Automobile charging station load intelligent prediction method and system

Abstract

The invention discloses an automobile charging station load intelligent prediction method and system, and relates to the technical field of new energy, and the method comprises the steps: collecting the charging state information of each automobile charging station in a target range, so as to construct a plurality of load intensity coefficients Xfh of the automobile charging stations, and carrying out the comparison of the load intensity coefficients Xfh, marking a number group of high-load automobile charging stations, and sending a load demand prediction instruction; according to the method, charging piles in a high-load automobile charging station number group are continuously monitored, a radio frequency identification technology is combined, a load demand index Zyc is obtained through fitting, after threshold comparison is carried out on the load demand index Zyc, whether the automobile charging stations in the high-load automobile charging station number group meet the automobile charging demand of the day or not is judged, and a corresponding grade regulation and control instruction is generated and executed; according to the method, through real-time monitoring, dynamic analysis and accurate regulation and control, the resource allocation of the automobile charging station is effectively optimized, and the charging user experience is improved.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a method and system for intelligently predicting load at a vehicle charging station. Background Art

[0002] With the rapid development of new energy technologies around the world, electric vehicles, as low-carbon and environmentally friendly means of transportation, have gradually occupied the mainstream market. Unlike traditional fuel vehicles, the operation of electric vehicles is highly dependent on charging infrastructure, and car charging stations, as core support, have a direct impact on the promotion and popularization of electric vehicles and user experience. In recent years, the number of car charging stations has increased significantly, and various fast charging stations and slow charging stations have been gradually deployed in cities and villages. However, with the increasing regionalization and personalization of charging demand, the imbalance of resource allocation of different car charging stations has gradually emerged. In order to improve charging efficiency and user experience, and optimize the utilization of power grid resources, intelligent load prediction for car charging stations has become a key means to solve these problems.

[0003] Although the existing load forecasting methods for automobile charging stations have achieved some results in practical applications, there are still some shortcomings. First, the existing load forecasting methods for automobile charging stations are usually based on single and static indicators, and fail to consider the real-time status and dynamic changes of automobile charging stations. At the same time, they rely on load monitoring records over the past period of time, while ignoring the changes in real-time status data, which leads to poor sensitivity of the forecast results to sudden load changes. In addition, the existing load forecasting methods for automobile charging stations usually lack in-depth analysis of the load intensity differences and operating characteristics of each automobile charging station, making it difficult to accurately evaluate the actual load intensity distribution of each automobile charging station. More importantly, these methods often focus on the current load status and lack future-oriented regulation and planning capabilities, especially the prediction of the next day's charging demand and the advance regulation of automobile charging station resources. This situation seriously affects the actual application value of the forecast. Therefore, it is urgent to establish a set of intelligent, efficient and targeted load forecasting and regulation methods to improve the resource utilization efficiency of automobile charging stations and optimize user experience, and provide support for the development and transformation of the new energy vehicle industry. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method and system for intelligent prediction of load at a vehicle charging station, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for intelligent prediction of load at a vehicle charging station, comprising the following steps; S1. Select several automobile charging stations within the target range as monitoring objects, and collect charging status information of each automobile charging station in combination with Internet of Things technology to obtain relevant charging status data information; S2. Construct the load intensity coefficient Xfh of several vehicle charging stations based on the relevant charging status data information, mark the number group of high-load vehicle charging stations after comparison, and issue a load demand forecasting instruction; S3. After receiving the load demand forecasting instruction, the charging piles in the high-load vehicle charging station quantity group are continuously monitored, and the real-time status data information and charging pile quantity data information are obtained by combining the radio frequency identification technology. After dimensionless processing, the load demand index Zyc is obtained by fitting; S4. Preset the demand threshold Y, compare the load demand index Zyc with the preset demand threshold Y, and determine whether the vehicle charging stations in the high-load vehicle charging station number group meet the vehicle charging demand of the day, so as to generate and execute the corresponding level control instructions.

[0006] Preferably, the specific steps of S1 include: S11. Select several automobile charging stations within the target range as monitoring objects, combine the Internet of Things technology, connect several automobile charging stations with the central data center, and transmit data through the OCPP protocol. Among them, the charging piles in the automobile charging stations are equipped with power metering units, fault diagnosis units and RFID chips for identity recognition; S12, connecting the data source interface of each vehicle charging station through the API interface of the central data center, collecting the charging status information of each vehicle charging station to obtain relevant charging status data information, wherein the relevant charging status data information includes the total vehicle charging time Tzs, the total number of vehicle charging times Ncs and the total number of vehicle fast charging times Nkc of each vehicle charging station, wherein the collection time is one day and one night; S13, preprocessing the relevant charging status data information, the preprocessing including noise removal, missing value filling and data smoothing operations, wherein the missing values ​​are filled by mean filling, median filling and interpolation filling.

[0007] Preferably, the specific steps of S2 include: S21, constructing a load intensity coefficient Xfh of several vehicle charging stations based on relevant charging status data information, taking the load intensity coefficient Xfh of the i-th vehicle charging station as i For example, it is obtained in the following way: ; In the formula, Tzs i Ncs is the total charging time of the vehicle at the i-th vehicle charging station.i Nkc is the total number of vehicle charging times at the i-th vehicle charging station. i It is represented by the total number of fast charging times of vehicles at the i-th vehicle charging station, where , and Both are represented as weight values, and A is represented as the first correction constant.

[0008] Preferably, the specific step S2 also includes: S22, based on the load intensity coefficient Xfh of the i-th vehicle charging station i The load intensity coefficient Xfh of several vehicle charging stations is obtained respectively, and the mean value of the load intensity coefficient is calculated based on the statistical mean value algorithm. ; S23, by comparing the load intensity coefficients Xfh of several vehicle charging stations with the average Compare the size to get the value above the mean The vehicle charging stations corresponding to the load intensity coefficient Xfh are marked, and a high-load vehicle charging station number group is constructed based on the marked vehicle charging stations. When the high-load vehicle charging station number group exceeds 50% of the total number of vehicle charging stations, a load demand forecast instruction is issued.

