Electric vehicle charging station leasing method and device

Abstract

The invention discloses an electric vehicle charging station leasing method and device, and the method comprises the steps: obtaining project information from a charging construction request after the charging station construction request is received, and converting the project information into vector data; generating a feasibility evaluation rule based on the vector data and a pre-constructed final charging leasing process; generating and feeding back charging station construction information based on a feasibility evaluation rule; after the construction of the charging station is completed and a charging station leasing request is received, obtaining historical use data and a current use demand, and generating a mode feature vector based on the historical use data and the current use demand; and screening out a lease scheme corresponding to the mode feature vector from all the lease schemes by using a recommendation algorithm, and feeding back the lease scheme. According to the charging station leasing method and device, after the leasing request sent by the client online is received, the corresponding leasing scheme is screened out according to the historical use condition of the charging station and fed back, the client only needs to perform online operation, offline tedious operation is not needed, the efficiency of the leasing process is improved, and then the experience feeling of the client is improved.

Description

Technical Field

[0001] The present application relates to the field of new energy technology, and in particular to a method and device for leasing an electric vehicle charging station. Background Art

[0002] With the popularization of electric vehicles, the construction and rental management of charging stations has become an important and urgent task.

[0003] Currently, during the construction of charging stations, the site selection stage often relies on manual experience due to the lack of systematic planning and intelligent tool support. In addition, after the construction of the charging station is completed, the traditional charging station rental business process mostly relies on offline operations, and customers need to submit applications in person. The entire rental process is cumbersome and time-consuming, and the customer experience is poor.

[0004] Therefore, how to improve customer experience has become an urgent problem that needs to be solved in this field. Summary of the Invention

[0005] The present application provides a method and device for leasing an electric vehicle charging station, with the aim of improving the customer experience.

[0006] In order to achieve the above objectives, this application provides the following technical solutions:

[0007] A method for leasing an electric vehicle charging station, comprising:

[0008] Upon receiving a charging station construction request, obtaining project information from the charging station construction request and converting the project information into vector data; the project information includes at least charging station site selection information data, equipment list data, construction plan data, budget details data, and expected revenue data;

[0009] Generate feasibility assessment rules based on the vector data and a pre-built final charging rental process; the final charging rental process is obtained by pre-optimizing the charging rental process;

[0010] generating charging station construction information based on the feasibility assessment rule, and feeding back the charging station construction information so as to construct a charging station according to the charging station construction information;

[0011] When the charging station is built and a charging station rental request is received, the historical usage data and current usage demand of the charging station are obtained;

[0012] generating a pattern feature vector based on the historical usage data and the current usage demand; wherein the pattern feature vector indicates a feature vector of a usage pattern of the charging station;

[0013] A recommendation algorithm is used to screen out a lease plan corresponding to the pattern feature vector from all lease plans, and the lease plan corresponding to the pattern feature vector is fed back.

[0014] Optionally, after receiving the charging station construction request, obtaining project information from the charging station construction request and converting the project information into vector data, the method further includes:

[0015] Obtain the amount of external financing for the project information;

[0016] Calculating the difference between the budget details and the external financing amount to obtain a target financing amount, and feeding back the target financing amount to the user;

[0017] Upon receiving a financing request carrying the target financing amount, reviewing the target financing amount and the project information to obtain a review result;

[0018] An electronic archive file is generated based on the project information and the audit result, and the electronic archive file is stored in a database.

[0019] Optionally, the process of pre-optimizing the charging lease process to obtain the final charging lease process includes:

[0020] Obtaining a charging station leasing process, execution rules for each task node in the charging station leasing process, and real-time data; the real-time data includes at least customer behavior data, external environment data, device status data, and event data;

[0021] Adjusting the overall structure matrix of the charging station leasing process according to the execution rules of each task node to obtain an adjusted charging station leasing process;

[0022] Optimizing the adjusted charging station leasing process according to the real-time data to obtain an optimized charging station leasing process;

[0023] The optimized charging station leasing process is optimized using an optimization function to obtain a final charging station leasing process.

[0024] Optionally, also include:

[0025] Acquire real-time construction data of the charging station; the real-time construction data includes at least key data, image data, and video data; the key data includes at least construction progress, equipment status, and environmental conditions;

[0026] Calculating a difference between the real-time construction data and preset construction data in the charging station construction information to obtain deviation information;

[0027] The preset construction data in the charging station construction information is optimized according to the deviation information to obtain optimized charging station construction information, and the optimized charging station construction information is fed back.

