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

Abstract

The invention discloses an intelligent temperature control method for a direct current charging pile and a related device, and relates to the technical field of data processing, and the method comprises the steps: transmitting the obtained real-time environment data and real-time temperature data to a main control module; performing environmental influence analysis based on the real-time environmental data; performing temperature field distribution analysis based on the environmental influence analysis data and the real-time temperature data; generating point position temperature of each target device based on the temperature field distribution data and the real-time temperature data; the point position temperature is compared with a preset threshold value, if the point position temperature exceeds the preset threshold value, the difference value between the point position temperature and the preset threshold value is calculated to conduct correction matching of the charging current, a target correction value is obtained, and the charging current of the direct-current charging pile is adjusted based on the target correction value; and determining a corresponding heat dissipation strategy based on the target correction value to carry out heat dissipation processing on the direct current charging pile. According to the invention, the temperature adjusting effect of the DC charging pile is improved, and an overheating event can be effectively prevented.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an intelligent temperature control method and related device for a DC charging pile. Background Art

[0002] At present, my country's new energy industry is in a stage of rapid development. The new energy vehicles derived from this, which use electricity as a driving energy source, have gradually become the most popular type of vehicles. The amount of charging services has also increased year by year, and more and more DC charging piles are used to deliver electricity to new energy vehicles. DC charging piles generate a lot of heat when charging cars. In order to avoid safety accidents caused by overheating of DC charging piles, it is necessary to control the temperature of DC charging piles. In the current temperature control method of DC charging piles, the influence of environmental factors of DC charging piles is usually ignored, resulting in the inability to accurately analyze the temperature distribution of DC charging piles, making the reliability of temperature control judgment of DC charging piles insufficient. The point temperature of the DC charging pile device can better understand the temperature state of the device. At present, it is usually analyzed through the junction temperature algorithm, but the point temperature obtained by this method deviates too much from the actual situation, affecting the accuracy of temperature control. At the same time, how to analyze the adjustment of charging current and heat dissipation strategy based on the point temperature of the device is also a problem that needs to be considered. If the adjustment of charging current and the analysis of heat dissipation strategy are inaccurate, the temperature regulation effect of DC charging piles will be poor, and overheating events cannot be effectively prevented. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art. The present invention provides an intelligent temperature control method and related devices for a DC charging pile, which improves the temperature regulation effect of the DC charging pile and can effectively prevent the occurrence of overheating events.

[0004] In order to solve the above technical problems, the present invention provides an intelligent temperature control method for a DC charging pile, which is applied to a main control module and a data acquisition module of the DC charging pile, wherein the main control module is communicatively connected with the data acquisition module; the method comprises:

[0005] 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;

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

[0007] Perform temperature field distribution analysis based on the environmental impact analysis data and the real-time temperature data to obtain temperature field distribution data;

[0008] Generate the point temperature of each target device based on the temperature field distribution data and the real-time temperature data;

[0009] Compare the point temperature with a preset threshold value. If the point temperature exceeds the preset threshold value, calculate the difference between the point temperature and the preset threshold value, perform 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;

[0010] A corresponding heat dissipation strategy is determined based on the target correction value, and heat dissipation processing of the DC charging pile is performed based on the heat dissipation strategy.

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

[0012] Acquire temperature data of each target device of the DC charging pile during the historical charging process, and perform temperature change point screening based on the temperature data to obtain a plurality of temperature change points;

[0013] Generate several fluctuation intervals based on several temperature sudden change points, and calculate the difference between adjacent extreme value points in each fluctuation interval;

[0014] Performing environmental membership analysis based on the differences between adjacent extreme value points in each fluctuation interval to obtain a target environmental membership, and constructing an environmental impact analysis model based on the target environmental membership combined with environmental factors;

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

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

[0017] Construct a 3D geometric model based on the geometric numerical value of the DC charging pile;

[0018] Calculate heat generation parameters based on the three-dimensional geometric model and the real-time temperature data to obtain target heat generation parameters;

[0019] Perform Kalman filter fusion analysis based on the target heat generation parameters and environmental impact analysis data to obtain device temperature distribution estimation results;

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

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

[0022] Inputting the geometric values ​​into a preset three-dimensional geometric model framework to generate an initial three-dimensional geometric model;

[0023] Extracting observation data of the initial three-dimensional geometric model, and performing model correction parameter analysis based on the observation data using the least square method to obtain target model correction parameters;

[0024] The initial three-dimensional geometric model is corrected and repaired based on the target model correction parameters 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 the real-time temperature data includes:

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

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

[0029] Obtain the current charging current of the DC charging pile, perform feature extraction and cross-modal fusion based on the temperature data of each target device in the DC charging pile during the historical charging process and the current charging current, and obtain the current time series feature matrix;

[0030] Based on the current time series characteristic matrix, a charging current-point temperature relationship curve is obtained by using interval interception;

[0031] The difference is used to match a target correction value of the charging current based on the charging current-point temperature relationship curve.

