Intelligent temperature control method for direct current charging pile and related device

Abstract

The application discloses an intelligent temperature control method for a direct-current charging pile and related devices, and relates to the technical field of data processing.The method comprises the following steps: transmitting real-time environment data and real-time temperature data obtained to a main control module; performing environment influence analysis based on the real-time environment data; performing temperature field distribution analysis based on the environment influence analysis data and the real-time temperature data; generating point temperature of each target device based on the temperature field distribution data and the real-time temperature data; comparing the point temperature with a preset threshold value, and if the point temperature exceeds the preset threshold value, calculating the difference between the point temperature and the preset threshold value to correct and match the charging current, obtaining a target correction value, and adjusting the charging current of the direct-current charging pile based on the target correction value; and determining a corresponding heat dissipation strategy based on the target correction value to perform heat dissipation treatment on the direct-current charging pile.The application improves the temperature regulation effect of the direct-current charging pile and can effectively prevent the occurrence of overheating events.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to an intelligent temperature control method and related device for DC charging piles. Background Technology

[0002] my country's new energy industry is currently experiencing rapid development, and electric vehicles, powered by electricity, have gradually become the most popular type of car. The demand for charging services is also increasing year by year, with more and more DC charging piles being used to supply electricity to these vehicles. DC charging piles generate a significant amount of heat during charging, and temperature control is necessary to prevent overheating and potential safety accidents. Current temperature control methods for DC charging piles often ignore environmental factors, leading to inaccurate analysis of temperature distribution and insufficient reliability in temperature control decisions. Monitoring the point temperatures of the charging pile components can provide a better understanding of their temperature status, currently often achieved through junction temperature algorithms. However, this method yields temperatures that deviate significantly from actual conditions, affecting the accuracy of temperature control. Furthermore, analyzing the charging current adjustment and heat dissipation strategies based on the component point temperatures is also crucial. Inaccurate analysis of these strategies will result in poor temperature regulation and an inability to effectively prevent overheating. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides an intelligent temperature control method and related device for DC charging piles, which improves the temperature regulation effect of DC charging piles and can effectively prevent overheating events.

[0004] To address the aforementioned technical problems, this invention provides an intelligent temperature control method for DC charging piles, applied to the main control module and data acquisition module of the DC charging pile, wherein the main control module and the data acquisition module are communicatively connected; the method includes:

[0005] The data acquisition module acquires real-time environmental data and real-time temperature data of each target device in the DC charging pile, and transmits the real-time environmental data and real-time temperature data to the main control module.

[0006] The main control module performs environmental impact analysis based on the real-time environmental data to obtain environmental impact analysis data.

[0007] Temperature field distribution data is obtained by performing temperature field distribution analysis based on the environmental impact analysis data and real-time temperature data.

[0008] The point temperature of each target device is generated based on the temperature field distribution data and real-time temperature data.

[0009] The temperature at the specified point is compared with a preset threshold. If the temperature at the specified point exceeds the preset threshold, the difference between the temperature at the specified point and the preset threshold is calculated. Based on the difference, the charging current is corrected and matched to obtain a target correction value. The charging current of the DC charging pile is then adjusted based on the target correction value.

[0010] Based on the target correction value, a corresponding heat dissipation strategy is determined, and the heat dissipation treatment of the DC charging pile is carried out based on the heat dissipation strategy.

[0011] Optionally, the step of performing environmental impact analysis based on the real-time environmental data to obtain environmental impact analysis data includes:

[0012] The temperature data of each target device in the DC charging pile during the historical charging process is obtained, and the sudden change points are screened based on the temperature data to obtain several temperature sudden change points.

[0013] Several fluctuation intervals are generated based on several temperature abrupt change points, and the difference between adjacent extreme points within each fluctuation interval is calculated.

[0014] Environmental membership analysis is performed based on the differences between adjacent extreme points within each fluctuation range to obtain the target environmental membership. An environmental impact analysis model is then constructed based on the target environmental membership and environmental factors.

[0015] Based on the environmental impact analysis model, environmental impact analysis is performed using the real-time environmental data to obtain environmental impact analysis data.

[0016] Optionally, the step of performing temperature field distribution analysis based on the environmental impact analysis data and real-time temperature data to obtain temperature field distribution data includes:

[0017] A three-dimensional geometric model is constructed based on the geometric numerical model of a DC charging pile;

[0018] Based on the three-dimensional geometric model and the real-time temperature data, the heat generation parameters are calculated to obtain the target heat generation parameters.

[0019] Based on the target heat generation parameters and environmental impact analysis data, Kalman filter fusion analysis is performed to obtain the device temperature distribution estimation results.

[0020] Based on the temperature distribution estimation results of the device, spatiotemporal variation characteristic analysis and three-dimensional spatial interpolation are performed to obtain temperature field distribution data.

[0021] Optionally, the three-dimensional geometric model constructed based on the geometric numerical model of the DC charging pile includes:

[0022] The geometric values ​​are input into a preset three-dimensional geometric model framework to generate an initial three-dimensional geometric model;

[0023] The observation data of the initial three-dimensional geometric model is extracted, and the least squares method is used to analyze the model correction parameters based on the observation data to obtain the target model correction parameters.

