New energy charging pile charging control method and system

Abstract

The invention discloses a new energy charging pile charging control method and system, and relates to the technical field of new energy, and the method comprises the steps: collecting user historical charging data, power grid load data, fixed electricity price information and weather forecast data in real time, and recognizing user charging habits and demands; generating a personalized charging scheduling strategy according to the charging habit of the user and the real-time power grid state; the availability of various energy sources is monitored in real time, the generating capacity and the cost of each energy source are evaluated, the priority of energy source selection is adjusted by using a personalized charging scheduling strategy, and an optimal charging mode is automatically selected; executing charging operation based on the charging mode selected by the user, and performing adjustment according to the power grid load and the energy consumption condition; the availability of various energy sources is monitored in real time, the generating capacity and the cost are evaluated, and the priority of the energy sources is dynamically adjusted, so that the response capability of the charging pile to environment change is improved, the charging cost of a user can be reduced, and renewable energy sources are fully utilized.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a new energy charging pile charging control method and system. Background Art

[0002] In recent years, with the global attention to renewable energy and the rapid development of the electric vehicle market, charging pile technology has gradually become an important part of new energy applications. Traditional charging piles mainly rely on fixed electricity prices and simple charging modes, and lack real-time response to user behavior and grid status. This model has met the initial charging needs to a certain extent, but with the popularization of electric vehicles, the utilization rate of charging facilities and the volatility of grid load have become increasingly severe, and traditional methods have gradually revealed their shortcomings. Specifically, existing technologies often fail to fully consider the personalized needs of users and the real-time status of the grid, resulting in waste of resources and poor user experience during the charging process. In addition, the lack of intelligent scheduling and priority selection of renewable energy also makes the utilization efficiency of power resources inefficient and fails to achieve the goal of sustainable development.

[0003] The deficiencies of existing charging control technology are mainly reflected in several aspects. First, the lack of in-depth analysis of user charging habits leads to the simplification of charging solutions, which cannot meet the charging needs of different users in different time periods. For example, users' charging needs during peak hours may impose additional burdens on the power grid. If they are not effectively dispatched, they may cause the risk of overload of the power grid. Secondly, existing systems often rely only on static electricity price information, but fail to respond in real time to dynamic load changes in the power grid and the availability of renewable energy. This static mode not only reduces charging efficiency, but also increases users' charging costs. In addition, the lack of an intelligent cost feedback mechanism makes users lack awareness of costs during the charging process, affecting their charging decisions. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a new energy charging pile charging control method and system to solve the dynamic balance problem between user charging demand and grid load, and optimize the charging strategy to achieve cost-effectiveness and efficient use of renewable energy.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a new energy charging pile charging control method, which comprises:

[0008] Collect user historical charging data, grid load data, fixed electricity price information and weather forecast data in real time to identify user charging habits and needs;

[0009] Generate personalized charging scheduling strategies based on user charging habits and real-time grid status;

[0010] Monitor the availability of multiple energy sources in real time, evaluate the power generation and cost of each energy source, use personalized charging scheduling strategies to adjust the priority of energy selection, and automatically select the optimal charging mode;

[0011] Execute charging operations based on the charging mode selected by the user and adjust according to grid load and energy consumption;

[0012] The charging cost is calculated in real time based on the fixed electricity price and the charging mode selected by the user, and the cost information is fed back on the user's mobile device to generate the final charging record.

[0013] As a preferred solution of the new energy charging pile charging control method of the present invention, wherein: the historical charging data includes charging time, charging frequency and charging amount;

[0014] The power grid load data includes real-time load and historical load;

[0015] The fixed electricity price information includes peak electricity price, average peak electricity price and low peak electricity price;

[0016] The weather forecast data includes solar energy and wind energy data.