[0032] Optionally, determining a corresponding heat dissipation strategy based on the target correction value, and performing heat dissipation processing of 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 diagram using a relationship matrix based on the historical heat dissipation processing data;

[0034] Based on the topological relationship diagram and the heat dissipation processing rules, a heat dissipation processing tree is constructed using a binary decision diagram;

[0035] Determine a corresponding heat dissipation strategy based on the heat dissipation processing tree using the target correction value;

[0036] A plurality of heat dissipation components in a corresponding area are selected, and the plurality of heat dissipation components perform heat dissipation processing on target devices 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, which is applied to a main control module and a data acquisition module of the DC charging pile, wherein the main control module is communicatively connected with the data acquisition module; the device comprises:

[0038] Data acquisition and transmission module: used to obtain 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 for the main control module to perform environmental impact analysis based on the real-time environmental data to 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 to 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 the real-time temperature data;

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

[0043] Heat dissipation processing module: used to determine the corresponding heat dissipation strategy based on the target correction value, and perform heat dissipation processing of 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 comprising 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 above-mentioned intelligent temperature control method for the DC charging pile.

[0045] In addition, the present invention provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the above-mentioned intelligent temperature control method for a DC charging pile.

[0046] In the embodiment of the present invention, the environmental impact analysis is performed based on the environmental impact analysis model using real-time environmental data, which can accurately analyze the impact of environmental factors on temperature fluctuations and improve the reliability of temperature control judgment of the DC charging pile. The temperature field distribution analysis is performed based on the environmental impact analysis data and the real-time temperature data. The comprehensiveness of temperature detection can be improved through the temperature field distribution analysis, so that the internal global temperature distribution of the DC charging pile can be understood. The point temperature of each target device is generated based on the temperature field distribution data and the real-time temperature data, which can improve the accuracy of the point temperature analysis and avoid the deviation between the obtained point temperature and the actual situation. When it is judged that the point temperature exceeds the preset threshold, the charging current is corrected and matched based on the difference between the point temperature and the preset threshold to adjust the charging current of the DC charging pile based on the target correction value, and the corresponding heat dissipation strategy is determined according to the target correction value for heat dissipation processing, which can improve the reliability of the charging current adjustment analysis and the heat dissipation strategy analysis, improve the temperature regulation effect of the DC charging pile, effectively prevent the occurrence of overheating events, and improve the operation safety of the DC charging pile. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] 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, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 is a flow chart of an intelligent temperature control method for a DC charging pile in an embodiment of the present invention;

[0049] Figure 2 is a flow chart of an intelligent temperature control method for a DC charging pile in another embodiment of the present invention;

[0050] Figure 3 Schematic diagram of the structure of the intelligent temperature control system of the DC charging pile in the embodiment of the present invention;

[0051] Figure 4 It is a schematic diagram of the structural composition of the intelligent temperature control device of the DC charging pile in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] Embodiment 1

[0054] See also Figure 1 , Figure 1 : is a flow chart of an intelligent temperature control method for a DC charging pile in an embodiment of the present invention, the method is applied to a main control module and a data acquisition module of a DC charging pile, the main control module is communicatively connected with the data acquisition module; the method comprises:

[0055] S11: acquiring 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 transmitting the real-time environmental data and real-time temperature data to the main control module;