[0024] Based on the target model correction parameters, the initial three-dimensional geometric model is corrected and patch repaired to obtain a three-dimensional geometric model.

[0025] Optionally, generating the point temperature of each target device based on the temperature field distribution data and real-time temperature data includes:

[0026] Based on the three-dimensional geometric model of the DC charging pile, a preset flow balance model is used to perform flow balance analysis at the cooling points to obtain flow balance data.

[0027] The point temperature of each target device is analyzed based on the flow balance data, combined with the temperature field distribution data and real-time temperature data.

[0028] Optionally, the step of correcting and matching the charging current based on the difference to obtain the target correction value includes:

[0029] The current charging current of the DC charging pile is obtained. Based on the temperature data of each target device of the DC charging pile in the historical charging process and the current charging current, feature extraction and cross-modal fusion are performed to obtain the current time series feature matrix.

[0030] Based on the current timing feature matrix, the charging current-point temperature relationship curve is obtained by interval truncation.

[0031] Based on the charging current-point temperature relationship curve, the target correction value of the charging current is matched using the difference.

[0032] Optionally, determining the corresponding heat dissipation strategy based on the target correction value and performing heat dissipation treatment on the DC charging pile based on the heat dissipation strategy includes:

[0033] Acquire historical heat dissipation processing data and heat dissipation processing rules, and construct a topological relationship graph based on the historical heat dissipation processing data using a relationship matrix;

[0034] Based on the aforementioned topology graph and heat dissipation rules, a heat dissipation processing tree is constructed using a binary decision graph.

[0035] Based on the heat dissipation processing tree, the corresponding heat dissipation strategy is determined using the target correction value;

[0036] Several heat dissipation components are selected in the corresponding area, and the heat dissipation components perform heat dissipation treatment on the target device corresponding to the DC charging pile based on the heat dissipation strategy.

[0037] In addition, the present invention also provides an intelligent temperature control device for a DC charging pile, applied to the main control module and data acquisition module of the DC charging pile, wherein the main control module and the data acquisition module are communicatively connected; the device includes:

[0038] Data acquisition and transmission module: used to acquire real-time environmental data and real-time temperature data of each target device in the DC charging pile based on the data acquisition module, and transmit the real-time environmental data and real-time temperature data to the main control module;

[0039] Environmental impact analysis module: used by the main control module to perform environmental impact analysis based on the real-time environmental data and obtain environmental impact analysis data;

[0040] Temperature field distribution analysis module: used to perform temperature field distribution analysis based on the environmental impact analysis data and real-time temperature data, and obtain temperature field distribution data;

[0041] Point temperature generation module: used to generate the point temperature of each target device based on the temperature field distribution data and real-time temperature data;

[0042] Charging current adjustment module: used to compare the temperature of the point with a preset threshold. If the temperature of the point exceeds the preset threshold, the difference between the temperature of the point and the preset threshold is calculated. Based on the difference, the charging current is corrected and matched to obtain a target correction value. The charging current of the DC charging pile is adjusted based on the target correction value.

[0043] Heat dissipation module: used to determine the corresponding heat dissipation strategy based on the target correction value, and to perform heat dissipation treatment on the DC charging pile based on the heat dissipation strategy.

[0044] In addition, the present invention also provides an intelligent temperature control system for a DC charging pile. The system includes a main control module and a data acquisition module. The main control module is communicatively connected to the data acquisition module. The system is configured to execute the above-described intelligent temperature control method for a DC charging pile.

[0045] In addition, the present invention provides a computer-readable storage medium that stores computer instructions, which, when executed on an electronic device, cause the electronic device to perform the above-described intelligent temperature control method for a DC charging pile.

[0046] In this embodiment of the invention, an environmental impact analysis model is used to perform environmental impact analysis based on real-time environmental data. This accurately analyzes the impact of environmental factors on temperature fluctuations, improving the reliability of temperature control judgment in DC charging piles. Temperature field distribution analysis is performed based on environmental impact analysis data and real-time temperature data. This analysis improves the comprehensiveness of temperature detection, allowing for an understanding of the internal global temperature distribution of the DC charging pile. Generating point temperatures for each target device based on temperature field distribution data and real-time temperature data improves the accuracy of point temperature analysis, preventing excessive deviations between the obtained point temperatures and actual conditions. When a point temperature exceeds a preset threshold, the charging current is corrected and matched based on the difference between the point temperature and the preset threshold. The charging current of the DC charging pile is adjusted based on a target correction value, and a corresponding heat dissipation strategy is determined according to the target correction value for heat dissipation treatment. This improves the reliability of charging current adjustment analysis and heat dissipation strategy analysis, enhances the temperature regulation effect of the DC charging pile, effectively prevents overheating events, and improves the operational safety of the DC charging pile. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart illustrating the intelligent temperature control method for a DC charging pile in an embodiment of the present invention.

[0049] Figure 2 This is a flowchart illustrating an intelligent temperature control method for a DC charging pile according to another embodiment of the present invention.

