A charging pile intelligent control method and system based on the Internet of Things

Abstract

The present invention provides an Internet of Things-based charging pile intelligent control method and system, which relates to the technical field of charging pile and data intelligent control, electrically connects the charging pile to a target car, completes the connection establishment between the charging pile and a communication device, obtains charging demand information to generate charging plan data, controls the charging pile to control the charging of the target car, generates charging supervision data, monitors and collects first temperature data and second temperature data, obtains an optimization control result based on an optimization control model, and performs charging optimization intelligent control on the target car, so as to solve the technical problems in the prior art that the control method for the charging pile is not intelligent enough, the consideration of charging influencing factors is not comprehensive enough, the charging coordination method is not rigorous enough, the charging needs of users cannot be met, and the charging control accuracy is not enough, charging planning is performed based on multi-dimensional influencing factors, and real-time regulation is performed based on real-time charging conditions, so as to realize intelligent charging optimization control.

Description

Technical Field

[0001] The present invention relates to the technical field of charging piles and data intelligent control, and in particular to a charging pile intelligent control method and system based on the Internet of Things. Background Art

[0002] In response to the sustainable development of energy, new energy vehicles are becoming more and more popular. With the rapid development of the new energy vehicle industry, charging piles, as necessary equipment for automobile energy supply, are deployed in wider and wider areas. Therefore, the charging of new energy vehicles has become a current hot issue. The popularity of new energy vehicles has caused charging difficulties, and it is impossible to meet the charging needs of users in terms of charging time and charging amount. Nowadays, the charging time is mainly determined by user reservations, and the charging parameters are mostly quantitative. In order to meet the charging needs of users, the control technology of charging piles needs to be further optimized and improved to achieve intelligent control of automobile charging.

[0003] In the prior art, the control method for charging piles is not intelligent enough, the consideration of factors affecting charging is not comprehensive enough, the charging coordination method is not rigorous enough, and it cannot meet the charging needs of users. At the same time, the charging control accuracy is insufficient. Summary of the invention

[0004] The present application provides an intelligent control method and system for charging piles based on the Internet of Things, which is used to solve the technical problems existing in the prior art that the control method for charging piles is not intelligent enough, the consideration of factors affecting charging is not comprehensive enough, the charging coordination method is not rigorous enough, and the charging needs of users cannot be met, and the charging control accuracy is not enough.

[0005] In view of the above problems, the present application provides a charging pile intelligent control method and system based on the Internet of Things.

[0006] In a first aspect, the present application provides a charging pile intelligent control method based on the Internet of Things, the method comprising:

[0007] Sending a connection establishment application to the communication device through the device identification code, and completing the connection establishment between the charging pile and the communication device when the detection charging pile and the target car are successfully electrically connected;

[0008] obtaining charging demand information through the communication device, and generating charging plan data based on the charging demand information;

[0009] Controlling the charging pile to charge the target vehicle through the charging plan data, and generating charging supervision data according to the charging plan data;

[0010] The charging temperature is monitored and collected by the temperature sensor to obtain first temperature data, and the temperature data of the target vehicle battery monitoring is read to obtain second temperature data;

[0011] Inputting the charging supervision data, the first temperature data and the second temperature data into an optimization control model, and outputting an optimization control result;

[0012] The charging optimization intelligent control of the target vehicle is performed based on the optimization control result.

[0013] In a second aspect, the present application provides an intelligent control system for charging piles based on the Internet of Things, the system comprising:

[0014] A connection establishment module, the connection establishment module is used to send a connection establishment application to the communication device through a device identification code, and when it is detected that the charging pile is electrically connected to the target car, the connection establishment between the charging pile and the communication device is completed;

[0015] A charging plan data acquisition module, the charging plan data acquisition module is used to obtain charging demand information through the communication device, and generate charging plan data based on the charging demand information;

[0016] A charging supervision data acquisition module, the charging supervision data acquisition module is used to control the charging pile to charge the target vehicle through the charging plan data, and generate charging supervision data according to the charging plan data;

[0017] A temperature data acquisition module, the temperature data acquisition module is used to monitor and collect charging temperature through the temperature sensor to obtain first temperature data, and read the temperature data of the target vehicle battery monitoring to obtain second temperature data;

[0018] A result output module, the result output module is used to input the charging supervision data, the first temperature data and the second temperature data into an optimization control model, and output an optimization control result;

[0019] A charging control module is used to perform charging optimization intelligent control of the target vehicle according to the optimization control result.