[0056] In the specific implementation process of the present 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, real-time humidity, etc. of the environment in which the DC charging pile is located, and the target devices include charging guns, AC contactors and relays, etc., and 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 process of the present invention, the environmental impact analysis is performed based on the real-time environmental data to obtain environmental impact analysis data, including: obtaining temperature data of each target device of the DC charging pile in the historical charging process, and screening sudden change points based on the temperature data to obtain a plurality of temperature sudden change points; generating a plurality of fluctuation intervals based on the plurality of temperature sudden change points, and calculating the difference between adjacent extreme points in each fluctuation interval; performing environmental membership analysis based on the difference between adjacent extreme points in each fluctuation interval to obtain a target environmental membership, and constructing an environmental impact analysis model based on the target environmental membership combined with environmental factors; 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, the temperature data of each target device of the DC charging pile in the historical charging process is obtained, that is, the temperature data of each time point in the historical charging process, and the sudden change point is screened based on the temperature data, and several corresponding temperature data curves are generated according to the temperature data of the historical charging process. In each temperature data curve, the two adjacent data points on the left and right of any temperature data point are linearly fitted to obtain the corresponding fitting straight line, and the target slope of the obtained fitting straight line is calculated. The possibility of the temperature data point being a sudden change point is calculated according to the target slope. When the possibility is greater than or equal to the preset possibility threshold, the data point is used as a temperature sudden change point. The above steps are repeated until the temperature sudden change points in all temperature data curves are analyzed and several temperature sudden change points are obtained. Several fluctuation intervals are generated based on several temperature sudden change points, and the interval between two adjacent temperature sudden change points is used as the fluctuation interval, and the difference between adjacent extreme value points in each fluctuation interval is calculated, that is, the difference between adjacent extreme value points in each fluctuation interval is calculated. Based on the differences between adjacent extreme points in each fluctuation interval, an environmental membership analysis is performed. Based on the differences between adjacent extreme points in each fluctuation interval, the degree of influence of device temperature under different external high temperature environment levels is analyzed to obtain the degree of influence of device temperature corresponding to different high temperature levels. According to the degree of influence of device temperature, a membership analysis of environmental factors is performed. Environmental factors include general high temperature, medium high temperature and ultra-high temperature, etc., to obtain the target environmental membership. Based on the target environmental membership and environmental factors, an environmental impact analysis model is constructed. The target environmental membership and environmental factors are combined with an artificial intelligence model to construct an environmental impact analysis model. Based on the environmental impact analysis model, an environmental impact analysis is performed using the real-time environmental data, that is, the degree of influence of the real-time environmental data on the temperature of the DC charging pile is analyzed to obtain environmental impact analysis data.

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

[0061] In the specific implementation process of the present invention, the temperature field distribution analysis is performed based on the environmental impact analysis data and the real-time temperature data to obtain the temperature field distribution data, including: constructing a three-dimensional geometric model based on the geometric numerical values ​​of the DC charging pile; calculating the heat generation parameters based on the three-dimensional geometric model in combination with the real-time temperature data to obtain the target heat generation parameters; performing Kalman filter fusion analysis based on the target heat generation parameters and the environmental impact analysis data to obtain the device temperature distribution estimation result; performing spatiotemporal change characteristic analysis and three-dimensional space interpolation based on the device temperature distribution estimation result to obtain temperature field distribution data.