[0028] Optionally, also include:

[0029] generating construction progress information based on the image data and the video data;

[0030] Inputting the construction progress information into an audit model to obtain an audit result; the audit model is pre-built based on a machine learning algorithm;

[0031] Inputting the audit results into an analysis model to obtain an analysis result; the analysis model is a deep learning model;

[0032] Optimizing the construction progress information according to the analysis result to obtain optimized construction progress information;

[0033] Comparing the construction progress information with the optimized construction progress information to obtain a comparison result;

[0034] The comparison result, the construction progress information and the optimized construction progress information are stored in a database.

[0035] Optionally, generating charging station construction information based on the feasibility assessment rule includes:

[0036] The feasibility assessment rules are converted using augmented reality technology to obtain a virtual charging station model;

[0037] Acquire and utilize real-time environmental data and the virtual charging station model to adjust the final charging station leasing process to obtain a final planning model; the real-time environmental data includes at least real-time traffic data and real-time power demand data;

[0038] The final planning model and the feasibility assessment rules are integrated to obtain charging station construction information.

[0039] A rental device for an electric vehicle charging station, comprising:

[0040] a conversion unit configured to, upon receiving a charging station construction request, obtain project information from the charging station construction request and convert the project information into vector data; the project information including at least charging station site selection information data, equipment list data, construction plan data, budget details data, and expected revenue data;

[0041] A first generating unit is configured to generate a feasibility assessment rule based on the vector data and a pre-constructed final charging rental process; the final charging rental process is obtained by pre-optimizing the charging rental process;

[0042] a second generating unit, configured to generate charging station construction information based on the feasibility assessment rule, and feed back the charging station construction information so as to construct a charging station according to the charging station construction information;

[0043] An acquisition unit, configured to acquire historical usage data and current usage demand of a charging station after the construction of the charging station is completed and a charging station lease request is received;

[0044] a third generating unit, configured to generate a pattern feature vector based on the historical usage data and the current usage demand; the pattern feature vector indicating a feature vector of a usage pattern of the charging station;

[0045] The screening unit is used to screen out the rental plan corresponding to the pattern feature vector from all the rental plans by using a recommendation algorithm, and feed back the rental plan corresponding to the pattern feature vector.

[0046] Optionally, also include:

[0047] an acquisition unit, configured to acquire the external financing amount of the project information;

[0048] a calculation unit, configured to calculate the difference between the budget detail data and the external financing amount to obtain a target financing amount, and to feed back the target financing amount to the user;

[0049] an audit unit, configured to, upon receiving a financing request carrying the target financing amount, audit the target financing amount and the project information to obtain an audit result;

[0050] The storage unit is used to generate an electronic archive file based on the project information and the audit result, and store the electronic archive file in a database.

[0051] Optionally, the first generating unit is specifically configured to:

[0052] Obtaining a charging station leasing process, execution rules for each task node in the charging station leasing process, and real-time data; the real-time data includes at least customer behavior data, external environment data, device status data, and event data;

[0053] Adjusting the overall structure matrix of the charging station leasing process according to the execution rules of each task node to obtain an adjusted charging station leasing process;

[0054] Optimizing the adjusted charging station leasing process according to the real-time data to obtain an optimized charging station leasing process;

[0055] The optimized charging station leasing process is optimized using an optimization function to obtain a final charging station leasing process.

[0056] Optionally, also include:

[0057] An information acquisition unit, configured to acquire real-time construction data of the charging station; the real-time construction data includes at least key data, image data, and video data; the key data includes at least construction progress, equipment status, and environmental conditions;

[0058] a difference calculation unit, configured to calculate a difference between the real-time construction data and the preset construction data in the charging station construction information to obtain deviation information;

[0059] The optimization unit is used to optimize the preset construction data in the charging station construction information according to the deviation information, obtain the optimized charging station construction information, and feed back the optimized charging station construction information.

[0060] The technical solution provided by this application, when receiving a charging station construction request, obtains project information from the charging construction request and converts the project information into vector data; generates feasibility assessment rules based on the vector data and the pre-built final charging lease process; generates charging station construction information based on the lease feasibility assessment rules, and feeds back the leased charging station construction information to build a charging station according to the leased charging station construction information; when the charging station construction is completed and a charging station lease request is received, obtains the charging station's historical usage data and current usage requirements; generates a pattern feature vector based on the lease historical usage data and the lease current usage requirements; uses a recommendation algorithm to filter out the lease plan corresponding to the lease pattern feature vector from all lease plans, and feeds back the lease plan corresponding to the lease pattern feature vector. In this application, after receiving a lease request sent online by a customer, the corresponding lease plan is filtered out and fed back based on the historical usage of the charging station. The customer only needs to operate online and does not need tedious offline operations, which improves the efficiency of the lease process and thus improves the customer experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0062] Figure 1A flowchart of leasing an electric vehicle charging station provided in an embodiment of the present application;

