Electric vehicle charging power distribution system and method based on big data

Abstract

The present invention discloses a system and method for distributing charging power of electric vehicles based on big data, which relates to the technical field of intelligent charging, and includes the following steps: obtaining information of an object to be tested and actual interference information, matching a first charging power, sending a first charging power signal, constructing a power compensation model according to weights, calculating a second charging power, sending a second charging power signal, updating the weight of the power compensation model according to the comparison result, obtaining a third charging power, performing a weighted operation on the third charging power and the first charging power, obtaining an optimal power, and sending an optimal power signal. The present invention improves charging efficiency by intelligently selecting a charging mode and setting a charging amount, dynamically adjusting charging power, improving charging efficiency, establishing and optimizing a power compensation model, maintaining the stability and efficiency of the charging process, improving charging safety, sending an optimal power signal to a charging device, and saving energy costs.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent charging, and in particular to a big data-based electric vehicle charging power distribution system and method. Background Art

[0002] In recent years, with the popularization of electric vehicles, intelligent charging management systems have become increasingly important. By real-time monitoring of factors such as the usage of charging equipment, grid load and user demand, the distribution and scheduling of charging power are optimized. The charging power can be adjusted according to the grid load in different time periods to avoid grid overload, thereby improving charging efficiency.

[0003] At present, in the Chinese invention patent with publication number CN 114211978 B, a charging power distribution method, system, equipment and storage medium for electric vehicles are disclosed. By designing the on-board charger to increase in a step-by-step manner, and cooperating with the DC or DC converter and the air conditioning management module to jointly realize power regulation, the problem of system failure caused by excessive load power fluctuation in the scenario of taking power from the charging pile is solved, and the robustness of the system is improved. However, the relevant technology does not analyze the power distribution based on big data, lacks representativeness, does not adjust the distribution model according to the actual value and the ideal value, and lacks applicability and accuracy. Summary of the invention

[0004] The technical problem solved by the present invention is that the related technology does not analyze power distribution based on big data, lacks representativeness, does not adjust the distribution model based on actual values ​​and ideal values, and lacks applicability and accuracy.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: In the first aspect, a method for allocating charging power of electric vehicles based on big data comprises the following steps:

[0006] Step S1, setting a location tag of the object to be measured, and when the location tag is in the charging area, obtaining information of the object to be measured and obtaining actual interference information;

[0007] Step S2, analyzing the information of the object to be tested, selecting a charging method, setting a charging amount, matching a first charging power according to the charging method and the charging amount, and sending a first charging power signal;

[0008] Step S3, obtaining historical interference information, calculating the weight of the historical interference information in the historical power loss, building a power compensation model according to the weight, calculating the second charging power according to the power compensation model, and sending a second charging power signal;

[0009] Step S4, obtaining the actual charging current and charging voltage, calculating the actual charging power, calculating a first value according to the actual charging power and the first charging power, comparing the first value with the second charging power to obtain a first comparison result, updating the weight of the power compensation model according to the comparison result, obtaining a third charging power according to the power compensation model after the weight update, performing weighted operation on the third charging power and the first charging power to obtain an optimal power, and sending an optimal power signal.

[0010] As a preferred solution of the electric vehicle charging power allocation method based on big data described in the present invention, step S1 includes the following sub-steps:

[0011] Step S11, setting a location tag of the object to be measured, tracking the location tag, and setting an identification range of the charging area;

[0012] Step S12, when the location tags are distributed within the corresponding identification range, obtaining information of the object to be detected, the information of the object to be detected including the model of the object to be detected, parking time, total mileage and remaining mileage;

[0013] Step S13, obtaining actual interference information, wherein the actual interference information includes actual ambient temperature, actual battery temperature and actual battery consumption.

[0014] As a preferred solution of the electric vehicle charging power allocation method based on big data described in the present invention, step S2 includes the following sub-steps:

[0015] Step S21, obtaining the model, parking time, total mileage and remaining mileage of the object to be tested;

[0016] Step S22, selecting a charging method according to the parking time, total mileage and remaining mileage, and setting the charging amount according to the remaining mileage, wherein the charging method includes fast charging and slow charging;

[0017] Step S23, matching a first charging power according to the charging method and the charging amount, and sending a first charging power signal.

