Get charging service personnel optimal selection method and system

Abstract

The invention discloses an optimal selection method for valet charging service personnel, and the method comprises the steps: receiving and analyzing a valet charging request of a vehicle owner, automatically obtaining a valet charging parameter, and carrying out the analysis of the sensitivity data of the vehicle owner according to the valet charging historical behavior data of the vehicle owner and different charging scenes; according to the position of the vehicle owner, the sensitivity of the vehicle owner and the charging parameter, carrying out weight value dynamic adjustment based on a charging scene; and pre-constructing a multi-dimensional scoring model, inputting the weight value and the charging request into the multi-dimensional scoring model, obtaining a preferable scoring result of the charging person, and distributing a preferred charging person for the vehicle owner to carry out charging on the charging person. According to the method, the quality and efficiency of the electric vehicle charging service are effectively improved through the intelligent application of the substitute charging person optimization method, the substitute charging person management and task allocation mechanism is optimized, and the user experience and the service quality are improved.

Description

Technical Field

[0001] The present application relates to the technical field of valet charging, and in particular to a method and system for selecting valet charging service personnel. Background Art

[0002] In recent years, with the popularity of electric vehicles and the growth of user demand, the convenience and efficiency of charging services have become a hot topic. Traditional charging methods have problems such as high user time cost and uneven distribution of charging station resources, and a more intelligent and convenient solution is needed.

[0003] In order to meet the charging needs of specific groups such as users who are busy at work during the day and online car-hailing drivers, a more personalized and customized charging service method is needed. However, there is still a lack of an effective method for selecting charging agents. The existing technical methods cannot solve the management of charging agent information and the efficient allocation of tasks; they cannot intelligently allocate charging tasks during peak hours, and there are technical problems such as long waiting time for users and low charging efficiency. How to quickly and effectively select the most suitable and highest-rated charging service personnel is a technical problem that needs to be solved urgently.

[0004] In order to solve the above-mentioned technical problems, it is urgent to propose a method for optimizing charging service personnel (i.e., charging agents), which aims to achieve smarter and more efficient charging services through the intelligent application of charging agent methods, optimize the charging agent management and task allocation mechanism, and improve user experience and service quality, which is of great significance to improving the level of charging services and operational efficiency. Summary of the invention

[0005] In order to solve the above-mentioned defects of the prior art, the present invention proposes a method and system for selecting charging service personnel for customers.

[0006] In a first aspect, an embodiment of the present application provides a method for selecting a charging service personnel for a customer, the method comprising:

[0007] Receive and analyze the charging request from the car owner, and automatically obtain the charging parameters, which include: charging agent parameters and car owner parameters;

[0008] Based on the owner's historical charging behavior data and different charging scenarios, analyze and obtain the owner's sensitivity data, including: charging speed sensitivity, charging price sensitivity and charging distance sensitivity;

[0009] Dynamically adjust the weight value based on the charging scenario according to the owner's sensitivity and charging parameters;

[0010] A multi-dimensional scoring model is pre-built, and the weight value and the charging request are input into the multi-dimensional scoring model to obtain the preferred scoring result of the charging agent, and the preferred charging agent is assigned to the car owner for charging.

[0011] In a specific embodiment of the present invention, the above-mentioned method for selecting a preferred method for charging service personnel for customers further includes:

[0012] If the preferred charging agent is unable to complete the charging task, an alternative charging agent will be automatically selected based on the preferred scoring results to complete the charging on behalf of the customer.

[0013] In a specific embodiment of the present invention, the above-mentioned receiving and parsing the charging request of the vehicle owner and automatically obtaining the charging parameters include:

[0014] Automatically obtain the parameters of the charging agent, including: the current status of the charging agent, the charging distance of the charging agent, the time parameters of the charging agent arriving at the charging location, the historical behavior of the charging agent, and the historical evaluation data of the charging agent;

[0015] Automatically obtain the owner's parameters, including: the owner's current location, the owner's current charging request, and the owner's historical behavior data.

[0016] In a specific embodiment of the present invention, the above-mentioned automatic acquisition of charging agent parameters also includes:

[0017] Check whether the online status of the charging agent is active, and check whether the corresponding time period of the charging agent has been assigned a task. If so, exclude the charging agent;

[0018] Calculate the distance between the charger's current location and the charging location, and estimate the time it will take the charger to arrive at the charging location based on real-time traffic data;

[0019] Analyze the charger's historical task completion status, including punctuality rate and user ratings; analyze the charger's skill level in completing charging tasks.

[0020] In a specific embodiment of the present invention, the above-mentioned automatic acquisition of vehicle owner parameters further includes:

[0021] Receive the charging request from the car owner, verify the legal identity of the car owner, and check the validity of the charging request;

[0022] The geographic address provided by the user's car owner is converted into longitude and latitude coordinates, and the owner's current charging request is parsed and stored. The parameters of the charging request include: charging location, expected starting time for charging, power required for charging, location of the charging user's vehicle, charging vehicle information, and expected time of use of the vehicle for charging.

