Electric appliance temperature monitoring method and device, equipment and storage medium

Abstract

The invention relates to the technical field of electric appliance monitoring, in particular to an electric appliance temperature monitoring method, device and equipment and a storage medium. The method comprises the following steps: acquiring electric appliance structure data; topological modeling is carried out on the electric appliance structure data to generate electric appliance operation circuit topological modeling data; performing circuit connection line construction on the electric appliance structure data based on the electric appliance operation circuit topology modeling data to generate electric appliance operation circuit connection line data; and performing electric appliance structure division on the electric appliance structure data through the electric appliance operation circuit connection line data to obtain electric appliance operation structure data. Through topological modeling, multi-dimensional sensor deployment, heat flow simulation and intelligent temperature threshold analysis of the electric appliance structure, more accurate, comprehensive and intelligent electric appliance temperature monitoring is realized, and the defects of insufficient precision, incomplete local monitoring and non-intelligent abnormal temperature monitoring in the traditional technology are solved.

Description

Technical Field

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

[0002] Initially, temperature monitoring mainly relied on simple mechanical thermometers or thermocouple sensors. Although these technologies can provide basic temperature measurements, they have shortcomings 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 temperature monitoring of electrical equipment, especially in high-temperature environments with good stability and sensitivity. With the rise of digital technology, intelligent temperature sensors began to appear, which can provide digital signal output and facilitate integration with control systems. During this period, temperature monitoring systems gradually developed towards automation and remoteness. Temperature data can be monitored and fed back in real time through wireless transmission technology, greatly improving the safety and intelligence level of electrical equipment. The rise of Internet of Things (IoT) technology has further improved the temperature monitoring system of electrical appliances. Through networked transmission, users can view the temperature data of the device in real time anywhere, and perform predictive analysis through intelligent algorithms to identify potential fault risks in advance. However, temperature monitoring in existing technologies often fails to accurately capture 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 source of the electrical appliance and the external air flow is usually ignored, resulting in low accuracy in pipeline temperature monitoring of the electrical operating circuit. Summary of the invention

[0003] Based on this, it is necessary to provide an electrical appliance temperature monitoring method, device, equipment and storage medium 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 is provided, the method comprising the following steps: 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: Perform 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; 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 operation of the electrical circuit pipeline.

[0005] The present invention performs topological modeling, constructs circuit connection routes and divides electrical structure data. This step can accurately reconstruct the structure of the internal circuit of the electrical appliance, provide complete electrical operation circuit data, and lay a solid foundation for subsequent temperature monitoring and heat source identification, thereby improving the accuracy and comprehensiveness of the monitoring process. By using the electrical operation structure data for multi-dimensional sensor deployment, sensors can be accurately arranged at key positions of the electrical circuit to obtain more accurate temperature field data. The hot spots of the electrical circuit are extracted through the temperature field calculation model, and heat source mapping is performed in combination with the material characteristics of the circuit pipeline, which helps to accurately identify the high-temperature area and heat source distribution, thereby discovering the overheating risk of 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 to generate accurate heat flow path predictions, which 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 inside and outside the electrical appliance, and avoids temperature deviations caused by air flow changes. By combining internal and external temperature mapping data for heat analysis, the actual heating conditions of the electrical circuit pipeline can be more accurately evaluated. Abnormal temperature monitoring based on 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 electrical operation. Therefore, the present invention achieves more accurate, comprehensive and intelligent electrical temperature monitoring through topological modeling of electrical structure, multi-dimensional sensor deployment, heat flow simulation and intelligent temperature threshold analysis, solving the defects of insufficient accuracy, incomplete local monitoring and unintelligent abnormal temperature monitoring in traditional technologies.

[0006] Preferably, step S1 comprises 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.

[0007] Through circuit topology analysis, the present invention can clearly understand the connection relationship between various circuit components inside an electrical appliance, thereby effectively identifying potential circuit design problems or conflicts and providing data support for subsequent design optimization. Through topological modeling of electrical appliance structure and circuit, comprehensive integration of structure and circuit can be achieved to ensure that the electrical appliance can operate smoothly in practical applications and avoid mutual influence or constraint problems between circuit and structure. Based on the topological modeling data of the operating circuit of the electrical appliance, 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 structure of the electrical appliance, the role and functional area of ​​each component of the electrical appliance can be clarified, and the maintainability and scalability of the electrical appliance can be enhanced.

[0008] Preferably, step S2 comprises the following steps: Step S21: using the electrical appliance operation structure data to perform multi-dimensional sensor deployment on the electrical appliance circuit pipeline 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.

[0009] The present invention can comprehensively monitor the operation status of electrical appliances by deploying a variety of sensors (MEMS micro temperature sensors, circuit operation sensors and ultrasonic flow field sensors), covering multiple dimensions such as temperature, current and flow field, and ensuring the accuracy and integrity of data acquisition. By screening the circuit operation sensor data, the power density distribution data of the electrical appliance 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 appliance, identify high-temperature areas in a timely manner, and help prevent failures caused by overheating. By collecting the current distribution of the circuit operation data, the hot spot areas in the circuit can be accurately located, providing data support for the optimized design of the electrical appliance, which can effectively avoid failures caused by local overload or poor heat dissipation, and improve the operation safety and stability of the electrical appliance. Heat source mapping is performed based on the material characteristics of the electrical circuit pipeline, and the temperature mapping data inside the pipeline is generated in combination with the temperature field data. In this way, the temperature of each part of the pipeline can be monitored in real time to ensure that the heat is effectively dispersed and prevent risks such as local overheating or fire.

[0010] Preferably, 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; calculating the material characteristics and geometric shape of the circuit pipeline according to Ohm's law to obtain the resistance data of the circuit pipeline; 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.