[0063] Figure 2 A flowchart of a method for reviewing a financing amount provided in an embodiment of the present application;

[0064] Figure 3 A schematic diagram of a method for generating a lease contract provided in an embodiment of the present application;

[0065] Figure 4 A flowchart of a method for optimizing a charging rental process provided in an embodiment of the present application;

[0066] Figure 5 A flowchart of a method for generating charging station construction information provided in an embodiment of the present application;

[0067] Figure 6 A flowchart of a construction data optimization method provided in an embodiment of the present application;

[0068] Figure 7 A flowchart of a method for storing rectification data provided in an embodiment of the present application;

[0069] Figure 8 A schematic diagram of a leasing process provided in an embodiment of the present application;

[0070] Figure 9 A schematic diagram of a construction management process provided in an embodiment of the present application;

[0071] Figure 10 A schematic diagram of the architecture for leasing an electric vehicle charging station provided in an embodiment of the present application. DETAILED DESCRIPTION

[0072] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0073] In this application, the terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0074] like Figure 1 FIG. 1 is a flowchart of leasing an electric vehicle charging station provided in an embodiment of the present application, comprising the following steps:

[0075] S101: After receiving a charging station construction request, obtain project information from the charging station construction request and convert the project information into vector data.

[0076] The project information includes at least charging station site selection information data, equipment list data, construction plan data, budget details data and expected revenue data.

[0077] It is understandable that when the login information entered by the user is received, the login information is first verified. After the verification is passed, a prompt message indicating the verification is passed is fed back to the user. When a request for access to charging station construction financing is received, the charging station construction financing page is displayed to allow the user to fill in the project information on the charging station construction financing page. When the user confirms the project information, the charging station construction request can be received.

[0078] It should be noted that the specific form of converting project information into vector data is: c1 ={P s ,P e ,P c ,P b ,P r}. Among them, P s is the location information data, P e is the equipment list data, P c is the construction plan data, P b is the budget detail data, P r Expected earnings data.

[0079] Optionally, after step S101, since the charging station construction request also includes a financing request, the financing request needs to be reviewed. By reviewing the financing amount, the feasibility of the project can be further evaluated. Therefore, another embodiment of the present application provides a method for reviewing the financing amount, such as Figure 2 As shown, the following steps are included:

[0080] S201: Obtain the external financing amount of the project information.

[0081] The external financing amount indicates the user’s own funds or the amount of external financing already obtained.

[0082] S202: Calculate the difference between the budget details and the external financing amount to obtain the target financing amount, and feedback the target financing amount to the user.

[0083] The specific form of calculating the difference between the budget details data and the external financing amount is shown in formula (1).

[0084] F c =P b -R e (1)

[0085] In formula (1), P b is the budget detail data, R e is the amount of external financing, F c The target financing amount (i.e. the financing amount that needs to be applied for).

[0086] S203: After receiving the financing request carrying the target financing amount, the target financing amount and project information are reviewed to obtain the review result.

[0087] Among them, the review result indicates whether the project financing is approved.

[0088] It is understandable that after the target financing amount is fed back to the user, the user confirms the target financing amount, that is, after receiving the financing request carrying the target financing amount, the user reviews the target financing amount and project information.

[0089] Optionally, the target financing amount and project information may be submitted to an approval module for review to obtain a review result.

[0090] S204: Generate an electronic archive file based on the project information and the audit results, and store the electronic archive file in a database.

[0091] It is understandable that electronic archive files are generated based on project information and audit results, and stored in the database. The electronic archive files can serve as records of the project review and financing process, facilitating subsequent tracking, auditing and compliance inspections.

[0092] Combine Figure 2 For the contents shown, see Figure 3 A schematic diagram of a method for generating a lease contract is shown. After receiving a charging station construction request (i.e., a financing application) submitted by a customer, the customer's identity is verified. Once the customer's identity is verified, a preliminary review of the financing application is conducted. After the preliminary review is passed, the suitability of the construction address of the charging station in the project information is evaluated. If so, a financing plan is generated, and then a financing contract is generated based on the financing plan.

[0093] S102: Generate feasibility assessment rules based on the vector data and the pre-built final charging rental process.

[0094] Among them, the final charging rental process is obtained by optimizing the charging rental process in advance.

[0095] It should be noted that the specific form of the feasibility evaluation rule is generated based on the vector data and the pre-built final charging rental process, as shown in formula (2).

[0096]

[0097] In formula (2), Model adjusted For the final charging leasing process, L g is the geographic location data matrix, which represents the geographic information of the site selection, P si is the location information data, P ei is the equipment list data, P ci is the construction plan data, P bi is the budget detail data, P ri is the expected return data, M s is the market data, T rj For real-time traffic data, D ej Real-time electricity demand data.