[0050] Figure 3 This is a schematic diagram of the structural composition of the intelligent temperature control system for a DC charging pile in an embodiment of the present invention;

[0051] Figure 4 This is a schematic diagram of the structure of the intelligent temperature control device for a DC charging pile in an embodiment of the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Example 1

[0054] Please see Figure 1 , Figure 1 This is a flowchart illustrating the intelligent temperature control method for a DC charging pile according to an embodiment of the present invention. The method is applied to the main control module and the data acquisition module of the DC charging pile, and the main control module and the data acquisition module are communicatively connected. The method includes:

[0055] S11: Based on the data acquisition module, acquire real-time environmental data and real-time temperature data of each target device in the DC charging pile, and transmit the real-time environmental data and real-time temperature data to the main control module;

[0056] In the specific implementation of this invention, real-time environmental data and real-time temperature data of each target device in the DC charging pile are obtained based on the data acquisition module. The real-time environmental data includes the real-time temperature and humidity of the environment where the DC charging pile is located. The target devices include charging guns, AC contactors and relays, etc. The real-time environmental data and real-time temperature data are transmitted to the main control module.

[0057] S12: The main control module performs environmental impact analysis based on the real-time environmental data to obtain environmental impact analysis data;

[0058] In the specific implementation of this invention, the step of performing environmental impact analysis based on the real-time environmental data to obtain environmental impact analysis data includes: acquiring temperature data of each target device of the DC charging pile during the historical charging process, and filtering abrupt change points based on the temperature data to obtain several temperature abrupt change points; generating several fluctuation intervals based on the several temperature abrupt change points, and calculating the difference between adjacent extreme points within each fluctuation interval; performing environmental membership analysis based on the difference between adjacent extreme points within each fluctuation interval to obtain target environmental membership, and constructing an environmental impact analysis model based on the target environmental membership and environmental factors; and performing environmental impact analysis using the real-time environmental data based on the environmental impact analysis model to obtain environmental impact analysis data.

[0059] Specifically, temperature data of each target device in the DC charging pile during the historical charging process is acquired, i.e., temperature data at each time point during the historical charging process. Based on this temperature data, abrupt change points are screened. Several temperature data curves are generated according to the temperature data of the historical charging process. In each temperature data curve, a straight line is fitted to the two adjacent data points of any given temperature data point to obtain a corresponding fitted straight line. The target slope of the fitted straight line is calculated, and the probability that the temperature data point is an abrupt change point is calculated based on the target slope. When the probability is greater than or equal to a preset probability threshold, the data point is identified as a temperature abrupt change point. The above steps are repeated until all temperature abrupt change points in the temperature data curves are analyzed, resulting in several temperature abrupt change points. Several fluctuation intervals are generated based on these temperature abrupt change points. The interval between two adjacent temperature abrupt change points is taken as the fluctuation interval, and the difference between adjacent extreme points within each fluctuation interval is calculated, i.e., the difference between adjacent extreme points within each fluctuation interval is calculated. Environmental membership analysis is performed based on the differences between adjacent extreme points within each fluctuation range. This analysis then examines the impact of these differences on device temperature under different high-temperature environmental levels, obtaining the corresponding impact levels. Based on this impact, membership analysis is conducted on environmental factors, including general high temperature, moderate high temperature, and ultra-high temperature, to obtain target environmental membership. An environmental impact analysis model is then constructed based on this target environmental membership and the environmental factors, combined with an artificial intelligence model. Finally, environmental impact analysis is performed using real-time environmental data based on this model, specifically analyzing the impact of real-time environmental data on the temperature of the DC charging pile, thus obtaining environmental impact analysis data.

[0060] S13: Based on the environmental impact analysis data and real-time temperature data, perform temperature field distribution analysis to obtain temperature field distribution data;

[0061] In the specific implementation of this invention, the step of performing temperature field distribution analysis based on the environmental impact analysis data and real-time temperature data to obtain temperature field distribution data includes: constructing a three-dimensional geometric model based on the geometric values ​​of the DC charging pile; calculating heat generation parameters based on the three-dimensional geometric model combined with the real-time temperature data to obtain target heat generation parameters; performing Kalman filter fusion analysis based on the target heat generation parameters and environmental impact analysis data to obtain device temperature distribution estimation results; and performing spatiotemporal variation feature analysis and three-dimensional spatial interpolation based on the device temperature distribution estimation results to obtain temperature field distribution data.

[0062] Furthermore, the construction of a three-dimensional geometric model based on the geometric numerical data of a DC charging pile includes: inputting the geometric numerical data into a preset three-dimensional geometric model framework to generate an initial three-dimensional geometric model; extracting observation data from the initial three-dimensional geometric model and performing model correction parameter analysis based on the observation data using the least squares method to obtain target model correction parameters; and correcting and patching the initial three-dimensional geometric model based on the target model correction parameters to obtain a three-dimensional geometric model.