[0023] In a specific embodiment of the present invention, the above-mentioned analysis and acquisition of the car owner's sensitivity data based on the car owner's charging history data for other people and different charging scenarios includes:

[0024] Analyze the sensitivity of car owners based on their historical behavior data and charging scenarios. The historical behavior data of car owners includes historical charging orders and charging orders for customers. The charging scenarios include home, office and leisure scenarios.

[0025] The sensitivity of the car owner to charging speed is obtained by analyzing the car owner's choice of fast or slow charging behavior; the car owner's choice of charging time, station distance, and charging price historical habits are calculated based on the car owner's choice of electricity price to obtain the car owner's sensitivity to charging price.

[0026] In a specific embodiment of the present invention, the above-mentioned dynamic adjustment of the weight value based on the charging scenario according to the sensitivity of the owner and the charging parameters includes:

[0027] According to different charging scenarios and the parameters of the car owner's charging request, the weight of each scoring item is dynamically adjusted to adapt to different business models, including: home mode, office mode and leisure mode.

[0028] In a specific embodiment of the present invention, the above-mentioned pre-construction of a multi-dimensional scoring model, inputting the weight value and the charging request into the multi-dimensional scoring model, obtaining the preferred scoring result of the charging agent, and allocating the preferred charging agent to the car owner for charging on behalf of the customer includes:

[0029] The data of charging test stations are used as training data to train a multi-dimensional scoring model;

[0030] Based on the rating results of recharge agents and user feedback, we continuously iterate and optimize the multi-dimensional rating model;

[0031] Sort the rating results of the recharge agents according to the scores, and select the recharge agent with the highest score as the preferred recharge agent;

[0032] The task instructions are sent to the selected charger via mobile application or text message. The instructions include: charging location, expected start time, required power, user's vehicle location, vehicle information, and expected use time.

[0033] In a specific embodiment of the present invention, if the preferred charging agent cannot complete the charging task, automatically selecting a candidate charging agent to complete the charging on behalf of the customer according to the preferred scoring result of the charging agent includes:

[0034] If the first choice agent fails to accept the task, the next agent with the highest score will be automatically selected from the backup queue, and the backup agent will be selected based on the ranking results of the agents' scores;

[0035] The standby queue is dynamically adjusted based on real-time conditions to ensure that there are always available replacements on standby.

[0036] In a second aspect, an embodiment of the present application provides a system for selecting charging service personnel for valet customers, using a method for selecting charging service personnel for valet customers, and the system includes:

[0037] Charging parameter acquisition module: used to receive the charging request from the owner, parse the request, and automatically obtain the charging parameters. The charging parameters include: charging staff parameters and owner parameters:

[0038] Valet charging sensitivity analysis module: used to analyze and obtain the owner's sensitivity data based on the owner's valet charging history behavior data and different charging scenarios. The owner's sensitivity includes: charging speed sensitivity, charging price sensitivity and charging distance sensitivity;

[0039] Weight adjustment module: used to dynamically adjust the weight value based on the charging scenario according to the owner's location, owner sensitivity and charging parameters;

[0040] The charging agent selection module is used to pre-build a multi-dimensional scoring model, input the weight value and charging request into the multi-dimensional scoring model, obtain the charging agent selection scoring result, and assign the preferred charging agent to the car owner for charging;

[0041] Charging agent alternative module: if the preferred charging agent is unable to complete the charging task, the alternative charging agent will be automatically selected based on the charging agent's preferred scoring results to complete the charging task on behalf of the customer.

[0042] In a third aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which implements a method for selecting a valet charging service personnel when the program is executed by a processor.

[0043] In a fourth aspect, an embodiment of the present application provides a computer program product, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for selecting charging service personnel for valet customers is implemented.

[0044] Compared with the related prior art, it has the following outstanding beneficial effects:

[0045] 1) The method of the present invention proposes a multi-dimensional scoring model, which comprehensively considers factors such as charging service distance, arrival time, and historical performance, and scores each charging agent, so that the optimization method of the present invention combines the multi-dimensional scoring criteria to select the best charging agent;

[0046] 2) The method of the present invention can dynamically adjust the weight of the scoring items of each recharger according to different scenarios and needs to adapt to different business models, realize the recharger selection method combined with different technical scenarios, and improve the flexibility and adaptability of the system;

[0047] 3) The method of the present invention adopts a standby charging queue to automatically select the next charging agent with the highest score from the standby queue, ensure timely assignment of tasks, and realize dynamic adjustment of the charging agent standby queue;

[0048] 4) The method of the present invention adopts real-time monitoring and exception handling: the location of the charging agent is monitored in real time through GPS and LBS technology, and abnormal situations are handled in time in combination with emergency plans to ensure the stability and reliability of the service;

[0049] 5) The method of the present invention dynamically adjusts the task allocation of charging agents according to real-time traffic data and charging demand conditions, thereby improving the flexibility and adaptability of charging services. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0051] Figure 1 This is a flow chart of the method for selecting a charging service personnel for a customer according to the present invention;

[0052] Figure 2 This is a flow chart of a method for selecting a charging service personnel for a customer according to an embodiment of the present invention;

[0053] Figure 3 This is a decomposition diagram of charging user requirements according to an embodiment of the present invention;

[0054] Figure 4 This is a diagram showing the requirements of charging personnel according to an embodiment of the present invention;