[0020] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0021] An embodiment of the present application provides an intelligent control method for a charging pile based on the Internet of Things. The method sends a connection application to the communication device through a device identification code. When it is detected that the charging pile is electrically connected to a target vehicle, the connection between the charging pile and the communication device is established, charging demand information is obtained and charging plan data is generated, the charging pile is controlled to perform charging control on the target vehicle, charging supervision data is generated according to the charging plan data, and charging temperature is monitored and collected by the temperature sensor to obtain first temperature data; the target vehicle battery monitoring temperature data is read to obtain second temperature data, the charging supervision data, the first temperature data and the second temperature data are input into an optimization control model, an optimization control result is output, and charging optimization intelligent control is performed on the target vehicle. The method solves the technical problems existing in the prior art that the control method for the charging pile is not intelligent enough, the consideration of charging influencing factors is not comprehensive enough, the charging coordination method is not rigorous enough, and the charging needs of users cannot be met, and the charging control accuracy is not enough. Charging planning is performed based on multi-dimensional influencing factors, and real-time regulation is performed based on real-time charging conditions to achieve intelligent charging optimization control. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of a charging pile intelligent control method based on the Internet of Things is provided for this application;

[0023] Figure 2 A schematic diagram of the charging plan data generation process in the charging pile intelligent control method based on the Internet of Things is provided for this application;

[0024] Figure 3 A schematic diagram of the process of generating an optimized control model in an intelligent control method for charging piles based on the Internet of Things is provided for this application;

[0025] Figure 4 A schematic diagram of the structure of an intelligent control system for charging piles based on the Internet of Things is provided for this application.

[0026] Explanation of the reference numerals: connection establishment module 11 , charging plan data acquisition module 12 , charging supervision data acquisition module 13 , temperature data acquisition module 14 , result output module 15 , charging control module 16 . DETAILED DESCRIPTION

[0027] The present application provides an intelligent control method and system for charging piles based on the Internet of Things, electrically connects the charging pile to a target vehicle, completes the connection establishment between the charging pile and a communication device, obtains charging demand information to generate charging plan data, controls the charging pile to control the charging of the target vehicle, generates charging supervision data, monitors and collects first temperature data and second temperature data, obtains an optimization control result based on an optimization control model, and performs charging optimization intelligent control on the target vehicle. The application is used to solve the technical problems existing in the prior art that the control method for the charging pile is not intelligent enough, the consideration of factors affecting charging is not comprehensive enough, the charging coordination method is not rigorous enough, and the charging needs of users cannot be met, and the charging control accuracy is not enough.

[0028] Embodiment 1

[0029] like Figure 1 As shown, the present application provides a charging pile intelligent control method based on the Internet of Things, the method is applied to the charging pile intelligent control system, the charging pile intelligent control system is communicatively connected with a temperature sensor and a communication device, and the method includes:

[0030] Step S100: sending a connection establishment application to the communication device through the device identification code, and when it is detected that the charging pile is electrically connected to the target car successfully, the connection establishment between the charging pile and the communication device is completed;

[0031] Specifically, with the popularization of new energy vehicles, charging piles have become necessary equipment for automobile energy supply. In order to realize intelligent control of automobile charging, an Internet of Things-based charging pile intelligent control method provided in this application is applied to the charging pile intelligent control system. The system is communicatively connected with the temperature sensor and the communication device. The temperature sensor is used to collect real-time temperature of the charging process to adaptively adjust and control the charging parameters, obtain the device identification code, that is, the scan code corresponding to the charging pile, and generate the connection establishment application based on the communication device scanning the device identification code to send it to the communication device. The communication device is a user mobile terminal device, which accepts the application and electrically connects the charging pile with the target car. When the connection is detected to be successful, the charging pile and the communication device are synchronously connected to visualize the charging process, indicating that the charging preparation work is completed, and charging control is performed after ensuring that the connection is correct.

[0032] Step S200: obtaining charging demand information through the communication device, and generating charging plan data based on the charging demand information;

[0033] Step S300: controlling the charging pile to charge the target vehicle through the charging plan data, and generating charging supervision data according to the charging plan data;

[0034] Specifically, the charging demand information, i.e., the charging time interval and power required by the user, is obtained based on the communication device, and multi-dimensional charging influencing factors are determined, such as matching charging piles, power scheduling problems caused by community electricity consumption, and charging time adjustability, etc. Charging coordination planning is carried out in conjunction with the charging demand information to obtain the charging plan data. Further, based on the charging plan data, the charging pile and the target vehicle are controlled, and charging control parameters, including charging rate, etc., are determined as the charging supervision data. Based on the charging supervision data, it is determined whether the charging parameter indicators of the target vehicle are consistent with the charging supervision data to ensure that it is in a normal charging state, which lays a solid foundation for subsequent charging control.