[0009] Preferably, the specific steps of S3 include: S31. After receiving the issued load demand forecasting instruction, the charging piles in the high-load automobile charging station group are continuously monitored, and a monitoring time period is set. The electric energy metering unit and the fault diagnosis unit equipped on the charging piles are continuously monitored for the load power change status and fault shutdown status of the charging piles in the high-load automobile charging station group, and the real-time status data information of the high-load automobile charging station group is summarized and obtained. The real-time status data information includes the total load power Pfh and the total number of faults Ncs at each monitoring time point in the monitoring time period. The charging piles in the high-load automobile charging station group are identified and the quantity is summarized through the radio frequency identification technology to obtain the charging pile quantity data information, which includes the total number of charging piles Ncd in the high-load automobile charging station group. The monitoring time period is one day and one night.

[0010] Preferably, the specific step S3 also includes: S32: Analyze the real-time status data information and the charging pile quantity data information, and after dimensionless processing, construct a charging pile load density coefficient Xmd at several monitoring time points, and use the charging pile load density coefficient Xmd at the tth monitoring time point as the value. t For example, it is obtained in the following way: ; In the formula, It is expressed as the total load power at the tth monitoring time point within the monitoring period, It is expressed as the total number of charging piles in the high-load vehicle charging station number group.

[0011] Preferably, the specific step S3 also includes: S33, based on the charging pile load density coefficient Xmd obtained at the tth monitoring time point t In this way, the charging pile load density coefficient Xmd at several monitoring time points is obtained respectively, and the charging pile load density coefficient Xmd at several monitoring time points is associated with the real-time status data information and the charging pile quantity data information. After dimensionless processing, the load satisfaction coefficient Xmz at several monitoring time points is constructed, and the load satisfaction coefficient Xmz at the tth monitoring time point is used. t For example, it is obtained in the following way: ; In the formula, The charging pile load density coefficient at the tth monitoring time point, It is represented by the total number of charging piles in the high-load vehicle charging station number group. Expressed as the total number of failures at the tth monitoring time point, It is expressed as the failure rate of the charging pile at the tth monitoring time point.

[0012] Preferably, the specific step S3 also includes: S34, based on the load satisfaction coefficient Xmz obtained at the t-th monitoring time point t The load satisfaction coefficient Xmz of several monitoring time points is obtained respectively, and the total design load capacity Cfh of the high-load vehicle charging station group is obtained according to the power supply line load level of the vehicle charging station during the power grid planning and design, and combined with the design capacity of the power supply transformer; S35, by associating the load satisfaction coefficient Xmz at several monitoring time points with the total design load capacity Cfh, and combining the real-time status data information, after dimensionless processing, the load demand index Zyc is obtained by fitting, and the load demand index Zyc is obtained by the following formula: ; In the formula, Xmz t It is expressed as the load satisfaction coefficient at the tth monitoring time point, It is expressed as the total load power at the t-th monitoring time point within the monitoring period, t=1, 2, 3, ..., T, T represents the number of monitoring time points, It is expressed as the ratio of the total load power to the total design load capacity at the t-th monitoring time point, representing the operating pressure level of the vehicle charging stations in the high-load vehicle charging station number group at the current monitoring time point. and Both are represented as weight values, and B is represented as the second correction constant.

[0013] Preferably, the specific steps of S4 include: S41, pre-set the demand threshold Y, compare the load demand index Zyc with the preset demand threshold Y, to determine whether the vehicle charging stations in the high-load vehicle charging station number group meet the vehicle charging demand of the day, and generate corresponding level control instructions, the specific contents are as follows: If the load demand index Zyc>demand threshold Y, it means that the car charging stations in the high-load car charging station number group do not meet the car charging demand of the day, and a first-level control instruction is obtained. The control content is to formulate a user guidance strategy for the next day based on the load intensity coefficient Xfh distribution of the car charging stations in the high-load car charging station number group; use the charging navigation system to recommend surrounding low-load car charging stations to electric vehicles as a priority choice, deploy temporary energy storage equipment and mobile charging vehicles in the surrounding areas of the high-load car charging station number group as a supplementary power source during peak hours, and arrange a maintenance team to repair the faulty charging piles of the car charging stations in the high-load car charging station number group in combination with the total number of faults Ncs indicator, and launch a peak-time staggered charging discount the next day; If the load demand index Zyc ≤ demand threshold Y, it means that the automobile charging stations in the high-load automobile charging station number group meet the automobile charging demand of the day, and the secondary control instruction is obtained. The control content is: continue to implement high-frequency dynamic monitoring of the automobile charging stations in the high-load automobile charging station number group the next day, guide the distribution of charging vehicles through the charging navigation system, promote the use of low-load automobile charging stations through charging discounts and operation publicity the next day, arrange emergency response teams to pay close attention, and prepare to deal with potential charging pile failure risks and sudden load growth measures, and use the load demand index Zyc of the next day as the basis for predicting subsequent automobile charging demand.

[0014] An intelligent load prediction system for a vehicle charging station, comprising a data acquisition module, a marking module, a load demand prediction module and a level control module; The data acquisition module is used to select several automobile charging stations within the target range as monitoring objects, and collect charging status information of each automobile charging station in combination with the Internet of Things technology to obtain relevant charging status data information; The marking module is used to construct the load intensity coefficient Xfh of several vehicle charging stations according to the relevant charging status data information, mark the number group of high-load vehicle charging stations after comparison, and issue a load demand prediction instruction; The load demand forecasting module is used to continuously monitor the charging piles in the high-load vehicle charging station quantity group after receiving the issued load demand forecasting instruction, and obtain the real-time status data information and charging pile quantity data information respectively by combining the radio frequency identification technology, and obtain the load demand index Zyc by fitting after dimensionless processing; The level control module is used to pre-set a demand threshold Y, compare the load demand index Zyc with the preset demand threshold Y, and determine whether the vehicle charging stations in the high-load vehicle charging station number group meet the vehicle charging demand of the day, so as to generate and execute corresponding level control instructions.