[0055] Figure 5 This is a diagram of the operator's needs decomposition in an embodiment of the present invention;

[0056] Figure 6 This is a charging flow chart of an embodiment of the present invention;

[0057] Figure 7 This is a flowchart of user identity authentication according to an embodiment of the present invention;

[0058] Figure 8 This is a decomposition diagram of a charging scenario according to an embodiment of the present invention;

[0059] Fig. 9 This is a diagram of a charging use case in an embodiment of the present invention;

[0060] Fig.10 This is a schematic diagram of the preferred system for charging service personnel of the present invention;

[0061] Fig.11 Schematic diagram of computer hardware of the present invention. DETAILED DESCRIPTION

[0062] It should be noted that the processor described in the present invention is the control center of the electronic device, which may be a processor or a general term for multiple processing elements. For example, it may be one or more central processing units (CPUs), or application specific integrated circuits (ASICs), or one or more integrated circuits configured to implement the embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs).

[0063] Optionally, the processor can perform various functions of the electronic device by running or executing a software program stored in the memory, and calling data stored in the memory.

[0064] In a specific implementation, as an embodiment, the processor may include one or more CPUs. Each of these processors may be a single core processor or a multi-core processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions). Electronic devices may include: servers, desktop computers, laptops, smart phones, tablet computers, embedded computers, etc., wherein the embedded computers include vehicles and robots, etc.

[0065] The memory is used to store the software program for executing the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0066] It should be noted that the structure of the electronic device shown in the drawings of the present invention does not constitute a limitation thereto, and the actual knowledge structure recognition device may include more or fewer components than shown in the drawings, or a combination of certain components, or a different arrangement of components.

[0067] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0068] It should also be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0069] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0070] It should also be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0071] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0072] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0073] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0074] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0075] In order to make the above features and effects of the present invention more clearly and understandably described, embodiments are given below and described in detail with reference to the accompanying drawings. This specification discloses one or more embodiments that include the features of the present invention. The disclosed embodiments are only for illustration. The scope of protection of the present invention is not limited to the disclosed embodiments, and the present invention is defined by the attached claims.

[0076] The following is a system embodiment corresponding to the above method embodiment. This embodiment can be implemented in conjunction with the above embodiment. The relevant technical details mentioned in the above embodiment are still valid in this embodiment. In order to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied in the above embodiment.

[0077] The following is a detailed description with reference to specific embodiments.

[0078] Embodiment 1

[0079] like Figure 1 As shown, Figure 1 This is a flow chart of a method for selecting a charging service personnel for a customer disclosed in an embodiment of the present invention. Figure 1 The method for selecting charging service personnel for valet charging can be applied to a system or device for selecting charging service personnel for valet charging, and the embodiment of the present invention does not limit this. Figure 1 As shown, the method includes:

[0080] Step 101: receiving and parsing the charging request from the car owner, and automatically obtaining charging parameters, which include: charging agent parameters and car owner parameters;

[0081] In a specific embodiment of the present invention, the parameters on the charging agent side are obtained, including: the current status of the charging agent, the historical behavior of the charging agent, and the historical evaluation; the parameters on the car owner side are obtained, including: the current location of the user car owner and the user's current charging demand, and historical behavior data; the parameters on the charging agent side are obtained by filtering the charging agent information, and the current location of the car owner and the user's current charging demand are obtained by user request and user request analysis, wherein the car owner's historical behavior data includes: the car owner's historical charging orders and charging orders on behalf of customers.

[0082] Step 102: Analyze and obtain the sensitivity data of the car owner based on the car owner's historical charging behavior data and different charging scenarios. The car owner's sensitivity includes: charging speed sensitivity, charging price sensitivity and charging distance sensitivity;

[0083] In a specific embodiment of the present invention, the sensitivity of the car owner is analyzed based on the car owner's historical behavior data combined with the scenario, for example: obtaining the car owner's historical charging orders and valet orders;

[0084] The sensitivity of car owners to charging speed is analyzed based on the car owners' choice behavior of fast or slow charging;

[0085] Based on the owner's choice of electricity price, the owner's choice of charging time, distance from the station, and historical habits of charging price are comprehensively considered to obtain the result and obtain the owner's sensitivity to charging price;

[0086] The location of the car owner obtained during the request analysis (such as home, company, stadium, etc.), but the present invention is not limited to this and can also include other more scenarios.

[0087] Step 103: dynamically adjusting the weight value based on the charging scenario according to the vehicle owner's location, the vehicle owner's sensitivity and the charging parameters;

[0088] Step 104: pre-construct a multi-dimensional scoring model, input the weight value and the charging request into the multi-dimensional scoring model, obtain the preferred scoring result of the charging agent, and assign the preferred charging agent to the car owner for charging.

[0089] The parameters obtained in step 101 and the weight setting results obtained in step 103 are input into the multi-dimensional scoring model; the charging agent score is obtained and a charging agent is assigned to the car owner.