[0062] Furthermore, the three-dimensional geometric model is constructed based on the geometric numerical values ​​of the DC charging pile, including: inputting the geometric numerical values ​​into a preset three-dimensional geometric model framework to generate an initial three-dimensional geometric model; extracting observation data of 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 patch-repairing 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 three-dimensional geometric model framework, which is pre-stored in a database. The geometric values ​​of the DC charging pile include the size and distance of each device, etc., to generate an initial three-dimensional geometric model. The observation data of the initial three-dimensional geometric model is extracted, that is, the point cloud data observation of the initial three-dimensional geometric model is extracted, and the model correction parameter analysis is performed based on the observation data using the least squares method, the corresponding partial derivatives are constructed through the observation data, and the estimation model is established based on the partial derivatives using the least squares method. The model correction analysis is performed through the estimation model to obtain the target model correction parameters. The initial three-dimensional geometric model is corrected and the patch is repaired based on the target model correction parameters, the device position and size in the initial three-dimensional geometric model are corrected according to the target model correction parameters, and the redundant spatial pixels in the initial three-dimensional geometric model are deleted according to the target model parameters to obtain the three-dimensional geometric model. The heat generation parameters are calculated based on the three-dimensional geometric model combined with the real-time temperature data, and the heat flow distance, specific heat capacity and heat consumption of the DC charging pile are obtained according to the three-dimensional geometric model, that is, the target heat generation parameters are obtained. A Kalman filter fusion analysis is performed based on the target heat generation parameters and the environmental impact analysis data, and a temperature change prediction of the time series is performed according to the target heat generation parameters and the environmental impact analysis data to obtain temperature change prediction data. A temperature dynamic model is constructed according to the temperature change prediction, and the temperature dynamic model is discretized to convert the model of the continuous time series into a discrete time model to obtain a discrete state equation. A linear random model is constructed according to the discrete state equation, and the linear random model is linearized to obtain a linearized state transfer matrix and an observation matrix. The device temperature distribution is estimated according to the linearized state transfer matrix and the observation matrix to obtain a device temperature distribution estimation result. Based on the device temperature distribution estimation result, a spatiotemporal variation characteristic analysis and three-dimensional space interpolation are performed, a multi-scale time characteristic is generated according to the device temperature distribution estimation result, a time autocorrelation function and a partial autocorrelation function are calculated according to the multi-scale time characteristic, a spatial autocorrelation analysis is performed according to the device temperature distribution estimation result to obtain a spatial hotspot distribution map, a spatiotemporal feature representation is generated according to the time autocorrelation function, the partial autocorrelation function and the spatial hotspot distribution map, a nonlinear spatiotemporal feature embedding is obtained according to the spatiotemporal feature representation, and a nonlinear spatiotemporal feature embedding is performed nonlinearly on the nonlinear spatiotemporal feature embedding to obtain a spatiotemporal variation law of the temperature distribution, a three-dimensional temperature field surface is constructed according to the spatiotemporal variation law of the temperature distribution, and the three-dimensional temperature field surface is adaptively meshed and three-dimensional thin plate spline interpolated to obtain temperature field distribution data.

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

[0065] In the specific implementation process of the present invention, the point temperature of each target device is generated based on the temperature field distribution data and the real-time temperature data, including: performing flow balance analysis on the cooling point using a preset flow balance model based on the three-dimensional geometric model of the DC charging pile to obtain flow balance data; analyzing the point temperature of each target device based on the flow balance data combined with the temperature field distribution data and the 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 perform flow balance analysis of cooling points, and the current heat dissipation data of the DC charging pile is obtained, including the current cooling medium and the corresponding cooling medium transmission rate and range, etc. The upper and lower limits of the adjustable flow of the cooling medium are determined according to the three-dimensional geometric model of the DC charging pile. According to the upper and lower limits of the adjustable flow and the current heat dissipation data, the preset flow balance model is used to perform flow balance analysis of cooling points. The preset flow balance model is a convergence model obtained by inputting a sample data set into a deep neural network for training, and flow balance data is obtained, that is, flow balance distribution data of the cooling medium flowing through each cooling point is obtained. Based on the flow balance data combined with the temperature field distribution data and the real-time temperature data, the point temperature of each target device is analyzed, and the initial point temperature of each target device is determined according to the temperature field distribution data and the real-time temperature data. The initial point temperature is corrected according to the flow balance data to obtain the final point temperature. The point temperature of the target device is the temperature of its key operating point.

[0067] S15: 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, performing correction matching of the charging current based on the difference, obtaining a target correction value, and adjusting the charging current of the DC charging pile based on the target correction value;

[0068] In the specific implementation process of the present invention, the correction matching of the charging current is performed based on the difference to obtain the target correction value, including: obtaining the current charging current of the DC charging pile, extracting features and cross-modal fusion based on the temperature data of each target device of the DC charging pile in the historical charging process and the current charging current, to obtain a current time series feature matrix; based on the current time series feature matrix, using interval interception to obtain a charging current-point temperature relationship curve; based on the charging current-point temperature relationship curve, using the difference to match the target correction value of the charging current.

[0069] Specifically, the point temperature is compared with a preset threshold value. If the point temperature does not exceed the preset threshold value, real-time environmental data and real-time temperature data are continuously collected for analysis. If the point temperature exceeds the preset threshold value, it indicates that the operating temperature of the device is abnormal. The difference between the point temperature and the preset threshold value 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. Feature extraction and cross-modal fusion are performed based on the temperature data and the current charging current of each target device of the DC charging pile in the historical charging process. 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 including 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 characteristic matrix, the charging current-point temperature relationship curve is obtained by using interval interception, and the charging current and corresponding temperature data of the current time series characteristic matrix are intercepted according to the preset interception interval range. The relationship between the point temperature and the charging current correction is analyzed according to the intercepted data to form a charging current-point temperature relationship curve. Based on the charging current-point temperature relationship curve, the target correction value of the charging current is matched by using the difference, and the correction value is matched in the relationship curve according to the difference to obtain the target correction value. The charging current of the DC charging pile is adjusted based on the target correction value. The operating load of the device can be reduced by adjusting the charging current, thereby reducing the temperature of the device and avoiding overheating of the device.