[0015] The present invention provides a method and system for intelligently predicting the load of a vehicle charging station, which has the following beneficial effects: (1) By combining the Internet of Things technology, radio frequency identification technology and real-time data analysis methods, many shortcomings of existing technologies are effectively overcome, the resource utilization efficiency of automobile charging stations is improved, the user experience is optimized, and support is provided for the development and transformation of the new energy vehicle industry. First, based on real-time dynamic monitoring and multi-source data fusion, the load intensity coefficient Xfh of several automobile charging stations is constructed. After comparison, the number group of high-load automobile charging stations is marked to reflect the dynamic operation status of automobile charging stations. This process effectively solves the limitation of relying on a single static indicator in traditional load forecasting methods, and significantly improves the sensitivity and accuracy of load changes. Secondly, by accurately identifying high-load automobile charging stations and dynamically fitting the load demand index Zyc based on real-time status data, the ability to effectively predict and predict the future demand of high-load automobile charging stations is achieved, providing a reliable basis for the optimal scheduling of automobile charging station resources. Compared with traditional methods, the ability to respond to regional and personalized changes in charging demand is significantly improved. At the same time, On the basis of load forecasting, a hierarchical control mechanism is further introduced. According to the comparison result of the load demand index Zyc and the preset demand threshold Y, different levels of control instructions are generated, and through control strategies, including user guidance, energy storage deployment, mobile charging vehicle deployment, fault repair and next-day off-peak charging preferential measures, resource allocation is effectively optimized; this future-oriented control planning capability not only improves the resource utilization efficiency of automobile charging stations, but also effectively alleviates the charging pressure during peak hours and ensures the stability of user experience. In addition, through real-time dynamic monitoring and high-frequency data collection, while improving the load forecasting accuracy, it also enhances the ability to handle sudden load changes, providing strong technical support for power grid load optimization and the sustainable development of the new energy vehicle industry; in summary, a method and system for intelligent prediction of automobile charging station load, through intelligent, efficient and deeply optimized load prediction and control methods, significantly improves the operating efficiency and service quality of automobile charging stations, and plays an important role in promoting the transformation and upgrading of the new energy transportation field.

[0016] (2) By combining the Internet of Things technology with the central data center, the intelligent level of load monitoring and analysis at the vehicle charging station is improved; by connecting each charging station through the OCPP protocol and equipping it with an energy metering unit, a fault diagnosis unit and an RFID chip, it is possible to collect key data such as the total charging time Tzs, the total number of vehicle charging times Ncs and the total number of vehicle fast charging times Nkc throughout the day; this multi-dimensional data collection method makes up for the defects of the traditional system in single data collection and lack of real-time performance, and at the same time ensures the quality and accuracy of the data through noise removal, missing value filling and data smoothing; in the load In the load intensity analysis, the operating load levels of different charging stations are quantified by constructing the load intensity coefficient Xfh, and the high-load stations are screened out through the mean algorithm, and the number groups of high-load stations are accurately marked; when the proportion of high-load stations exceeds 50%, load demand forecasting instructions are automatically issued, providing a scientific basis for dynamic resource allocation; this method not only effectively solves the problem of insufficient analysis of the regional distribution of charging demand in traditional load forecasting methods, but also significantly improves the accuracy and sensitivity of load forecasting, providing strong technical support for efficient management of charging station resources and improving the utilization rate of power grid resources.

[0017] (3) Through continuous monitoring and multi-dimensional data analysis of the number of high-load vehicle charging stations, the accuracy and dynamic response capability of load management are significantly improved. After receiving the load demand forecasting instruction, the total load power Pfh is monitored in real time through the power metering unit, and the total number of faults Ncs is obtained by combining with the fault diagnosis unit. The number of charging piles Ncd is summarized by radio frequency identification technology, realizing high-frequency data collection and accurate state perception. Furthermore, the charging pile load density coefficient Xmd and load satisfaction coefficient Xmz are constructed through dimensionless processing, reflecting the load intensity distribution and satisfaction capacity at different monitoring time points, and combined with the total design load capacity C of the station. fh, the load demand index Zyc of the high-load automobile charging station group is fitted, and the operating pressure and demand trend of the high-load stations are quantified; this method effectively solves the problem of insufficient prediction of load dynamic changes and future demand in traditional technologies, and provides an accurate basis for resource regulation through the analysis of load satisfaction coefficient Xmz and load demand index Zyc; in addition, this method can identify charging peaks and uneven distribution problems, and formulate targeted regulation strategies based on design capacity and real-time status data, which significantly improves the resource utilization efficiency and operation reliability of charging stations, and provides important technical support for realizing intelligent load prediction of automobile charging stations.

[0018] (4) Based on the comparison between the load demand index Zyc and the preset demand threshold Y, the corresponding level of control instructions are generated and executed. Through the execution of the control strategy, the future-oriented control planning capability is realized, especially the prediction of the charging demand for the next day and the advance control of the resources of the vehicle charging station; specific resource scheduling plans are formulated for different load conditions, including peak-shifting charging discounts, user guidance strategies, temporary energy storage equipment deployment and maintenance team arrangements, which effectively alleviate the peak charging pressure, optimize the efficiency of power grid resource utilization, and improve user experience; Overall, the present invention takes intelligent, efficient and precise load prediction and control as the core, which can effectively solve the problems of uneven resource allocation and sudden load pressure in vehicle charging stations, and provide important support for the development of the new energy vehicle industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a schematic diagram of a flow chart of a method for intelligent prediction of load at a vehicle charging station according to the present invention; Figure 2 This is a block diagram of an intelligent prediction system for load at a vehicle charging station according to the present invention. DETAILED DESCRIPTION

[0020] 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.

[0021] Example 1 See also Figure 1 ,The present invention provides a method for intelligent prediction of vehicle charging station load, comprising the following steps; S1. Select several automobile charging stations within the target range as monitoring objects, and collect charging status information of each automobile charging station in combination with Internet of Things technology to obtain relevant charging status data information; S2. Construct a load intensity coefficient Xfh of several vehicle charging stations based on relevant charging status data information, mark a high-load vehicle charging station number group after comparison, and issue a load demand forecasting instruction; S3. After receiving the load demand forecasting instruction, the charging piles in the high-load vehicle charging station quantity group are continuously monitored, and the real-time status data information and charging pile quantity data information are obtained by combining the radio frequency identification technology, and the load demand index Zyc is obtained by fitting after dimensionless processing; S4. Preset the demand threshold Y, compare the load demand index Zyc with the preset demand threshold Y, and determine whether the vehicle charging stations in the high-load vehicle charging station number group meet the vehicle charging demand of the day, so as to generate and execute the corresponding level control instructions.

