Thermal management method and system applied to four-wheel drive intelligent trolley

Abstract

The invention discloses a thermal management method and system applied to a four-wheel drive intelligent trolley, and the method comprises the steps: building a standard three-dimensional model of a target four-wheel drive intelligent trolley based on a three-dimensional modeling technology, and defining a heat source region and a non-heat source region of the standard three-dimensional model; presetting a plurality of operation conditions, performing internal temperature simulation according to the standard three-dimensional model, acquiring historical internal temperature monitoring information under the corresponding operation conditions, and analyzing an internal temperature transfer rule of the target four-wheel drive intelligent trolley; generating an internal region summary graph through the standard three-dimensional model of the target four-wheel drive intelligent trolley, and constructing a temperature prediction model based on a graph neural network; operation condition monitoring information is obtained, and the internal temperature change of the four-wheel drive intelligent trolley under the current operation working condition is predicted; a plurality of risk thresholds are preset, whether the heat accumulation risk exists or not is judged, and heat management of the four-wheel drive intelligent trolley is carried out, so that the running safety and task completion efficiency of the trolley are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of four-wheel drive intelligent vehicles, and in particular to a thermal management method and system applied to a four-wheel drive intelligent vehicle. Background Art

[0002] With the widespread application of four-wheel drive smart cars in the fields of unmanned driving, smart logistics and exploration, the heat management problem generated during its operation has become a technical difficulty that needs to be solved urgently. The main heat sources inside the smart car include the power motor, controller, battery pack and drive module. These components will generate a lot of heat under high load and continuous operation. If the heat cannot be dissipated in time, the temperature of the core components will rise, causing performance degradation, shortened life, and even equipment damage.

[0003] In addition, the heat inside the car is prone to excessive accumulation in certain areas in a high temperature environment or under long-term operation, resulting in uneven temperature field distribution, which affects the stability and safety of the vehicle. However, traditional thermal management is often triggered by a single temperature monitoring threshold and cannot achieve dynamic management. At the same time, once the thermal risk threshold is triggered, the corresponding car will often stop running to dissipate heat, thus affecting the efficiency of use.

[0004] Therefore, how to perform more intelligent thermal management on the four-wheel drive smart car that is performing work tasks, so as to improve the vehicle's operating safety and task completion efficiency, is an urgent problem to be solved. Summary of the invention

[0005] The present invention overcomes the deficiencies of the prior art and provides a thermal management method and system for a four-wheel drive intelligent vehicle, the important purpose of which is to improve the vehicle's operating safety and task completion efficiency.

[0006] To achieve the above-mentioned purpose, the first aspect of the present invention provides a thermal management method applied to a four-wheel drive intelligent vehicle, comprising: Obtain a multi-dimensional design drawing of a target four-wheel drive smart car, construct a standard three-dimensional model of the target four-wheel drive smart car based on three-dimensional modeling technology, and define a heat source area and a non-heat source area of ​​the standard three-dimensional model; Preset several operating conditions, perform internal temperature simulation according to the standard three-dimensional model, obtain historical internal temperature monitoring information under corresponding operating conditions, analyze the internal temperature transfer law of the target four-wheel drive smart car in combination with the internal temperature simulation results, and obtain temperature transfer law analysis information; Generate an internal area summary map through the standard three-dimensional model of the target four-wheel drive smart car, build a knowledge graph based on the temperature transfer law analysis information, and build a temperature prediction model based on the graph neural network; Monitor the real-time operating status of the target four-wheel drive smart car to obtain operating status monitoring information, input it into the temperature prediction model for analysis, predict the internal temperature change of the four-wheel drive smart car under the current operating conditions, and obtain internal temperature prediction information; A number of risk thresholds are preset, and the internal prediction information is judged against the preset risk thresholds. Based on the judgment results, it is analyzed whether there is a heat accumulation risk in the real-time operation scenario of the target four-wheel drive smart car, and thermal management of the four-wheel drive smart car is performed.

[0007] In this solution, the multi-dimensional design drawings of the target four-wheel drive smart car are obtained, a standard three-dimensional model of the target four-wheel drive smart car is constructed based on the three-dimensional modeling technology, and the heat source area and non-heat source area of ​​the standard three-dimensional model are defined, specifically including: Obtain a multi-dimensional design drawing of a target four-wheel drive smart car, and obtain a component library through a three-dimensional model design software, wherein the component library contains three-dimensional components for constructing the four-wheel drive smart car; Based on the three-dimensional modeling technology, three-dimensional components are matched in the component library through the multi-dimensional design drawings to obtain three-dimensional components used to construct the three-dimensional model of the target four-wheel drive intelligent car, which are defined as candidate three-dimensional components; Acquire the preparation information of the target four-wheel drive smart car through data retrieval, extract the preparation material characteristics and properties of the target four-wheel drive smart car based on the preparation information, match the obtained candidate three-dimensional components, select the target three-dimensional component and define the material properties, including thermal conductivity, specific heat capacity and density; Obtaining a device specification sheet of a target four-wheel drive smart car, extracting preset operating parameters of internal components of the target four-wheel drive smart car in each mode based on the device specification sheet, performing internal heat source analysis, and obtaining internal heat source analysis information; A standard three-dimensional model of the target four-wheel drive intelligent car is constructed in the three-dimensional design software by using the three-dimensional modeling technology according to the selected target three-dimensional components and the defined material properties, and the heat source area and the non-heat source area of ​​the standard three-dimensional model are defined by the internal heat source analysis information.

[0008] In this solution, the preset operating conditions are used to simulate the internal temperature according to the standard three-dimensional model, and the historical internal temperature monitoring information under the corresponding operating conditions is obtained. The internal temperature transfer law of the target four-wheel drive smart car is analyzed in combination with the internal temperature simulation results to obtain the temperature transfer law analysis information, which specifically includes: Acquire a standard three-dimensional model of a target four-wheel drive smart car, extract internal structural features and internal area features of the target four-wheel drive smart car based on the standard three-dimensional model, and mesh the standard three-dimensional model; Based on the internal structural characteristics obtained, fluid boundary conditions are set, including natural convection boundary conditions and forced convection boundary conditions, and radiation boundary conditions are set according to the internal area characteristics. Several operating conditions are preset, and simulation analysis is performed on the internal temperature conditions under each operating condition to obtain temperature simulation analysis information; Based on a number of preset operating conditions, historical internal temperature monitoring information under corresponding operating conditions is obtained by using big data retrieval means, data preprocessing is performed on the historical internal temperature monitoring information, and a historical internal temperature time series sequence under each operating condition is generated according to the preprocessing result; Performing time sequence processing on the temperature simulation information to generate an internal temperature simulation sequence for each operating condition, performing time sequence alignment with the historical internal temperature time sequence, and performing data supplementation and data correction on the internal temperature simulation sequence based on the time sequence alignment result to obtain a new internal temperature simulation sequence; Based on the new internal temperature simulation sequence, a thermal model is used to generate an internal temperature simulation thermodynamic map of the target four-wheel drive smart car under various operating conditions, the internal temperature simulation thermodynamic map is rasterized, and a spline function interpolation method is used to interpolate unknown unit grids; Extracting thermal chromaticity features through internal temperature simulation heat map, obtaining internal area position features corresponding to the thermal chromaticity features based on the temperature simulation information and correlating them, introducing a Bayesian inference network, and inputting the correlated thermal chromaticity features into the Bayesian inference network for training; The transient prior distribution and probability likelihood function are obtained through the Bayesian inference network. The transient posterior distribution of the thermal chromaticity feature data is calculated by combining the transient prior distribution and the probability likelihood function. Based on the transient posterior distribution, the temperature transfer probability and transfer direction inside the target four-wheel drive smart car are inferred in the Bayesian network to obtain the temperature transfer law analysis information.