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

[0071] In the specific implementation process of the present invention, the corresponding heat dissipation strategy is determined based on the target correction value, and the heat dissipation treatment of the DC charging pile is performed based on the heat dissipation strategy, including: obtaining historical heat dissipation treatment data and heat dissipation treatment rules, and constructing a topological relationship diagram based on the historical heat dissipation treatment data using a relationship matrix; constructing a heat dissipation treatment tree based on the topological relationship diagram and the heat dissipation treatment rules using a binary decision diagram; determining the corresponding heat dissipation strategy based on the heat dissipation treatment tree using the target correction value; selecting a number of heat dissipation components in the corresponding area, and the several heat dissipation components perform heat dissipation treatment on the target devices corresponding to the DC charging pile based on the heat dissipation strategy.

[0072] Specifically, historical heat dissipation processing data and heat dissipation processing rules are obtained, the historical heat dissipation processing data includes the difference between different point temperatures and preset temperature thresholds and the transfer rate and intensity of the heat dissipation medium under the charging current correction value, and the heat dissipation processing rules include the limits of the transfer rate and intensity of the heat dissipation medium, and a topological relationship diagram is constructed based on the historical heat dissipation processing data using a relationship matrix, the association relationship between the historical heat dissipation processing data and the heat dissipation processing rules is obtained, and a directed acyclic graph is constructed based on the association relationship. The directed acyclic graph is provided with a number of nodes, and target features are extracted from the directed acyclic graph, the cosine similarity of each target feature is calculated, and a similarity network is generated through the cosine similarity between each target feature. A relationship matrix is ​​generated based on the similarity network and the target features, and a feature coefficient is calculated through the relationship matrix. A feature coefficient matrix is ​​constructed based on the feature coefficient, and a topological relationship diagram is generated through the feature coefficient matrix using a three-adjacent matrix. A heat dissipation processing tree is constructed based on the topological relationship diagram and the heat dissipation processing rules using a binary decision diagram, and a root node and a child node are constructed according to the topological relationship diagram and the heat dissipation processing rules to form an initial heat dissipation processing tree, and the initial heat dissipation processing tree is optimized through the binary decision diagram to obtain a final heat dissipation processing tree. Based on the heat dissipation processing tree, the corresponding heat dissipation strategy is determined using the target correction value, and the target correction value is input into the heat dissipation processing tree to obtain corresponding heat dissipation processing data, which is the corresponding heat dissipation strategy. Select a number of heat dissipation components in the corresponding area, that is, select a number of heat dissipation components closest to the target device, and the several heat dissipation components perform heat dissipation processing on the target device corresponding to the DC charging pile based on the heat dissipation strategy, turn each heat dissipation component toward the target area, and perform heat dissipation processing on the target device corresponding to the DC charging pile according to the heat dissipation strategy.

[0073] In the embodiment of the present invention, the environmental impact analysis is performed based on the environmental impact analysis model using real-time environmental data, which can accurately analyze the impact of environmental factors on temperature fluctuations and improve the reliability of temperature control judgment of the DC charging pile. The temperature field distribution analysis is performed based on the environmental impact analysis data and the real-time temperature data. The comprehensiveness of temperature detection can be improved through the temperature field distribution analysis, so that the internal global temperature distribution of the DC charging pile can be understood. The point temperature of each target device is generated based on the temperature field distribution data and the real-time temperature data, which can improve the accuracy of the point temperature analysis and avoid the deviation between the obtained point temperature and the actual situation. When it is judged that the point temperature exceeds the preset threshold, the charging current is corrected and matched based on the difference between the point temperature and the preset threshold to adjust the charging current of the DC charging pile based on the target correction value, and the corresponding heat dissipation strategy is determined according to the target correction value for heat dissipation processing, which can improve the reliability of the charging current adjustment analysis and the heat dissipation strategy analysis, improve the temperature regulation effect of the DC charging pile, effectively prevent the occurrence of overheating events, and improve the operation safety of the DC charging pile.