[0017] As a preferred solution of the new energy charging pile charging control method of the present invention, the specific operation steps of identifying the user's charging habits and needs are as follows:

[0018] Format and clean the collected data;

[0019] Extract the start time of each charging record from the processed database and convert it into a format suitable for cluster analysis, and assign a sample tag to each record;

[0020] Set the number of clusters to N, randomly select M initial cluster centers, and select the initial centers by analyzing the most commonly used charging time periods of users;

[0021] According to the charging time data, the distance from each data point to the cluster center is calculated, and each record with a sample mark is assigned to the nearest cluster center;

[0022] When the cluster center no longer changes significantly, the iteration is stopped and the final clustering result is determined;

[0023] For each cluster, count the number of charging times in the time period and calculate the charging frequency;

[0024] Compare the charging frequencies in different time periods and identify the most frequently charged periods;

[0025] Set a charging frequency threshold for the peak charging period, and define the time period when the frequency exceeds the threshold as the peak period;

[0026] Based on the results of the charging peak time analysis, identify the user's common charging time period;

[0027] Extracting the charging amount and the corresponding time period from the processed database and converting them into a format suitable for processing by the association rule learning algorithm to form a transaction database;

[0028] Define the combination of charging time and charging amount as an item set, and use the combination algorithm to generate all candidate item sets;

[0029] For each candidate item set, calculate its support and set a support threshold based on historical data analysis of user behavior. When the support is greater than the set support threshold, the candidate item set is selected as a frequent item set.

[0030] Generate association rules from frequent item sets and calculate the confidence and lift of each rule;

[0031] Filter out the most representative association rules based on confidence and lift;

[0032] Summarize and classify the most representative association rules identified, and extract common features from the summarized rules;

[0033] Combined with the user's actual charging data, analyze whether the behavior reflected by the association rules is consistent with the actual situation, and obtain the user's charging behavior;

[0034] Analyze users' charging behavior during different electricity price periods and identify their charging needs.

[0035] As a preferred solution of the new energy charging pile charging control method of the present invention, wherein: generating a personalized charging scheduling strategy according to the user's charging habits and the real-time power grid status includes the following steps:

[0036] Merge user charging records with real-time grid load data to form a comprehensive data set;

[0037] Select users’ common charging time periods, average charging amounts, electricity price sensitivity, and grid load forecasting characteristics from the comprehensive dataset;

[0038] Build a load forecasting model based on historical data to predict future grid loads;

[0039] Group users’ charging time periods and calculate charging demand and grid load for each time period;

[0040] Set the charging power according to the grid load forecast;

[0041] Generate a personalized charging scheduling strategy based on user charging habits and grid load conditions.

[0042] As a preferred solution of the new energy charging pile charging control method of the present invention, the following steps are included: real-time monitoring of the availability of multiple energy sources, evaluation of the power generation and cost of each energy source, use of personalized charging scheduling strategy to adjust the priority of energy selection, and automatic selection of the optimal charging mode.

[0043] Real-time monitoring of the availability of multiple energy sources, evaluation of the power generation and cost of each energy source, use of personalized charging scheduling strategies to adjust the priority of energy selection, and automatic selection of the optimal charging mode include the following steps:

[0044] Integrate current grid load and electricity price information to obtain real-time data on renewable energy generation, solar and wind power generation;

[0045] Evaluate the power generation of each energy source and calculate the current power generation cost;

[0046] Prioritize each energy source based on generation costs, current generation, renewable energy data, and electricity price information;

[0047] According to the set priority, sort the priorities of all energy sources, and set the energy selection logic and the charging mode threshold T of each energy source;

[0048] Determine the current charging mode to be adopted based on the current grid load, electricity price information and renewable energy generation;

[0049] When the renewable energy supply exceeds the set threshold T, it switches to green charging mode;

[0050] When the grid load is low and user demand is high, it switches to high-efficiency charging mode;

[0051] When the grid load is high and user demand is moderate, it switches to economic charging mode;

[0052] Based on the judgment results, it automatically switches to the optimal charging mode.

[0053] As a preferred solution of the new energy charging pile charging control method of the present invention, wherein: performing charging operation based on the charging mode selected by the user and adjusting according to the grid load and energy consumption includes the following steps:

[0054] Confirm the charging mode selected by the user and configure corresponding charging parameters according to the charging mode selected by the user;

[0055] Real-time monitoring of various parameters during the charging process, the consumption of each energy source and the real-time load of the power grid;

[0056] When the monitored current and voltage exceed the safe range, the charging parameters are automatically adjusted;

[0057] Set the abnormal detection threshold of the parameter, and adjust the charging power immediately when an abnormality is detected;

[0058] Conduct cost-benefit analysis based on the consumption of each energy source and user charging needs, analyze the matching degree between charging cost and user needs, and evaluate the economic feasibility of each energy source;

[0059] Based on the results of the cost-benefit analysis, adjustments are made in the generated personalized charging strategy.