[0022] In this embodiment, through the intelligent prediction of the load of the automobile charging station, many shortcomings in the traditional technology are effectively overcome, showing unique advantages and practical value; by combining the Internet of Things technology and the radio frequency identification technology, the real-time dynamic monitoring of the charging status information of the automobile charging station is realized, breaking through the limitation of relying on a single static indicator in the existing method, and greatly improving the accuracy and sensitivity of the load prediction; at the same time, by constructing a multi-dimensional analysis process of the load intensity coefficient Xfh, the charging pile load density coefficient Xmd and the load satisfaction coefficient Xmz, and performing dimensionless processing, the load differences and dynamic change characteristics between the automobile charging stations are taken into account, and the traditional method of load prediction is effectively solved. In addition, with the load demand index Zyc as the core, the demand threshold Y is preset and targeted grade control instructions are generated, realizing future-oriented resource optimization control and next-day demand prediction capabilities, thereby effectively addressing the problem of lack of forward-looking planning in traditional methods; this method not only optimizes the resource allocation efficiency of vehicle charging stations, but also effectively alleviates peak load pressure through staggered charging, energy storage deployment and dynamic monitoring mechanisms, and improves the stability and satisfaction of user experience; overall, it provides an intelligent and refined solution for the efficient operation and maintenance of new energy vehicle charging infrastructure, injecting new impetus into the sustainable development of the new energy vehicle industry.

[0023] Example 2 Please refer to Figure 1 , specifically: S1 specific steps include: S11. Select several automobile charging stations within the target range as monitoring objects, combine the Internet of Things technology, connect several automobile charging stations with the central data center, and transmit data through the OCPP protocol. Among them, the charging piles in the automobile charging stations are equipped with power metering units, fault diagnosis units and RFID chips for identity recognition; It should be noted that the energy metering unit, fault diagnosis unit and RFID chip for identity identification are used to realize the core status monitoring and precise management of the charging pile respectively; the energy metering unit is responsible for real-time monitoring of the energy consumption of the charging pile, providing accurate data support for subsequent load analysis and regulation; the fault diagnosis unit is used to detect the operating status of the charging pile, quickly identify and report faults, and ensure the normal operation and timely maintenance of the charging pile; the RFID chip is used to identify the identity information of the charging pile, and improve the identity authentication security and data traceability of the charging pile; S12, connecting the data source interface of each vehicle charging station through the API interface of the central data center, collecting the charging status information of each vehicle charging station to obtain relevant charging status data information, wherein the relevant charging status data information includes the total vehicle charging time Tzs, the total number of vehicle charging times Ncs and the total number of vehicle fast charging times Nkc of each vehicle charging station, wherein the collection time is one day and one night; S13, preprocessing the relevant charging status data information, the preprocessing including noise removal, missing value filling and data smoothing operations, wherein the missing values ​​are filled by mean filling, median filling and interpolation filling.

[0024] It should be noted that the data of the total vehicle charging time Tzs, the total number of vehicle charging times Ncs and the total number of vehicle fast charging times Nkc are collected in real time through the data source interface of each automobile charging station connected to the central data center; the electric energy metering unit monitors the start and end time of each charging in real time, automatically calculates the total vehicle charging time Tzs, and accumulates and counts the total number of vehicle charging times Ncs according to the number of charging events; the fast charging mode is determined by the charging power threshold of the charging pile to generate the total number of vehicle fast charging times Nkc. These data not only reflect the real-time operating load of the automobile charging station, but also quantify the differences in charging demand of each automobile charging station, providing key data support for load forecasting, load intensity analysis and subsequent control strategies; this data acquisition method effectively overcomes the problems of single data collection dimension and delayed update in traditional systems, laying a solid foundation for improving the intelligence and real-time management of automobile charging stations, and at the same time provides an important basis for optimizing resource allocation and improving user experience.

[0025] In this embodiment, by introducing the Internet of Things technology and the OCPP protocol, multiple vehicle charging stations are efficiently connected to the central data center, achieving high-precision transmission of real-time data, and relying on the power metering unit, fault diagnosis unit and RFID chip on the charging pile, the reliability of data collection is ensured; through the API interface of the central data center, it is possible to obtain multi-dimensional core charging status information covering the total vehicle charging time Tzs, the total number of vehicle charging times Ncs and the total number of vehicle fast charging times Nkc, thereby significantly improving the dynamic grasp of the operating status of the vehicle charging station; in addition, through the preprocessing of the collected data, including removing noise, filling missing values ​​and data averaging, Slip processing effectively solves the impact of data missing and noise interference on the prediction results, and ensures data quality and analysis accuracy; compared with the shortcomings of static single indicator analysis in traditional load forecasting methods, it realizes the fusion analysis of multi-dimensional real-time data, laying a solid data foundation for subsequent load intensity coefficient construction and demand forecasting; this data collection and preprocessing method not only improves the accuracy of vehicle charging station status monitoring, but also significantly improves the dynamic response capability to load changes, and provides innovative technical support for solving the problems of uneven resource allocation and difficulty in coping with sudden load changes, meeting the actual needs of the new energy vehicle industry for efficient and intelligent load forecasting systems.

[0026] Example 3 Please refer to Figure 1 , specifically: S2 specific steps include: S21, constructing a load intensity coefficient Xfh of several vehicle charging stations based on relevant charging status data information, taking the load intensity coefficient Xfh of the i-th vehicle charging station as i For example, it is obtained in the following way: ; In the formula, Tzs i Ncs is the total charging time of the vehicle at the i-th vehicle charging station. i Nkc is the total number of vehicle charging times at the i-th vehicle charging station. i It is represented by the total number of fast charging times of vehicles at the i-th vehicle charging station, where , and Both are represented as weight values, and A is represented as the first correction constant.

[0027] Specifically, the specific steps of S2 also include: S22, based on the load intensity coefficient Xfh of the i-th vehicle charging station i The load intensity coefficient Xfh of several vehicle charging stations is obtained respectively, and the mean value of the load intensity coefficient is calculated based on the statistical mean value algorithm. ; S23, by comparing the load intensity coefficients Xfh of several vehicle charging stations with the average Compare the size to get the value above the mean The vehicle charging stations corresponding to the load intensity coefficient Xfh are marked, and a high-load vehicle charging station number group is constructed based on the marked vehicle charging stations. When the high-load vehicle charging station number group exceeds 50% of the total number of vehicle charging stations, a load demand forecast instruction is issued.