[0009] In this solution, the internal area summary map is generated by using the standard three-dimensional model of the target four-wheel drive smart car, a knowledge graph is constructed in combination with the temperature transfer law analysis information, and a temperature prediction model is constructed based on a graph neural network, specifically including: Generate an internal area summary map through a standard three-dimensional model of the target four-wheel drive smart car, wherein the internal area summary map includes a heat source area and a non-heat source area inside the four-wheel drive smart car, and obtain temperature transfer law analysis information; Combining the temperature transfer law analysis information and the internal area summary map to generate an internal temperature transfer law map of the target four-wheel drive intelligent vehicle, the internal temperature transfer law map including the temperature transfer probability and trend between the internal heat source area and the non-heat source area under different operating conditions and the temperature transfer probability and trend between the non-heat source areas; According to the internal temperature transfer law diagram, the temperature transfer paths under different operating conditions are constructed with the heat source area as the starting node and the non-heat source area as the individual nodes, and the temperature transfer probability and trend of each node are associated, and a knowledge graph is constructed based on the constructed temperature transfer path; Obtain temperature simulation analysis information, use the MetaPath random walk algorithm to represent the constructed knowledge graph, build a training sample set based on the temperature simulation analysis information, learn the graph representation through a graph neural network and build a temperature prediction model, use the training sample set to train the model, and obtain a temperature prediction model that meets the expectations.

[0010] In this solution, the real-time operating status of the monitoring target four-wheel drive smart car is obtained to obtain operating status monitoring information, which is input into the temperature prediction model for analysis to predict the internal temperature change of the four-wheel drive smart car under the current operating conditions, and obtain the internal temperature prediction information, which specifically includes: A sensor array is installed in the target four-wheel drive smart car, and the real-time operating status of the target four-wheel drive smart car is monitored based on the sensor array to obtain operating status monitoring information, wherein the operating status monitoring information includes internal temperature data and operating condition data of the target four-wheel drive smart car; Inputting the operating status monitoring information into the temperature prediction model to generate a target node, performing first-order neighborhood sampling on the target node to obtain a number of neighboring nodes, and calculating the cosine similarity value between the target node and each neighboring node; The neighbor nodes are sorted by the calculated cosine similarity value, and the neighbor node with the greatest similarity is selected as the target neighbor node. The temperature transfer probability and trend corresponding to the target neighbor node are obtained to form the feature vector of the target neighbor node. The feature vectors of the target neighbor nodes are merged with the target node through an aggregation mechanism to update the feature vector of the target node, and the target node of the next temperature transfer state is formed based on the updated feature vector of the target node; Based on the target node of the next temperature transfer state, neighborhood sampling is performed to obtain neighbor nodes, and iterative updates are performed until a preset number of times are reached to obtain the feature vector of the final node. Based on the feature vector of the final node, the internal temperature prediction information of the target four-wheel drive intelligent car under the current operating conditions is generated.

[0011] In this solution, the preset risk thresholds are used to judge the internal prediction information against the preset risk thresholds, and based on the judgment results, it is analyzed whether there is a risk of heat accumulation in the real-time operation scenario of the target four-wheel drive smart car, and thermal management of the four-wheel drive smart car is performed, specifically including: Acquire internal temperature prediction information, extract temperature transfer time features based on the internal temperature prediction information, and construct an internal temperature transfer prediction path of the target four-wheel drive smart car based on the temperature transfer time features, wherein the internal temperature transfer prediction path represents a migration path and migration state of the internal temperature of the target four-wheel drive smart car over time under the current operating condition; Extract predicted temperature characteristics of the heat source area and the non-heat source area inside the target four-wheel drive smart car in the future time period based on the internal temperature transfer prediction path, and analyze the heat accumulation degree of each area inside the smart car in the future time period according to the extracted predicted temperature characteristics to obtain first analysis information; Acquire preparation information of the target four-wheel drive smart car, acquire heat resistance information of the internal preparation components of the target four-wheel drive smart car based on the preparation information, and set risk thresholds of various areas inside the smart car according to the heat resistance information; The first analysis information is judged against a preset risk threshold, and based on the judgment result, a local heat accumulation risk area of ​​the target four-wheel drive smart car in a future time period is identified to obtain local heat accumulation risk area identification information; Based on the local heat accumulation risk area identification information, determining whether there is a thermal runaway risk; if there is a thermal runaway risk, obtaining risk area type characteristics through the local heat accumulation risk area identification information, and formulating a thermal management strategy; If the local heat accumulation risk area is a heat source area, it means that the internal cooling mode of the car in the current operation control strategy cannot bear the current car operation mode, so the real-time internal cooling mode of the target four-wheel drive smart car is extracted for regulation; If the real-time internal heat dissipation mode is not the highest heat dissipation efficiency mode, the real-time internal heat dissipation mode is replaced with the next heat dissipation efficiency mode, and a control instruction is generated to control the heat dissipation module to switch the heat dissipation mode; If the real-time internal heat dissipation mode is the highest heat dissipation efficiency mode, the real-time operation control parameters of the target four-wheel drive smart car are obtained, marked as an incompatible mode, and another operation control parameter combination is retrieved to generate a control instruction to control the target four-wheel drive smart car; If the local heat accumulation risk area is a non-heat source area, the real-time internal heat dissipation mode of the target four-wheel drive smart car is adjusted and switched to the next heat dissipation efficiency mode; if the real-time internal heat dissipation mode is the highest heat dissipation efficiency mode, the operation control mode of the target four-wheel drive smart car is switched.