[0098] Optionally, in another embodiment of the present application, the charging lease process is optimized in advance to obtain a specific implementation method of the final charging lease process, such as Figure 4 As shown, the following steps are included:

[0099] S401: Obtain the charging station leasing process, the execution rules and real-time data of each task node in the charging station leasing process.

[0100] Among them, real-time data includes at least customer behavior data, external environment data, device status data and event data.

[0101] In step S401, the specific form of the charging station leasing process is shown in formula (3).

[0102]

[0103] In formula (3), M initial It is the overall structure of the charging station leasing process, covering all components of the charging station leasing business process. ij is the state of task node i on process path j, i=1,2,...,m, j=1,2,...,n, P ij A priority matrix for decision points and path selection is used to determine the specific execution path and priority of the process.

[0104] In step S401, the specific form of the execution rules of each task node in the charging station leasing process is shown in formula (4).

[0105]

[0106] In formula (4), R i is the comprehensive execution rule of the i-th task node; C ij is the condition vector, which defines the conditions for task execution, E ij is the event trigger vector, indicating the critical time associated with the task node, A ij It is an action vector, which indicates the corresponding action when the condition is met or the event is triggered.

[0107] It should be noted that the rule engine module of the adaptive process engine is used to define the execution rules and conditions of each task node, and in the development process with separated front-end and back-end, the process engine is used to judge the logic on the back-end to ensure the correctness of the process logic. At the same time, when the process is disordered, the system will prevent the process from continuing to flow and record the error node for subsequent re-pushing of the error node and process correction.

[0108] S402: According to the execution rules of each task node, the overall structure matrix of the charging station leasing process is adjusted to obtain an adjusted charging station leasing process.

[0109] Among them, according to the execution rules of each task node, the specific form of adjusting the overall structural matrix of the charging station leasing process is shown in formula (5).

[0110]

[0111] In formula (5), Model dynamic is the charging station leasing process model after dynamic adjustment; Tk represents the specific operation matrix of each task; R k (ie R i ) represents the execution rule matrix of the task node, which represents how the rules of the task node affect the dynamic update and adjustment of the charging station rental process; D k is the feedback matrix of real-time data during task execution; P k represents the decision path matrix related to tasks, and p is the number of tasks involved in the process.

[0112] It should be noted that, based on the charging station leasing process, the dynamic modeling module of the adaptive process engine is used to model and adjust the various steps, tasks and decision points in the process in real time according to the needs of the leasing business (i.e. the execution rules of each task node).

[0113] S403: Optimize the adjusted charging station leasing process according to the real-time data to obtain an optimized charging station leasing process.

[0114] The specific implementation process of optimizing the adjusted charging station leasing process based on real-time data is as follows: optimizing the adjusted charging station leasing process using the optimization formula. The specific expression of the optimization formula is shown in formula (6).

[0115] Model adjusted =Model dynamic +α·[D c W c +D e W e +D s W s +D' e W e '] (6)

[0116] In formula (6), α is the adjustment factor, which is used to adjust the adjustment range of the charging station rental process model. c is the weight matrix of customer behavior data, W e is the weight matrix of external environment data, W s is the weight matrix of device status data, W e ' is the weight matrix of event data, D c For customer behavior data, D e For external environment data, D s is the device status data, D' e For event data.

[0117] S404: Optimize the optimized charging station leasing process using an optimization function to obtain a final charging station leasing process.

[0118] The specific expression of the optimization function is shown in formula (7).

[0119]

[0120] In formula (7), is an optimization function used to determine the optimal state of the final charging station rental process, Cost k Represents the cost function of each optimization objective, measuring the cost of different optimization objectives; W k is the weight matrix of the cost function; k is the factor by which the cost function decays over time; Δt k is the time increment; F i is the real-time feedback function vector; V i is the weight vector of the event adjustment function; ω i is the frequency of event response, and t is the current time point.

[0121] It can be understood that the optimized charging station leasing process is optimized by using the optimization function, so that the leasing process can achieve the best effect in different environments.

[0122] It should be noted that after obtaining the final charging station leasing process, each task node can be monitored and adjusted in real time to ensure that the execution status of the process is consistent with the final charging station leasing process, and to prevent the process from continuing to flow if the process is disordered. The specific implementation method for real-time monitoring and adjustment of each task node is shown in Formula (8).

[0123]

[0124] In formula (8), is the updated task node state vector; is the initial state vector; β ij is the coupling factor between node i and event j; θ ij is the initial phase; φ j is the frequency response factor of the event trigger, and t is the current time point.

[0125] S103: Generating charging station construction information based on the feasibility assessment rule, and feeding back the charging station construction information so as to construct a charging station according to the charging station construction information.