[0035] Step S400: monitoring and collecting charging temperature through the temperature sensor to obtain first temperature data, and reading the monitoring temperature data of the target vehicle battery to obtain second temperature data;

[0036] Step S500: inputting the charging supervision data, the first temperature data and the second temperature data into an optimization control model, and outputting an optimization control result;

[0037] Step S600: performing charging optimization intelligent control of the target vehicle according to the optimization control result.

[0038] Specifically, based on the temperature sensor, real-time temperature monitoring and collection is performed on the connection port position, and the temperature collection result of the connection port position is used as the first temperature data. The real-time temperature of the battery during the charging process of the target vehicle is monitored and read as the second temperature data. When the first temperature data and the second temperature data exceed the temperature threshold, charging abnormality may be caused, and adaptive adjustment needs to be made to the real-time monitored temperature.

[0039] The optimization control model is constructed, and multiple temperature intervals are set for the first temperature data and the second temperature data. Based on big data, the adjustment control information under the dynamic fluctuation of the multiple temperature intervals and the charging supervision data can be retrieved. The adjustment control information can be determined based on the actual charging status. The multiple temperature intervals and the fluctuation value of the charging supervision data are used as matching nodes, and the adjustment control information corresponding to each matching node is determined as a decision node. The matching nodes are associated with the decision nodes to construct a matching decision layer, and then an implicit constraint layer is placed after the matching decision layer. The optimization control model is generated based on the matching decision layer and the implicit constraint layer. The charging supervision data, the first temperature data and the second temperature data are input into the optimization control model, and the real-time charging parameters are adaptively adjusted, and the adjustment scale and adjustment direction are determined. The optimization control results are outputted, and the charging regulation and control analysis is performed based on the optimization control model, which can effectively ensure the objectivity and accuracy of the output results. Based on the optimization control results, the current charging parameters of the target vehicle are adjusted to ensure that the charging parameters avoid abnormal charging status on the basis of meeting the user's charging needs, and realize the charging optimization intelligent control of the target vehicle.

[0040] Furthermore, if Figure 2 As shown, step S200 of the present application also includes:

[0041] Step S210-1: Collect and obtain community charging pile layout information, and construct charging pile related data according to the community charging pile layout information;

[0042] Step S220-1: Read the community's historical charging pile charging data and community electricity consumption data;

[0043] Step S230-1: Perform charging characteristic analysis based on the charging pile association data and the historical charging pile charging data of the community, and generate first impact data based on the analysis result;

[0044] Step S240-1: performing a peak power consumption analysis based on the community power consumption data, and generating second impact data based on the analysis result;

[0045] Step S250 - 1 : Generate charging plan data based on the charging demand information, the first impact data and the second impact data.

[0046] Furthermore, step S230 of the present application also includes:

[0047] Step S231-1: Collect and obtain vehicle travel information of community users, perform charging feature statistics based on the vehicle travel information, and obtain charging feature statistics results;

[0048] Step S232-1: Generate predicted charging demand information based on the charging feature statistics and the community historical charging pile charging data;

[0049] Step S233 - 1 : performing charging association constraints on the predicted charging demand data according to the charging pile association data, and generating the first impact data based on the association constraint results.

[0050] Specifically, the community charging pile layout information is collected, including the layout location, power, and shared charging lines of the charging piles, including information on the line material, the maximum current allowed to pass, etc. The above information is associated and matched, and information identification is performed to generate the charging pile associated data, a predetermined time interval is set, and the historical charging pile data of the community and the community electricity consumption data are retrieved based on the predetermined time interval, and the retrieved data is time-series identified.