[0012] A second aspect of the present invention provides a thermal management system for a four-wheel drive smart car, the system comprising: a memory, a processor, the memory containing a thermal management method program for the four-wheel drive smart car, the thermal management method program for the four-wheel drive smart car being executed by the processor to implement the following steps: Obtain a multi-dimensional design drawing of a target four-wheel drive smart car, construct a standard three-dimensional model of the target four-wheel drive smart car based on three-dimensional modeling technology, and define a heat source area and a non-heat source area of ​​the standard three-dimensional model; Preset several operating conditions, perform internal temperature simulation according to the standard three-dimensional model, obtain historical internal temperature monitoring information under corresponding operating conditions, analyze the internal temperature transfer law of the target four-wheel drive smart car in combination with the internal temperature simulation results, and obtain temperature transfer law analysis information; Generate an internal area summary map through the standard three-dimensional model of the target four-wheel drive smart car, build a knowledge graph based on the temperature transfer law analysis information, and build a temperature prediction model based on the graph neural network; Monitor the real-time operating status of the target four-wheel drive smart car to obtain operating status monitoring information, input it into the temperature prediction model for analysis, predict the internal temperature change of the four-wheel drive smart car under the current operating conditions, and obtain internal temperature prediction information; A number of risk thresholds are preset, and the internal prediction information is judged against the preset risk thresholds. Based on the judgment results, it is analyzed whether there is a heat accumulation risk in the real-time operation scenario of the target four-wheel drive smart car, and thermal management of the four-wheel drive smart car is performed.

[0013] The present invention discloses a thermal management method and system for a four-wheel drive smart car, comprising: constructing a standard three-dimensional model of a target four-wheel drive smart car based on three-dimensional modeling technology, and defining a heat source area and a non-heat source area of ​​the standard three-dimensional model; presetting a number of operating conditions, performing internal temperature simulation according to the standard three-dimensional model, and obtaining historical internal temperature monitoring information under corresponding operating conditions, and analyzing the internal temperature transfer law of the target four-wheel drive smart car; generating an internal area summary map through the standard three-dimensional model of the target four-wheel drive smart car, and constructing a temperature prediction model based on a graph neural network; obtaining operating status monitoring information, and predicting the internal temperature change of the four-wheel drive smart car under the current operating conditions; presetting a number of risk thresholds, judging whether there is a heat accumulation risk, and performing thermal management of the four-wheel drive smart car, thereby improving the vehicle operation safety and task completion efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0015] Figure 1A flow chart of a thermal management method applied to a four-wheel drive smart car provided by one embodiment of the present invention; Figure 2 A flow chart of a method for regulating heat accumulation risk in a four-wheel drive intelligent vehicle provided in one embodiment of the present invention; Figure 3 A block diagram of a thermal management system for a four-wheel drive smart car provided by an embodiment of the present invention: 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

[0016] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0017] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0018] Figure 1 A flow chart of a thermal management method applied to a four-wheel drive smart car provided by one embodiment of the present invention; like Figure 1 As shown, the present invention provides a flow chart of a thermal management method applied to a four-wheel drive intelligent vehicle, comprising: S102, obtaining a multi-dimensional design drawing of a target four-wheel drive smart car, constructing a standard three-dimensional model of the target four-wheel drive smart car based on three-dimensional modeling technology, and defining a heat source area and a non-heat source area of ​​the standard three-dimensional model; S104, presetting a number of operating conditions, performing internal temperature simulation according to the standard three-dimensional model, and obtaining historical internal temperature monitoring information under the corresponding operating conditions, analyzing the internal temperature transfer law of the target four-wheel drive smart car in combination with the internal temperature simulation results, and obtaining temperature transfer law analysis information; S106, generating an internal area summary map through a standard three-dimensional model of the target four-wheel drive smart car, building a knowledge graph based on the temperature transfer law analysis information, and building a temperature prediction model based on a graph neural network; S108, monitoring the real-time operating status of the target four-wheel drive smart car to obtain operating status monitoring information, inputting the information into the temperature prediction model for analysis, predicting the internal temperature change of the four-wheel drive smart car under the current operating condition, and obtaining internal temperature prediction information; S110, presetting a number of risk thresholds, comparing the internal prediction information with the preset risk thresholds, analyzing whether there is a risk of heat accumulation in the real-time operation scenario of the target four-wheel drive smart car based on the judgment result, and performing thermal management of the four-wheel drive smart car.

[0019] It should be noted that the present invention provides a thermal management method and system for a four-wheel drive smart car. A standard three-dimensional model of a target vehicle can be constructed using three-dimensional modeling technology, and the heat source area and non-heat source area in the model can be further clarified, wherein the heat source area usually refers to heat-generating components such as power motors, battery packs, and controllers, while the non-heat source area mainly includes the body structure and the heat dissipation area. Subsequently, based on this three-dimensional model, several typical operating conditions are preset, and the dynamic analysis of the internal temperature is performed using simulation technology to simulate the temperature distribution of the vehicle under different operating conditions, and the accuracy of the simulation results is verified in combination with historical internal temperature monitoring information. The spatiotemporal law of temperature transfer is further analyzed, and the path and characteristics of heat diffusion from the heat source area to the non-heat source area are clarified, thereby generating temperature transfer law analysis information. On the basis of analyzing the temperature distribution and transfer law, the internal area of ​​the standard three-dimensional model is summarized and layered, and a simplified internal area summary diagram is generated for visualization of thermodynamic relationships. At the same time, the temperature transfer law analysis information and the summary diagram are combined to construct a knowledge map of the smart car to describe the structured knowledge of internal heat flow. Based on this knowledge graph, a temperature prediction model is established using a graph neural network. By mining the heat transfer relationship between regions, the temperature changes of different regions under specific operating conditions can be dynamically predicted. Then, the future temperature distribution of different areas inside the vehicle is predicted through the operating status monitoring information, and the internal temperature prediction information is generated. Several risk thresholds are set to determine whether the temperature of a local area exceeds the safe range. If a potential heat accumulation risk is detected, the system will conduct a risk assessment of the real-time operating scenario and take corresponding thermal management measures to effectively reduce the risk of heat accumulation and ensure the long-term stable operation of the four-wheel drive smart car.

[0020] Further, in a preferred embodiment of the present invention, the multi-dimensional design drawings of the target four-wheel drive smart car are obtained, a standard three-dimensional model of the target four-wheel drive smart car is constructed based on the three-dimensional modeling technology, and a heat source area and a non-heat source area of ​​the standard three-dimensional model are defined, specifically including: Obtain a multi-dimensional design drawing of a target four-wheel drive smart car, and obtain a component library through a three-dimensional model design software, wherein the component library contains three-dimensional components for constructing the four-wheel drive smart car; Based on the three-dimensional modeling technology, three-dimensional components are matched in the component library through the multi-dimensional design drawings to obtain three-dimensional components used to construct the three-dimensional model of the target four-wheel drive intelligent car, which are defined as candidate three-dimensional components; Acquire the preparation information of the target four-wheel drive smart car through data retrieval, extract the preparation material characteristics and properties of the target four-wheel drive smart car based on the preparation information, match the obtained candidate three-dimensional components, select the target three-dimensional component and define the material properties, including thermal conductivity, specific heat capacity and density; Obtaining a device specification sheet of a target four-wheel drive smart car, extracting preset operating parameters of internal components of the target four-wheel drive smart car in each mode based on the device specification sheet, performing internal heat source analysis, and obtaining internal heat source analysis information; A standard three-dimensional model of the target four-wheel drive intelligent car is constructed in the three-dimensional design software by using the three-dimensional modeling technology according to the selected target three-dimensional components and the defined material properties, and the heat source area and the non-heat source area of ​​the standard three-dimensional model are defined by the internal heat source analysis information.