[0018] As a preferred solution of the electric vehicle charging power allocation method based on big data described in the present invention, the selection logic of the charging mode is:

[0019] Acquire the parking time, the total mileage and the remaining mileage, calculate the ratio of the untraveled mileage to the total mileage, and set the ratio of the remaining mileage to the total mileage to a first ratio;

[0020] Retrieving a car database, inputting a model of the object to be tested into the car database, obtaining a full-charge endurance time of the object to be tested, calculating a ratio of the parking time to the full-charge endurance time, and recording the ratio of the parking time to the full-charge endurance time as a second ratio;

[0021] When the second ratio is greater than the first ratio, slow charging is selected, and when the second ratio is less than or equal to the first ratio, fast charging is selected.

[0022] As a preferred solution of the electric vehicle charging power allocation method based on big data described in the present invention, the setting logic of the charging amount is:

[0023] Obtaining the remaining mileage and the total mileage, calling a car database, inputting the model of the object to be tested into the car database, matching the corresponding full power, obtaining a first ratio, calculating the product of the first ratio and the full power, and setting the product of the first ratio and the full power as the charging amount;

[0024] The matching logic of the first charging power includes: calling a charging record database, wherein the charging record database includes a historical vehicle model, a corresponding charging time, a corresponding charging method, a corresponding charging power, and a corresponding charging amount.

[0025] As a preferred solution of the electric vehicle charging power allocation method based on big data described in the present invention, the matching logic of the first charging power includes:

[0026] receiving a first trigger condition, identifying the first trigger condition to obtain a first identification result, setting the first identification result as a second trigger condition, accepting the second trigger condition, identifying the second trigger condition to obtain a second identification result;

[0027] The first trigger condition is to match a historical car model that is consistent with the model of the object to be tested, and the first recognition result is to retrieve a charging record corresponding to the model of the object to be tested;

[0028] The second trigger condition is to retrieve a charging record in the first recognition result that is consistent with the charging mode and charging amount of the object to be tested in the charging mode, and the second recognition result is to obtain the charging record corresponding to the second trigger condition and obtain the charging power in the charging record corresponding to the second trigger condition;

[0029] The charging power in the charging record corresponding to the second trigger condition is set to the first charging power, and a first charging power signal is sent.

[0030] As a preferred solution of the electric vehicle charging power allocation method based on big data described in the present invention, step S3 includes the following sub-steps:

[0031] Step S31, obtaining interference information, wherein the interference information includes ambient temperature, battery temperature and battery consumption;

[0032] Step S32, retrieve the car database, input the model of the object to be tested, obtain the historical ambient temperature, historical battery temperature, historical battery loss and historical power loss, calculate the weights of the historical ambient temperature, historical battery temperature and historical battery loss in the historical power loss, and the calculation logic of the weights includes:

[0033] Taking the historical power loss as the dependent variable and the historical ambient temperature, historical battery temperature and historical battery loss as the independent variables, a multivariate regression analysis was performed to obtain the third result;

[0034] Using the coefficient corresponding to the historical ambient temperature in the third result as the weight of the historical ambient temperature, using the coefficient corresponding to the historical battery temperature as the weight of the historical battery temperature, and using the coefficient corresponding to the historical battery loss as the weight of the historical battery loss;

[0035] Step S33, constructing a power compensation model according to the weights, and calculating the second charging power according to the power compensation model, wherein the input of the power compensation model is the actual ambient temperature, the actual battery temperature and the actual battery loss, and the output of the power compensation model is the actual power loss, and the actual power loss is set as the second charging power, and a second charging power signal is sent according to the second charging power.

[0036] As a preferred solution of the electric vehicle charging power allocation method based on big data described in the present invention, the calculation expression of the weight is:

[0037]

[0038] Where y is the actual power loss, c i is the coefficient of the ith actual interference information, x i is the ith actual interference information, and n is the number of actual interference information.