[0028] In this embodiment, by constructing the load intensity coefficient Xfh of several automobile charging stations, the load conditions of each automobile charging station are accurately quantified from multiple dimensions, ensuring the flexibility and adaptability of the calculation model; compared with the static analysis of a single indicator in the traditional method, it can characterize the operating characteristics of the automobile charging station, and effectively solve the problem of insufficient evaluation of load intensity differences and actual distribution in the prior art; in addition, the load intensity coefficient Xfh of several automobile charging stations is evaluated as a whole through the statistical mean algorithm, and the load intensity coefficients Xfh exceeding the mean are marked. high-load automobile charging stations, and further construct a number group of high-load automobile charging stations, realizing the accurate screening and identification of high-load automobile charging stations; this method not only improves the scientific nature of load forecasting and data processing efficiency, but also automatically generates load demand forecasting instructions by setting the trigger condition of more than 50% of the high-load automobile charging stations, providing a basis for subsequent demand forecasting and regulation; this innovative step effectively makes up for the problem of insufficient response to the dynamic changes of high-load automobile charging stations in the existing load forecasting methods, significantly improves the identification and early warning capabilities of uneven resource allocation of automobile charging stations, and provides reliable support for the optimal scheduling of automobile charging stations.

[0029] Example 4 Please refer to Figure 1 , specifically: S3 specific steps include: S31. After receiving the issued load demand forecasting instruction, the charging piles in the high-load automobile charging station group are continuously monitored, and a monitoring time period is set. The electric energy metering unit and the fault diagnosis unit equipped on the charging piles are continuously monitored for the load power change status and fault shutdown status of the charging piles in the high-load automobile charging station group, and the real-time status data information of the high-load automobile charging station group is summarized and obtained. The real-time status data information includes the total load power Pfh and the total number of faults Ncs at each monitoring time point in the monitoring time period. The charging piles in the high-load automobile charging station group are identified and the quantity is summarized through the radio frequency identification technology to obtain the charging pile quantity data information, which includes the total number of charging piles Ncd in the high-load automobile charging station group. The monitoring time period is one day and one night.

[0030] It should be noted that the data of total load power Pfh and total number of faults Ncs at each monitoring time point within the monitoring period and the total number of charging piles Ncd in the high-load vehicle charging station group are obtained through continuous monitoring of high-load vehicle charging stations, which is specifically completed by the power metering unit, fault diagnosis unit and radio frequency identification technology in the charging piles; the power metering unit records the power output of each charging pile in real time, and summarizes the total load power Pfh at each monitoring time point; the fault diagnosis unit identifies fault events by detecting the operating status of the equipment, and accumulates and counts the total number of faults Ncs at each time point; the radio frequency identification technology authenticates the identity through the RFID chip in the charging pile With data collection, ensure the accurate summary of the number of charging piles Ncd; the role of these data is to accurately reflect the operating status of high-load automobile charging stations; the total load power Pfh describes the real-time load intensity of the automobile charging station, the total number of failures Ncs provides a key indicator of equipment reliability and operating risk, and the total number of charging piles Ncd in the high-load automobile charging station group provides the basis for subsequent load density analysis and resource scheduling; this multi-dimensional data acquisition method significantly improves the ability to perceive the status of high-load automobile charging stations, provides scientific support for the formulation of accurate load forecasting and control strategies, and effectively responds to the challenges of charging demand fluctuations and sudden equipment failures.

[0031] In this embodiment, by continuously monitoring the charging piles in the high-load automobile charging station number group, the dynamic control capability of the operation status of the automobile charging station is significantly enhanced; by using the power metering unit and fault diagnosis unit equipped on the charging pile, the load power change and fault shutdown status of the charging pile can be captured in real time, and the identity and quantity of the charging piles can be summarized by combining the radio frequency identification technology, and the total number of charging piles Ncd and real-time status data information can be accurately obtained; this process not only improves the perception capability of the real-time operation status of the automobile charging station, but also provides a basis for subsequent load forecasting and control strategies by monitoring the total load power Pfh and the total number of faults Ncs at the time point. It provides key data support; in particular, by setting a monitoring period of one day and one night, it can achieve high-frequency data collection and analysis, and quickly respond to load changes and sudden failure problems. Compared with the static prediction method that relies on historical data in the traditional method, its dynamic and real-time performance is significantly enhanced; it effectively solves the problem of lack of real-time monitoring of charging pile fault status and load dynamic changes in the existing technology, and at the same time optimizes the timeliness of data collection, providing a solid foundation for the precise fitting of subsequent load demands and the formulation of control plans, thereby effectively alleviating the operating pressure of high-load automobile charging stations and improving the overall resource allocation efficiency and operational reliability of automobile charging stations.

[0032] Example 5 Please refer to Figure 1, specifically: S3 specific steps also include: S32: Analyze the real-time status data information and the charging pile quantity data information, and after dimensionless processing, construct a charging pile load density coefficient Xmd at several monitoring time points, and use the charging pile load density coefficient Xmd at the tth monitoring time point as the value. t For example, it is obtained in the following way: ; In the formula, It is expressed as the total load power at the tth monitoring time point within the monitoring period, It is expressed as the total number of charging piles in the high-load vehicle charging station number group.

[0033] It should be noted that the charging pile load density coefficient Xmd is an important indicator used to quantify the load distribution of charging piles at a vehicle charging station at a specific monitoring time point. It is specifically defined as the ratio of the total load power Pfh at each monitoring time point to the total number of charging piles Ncd. It reflects the average load intensity carried by each charging pile per unit time and is a key parameter for evaluating the operating efficiency and load distribution balance of vehicle charging stations. By constructing the charging pile load density coefficient Xmd, it is possible to effectively identify high-load and low-load time periods and the load distribution of vehicle charging stations, providing data support for optimizing resource allocation and improving operating efficiency. In addition, the dynamic change trend of the charging pile load density coefficient Xmd can reveal the law of load fluctuations, help predict future load changes, and provide a scientific basis for formulating precise control strategies, improving user charging experience, and improving the overall management level of vehicle charging stations. This indicator has good versatility after dimensionless processing, which is convenient for comparison and analysis at different time points.

[0034] Specifically, the S3 specific steps also include: S33, based on the charging pile load density coefficient Xmd obtained at the tth monitoring time point t In this way, the charging pile load density coefficient Xmd at several monitoring time points is obtained respectively, and the charging pile load density coefficient Xmd at several monitoring time points is associated with the real-time status data information and the charging pile quantity data information. After dimensionless processing, the load satisfaction coefficient Xmz at several monitoring time points is constructed, and the load satisfaction coefficient Xmz at the tth monitoring time point is used. t For example, it is obtained in the following way: ; In the formula, The charging pile load density coefficient at the tth monitoring time point, It is represented by the total number of charging piles in the high-load vehicle charging station number group. Expressed as the total number of failures at the tth monitoring time point, It is expressed as the failure rate of the charging pile at the tth monitoring time point.