[0051] Specifically, the vehicle travel information of community users within the predetermined time interval is collected, and the regularity of the vehicle travel information is analyzed, such as vehicle travel in fixed time periods on weekdays; travel patterns on holidays and rest days, etc., and charging characteristics are extracted and counted, including charging time nodes, duration, etc. User identification is performed based on the statistical results to generate the charging characteristic statistical results. The charging characteristic statistical results are mapped and matched with the charging data of the historical charging piles in the community. Generally speaking, the fluctuation states of the two are synchronized, and charging demand information is predicted based on this. As the charging pile statistical data and the charging data of the historical charging piles in the community increase, the corresponding predicted charging demand information becomes higher, and the generated predicted charging demand information is time-series identified. Further, based on the charging pile associated data, charging associated constraints are performed on the predicted charging demand data, such as the impact of line sharing on the charging parameters of the associated charging piles, and the first impact data is obtained.

[0052] Furthermore, based on the community electricity consumption data, the charging of the charging pile is analyzed for impact, the community electricity consumption peak is determined, when in the community electricity consumption peak period, whether the community electricity consumption peak and the charging peak can be adjusted, whether the current scheduling of the charging pile can meet the predicted charging demand information, and the second impact data is generated by performing electricity consumption peak analysis. Based on the charging demand information, the first impact data and the second impact data, charging planning is performed to generate the charging plan data. Based on the multi-dimensional charging impact data, charging planning is performed reasonably without affecting the normal travel status of users to ensure the actual adaptability of the charging status.

[0053] Furthermore, step S200 of the present application also includes:

[0054] Step S210-2: performing demand category determination on the charging demand information to obtain a category determination result;

[0055] Step S220-2: When the category determination result is the first category of charging demand, matching the electricity price during the demand period according to the charging demand information, and performing scheduled charging management through the electricity price during the demand period and the charging demand information;

[0056] Step S230-2: When the category determination result is the second category charging demand, detecting and obtaining the battery power information and charging information of the target vehicle;

[0057] Step S240-2: Collect and obtain planned charging information, and plan and obtain the charging plan data according to the planned charging information, the battery power information, the charging information and the charging demand information.

[0058] Specifically, fixed-point charging and adjustable flexible charging are used as classification basis, and the charging type is judged for the charging demand information, the charging demand information includes the total charging demand of users in the control area, and the category determination result is obtained, including the charging demand category and the corresponding charging time. When the category determination result is the first type of charging demand, that is, fixed-point charging, for example, charging must start at 8 o'clock or something, and the electricity price of the scheduled charging period is charged. There are differences in the electricity prices corresponding to different charging time periods. The electricity price of the demand period is matched with the charging demand information, and scheduled charging management is performed based on the matching result.

[0059] When the category determination result is the second type of charging demand, that is, flexible charging demand, such as fully charging before 6 o'clock, the charging time node can be adaptively adjusted, the battery power information of the target car and the charging information are detected, and charging coordination management is performed based on the detection result. Further collect the planned charging information, that is, the charging information that has been scheduled and determined, including the charging time. For example, for the second type of charging demand, there may already be some planned charging time intervals before 6 o'clock. When coordinating charging for the user, plan within the remaining time interval. Based on the planned charging information, the battery power information, the charging information and the charging demand information, determine the specific coordinated charging time interval of the target user, allocate charging piles, battery charging amount and other information as the charging plan data, wherein the charging collection data needs to be equipped with a certain reserved time to ensure that when there is a charging anomaly, the user's charging needs can be met to avoid affecting the user's travel.

[0060] Furthermore, step S240-2 of the present application also includes:

[0061] Step S241-2: collecting historical charging information of the target vehicle to obtain a charging information collection result;

[0062] Step S242-2: performing charging abnormality adjustment analysis according to the charging information collection result to obtain an adjustment analysis result;

[0063] Step S243 - 2 : Generate an abnormal adjustment interval according to the adjustment analysis result, and obtain the charging plan data according to the abnormal adjustment interval.

[0064] Specifically, historical charging information of the target vehicle is collected based on a predetermined time interval, and the collected information is integrated based on time sequence to generate the charging information collection result. Abnormal information is further extracted from the charging information collection result, and abnormal adjustment analysis is performed, such as charging time adjustment, abnormal charging efficiency that cannot meet the predetermined demand, etc., and relevant information is integrated to generate the adjustment analysis result. The adjustment analysis result is used as reference information to determine the abnormal adjustment interval, such as charging time node adjustment, charging pile matching adjustment, etc., and the charging plan data is adjusted based on the abnormal adjustment interval to improve the fit between the charging plan data and user needs.

