuit operation hot spot area; extract the material characteristics of the electrical appliance circuit pipeline from the electrical appliance operation structure data, and perform electrical appliance circuit operation heat source mapping on the electrical appliance circuit operation hot spot area based on the material characteristics of the electrical appliance circuit pipeline to generate the electrical appliance pipeline internal temperature mapping data; The external temperature monitoring module is used to calculate the air flow according to the multi-dimensional sensor deployment data to obtain the circuit operation air flow data; perform air heat flow simulation on the circuit operation air flow data based on the internal temperature mapping data of the electrical pipeline to generate the circuit operation air heat flow simulation data; perform heat flow flow path prediction on the circuit operation air heat flow simulation data to generate heat flow prediction path data; use the heat flow prediction path data to perform electrical circuit heat conduction mapping on the electrical circuit operation hot spot area to generate electrical pipeline external temperature mapping data; The actual thermal analysis module is used to perform actual thermal analysis on the electrical circuit pipeline based on the internal temperature mapping data of the electrical circuit pipeline and the external temperature mapping data of the electrical circuit pipeline to generate the actual heating temperature of the electrical circuit pipeline; analyze the heating temperature threshold of the electrical circuit pipeline based on the material characteristics of the electrical circuit pipeline, and use the actual heating temperature of the electrical circuit pipeline to monitor the abnormal temperature of the electrical circuit pipeline based on the heating temperature threshold, so as to perform abnormal temperature monitoring operations on the electrical circuit pipeline.

9. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for monitoring the temperature of an electrical appliance as claimed in any one of claims 1 to 7 is implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for monitoring the temperature of an electrical appliance as described in any one of claims 1 to 7 is implemented.

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