[0063] Specifically, the geometric values ​​are input into a preset 3D geometric model framework, which is pre-stored in a database. The geometric values ​​of the DC charging pile include the dimensions and spacing of each component, generating an initial 3D geometric model. Observational data from this initial 3D geometric model is extracted, specifically the point cloud data observations. Based on this observation data, the least squares method is used to analyze model correction parameters. Partial derivatives are constructed from the observation data, and an estimation model is established using the least squares method based on these partial derivatives. The estimated model is then used for model correction analysis to obtain the target model correction parameters. Based on these target model correction parameters, the initial 3D geometric model is corrected and patch repairs are performed. The positions and dimensions of components in the initial 3D geometric model are corrected according to the target model correction parameters. Redundant spatial pixels in the initial 3D geometric model are deleted according to the target model parameters to obtain the final 3D geometric model. Based on the 3D geometric model and the real-time temperature data, heat generation parameters are calculated. The heat flow path, specific heat capacity, and heat consumption of the DC charging pile are obtained from the 3D geometric model, thus obtaining the target heat generation parameters. Kalman filter fusion analysis is performed based on the target heat generation parameters and environmental impact analysis data. Temperature change prediction is performed on the time series data based on the target heat generation parameters and environmental impact analysis data to obtain temperature change prediction data. A temperature dynamic model is constructed based on the temperature change prediction. The temperature dynamic model is discretized to convert the continuous time series model into a discrete time model to obtain a discrete state equation. A linear stochastic model is constructed based on the discrete state equation. The linear stochastic model is linearized to obtain a linearized state transition matrix and an observation matrix. The device temperature distribution is estimated based on the linearized state transition matrix and the observation matrix to obtain the device temperature distribution estimation result. Based on the device temperature distribution estimation results, spatiotemporal variation feature analysis and three-dimensional spatial interpolation are performed. Multi-scale time features are generated based on the device temperature distribution estimation results. The time autocorrelation function and partial autocorrelation function are calculated based on the multi-scale time features. Spatial autocorrelation analysis is performed based on the device temperature distribution estimation results to obtain a spatial hotspot distribution map. Spatiotemporal feature representation is generated based on the time autocorrelation function, partial autocorrelation function, and spatial hotspot distribution map. Nonlinear spatiotemporal feature embedding is obtained based on the spatiotemporal feature representation, and nonlinear mapping is performed on the nonlinear spatiotemporal feature embedding to obtain the spatiotemporal variation law of temperature distribution. A three-dimensional temperature field surface is constructed based on the spatiotemporal variation law of temperature distribution, and adaptive meshing and three-dimensional thin plate spline interpolation are performed on the three-dimensional temperature field surface to obtain temperature field distribution data.

[0064] S14: Generate the point temperature of each target device based on the temperature field distribution data and real-time temperature data;

[0065] In the specific implementation of this invention, the step of generating the point temperature of each target device based on the temperature field distribution data and real-time temperature data includes: performing flow balance analysis of the cooling points using a preset flow balance model based on the three-dimensional geometric model of the DC charging pile to obtain flow balance data; and analyzing the point temperature of each target device based on the flow balance data combined with the temperature field distribution data and real-time temperature data.

[0066] Specifically, based on the three-dimensional geometric model of the DC charging pile, a preset flow balance model is used to analyze the flow balance of cooling points. This obtains the current heat dissipation data of the DC charging pile, including the current cooling medium, its corresponding inflow rate, and range. The upper and lower limits of the adjustable flow rate of the cooling medium are determined according to the three-dimensional geometric model of the DC charging pile. Based on these adjustable flow limits and the current heat dissipation data, the preset flow balance model is used to analyze the flow balance of cooling points. This preset flow balance model is a convergent model obtained by training a deep neural network with a sample dataset. The resulting flow balance data represents the flow balance distribution data of the cooling medium flowing through each cooling point. Based on this flow balance data, combined with the temperature field distribution data and real-time temperature data, the point temperature of each target device is analyzed. The initial point temperature of each target device is determined based on the temperature field distribution data and real-time temperature data. The initial point temperature is then corrected based on the flow balance data to obtain the final point temperature. The point temperature of the target device is the temperature of its critical operating point.

[0067] S15: Compare the temperature at the point with a preset threshold. If the temperature at the point exceeds the preset threshold, calculate the difference between the temperature at the point and the preset threshold. Based on the difference, perform a correction and matching of the charging current to obtain a target correction value. Then, adjust the charging current of the DC charging pile based on the target correction value.

[0068] In a specific implementation of this invention, the step of correcting and matching the charging current based on the difference to obtain the target correction value includes: acquiring the current charging current of the DC charging pile; performing feature extraction and cross-modal fusion based on the temperature data of each target device of the DC charging pile during the historical charging process and the current charging current to obtain a current time series feature matrix; obtaining the charging current-point temperature relationship curve based on the current time series feature matrix using interval truncation; and matching the target correction value of the charging current based on the charging current-point temperature relationship curve using the difference.