[0090] Embodiment 2

[0091] like Figure 2 As shown, Figure 2 The method for selecting charging service personnel for valet charging can be applied to a system or device for selecting charging service personnel for valet charging, and the embodiment of the present invention does not limit this. Figure 2 As shown, the method includes:

[0092] Step 201: receiving and parsing the charging request from the car owner, and automatically obtaining charging parameters, which include: charging agent parameters and car owner parameters;

[0093] like Figure 3 , Figure 4 and Figure 5 As shown, in a specific embodiment of the present invention, the above step 201 includes:

[0094] Automatically obtain the parameters of the charging agent, including: the current status of the charging agent, the charging distance of the charging agent, the time parameters of the charging agent arriving at the charging location, the historical behavior of the charging agent, and the historical evaluation data of the charging agent;

[0095] Automatically obtain the owner's parameters, including: the owner's current location, the owner's current charging request, and the owner's historical behavior data.

[0096] In a specific embodiment of the present invention, the above-mentioned automatic acquisition of charging agent parameters also includes:

[0097] Detect whether the online status of the charging agent is active, check whether the corresponding time period of the charging agent has been assigned a task, and exclude it if it has been assigned;

[0098] Calculate the distance between the charger's current location and the charging location, and estimate the time it will take the charger to arrive at the charging location based on real-time traffic data;

[0099] Analyze the charger's historical task completion status, including punctuality rate and user ratings; analyze the charger's skill level in completing charging tasks.

[0100] like Figure 6 As shown, in a specific embodiment of the present invention, the selection of the agent specifically includes:

[0101] 1) Collection and management of recharge agent information:

[0102] In actual applications, the agent needs to fill in personal information through registration and submit relevant documents for real-name authentication. The platform will verify and store this information to ensure the authenticity and reliability of the agent's identity. Through the API interface, the agent can submit a request to receive the agent's recharge task. The system will check the format of the request to ensure the correctness of the information transmission. At the same time, the address provided by the agent will be converted into longitude and latitude coordinates through geocoding for subsequent route planning and matching.

[0103] 2) Availability check

[0104] Online status detection: After the charging agent enters the working mode, he / she sends a designation to the background, indicating that he / she can receive charging tasks; in order to prevent the charging agent from having other things to deal with temporarily, the charging agent is required to go online regularly to indicate that he / she is in an active state.

[0105] Task status query: Check whether the time period corresponding to the charger has been assigned a task. If so, exclude it.

[0106] 3) Position matching

[0107] Distance calculation: Use the Amap API to calculate the distance between the charger’s current location and the charging location. Time estimation: Combine real-time traffic data to estimate the time it takes the charger to reach the charging location.

[0108] 4) Capacity Assessment

[0109] Historical performance: Analyze the historical task completion status of the charger, including punctuality rate, user ratings, etc.

[0110] Skill level: Consider the operator’s operational skills and experience to ensure they can complete the task efficiently.

[0111] 5) Screening and matching of recharge agents:

[0112] The system will detect the online status of the charger in real time. If the charger is available to receive tasks, the system will send a notification to the charger. Amap API will calculate the distance between the charger's current location and the charging location, and estimate the arrival time based on real-time traffic data in order to select the best charger. Historical performance and skill level assessment will also be taken into consideration, and the system will eventually assign tasks to the best charger.

[0113] like Figure 7 As shown, in a specific embodiment of the present invention, the above-mentioned automatic acquisition of vehicle owner parameters also includes:

[0114] Receive the charging request from the car owner, verify the legal identity of the car owner, and check the validity of the charging request;

[0115] The geographic address provided by the user is converted into longitude and latitude coordinates, and the owner's current charging request is parsed and stored. The parameters of the charging request include: charging location, expected starting time of charging, required power of charging, location of the charging user's vehicle, charging vehicle information, and expected time of use of the vehicle for charging.

[0116] In a specific embodiment of the present invention, the parsing of the car owner's charging request includes:

[0117] 1) Receive a charging request from the user's car owner

[0118] User request processing

[0119] Authentication: Use a secure authentication mechanism (such as OAuth 2.0) to verify the user's identity and ensure that the request comes from a legitimate user.

[0120] Validity check: Receive requests through the API interface and check whether the request format is correct, whether the necessary parameters are complete, and whether the user account has sufficient balance or credit.

[0121] 2) Request parsing

[0122] Geocoding: Converts a user-supplied address into longitude and latitude coordinates for subsequent location matching.

[0123] Demand analysis: Analyze the specific requirements in the user's request, such as charging location, expected start time, required power, user vehicle location, vehicle information, expected vehicle usage time, etc., and store them in a temporary database.

[0124] Step 202: Analyze and obtain the sensitivity data of the car owner based on the car owner's historical charging behavior data and different charging scenarios. The car owner's sensitivity includes: charging speed sensitivity, charging price sensitivity and charging distance sensitivity;

[0125] like Figure 8 and Fig. 9 As shown, in a specific embodiment of the present invention, the above step 202 also includes:

[0126] Analyze the sensitivity of car owners based on their historical behavior data and charging scenarios. The historical behavior data of car owners includes historical charging orders and charging orders for customers. The charging scenarios include home, office and leisure scenarios.

[0127] The sensitivity of the car owner to charging speed is obtained by analyzing the car owner's choice of fast or slow charging behavior; the car owner's choice of charging time, station distance, and charging price historical habits are calculated based on the car owner's choice of electricity price to obtain the car owner's sensitivity to charging price.