[0065] Furthermore, step S400 of the present application also includes:

[0066] Step S410: determining whether the second temperature data fails to meet a first preset temperature threshold;

[0067] Step S420: when the second temperature data cannot meet the first preset temperature threshold, generating a preheating adjustment time interval;

[0068] Step S430: Obtaining the charging plan data through the preheating adjustment time interval.

[0069] Specifically, by reading the target vehicle battery monitoring temperature data, obtaining the second temperature data, setting the first preset temperature threshold, that is, the low temperature critical value, when the battery temperature is too low, it will affect the battery charging efficiency, etc., causing a deviation between the charging amount and the required power, and judging whether the second temperature meets the first preset temperature threshold. When the second temperature data cannot meet the first preset temperature threshold, it indicates that the current battery temperature of the target vehicle is too low, affecting the charging effect, and generating a preheating adjustment time interval, that is, a time interval for preheating the battery temperature, to ensure normal charging of the battery, and adding the preheating adjustment time interval to the charging plan data to further improve the actual fit of the charging plan data.

[0070] Furthermore, if Figure 3 As shown, step S500 of the present application also includes:

[0071] Step S510: setting multiple temperature levels;

[0072] Step S520: constructing a level temperature rise rate constraint value according to the multi-level temperature levels;

[0073] Step S530: constructing an implicit constraint layer through the hierarchical temperature rise rate constraint value;

[0074] Step S540: adding the implicit constraint layer to the optimization control model.

[0075] Specifically, a monitoring temperature adjustment interval is determined, and multiple temperature levels are set based on the interval. Temperature rise rate constraint values, i.e., temperature rise rate critical values, are set for the multiple temperature levels. When the real-time monitoring temperature reaches the corresponding temperature rise rate constraint value, it indicates that the current charging rate is abnormal compared with the monitoring temperature and needs to be adjusted appropriately. For example, when the temperature rise rate is high, the current charging rate needs to be appropriately reduced. When the temperature rise rate is low, the charging rate can be appropriately accelerated to achieve positive feedback. Corresponding charging rate adjustment amplitudes are configured for the multiple temperature levels, and the implicit constraint layer is constructed. The multiple temperature levels embedded in the implicit constraint layer are identified based on the charging rate adjustment amplitude. The implicit constraint layer is then added to the optimization control model to optimize and improve the model, thereby further improving the final optimization control accuracy.

[0076] Embodiment 2

[0077] Based on the same inventive concept as the intelligent control method of a charging pile based on the Internet of Things in the aforementioned embodiment, Figure 4 As shown, the present application provides an intelligent control system for charging piles based on the Internet of Things, the system comprising:

[0078] A connection establishment module 11, the connection establishment module 11 is used to send a connection establishment application to the communication device through a device identification code, and when it is detected that the charging pile is electrically connected to the target car, the connection establishment between the charging pile and the communication device is completed;

[0079] A charging plan data acquisition module 12, the charging plan data acquisition module 12 is used to obtain charging demand information through the communication device, and generate charging plan data based on the charging demand information;

[0080] A charging supervision data acquisition module 13, wherein the charging supervision data acquisition module 13 is used to control the charging pile to charge the target vehicle through the charging plan data, and to generate charging supervision data according to the charging plan data;

[0081] A temperature data acquisition module 14, the temperature data acquisition module 14 is used to monitor and collect charging temperature through the temperature sensor to obtain first temperature data, and read the temperature data of the target vehicle battery monitoring to obtain second temperature data;

[0082] A result output module 15, the result output module 15 is used to input the charging supervision data, the first temperature data and the second temperature data into an optimization control model, and output an optimization control result;

[0083] The charging control module 16 is used to perform charging optimization intelligent control of the target vehicle according to the optimization control result.

[0084] Furthermore, the system further comprises:

[0085] A data construction module, the data construction module is used to collect and obtain community charging pile layout information, and construct charging pile related data according to the community charging pile layout information;

[0086] A data reading module, which is used to read the community's historical charging pile charging data and community electricity consumption data;

[0087] A first impact data generation module, the first impact data generation module is used to perform charging feature analysis based on the charging pile association data and the community historical charging pile charging data, and generate first impact data based on the analysis result;

[0088] A second impact data generating module, the second impact data generating module is used to perform power consumption peak analysis according to the community power consumption data, and generate second impact data according to the analysis result;

[0089] A charging plan data generating module is used to generate charging plan data based on the charging demand information, the first impact data and the second impact data.