[0126] Among them, the project information is evaluated according to the feasibility evaluation rules to evaluate whether the site selection of the project is reasonable, thereby obtaining the evaluation results, and the charging station construction information is generated according to the evaluation results.

[0127] Optionally, in another embodiment of the present application, the specific implementation process of generating the charging station construction information based on the feasibility assessment rule in step S103 is as follows: Figure 5 As shown, the following steps are included:

[0128] S501: Using augmented reality technology to transform the feasibility assessment rules to obtain a virtual charging station model.

[0129] The specific form of converting the feasibility assessment rules using augmented reality technology is shown in formula (9).

[0130]

[0131] In formula (9), represents a subset of geolocation data, The components of the augmented reality generation matrix, M s is the market data, P c For the project planning data vector, D em and T rmRepresent the items related to real-time power demand data and real-time traffic data, V cs A virtual charging station model is generated to demonstrate the virtual feasibility of a charging station project under specific geographical locations and market conditions.

[0132] S502: Acquire and use real-time environmental data and the virtual charging station model to adjust the final charging station leasing process and obtain a final planning model.

[0133] The real-time environmental data includes at least real-time traffic data and real-time power demand data.

[0134] It can be understood that the specific form of adjusting the final charging station leasing process is as shown in formula (10).

[0135]

[0136] In formula (10), is the square root of the real-time traffic data matrix, D e is the power demand data matrix, P c is the project plan data vector (the overall data vector of the project plan, representing the collection of all relevant project plan data), ∥L g ∥ is the norm of the geographic location data, P d It is a dynamically adjusted planning model used to optimize the construction and layout of charging stations.

[0137] S503: Integrate the final planning model and feasibility assessment rules to obtain charging station construction information.

[0138] Among them, the charging station construction information indicates the feasibility of charging station construction.

[0139] It is understandable that the final planning model and feasibility assessment rules are integrated. Specifically, the feasibility planning rules provide basic analysis for the overall feasibility of the project, and the final planning model optimizes and adjusts the project according to real-time changes, thereby obtaining charging station construction information and providing feedback on charging station construction information.

[0140] Optionally, the charging station construction information can be displayed through an augmented reality platform.

[0141] Optionally, after step S505, it is also necessary to pay attention to the real-time construction data of the charging station. By monitoring the real-time construction data, overspending can be discovered and corrected in a timely manner, thereby controlling the overall construction cost and avoiding unnecessary financial losses. Therefore, another embodiment of the present application provides a construction data optimization method, such as Figure 6 As shown, the following steps are included:

[0142] S601: Acquire real-time construction data of charging stations.

[0143] Among them, the real-time construction data includes at least key data, image data and video data.

[0144] Optionally, key data includes but is not limited to: construction progress, equipment status, and environmental conditions.

[0145] It should be noted that key data of the charging station construction site can be obtained in real time through the IoT sensor network, and the construction site can be regularly inspected through the drone network to obtain image data and video data of the construction site.

[0146] Optionally, after step S601, a construction progress report can be generated based on real-time construction data, and the construction progress report can be reviewed by the model, thereby providing intelligent problem feedback and automatic rectification suggestions based on the review results, and storing the data before and after the rectification so that the data involved in the rectification can be queried later. Therefore, another embodiment of the present application provides a storage method for rectification data, such as Figure 7 As shown, the following steps are included:

[0147] S701: Generate construction progress information based on image data and video data.

[0148] It is understandable that the construction progress information is generated based on the image data and the video data. Specifically, the image data and the video data are integrated into the construction progress report, thereby generating the construction progress information including dynamic images.

[0149] S702: Input the construction progress information into the audit model to obtain the audit results.

[0150] Among them, the audit model is pre-built based on machine learning algorithms.

[0151] Optionally, the audit results indicate potential problems and abnormal results in the construction progress information.

[0152] In step S702, the construction progress information is input into the audit model, so that the audit model evaluates the rationality of the construction progress, the standardization of equipment installation and the accuracy of budget execution to obtain an evaluation result.

[0153] It can be understood that the specific implementation method of the audit model for auditing the construction progress information is as shown in formula (11).

[0154]

[0155] In formula (11), A audit is the audit result matrix; C pi Construction progress data set (i.e. construction progress information); Sqi Install the status matrix for the device; B ri Budget execution data; T ei is the planned time node; W mi It is a weight matrix used to adjust the impact factors of the audit results.

[0156] S703: Input the audit results into the analysis model to obtain the analysis results.

[0157] Among them, the analysis model is a deep learning model.

[0158] Optionally, the analysis results indicate the root cause of the problem and the possible impact.

[0159] It is understandable that the problems identified in the audit results are input into the analysis model for further analysis to determine the severity and scope of the problem. The analysis process comprehensively considers the project historical data and the nature of the problem to determine the root cause and possible impact of the problem.