[0039] As a preferred solution of the electric vehicle charging power allocation method based on big data described in the present invention, step S4 includes the following sub-steps:

[0040] Step S41, obtaining the actual charging current and charging voltage, calculating the actual charging power, calculating the difference between the actual charging power and the first charging power, taking an absolute value of the difference between the actual charging power and the first charging power, and setting the absolute value as the first value;

[0041] Step S42, comparing the first value with the second charging power to obtain a first comparison result, when the first value is not equal to the second charging power, calculating the ratio of the first value to the second charging power, setting the ratio of the first value to the second charging power as a third ratio, multiplying the historical power loss amount by the third ratio to obtain an updated historical power loss amount, taking the updated historical power loss amount as the dependent variable, taking the historical ambient temperature, the historical battery temperature and the historical battery loss amount as independent variables, re-performing a multiple regression analysis, and updating the weight corresponding to the historical ambient temperature, the weight corresponding to the historical battery temperature and the weight corresponding to the historical battery loss amount;

[0042] Step S43: obtaining a third charging power according to the power compensation model after weight update, performing weighted operation on the third charging power and the first charging power to obtain an optimal power, and sending an optimal power signal.

[0043] In the second aspect, a big data-based electric vehicle charging power distribution system includes a collection module, an analysis module and a feedback module;

[0044] The acquisition module sets a position tag of the object to be measured, and when the position tag is in the charging area, obtains information of the object to be measured and obtains actual interference information;

[0045] The analysis module analyzes the information of the object to be tested, selects a charging method, sets a charging amount, matches a first charging power according to the charging method and the charging amount, sends a first charging power signal, obtains historical interference information, calculates the weight of the historical interference information in the historical power loss amount, constructs a power compensation model according to the weight, calculates a second charging power according to the power compensation model, and sends a second charging power signal;

[0046] The feedback module is used to obtain the actual charging current and the charging voltage, calculate the actual charging power, calculate a first value according to the actual charging power and the first charging power, compare the first value with the second charging power to obtain a first comparison result, update the weight of the power compensation model according to the comparison result, obtain the third charging power according to the power compensation model after the weight is updated, perform weighted operation on the third charging power and the first charging power to obtain the optimal power, and send the optimal power signal.

[0047] The beneficial effects of the present invention are as follows: by setting the location tag of the object to be tested and detecting the actual situation in the charging area, the charging method is intelligently selected and the charging amount is set, the charging process is optimized, the charging efficiency and the utilization rate of the charging equipment are improved, and by obtaining real-time interference information and historical interference information, the charging power can be dynamically adjusted to reduce the power loss caused by external interference, and the charging efficiency and stability are improved. Based on the historical interference information and the actual charging situation, the system establishes and optimizes the power compensation model, more accurately predicts and adjusts the charging power, maintains the stability and efficiency of the charging process, monitors the actual charging current, voltage and power in real time, and adjusts the charging power according to the actual situation, improves the safety of charging, obtains the optimal power through weighted calculation, and sends the optimal power signal to the charging equipment, so as to achieve the best charging effect and save charging time and energy costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic diagram of the basic flow of a method for allocating charging power for electric vehicles based on big data is provided as an embodiment of the present invention. DETAILED DESCRIPTION

[0049] 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 drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0050] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a method for allocating charging power of electric vehicles based on big data, comprising the following steps:

[0051] Step S1, setting a location tag of the object to be measured, and when the location tag is in the charging area, obtaining information of the object to be measured and obtaining actual interference information;

[0052] Step S2, analyzing the information of the object to be tested, selecting a charging method, setting a charging amount, matching a first charging power according to the charging method and the charging amount, and sending a first charging power signal;

[0053] Step S3, obtaining historical interference information, calculating the weight of the historical interference information in the historical power loss, building a power compensation model according to the weight, calculating the second charging power according to the power compensation model, and sending a second charging power signal;

[0054] Step S4, obtaining the actual charging current and charging voltage, calculating the actual charging power, calculating a first value according to the actual charging power and the first charging power, comparing the first value with the second charging power to obtain a first comparison result, updating the weight of the power compensation model according to the comparison result, obtaining a third charging power according to the power compensation model after the weight update, performing weighted operation on the third charging power and the first charging power to obtain an optimal power, and sending an optimal power signal.