[0090] Furthermore, the system further comprises:

[0091] A feature statistics module, the feature statistics module is used to collect and obtain the vehicle travel information of community users, perform charging feature statistics based on the vehicle travel information, and obtain charging feature statistics results;

[0092] A demand prediction module, the demand prediction module is used to generate predicted charging demand information according to the charging feature statistics and the charging data of the community's historical charging piles;

[0093] A charging associated constraint module is used to perform charging associated constraints on the predicted charging demand data according to the charging pile associated data, and generate the first impact data based on the associated constraint result.

[0094] Furthermore, the system further comprises:

[0095] An information category determination module, the information category determination module is used to perform demand category determination on the charging demand information to obtain a category determination result;

[0096] A charging management module, wherein when the category determination result is a first category of charging demand, the charging management module is used to match the electricity price during the demand period according to the charging demand information, and perform timed charging management through the electricity price during the demand period and the charging demand information;

[0097] An information detection module, wherein the information detection module is used to detect and obtain battery power information and charging information of the target vehicle when the category determination result is the second category of charging demand;

[0098] A charging plan data acquisition module is used to collect and obtain planned charging information, and to plan and obtain the charging plan data according to the planned charging information, the battery power information, the charging information and the charging demand information.

[0099] Furthermore, the system further comprises:

[0100] An information collection module, the information collection module is used to collect historical charging information of the target vehicle and obtain charging information collection results;

[0101] A result analysis module, the result analysis module is used to perform charging abnormality adjustment analysis according to the charging information collection result to obtain an adjustment analysis result;

[0102] A data acquisition module is used to generate an abnormal adjustment interval according to the adjustment analysis result, and obtain the charging plan data according to the abnormal adjustment interval.

[0103] Furthermore, the system further comprises:

[0104] A data judgment module, the data judgment module is used to judge whether the second temperature data cannot meet the first preset temperature threshold;

[0105] An interval generation module, wherein the interval generation module is used to generate a preheating adjustment time interval when the second temperature data cannot meet the first preset temperature threshold;

[0106] A charging plan data determination module is used to obtain the charging plan data through the preheating adjustment time interval.

[0107] Furthermore, the system further comprises:

[0108] A level setting module, the level setting module is used to set multiple temperature levels;

[0109] A constraint value building module, the constraint value building module is used to build a level temperature rise rate constraint value according to the multi-level temperature level;

[0110] A constraint layer construction module, wherein the constraint layer construction module is used to construct an implicit constraint layer through the layer temperature rise rate constraint value;

[0111] A constraint layer adding module is used to add the implicit constraint layer to the optimization control model.

[0112] Through the above detailed description of an intelligent control method for charging piles based on the Internet of Things in this specification, those skilled in the art can clearly understand an intelligent control method and system for charging piles based on the Internet of Things in this embodiment. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0113] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A charging pile intelligent control method based on the Internet of Things, characterized in that: The method is applied to a charging pile intelligent control system, wherein the charging pile intelligent control system is communicatively connected with a temperature sensor and a communication device, and the method comprises: Sending a connection establishment application to the communication device through the device identification code, and completing the connection establishment between the charging pile and the communication device when the detection charging pile and the target car are successfully electrically connected; obtaining charging demand information through the communication device, and generating charging plan data based on the charging demand information; Controlling the charging pile to charge the target vehicle through the charging plan data, and generating charging supervision data according to the charging plan data; The charging temperature is monitored and collected by the temperature sensor to obtain the first temperature data, and the temperature data of the battery of the target vehicle is read to obtain the second temperature data. Specifically, the temperature of the connection port is monitored and collected in real time based on the temperature sensor, and the temperature collection result of the connection port is used as the first temperature data. The real-time temperature of the battery of the target vehicle during the charging process is monitored and read as the second temperature data; Input the charging supervision data, the first temperature data and the second temperature data into the optimization control model, and output the optimization control result, wherein the optimization control model is constructed, a multi-level temperature range is set for the first temperature data and the second temperature data, and the adjustment control information under the dynamic fluctuation of the multi-level temperature range and the charging supervision data is retrieved based on big data, which can be determined based on the actual charging status, and the multi-level temperature range and the charging supervision data fluctuation value are used as matching nodes, and the adjustment control information corresponding to each matching node is determined as a decision node, and the matching nodes are associated with the decision nodes to construct a matching decision layer, and then an implicit constraint layer is placed after the matching decision layer, and the optimization control model is generated based on the matching decision layer and the implicit constraint layer; Performing charging optimization intelligent control of the target vehicle according to the optimization control result; Wherein, the method comprises: Collect and obtain community charging pile layout information, and construct charging pile related data according to the community charging pile layout information; Read the community's historical charging data and community electricity consumption data; Perform charging characteristic analysis based on the charging pile association data and the historical charging pile charging data of the community, and generate first impact data based on the analysis result; Performing a peak power consumption analysis based on the community power consumption data, and generating second impact data based on the analysis result; Charging plan data is generated based on the charging demand information, the first impact data, and the second impact data.