[0160] It should be noted that the specific form of analyzing the audit results through the analysis model is shown in formula (12).

[0161]

[0162] In formula (12), P analysis is the problem analysis matrix; E sj Key event data in the project; H tj is the similarity matrix of historical project data; R uj is the problem severity function; E vj is the weight matrix of the deep learning model.

[0163] S704: Optimize the construction progress information according to the analysis results to obtain optimized construction progress information.

[0164] The specific form of optimizing the construction progress information according to the analysis results is shown in formula (13).

[0165]

[0166] In formula (13), P feedback is the intelligent feedback report matrix (i.e. the optimized construction progress); D ck Decision-making data for rectification suggestions; L fk The task load data of the project leader; S gk Prioritization matrix for feedback reports.

[0167] S705: Compare the construction progress information with the optimized construction progress information to obtain a comparison result.

[0168] Among them, the comparison results indicate the effects before and after rectification.

[0169] It can be understood that the specific form of comparing the construction progress information with the optimized construction progress information is as shown in formula (14).

[0170]

[0171] In formula (14), E effect M is the rectification effect evaluation matrix; nl Implementation progress data; tl Construction progress information before rectification, N wl It is the construction progress information after rectification; V xl is the effect weight matrix.

[0172] S706: Storing the comparison results, construction progress information and optimized construction progress information in a database.

[0173] It is understandable that the comparison results, construction progress information and optimized construction progress information are stored in the database so that the audit model and analysis model can be further optimized based on the above information.

[0174] S602: Calculate the difference between the real-time construction data and the preset construction data in the charging station construction information to obtain deviation information.

[0175] The deviation information indicates deviation information of the construction progress.

[0176] S603: Optimize the preset construction data in the charging station construction information according to the deviation information to obtain optimized charging station construction information, and feed back the optimized charging station construction information.

[0177] It can be understood that the preset construction data in the charging station construction information is optimized according to the deviation information, that is, resource scheduling and task scheduling are carried out according to the deviation information, and the allocation and scheduling of resources are optimized to obtain the optimized charging station construction information to ensure that the project proceeds as planned (that is, according to the preset construction data), thereby reducing delays and waste of resources.

[0178] It should be noted that the optimized charging station construction information (i.e., the optimized resource scheduling plan) can be fed back to the construction team through the project management platform, and the implementation of the resource scheduling plan can be monitored to ensure that the construction task is successfully completed according to the optimized plan.

[0179] S104: When the construction of the charging station is completed and a charging station lease request is received, historical usage data and current usage demand of the charging station are obtained.

[0180] The lease request indicates a request for obtaining a charging station lease plan.

[0181] It is understandable that the historical usage data of the charging station includes but is not limited to: the number of charges, charging time, peak usage period, user type, and device type, and the current usage demand includes but is not limited to: the number of electric vehicles in use and charging demand forecast.

[0182] S105: Generate a pattern feature vector based on historical usage data and current usage demand.

[0183] The mode feature vector indicates a feature vector of a usage mode of the charging station.

[0184] It is understandable that a pattern feature vector is generated based on historical usage data and current usage demand, so that a charging station rental plan can be recommended subsequently based on the pattern feature vector.

[0185] S106: Filter out a leasing plan corresponding to the pattern feature vector from all leasing plans using a recommendation algorithm, and feed back the leasing plan corresponding to the pattern feature vector.

[0186] The specific expression of the recommendation algorithm is shown in formula (15).

[0187]

[0188] In formula (15), R recommend is the recommended leasing solution matrix, L pj is the characteristic matrix of the optional leasing options, Q rj represents the adaptability score of the leasing scheme, M sj Market acceptance of leasing solutions.

[0189] Optionally, a financing approval process will be generated based on the recommended leasing plan, and the financing approval process will be sent to the approval department. Based on the execution of the approval process, the leasing plan status of the charging station will be updated until the approval process is completed.

[0190] Optionally, when a financing approval process is generated and the preset financing amount (eg, five million) is not reached, the financing approval process can be approved by the risk control model to obtain an approval result.

[0191] It should be noted that once the financing approval process is approved, blockchain technology can be used to generate and sign an electronic contract; after the electronic contract is signed, the user can pay the lease fee through a variety of payment methods (for example, platforms, mobile applications, smart watches); when the fee payment is completed, a settlement document can be generated and feedback can be provided.

[0192] It should be emphasized that any problems encountered during the charging station rental process can be promptly resolved through the pre-integrated customer service system and real-time technical support. The operating status of the charging equipment in the charging station can also be monitored in real time through the Internet of Things and edge computing technologies, providing fault alarms and automated maintenance suggestions.