[0055] The present invention sets the location tag of the object to be tested and detects the actual situation in the charging area, intelligently selects the charging mode and sets the charging amount, optimizes the charging process, improves the charging efficiency and the utilization rate of the charging equipment, and can dynamically adjust the charging power by obtaining real-time interference information and historical interference information, thereby reducing the power loss caused by external interference and improving the charging efficiency and stability. Based on the historical interference information and the actual charging situation, the system establishes and optimizes the power compensation model, more accurately predicts and adjusts the charging power, maintains the stability and efficiency of the charging process, monitors the actual charging current, voltage and power in real time, adjusts the charging power according to the actual situation, improves the safety of charging, obtains the optimal power through weighted calculation, and sends the optimal power signal to the charging equipment, thereby achieving the best charging effect and saving charging time and energy costs.

[0056] The step S1 includes the following sub-steps:

[0057] Step S11, setting a location tag of the object to be measured, tracking the location tag, and setting an identification range of the charging area;

[0058] Step S12, when the location tags are distributed within the corresponding identification range, obtaining information of the object to be detected, the information of the object to be detected including the model of the object to be detected, parking time, total mileage and remaining mileage;

[0059] Step S13, obtaining actual interference information, wherein the actual interference information includes actual ambient temperature, actual battery temperature and actual battery consumption.

[0060] In specific implementation, by obtaining real-time interference information and historical interference information, the charging power can be dynamically adjusted to reduce power loss due to external interference, thereby improving charging efficiency and stability.

[0061] The step S2 includes the following sub-steps:

[0062] Step S21, obtaining the model, parking time, total mileage and remaining mileage of the object to be tested;

[0063] Step S22, selecting a charging method according to the parking time, total mileage and remaining mileage, and setting the charging amount according to the remaining mileage, wherein the charging method includes fast charging and slow charging;

[0064] Step S23, matching a first charging power according to the charging method and the charging amount, and sending a first charging power signal.

[0065] The selection logic of the charging mode is:

[0066] Acquire the parking time, the total mileage and the remaining mileage, calculate the ratio of the untraveled mileage to the total mileage, and set the ratio of the remaining mileage to the total mileage to a first ratio;

[0067] Retrieving a car database, inputting a model of the object to be tested into the car database, obtaining a full-charge endurance time of the object to be tested, calculating a ratio of the parking time to the full-charge endurance time, and recording the ratio of the parking time to the full-charge endurance time as a second ratio;

[0068] When the second ratio is greater than the first ratio, slow charging is selected, and when the second ratio is less than or equal to the first ratio, fast charging is selected.

[0069] The setting logic of the charging amount is:

[0070] Obtaining the remaining mileage and the total mileage, calling a car database, inputting the model of the object to be tested into the car database, matching the corresponding full power, obtaining a first ratio, calculating the product of the first ratio and the full power, and setting the product of the first ratio and the full power as the charging amount;

[0071] The matching logic of the first charging power includes: calling a charging record database, wherein the charging record database includes a historical vehicle model, a corresponding charging time, a corresponding charging method, a corresponding charging power, and a corresponding charging amount.

[0072] In the specific implementation, by setting the location tag of the object to be tested and detecting the actual situation in the charging area, the charging method is intelligently selected and the charging amount is set, which optimizes the charging process and improves the charging efficiency and the utilization rate of the charging equipment.

[0073] The matching logic of the first charging power includes:

[0074] receiving a first trigger condition, identifying the first trigger condition to obtain a first identification result, setting the first identification result as a second trigger condition, accepting the second trigger condition, identifying the second trigger condition to obtain a second identification result;

[0075] The first trigger condition is to match a historical car model that is consistent with the model of the object to be tested, and the first recognition result is to retrieve a charging record corresponding to the model of the object to be tested;

[0076] The second trigger condition is to retrieve a charging record in the first recognition result that is consistent with the charging mode and charging amount of the object to be tested in the charging mode, and the second recognition result is to obtain the charging record corresponding to the second trigger condition and obtain the charging power in the charging record corresponding to the second trigger condition;

[0077] The charging power in the charging record corresponding to the second trigger condition is set to the first charging power, and a first charging power signal is sent.