[0021] It should be noted that by obtaining the multi-dimensional design drawings of the target four-wheel drive smart car, and with the help of the component library in the three-dimensional model design software, the multi-dimensional design drawings are parsed through the three-dimensional modeling technology, and the three-dimensional components consistent with the design requirements of the target smart car are matched from the component library, and these qualified components are defined as candidate three-dimensional components. Subsequently, the preparation information related to the target four-wheel drive smart car, such as production process, material selection and performance indicators, can be obtained through data retrieval. Based on this preparation information, the preparation material characteristics and properties of the car can be extracted, including thermal performance parameters such as thermal conductivity, specific heat capacity and density. After matching and analyzing these material properties with the candidate three-dimensional components, the target three-dimensional components that meet the design and material requirements are finally selected, and the corresponding material properties are defined for each component. Subsequently, the equipment usage specification is obtained, which contains the operating conditions and parameters of each internal component in different modes, which is used to clarify the power consumption characteristics and working status of the component, and further perform internal heat source analysis, so as to obtain internal heat source analysis information reflecting the distribution and intensity of the heat source. Combined with the aforementioned selected target three-dimensional components and defined material properties, the standard three-dimensional model of the target four-wheel drive smart car is built using three-dimensional modeling technology in the three-dimensional design software. Using the internal heat source analysis information, high-power heat-generating components, heat sinks, and auxiliary components in the model are defined as heat source areas, while other non-critical components and structural frames are defined as non-heat source areas.

[0022] Further, in a preferred embodiment of the present invention, the preset operating conditions are used to perform internal temperature simulation according to the standard three-dimensional model, and obtain historical internal temperature monitoring information under the corresponding operating conditions. The internal temperature transfer law of the target four-wheel drive intelligent car is analyzed in combination with the internal temperature simulation results to obtain temperature transfer law analysis information, which specifically includes: Acquire a standard three-dimensional model of a target four-wheel drive smart car, extract internal structural features and internal area features of the target four-wheel drive smart car based on the standard three-dimensional model, and mesh the standard three-dimensional model; Based on the internal structural characteristics obtained, fluid boundary conditions are set, including natural convection boundary conditions and forced convection boundary conditions, and radiation boundary conditions are set according to the internal area characteristics. Several operating conditions are preset, and simulation analysis is performed on the internal temperature conditions under each operating condition to obtain temperature simulation analysis information; Based on a number of preset operating conditions, historical internal temperature monitoring information under corresponding operating conditions is obtained by using big data retrieval means, data preprocessing is performed on the historical internal temperature monitoring information, and a historical internal temperature time series sequence under each operating condition is generated according to the preprocessing result; Performing time sequence processing on the temperature simulation information to generate an internal temperature simulation sequence for each operating condition, performing time sequence alignment with the historical internal temperature time sequence, and performing data supplementation and data correction on the internal temperature simulation sequence based on the time sequence alignment result to obtain a new internal temperature simulation sequence; Based on the new internal temperature simulation sequence, a thermal model is used to generate an internal temperature simulation thermodynamic map of the target four-wheel drive smart car under various operating conditions, the internal temperature simulation thermodynamic map is rasterized, and a spline function interpolation method is used to interpolate unknown unit grids; Extracting thermal chromaticity features through internal temperature simulation heat map, obtaining internal area position features corresponding to the thermal chromaticity features based on the temperature simulation information and correlating them, introducing a Bayesian inference network, and inputting the correlated thermal chromaticity features into the Bayesian inference network for training; The transient prior distribution and probability likelihood function are obtained through the Bayesian inference network. The transient posterior distribution of the thermal chromaticity feature data is calculated by combining the transient prior distribution and the probability likelihood function. Based on the transient posterior distribution, the temperature transfer probability and transfer direction inside the target four-wheel drive smart car are inferred in the Bayesian network to obtain the temperature transfer law analysis information.

[0023] It should be noted that the internal structural features and regional features of the target four-wheel drive smart car are extracted through the standard three-dimensional model, and the standard three-dimensional model is meshed based on this to provide accurate calculation units for subsequent simulation analysis. After meshing, fluid boundary conditions are set according to the internal structural characteristics of the car, including natural convection and forced convection boundary conditions, to simulate the internal air flow and heat transfer behavior. At the same time, radiation boundary conditions are set according to the internal regional characteristics to simulate the radiation heat exchange between components. On this basis, multiple operating conditions are preset and simulation analysis is performed to evaluate the internal temperature distribution of the target smart car under different operating conditions and obtain the corresponding temperature simulation analysis information. Subsequently, the historical internal temperature monitoring information of the car under the corresponding operating conditions in actual operation is obtained through big data retrieval and converted into a time series to generate the historical internal temperature time series sequence under each operating condition. Then, the temperature simulation analysis information is time-series processed to obtain the simulation temperature sequence under each operating condition, and the time series is aligned with the historical temperature time series sequence. The simulation data is supplemented and corrected according to the historical data to generate a more accurate internal temperature simulation sequence. Using the updated temperature simulation sequence, the internal temperature simulation thermal map of the target smart car under different working conditions is generated through the thermal model and rasterized. The temperature values ​​of the unknown unit grids are interpolated by the spline function interpolation method to further improve the thermal map data. Based on the thermal map, the thermal chromaticity features related to temperature are extracted, and the color features are linked to the spatial position to clarify the regional characteristics of the temperature distribution. The Bayesian inference network is introduced and trained using the extracted thermal chromaticity features. The transient posterior distribution of the thermal chromaticity feature data is calculated through the Bayesian inference network. Based on the transient posterior distribution, the transfer probability and transfer direction of the internal temperature of the car are inferred, so as to obtain a more accurate temperature transfer law, which helps to improve the efficiency and reliability of the thermal management system.