[0060] As a preferred solution of the new energy charging pile charging control method of the present invention, the charging cost is calculated in real time according to the fixed electricity price and the charging mode selected by the user, and the cost information is fed back on the user's mobile device to generate the final charging record, including the following steps:

[0061] Obtain current basic electricity prices and renewable energy information, and calculate real-time charging costs based on charging power, charging time, and electricity prices;

[0062] When charging is finished, a detailed cost report is generated and sent to the user's mobile device, asking the user to confirm the cost;

[0063] After the user confirms the fee, the transaction is recorded and the payment is made;

[0064] Send instructions to automatically shut down the charging device, cut off the power supply, and generate detailed charging records.

[0065] In a second aspect, the present invention provides a new energy charging pile charging control system, comprising:

[0066] The data collection module collects user historical charging data, grid load data, fixed electricity price information and weather forecast data in real time to identify user charging habits and needs;

[0067] Personalized scheduling strategy generation module generates personalized charging scheduling strategies based on user charging habits and real-time grid status;

[0068] Optimize the charging mode selection module, monitor the availability of multiple energy sources in real time, evaluate the power generation and cost of each energy source, use personalized charging scheduling strategies to adjust the priority of energy selection, and automatically select the optimal charging mode;

[0069] A charging execution module executes charging operations based on the charging mode selected by the user and makes adjustments based on the grid load and energy consumption;

[0070] The cost calculation module calculates the charging cost in real time based on the fixed electricity price and the charging mode selected by the user, and feeds back the cost information on the user's mobile device to generate the final charging record.

[0071] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the new energy charging pile charging control method as described in the first aspect of the present invention is implemented.

[0072] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the new energy charging pile charging control method as described in the first aspect of the present invention is implemented.

[0073] The beneficial effects of the present invention are as follows: by collecting historical charging data, grid load information, fixed electricity prices and weather forecast data, the user's charging habits can be accurately identified; when generating a personalized charging scheduling strategy, the user records and grid load data are merged to build a load prediction model, so that when the grid load is low, the charging pile can provide higher charging power, meet user needs while reducing charging costs and optimizing charging efficiency; real-time monitoring of the availability of multiple energy sources, evaluation of power generation and cost, and dynamic adjustment of energy priority not only improves the charging pile's ability to respond to environmental changes, but also reduces users' charging costs, makes full use of renewable energy, ensures the charging process is safe and efficient through real-time monitoring of charging parameters and cost feedback, and improves users' transparency of costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0075] Figure 1 This is a flow chart of the new energy charging pile charging control method in Example 1.

[0076] Figure 2 This is a schematic diagram of the new energy charging pile charging control system in Example 1. DETAILED DESCRIPTION

[0077] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0078] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0079] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0080] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a new energy charging pile charging control method, comprising the following steps:

[0081] S1. Collect user historical charging data, grid load data, fixed electricity price information and weather forecast data in real time to identify user charging habits and needs.

[0082] S1.1. Historical charging data includes charging time, charging frequency and charging amount; grid load data includes real-time load and historical load; fixed electricity price information includes peak electricity price, off-peak electricity price and low-peak electricity price; weather forecast data includes solar energy and wind energy data.

[0083] S1.2. Identify user charging habits and needs.

[0084] S1.2.1. Format and clean the collected data.

[0085] Specifically, data formatting refers to converting data from different sources (such as charging records, grid load, fixed electricity prices, and weather forecasts) into a standard data structure. Specific operations include:

[0086] The JSON format is selected as the unified data storage format because it has good readability and easy parsing characteristics. It is suitable for storing hierarchical data and defining standard fields for each data type. For example:

[0087] For historical charging data: including fields such as charging time, charging frequency and charging amount.