[0128] Step 203: dynamically adjusting the weight value based on the charging scenario according to the vehicle owner's location, the vehicle owner's sensitivity and the charging parameters;

[0129] In a specific embodiment of the present invention, the above step 203 includes:

[0130] According to different charging scenarios and the parameters of the car owner's charging request, the weight of each scoring item is dynamically adjusted to adapt to different business models, including: home mode, office mode and leisure mode.

[0131] The sensitivity of the car owner will affect the weight of each weight value. For example, the sensitivity of charging price will increase the proportion of the weight value of charging price, as well as the proportion of the weight value of the distance / time for the charging agent to pick up the car from the car owner's home (the farther the distance of the charging agent, the higher the price); the sensitivity of charging time will increase the proportion of the weight value of the distance / time for the charging agent to pick up the car from the car owner's home, the distance / time from the charging station to the car owner's home, and the type of charging station; the sensitivity of charging experience will increase the proportion of the weight value of the type of charging station, charging speed, historical behavior of the charging agent, and evaluation of the charging agent;

[0132] The weight values ​​include: order completion time, charging price, charging time (analyzing the owner's historical charging orders / general data of the car model), charging speed, the distance / time for the charging agent to pick up the car from the owner's home, the distance / time from the charging station to the owner's home, the charging agent's score (obtained from the charging agent's historical behavior and historical evaluation), the charging station type, etc.

[0133] 1. When the car owner initiates a charging request at home, the request is parsed and the owner's request is to pick up the car as soon as possible and use it at 6 am tomorrow;

[0134] Obtain the car owner's historical order information, and use the model analysis to obtain the car owner's sensitivity to charging price, charging location, charging speed, charging station type, charging time, charging experience, etc. The analysis found that the car owner is highly sensitive to charging price (car owners often charge at night when charging prices are low, or charge sporadically during the day when charging prices are low)

[0135] At this time, the weight calculation formula can be: whether the order completion time meets the needs of the car owner (if it meets the needs, it is 1, if it does not meet the needs, it is 0) * (charging price * 70% + charging time * 0% + charging speed * 0% + distance / time from the charging agent to the car owner's location to pick up the car * 20% + distance / time from the charging station to the car owner's location * 0% + charging agent score * 10% + charging station type * 0%);

[0136] 2. When the car owner initiates a charging request in the office, the request is parsed to find that the car owner needs to fully charge the car for use after get off work;

[0137] Obtain the car owner's historical order information and analyze the car owner's sensitivity to charging prices, charging locations, charging speeds, etc. The analysis found that car owners are highly sensitive to charging prices (car owners often charge at night when charging prices are low, or charge sporadically during the day when charging prices are low; and they pay more attention to battery health)

[0138] At this time, the weight calculation formula can be: whether the order completion time meets the needs of the car owner (if it meets the needs, it is 1, if it does not meet the needs, it is 0) * (charging price * 50% + charging time * 0% + charging speed * 20% + distance / time from the charging agent to the car owner's location to pick up the car * 20% + distance / time from the charging station to the car owner's location * 0% + charging agent score * 10% + charging station type * 0%);

[0139] 3. When the car owner initiates a charging request at the golf course, the request is parsed to find that the car owner wants to fully charge the car and use it after 2 hours;

[0140] Obtain the car owner's historical order information and analyze the car owner's sensitivity to charging prices, charging locations, charging speeds, etc. The analysis found that car owners are highly sensitive to charging prices (car owners mostly charge at VIP stations; and they pay more attention to battery health)

[0141] At this time, the weight calculation formula can be: whether the order completion time meets the needs of the car owner (if it meets the needs, it is 1, if it does not meet the needs, it is 0) * (charging price * 0% + charging time * 10% + charging speed * 30% + distance / time from the charging agent to the car owner's location to pick up the car * 10% + distance / time from the charging station to the car owner's location * 10% + charging agent score * 10% + charging station type * 30%);

[0142] 4. The car owner initiates a charging request on the way to the hospital / business district / office building (the car owner needs to stay for a short time at the hospital / business district / office building but there is no vacant parking lot, so charging can solve the parking problem at the same time). The request is analyzed to find that the car owner's demand is that the battery power can support the car to the home, and the car can be used after 3 hours;

[0143] At this time, the weight calculation formula can be: whether the order completion time meets the needs of the car owner (if it meets the needs, it is 1, if it does not meet the needs, it is 0) * (charging price * 10% + charging time * 30% + charging speed * 10% + distance / time from the charging agent to the car owner's location to pick up the car * 30% + distance / time from the charging station to the car owner's location * 20% + charging agent score * 0% + charging station type * 0%);

[0144] In a specific embodiment of the present invention, each weight value has a value range of [0, 1], but the present invention is not limited thereto, and the weight value may also be set to a value range greater than 1.

[0145] Step 204: pre-construct a multi-dimensional scoring model, input the weight value and the charging request into the multi-dimensional scoring model, obtain the preferred scoring result of the charging agent, and assign the preferred charging agent to the car owner for charging.