[0078] In the specific implementation, the first charging power is obtained through the database, which effectively utilizes big data to obtain the required data, improves the practical application value of the analysis, and improves the practicality and simplicity of the analysis.

[0079] The step S3 includes the following sub-steps:

[0080] Step S31, obtaining interference information, wherein the interference information includes ambient temperature, battery temperature and battery consumption;

[0081] Step S32, retrieve the car database, input the model of the object to be tested, obtain the historical ambient temperature, historical battery temperature, historical battery loss and historical power loss, calculate the weights of the historical ambient temperature, historical battery temperature and historical battery loss in the historical power loss, and the calculation logic of the weights includes:

[0082] Taking the historical power loss as the dependent variable and the historical ambient temperature, historical battery temperature and historical battery loss as the independent variables, a multivariate regression analysis was performed to obtain the third result;

[0083] Using the coefficient corresponding to the historical ambient temperature in the third result as the weight of the historical ambient temperature, using the coefficient corresponding to the historical battery temperature as the weight of the historical battery temperature, and using the coefficient corresponding to the historical battery loss as the weight of the historical battery loss;

[0084] Step S33, constructing a power compensation model according to the weights, and calculating the second charging power according to the power compensation model, wherein the input of the power compensation model is the actual ambient temperature, the actual battery temperature and the actual battery loss, and the output of the power compensation model is the actual power loss, and the actual power loss is set as the second charging power, and a second charging power signal is sent according to the second charging power.

[0085] The calculation expression of the weight is:

[0086]

[0087] Where y is the actual power loss, c i is the coefficient of the ith actual interference information, x iis the ith actual interference information, and n is the number of actual interference information.

[0088] In the specific implementation, based on historical interference information and actual charging conditions, the system establishes and optimizes the power compensation model to more accurately predict and adjust the charging power, thereby maintaining the stability and efficiency of the charging process.

[0089] The step S4 comprises the following sub-steps:

[0090] Step S41, obtaining the actual charging current and charging voltage, calculating the actual charging power, calculating the difference between the actual charging power and the first charging power, taking an absolute value of the difference between the actual charging power and the first charging power, and setting the absolute value as the first value;

[0091] Step S42, comparing the first value with the second charging power to obtain a first comparison result, when the first value is not equal to the second charging power, calculating the ratio of the first value to the second charging power, setting the ratio of the first value to the second charging power as a third ratio, multiplying the historical power loss amount by the third ratio to obtain an updated historical power loss amount, taking the updated historical power loss amount as the dependent variable, taking the historical ambient temperature, the historical battery temperature and the historical battery loss amount as independent variables, re-performing a multiple regression analysis, and updating the weight corresponding to the historical ambient temperature, the weight corresponding to the historical battery temperature and the weight corresponding to the historical battery loss amount;

[0092] Step S43: obtaining a third charging power according to the power compensation model after weight update, performing weighted operation on the third charging power and the first charging power to obtain an optimal power, and sending an optimal power signal.

[0093] During the specific implementation, the actual charging current, voltage and power are monitored in real time, and the charging power is adjusted according to the actual situation to improve the safety of charging. The optimal power is obtained through weighted calculation, and the optimal power signal is sent to the charging device to achieve the best charging effect and save charging time and energy costs.

[0094] The present invention sets the location tag of the object to be tested and detects the actual situation in the charging area, intelligently selects the charging mode and sets the charging amount, optimizes the charging process, improves the charging efficiency and the utilization rate of the charging equipment, and can dynamically adjust the charging power by obtaining real-time interference information and historical interference information, thereby reducing the power loss caused by external interference and improving the charging efficiency and stability. Based on the historical interference information and the actual charging situation, the system establishes and optimizes the power compensation model, more accurately predicts and adjusts the charging power, maintains the stability and efficiency of the charging process, monitors the actual charging current, voltage and power in real time, adjusts the charging power according to the actual situation, improves the safety of charging, obtains the optimal power through weighted calculation, and sends the optimal power signal to the charging equipment, thereby achieving the best charging effect and saving charging time and energy costs.