[0088] For power grid load: includes fields such as real-time load and historical load.

[0089] For fixed tariffs: include the Time Period and Price fields.

[0090] For weather data: including fields such as temperature, humidity, and sunshine duration.

[0091] By writing a data conversion module, data from different sources can be converted according to defined fields. For example, the charging record data can be converted from CSV format to JSON format and each field can be filled.

[0092] Data cleaning refers to the evaluation of formatted data to identify and remove redundant and erroneous data to ensure the quality of the data set. The specific steps include:

[0093] Identify redundant data: The system checks if there are duplicate records in the dataset, such as duplicate entries for the same charging time and charging amount. By removing duplicates from the "charging time" field, the system can maintain the uniqueness of the dataset.

[0094] Erroneous data rejection includes charge level checking and timestamp verification.

[0095] Furthermore, the system will set a threshold to remove the records with a charge amount less than 0. This is because the charge amount cannot be negative, and any such record is considered an error. Timestamp verification: The system will verify the timestamp to ensure that the time of each record is reasonable. For example, check whether the charging end time is later than the charging start time. If not, the record will be removed.

[0096] Data consistency check: During the data cleaning process, the logical consistency between different data sources is also verified. For example, the correlation between grid load data and weather data is checked to ensure that high load periods are consistent with adverse weather conditions.

[0097] S1.2.2. Extract the start time of each charging record from the processed database, convert it into a format suitable for cluster analysis, and assign a sample tag to each record; set the number of clusters to N based on user behavior analysis, randomly select M initial cluster centers from the charging time periods most commonly used by users, and select the initial center by analyzing the charging time periods most commonly used by users; calculate the distance from each data point to the cluster center based on the charging time data, and assign each record with a sample tag to the nearest cluster center; stop iteration when the cluster center no longer changes significantly, and determine the final clustering result; for each cluster, count the number of charging times and the length of the charging time period in the time period, and calculate the charging frequency; compare the charging frequencies of each time period to identify the most frequent charging period; set a charging frequency threshold for the peak charging period, and define the time period when the frequency exceeds the threshold as the peak period; identify the user's commonly used charging time period based on the results of the peak charging time analysis.

[0098] S1.2.3. Extract the charging amount and the corresponding time period from the processed database, and convert them into a format suitable for processing by the association rule learning algorithm to form a transaction database; define the combination of charging time and charging amount as an item set, and use the combination algorithm to generate all candidate item sets; for each candidate item set, calculate its support; set the support threshold based on the historical data analysis of user behavior; when the candidate item set with support greater than the set support threshold is filtered as a frequent item set; generate association rules from the frequent item set, and calculate the confidence and lift of each rule; filter out the most representative association rules based on the confidence and lift; summarize and classify the most representative association rules identified, and extract common features from the summarized rules; combine the user's actual charging data to analyze whether the behavior reflected by the association rules is consistent with the actual situation, and obtain the user's charging behavior; analyze the user's charging behavior in different electricity price periods to identify the user's charging needs.

[0099] S2. Generate personalized charging scheduling strategies based on user charging habits and real-time grid status.

[0100] S2.1. Combine the user's charging records (including time period, charging amount, etc.) with the real-time grid load data (such as electricity price, load forecast) to form a comprehensive data set; select the user's commonly used charging time period, average charging amount, electricity price sensitivity and grid load forecast characteristics from the comprehensive data set.

[0101] Specifically, the charging records are grouped by time periods (such as hours), and the number of charging times in each time period is calculated; the time period with the most charging times is identified to determine the user's commonly used charging time period. For example, the top three time periods with the most charging times are selected; the user's charging amount in each commonly used time period is summarized, and the user's total charging amount in these time periods is calculated. The total charging amount is divided by the number of charging times to obtain the average charging amount of each user, and the average charging amount in different time periods is analyzed to understand the user's charging preferences in different time periods; by identifying commonly used charging time periods, personalized scheduling can be performed based on user habits, improving charging convenience and user satisfaction, and understanding the user's average charging amount can help the system estimate power demand when setting charging strategies, thereby ensuring the effectiveness of the charging plan.