[0146] In a specific embodiment of the present invention, the above step 204 includes:

[0147] The data of charging test stations are used as training data to train a multi-dimensional scoring model;

[0148] In a specific embodiment of the present invention, a multi-dimensional scoring model is established to comprehensively consider factors such as distance, arrival time, historical performance, etc. to score each charging agent. The multi-dimensional scoring model is obtained by training and optimization of a greedy algorithm or a genetic algorithm; the training data is the charging test site data, and the specific data required for the test is as mentioned above, but the present invention is not limited to this, and other algorithms can also be used in the multi-dimensional scoring model.

[0149] Based on the rating results of recharge agents and user feedback, we continuously iterate and optimize the multi-dimensional rating model;

[0150] Sort the rating results of the recharge agents according to the scores, and select the recharge agent with the highest score as the preferred recharge agent;

[0151] The task instructions are sent to the selected charger via mobile application or text message. The instructions include: charging location, expected start time, required power, user's vehicle location, vehicle information, and expected use time.

[0152] In a specific embodiment of the present invention, the charging task assignment notification sends a task instruction to the selected charging agent via a mobile application or text message, including the charging location, expected start time, required power, user vehicle location, vehicle information, expected vehicle use time, etc.;

[0153] In a specific embodiment of the present invention, the charging task execution monitoring is performed. Through a greedy algorithm or a genetic algorithm, the system selects the charging agent with the highest score to perform the task, so as to improve the efficiency of task allocation. The location of the charging agent is monitored in real time, videos are recorded, and the charging completion time and the return time are predicted to ensure the smooth execution of the task.

[0154] Step 205: If the preferred charging agent cannot complete the charging task, an alternative charging agent is automatically selected to complete the charging task based on the charging agent's preferred scoring result.

[0155] In a specific embodiment of the present invention, the above step 205 includes:

[0156] If the first choice agent fails to accept the task, the next agent with the highest score will be automatically selected from the backup queue, and the backup agent will be selected based on the ranking results of the agents' scores;

[0157] The standby queue is dynamically adjusted based on real-time conditions to ensure that there are always available replacements on standby.

[0158] In a specific embodiment of the present invention, the backup plan adopts a standby queue method: if the preferred substitute fails to accept the task, the next substitute with the highest score is automatically selected from the standby queue to ensure timely distribution of the task, and the next substitute is selected based on the substitute's score ranking.

[0159] In a specific embodiment of the present invention, the backup scheme adopts dynamic adjustment, and the standby queue is dynamically adjusted according to the real-time situation to ensure that there are always available substitutes on standby.

[0160] In a specific embodiment of the present invention, the monitoring of the execution of the charging task includes:

[0161] 1) Real-time positioning: Use GPS and LBS technology to monitor the location of the charger in real time to ensure that he or she follows the scheduled route.

[0162] 2) Key event monitoring: The charging agent needs to record the video when getting on and off the vehicle and upload it to the background for the car owner to view.

[0163] 3) Charging completion time prediction: The charging completion time is predicted based on the current busy and idle conditions of the station and the real-time charging power and SOC of the vehicle.

[0164] 4) Return time prediction: Combine the route planning of Amap and the charging time to calculate the estimated return time of the charger.

[0165] In a specific embodiment of the present invention, the replacement charging task status update includes:

[0166] 1) Two-way communication: Maintain real-time communication with the charger through mobile applications or communication platforms to keep abreast of the task execution status.

[0167] 2) Status report: The charger regularly reports the progress of the task, including the current charging amount, estimated completion time, etc.

[0168] In a specific embodiment of the present invention, the confirmation of completion of the charging task includes:

[0169] 1) Result feedback;

[0170] User confirmation: After the task is completed, the user will be reminded via mobile app or text message to confirm whether the charging amount has reached the expectation and to evaluate the service quality.

[0171] 2) Evaluation collection: Collect user feedback and ratings for subsequent agent evaluation and service optimization.

[0172] In a specific embodiment of the present invention, the charging task data record includes:

[0173] 1) Log Recording: Store the relevant information of this service (such as task details, execution process, user feedback, etc.) into the database for subsequent data analysis and optimization.

[0174] 2) Data analysis: Regularly analyze service data, extract useful information, and improve service processes.

[0175] In a specific embodiment of the present invention, the continuous optimization of the substitute charging task model includes:

[0176] 1) Performance evaluation

[0177] 2) Key indicators: Regularly evaluate the operating performance of the scheduling system, including task response time, user satisfaction, and charger utilization rate.

[0178] 3) User research: Collect user opinions and suggestions through questionnaires, user interviews, etc. to understand changes in user needs.

[0179] 4) Algorithm iteration

[0180] Model optimization: Based on evaluation results and market feedback, we continuously optimize the scheduling algorithm to improve scheduling efficiency and service quality.

[0181] Technological innovation: Explore new technologies and methods, such as introducing advanced algorithms such as deep learning and reinforcement learning, to further improve the intelligence level of the dispatching system.

[0182] In a specific embodiment of the present invention, abnormal handling and continuous optimization of charging tasks include:

[0183] The system will preset a variety of emergency plans, such as abnormal situations such as busy charging agents and damaged chargers, and handle them in a timely manner to ensure the stability and reliability of the service. Regularly analyze service data, extract useful information for continuous optimization, and improve the operation effect of the dispatching system. This continuous optimization will ensure that the user experience is continuously improved and the system runs more efficiently and stably.