[0069] Specifically, the temperature at the specified location is compared with a preset threshold. If the temperature at the location does not exceed the preset threshold, real-time environmental data and real-time temperature data are collected and analyzed. If the temperature at the location exceeds the preset threshold, it indicates that the operating temperature of the device is abnormal. The difference between the temperature at the location and the preset threshold is calculated to obtain the current charging current of the DC charging pile and the charging current of the DC charging pile in the current time period. Based on the temperature data of each target device of the DC charging pile in the historical charging process and the current charging current, feature extraction and cross-modal fusion are performed. The temperature data of multiple time points in the historical charging process are arranged according to a preset time dimension to form a temperature time-series input vector. The current charging current is formed into a charging current time-series input vector. The charging current time-series input vector and the temperature time-series input vector are jointly encoded by a cross-modal joint encoder containing a current time-series feature extractor and a temperature time-series feature extractor to obtain a current time-series feature matrix. Based on the current time-series feature matrix, a charging current-point temperature relationship curve is obtained by interval truncation. Data on charging current and corresponding temperature are truncated from the current time-series feature matrix according to a preset truncation range. The relationship between point temperature and charging current correction is analyzed based on the truncated data, forming a charging current-point temperature relationship curve. Based on this curve, a target correction value for the charging current is matched using the difference. The correction value is then matched against the relationship curve based on the difference to obtain the target correction value. The charging current of the DC charging pile is adjusted based on this target correction value. Adjusting the charging current reduces the operating load on the device, thereby lowering the device temperature and preventing overheating.

[0070] S16: Determine the corresponding heat dissipation strategy based on the target correction value, and perform heat dissipation treatment on the DC charging pile based on the heat dissipation strategy.

[0071] In a specific implementation of this invention, the step of determining the corresponding heat dissipation strategy based on the target correction value and performing heat dissipation processing on the DC charging pile based on the heat dissipation strategy includes: acquiring historical heat dissipation processing data and heat dissipation processing rules, and constructing a topology graph based on the historical heat dissipation processing data using a relation matrix; constructing a heat dissipation processing tree based on the topology graph and heat dissipation processing rules using a binary decision graph; determining the corresponding heat dissipation strategy based on the heat dissipation processing tree using the target correction value; selecting several heat dissipation components in a corresponding region, and the several heat dissipation components performing heat dissipation processing on the target device corresponding to the DC charging pile based on the heat dissipation strategy.

[0072] Specifically, historical heat dissipation data and rules are acquired. Historical heat dissipation data includes the difference between the temperature at different points and a preset temperature threshold, as well as the inflow rate and intensity of the heat dissipation medium under charging current correction values. Heat dissipation rules include the limits of the inflow rate and intensity of the heat dissipation medium. Based on the historical heat dissipation data, a topological relationship graph is constructed using a relationship matrix to obtain the association between the historical heat dissipation data and the heat dissipation rules. A directed acyclic graph (DAG) is constructed based on the association, with several nodes. Target features are extracted from the DAG, and the cosine similarity of each target feature is calculated. A similarity network is generated based on the cosine similarity between each target feature. A relationship matrix is ​​generated based on the similarity network and the target features. Feature coefficients are calculated using the relationship matrix, and a feature coefficient matrix is ​​constructed based on the feature coefficients. A topological relationship graph is generated using a three-adjacency matrix based on the feature coefficient matrix. A heat dissipation tree is constructed using a binary decision graph based on the topological relationship graph and the heat dissipation rules. Root nodes and child nodes are constructed according to the topological relationship graph and the heat dissipation rules to form an initial heat dissipation tree. The initial heat dissipation tree is optimized using the binary decision graph to obtain the final heat dissipation tree. Based on the heat dissipation processing tree, the corresponding heat dissipation strategy is determined using the target correction value. The target correction value is input into the heat dissipation processing tree to obtain the corresponding heat dissipation processing data, which is the corresponding heat dissipation strategy. Several heat dissipation components are selected in the corresponding area, that is, several heat dissipation components closest to the target device are selected. These heat dissipation components perform heat dissipation processing on the target device corresponding to the DC charging pile based on the heat dissipation strategy. The orientation of each heat dissipation component is turned towards the target area, and heat dissipation processing is performed on the target device corresponding to the DC charging pile according to the heat dissipation strategy.

[0073] In this embodiment of the invention, an environmental impact analysis model is used to perform environmental impact analysis based on real-time environmental data. This accurately analyzes the impact of environmental factors on temperature fluctuations, improving the reliability of temperature control judgment in DC charging piles. Temperature field distribution analysis is performed based on environmental impact analysis data and real-time temperature data. This analysis improves the comprehensiveness of temperature detection, allowing for an understanding of the internal global temperature distribution of the DC charging pile. Generating point temperatures for each target device based on temperature field distribution data and real-time temperature data improves the accuracy of point temperature analysis, preventing excessive deviations between the obtained point temperatures and actual conditions. When a point temperature exceeds a preset threshold, the charging current is corrected and matched based on the difference between the point temperature and the preset threshold. The charging current of the DC charging pile is adjusted based on a target correction value, and a corresponding heat dissipation strategy is determined according to the target correction value for heat dissipation treatment. This improves the reliability of charging current adjustment analysis and heat dissipation strategy analysis, enhances the temperature regulation effect of the DC charging pile, effectively prevents overheating events, and improves the operational safety of the DC charging pile.