[0011] The present invention can accurately predict the resistance characteristics of the circuit pipeline by extracting the material characteristics and geometric shape of the electrical circuit pipeline and calculating the resistance according to Ohm's law, which provides a reliable basis for the subsequent power dissipation analysis and ensures that the electrical performance of the electrical circuit is accurately mastered. Based on the circuit pipeline resistance data and the electrical operation circuit distribution data, the power dissipation density data of the circuit pipeline can be calculated, and the heat source intensity of the pipeline can be further analyzed, which makes it possible to timely find the area causing overheating in the operation of the electrical appliance, optimize the heat dissipation design, and prevent the performance of the electrical appliance from being reduced or damaged due to overheating. Using the pipeline heat source intensity data, the heat source distribution mapping is performed on the hot spot area of ​​the electrical circuit operation to generate accurate heat source distribution data. This process helps identify the key heat source area in the electrical system, and through temperature field simulation, predict the temperature change inside the pipeline, and provide decision 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 conductive 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.

[0012] Preferably, step S3 comprises 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.

[0013] The present invention can accurately simulate the air flow inside the electrical appliance by screening the air velocity data and using the Navier-Stokes equation to calculate the air flow. This process helps analyze the air flow characteristics inside the electrical appliance, so as to better understand the performance of the heat dissipation and air circulation system. Based on the temperature mapping data inside the electrical pipeline, the heat flow simulation of the circuit operation air flow is performed. This process can simulate the propagation path of heat in the air, accurately predict the heat distribution and flow trend inside the electrical appliance, and provide a scientific basis for heat dissipation design and optimization. According to the air heat flow simulation data of the 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 where air flow is not smooth can be identified, and timely optimization can be performed to reduce the risks caused by heat concentration. Using the heat flow prediction path data, heat conduction mapping is performed on the hot spots of the electrical circuit operation, and temperature mapping data outside the pipeline is generated, which provides guidance for external heat dissipation and thermal management of the electrical appliance, optimizes the external temperature distribution of the electrical appliance, and reduces the loss or safety hazards caused by heat leakage. Through these detailed heat flow simulation and prediction steps, the thermal management system of electrical appliances can be comprehensively improved, which not only optimizes air flow and heat dissipation, but also reduces the risk of uneven or excessively concentrated heat sources, thereby improving the operating efficiency of electrical appliances, extending their service life and reducing failure rates.

[0014] Preferably, 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.

[0015] The present invention can accurately identify the temperature distribution and air flow conditions of different areas inside the electrical appliance by extracting the temperature field data inside the electrical pipeline and dividing the air flow of the circuit operation into regions, which helps to fully understand the heat distribution inside the electrical appliance, timely discover potential overheating areas, and provide detailed basis for heat dissipation design. By modeling the path of the air flow area, the air flow path data is obtained. This step helps to build an accurate air flow model, predict the dynamic changes of air flow, thereby optimizing the air flow circulation inside the electrical appliance, and improving the heat dissipation effect and air flow efficiency. Based on the temperature field data and the air flow path data, a heat flow transfer model is constructed. This model can accurately describe the heat transfer relationship between the temperature inside the electrical appliance and the air flow, provide a theoretical basis for the thermal management system of the electrical appliance, and optimize the distribution and transfer of thermal energy. The air flow path is simulated and calculated by the heat flow transfer data to obtain the heat flow simulation data of the air inside the electrical appliance. 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 each component of the electrical appliance can be evenly dissipated, and avoid local overheating problems. These steps provide all-round support for the thermal management system of the electrical appliance through detailed heat flow and air flow analysis. By optimizing air flow and heat distribution, the heat dissipation capacity of the appliance is significantly improved, thereby reducing the risk of overheating and improving the stability, efficiency and service life of the appliance.

[0016] Preferably, 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.

[0017] The present invention extracts heat flow features from circuit operation air heat flow simulation data to obtain temperature distribution and heat flux density data. This process can accurately identify the heat distribution features inside the electrical appliance and provide detailed preliminary data for subsequent heat flow prediction, which helps to understand the thermal behavior of the electrical appliance under different operating conditions. The heat flow feature extraction data is divided into a model training set and a test set, and trained using a support vector machine algorithm. This method enables the circuit operation heat flow prediction model to learn patterns from a large amount of data and achieve efficient heat flow prediction. The circuit operation heat flow prediction pre-model 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 air heat flow of the circuit operation can be distributed and predicted to generate heat flow distribution prediction data, which enables the thermal distribution of the electrical appliance to be comprehensively predicted and the corresponding thermal management adjustment to be made. Based on the heat flow distribution prediction data, the heat flow path is evolved and predicted to obtain the heat flow prediction path data. This process can simulate the evolution of heat flow and predict how heat propagates inside the electrical system, providing decision support for heat dissipation design and optimization. Through these steps, more accurate prediction and control methods can be provided for the thermal management system of electrical appliances, helping to identify and solve potential overheating problems and ensure that electrical appliances can operate stably under high load. In addition, the optimized heat flow path evolution helps to evenly distribute the heat inside the appliance to avoid local overheating or uneven cooling.

[0018] In this specification, an electrical appliance temperature monitoring device is provided, which is used to execute the above-mentioned electrical appliance temperature monitoring method. The electrical appliance temperature monitoring system includes: 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.

[0019] The present invention provides a computer device, comprising 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 above-mentioned electrical appliance temperature monitoring method is implemented.

[0020] 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 method for monitoring the temperature of an electrical appliance is implemented.