[0074] Embodiment 2

[0075] See also Figure 2 , Figure 2 : is a flow chart of an intelligent temperature control method for a DC charging pile in another embodiment of the present invention, the method is applied to a main control module and a data acquisition module of a DC charging pile, the main control module is communicatively connected with the data acquisition module; the method comprises:

[0076] S201: acquiring 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 transmitting 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 values ​​of the DC charging pile;

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

[0080] S205: performing Kalman filter fusion analysis based on the target heat generation parameter and the environmental impact analysis data to obtain a device temperature distribution estimation result;

[0081] S206: performing spatiotemporal variation characteristic analysis and three-dimensional space interpolation based on the device temperature distribution estimation result to obtain temperature field distribution data;

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

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

[0084] S209: If the point temperature exceeds a preset threshold, a difference between the point temperature and the preset threshold is calculated, a charging current is corrected and matched based on the difference, a target correction value is obtained, and a charging current of the DC charging pile is adjusted based on the target correction value;

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

[0086] In the embodiment of the present invention, the environmental impact analysis is performed based on the environmental impact analysis model using real-time environmental data, which can accurately analyze the impact of environmental factors on temperature fluctuations and improve the reliability of temperature control judgment of the DC charging pile. The temperature field distribution analysis is performed based on the environmental impact analysis data and the real-time temperature data. The comprehensiveness of temperature detection can be improved through the temperature field distribution analysis, so that the internal global temperature distribution of the DC charging pile can be understood. The point temperature of each target device is generated based on the temperature field distribution data and the real-time temperature data, which can improve the accuracy of the point temperature analysis and avoid the deviation between the obtained point temperature and the actual situation. When it is judged that the point temperature exceeds the preset threshold, the charging current is corrected and matched based on the difference between the point temperature and the preset threshold to adjust the charging current of the DC charging pile based on the target correction value, and the corresponding heat dissipation strategy is determined according to the target correction value for heat dissipation processing, which can improve the reliability of the charging current adjustment analysis and the heat dissipation strategy analysis, improve the temperature regulation effect of the DC charging pile, effectively prevent the occurrence of overheating events, and improve the operation safety of the DC charging pile.

[0087] Embodiment 3

[0088] See also Figure 3 , Figure 3 It is a schematic diagram of the structural composition of the intelligent temperature control system of the DC charging pile in the embodiment of the present invention. The system includes a main control module 31 and a data acquisition module 32. The main control module 31 is communicatively connected with the data acquisition module 32. The system is configured to execute the intelligent temperature control method of the DC charging pile in the above embodiment.

[0089] In the specific implementation process of the present invention, the main control module 31 has functions such as intelligent control and communication, and integrates advanced algorithms. The main control module can realize the control of the operation of the charging module and the closure of the contactor, the monitoring and adjustment of the output voltage and current, and the heat dissipation adjustment in the cabin. Based on the dual-processor design of microcontroller + microprocessor, more complex data calculations and more efficient and flexible application designs can be realized, and multiple module control modes such as full matrix, dual matrix, and ring can be supported to adapt to different application scenarios. The data acquisition module 32 adopts a combination of intelligent sensors, which can collect the real-time temperature of each device and the real-time ambient temperature and humidity of the environment where the DC charging pile is located.

[0090] at the same time, Figure 3 The illustrated intelligent temperature control system for the DC charging pile does not limit all components, and may include more or fewer components than illustrated, or combine certain components. The specific implementation method can be found in the above embodiments, which will not be described in detail here.

[0091] In the embodiment of the present invention, the environmental impact analysis is performed based on the environmental impact analysis model using real-time environmental data, which can accurately analyze the impact of environmental factors on temperature fluctuations and improve the reliability of temperature control judgment of the DC charging pile. The temperature field distribution analysis is performed based on the environmental impact analysis data and the real-time temperature data. The comprehensiveness of temperature detection can be improved through the temperature field distribution analysis, so that the internal global temperature distribution of the DC charging pile can be understood. The point temperature of each target device is generated based on the temperature field distribution data and the real-time temperature data, which can improve the accuracy of the point temperature analysis and avoid the deviation between the obtained point temperature and the actual situation. When it is judged that the point temperature exceeds the preset threshold, the charging current is corrected and matched based on the difference between the point temperature and the preset threshold to adjust the charging current of the DC charging pile based on the target correction value, and the corresponding heat dissipation strategy is determined according to the target correction value for heat dissipation processing, which can improve the reliability of the charging current adjustment analysis and the heat dissipation strategy analysis, improve the temperature regulation effect of the DC charging pile, effectively prevent the occurrence of overheating events, and improve the operation safety of the DC charging pile.