2. The method according to claim 1, characterized in that The method further comprises: Collecting and obtaining vehicle travel information of community users, performing charging characteristic statistics based on the vehicle travel information, and obtaining charging characteristic statistical results; Generate predicted charging demand information based on the charging feature statistics and the community's historical charging pile charging data; The predicted charging demand data is subjected to charging association constraints according to the charging pile association data, and the first impact data is generated based on the association constraint result.

3. The method according to claim 1, characterized in that The method comprises: Performing demand category determination on the charging demand information to obtain a category determination result; When the category determination result is the first category of charging demand, the electricity price during the demand period is matched according to the charging demand information, and scheduled charging management is performed through the electricity price during the demand period and the charging demand information; When the category determination result is the second category charging demand, detecting and obtaining the battery power information and charging information of the target vehicle; The planned charging information is collected and obtained, and the charging plan data is obtained according to the planned charging information, the battery power information, the charging information and the charging demand information.

4. The method according to claim 3, characterized in that The method comprises: Collecting historical charging information of the target vehicle to obtain a charging information collection result; Performing charging abnormality adjustment analysis according to the charging information collection result to obtain an adjustment analysis result; An abnormal adjustment interval is generated according to the adjustment analysis result, and the charging plan data is obtained according to the abnormal adjustment interval.

5. The method according to claim 4, characterized in that The method comprises: Determining whether the second temperature data fails to meet a first preset temperature threshold; When the second temperature data cannot meet the first preset temperature threshold, a preheating adjustment time interval is generated; The charging plan data is obtained through the preheating adjustment time interval.

6. The method according to claim 1, characterized in that The method comprises: Set multiple temperature levels; Constructing a level temperature rise rate constraint value according to the multi-level temperature levels; Constructing an implicit constraint layer through the layer temperature rise rate constraint value; The implicit constraint layer is added to the optimization control model.

7. An intelligent control system for charging piles based on the Internet of Things, characterized in that: Used to execute the intelligent control method of charging pile based on the Internet of Things according to any one of claims 1 to 6, the system is communicatively connected with a temperature sensor and a communication device, and the system includes: A connection establishment module, the connection establishment module is used to send a connection establishment application to the communication device through a device identification code, and when it is detected that the charging pile is electrically connected to the target car, the connection establishment between the charging pile and the communication device is completed; A charging plan data acquisition module, the charging plan data acquisition module is used to obtain charging demand information through the communication device, and generate charging plan data based on the charging demand information; A charging supervision data acquisition module, the charging supervision data acquisition module is used to control the charging pile to charge the target vehicle through the charging plan data, and generate charging supervision data according to the charging plan data; a temperature data acquisition module, the temperature data acquisition module is used to monitor and collect charging temperature through the temperature sensor to obtain first temperature data, read the temperature data of the target vehicle battery monitoring, and obtain second temperature data. Specifically, based on the temperature sensor, real-time temperature monitoring and collection is performed on the connection port position, and the temperature collection result of the connection port position is used as the first temperature data, and the real-time temperature of the battery of the target vehicle during the charging process is monitored and read as the second temperature data; A result output module, the result output module is used to input the charging supervision data, the first temperature data and the second temperature data into the optimization control model, and output the optimization control result, wherein the optimization control model is constructed, a multi-level temperature range is set for the first temperature data and the second temperature data, and the adjustment control information under the dynamic fluctuation of the multi-level temperature range and the charging supervision data is retrieved based on big data, which can be determined based on the actual charging status, and the multi-level temperature range and the charging supervision data fluctuation value are used as matching nodes, and the adjustment control information corresponding to each matching node is determined as a decision node, and the matching nodes are associated with the decision nodes to construct a matching decision layer, and then an implicit constraint layer is placed after the matching decision layer, and the optimization control model is generated based on the matching decision layer and the implicit constraint layer; A charging control module is used to perform charging optimization intelligent control of the target vehicle according to the optimization control result.

Images