[0102] S2.2. Build a load forecasting model based on historical data to predict future grid loads.

[0103] Specifically, historical data related to grid load is collected; a linear regression model is established, and the data is divided into a training set and a test set; the model is trained using the training set, and the accuracy of the model is verified using the test set, and the performance indicators of the model are evaluated; real-time meteorological data and user behavior data are input into the trained model, and the model outputs a forecast of grid load for a specific time period in the future, usually a short-term (such as the next 24 hours) or medium-term (such as the next week) load forecast; the trend of the forecast results is analyzed, peak and trough periods are identified, and the charging strategy is dynamically adjusted according to the predicted grid load, limiting or postponing charging during high-load periods, and increasing charging power during low-load periods to optimize resource utilization.

[0104] S2.3. Group the users' charging time periods and calculate the charging demand and grid load in each time period.

[0105] Specifically, the grouping method of charging time periods is defined according to the analysis objectives, such as grouping by hour, grouping by peak hours, and custom time periods.

[0106] Furthermore, grouping is performed by hour: the 24 hours of a day are divided into 24 time periods, each time period is 1 hour (eg 08:00-09:00).

[0107] Group by peak hours: Identify the peak hours of the power grid (such as morning peak and evening peak) and divide the charging records into peak hours and off-peak hours.

[0108] Custom time periods: Create custom time periods (such as morning, afternoon, evening) based on the user's charging habits.

[0109] The charging records in each time period are counted to calculate the total amount of charging by users in that time period. The specific steps include:

[0110] Initialize a dictionary or data frame to store the charging amount in each time period, traverse the user's charging records, and accumulate the charging amount in each time period. For each time period T, the charging demand D T It can be expressed as: Among them, i is the charging number index, n is the charging number, Q i is the i-th charging amount in time period T; extract the grid load information corresponding to the charging time period from the grid load data set, including the current load and the predicted load, compare the charging demand and grid load in each time period, and analyze the relationship between the user's charging preference and grid load in different time periods.

[0111] S2.4. Set the charging power according to the grid load forecast; generate a personalized charging scheduling strategy based on the user's charging habits and grid load conditions.

[0112] Specifically, in the time period with high predicted load (such as morning peak and evening peak), set the upper limit of charging power to prevent grid overload. For example, set the charging power limit to 70% of the maximum allowable value, and allow higher charging power in the time period with low load, such as 90% or higher of the maximum allowable value. By setting the charging power limit reasonably, the safety of the grid can be effectively protected and grid failure caused by excessive charging demand can be prevented; set rules to automatically start charging in the time period with low electricity price (such as night or non-peak time) to ensure that users can enjoy the lowest electricity price; limit the charging power in the time period with high predicted load to ensure the safety of the grid, and charge in the time period with low user charging demand; design priority charging strategies for users who are sensitive to electricity prices, such as starting charging in advance before the price rises to reduce the overall charging cost; clear scheduling rules can help users better understand the charging strategy, improve their participation and satisfaction, and optimize the charging cost.

[0113] S3, real-time monitoring of the availability of multiple energy sources, evaluation of the power generation and cost of each energy source, use of personalized charging scheduling strategy to adjust the priority of energy selection, and automatic selection of the optimal charging mode include the following steps,

[0114] Real-time monitoring of the availability of multiple energy sources, evaluation of the power generation and cost of each energy source, use of personalized charging scheduling strategies to adjust the priority of energy selection, and automatic selection of the optimal charging mode include the following steps:

[0115] Integrate current grid load and electricity price information, and obtain real-time renewable energy power generation data of solar and wind power; evaluate the power generation of each energy source and calculate the current power generation cost; set the priority of each energy source based on the power generation cost, current power generation, renewable energy data and electricity price information; sort the priorities of all energy sources according to the set priority, and set the energy selection logic and charging mode threshold T of each energy source; determine the current charging mode to be adopted according to the current grid load, electricity price information and renewable energy power generation; switch to green charging mode when the renewable energy supply exceeds the set threshold T; switch to high-efficiency charging mode when the grid load is low and user demand is high; switch to economic charging mode when the grid load is high and user demand is moderate; automatically switch to the optimal charging mode based on the judgment result.