[0095] It should be understood by those skilled in the art that the embodiments of the present invention can be provided as methods, systems or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. Among them, 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 memory, flash memory, disk or optical disk. These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0096] 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 method for allocating charging power for electric vehicles based on big data, characterized in that: The following steps are involved: Step S1, setting a location tag of the object to be measured, and when the location tag is in the charging area, obtaining information of the object to be measured and obtaining actual interference information; Step S2, analyzing the information of the object to be tested, selecting a charging method, setting a charging amount, matching a first charging power according to the charging method and the charging amount, and sending a first charging power signal; Step S3, obtaining historical interference information, calculating the weight of the historical interference information in the historical power loss, building a power compensation model according to the weight, calculating the second charging power according to the power compensation model, and sending a second charging power signal; Step S4, obtaining the actual charging current and charging voltage, calculating the actual charging power, calculating a first value according to the actual charging power and the first charging power, comparing the first value with the second charging power to obtain a first comparison result, updating the weight of the power compensation model according to the comparison result, obtaining a third charging power according to the power compensation model after the weight update, performing weighted operation on the third charging power and the first charging power to obtain an optimal power, and sending an optimal power signal.

2. The method for allocating charging power of an electric vehicle based on big data as claimed in claim 1, characterized in that: The step S1 includes the following sub-steps: Step S11, setting a location tag of the object to be measured, tracking the location tag, and setting an identification range of the charging area; Step S12, when the location tags are distributed within the identification range, obtaining information of the object to be detected, the information of the object to be detected including the model of the object to be detected, parking time, total mileage and remaining mileage; Step S13, obtaining actual interference information, wherein the actual interference information includes actual ambient temperature, actual battery temperature and actual battery consumption.

3. The method for allocating charging power of electric vehicles based on big data as claimed in claim 1, characterized in that: The step S2 includes the following sub-steps: Step S21, obtaining the model, parking time, total mileage and remaining mileage of the object to be tested; Step S22, selecting a charging method according to the parking time, total mileage and remaining mileage, and setting the charging amount according to the remaining mileage, wherein the charging method includes fast charging and slow charging; Step S23, matching a first charging power according to the charging method and the charging amount, and sending a first charging power signal.

4. The method for allocating charging power of electric vehicles based on big data as claimed in claim 3, characterized in that: The selection logic of the charging mode is: acquiring parking time, total mileage, and remaining mileage, calculating a ratio of the remaining mileage to the total mileage, and setting the ratio of the remaining mileage to the total mileage to a first ratio; Retrieving a car database, inputting a model of the object to be tested into the car database, obtaining a full-charge endurance time of the object to be tested, calculating a ratio of the parking time to the full-charge endurance time, and recording the ratio of the parking time to the full-charge endurance time as a second ratio; When the second ratio is greater than the first ratio, slow charging is selected, and when the second ratio is less than or equal to the first ratio, fast charging is selected.

5. The method for allocating charging power of electric vehicles based on big data as claimed in claim 4, characterized in that: The setting logic of the charging amount is: Obtaining the remaining mileage and the total mileage, calling a car database, inputting the model of the object to be tested into the car database, matching the corresponding full power, obtaining a first ratio, calculating the product of the first ratio and the full power, and setting the product of the first ratio and the full power as the charging amount; The matching logic of the first charging power includes: calling a charging record database, wherein the charging record database includes a historical vehicle model, a corresponding charging time, a corresponding charging method, a corresponding charging power, and a corresponding charging amount.

6. The method for allocating charging power of electric vehicles based on big data as claimed in claim 5, characterized in that: The matching logic of the first charging power includes: receiving a first trigger condition, identifying the first trigger condition to obtain a first identification result, setting the first identification result as a second trigger condition, receiving the second trigger condition, identifying the second trigger condition to obtain a second identification result; The first trigger condition is to match a historical car model that is consistent with the model of the object to be tested, and the first recognition result is to retrieve a charging record corresponding to the model of the object to be tested; The second trigger condition is to retrieve a charging record in the first recognition result that is consistent with the charging method and charging amount of the object to be tested, and the second recognition result is to obtain the charging record corresponding to the second trigger condition and obtain the charging power in the charging record corresponding to the second trigger condition; The charging power in the charging record corresponding to the second trigger condition is set to the first charging power, and a first charging power signal is sent.