[0074] Example 2

[0075] Please see Figure 2 , Figure 2 This is a flowchart illustrating an intelligent temperature control method for a DC charging pile according to another embodiment of the present invention. The method is applied to the main control module and data acquisition module of the DC charging pile, and the main control module and the data acquisition module are communicatively connected. The method includes:

[0076] S201: Based on the data acquisition module, acquire real-time environmental data and real-time temperature data of each target device in the DC charging pile, and transmit the real-time environmental data and real-time temperature data to the main control module;

[0077] S202: The main control module performs environmental impact analysis based on the real-time environmental data to obtain environmental impact analysis data;

[0078] S203: Constructing a three-dimensional geometric model based on the geometric numerical model of a DC charging pile;

[0079] S204: Calculate the heat generation parameters based on the three-dimensional geometric model and the real-time temperature data to obtain the target heat generation parameters;

[0080] S205: Based on the target heat generation parameters and environmental impact analysis data, perform Kalman filter fusion analysis to obtain the device temperature distribution estimation results;

[0081] S206: Based on the temperature distribution estimation results of the device, perform spatiotemporal variation characteristic analysis and three-dimensional spatial interpolation to obtain temperature field distribution data;

[0082] S207: Generate the point temperature of each target device based on the temperature field distribution data and real-time temperature data;

[0083] S208: Determine whether the temperature at the specified point exceeds a preset threshold;

[0084] S209: If the temperature at the point exceeds a preset threshold, calculate the difference between the temperature at the point and the preset threshold, perform a correction matching of the charging current based on the difference, obtain a target correction value, and adjust the charging current of the DC charging pile based on the target correction value.

[0085] S210: Determine the corresponding heat dissipation strategy based on the target correction value, and perform heat dissipation treatment on the DC charging pile based on the heat dissipation strategy.

[0086] In this embodiment of the invention, an environmental impact analysis model is used to perform environmental impact analysis based on real-time environmental data. This accurately analyzes the impact of environmental factors on temperature fluctuations, improving the reliability of temperature control judgment in DC charging piles. Temperature field distribution analysis is performed based on environmental impact analysis data and real-time temperature data. This analysis improves the comprehensiveness of temperature detection, allowing for an understanding of the internal global temperature distribution of the DC charging pile. Generating point temperatures for each target device based on temperature field distribution data and real-time temperature data improves the accuracy of point temperature analysis, preventing excessive deviations between the obtained point temperatures and actual conditions. When a point temperature exceeds a preset threshold, the charging current is corrected and matched based on the difference between the point temperature and the preset threshold. The charging current of the DC charging pile is adjusted based on a target correction value, and a corresponding heat dissipation strategy is determined according to the target correction value for heat dissipation treatment. This improves the reliability of charging current adjustment analysis and heat dissipation strategy analysis, enhances the temperature regulation effect of the DC charging pile, effectively prevents overheating events, and improves the operational safety of the DC charging pile.

[0087] Example 3

[0088] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of the intelligent temperature control system for a DC charging pile in an embodiment of the present invention. The system includes a main control module 31 and a data acquisition module 32. The main control module 31 and the data acquisition module 32 are communicatively connected. The system is configured to execute the intelligent temperature control method for the DC charging pile in the above embodiment.

[0089] In the specific implementation of this invention, the main control module 31 possesses intelligent control and communication functions, integrates advanced algorithms, and can control the operation of the charging module and the closing of the contactor, monitor and adjust the output voltage and current, and regulate the heat dissipation within the charging chamber. Based on a dual-processor design of microcontroller + microprocessor, it can achieve more complex data calculations and more efficient and flexible application designs, supporting various module control modes such as full matrix, dual matrix, and ring to adapt to different application scenarios. The data acquisition module 32 adopts a combination of intelligent sensors, capable of collecting real-time temperature data of each device and real-time ambient temperature and humidity data of the DC charging pile's environment.

[0090] at the same time, Figure 3 The intelligent temperature control system of the DC charging pile shown is not intended to limit all components; it may include more or fewer components than shown, or combine certain components. Specific implementation details can be found in the above embodiments, and will not be repeated here.

[0091] In this embodiment of the invention, an environmental impact analysis model is used to perform environmental impact analysis based on real-time environmental data. This accurately analyzes the impact of environmental factors on temperature fluctuations, improving the reliability of temperature control judgment in DC charging piles. Temperature field distribution analysis is performed based on environmental impact analysis data and real-time temperature data. This analysis improves the comprehensiveness of temperature detection, allowing for an understanding of the internal global temperature distribution of the DC charging pile. Generating point temperatures for each target device based on temperature field distribution data and real-time temperature data improves the accuracy of point temperature analysis, preventing excessive deviations between the obtained point temperatures and actual conditions. When a point temperature exceeds a preset threshold, the charging current is corrected and matched based on the difference between the point temperature and the preset threshold. The charging current of the DC charging pile is adjusted based on a target correction value, and a corresponding heat dissipation strategy is determined according to the target correction value for heat dissipation treatment. This improves the reliability of charging current adjustment analysis and heat dissipation strategy analysis, enhances the temperature regulation effect of the DC charging pile, effectively prevents overheating events, and improves the operational safety of the DC charging pile.