[0184] Embodiment 3

[0185] like Fig.10 As shown, Fig.10 The embodiment of the present application provides a system for selecting charging service personnel for valet customers, using a method for selecting charging service personnel for valet customers, and the system includes:

[0186] The charging parameter acquisition module 301 is used to receive the charging request from the car owner, analyze the request, and automatically obtain the charging parameters. The charging parameters include: charging personnel parameters and car owner parameters:

[0187] Valet charging sensitivity analysis module 302: used to analyze and obtain the owner's sensitivity data based on the owner's valet charging history behavior data and different charging scenarios. The owner's sensitivity includes: charging speed sensitivity, charging price sensitivity and charging distance sensitivity;

[0188] Weight adjustment module 303: used to dynamically adjust the weight value based on the charging scenario according to the location of the owner, the sensitivity of the owner and the charging parameters;

[0189] The charging agent optimization module 304 is used to pre-build a multi-dimensional scoring model, input the weight value and the charging request into the multi-dimensional scoring model, obtain the charging agent optimization scoring result, and assign the preferred charging agent to the car owner for charging.

[0190] In a specific embodiment of the present invention, the above-mentioned valet charging service personnel selection system further includes:

[0191] The charging agent selection module 305 is used to automatically select a charging agent to complete the charging task based on the charging agent's preferred scoring result if the preferred charging agent cannot complete the charging task.

[0192] Embodiment 4

[0193] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method for selecting a preferred method for charging service personnel for valet customers.

[0194] Embodiment 5

[0195] An embodiment of the present application provides a computer program product, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method for optimizing the charging service personnel for valet charging are implemented.

[0196] In addition, combined Figure 1 The preferred method for charging service personnel in the embodiment of the present application described above can be implemented by an electronic device, such as a computer device. Fig.11 A schematic diagram of the hardware structure of a computer device of a computer program product according to an embodiment of the present application.

[0197] In some of these embodiments, the computer device may further include a communication interface 83 and a bus 80. Fig.11 As shown, the processor 81, the memory 82, and the communication interface 83 are connected via a bus 80 and communicate with each other.

[0198] Specifically, the processor 81 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0199] The memory 82 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 81 .

[0200] The processor 81 reads and executes the computer program instructions stored in the memory 82 to implement any one of the methods for selecting the best charging service personnel for valet charging in the above-mentioned embodiments.

[0201] In summary, the present invention has positive technical effects compared with the prior art: the method of the present invention can be applied to more charging technology scenarios:

[0202] 1) Applied to electric vehicle shared charging platform:

[0203] The method of the present invention can realize the management of charging agent information and task allocation, improve the efficiency of task execution and the service quality of charging agents, and enhance the user experience; it can intelligently allocate tasks during peak hours, reduce user waiting time, improve charging efficiency, and provide users with a better service experience. According to user feedback and historical data, the charging agent screening and matching mechanism is continuously optimized to improve the overall operation effect of the shared charging platform. Real-time monitoring of task execution and timely handling of abnormal situations ensure the smooth progress of the charging process and the reliability and stability of the service.

[0204] 2) Applied to the construction of urban electric vehicle charging network:

[0205] The algorithm of the present invention can optimize the selection of charging service points, rationally plan the layout of the charging network, and improve the utilization rate and service efficiency of charging stations. The multi-dimensional scoring model is continuously optimized to improve the service quality of charging agents, increase the reputation of charging agents and user satisfaction. Abnormal situations are handled promptly and effectively to ensure the continuity of charging services and provide stable and reliable charging services for urban electric vehicle users.

[0206] In summary, by applying the algorithm of the present invention in the above fields, the quality and efficiency of electric vehicle charging services can be effectively improved, the management of charging agents and the task allocation mechanism can be optimized, thereby improving user experience, enhancing the stability and reliability of services, and promoting the development and popularization of the electric vehicle industry.

[0207] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0208] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for selecting charging service personnel for customers, characterized in that: The method comprises: Receive and analyze the charging request from the car owner, and automatically obtain charging parameters, which include: charging agent parameters and car owner parameters; Analyze and obtain the car owner's sensitivity data based on the car owner's historical charging behavior data and different charging scenarios. The car owner's sensitivity includes: charging speed sensitivity, charging price sensitivity and charging distance sensitivity; According to the sensitivity of the vehicle owner and the proxy charging parameters, dynamically adjusting the weight value based on the charging scenario; A multi-dimensional scoring model is pre-constructed, the weight value and the charging request are input into the multi-dimensional scoring model, a preferred scoring result of the charging agent is obtained, and a preferred charging agent is assigned to the car owner for charging on behalf of the customer.

2. The method for selecting charging service personnel for customers according to claim 1, characterized in that: The method further comprises: If the preferred charging agent is unable to complete the charging task, an alternative charging agent is automatically selected to complete the charging task based on the preferred scoring result of the charging agent.