[0035] Specifically, the S3 specific steps also include: S34, based on the load satisfaction coefficient Xmz obtained at the t-th monitoring time point t The load satisfaction coefficient Xmz of several monitoring time points is obtained respectively, and the total design load capacity Cfh of the high-load vehicle charging station group is obtained according to the power supply line load level of the vehicle charging station during the power grid planning and design, and combined with the design capacity of the power supply transformer; It should be noted that the total design load capacity Cfh of the high-load vehicle charging station group is calculated by comprehensively analyzing the load level of the power supply line and the design capacity of the power supply transformer during power grid planning and design; specifically, the load level of the power supply line provides the theoretical upper limit of the station power supply system when operating at full load, while the design capacity of the power supply transformer defines the upper limit of the actual power supply capacity of the station; the total design load capacity Cfh is obtained by adding up the design capacity of each charging station in the high-load station group; its role is to provide a benchmark for the analysis of load demand, and by comparing the actual load satisfaction with the total design load capacity Cfh, the operating pressure and resource utilization efficiency of the high-load vehicle charging station group can be effectively evaluated; this indicator can not only identify potential resource bottlenecks, but also provide key data support for formulating load control strategies, optimizing grid resource allocation and ensuring station operation stability.

[0036] S35, by associating the load satisfaction coefficient Xmz at several monitoring time points with the total design load capacity Cfh, and combining the real-time status data information, after dimensionless processing, the load demand index Zyc is obtained by fitting, and the load demand index Zyc is obtained by the following formula: ; In the formula, Xmz t It is expressed as the load satisfaction coefficient at the tth monitoring time point, It is expressed as the total load power at the t-th monitoring time point within the monitoring period, t=1, 2, 3, ..., T, T represents the number of monitoring time points, It is expressed as the ratio of the total load power to the total design load capacity at the t-th monitoring time point, representing the operating pressure level of the vehicle charging stations in the high-load vehicle charging station number group at the current monitoring time point. and Both are represented as weight values, and B is represented as the second correction constant.

[0037] It should be noted that the load demand index Zyc is a comprehensive indicator used to quantify the degree of matching between charging demand and resource carrying capacity within a group of high-load vehicle charging stations. The load demand index Zyc constructed by combining the load satisfaction coefficient Xmz and the total design load capacity Cfh at several monitoring time points, as well as real-time status data information, reflects the operating pressure level and load demand trend of high-load vehicle charging stations during the monitoring period. Specifically, the calculation of the load demand index Zyc takes into account the ratio of the actual load power to the design capacity of the vehicle charging station at each time point, and introduces the weight value , And the second correction constant B ensures that the load demand index Zyc can flexibly adapt to the operating conditions of different vehicle charging stations. The role of the load demand index Zyc is to provide a scientific basis for load forecasting and resource regulation, as well as the ability to dynamically analyze load changes.

[0038] In this embodiment, by constructing the charging pile load density coefficient Xmd and the load satisfaction coefficient Xmz, the load distribution and operation characteristics of the high-load automobile charging station are dynamically analyzed from multiple monitoring time points, which effectively solves the limitations of the static analysis of load status in traditional technologies; firstly, based on the real-time status data information and the charging pile quantity data information, the dimensionless processing technology is used to construct the charging pile load density coefficient Xmd with the monitoring time point as the unit, and the load intensity distribution of the charging pile at each moment is accurately quantified; this method can clearly reflect the dynamic changes of the load in the time dimension, and make up for the problem that the description of the load distribution characteristics in the traditional load forecasting method is not comprehensive enough; in addition, by associating the load density coefficient Xmd with the real-time status data information and the charging pile quantity data information, the load satisfaction coefficient Xmz is further constructed, which can more comprehensively evaluate the load carrying capacity and operation stability of the automobile charging station at different time points, and further combined with the power supply line load level of the automobile charging station during the power grid planning and design, and The design capacity of the power supply transformer is used to obtain the total design load capacity Cfh of the high-load vehicle charging station, and based on this, the load demand index Zyc within the high-load vehicle charging station number group is dynamically fitted; this process realizes the accurate quantification of the operating pressure level of the vehicle charging station, and effectively solves the problem of lack of in-depth analysis of load dynamic changes and future demand trends in the existing technology; in particular, through the comprehensive evaluation of several monitoring time points, it can capture the key periods of load fluctuations and peak pressure, and provide a scientific basis for formulating more efficient and accurate resource regulation strategies; this method not only improves the accuracy of load forecasting, but also provides technical support for the optimal allocation of power grid resources and the forward-looking regulation of high-load vehicle charging stations, thereby significantly improving the user experience and improving the intelligent management level of new energy vehicle charging infrastructure; overall, this process has unique advantages in dynamic load analysis and future demand forecasting, and provides an innovative solution for effectively solving uneven resource allocation and sudden load pressure.

[0039] Example 6 Please refer to Figure 1 , specifically: S4 specific steps include: S41, pre-set the demand threshold Y, compare the load demand index Zyc with the preset demand threshold Y, to determine whether the vehicle charging stations in the high-load vehicle charging station number group meet the vehicle charging demand of the day, and generate corresponding level control instructions, the specific contents are as follows: If the load demand index Zyc>demand threshold Y, it means that the car charging stations in the high-load car charging station number group do not meet the car charging demand of the day, and a first-level control instruction is obtained. The control content is to formulate a user guidance strategy for the next day based on the load intensity coefficient Xfh distribution of the car charging stations in the high-load car charging station number group; use the charging navigation system to recommend surrounding low-load car charging stations to electric vehicles as a priority choice, deploy temporary energy storage equipment and mobile charging vehicles in the surrounding areas of the high-load car charging station number group as a supplementary power source during peak hours, and arrange a maintenance team to repair the faulty charging piles of the car charging stations in the high-load car charging station number group in combination with the total number of faults Ncs indicator, and launch a peak-time staggered charging discount the next day; If the load demand index Zyc ≤ demand threshold Y, it means that the automobile charging stations in the high-load automobile charging station number group meet the automobile charging demand of the day, and the secondary control instruction is obtained. The control content is: continue to implement high-frequency dynamic monitoring of the automobile charging stations in the high-load automobile charging station number group the next day, guide the distribution of charging vehicles through the charging navigation system, promote the use of low-load automobile charging stations through charging discounts and operation publicity the next day, arrange emergency response teams to pay close attention, and prepare to deal with potential charging pile failure risks and sudden load growth measures, and use the load demand index Zyc of the next day as the basis for predicting subsequent automobile charging demand.