[0021] The beneficial effect of the present invention is that by topological modeling of electrical structure data, the system can accurately construct the topological structure and connection route of the electrical operation circuit, ensure the correct connection and operation path of the electrical circuit, and thus optimize the design and performance of the electrical appliance. By dividing the structure through the electrical circuit connection route data, it is possible to scientifically divide the various components and functional modules of the electrical appliance, which is helpful for subsequent temperature monitoring and thermal management operations, and improves 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 circuit, and generate electrical operation temperature field data, help identify the hot spots in the electrical circuit, and ensure that the electrical equipment operates within a safe temperature range. According to the material characteristics of the electrical circuit pipeline, the system can perform heat source mapping on the hot spots of the electrical circuit, accurately draw the temperature distribution inside the pipeline, and warn potential thermal damage areas in advance to avoid overheating damage. By calculating the air flow data of the electrical circuit and combining the internal temperature mapping data to perform air heat flow simulation, the heat flow path inside the electrical appliance can be effectively simulated, providing a basis for heat dissipation optimization. Through the prediction of the heat flow path, 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 circuit and the 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 analysis, it is possible to accurately evaluate the heat impact on the electrical circuit pipeline during operation, ensuring that the electrical equipment is not damaged by overheating. The system can set a reasonable temperature threshold according to the material characteristics of the electrical circuit pipeline, monitor the temperature change in real time, and provide timely warning when abnormal temperature occurs, so as to prevent the electrical equipment from overheating, short circuit or fire and other safety hazards, and ensure the long-term safe operation of the electrical equipment. Therefore, the present invention realizes more accurate, comprehensive and intelligent electrical temperature monitoring through topological modeling of the electrical structure, multi-dimensional sensor deployment, heat flow simulation and intelligent temperature threshold analysis, solving the defects of insufficient accuracy, incomplete local monitoring and unintelligent abnormal temperature monitoring in traditional technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of the steps of a method for monitoring the temperature of an electrical appliance; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0023] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0024] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying 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 implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0025] It should be understood that although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, and the term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0026] To achieve this, please refer to Figures 1 to 3 , a method for monitoring the temperature of an electrical appliance, the method comprising the following steps: 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: Perform 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; 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 operation of the electrical circuit pipeline.

[0027] The present invention performs topological modeling, constructs circuit connection routes and divides electrical structure data. This step can accurately reconstruct the structure of the internal circuit of the electrical appliance, provide complete electrical operation circuit data, and lay a solid foundation for subsequent temperature monitoring and heat source identification, thereby improving the accuracy and comprehensiveness of the monitoring process. By using the electrical operation structure data for multi-dimensional sensor deployment, sensors can be accurately arranged at key positions of the electrical circuit to obtain more accurate temperature field data. The hot spots of the electrical circuit are extracted through the temperature field calculation model, and heat source mapping is performed in combination with the material characteristics of the circuit pipeline, which helps to accurately identify the high-temperature area and heat source distribution, thereby discovering the overheating risk of 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 to generate accurate heat flow path predictions, which 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 inside and outside the electrical appliance, and avoids temperature deviations caused by air flow changes. By combining internal and external temperature mapping data for heat analysis, the actual heating conditions of the electrical circuit pipeline can be more accurately evaluated. Abnormal temperature monitoring based on 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 electrical operation. Therefore, the present invention achieves more accurate, comprehensive and intelligent electrical temperature monitoring through topological modeling of electrical structure, multi-dimensional sensor deployment, heat flow simulation and intelligent temperature threshold analysis, solving the defects of insufficient accuracy, incomplete local monitoring and unintelligent abnormal temperature monitoring in traditional technologies.

[0028] In the embodiment of the present invention, reference Figure 1The above is a schematic diagram of the steps of a method for monitoring the temperature of an electrical appliance of the present invention. In this example, the method for monitoring the temperature of an electrical appliance includes the following steps: 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; In an embodiment of the present invention, the physical structure data of the electrical appliance is obtained by scanning technology (such as three-dimensional laser scanning or CT scanning), and these data may include information such as the external shape of the electrical appliance, the internal circuit layout, and the location of components. For complex electrical appliances, CAD (computer-aided design) data can be used to supplement and ensure the integrity of the structural data. Using the obtained electrical appliance structure data, a topological modeling method (such as a modeling method based on graph theory) is used to model the internal circuit of the electrical appliance. By mathematically describing the various components of the internal circuit of the electrical appliance and their connection methods, the operating circuit topological structure data of the electrical appliance is generated, which is expressed as a directed graph or undirected graph model containing various electrical components and connection lines of the electrical appliance. Based on the electrical appliance operating circuit topological modeling data, an algorithm (such as a depth-first search or a breadth-first search) is used to construct the circuit connection route inside the electrical appliance. The electrical connection relationship between the various components in the circuit is identified by these algorithms, and then the circuit connection route data of the electrical appliance is generated. These data specifically describe how the various electrical components are connected through the circuit line, and can be further used to guide the actual sensor layout. Based on the circuit connection route data generated earlier, the structure of the appliance is divided. The implementation process of this step involves dividing the appliance into different functional blocks or modules, such as power modules, control modules, load modules, etc., to ensure that the electrical connections within each module can be made through appropriate lines. Ultimately, the entire structure of the appliance is divided into different areas and the operating structure data of the appliance is formed. This data will help with subsequent thermal flow simulation, temperature field analysis, and other work.