[0092] Example 4

[0093] Please see Figure 4 , Figure 4 This is a schematic diagram of the intelligent temperature control device for a DC charging pile according to an embodiment of the present invention. The device is applied to the main control module and data acquisition module of the DC charging pile, and the main control module and the data acquisition module are communicatively connected. The device includes:

[0094] Data acquisition and transmission module 41: used to acquire real-time environmental data and real-time temperature data of each target device in the DC charging pile based on the data acquisition module, and transmit the real-time environmental data and real-time temperature data to the main control module;

[0095] Environmental impact analysis module 42: used by the main control module to perform environmental impact analysis based on the real-time environmental data and obtain environmental impact analysis data;

[0096] Temperature field distribution analysis module 43: used to perform temperature field distribution analysis based on the environmental impact analysis data and real-time temperature data, and obtain temperature field distribution data;

[0097] Point temperature generation module 44: used to generate point temperatures of each target device based on the temperature field distribution data and real-time temperature data;

[0098] Charging current adjustment module 45: used to compare the temperature of the point with a preset threshold. If the temperature of the point exceeds the preset threshold, the difference between the temperature of the point and the preset threshold is calculated. Based on the difference, the charging current is corrected and matched to obtain a target correction value. The charging current of the DC charging pile is adjusted based on the target correction value.

[0099] Heat dissipation module 46: used to determine the corresponding heat dissipation strategy based on the target correction value, and to perform heat dissipation treatment of DC charging pile based on the heat dissipation strategy.

[0100] In the specific implementation of this invention, the specific implementation methods of the device can be referred to the above embodiments, and will not be repeated here.

[0101] In this embodiment of the invention, an environmental impact analysis model is used to perform environmental impact analysis based on real-time environmental data. This accurately analyzes the impact of environmental factors on temperature fluctuations, improving the reliability of temperature control judgment in DC charging piles. Temperature field distribution analysis is performed based on environmental impact analysis data and real-time temperature data. This analysis improves the comprehensiveness of temperature detection, allowing for an understanding of the internal global temperature distribution of the DC charging pile. Generating point temperatures for each target device based on temperature field distribution data and real-time temperature data improves the accuracy of point temperature analysis, preventing excessive deviations between the obtained point temperatures and actual conditions. When a point temperature exceeds a preset threshold, the charging current is corrected and matched based on the difference between the point temperature and the preset threshold. The charging current of the DC charging pile is adjusted based on a target correction value, and a corresponding heat dissipation strategy is determined according to the target correction value for heat dissipation treatment. This improves the reliability of charging current adjustment analysis and heat dissipation strategy analysis, enhances the temperature regulation effect of the DC charging pile, effectively prevents overheating events, and improves the operational safety of the DC charging pile.

[0102] This invention provides a computer-readable storage medium storing a computer program. When executed by a processor, this program implements the intelligent temperature control method for a DC charging pile according to any of the above embodiments. The computer-readable storage medium includes, but is not limited to, any type of disk (including floppy disk, hard disk, optical disk, CD-ROM, and magneto-optical disk), ROM (Read-Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic cards, or optical cards. In other words, the storage device includes any medium that stores or transmits information in a readable form by a device (e.g., a computer, a mobile phone), and can be a read-only memory, a disk, or an optical disk, etc.

[0103] Furthermore, the above provides a detailed description of the intelligent temperature control method and related devices for a DC charging pile provided by the embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A smart temperature control method for a DC charging pile, characterized in that, A main control module and a data acquisition module are applied to a DC charging pile, wherein the main control module and the data acquisition module are communicatively connected; the method includes: The data acquisition module acquires real-time environmental data and real-time temperature data of each target device in the DC charging pile, and transmits the real-time environmental data and real-time temperature data to the main control module. The main control module performs environmental impact analysis based on the real-time environmental data to obtain environmental impact analysis data, including: acquiring temperature data of each target device of the DC charging pile during the historical charging process, and filtering abrupt change points based on the temperature data to obtain several temperature abrupt change points; generating several fluctuation intervals based on the several temperature abrupt change points, and calculating the difference between adjacent extreme points within each fluctuation interval; performing environmental membership analysis based on the difference between adjacent extreme points within each fluctuation interval to obtain the target environmental membership, and constructing an environmental impact analysis model based on the target environmental membership and environmental factors; and performing environmental impact analysis using the real-time environmental data based on the environmental impact analysis model to obtain environmental impact analysis data. Temperature field distribution analysis is performed based on the environmental impact analysis data and real-time temperature data to obtain temperature field distribution data. This includes: constructing a three-dimensional geometric model based on the geometric values ​​of the DC charging pile; calculating heat generation parameters based on the three-dimensional geometric model and the real-time temperature data to obtain target heat generation parameters; performing Kalman filter fusion analysis based on the target heat generation parameters and environmental impact analysis data to obtain device temperature distribution estimation results; and performing spatiotemporal variation characteristic analysis and three-dimensional spatial interpolation based on the device temperature distribution estimation results to obtain temperature field distribution data. The point temperature of each target device is generated based on the temperature field distribution data and real-time temperature data, including: performing flow balance analysis of the cooling points using a preset flow balance model based on the three-dimensional geometric model of the DC charging pile to obtain flow balance data; and analyzing the point temperature of each target device based on the flow balance data combined with the temperature field distribution data and real-time temperature data. The process involves comparing the temperature at a specific point with a preset threshold. If the temperature exceeds the preset threshold, the difference between the temperature and the threshold is calculated. Based on this difference, the charging current is corrected and matched to obtain a target correction value. The charging current of the DC charging pile is then adjusted based on the target correction value. This includes: acquiring the current charging current of the DC charging pile; performing feature extraction and cross-modal fusion based on the temperature data of each target device in the historical charging process and the current charging current to obtain a current time-series feature matrix; obtaining a charging current-temperature relationship curve based on the current time-series feature matrix using interval truncation; and matching the target correction value of the charging current using the difference in the charging current-temperature relationship curve. Based on the target correction value, a corresponding heat dissipation strategy is determined, and the heat dissipation treatment of the DC charging pile is carried out based on the heat dissipation strategy.