[0116] Specifically, the priority setting rules are as follows: if the cost of renewable energy power generation is lower than the grid electricity price, then this energy has a higher priority. The higher the power generation capacity of an energy source, the higher its priority. Weather forecast data is integrated to predict the future power generation potential of renewable energy and dynamically adjust the priority.

[0117] Mode judgment: According to the current grid load, electricity price information and renewable energy generation, the current charging mode to be adopted is judged:

[0118] Efficient charging mode: When the grid load is less than 75%, the maximum charging power is selected.

[0119] Economic charging mode: When the grid load exceeds 90%, the charging power is limited.

[0120] Green charging mode: When the proportion of renewable energy exceeds 50%, renewable energy is given priority.

[0121] Intelligent scheduling mode: select the best charging time period based on the user's historical charging habits and grid load forecast.

[0122] S4, performing charging operation based on the charging mode selected by the user and adjusting according to the grid load and energy consumption, including the following steps:

[0123] Confirm the charging mode selected by the user, and configure the corresponding charging parameters according to the charging mode selected by the user, including charging current, voltage, charging time and power; monitor the various parameters in the charging process, the consumption of each energy source and the real-time load of the power grid in real time; the consumption of each energy source includes solar energy, wind energy and power grid; when the monitored current and voltage exceed the safe range, automatically adjust the charging parameters; set the abnormal detection threshold of the parameters, and immediately adjust the charging power when an abnormality is detected; conduct a cost-benefit analysis based on the consumption of each energy source and the user's charging needs, analyze the matching degree of charging cost with user needs, and evaluate the economy of each energy source; based on the results of the cost-benefit analysis, make adjustments in the personalized charging strategy.

[0124] Specifically, parameter setting: configure the corresponding charging parameters according to the charging mode selected by the user. For example:

[0125] Fast charging: Set high current and short charging time.

[0126] Timed charging: Set the specific time for charging to start and end, and select the appropriate power.

[0127] Green charging: Give priority to the use of renewable energy and set the corresponding charging power.

[0128] Cost-benefit analysis includes the following steps:

[0129] Data collection: Collect data on consumption, charging time and related costs of each energy source based on monitoring results.

[0130] Economic Assessment: Analyze the economics of different energy sources, including:

[0131] Solar Energy Costs: Calculate the cost-effectiveness of using solar energy.

[0132] Wind energy costs: Assessing the economics of using wind energy.

[0133] Grid power cost: Compare the cost of grid power, taking into account electricity price fluctuations.

[0134] User demand matching: Based on the user's charging needs and the cost of each energy source, the matching degree between charging cost and demand is evaluated.

[0135] It should be noted that cost-benefit analysis can help formulate more reasonable charging strategies to ensure that users get the best charging experience economically.

[0136] The specific steps for adjusting the personalized charging strategy are as follows:

[0137] Strategy evaluation: Based on the results of cost-benefit analysis, evaluate the effectiveness and economy of the current charging strategy.

[0138] Strategy adjustment: Adjust the personalized charging strategy based on the analysis results, for example:

[0139] Prioritize the use of low-cost renewable energy, limit the use of grid power during periods of higher electricity prices, and dynamically adjust charging time and power according to user needs.

[0140] It should be noted that the adjusted charging strategy can more effectively meet user needs, while optimizing energy use and reducing overall charging costs.

[0141] S5, calculating the charging cost in real time according to the fixed electricity price and the charging mode selected by the user, and feeding back the cost information on the user's mobile device, and generating the final charging record includes the following steps:

[0142] Obtain the current basic electricity price and renewable energy information, and calculate the real-time charging cost through charging power, charging time and electricity price; when charging is completed, generate a detailed cost report and send it to the user's mobile device, asking the user to confirm the cost; after the user confirms the cost, record the transaction and make the payment; send a command to automatically shut down the charging device, cut off the power supply, and generate a detailed charging record.