[0024] Furthermore, in a preferred embodiment of the present invention, the internal area summary map is generated by using the standard three-dimensional model of the target four-wheel drive smart car, a knowledge graph is constructed in combination with the temperature transfer law analysis information, and a temperature prediction model is constructed based on a graph neural network, specifically including: Generate an internal area summary map through a standard three-dimensional model of the target four-wheel drive smart car, wherein the internal area summary map includes a heat source area and a non-heat source area inside the four-wheel drive smart car, and obtain temperature transfer law analysis information; Combining the temperature transfer law analysis information and the internal area summary map to generate an internal temperature transfer law map of the target four-wheel drive intelligent vehicle, the internal temperature transfer law map including the temperature transfer probability and trend between the internal heat source area and the non-heat source area under different operating conditions and the temperature transfer probability and trend between the non-heat source areas; According to the internal temperature transfer law diagram, the temperature transfer paths under different operating conditions are constructed with the heat source area as the starting node and the non-heat source area as the individual nodes, and the temperature transfer probability and trend of each node are associated, and a knowledge graph is constructed based on the constructed temperature transfer path; Obtain temperature simulation analysis information, use the MetaPath random walk algorithm to represent the constructed knowledge graph, build a training sample set based on the temperature simulation analysis information, learn the graph representation through a graph neural network and build a temperature prediction model, use the training sample set to train the model, and obtain a temperature prediction model that meets the expectations.

[0025] It should be noted that the standard three-dimensional model of the target four-wheel drive smart car is used to generate a general diagram of its internal area, with the distinction between the heat source area and the non-heat source area as the core, which clearly shows the thermal structure layout inside the smart car. On this basis, the internal temperature transfer law diagram of the target smart car is further generated in combination with the temperature transfer law analysis information, so as to fully reflect the temperature dynamics inside the car. Based on the internal temperature transfer law diagram, the temperature transfer path of the car under different operating conditions is constructed with the heat source area as the starting node and the non-heat source area as the individual node. In the process of path construction, the temperature transfer probability and trend of each node are associated with the path, so that the path expression not only contains the location information, but also contains the temperature dynamic characteristics, and a knowledge graph is constructed to present the temperature distribution law in a structured form. Combined with the obtained temperature simulation analysis information, the MetaPath random walk algorithm is used to represent the constructed knowledge graph in order to capture the deep relationship and transfer characteristics between nodes in the knowledge graph. The graph representation is learned using a graph neural network. The graph neural network extracts core features from the knowledge graph by parsing the complex relationship between nodes and paths, and then constructs a temperature prediction model for the target smart car. Finally, the model is fully trained through the training sample set to optimize the prediction performance of the model, and finally a temperature prediction model that meets the expectations is obtained to predict the temperature distribution and dynamic change law of the target four-wheel drive intelligent car.

[0026] Further, in a preferred embodiment of the present invention, the real-time operating status of the monitoring target four-wheel drive smart car is obtained to obtain operating status monitoring information, which is input into the temperature prediction model for analysis, and the internal temperature change of the four-wheel drive smart car under the current operating conditions is predicted to obtain the internal temperature prediction information, which specifically includes: A sensor array is installed in the target four-wheel drive smart car, and the real-time operating status of the target four-wheel drive smart car is monitored based on the sensor array to obtain operating status monitoring information, wherein the operating status monitoring information includes internal temperature data and operating condition data of the target four-wheel drive smart car; Inputting the operating status monitoring information into the temperature prediction model to generate a target node, performing first-order neighborhood sampling on the target node to obtain a number of neighboring nodes, and calculating the cosine similarity value between the target node and each neighboring node; The neighbor nodes are sorted by the calculated cosine similarity value, and the neighbor node with the greatest similarity is selected as the target neighbor node. The temperature transfer probability and trend corresponding to the target neighbor node are obtained to form the feature vector of the target neighbor node. The feature vectors of the target neighbor nodes are merged with the target node through an aggregation mechanism to update the feature vector of the target node, and the target node of the next temperature transfer state is formed based on the updated feature vector of the target node; Based on the target node of the next temperature transfer state, neighborhood sampling is performed to obtain neighbor nodes, and iterative updates are performed until a preset number of times are reached to obtain the feature vector of the final node. Based on the feature vector of the final node, the internal temperature prediction information of the target four-wheel drive intelligent car under the current operating conditions is generated.

[0027] It should be noted that a sensor array is deployed in the target four-wheel drive smart car to monitor the running status of the car in real time. The collected running status monitoring information includes internal temperature data and dynamic information under the corresponding running conditions, which is passed as input to the constructed temperature prediction model to generate the target node. For each target node, the first-order neighbor node set around the node is obtained by the neighborhood sampling method, and the cosine similarity value between the target node and these neighbor nodes is calculated to measure the similarity and association between the nodes. According to the calculated cosine similarity value, the neighbor node set is sorted, and the neighbor node most similar to the target node is selected as the target neighbor node. The temperature transfer probability and trend information of the target neighbor node are extracted, and the corresponding feature vector is constructed to reflect the state characteristics and transfer information of the node. Next, the feature vector of the target neighbor node is fused with the feature vector of the target node through the aggregation mechanism to obtain the updated features of the target node. Reflect the change and transfer trend of the temperature distribution in the current state, and generate the target node of the next temperature transfer state. Then, the neighborhood sampling is performed again on the newly generated target node, and the above process is repeated to obtain a new set of neighbor nodes, and the node feature vector is iteratively updated until the preset number of iterations or convergence conditions are reached. Finally, based on the last updated target node feature vector, the internal temperature prediction information of the target four-wheel drive smart car under the current operating conditions is obtained. The accuracy and timeliness of temperature control are improved, while reducing the risk of heat accumulation and the possibility of performance degradation.

[0028] Further, in a preferred embodiment of the present invention, the preset risk thresholds are used to judge the internal prediction information against the preset risk thresholds, and based on the judgment results, the real-time operation scenario of the target four-wheel drive smart car is analyzed to determine whether there is a risk of heat accumulation, and the thermal management of the four-wheel drive smart car is performed, specifically including: Acquire internal temperature prediction information, extract temperature transfer time features based on the internal temperature prediction information, and construct an internal temperature transfer prediction path of the target four-wheel drive smart car based on the temperature transfer time features, wherein the internal temperature transfer prediction path represents a migration path and migration state of the internal temperature of the target four-wheel drive smart car over time under the current operating condition; Extract predicted temperature characteristics of the heat source area and the non-heat source area inside the target four-wheel drive smart car in the future time period based on the internal temperature transfer prediction path, and analyze the heat accumulation degree of each area inside the smart car in the future time period according to the extracted predicted temperature characteristics to obtain first analysis information; Acquire preparation information of the target four-wheel drive smart car, acquire heat resistance information of the internal preparation components of the target four-wheel drive smart car based on the preparation information, and set risk thresholds of various areas inside the smart car according to the heat resistance information; The first analysis information is judged against a preset risk threshold, and based on the judgment result, a local heat accumulation risk area of ​​the target four-wheel drive smart car in a future time period is identified to obtain local heat accumulation risk area identification information; Based on the local heat accumulation risk area identification information, determining whether there is a thermal runaway risk; if there is a thermal runaway risk, obtaining risk area type characteristics through the local heat accumulation risk area identification information, and formulating a thermal management strategy; If the local heat accumulation risk area is a heat source area, it means that the internal cooling mode of the car in the current operation control strategy cannot bear the current car operation mode, so the real-time internal cooling mode of the target four-wheel drive smart car is extracted for regulation; If the real-time internal heat dissipation mode is not the highest heat dissipation efficiency mode, the real-time internal heat dissipation mode is replaced with the next heat dissipation efficiency mode, and a control instruction is generated to control the heat dissipation module to switch the heat dissipation mode; If the real-time internal heat dissipation mode is the highest heat dissipation efficiency mode, the real-time operation control parameters of the target four-wheel drive smart car are obtained, marked as an incompatible mode, and another operation control parameter combination is retrieved to generate a control instruction to control the target four-wheel drive smart car; If the local heat accumulation risk area is a non-heat source area, the real-time internal heat dissipation mode of the target four-wheel drive smart car is adjusted and switched to the next heat dissipation efficiency mode; if the real-time internal heat dissipation mode is the highest heat dissipation efficiency mode, the operation control mode of the target four-wheel drive smart car is switched.