[0029] 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; In an embodiment of the present invention, by using the electrical operation structure data obtained in step S1, multi-dimensional sensor layout of the circuit pipeline inside the electrical appliance is performed. Sensor types may include temperature sensors, humidity sensors, pressure sensors, etc. The layout positions of the sensors should cover various key areas of the electrical appliance, especially areas with dense current or high heat generation, such as power modules, power conversion modules and near radiators. The deployment process can be implemented in the following ways: According to the three-dimensional structure data of the electrical appliance, use three-dimensional modeling software (such as SolidWorks, AutoCAD) to deploy virtual sensors to ensure the rationality of the sensor position. Use sensor deployment optimization algorithms (such as genetic algorithms, simulated annealing algorithms, etc.) to improve the coverage and accuracy of monitoring. The deployed sensors collect parameters such as temperature, pressure, humidity, etc. 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 by numerical simulation methods (such as finite element method, computational fluid dynamics CFD model, etc.). The model will take into account the internal heat source of the electrical appliance, sensor data, and the heat dissipation characteristics of electrical components to simulate the temperature distribution of various parts of the electrical appliance. The implementation steps are as follows: Input parameters such as the internal structure of the appliance, sensor data, heat source distribution, and ambient temperature. Use simulation software (such as COMSOL, ANSYS, etc.) to calculate the temperature field and obtain the temperature distribution data inside the appliance. Through the temperature field data, use the threshold method or cluster analysis method to extract the hot spot area in the electrical circuit. 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 appliance. The area exceeding the threshold is the hot spot area. Use the heat map analysis method (such as hot zone identification algorithm, regional clustering algorithm, etc.) to extract the hot spot area from the temperature field data. According to the operating structure data of the appliance, extract the material characteristics of the electrical circuit pipeline, such as thermal conductivity, resistivity, thermal expansion coefficient, etc. Different materials have different effects on heat conduction, so it is necessary to accurately extract material parameters: obtain the material information of the circuit pipeline (such as copper, aluminum, iron, etc.) from the CAD drawing or material database, and accurately model it according to the actual situation. Use the existing material performance database to obtain parameters such as thermal conductivity and thermal stability of different materials. Combined with the extracted material characteristics and temperature field data of the electrical circuit pipeline, heat source mapping is performed on the hot spot area. This step can be implemented in the following ways: Identify the main heat source in the hot spot area through temperature distribution data. Based on the pipeline material characteristics (such as thermal conductivity) and the temperature distribution of the hot spot area, heat source mapping is performed to generate a heat source distribution map inside the electrical circuit. Through the heat source mapping data, we can further understand the heat conduction paths in different areas and their impact on electrical performance. Finally, based on the temperature field and heat source mapping data, generate the temperature mapping data inside the electrical pipeline.

[0030] 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; In an embodiment of the present invention, the air flow inside the electrical appliance is calculated according to the multi-dimensional sensor deployment data to obtain the air flow data in the circuit operation. Air flow is crucial to the heat dissipation effect of the electrical appliance, so accurate calculation of air flow is an important part of temperature monitoring. The implementation method is as follows: collect data through wind speed, pressure, and temperature sensors deployed inside the electrical appliance, and these data include air flow rate, flow direction, temperature distribution, etc. Use computational fluid dynamics (CFD) software (such as ANSYS Fluent, OpenFOAM, etc.) to combine sensor data to perform numerical simulation of the air flow inside the electrical appliance, 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, and the location of the air inlet and outlet. Based on the temperature mapping data inside the electrical pipeline and the calculated air flow data, air heat flow simulation is performed 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: Introduce the heat transfer equation in the CFD simulation, and consider the coupling effect of air flow and heat conduction. The model will calculate the heat exchange process between air flow and electrical pipelines. Use simulation software (such as ANSYS Fluent, COMSOL Multiphysics, etc.) to simulate the air heat flow inside the appliance, simulate how heat is dissipated through air flow, and generate heat flow distribution data. 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 in order to more accurately understand the trend of heat propagation and its impact on other components of the appliance. The specific implementation steps are as follows: Based on the heat flow simulation results, use path tracing algorithms (such as Lagrangian path tracing or streamline analysis methods) to predict the flow path of the heat flow. Identify the main channels for heat transfer, and analyze different paths to predict how the heat will be distributed to other components of the appliance, especially hot spots. Using the predicted heat flow path data, perform heat conduction mapping on the hot spots where the electrical circuit is running, and generate temperature mapping data on the outside of the electrical pipeline. This step helps to fully understand the heat transfer from the inside to the outside, and evaluate the heat dissipation effect and potential high-temperature areas. The implementation method is as follows: Establish a heat conduction model outside the electrical circuit, consider factors such as the material, thermal conductivity, and heat dissipation surface area of ​​the electrical housing, and simulate how the internal heat is dissipated through the outside of the electrical pipeline. According to the heat flow path data, use the heat conduction model to simulate the external temperature of the electrical appliance, generate a temperature distribution map of the external electrical appliance, and display the temperature changes outside the electrical pipeline. Through the temperature mapping data, analyze the temperature conditions of various areas outside the electrical appliance, especially the external areas associated with the internal hot spots. Finally, combine the internal temperature mapping, air flow simulation, heat flow path prediction, and external temperature conduction data to generate a comprehensive temperature mapping result.

[0031] Step S4: Perform 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; 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 operation of the electrical circuit pipeline.

[0032] In an embodiment of the present invention, an actual heat analysis is performed 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 to generate the actual heating temperature of the electrical circuit pipeline. This process helps to accurately evaluate the heating conditions of the electrical circuit under different working conditions. The implementation method is as follows: the internal temperature mapping data of the electrical pipeline and the external temperature mapping data of the electrical pipeline are obtained as the basis for the actual heat analysis. The heating conditions of the electrical pipeline under the influence of the internal temperature and the external environment are calculated by heat conduction analysis. The heat distribution of the pipeline is evaluated in combination with the geometric shape, material properties and working conditions of the pipeline. Thermal analysis software (such as ANSYS, COMSOL, etc.) is used for simulation to calculate the actual heating temperature of the electrical pipeline under different conditions and obtain the temperature change data of the pipeline during operation. Based on the material characteristics of the electrical circuit pipeline, the heating temperature threshold of the electrical circuit pipeline is analyzed. Different pipeline materials have different temperature resistance characteristics. Therefore, it is necessary to set a reasonable temperature threshold for pipelines of different materials. The implementation steps are as follows: according to the material characteristics of the electrical circuit pipeline (such as copper, aluminum, steel, etc.), the physical properties of the pipeline such as thermal conductivity, melting point, and temperature resistance range are extracted. According to the material characteristics, the temperature threshold of each material is set through experimental data or standard data. The temperature threshold indicates whether the pipeline material will be damaged or deformed under high temperature conditions. A heating temperature threshold is set to indicate 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 heating temperature of the electrical circuit pipeline and the set temperature threshold, the abnormal temperature of the electrical circuit pipeline is monitored to timely detect whether the electrical circuit pipeline exceeds the set temperature range. The implementation steps are as follows: Use the deployed multi-dimensional sensors to continuously monitor the actual heating temperature of the electrical circuit pipeline. Compare the actual heating temperature obtained in real time with the set temperature threshold. When the actual heating temperature exceeds the preset threshold, an abnormal alarm is triggered. If the actual heating temperature of the electrical circuit pipeline exceeds the temperature threshold of the material, it is judged as an abnormal temperature state, the system immediately alarms, and records the abnormal data for subsequent analysis. When the abnormal temperature is detected, corresponding processing and response are carried out to ensure the safety of electrical equipment. The implementation method is as follows: When an abnormal temperature is detected, the system automatically triggers an alarm and notifies relevant personnel to handle it in time. 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 overheating. The system records abnormal temperature data, analyzes the time, duration and cause of abnormal temperature, and provides preventive measures and maintenance suggestions.