[0143] This embodiment also provides a new energy charging pile charging control system, including:

[0144] The data acquisition module collects the user's historical charging data, grid load data, fixed electricity price information and weather forecast data in real time to identify the user's charging habits and needs; the personalized scheduling strategy generation module generates a personalized charging scheduling strategy based on the user's charging habits and real-time grid status; the optimized charging mode selection module monitors the availability of multiple energy sources in real time, evaluates the power generation and cost of each energy source, uses personalized charging scheduling strategies to adjust the priority of energy selection, and automatically selects the optimal charging mode; the charging execution module executes charging operations based on the charging mode selected by the user, and adjusts it according to the grid load and energy consumption; the cost calculation module calculates the charging cost in real time based on the fixed electricity price and the charging mode selected by the user, and feeds back the cost information on the user's mobile device to generate the final charging record.

[0145] This embodiment also provides a computer device, which is suitable for the case of a new energy charging pile charging control method, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the new energy charging pile charging control method proposed in the above embodiment.

[0146] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0147] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the charging control method for a new energy charging pile proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, disk or optical disk.

[0148] In summary, the present invention can accurately identify users' charging habits by: collecting historical charging data, grid load information, fixed electricity prices and weather forecast data; when generating personalized charging scheduling strategies, merging user records with grid load data, and constructing a load prediction model, so that when the grid load is low, the charging pile can provide higher charging power, thereby meeting user needs while reducing charging costs and optimizing charging efficiency; real-time monitoring of the availability of multiple energy sources, evaluating power generation and costs, and dynamically adjusting energy priorities, which not only improves the charging pile's ability to respond to environmental changes, but also reduces users' charging costs, makes full use of renewable energy, ensures a safe and efficient charging process through real-time monitoring of charging parameters and cost feedback, and improves users' transparency of costs.

[0149] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A new energy charging pile charging control method, characterized in that: include, Collect user historical charging data, grid load data, fixed electricity price information and weather forecast data in real time to identify user charging habits and needs; Generate personalized charging scheduling strategies based on user charging habits and real-time grid status; Monitor the availability of multiple energy sources in real time, evaluate the power generation and cost of each energy source, use personalized charging scheduling strategies to adjust the priority of energy selection, and automatically select the optimal charging mode; Execute charging operations based on the charging mode selected by the user and adjust according to grid load and energy consumption; The charging cost is calculated in real time based on the fixed electricity price and the charging mode selected by the user, and the cost information is fed back on the user's mobile device to generate the final charging record.

2. The new energy charging pile charging control method according to claim 1, characterized in that: The historical charging data includes charging time, charging frequency and charging amount; The power grid load data includes real-time load and historical load; The fixed electricity price information includes peak electricity price, average peak electricity price and low peak electricity price; The weather forecast data includes solar energy and wind energy data.

3. The new energy charging pile charging control method according to claim 2, characterized in that: The specific steps of identifying the user's charging habits and needs are as follows: Format and clean the collected data; Extract the start time of each charging record from the processed database and convert it into a format suitable for cluster analysis, and assign a sample tag to each record; Set the number of clusters to N, randomly select M initial cluster centers, and select the initial centers by analyzing the most commonly used charging time periods of users; According to the charging time data, the distance from each data point to the cluster center is calculated, and each record with a sample mark is assigned to the nearest cluster center; When the cluster center no longer changes significantly, the iteration is stopped and the final clustering result is determined; For each cluster, count the number of charging times in the time period and calculate the charging frequency; Compare the charging frequencies in different time periods and identify the most frequently charged periods; Set a charging frequency threshold for the peak charging period, and define the time period when the frequency exceeds the threshold as the peak period; Based on the results of the charging peak time analysis, identify the user's common charging time period; Extracting the charging amount and the corresponding time period from the processed database and converting them into a format suitable for processing by the association rule learning algorithm to form a transaction database; Define the combination of charging time and charging amount as an item set, and use the combination algorithm to generate all candidate item sets; For each candidate item set, calculate its support and set a support threshold based on historical data analysis of user behavior. When the support is greater than the set support threshold, the candidate item set is selected as a frequent item set. Generate association rules from frequent item sets and calculate the confidence and lift of each rule; Filter out the most representative association rules based on confidence and lift; Summarize and classify the most representative association rules identified, and extract common features from the summarized rules; Combined with the user's actual charging data, analyze whether the behavior reflected by the association rules is consistent with the actual situation, and obtain the user's charging behavior; Analyze users' charging behavior during different electricity price periods and identify their charging needs.