2. The intelligent temperature control method for DC charging piles according to claim 1, characterized in that, The geometric numerical construction of the three-dimensional geometric model based on the DC charging pile includes: The geometric values ​​are input into a preset three-dimensional geometric model framework to generate an initial three-dimensional geometric model; The observation data of the initial three-dimensional geometric model is extracted, and the least squares method is used to analyze the model correction parameters based on the observation data to obtain the target model correction parameters. Based on the target model correction parameters, the initial three-dimensional geometric model is corrected and patch repaired to obtain a three-dimensional geometric model.

3. The intelligent temperature control method for DC charging piles according to claim 1, characterized in that, The step of determining the corresponding heat dissipation strategy based on the target correction value and performing heat dissipation treatment on the DC charging pile based on the heat dissipation strategy includes: Acquire historical heat dissipation processing data and heat dissipation processing rules, and construct a topological relationship graph based on the historical heat dissipation processing data using a relationship matrix; Based on the aforementioned topology graph and heat dissipation rules, a heat dissipation processing tree is constructed using a binary decision graph. Based on the heat dissipation processing tree, the corresponding heat dissipation strategy is determined using the target correction value; Several heat dissipation components are selected in the corresponding area, and the heat dissipation components perform heat dissipation treatment on the target device corresponding to the DC charging pile based on the heat dissipation strategy.

4. An intelligent temperature control device for a DC charging pile, characterized in that, A main control module and a data acquisition module are used in DC charging piles, wherein the main control module and the data acquisition module are communicatively connected; the device includes: Data acquisition and transmission module: used to acquire real-time environmental data and real-time temperature data of each target device in the DC charging pile based on the data acquisition module, and transmit the real-time environmental data and real-time temperature data to the main control module; Environmental Impact Analysis Module: This module is used by the main control module to perform environmental impact analysis based on the real-time environmental data, obtaining environmental impact analysis data. This includes: acquiring temperature data of each target device in the DC charging pile during the historical charging process, and filtering abrupt change points based on the temperature data to obtain several temperature abrupt change points; generating several fluctuation intervals based on the several temperature abrupt change points, and calculating the difference between adjacent extreme points within each fluctuation interval; performing environmental membership analysis based on the difference between adjacent extreme points within each fluctuation interval to obtain the target environmental membership, and constructing an environmental impact analysis model based on the target environmental membership and environmental factors; and performing environmental impact analysis using the real-time environmental data based on the environmental impact analysis model to obtain environmental impact analysis data. Temperature field distribution analysis module: used to perform temperature field distribution analysis based on the environmental impact analysis data and real-time temperature data to obtain temperature field distribution data, including: constructing a three-dimensional geometric model based on the geometric values ​​of the DC charging pile; calculating heat generation parameters based on the three-dimensional geometric model and the real-time temperature data to obtain target heat generation parameters; performing Kalman filter fusion analysis based on the target heat generation parameters and environmental impact analysis data to obtain device temperature distribution estimation results; and performing spatiotemporal variation characteristic analysis and three-dimensional spatial interpolation based on the device temperature distribution estimation results to obtain temperature field distribution data. Point temperature generation module: used to generate point temperatures of each target device based on the temperature field distribution data and real-time temperature data, including: performing flow balance analysis of cooling points using a preset flow balance model based on the three-dimensional geometric model of the DC charging pile to obtain flow balance data; and analyzing the point temperatures of each target device based on the flow balance data combined with the temperature field distribution data and real-time temperature data. Charging current adjustment module: This module compares the temperature at the charging point with a preset threshold. If the temperature at the charging point exceeds the preset threshold, it calculates the difference between the temperature at the charging point and the preset threshold. Based on the difference, it performs a correction matching of the charging current to obtain a target correction value. Then, it adjusts the charging current of the DC charging pile based on the target correction value. This includes: acquiring the current charging current of the DC charging pile; performing feature extraction and cross-modal fusion based on the temperature data of each target device in the historical charging process and the current charging current to obtain a current time-series feature matrix; obtaining a charging current-temperature relationship curve based on the current time-series feature matrix using interval truncation; and matching the target correction value of the charging current using the difference in the charging current-temperature relationship curve. Heat dissipation module: used to determine the corresponding heat dissipation strategy based on the target correction value, and to perform heat dissipation treatment on the DC charging pile based on the heat dissipation strategy.

5. An intelligent temperature control system for a DC charging pile, characterized in that, The system includes a main control module and a data acquisition module, the main control module being communicatively connected to the data acquisition module, and the system being configured to execute the intelligent temperature control method for a DC charging pile as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed on an electronic device, cause the electronic device to perform the intelligent temperature control method for a DC charging pile as described in any one of claims 1 to 3.

Images