[0193] In addition, based on all the data involved in the above-mentioned leasing process, operational reports and business insights are generated based on all the data, so as to assist in decision-making optimization based on the operational reports and business insights.

[0194] In order to better explain the above leasing process, the leasing process is described below in combination with specific application scenarios. Figure 8 First, determine whether personal authentication has been conducted. If so, conduct enterprise authentication. If not, conduct personal authentication first and then enterprise authentication. Establish an enterprise file, fill in information in the enterprise file, and determine whether the financing exposure in the information is not greater than the limit. If not, push it to the core charging station for details. If greater, supplement the guarantor information and then push it to the core charging station for details. Push the signing task to determine whether a guarantor is required to sign. If so, the legal person signs and the guarantor signs. If not, the legal person signs. Push the signing results and the preset model results, upload the self-certification materials, and push the core approval after the upload is completed. After the core review is passed, the process ends. If it fails, you need to re-upload the self-certification materials for review.

[0195] Combine Figure 1 For the contents shown, see Figure 9 The construction management process of the charging station can be simply summarized as follows: first establish the final leasing process. When a financing application is received, use the final leasing process and feasibility assessment rules to evaluate and obtain the charging station construction information; obtain the real-time construction data of the charging station, and compare the real-time construction data with the preset construction data in the charging station construction information to obtain the comparison results; optimize the preset construction data according to the comparison results, and build the charging station according to the optimized preset construction data; when the charging station construction is completed, a leasing plan can be generated, and a financing approval process can be generated according to the leasing plan, and the financing approval process can be reviewed.

[0196] from Figure 9As can be seen in the figure, the charging station leasing process is first introduced to achieve fully automated management of the charging station leasing process. The charging station leasing process is used in combination with the rule engine module of the adaptive process engine to define the execution rules and conditions of each task node. It can dynamically adjust and optimize the execution of each task node based on real-time data. This not only greatly reduces manual intervention and improves the execution efficiency of the process, but also ensures the continuity and correctness of the charging station leasing process through real-time monitoring and dynamic adjustment. In particular, when the charging station leasing process is disordered, the system can automatically prevent the charging station leasing process from continuing to flow and record the error node, realizing highly intelligent management of the charging station leasing process. Through the front-end and back-end separation processing of the rule engine module, the pressure on the front-end state judgment is reduced, making the logic of the entire process clearer and more controllable.

[0197] To sum up, after receiving the rental request sent by the customer online, the corresponding rental plan is screened and feedback is provided based on the historical usage of the charging station. The customer only needs to operate online and does not need tedious offline operations, which improves the efficiency of the rental process and thus enhances the customer experience.

[0198] In order to better Figure 1 The following is an explanation of the content of the .com file, which is combined with specific application scenarios, including:

[0199] A large electric vehicle operating company planned to build and operate a number of new electric vehicle charging stations in five new urban districts in City A. Due to the rapid development of these areas, demand for electric vehicles has increased rapidly, but existing charging facilities are unable to meet this growing demand. To ensure that the new charging stations can be put into use quickly and efficiently, the operating company decided to adopt the process engine-based online management method for customer charging station construction and leasing in this paper to optimize the entire process from site selection to operation.

[0200] On May 10, 2024, the operating company submitted an application to build 20 electric vehicle charging stations in five new urban districts of City A through the online management platform of an embodiment of the present application. The identity of the submitter was first verified, and the submitted project information, including site selection data, equipment list data, construction plan data, budget details data, and expected revenue data, was structured into vector data and analyzed.

[0201] During the analysis process, augmented reality technology was used in combination with real-time traffic data and power supply data to conduct a detailed site selection assessment for one of the five new districts. On May 12, geographic information system (GIS) was used to discover that one of the planned sites had a high traffic volume but insufficient power supply. A site selection optimization recommendation report was automatically generated, indicating that due to insufficient power supply, the site may not be able to meet charging demand during peak hours. After further analysis, it was recommended to adjust the location of the charging station to a nearby commercial area. Although the traffic volume in this area is low, the power supply is stable and there is great potential for future development. The operating company adopted the suggestion and confirmed the final site selection plan on May 14.

[0202] On May 20, 2024, the construction team began constructing charging stations in the confirmed site. IoT sensors installed at the construction site collected real-time data on construction progress and equipment status. Drones also regularly patrolled the construction site, capturing high-precision images and videos. All of this data was automatically integrated into a data center for real-time analysis.