[0033] Preferably, step S1 comprises 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.

[0034] In an embodiment of the present invention, by using technologies such as 3D scanners, CAD models or electrical schematics, the three-dimensional geometric structure data of the electrical appliance, including the size, position, shape and material properties of the parts, is collected. The acquired data is converted into a format suitable for subsequent analysis and processing, such as STL, OBJ format, or the electrical properties of the electrical appliance are imported into the model through a special tool. Redundant information is removed and missing structural data is supplemented to ensure the integrity and accuracy of the data. The electrical connection relationship between the electrical components (such as resistors, capacitors, inductors, semiconductor components, etc.) in the electrical appliance is analyzed using circuit analysis software (such as SPICE) or a custom algorithm. Based on the geometric information of the electrical structure, the electrical connections (such as wire connections, solder joints, etc.) between the electrical components are identified. At the same time, the functional distinction and connection rules of the circuit are analyzed to obtain the topological structure of the circuit. The topological relationship is converted into a structured data form (such as a graph database or an adjacency matrix), including circuit components and their connection methods, and the electrical characteristics (voltage, current, impedance, etc.) of each component are annotated. In combination with the operating environment of the electrical appliance and the functional requirements of the circuit, a circuit model that adapts to the operating conditions is created based on the circuit topology data, involving consideration of factors such as current load, operating frequency, and heat dissipation requirements. Optimize the circuit connection route through algorithms (such as the shortest path algorithm and the network flow algorithm) to ensure the stability and efficiency of the current flow and avoid interference and power loss. According to the circuit design requirements, generate the actual physical route of the circuit connection (including wire routing, PCB board design, etc.), and record the connection points, wire materials, path length and other information. According to the circuit connection route, divide the structural modules of the electrical appliance (such as power module, signal processing module, output module, etc.), and each module has a clear electrical function and connection method. By dividing the structural units, fault diagnosis, function optimization and post-maintenance can be better performed. The independence of each module improves the detachability and scalability of the electrical appliance, and generates operating structure data that reflects the internal structure, functional partitioning and circuit connection of the electrical appliance, including key indicators such as the working status and power consumption of each module.

[0035] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: using the electrical appliance operation structure data to perform multi-dimensional sensor deployment on the electrical appliance circuit pipeline 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.

[0036] In the embodiment of the present invention, MEMS micro temperature sensors are deployed at key locations of electrical circuit pipelines (such as power supply terminals, near heating elements, current-intensive areas, etc.). The sensors are small and sensitive, and can perform high-precision temperature monitoring without interfering with the normal operation of the electrical appliances. 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 appliances, and evaluate the working status of the electrical appliances. Ultrasonic sensors are deployed in areas where fluid flow (such as coolant, airflow, etc.) needs to be monitored, and ultrasonic technology is used to monitor parameters such as fluid velocity, temperature, and density, especially in electrical heat dissipation systems, to obtain accurate flow field data. According to the structural characteristics of the electrical appliances and the layout of the circuit pipelines, the deployment location of the sensors is reasonably planned to ensure that all functional areas of the electrical appliances are covered, and comprehensive data on the operating status can be effectively obtained. When deploying, factors such as electrical isolation, mechanical stability, and compatibility of sensors and circuits need to be considered. The circuit operation sensor obtains data such as current and voltage, and the power density of current and voltage (i.e., the spatial distribution of power) is calculated in combination with the working principle of the electrical appliances. Apply filtering and data analysis algorithms (such as Kalman filtering, least squares method, etc.) to process the data collected by the sensor, 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, 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 of different areas in the circuit is determined, and the high power density areas in the 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 the Fourier heat conduction equation). In the model, the heat source position is related to the power density distribution, and the law of temperature field change over time and space is calculated. Temperature field calculation formula: ;in, is the temperature distribution in space and time, is the thermal diffusivity, It is a heat source term related to power density and heat source. The current sensor deployed in the electrical circuit measures the current distribution in real time. Through multi-point acquisition, the current intensity data on each current path is obtained. A sensor array (such as a Hall effect sensor array) is applied to detect the current density at different locations. Through mathematical modeling or graphical methods, the current distribution of each area is visualized to obtain the spatial data of the current flow inside the electrical appliance. Based on the current distribution data of the electrical appliance, the relationship between heat flux density and current is used to identify the areas with dense current, which 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 spots in the temperature field, and the heat source points in the circuit are found through the correlation between current density and temperature. High temperature areas or overheated areas in the electrical appliance are marked for further design optimization or fault detection. The material information in the electrical structure data (such as the material of the wire, the material of the circuit board, etc.) is used to extract the physical properties of each material such as thermal conductivity, specific heat capacity, density, etc. Create a material library containing the material information of common electrical components and circuit pipelines as the basis for subsequent heat source mapping. Based on the material properties and temperature field data of the circuit, the hot spots of the electrical circuit operation are matched with the material properties of the electrical circuit pipeline. The heat conduction formula is used to combine the current density with the thermal properties of the material to generate heat source mapping data for the operation of the electrical circuit, revealing the process of heat transfer from the area where the current flows to the electrical pipeline. According to the thermal conductivity, thermal convection and other characteristics of the material, the temperature distribution of each pipeline part in the electrical circuit is simulated. The final internal temperature mapping data of the electrical pipeline shows the temperature conditions of each pipeline area for further heat dissipation design, fault warning or performance optimization.