[0040] In this embodiment, by presetting the demand threshold Y, the load demand index Zyc is compared with the preset demand threshold Y, and the first-level control instruction and the second-level control instruction are generated and executed, which significantly improves the intelligence and precision of the load management of the automobile charging station; when the automobile charging stations in the high-load automobile charging station number group do not meet the automobile charging demand of the day, a first-level control instruction is generated, and multi-level control measures are taken. Through the charging navigation system, users are guided to give priority to low-load automobile charging stations, and temporary energy storage equipment and mobile charging vehicles are deployed to alleviate the power consumption pressure during peak hours. In combination with the total number of faults Ncs indicator, a maintenance team is arranged to repair the faulty charging piles, and a discount strategy for off-peak charging during the next day's peak is introduced. These measures not only effectively alleviate the operating pressure of high-load automobile charging stations, but also optimize the resource scheduling efficiency and improve the user experience; in the high-load automobile charging station number group When the car charging stations within the city meet the car charging demand of the day, a secondary control instruction is generated, high-frequency dynamic monitoring is implemented, and the use of low-load car charging stations is promoted through charging discounts and operational publicity. At the same time, an emergency response team is arranged to pay close attention to potential risks. This control method can ensure the rational allocation of resources, and effectively prevent the adverse effects of sudden load growth on the power grid and user experience. Compared with the lack of forward-looking control planning and the lack of future demand forecasting capabilities in traditional methods, this control mechanism effectively solves the problems of uneven resource allocation and failure to respond to sudden demands in a timely manner through a highly targeted grading strategy. Moreover, the control measures dynamically adjust the resources of car charging stations and optimize user behavior to build an intelligent management system with efficient resource utilization and user-friendly experience, providing strong technical guarantees for the efficient operation and sustainable development of electric vehicle charging infrastructure.

[0041] Example 7 Please refer to Figure 1 and Figure 2 ,Specifically: A vehicle charging station load intelligent prediction system includes a data acquisition module, a marking module, a load demand prediction module and a level control module; The data acquisition module is used to select several automobile charging stations within the target range as monitoring objects, and collect charging status information of each automobile charging station in combination with the Internet of Things technology to obtain relevant charging status data information; The marking module is used to construct the load intensity coefficient Xfh of several vehicle charging stations according to the relevant charging status data information, mark the number group of high-load vehicle charging stations after comparison, and issue a load demand prediction instruction; The load demand forecasting module is used to continuously monitor the charging piles in the high-load vehicle charging station quantity group after receiving the issued load demand forecasting instruction, and obtain the real-time status data information and charging pile quantity data information respectively by combining the radio frequency identification technology, and obtain the load demand index Zyc by fitting after dimensionless processing; The level control module is used to pre-set a demand threshold Y, compare the load demand index Zyc with the preset demand threshold Y, and determine whether the vehicle charging stations in the high-load vehicle charging station number group meet the vehicle charging demand of the day, so as to generate and execute corresponding level control instructions.

[0042] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent prediction of vehicle charging station load, characterized by: The steps include: S1. Select several automobile charging stations within the target range as monitoring objects, and collect charging status information of each automobile charging station in combination with Internet of Things technology to obtain relevant charging status data information; S2. Construct the load intensity coefficient Xfh of several vehicle charging stations based on the relevant charging status data information, mark the number group of high-load vehicle charging stations after comparison, and issue a load demand forecasting instruction; S3. After receiving the load demand forecasting instruction, the charging piles in the high-load vehicle charging station quantity group are continuously monitored, and the real-time status data information and charging pile quantity data information are obtained by combining the radio frequency identification technology. After dimensionless processing, the load demand index Zyc is obtained by fitting; S4. Preset the demand threshold Y, compare the load demand index Zyc with the preset demand threshold Y, and determine whether the vehicle charging stations in the high-load vehicle charging station number group meet the vehicle charging demand of the day, so as to generate and execute the corresponding level control instructions.

2. According to claim 1, a method for intelligent prediction of vehicle charging station load is characterized by: The specific steps of S1 include: S11. Select several automobile charging stations within the target range as monitoring objects, combine the Internet of Things technology, connect several automobile charging stations with the central data center, and transmit data through the OCPP protocol. Among them, the charging piles in the automobile charging stations are equipped with power metering units, fault diagnosis units and RFID chips for identity recognition; S12, connecting the data source interface of each vehicle charging station through the API interface of the central data center, collecting the charging status information of each vehicle charging station to obtain relevant charging status data information, wherein the relevant charging status data information includes the total vehicle charging time Tzs, the total number of vehicle charging times Ncs and the total number of vehicle fast charging times Nkc of each vehicle charging station, wherein the collection time is one day and one night; S13, preprocessing the relevant charging status data information, the preprocessing including noise removal, missing value filling and data smoothing operations, wherein the missing values ​​are filled by mean filling, median filling and interpolation filling.

3. The method for intelligent prediction of vehicle charging station load according to claim 2 is characterized by: The specific steps of S2 include: S21, constructing a load intensity coefficient Xfh of several vehicle charging stations based on relevant charging status data information, taking the load intensity coefficient Xfh of the i-th vehicle charging station as i For example, it is obtained in the following way: ; In the formula, Tzs i Ncs is the total charging time of the vehicle at the i-th vehicle charging station. i Nkc is the total number of vehicle charging times at the i-th vehicle charging station. i It is represented by the total number of fast charging times of vehicles at the i-th vehicle charging station, where , and Both are represented as weight values, and A is represented as the first correction constant.

4. The method for intelligent prediction of vehicle charging station load according to claim 3 is characterized by: The specific steps of S2 also include: S22, based on the load intensity coefficient Xfh of the i-th vehicle charging station i The load intensity coefficient Xfh of several vehicle charging stations is obtained respectively, and the mean value of the load intensity coefficient is calculated based on the statistical mean value algorithm. ; S23, by comparing the load intensity coefficients Xfh of several vehicle charging stations with the average Compare the size to get more than the mean The vehicle charging stations corresponding to the load intensity coefficient Xfh are marked, and a high-load vehicle charging station number group is constructed based on the marked vehicle charging stations. When the high-load vehicle charging station number group exceeds 50% of the total number of vehicle charging stations, a load demand forecast instruction is issued.