4. The new energy charging pile charging control method according to claim 3, characterized in that: According to the user's charging habits and real-time grid status, generating a personalized charging scheduling strategy includes the following steps: Merge user charging records with real-time grid load data to form a comprehensive data set; Select users’ common charging time periods, average charging amounts, electricity price sensitivity, and grid load forecasting characteristics from the comprehensive dataset; Build a load forecasting model based on historical data to predict future grid loads; Group users’ charging time periods and calculate charging demand and grid load for each time period; Set the charging power according to the grid load forecast; Generate a personalized charging scheduling strategy based on user charging habits and grid load conditions.

5. The new energy charging pile charging control method according to claim 4, characterized in that: Real-time monitoring of the availability of multiple energy sources, evaluation of the power generation and cost of each energy source, use of personalized charging scheduling strategies to adjust the priority of energy selection, and automatic selection of the optimal charging mode include the following steps: Integrate current grid load and electricity price information to obtain real-time data on renewable energy generation, solar and wind power generation; Evaluate the power generation of each energy source and calculate the current power generation cost; Prioritize each energy source based on generation costs, current generation, renewable energy data, and electricity price information; According to the set priority, sort the priorities of all energy sources, and set the energy selection logic and the charging mode threshold T of each energy source; Determine the current charging mode to be adopted based on the current grid load, electricity price information and renewable energy generation; When the renewable energy supply exceeds the set threshold T, it switches to green charging mode; When the grid load is low and user demand is high, it switches to high-efficiency charging mode; When the grid load is high and user demand is moderate, it switches to economic charging mode; Based on the judgment results, it automatically switches to the optimal charging mode.

6. The new energy charging pile charging control method according to claim 5, characterized in that: Executing charging operation based on the charging mode selected by the user and adjusting it according to the grid load and energy consumption includes the following steps: Confirm the charging mode selected by the user and configure corresponding charging parameters according to the charging mode selected by the user; Real-time monitoring of various parameters during the charging process, the consumption of each energy source and the real-time load of the power grid; When the monitored current and voltage exceed the safe range, the charging parameters are automatically adjusted; Set the abnormality detection threshold of the parameter, and adjust the charging power immediately when an abnormality is detected; Conduct cost-benefit analysis based on the consumption of each energy source and user charging needs, analyze the matching degree between charging cost and user needs, and evaluate the economic feasibility of each energy source; Based on the results of the cost-effectiveness analysis, adjustments are made in the generated personalized strategy.

7. The new energy charging pile charging control method according to claim 6, characterized in that: The charging cost is calculated in real time based on the fixed electricity price and the charging mode selected by the user, and the cost information is fed back on the user's mobile device. The generation of the final charging record includes the following steps: Obtain current basic electricity prices and renewable energy information, and calculate real-time charging costs based on charging power, charging time, and electricity prices; When charging is finished, a detailed cost report is generated and sent to the user's mobile device, asking the user to confirm the cost; After the user confirms the fee, the transaction is recorded and the payment is made; Send instructions to automatically shut down the charging device, cut off the power supply, and generate detailed charging records.

8. A new energy charging pile charging control system, based on the new energy charging pile charging control method according to any one of claims 1 to 7, characterized in that: include, The data collection module collects user historical charging data, grid load data, fixed electricity price information and weather forecast data in real time to identify user charging habits and needs; Personalized scheduling strategy generation module generates personalized charging scheduling strategies based on user charging habits and real-time grid status; Optimize the charging mode selection module, monitor the availability of multiple energy sources in real time, evaluate the power generation and cost of each energy source, use personalized charging scheduling strategies to adjust the priority of energy selection, and automatically select the optimal charging mode; A charging execution module executes charging operations based on the charging mode selected by the user and makes adjustments based on the grid load and energy consumption; The cost calculation module calculates the charging cost in real time based on the fixed electricity price and the charging mode selected by the user, and feeds back the cost information on the user's mobile device to generate the final charging record.

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