[0037] Preferably, 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; calculating the material characteristics and geometric shape of the circuit pipeline according to Ohm's law to obtain the resistance data of the circuit pipeline; 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.

[0038] In an embodiment of the present invention, 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 are extracted from the electrical structure data. These material characteristics are usually extracted through data in a CAD model or a physical parameter library. According to the design drawings or three-dimensional models of the electrical appliance, the geometric characteristics of the circuit pipeline are extracted, including the diameter, length, surface area, etc. of the pipeline. When considering the direction and layout of the circuit pipeline, the geometric structure of the circuit pipeline is determined in combination with the connection between specific electrical components, and the three-dimensional geometric data of the pipeline is generated. According to Ohm's law, the resistance calculation formula is: ;in, 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 based on 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: ;in, is the power dissipation, is the current, is the resistance of the circuit pipeline. The power dissipation of the circuit pipeline is calculated using the current data in the electrical operation circuit distribution data, combined with the resistance data of each pipeline. Power density is the power dissipation per unit volume or 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, analyze the hot spots of the electrical circuit operation. The higher the power dissipation, the greater the heat source intensity. Based on the power density of different areas, generate pipeline heat source intensity data to identify areas with higher heat source intensity in the circuit pipeline. Use the pipeline heat source intensity data to visualize the heat source distribution in the three-dimensional space of the electrical appliance. Areas with higher heat source intensity will be displayed as hot spots in the mapping. Generate a heat source distribution map of the electrical circuit pipeline through the spatial distribution of the heat source intensity, which can help designers understand the concentrated distribution areas of heat in the electrical appliance. Use the heat conduction equation to simulate the temperature field inside the electrical circuit pipeline. Assume that the electrical pipeline is a three-dimensional heat conduction medium, and the heat source comes from the power dissipation generated when the current flows through it. 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 temperature distribution data for different areas inside the pipeline. By simulating the temperature field inside the pipeline, the area with the highest temperature, i.e. the heat source area, can be obtained. Based on the simulation data, a temperature map inside the pipeline can be generated to show the temperature distribution.

[0039] Preferably, 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; calculating the material characteristics and geometric shape of the circuit pipeline according to Ohm's law to obtain the resistance data of the circuit pipeline; 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.

[0040] In an embodiment of the present invention, the basic material characteristics of the electrical circuit pipeline are extracted from the electrical circuit diagram or circuit simulation data. For example, the conductor material (copper, aluminum, etc.), insulation material, outer sheath material, etc. of the cable, the electrical conductivity and thermal conductivity of these materials are key data for subsequent analysis. The geometric shape characteristics of the circuit pipeline are extracted, including the cross-sectional dimensions, length, bending angle, etc. of the pipeline. These geometric parameters directly affect the resistance and heat loss when the current passes through the pipeline. According to Ohm's law, the resistance of the circuit pipeline can be calculated by the following formula: ;in, 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, it is necessary to perform integral calculations based on the cross-sectional geometry, or use finite element analysis to simulate the current distribution more accurately. The resistance calculation results of the pipelines are summarized to generate resistance data for pipelines in different circuits, providing basic data for subsequent power dissipation calculations. When the current in the circuit passes through the resistor, power dissipation is generated, and the power dissipation density can be calculated using the following formula: ;in, is the power dissipation, is the current, is the resistance of the circuit pipeline. According to the circuit distribution data, the distribution of current in each circuit pipeline is analyzed, and the power dissipation density of each pipeline is calculated. Based on the power dissipation density of the circuit pipeline, the heat source intensity of each pipeline is calculated. 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 perform spatial distribution analysis of the heat source intensity and predict the temperature rise of each part of the electrical circuit. For each circuit pipeline, the pipeline heat source intensity data is generated according to the power dissipation density. According to the power dissipation data of different circuit areas, the heat source intensity of the corresponding area is calculated, thereby providing preliminary data for temperature field simulation. Using the heat source intensity data, based on the geometric shape and current distribution of the pipeline, the pipeline heat source distribution mapping data is generated. The mapping data visualizes the heat source distribution of each area in the electrical circuit and helps identify hot spots. Using a heat source model (such as a heat conduction equation), the heat source intensity is combined with the geometric structure of the circuit pipeline to simulate the heat distribution of different pipeline sections. Based on the heat source distribution mapping data, the heat conduction model is further used to simulate the temperature field inside the electrical circuit pipeline. This step takes into account factors such as temperature changes caused by heat sources (power dissipation), external ambient temperature, material thermal conductivity, and thermal convection effects. The simulation results of the temperature field inside the pipeline are visualized as temperature mapping data to show 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 perform heat dissipation design.

[0041] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: 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.