[0029] It should be noted that the temperature transfer time characteristics are extracted from the internal temperature prediction information, and the temperature transfer prediction path is constructed based on this. This path describes the migration law and migration state of the internal temperature of the target car over time under the current operating conditions. Subsequently, the predicted temperature characteristics of the heat source area and the non-heat source area in the future time period are extracted according to the path, and the heat accumulation degree of each area inside the car is further evaluated, thereby generating the first analysis information. At the same time, based on the preparation information of the target four-wheel drive intelligent car, the heat resistance information of each internal device is extracted, and the risk threshold is set for different internal areas in combination with the heat resistance characteristics. By comparing the first analysis information with the risk threshold, the local heat accumulation risk area that may be formed in the future time period can be identified, and the relevant area identification information can be obtained. Based on the identification information, it is evaluated whether there is a possibility of thermal runaway. If the risk of thermal runaway is found, a corresponding thermal management strategy is formulated for regulation. When the risk area is a heat source area, it means that the internal heat dissipation mode in the current operation control strategy can no longer meet the operation heat dissipation requirements. In this case, the real-time internal heat dissipation mode of the target car is first extracted. If the current mode is not the highest heat dissipation efficiency mode, the heat dissipation effect is enhanced by replacing it with the next higher efficiency heat dissipation mode, and a control instruction is generated to control the heat dissipation module to switch the mode. If the current mode is already the highest heat dissipation efficiency mode, it is considered that the existing operating mode is incompatible. At this time, the real-time operating control parameters are obtained and marked as incompatible modes. Then, other operating control parameter combinations are retrieved and selected to generate control instructions to adjust and optimize the operating mode of the target car, thereby reducing the operating efficiency of the target car, such as reducing the driving speed or switching the transportation path. If the local heat accumulation risk area is located in a non-heat source area, the real-time internal heat dissipation mode is preferentially adjusted to switch the current mode to a more efficient heat dissipation mode. Similarly, if the highest heat dissipation efficiency mode is enabled but still cannot solve the problem, the operating control mode of the target four-wheel drive smart car is further switched, thereby reducing the local heat accumulation risk in the non-heat source area in a global optimization manner, ensuring stable operation of the equipment and effective avoidance of thermal runaway risks.

[0030] Figure 2 A flow chart of a method for regulating heat accumulation risk in a four-wheel drive intelligent vehicle provided in one embodiment of the present invention; like Figure 2 As shown, the present invention provides a flow chart of a method for regulating heat accumulation risk of a four-wheel drive intelligent vehicle, including: S202, monitoring the real-time operating status of the target four-wheel drive smart car based on the sensor array, and inputting the real-time operating status monitoring information into the temperature prediction model to obtain internal temperature prediction information; S204, extracting temperature transfer time features based on the internal temperature prediction information, and constructing an internal temperature transfer prediction path for the target four-wheel drive smart car based on the temperature transfer time features; S206, extracting predicted temperature characteristics of the heat source area and the non-heat source area inside the target four-wheel drive smart car in the future time period based on the internal temperature transfer prediction path, and analyzing the heat accumulation degree of each area inside the smart car in the future time period according to the extracted predicted temperature characteristics to obtain first analysis information; S208, judging the first analysis information and a preset risk threshold, identifying a local heat accumulation risk area of ​​the target four-wheel drive smart car in a future time period based on the judgment result, and obtaining local heat accumulation risk area identification information; S210, judging whether there is a thermal runaway risk based on the local heat accumulation risk area identification information, and if there is a thermal runaway risk, obtaining risk area type characteristics through the local heat accumulation risk area identification information, and formulating a thermal management strategy.

[0031] Furthermore, in a thermal management method for a four-wheel drive intelligent vehicle provided by the present invention, the following steps are also included: Based on data retrieval, historical component maintenance instances of the four-wheel drive intelligent vehicle under different operating temperature environments are obtained, and maintenance life state characteristics and operating environment characteristics of each component during maintenance are extracted according to the historical component maintenance instances to obtain a first feature set; Calculating the Euclidean distance value between each historical component maintenance instance in the first feature set, comparing the Euclidean distance value with a preset distance threshold, and classifying the historical component maintenance instance categories according to the judgment result to obtain a plurality of category subsets; Perform feature extraction on each category subset respectively, introduce the PCA algorithm to perform principal component analysis on the extracted features, obtain the principal component score of the feature data corresponding to each category subset, and select the principal component features of the corresponding category subset according to the principal component score; Construct a component maintenance analysis model, construct a corresponding category subset feature portrait based on the principal component features of each category subset, and use the feature portrait as a training sample to perform deep learning and training on the component maintenance analysis model to obtain a component maintenance analysis model that meets expectations; Obtaining historical vehicle internal environment monitoring information of the target four-wheel drive smart car, inputting it into the component maintenance analysis model for analysis, determining whether the internal components of the target four-wheel drive smart car meet the maintenance standards under the historical internal temperature environment, and evaluating the life status of the corresponding components to obtain component maintenance analysis information; A maintenance warning report is generated based on the component maintenance analysis information and pushed.

[0032] It should be noted that during normal use and operation of the four-wheel drive smart car, since the internal components operate at different temperatures, their corresponding lifespan and health status may decay due to temperature, that is, if a component operates at the limit or exceeds the heat resistance for a certain period of time, its corresponding lifespan and health status will decay, thus posing a potential risk of operational failure. Therefore, monitoring and analysis based on the internal environment can help dynamically manage the maintenance cycle of the four-wheel drive smart car and avoid operational risks caused by abnormal internal temperatures.