7. The method for allocating charging power of electric vehicles based on big data as claimed in claim 1, characterized in that: The step S3 includes the following sub-steps: Step S31, obtaining historical interference information, wherein the historical interference information includes historical ambient temperature, historical battery temperature, and historical battery loss; Step S32, retrieve the car database, input the model of the object to be tested, obtain the historical ambient temperature, historical battery temperature, historical battery loss and historical power loss, calculate the weights of the historical ambient temperature, historical battery temperature and historical battery loss in the historical power loss, and the calculation logic of the weights includes: Taking the historical power loss as the dependent variable and the historical ambient temperature, historical battery temperature and historical battery loss as the independent variables, a multivariate regression analysis was performed to obtain the third result; Using the coefficient corresponding to the historical ambient temperature in the third result as the weight of the historical ambient temperature, using the coefficient corresponding to the historical battery temperature as the weight of the historical battery temperature, and using the coefficient corresponding to the historical battery loss as the weight of the historical battery loss; Step S33, constructing a power compensation model according to the weights, and calculating the second charging power according to the power compensation model, wherein the input of the power compensation model is the actual ambient temperature, the actual battery temperature and the actual battery loss, and the output of the power compensation model is the actual power loss, and the actual power loss is set as the second charging power, and a second charging power signal is sent according to the second charging power.

8. The method for allocating charging power of electric vehicles based on big data as claimed in claim 7, characterized in that: The calculation expression of the weight is: in, is the actual power loss, For the The coefficient of actual interference information, For the Actual interference information, is the actual amount of interference information.

9. The method for allocating charging power of electric vehicles based on big data as claimed in claim 1, characterized in that: The step S4 comprises the following sub-steps: Step S41, obtaining the actual charging current and charging voltage, calculating the actual charging power, calculating the difference between the actual charging power and the first charging power, taking an absolute value of the difference between the actual charging power and the first charging power, and setting the absolute value as the first value; Step S42, comparing the first value with the second charging power to obtain a first comparison result, when the first value is not equal to the second charging power, calculating the ratio of the first value to the second charging power, setting the ratio of the first value to the second charging power as a third ratio, multiplying the historical power loss amount by the third ratio to obtain an updated historical power loss amount, taking the updated historical power loss amount as the dependent variable, taking the historical ambient temperature, the historical battery temperature and the historical battery loss amount as independent variables, re-performing a multiple regression analysis, and updating the weight corresponding to the historical ambient temperature, the weight corresponding to the historical battery temperature and the weight corresponding to the historical battery loss amount; Step S43: obtaining a third charging power according to the power compensation model after weight update, performing weighted operation on the third charging power and the first charging power to obtain an optimal power, and sending an optimal power signal.

10. An electric vehicle charging power distribution system based on big data, characterized in that: It includes acquisition module, analysis module and feedback module; The acquisition module sets a position tag of the object to be measured, and when the position tag is in the charging area, obtains information of the object to be measured and obtains actual interference information; The analysis module analyzes the information of the object to be tested, selects a charging method, sets a charging amount, matches a first charging power according to the charging method and the charging amount, sends a first charging power signal, obtains historical interference information, calculates the weight of the historical interference information in the historical power loss amount, constructs a power compensation model according to the weight, calculates a second charging power according to the power compensation model, and sends a second charging power signal; The feedback module is used to obtain the actual charging current and the charging voltage, calculate the actual charging power, calculate a first value according to the actual charging power and the first charging power, compare the first value with the second charging power to obtain a first comparison result, update the weight of the power compensation model according to the comparison result, obtain the third charging power according to the power compensation model after the weight is updated, perform weighted operation on the third charging power and the first charging power to obtain the optimal power, and send the optimal power signal.

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