[0092] Embodiment 4

[0093] See also Figure 4 , Figure 4 : is a structural schematic diagram of an intelligent temperature control device for a DC charging pile in an embodiment of the present invention, the device is applied to a main control module and a data acquisition module of a DC charging pile, the main control module is communicatively connected with the data acquisition module; the device comprises:

[0094] Data acquisition and transmission module 41: used to obtain 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 for the main control module to perform environmental impact analysis based on the real-time environmental data to 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 the real-time temperature data to obtain temperature field distribution data;

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

[0098] Charging current adjustment module 45: used to compare the point temperature with a preset threshold value, and if the point temperature exceeds the preset threshold value, calculate the difference between the point temperature and the preset threshold value, perform 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;

[0099] The heat dissipation processing module 46 is used to determine a corresponding heat dissipation strategy based on the target correction value, and perform heat dissipation processing on the DC charging pile based on the heat dissipation strategy.

[0100] In the specific implementation process of the present invention, the specific implementation methods of the device items can be referred to the above embodiments, which will not be repeated here.

[0101] In the embodiment of the present invention, the environmental impact analysis is performed based on the environmental impact analysis model using real-time environmental data, which can accurately analyze the impact of environmental factors on temperature fluctuations and improve the reliability of temperature control judgment of the DC charging pile. The temperature field distribution analysis is performed based on the environmental impact analysis data and the real-time temperature data. The comprehensiveness of temperature detection can be improved through the temperature field distribution analysis, so that the internal global temperature distribution of the DC charging pile can be understood. The point temperature of each target device is generated based on the temperature field distribution data and the real-time temperature data, which can improve the accuracy of the point temperature analysis and avoid the deviation between the obtained point temperature and the actual situation. When it is judged that the point temperature exceeds the preset threshold, the charging current is corrected and matched based on the difference between the point temperature and the preset threshold to adjust the charging current of the DC charging pile based on the target correction value, and the corresponding heat dissipation strategy is determined according to the target correction value for heat dissipation processing, which can improve the reliability of the charging current adjustment analysis and the heat dissipation strategy analysis, improve the temperature regulation effect of the DC charging pile, effectively prevent the occurrence of overheating events, and improve the operation safety of the DC charging pile.

[0102] A computer-readable storage medium provided in an embodiment of the present invention stores a computer program on the computer-readable storage medium, and when the program is executed by a processor, the intelligent temperature control method of a DC charging pile in any one of the above embodiments is implemented. Wherein, 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 card or optical card. That is, the storage device includes any medium that stores or transmits information in a readable form by a device (for example, a computer, a mobile phone), which can be a read-only memory, a disk or an optical disk, etc.

[0103] In addition, the above is a detailed introduction to the intelligent temperature control method and related devices of a DC charging pile provided by an embodiment of the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. An intelligent temperature control method for a DC charging pile, characterized in that: A main control module and a data acquisition module applied to a DC charging pile, wherein the main control module is communicatively connected with the data acquisition module; the method comprises: 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; The main control module performs environmental impact analysis based on the real-time environmental data to obtain environmental impact analysis data; Perform temperature field distribution analysis based on the environmental impact analysis data and the real-time temperature data to obtain temperature field distribution data; Generate the point temperature of each target device based on the temperature field distribution data and the real-time temperature data; Compare the point temperature with a preset threshold value. If the point temperature exceeds the preset threshold value, calculate the difference between the point temperature and the preset threshold value, perform 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; A corresponding heat dissipation strategy is determined based on the target correction value, and heat dissipation processing of the DC charging pile is performed based on the heat dissipation strategy.

2. The intelligent temperature control method for a DC charging pile according to claim 1, characterized in that: The performing of environmental impact analysis based on the real-time environmental data to obtain environmental impact analysis data includes: Acquire temperature data of each target device of the DC charging pile during the historical charging process, and perform temperature change point screening based on the temperature data to obtain a plurality of temperature change points; Generate several fluctuation intervals based on several temperature sudden change points, and calculate the difference between adjacent extreme value points in each fluctuation interval; Performing environmental membership analysis based on the differences between adjacent extreme value points in each fluctuation interval to obtain a target environmental membership, and constructing an environmental impact analysis model based on the target environmental membership combined with environmental factors; An environmental impact analysis is performed based on the environmental impact analysis model using the real-time environmental data to obtain environmental impact analysis data.