[0033] Figure 3 A thermal management system 3 applied to a four-wheel drive smart car is provided in one embodiment of the present invention. The system includes: a memory 31 and a processor 32. The memory 31 contains a thermal management method program applied to the four-wheel drive smart car. When the thermal management method program applied to the four-wheel drive smart car is executed by the processor 32, the following steps are implemented: Obtain a multi-dimensional design drawing of a target four-wheel drive smart car, construct a standard three-dimensional model of the target four-wheel drive smart car based on three-dimensional modeling technology, and define a heat source area and a non-heat source area of ​​the standard three-dimensional model; Preset several operating conditions, perform internal temperature simulation according to the standard three-dimensional model, obtain historical internal temperature monitoring information under corresponding operating conditions, analyze the internal temperature transfer law of the target four-wheel drive smart car in combination with the internal temperature simulation results, and obtain temperature transfer law analysis information; Generate an internal area summary map through the standard three-dimensional model of the target four-wheel drive smart car, build a knowledge graph based on the temperature transfer law analysis information, and build a temperature prediction model based on the graph neural network; Monitor the real-time operating status of the target four-wheel drive smart car to obtain operating status monitoring information, input it into the temperature prediction model for analysis, predict the internal temperature change of the four-wheel drive smart car under the current operating conditions, and obtain internal temperature prediction information; A number of risk thresholds are preset, and the internal prediction information is judged against the preset risk thresholds. Based on the judgment results, it is analyzed whether there is a heat accumulation risk in the real-time operation scenario of the target four-wheel drive smart car, and thermal management of the four-wheel drive smart car is performed.

[0034] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0035] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0036] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0037] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0038] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0039] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A thermal management method applied to a four-wheel drive intelligent vehicle, characterized in that: include: Obtain a multi-dimensional design drawing of a target four-wheel drive smart car, construct a standard three-dimensional model of the target four-wheel drive smart car based on three-dimensional modeling technology, and define a heat source area and a non-heat source area of ​​the standard three-dimensional model; Preset several operating conditions, perform internal temperature simulation according to the standard three-dimensional model, obtain historical internal temperature monitoring information under corresponding operating conditions, analyze the internal temperature transfer law of the target four-wheel drive smart car in combination with the internal temperature simulation results, and obtain temperature transfer law analysis information; Generate an internal area summary map through the standard three-dimensional model of the target four-wheel drive smart car, build a knowledge graph based on the temperature transfer law analysis information, and build a temperature prediction model based on the graph neural network; Monitor the real-time operating status of the target four-wheel drive smart car to obtain operating status monitoring information, input it into the temperature prediction model for analysis, predict the internal temperature change of the four-wheel drive smart car under the current operating conditions, and obtain internal temperature prediction information; A number of risk thresholds are preset, and the internal prediction information is judged against the preset risk thresholds. Based on the judgment results, it is analyzed whether there is a heat accumulation risk in the real-time operation scenario of the target four-wheel drive smart car, and thermal management of the four-wheel drive smart car is performed.

2. A thermal management method for a four-wheel drive smart car according to claim 1, characterized in that: The method of obtaining a multi-dimensional design drawing of the target four-wheel drive smart car, constructing a standard three-dimensional model of the target four-wheel drive smart car based on three-dimensional modeling technology, and defining a heat source area and a non-heat source area of ​​the standard three-dimensional model specifically includes: Obtain a multi-dimensional design drawing of a target four-wheel drive smart car, and obtain a component library through a three-dimensional model design software, wherein the component library contains three-dimensional components for constructing the four-wheel drive smart car; Based on the three-dimensional modeling technology, three-dimensional components are matched in the component library through the multi-dimensional design drawings to obtain three-dimensional components used to construct the three-dimensional model of the target four-wheel drive intelligent vehicle, which are defined as candidate three-dimensional components; Acquire the preparation information of the target four-wheel drive smart car through data retrieval, extract the preparation material characteristics and properties of the target four-wheel drive smart car based on the preparation information, match the obtained candidate three-dimensional components, select the target three-dimensional component and define the material properties, including thermal conductivity, specific heat capacity and density; Obtaining a device specification of a target four-wheel drive smart car, extracting preset operating parameters of internal components of the target four-wheel drive smart car in each mode based on the device specification, performing internal heat source analysis, and obtaining internal heat source analysis information; A standard three-dimensional model of the target four-wheel drive intelligent car is constructed in the three-dimensional design software by using the three-dimensional modeling technology according to the selected target three-dimensional components and the defined material properties, and the heat source area and the non-heat source area of ​​the standard three-dimensional model are defined by the internal heat source analysis information.

3. The thermal management method for a four-wheel drive smart car according to claim 1, characterized in that: The preset several operating conditions, perform internal temperature simulation according to the standard three-dimensional model, obtain historical internal temperature monitoring information under the corresponding operating conditions, analyze the internal temperature transfer law of the target four-wheel drive smart car in combination with the internal temperature simulation results, and obtain temperature transfer law analysis information, which specifically includes: Acquire a standard three-dimensional model of a target four-wheel drive smart car, extract internal structural features and internal area features of the target four-wheel drive smart car based on the standard three-dimensional model, and mesh the standard three-dimensional model; Based on the internal structural characteristics obtained, fluid boundary conditions are set, including natural convection boundary conditions and forced convection boundary conditions, and radiation boundary conditions are set according to the internal area characteristics. Several operating conditions are preset, and simulation analysis is performed on the internal temperature conditions under each operating condition to obtain temperature simulation analysis information; Based on a number of preset operating conditions, historical internal temperature monitoring information under corresponding operating conditions is obtained by using big data retrieval means, data preprocessing is performed on the historical internal temperature monitoring information, and a historical internal temperature time series sequence under each operating condition is generated according to the preprocessing result; Performing time sequence processing on the temperature simulation information to generate an internal temperature simulation sequence for each operating condition, performing time sequence alignment with the historical internal temperature time sequence, and performing data supplementation and data correction on the internal temperature simulation sequence based on the time sequence alignment result to obtain a new internal temperature simulation sequence; Based on the new internal temperature simulation sequence, a thermal model is used to generate an internal temperature simulation thermodynamic map of the target four-wheel drive smart car under various operating conditions, the internal temperature simulation thermodynamic map is rasterized, and a spline function interpolation method is used to interpolate unknown unit grids; Extracting thermal chromaticity features through internal temperature simulation heat map, obtaining internal area position features corresponding to the thermal chromaticity features based on the temperature simulation information and correlating them, introducing a Bayesian inference network, and inputting the correlated thermal chromaticity features into the Bayesian inference network for training; The transient prior distribution and probability likelihood function are obtained through the Bayesian inference network. The transient posterior distribution of the thermal chromaticity feature data is calculated by combining the transient prior distribution and the probability likelihood function. Based on the transient posterior distribution, the temperature transfer probability and transfer direction inside the target four-wheel drive smart car are inferred in the Bayesian network to obtain the temperature transfer law analysis information.