5. The method for intelligent prediction of vehicle charging station load according to claim 4 is characterized in that: The specific steps of S3 include: S31. After receiving the issued load demand forecasting instruction, the charging piles in the high-load automobile charging station group are continuously monitored, and a monitoring time period is set. The electric energy metering unit and the fault diagnosis unit equipped on the charging piles are continuously monitored for the load power change status and fault shutdown status of the charging piles in the high-load automobile charging station group, and the real-time status data information of the high-load automobile charging station group is summarized and obtained. The real-time status data information includes the total load power Pfh and the total number of faults Ncs at each monitoring time point in the monitoring time period. The charging piles in the high-load automobile charging station group are identified and the quantity is summarized through the radio frequency identification technology to obtain the charging pile quantity data information, which includes the total number of charging piles Ncd in the high-load automobile charging station group. The monitoring time period is one day and one night.

6. The method for intelligent prediction of vehicle charging station load according to claim 5, characterized in that: The specific steps of S3 also include: S32: Analyze the real-time status data information and the charging pile quantity data information, and after dimensionless processing, construct a charging pile load density coefficient Xmd at several monitoring time points, and use the charging pile load density coefficient Xmd at the tth monitoring time point as the value. t For example, it is obtained in the following way: ; In the formula, It is expressed as the total load power at the tth monitoring time point within the monitoring period, It is expressed as the total number of charging piles in the high-load vehicle charging station number group.

7. The method for intelligent prediction of vehicle charging station load according to claim 6, characterized in that: The specific steps of S3 also include: S33, based on the charging pile load density coefficient Xmd obtained at the tth monitoring time point t In this way, the charging pile load density coefficient Xmd at several monitoring time points is obtained respectively, and the charging pile load density coefficient Xmd at several monitoring time points is associated with the real-time status data information and the charging pile quantity data information. After dimensionless processing, the load satisfaction coefficient Xmz at several monitoring time points is constructed, and the load satisfaction coefficient Xmz at the tth monitoring time point is used. t For example, it is obtained in the following way: ; In the formula, The charging pile load density coefficient at the tth monitoring time point, It is represented by the total number of charging piles in the high-load vehicle charging station number group. Expressed as the total number of failures at the tth monitoring time point, It is expressed as the failure rate of the charging pile at the tth monitoring time point.

8. The method for intelligent prediction of vehicle charging station load according to claim 7, characterized in that: The specific steps of S3 also include: S34, based on the load satisfaction coefficient Xmz obtained at the t-th monitoring time point t The load satisfaction coefficient Xmz of several monitoring time points is obtained respectively, and the total design load capacity Cfh of the high-load vehicle charging station group is obtained according to the power supply line load level of the vehicle charging station during the power grid planning and design, and combined with the design capacity of the power supply transformer; S35, by associating the load satisfaction coefficient Xmz at several monitoring time points with the total design load capacity Cfh, and combining the real-time status data information, after dimensionless processing, the load demand index Zyc is obtained by fitting, and the load demand index Zyc is obtained by the following formula: ; In the formula, Xmz t It is expressed as the load satisfaction coefficient at the tth monitoring time point, It is expressed as the total load power at the tth monitoring time point within the monitoring period, t=1, 2, 3, ..., T, T represents the number of monitoring time points, It is expressed as the ratio of the total load power to the total design load capacity at the t-th monitoring time point, representing the operating pressure level of the vehicle charging stations in the high-load vehicle charging station number group at the current monitoring time point. and Both are represented as weight values, and B is represented as the second correction constant.

9. The method for intelligent prediction of vehicle charging station load according to claim 8, characterized in that: The specific steps of S4 include: S41, pre-set the demand threshold Y, compare the load demand index Zyc with the preset demand threshold Y, to determine whether the vehicle charging stations in the high-load vehicle charging station number group meet the vehicle charging demand of the day, and generate corresponding level control instructions, the specific contents are as follows: If the load demand index Zyc>demand threshold Y, it means that the car charging stations in the high-load car charging station number group do not meet the car charging demand of the day, and a first-level control instruction is obtained. The control content is to formulate a user guidance strategy for the next day based on the load intensity coefficient Xfh distribution of the car charging stations in the high-load car charging station number group; use the charging navigation system to recommend surrounding low-load car charging stations to electric vehicles as a priority choice, deploy temporary energy storage equipment and mobile charging vehicles in the surrounding areas of the high-load car charging station number group as a supplementary power source during peak hours, and arrange a maintenance team to repair the faulty charging piles of the car charging stations in the high-load car charging station number group in combination with the total number of faults Ncs indicator, and launch a peak-time staggered charging discount the next day; If the load demand index Zyc ≤ demand threshold Y, it means that the automobile charging stations in the high-load automobile charging station number group meet the automobile charging demand of the day, and the secondary control instruction is obtained. The control content is: continue to implement high-frequency dynamic monitoring of the automobile charging stations in the high-load automobile charging station number group the next day, guide the distribution of charging vehicles through the charging navigation system, promote the use of low-load automobile charging stations through charging discounts and operation publicity the next day, arrange emergency response teams to pay close attention, and prepare to deal with potential charging pile failure risks and sudden load growth measures, and use the load demand index Zyc of the next day as the basis for predicting subsequent automobile charging demand.

10. An intelligent prediction system for vehicle charging station load, used to implement an intelligent prediction method for vehicle charging station load as described in any one of claims 1 to 9, characterized in that: It includes data collection module, marking module, load demand prediction module and grade control module; The data acquisition module is used to select several automobile charging stations within the target range as monitoring objects, and collect charging status information of each automobile charging station in combination with the Internet of Things technology to obtain relevant charging status data information; The marking module is used to construct the load intensity coefficient Xfh of several vehicle charging stations according to the relevant charging status data information, mark the number group of high-load vehicle charging stations after comparison, and issue a load demand prediction instruction; The load demand forecasting module is used to continuously monitor the charging piles in the high-load vehicle charging station quantity group after receiving the issued load demand forecasting instruction, and obtain the real-time status data information and charging pile quantity data information respectively by combining the radio frequency identification technology, and obtain the load demand index Zyc by fitting after dimensionless processing; The level control module is used to pre-set a demand threshold Y, compare the load demand index Zyc with the preset demand threshold Y, and determine whether the vehicle charging stations in the high-load vehicle charging station number group meet the vehicle charging demand of the day, so as to generate and execute corresponding level control instructions.

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