3. The intelligent temperature control method for a DC charging pile according to claim 1, characterized in that: The performing temperature field distribution analysis based on the environmental impact analysis data and the real-time temperature data to obtain temperature field distribution data includes: Construct a 3D geometric model based on the geometric numerical value of the DC charging pile; Calculate heat generation parameters based on the three-dimensional geometric model and the real-time temperature data to obtain target heat generation parameters; Perform Kalman filter fusion analysis based on the target heat generation parameters and environmental impact analysis data to obtain device temperature distribution estimation results; Based on the device temperature distribution estimation result, temporal and spatial variation characteristic analysis and three-dimensional space interpolation are performed to obtain temperature field distribution data.

4. The intelligent temperature control method for a DC charging pile according to claim 3, characterized in that: The three-dimensional geometric model is constructed based on the geometric numerical value of the DC charging pile, including: Inputting the geometric values ​​into a preset three-dimensional geometric model framework to generate an initial three-dimensional geometric model; Extracting observation data of the initial three-dimensional geometric model, and performing model correction parameter analysis based on the observation data using the least square method to obtain target model correction parameters; The initial three-dimensional geometric model is corrected and repaired based on the target model correction parameters to obtain a three-dimensional geometric model.

5. The intelligent temperature control method for a DC charging pile according to claim 1, characterized in that: The step of generating the point temperature of each target device based on the temperature field distribution data and the real-time temperature data includes: Based on the three-dimensional geometric model of the DC charging pile, the preset flow balance model is used to perform flow balance analysis at the cooling point to obtain flow balance data; The point temperature of each target device is analyzed based on the flow balance data combined with the temperature field distribution data and the real-time temperature data.

6. The intelligent temperature control method for a DC charging pile according to claim 1, characterized in that: The correcting and matching the charging current based on the difference to obtain a target correction value includes: Obtain the current charging current of the DC charging pile, perform feature extraction and cross-modal fusion based on the temperature data of each target device in the DC charging pile during the historical charging process and the current charging current, and obtain the current time series feature matrix; Based on the current time series characteristic matrix, a charging current-point temperature relationship curve is obtained by using interval interception; The difference is used to match a target correction value of the charging current based on the charging current-point temperature relationship curve.

7. The intelligent temperature control method for a DC charging pile according to claim 1, characterized in that: The determining a corresponding heat dissipation strategy based on the target correction value, and performing heat dissipation processing of 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 diagram using a relationship matrix based on the historical heat dissipation processing data; Based on the topological relationship diagram and the heat dissipation processing rules, a heat dissipation processing tree is constructed using a binary decision diagram; Determine a corresponding heat dissipation strategy based on the heat dissipation processing tree using the target correction value; A plurality of heat dissipation components in a corresponding area are selected, and the plurality of heat dissipation components perform heat dissipation processing on target devices corresponding to the DC charging pile based on the heat dissipation strategy.

8. An intelligent temperature control device for a DC charging pile, characterized in that: A main control module and a data acquisition module applied to a DC charging pile, wherein the main control module is communicatively connected with the data acquisition module; the device comprises: Data acquisition and transmission module: used to obtain 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: used for the main control module to perform environmental impact analysis based on the real-time environmental data 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; Point temperature generation module: used to generate the point temperature of each target device based on the temperature field distribution data and the real-time temperature data; Charging current adjustment module: used to compare the point temperature with a preset threshold value. If the point temperature exceeds the preset threshold value, the difference between the point temperature and the preset threshold value is calculated, and the charging current is corrected and matched based on the difference to obtain a target correction value, and the charging current of the DC charging pile is adjusted based on the target correction value; Heat dissipation processing module: used to determine the corresponding heat dissipation strategy based on the target correction value, and perform heat dissipation processing of the DC charging pile based on the heat dissipation strategy.

9. 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, wherein the main control module is communicatively connected to the data acquisition module, and the system is configured to execute the intelligent temperature control method for a DC charging pile according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed on an electronic device, the electronic device executes the intelligent temperature control method for a DC charging pile according to any one of claims 1 to 7.

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