3. The method for selecting charging service personnel for customers according to claim 1 or 2, characterized in that: The receiving and parsing of the charging request from the vehicle owner and automatically obtaining charging parameters includes: Automatically obtain the parameters of the charging agent, which include: the current status of the charging agent, the charging distance of the charging agent, the time parameter of the charging agent arriving at the charging location, the historical behavior of the charging agent, and the historical evaluation data of the charging agent; Automatically obtain the owner's parameters, which include: the owner's current location, the owner's current charging request, and the owner's historical behavior data.

4. The method for selecting charging service personnel for customers according to claim 3, characterized in that: The automatic acquisition of charging agent parameters also includes: Detect whether the online status of the charging agent is active, check whether the time period corresponding to the charging agent has been assigned a task, and if so, exclude the charging agent; Calculate the distance between the current location of the charger and the charging location, and estimate the time it takes for the charger to arrive at the charging location based on real-time traffic data; Analyze the charger's historical task completion status, including punctuality rate and user ratings; analyze the charger's skill level in completing charging tasks.

5. The method for selecting charging service personnel for customers according to claim 3, characterized in that: The automatic acquisition of the vehicle owner parameters also includes: receiving a charging request from the vehicle owner, performing a legal identity verification on the vehicle owner, and checking the validity of the charging request; The geographic address provided by the user car owner is converted into longitude and latitude coordinates, and the owner's current proxy charging request is parsed and stored. The parameters of the proxy charging request include: proxy charging location, expected start time of proxy charging, power required for proxy charging, location of the proxy charging user's vehicle, proxy charging vehicle information, and expected time of use of the vehicle for proxy charging.

6. The method for selecting charging service personnel for customers according to claim 1 or 2, characterized in that: The analyzing and obtaining the sensitivity data of the car owner according to the car owner's historical charging behavior data and different charging scenarios includes: Analyze the sensitivity of the car owner based on the car owner's historical behavior data combined with the proxy charging scenario, the car owner's historical behavior data includes: historical charging orders and proxy charging orders; the proxy charging scenarios include: home scenarios, office scenarios and leisure scenarios; The sensitivity of the car owner to charging speed is obtained by analyzing the car owner's choice of fast or slow charging behavior; the car owner's choice of charging time, station distance, and charging price historical habits are calculated based on the car owner's choice of electricity price to obtain the car owner's sensitivity to charging price.

7. The method for selecting charging service personnel for customers according to claim 1 or 2, characterized in that: The dynamically adjusting the weight value based on the charging scenario according to the vehicle owner sensitivity and the proxy charging parameter includes: According to the different charging scenarios and the parameters of the charging request by the vehicle owner, the weight of each scoring item is dynamically adjusted to adapt to different business modes, and the business modes include: home mode, office mode and leisure mode.

8. The method for selecting charging service personnel for customers according to claim 1 or 2, characterized in that: The pre-constructing of a multi-dimensional scoring model, inputting the weight value and the charging request into the multi-dimensional scoring model, obtaining the charging agent's preferred scoring result, and allocating the preferred charging agent to the car owner for charging on behalf of the customer comprises: Using charging test station data as training data to train the multi-dimensional scoring model; Continuously iterate and optimize the multi-dimensional scoring model based on the scoring results of the recharge agents and user feedback; Sorting the scoring results of the charging agents according to the scores, and selecting the charging agent with the highest score as the preferred charging agent; The task instructions are sent to the selected charger via a mobile application or text message, and the instructions include: charging location, expected start time, required power, user vehicle location, vehicle information, and expected vehicle usage time.

9. The method for selecting charging service personnel for customers according to claim 2, characterized in that: If the preferred charging agent cannot complete the charging task, automatically selecting a backup charging agent to complete the charging task based on the preferred scoring result of the charging agent includes: If the first choice agent fails to accept the task, the next agent with the highest score will be automatically selected from the backup queue, and the backup agent will be selected based on the ranking results of the agents' scores; The standby queue is dynamically adjusted based on real-time conditions to ensure that there are always available replacements on standby.

10. A system for selecting charging service personnel for customers, using the method for selecting charging service personnel for customers as claimed in any one of claims 1 to 9, characterized in that: The system comprises: The charging parameter acquisition module is used to receive the charging request from the car owner, analyze the request, and automatically obtain the charging parameters, which include the charging person parameters and the car owner parameters: Valet charging sensitivity analysis module: used to analyze and obtain the owner's sensitivity data based on the owner's valet charging history behavior data and different charging scenarios. The owner's sensitivity includes: charging speed sensitivity, charging price sensitivity and charging distance sensitivity; A weight adjustment module: used to dynamically adjust the weight value based on the charging scenario according to the location of the car owner, the sensitivity of the car owner and the charging parameters; A charging agent selection module is used to pre-build a multi-dimensional scoring model, input the weight value and the charging request into the multi-dimensional scoring model, obtain the charging agent selection scoring result, and assign the preferred charging agent to the car owner for charging; The charging agent selection module is used to automatically select a candidate charging agent to complete the charging task based on the charging agent's preferred scoring result if the preferred charging agent is unable to complete the charging task.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the preferred method for charging service personnel for valet charging described in any one of claims 1 to 9 is implemented.

12. A computer program product, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method for selecting a valet charging service personnel according to any one of claims 1 to 9 is implemented.

Images