4. The thermal management method for a four-wheel drive smart car according to claim 1, characterized in that: The method generates an internal area summary map through a standard three-dimensional model of the target four-wheel drive smart car, constructs a knowledge graph based on the temperature transfer law analysis information, and constructs a temperature prediction model based on a graph neural network, specifically including: Generate an internal area summary map through a standard three-dimensional model of the target four-wheel drive smart car, wherein the internal area summary map includes a heat source area and a non-heat source area inside the four-wheel drive smart car, and obtain temperature transfer law analysis information; Combining the temperature transfer law analysis information and the internal area summary map to generate an internal temperature transfer law map of the target four-wheel drive intelligent vehicle, the internal temperature transfer law map including the temperature transfer probability and trend between the internal heat source area and the non-heat source area under different operating conditions and the temperature transfer probability and trend between the non-heat source areas; According to the internal temperature transfer law diagram, the temperature transfer paths under different operating conditions are constructed with the heat source area as the starting node and the non-heat source area as the individual nodes, and the temperature transfer probability and trend of each node are associated, and a knowledge graph is constructed based on the constructed temperature transfer path; Obtain temperature simulation analysis information, use the MetaPath random walk algorithm to represent the constructed knowledge graph, build a training sample set based on the temperature simulation analysis information, learn the graph representation through a graph neural network and build a temperature prediction model, use the training sample set to train the model, and obtain a temperature prediction model that meets the expectations.

5. The thermal management method for a four-wheel drive smart car according to claim 1, characterized in that: The real-time operating status of the monitoring target four-wheel drive smart car is obtained by operating status monitoring information, which is input into the temperature prediction model for analysis, and the internal temperature change of the four-wheel drive smart car under the current operating condition is predicted to obtain the internal temperature prediction information, which specifically includes: A sensor array is installed in the target four-wheel drive smart car, and the real-time operating status of the target four-wheel drive smart car is monitored based on the sensor array to obtain operating status monitoring information, wherein the operating status monitoring information includes internal temperature data and operating condition data of the target four-wheel drive smart car; Inputting the operating status monitoring information into the temperature prediction model to generate a target node, performing first-order neighborhood sampling on the target node to obtain a number of neighboring nodes, and calculating the cosine similarity value between the target node and each neighboring node; The neighbor nodes are sorted by the calculated cosine similarity value, and the neighbor node with the greatest similarity is selected as the target neighbor node. The temperature transfer probability and trend corresponding to the target neighbor node are obtained to form the feature vector of the target neighbor node. The feature vectors of the target neighbor nodes are merged with the target node through an aggregation mechanism to update the feature vector of the target node, and the target node of the next temperature transfer state is formed based on the updated feature vector of the target node; Based on the target node of the next temperature transfer state, neighborhood sampling is performed to obtain neighbor nodes, and iterative updates are performed until a preset number of times are reached to obtain the feature vector of the final node. Based on the feature vector of the final node, the internal temperature prediction information of the target four-wheel drive intelligent car under the current operating conditions is generated.

6. The thermal management method for a four-wheel drive smart car according to claim 1, characterized in that: The preset risk thresholds are used to judge the internal prediction information against the preset risk thresholds, and based on the judgment results, the real-time operation scenario of the target four-wheel drive smart car is analyzed to determine whether there is a risk of heat accumulation, and the thermal management of the four-wheel drive smart car is performed, specifically including: Acquire internal temperature prediction information, extract temperature transfer time features based on the internal temperature prediction information, and construct an internal temperature transfer prediction path of the target four-wheel drive smart car based on the temperature transfer time features, wherein the internal temperature transfer prediction path represents a migration path and migration state of the internal temperature of the target four-wheel drive smart car over time under the current operating condition; Extract predicted temperature characteristics of the heat source area and the non-heat source area inside the target four-wheel drive smart car in the future time period based on the internal temperature transfer prediction path, and analyze the heat accumulation degree of each area inside the smart car in the future time period according to the extracted predicted temperature characteristics to obtain first analysis information; Acquire preparation information of the target four-wheel drive smart car, acquire heat resistance information of the internal preparation components of the target four-wheel drive smart car based on the preparation information, and set risk thresholds of various areas inside the smart car according to the heat resistance information; The first analysis information is judged against a preset risk threshold, and based on the judgment result, a local heat accumulation risk area of ​​the target four-wheel drive smart car in a future time period is identified to obtain local heat accumulation risk area identification information; Based on the local heat accumulation risk area identification information, determining whether there is a thermal runaway risk; if there is a thermal runaway risk, obtaining risk area type characteristics through the local heat accumulation risk area identification information, and formulating a thermal management strategy; If the local heat accumulation risk area is a heat source area, it means that the internal cooling mode of the car in the current operation control strategy cannot bear the current car operation mode, so the real-time internal cooling mode of the target four-wheel drive smart car is extracted for regulation; If the real-time internal heat dissipation mode is not the highest heat dissipation efficiency mode, the real-time internal heat dissipation mode is replaced with the next heat dissipation efficiency mode, and a control instruction is generated to control the heat dissipation module to switch the heat dissipation mode; If the real-time internal heat dissipation mode is the highest heat dissipation efficiency mode, the real-time operation control parameters of the target four-wheel drive smart car are obtained, marked as an incompatible mode, and another operation control parameter combination is retrieved to generate a control instruction to control the target four-wheel drive smart car; If the local heat accumulation risk area is a non-heat source area, the real-time internal heat dissipation mode of the target four-wheel drive smart car is adjusted and switched to the next heat dissipation efficiency mode; if the real-time internal heat dissipation mode is the highest heat dissipation efficiency mode, the operation control mode of the target four-wheel drive smart car is switched.

7. A thermal management system applied to a four-wheel drive intelligent vehicle, characterized in that: The system includes: a memory and a processor, wherein the memory contains a thermal management method program applied to a four-wheel drive smart car, and when the thermal management method program applied to a four-wheel drive smart car is executed by the processor, the following steps are implemented: Obtain a multi-dimensional design drawing of a target four-wheel drive smart car, construct a standard three-dimensional model of the target four-wheel drive smart car based on three-dimensional modeling technology, and define a heat source area and a non-heat source area of ​​the standard three-dimensional model; Preset several operating conditions, perform internal temperature simulation according to the standard three-dimensional model, obtain historical internal temperature monitoring information under corresponding operating conditions, analyze the internal temperature transfer law of the target four-wheel drive smart car in combination with the internal temperature simulation results, and obtain temperature transfer law analysis information; Generate an internal area summary map through the standard three-dimensional model of the target four-wheel drive smart car, build a knowledge graph based on the temperature transfer law analysis information, and build a temperature prediction model based on the graph neural network; Monitor the real-time operating status of the target four-wheel drive smart car to obtain operating status monitoring information, input it into the temperature prediction model for analysis, predict the internal temperature change of the four-wheel drive smart car under the current operating conditions, and obtain internal temperature prediction information; A number of risk thresholds are preset, and the internal prediction information is judged against the preset risk thresholds. Based on the judgment results, it is analyzed whether there is a heat accumulation risk in the real-time operation scenario of the target four-wheel drive smart car, and thermal management of the four-wheel drive smart car is performed.

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