[0042] In an embodiment of the present invention, ultrasonic flow field sensors are deployed in different areas of electrical equipment, and these sensors can monitor the air flow velocity in real time and calculate the air flow velocity through the echo reflection time. Sensors should be arranged in key areas of electrical circuits, such as the entrances and exits of circuit pipelines, near heat sources, etc., to obtain comprehensive air flow velocity data. The flow velocity data obtained by the ultrasonic sensor is filtered using a data preprocessing algorithm to remove noise and outliers. Common screening techniques include moving average filtering, Kalman filtering, etc. Focus on the impact of air flow velocity on electrical components, and filter out air flow velocity data in areas that have a greater impact on electrical thermal management. The filtered flow velocity data is organized into tables or time series data for subsequent air flow calculations and thermal flow simulations. The Navier-Stokes equation is a basic equation that describes the motion of viscous fluids (such as air). For incompressible fluids, the expression is: ;in, is the fluid density, is the velocity field, is the pressure field, is the fluid viscosity, is the volume force (such as gravity, heat generated by electronic equipment, etc.). The above equations are used for numerical calculations to calculate the air flow in the circuit area. Based on the flow rate data, computational fluid dynamics (CFD) software is used to simulate the air flow. The air flow rate in the electrical circuit is dynamically calculated in space and time to obtain the air flow data in the entire circuit area, including information such as the velocity field and streamline distribution. Through CFD simulation and the solution of the Navier-Stokes equations, the air flow data during the operation of the electrical circuit is generated, including the velocity field, turbulence characteristics and pressure distribution in each area. The internal temperature mapping data of the electrical pipeline is used as input, combined with the air flow data (such as velocity field, turbulence characteristics), to perform air thermal flow simulation. The influence of the temperature field on the air flow is described by the thermal convection model, and the heat transfer in the electrical circuit is calculated. Common thermal flow models are: ;in, is the thermal diffusivity, is the temperature field, The heat source is a heat source. The heat source inside the electrical appliance (such as the heat caused by the current passing through the resistor) and the fluid (air) are coupled and analyzed to simulate the interaction between heat flow and airflow. Through CFD simulation, the distribution of heat flow is calculated, and how the airflow carries heat in the circuit area is predicted. Based on the simulation results, the air heat flow simulation data in the circuit area is generated, which includes information such as heat flux density, temperature field distribution, and the flow path of air carrying heat. Combined with the heat flow simulation data obtained by simulation, a heat flow prediction model is constructed using machine learning or deep learning algorithms (such as regression models, neural networks, etc.). The model takes the input air flow characteristics and temperature field data as input to predict the heat flow distribution and hot spots of the electrical circuit. A large amount of historical data (such as heat flow data under different operating conditions of the electrical circuit) is used to train the heat flow prediction model and optimize the model parameters so that it can accurately predict the heat flow distribution under future operating conditions. Using the trained heat flow prediction model, the air flow data and temperature data of the current electrical circuit are analyzed to predict the flow path of the heat flow in the electrical circuit. The path prediction includes the flow path of heat from the heat source area to the cooling area, and predicts the heat conduction channels and airflow 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 temperature distribution of the airflow, which can provide decision support for the thermal management optimization of electrical equipment. Based on the heat flow prediction path data, use the heat conduction model to simulate the propagation of heat outside the electrical circuit pipeline. Perform heat conduction simulation on the outside of the electrical 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 to assist in heat dissipation design and optimization. Convert the heat conduction simulation results into temperature mapping data to display the temperature field outside the electrical 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.

[0043] Preferably, 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.

[0044] In an embodiment of the present invention, by obtaining the temperature mapping data inside the electrical pipeline, the data can be obtained by a temperature sensor or thermal imaging technology, covering the temperature distribution of each component in the electrical circuit. Data processing technology (such as image processing, interpolation or data fitting, etc.) is applied to extract temperature field data from the temperature mapping data. Temperature field data refers to the temperature distribution of each point inside the electrical circuit, including the temperature intensity of each area. The circuit operation air flow data obtained in step S31 is used, and the data describes the air flow rate, air flow direction and other information in the electrical circuit area. According to factors such as air flow rate, flow direction, temperature gradient, etc., the air flow area inside the electrical circuit is divided by clustering analysis (such as K-means or DBSCAN algorithm). After division, the air flow area of ​​the electrical circuit is divided into multiple sub-areas (for example, hot spot area, cooling area, slow flow area, etc.), and air flow area data is obtained. Based on the air flow area data, the air flow path is modeled by a fluid mechanics model (such as Navier-Stokes equation, particle tracking method, etc.). The purpose of modeling is to predict the flow path, flow direction and time and spatial distribution of air flow inside the electrical circuit. By simulating the movement trajectory of particles in the airflow, the path of air flow is inferred, and then the air flow path is obtained. In the air flow area, the air flow path is drawn using the streamline method based on the flow velocity data. The streamline shows the direction of the airflow and its distribution characteristics. Through calculation and simulation, the path data of air flow in the electrical circuit is generated, including the flow direction, speed and heat transfer path of the airflow. The input is temperature field data and air flow path data. The temperature field data describes the temperature distribution of each area in the electrical circuit, while the air flow path data describes the flow route of the air. Use the classic convection heat transfer equation: ;in, 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 temperature gradient, a heat flow transfer model is constructed to describe how heat flows from the heat source area to the cooling area through air. Considering the thermal diffusion characteristics of air and pipeline materials, the transfer of heat in the electrical circuit is not only through air flow, but also involves heat diffusion. Through the constructed heat flow transfer model, how heat is transferred in the electrical circuit is calculated to obtain heat flow transfer data. The heat flow transfer data includes: the heat transfer rate of each area, the heat flow path and heat flux density, and the temperature change of each point. The input includes temperature field data, air flow path data, and heat flow transfer data. This data will provide the temperature change, air flow path, and heat flow transfer method in the electrical circuit. CFD (computational fluid dynamics) software (such as ANSYS Fluent, OpenFOAM, etc.) is used to simulate and calculate the air heat flow of the electrical circuit. During the simulation process, the fluid dynamics equations (including the Navier-Stokes equations) and heat transfer equations are solved by numerical methods to calculate the heat transfer process in the air flow. During the simulation process, the coupling effect of airflow and heat flow is considered, that is, the airflow not only carries heat, but also changes the properties of the airflow (such as flow rate, flow direction, etc.) due to the existence of heat flow. The interaction between air flow and temperature change in the electrical circuit is simulated to obtain the air heat flow distribution and temperature distribution in the electrical circuit. Based on the CFD simulation results, the air heat flow simulation data of the electrical circuit is generated.

[0045] Preferably, 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.

[0046] In an embodiment of the present invention, the circuit running air heat flow simulation data obtained from step S32 is used as input. The data includes information such as temperature distribution, air flow velocity, heat flux density, etc. of each area in the electrical circuit. Use numerical analysis methods (such as temperature field gradient calculation, interpolation technology, etc.) to extract temperature distribution data of each area in the circuit. This data can be expressed as a spatial distribution of temperature (for example, the temperature of each point in a two-dimensional or three-dimensional coordinate system). By analyzing the temperature change trend, the heat source and cooling area are determined, so as to obtain the temperature change pattern 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. Heat flux density It is given by the following formula: ;in, 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 of each area of ​​the circuit. Through the above method, the heat flux characteristic data obtained includes temperature distribution and heat flux density, which provides data support for subsequent model training. According to the heat flow characteristics, data is extracted and the data set is divided. Usually, the data set is divided into a training set and a test set, and the ratio is generally 70% training set, 30% test set, or 80% training set, 20% test set. The training set is used to train the model, which contains known inputs (such as internal circuit temperature distribution, air flow path, etc.) and target outputs (such as heat flux density, heat flow path, etc.). The test set is used to verify the accuracy of the model and ensure the generalization ability of the model. The training set is trained using the support vector machine (SVM) algorithm. 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 the regression problem. According to the characteristics of the data, select an appropriate kernel function (such as linear kernel, radial basis kernel, etc.). The training is performed by maximizing the boundary 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 by the test set, the prediction error of the model is calculated, and the parameters are further adjusted and optimized. According to the optimization target, multiple model iterations are performed. The optimization process usually includes adjusting the penalty parameters (such as C) of the SVM and the parameters of the kernel function (such as γ). The optimized model is the circuit operation heat flow prediction model (circuit operation heat flow prediction pre-model). The trained circuit operation heat flow prediction model is used to predict the circuit operation air heat flow simulation data. According to the changes in the circuit operation environment (such as temperature distribution, airflow path, material properties, etc.), the trained support vector machine model is used to predict the heat flow distribution in the circuit. The generated prediction data includes: predicting the heat flux density and flow direction in different areas of the electrical circuit. By analyzing the prediction results, the heat source area and cooling area in the electrical circuit can be identified, and the design can be further optimized to generate the circuit operation heat flow distribution prediction data to describe the heat flow intensity and distribution of each area of ​​the circuit. According to the heat flow distribution prediction data generated in step S333, the flow path of the heat flow is further predicted. The heat flow path evolution is to infer the conduction path of heat from the source area to the cooling area by analyzing the predicted heat flow distribution data. Use numerical methods (such as streamline analysis, particle tracing, etc.) to simulate the heat propagation process in the circuit. Consider the mutual influence of the temperature field and the air flow path, and combine the heat flow transfer equation to calculate the evolution path of the heat flow. Through the evolution process of the heat flow path, the flow path of the heat flow in the electrical circuit is obtained, which shows how the heat flows from the high temperature area (heat source) to the low temperature area (cooling area).

[0047] In this specification, an electrical appliance temperature monitoring device is provided, which is used to execute the above-mentioned electrical appliance temperature monitoring method. The electrical appliance temperature monitoring system includes: 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.

[0048] The present invention provides a computer device, comprising 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 above-mentioned electrical appliance temperature monitoring method is implemented.

[0049] 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 method for monitoring the temperature of an electrical appliance is implemented.

[0050] The beneficial effect of the present invention is that by topological modeling of electrical structure data, the system can accurately construct the topological structure and connection route of the electrical operation circuit, ensure the correct connection and operation path of the electrical circuit, and thus optimize the design and performance of the electrical appliance. By dividing the structure through the electrical circuit connection route data, it is possible to scientifically divide the various components and functional modules of the electrical appliance, which is helpful for subsequent temperature monitoring and thermal management operations, and improves 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 circuit, and generate electrical operation temperature field data, help identify the hot spots in the electrical circuit, and ensure that the electrical equipment operates within a safe temperature range. According to the material characteristics of the electrical circuit pipeline, the system can perform heat source mapping on the hot spots of the electrical circuit, accurately draw the temperature distribution inside the pipeline, and warn potential thermal damage areas in advance to avoid overheating damage. By calculating the air flow data of the electrical circuit and combining the internal temperature mapping data to perform air heat flow simulation, the heat flow path inside the electrical appliance can be effectively simulated, providing a basis for heat dissipation optimization. Through the prediction of the heat flow path, 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 circuit and the 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 analysis, it is possible to accurately evaluate the heat impact on the electrical circuit pipeline during operation, ensuring that the electrical equipment is not damaged by overheating. The system can set a reasonable temperature threshold according to the material characteristics of the electrical circuit pipeline, monitor the temperature change in real time, and provide timely warning when abnormal temperature occurs, so as to prevent the electrical equipment from overheating, short circuit or fire and other safety hazards, and ensure the long-term safe operation of the electrical equipment. Therefore, the present invention realizes more accurate, comprehensive and intelligent electrical temperature monitoring through topological modeling of the electrical structure, multi-dimensional sensor deployment, heat flow simulation and intelligent temperature threshold analysis, solving the defects of insufficient accuracy, incomplete local monitoring and unintelligent abnormal temperature monitoring in traditional technologies.

[0051] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0052] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented 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; calculating the material characteristics and geometric shape of the circuit pipeline according to Ohm's law to obtain the resistance data of the circuit pipeline; 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, used to execute the electrical appliance temperature monitoring method according to claim 1, the electrical appliance temperature monitoring device comprising: Run the structure screening module to obtain electrical structure data; Carry out topological modeling on electrical structure data to generate electrical operation circuit topological modeling data; Based on the electrical appliance operation circuit topology modeling data, the circuit connection route is constructed for the electrical appliance structure data to generate the electrical appliance operation circuit connection route data; The electrical appliance structure data is divided into electrical appliance structure data according to the electrical appliance operation circuit connection route data, thereby obtaining the electrical appliance operation structure data; An 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; A temperature field calculation model is constructed based on the multi-dimensional sensor deployment data to obtain the electrical appliance operation temperature field data, and the electrical appliance operation temperature field data is used to extract the hot spot area of ​​the electrical appliance circuit to generate the electrical appliance circuit operation hot spot area; the electrical appliance circuit pipeline material characteristics of the electrical appliance operation structure data are extracted, and the electrical appliance circuit operation heat source mapping is performed on the electrical appliance circuit operation hot spot area based on the electrical appliance circuit pipeline material characteristics 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 according to the internal temperature mapping data of the electrical pipeline and the external temperature mapping data of the electrical pipeline, and 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.

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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