[0203] On May 25, when analyzing the construction progress information for that day, it was detected that the equipment installation progress at a certain construction site was lagging behind and the construction quality did not meet the standards. Specifically, a charging pile numbered "EQ1234" had a slight installation deviation in its connection line, which caused abnormal current fluctuations in the equipment during the initial test. A problem feedback report was automatically generated, which detailed the installation time, installers, test data, and recommended corrective measures for the equipment. The problem was also automatically fed back to the project manager, and it was recommended to readjust the installation position of the equipment and correct the line connection. The next day, the construction team made rectifications based on the suggestions, and the equipment problem was effectively resolved. Subsequently, it was confirmed that the equipment status had returned to normal, and the construction progress report was updated.

[0204] During the construction process, a sudden downpour in one area impacted the planned construction schedule. On May 30th, weather monitoring sensors detected the downpour and automatically generated an emergency dispatch plan. This reassigned previously planned construction resources to areas with better weather and recommended postponing construction in the affected area until weather conditions improved. This dynamic resource scheduling ensured that the entire project maintained its planned steady progress, avoiding any delays.

[0205] On June 10, 2024, construction of all charging stations was successfully completed. The operating company subsequently published leasing information for these charging stations through the platform. On June 12, the platform intelligently recommended a suitable leasing plan based on the company's historical charging records and current needs. The logistics company applied for long-term leases of two of the charging stations through the platform. The financing approval process was automatically generated and signed electronically, taking into account the company's financial situation and historical transaction data. The entire leasing process took only two days, a 93% reduction in time compared to traditional leasing approval methods.

[0206] It can be seen that the embodiments of the present application significantly shorten the time cost of each link, improve the accuracy of management and real-time response capabilities, and ensure the efficient execution of the entire project and high customer satisfaction.

[0207] like Figure 10 As shown, it is a schematic diagram of the architecture of leasing an electric vehicle charging station provided in an embodiment of the present application, and the leasing includes: a conversion unit 100, a first generation unit 200, a second generation unit 300, an acquisition unit 400, a third generation unit 500 and a screening unit 600.

[0208] The conversion unit 100 is used to obtain project information from the charging station construction request and convert the project information into vector data after receiving the charging station construction request; the project information includes at least charging station site selection information data, equipment list data, construction plan data, budget details data and expected revenue data.

[0209] The first generating unit 200 is configured to generate a feasibility evaluation rule based on the vector data and a pre-built final charging rental process; the final charging rental process is obtained by pre-optimizing the charging rental process.

[0210] The first generation unit 200 is specifically used to: obtain the charging station rental process, the execution rules of each task node in the charging station rental process and real-time data; the real-time data includes at least customer behavior data, external environment data, device status data and event data; according to the execution rules of each task node, adjust the overall structure matrix of the charging station rental process to obtain an adjusted charging station rental process; optimize the adjusted charging station rental process according to the real-time data to obtain an optimized charging station rental process; use the optimization function to optimize the optimized charging station rental process to obtain a final charging station rental process.

[0211] The second generating unit 300 is configured to generate charging station construction information based on the feasibility evaluation rule, and feed back the charging station construction information so as to construct a charging station according to the charging station construction information.

[0212] The second generation unit 300 is specifically used to: use augmented reality technology to convert the feasibility assessment rules to obtain a virtual charging station model; obtain and use real-time environmental data and the virtual charging station model to adjust the final charging station leasing process to obtain a final planning model; the real-time environmental data at least includes real-time traffic data and real-time power demand data; and integrate the final planning model and the feasibility assessment rules to obtain charging station construction information.

[0213] The acquisition unit 400 is configured to acquire historical usage data and current usage demand of the charging station after the construction of the charging station is completed and a charging station lease request is received.

[0214] The third generating unit 500 is configured to generate a pattern feature vector based on historical usage data and current usage demand; the pattern feature vector indicates a feature vector of a usage pattern of the charging station.

[0215] The screening unit 600 is configured to screen out a rental plan corresponding to the pattern feature vector from all rental plans using a recommendation algorithm, and to feed back the rental plan corresponding to the pattern feature vector.

[0216] To sum up, after receiving the rental request sent by the customer online, the corresponding rental plan is screened and feedback is provided based on the historical usage of the charging station. The customer only needs to operate online and does not need tedious offline operations, which improves the efficiency of the rental process and thus enhances the customer experience.

[0217] Preferably, combined Figure 10 In addition to the content shown, the lease also includes: an acquisition unit, a calculation unit, an audit unit and a storage unit.

[0218] The acquisition unit is used to obtain the external financing amount of the project information.

[0219] The calculation unit is used to calculate the difference between the budget detail data and the external financing amount, obtain the target financing amount, and feedback the target financing amount to the user.

[0220] The review unit is used to review the target financing amount and project information after receiving a financing request carrying the target financing amount, and obtain a review result.

[0221] The storage unit is used to generate electronic archive files based on project information and audit results, and store the electronic archive files in a database.

[0222] Preferably, combined Figure 10 As shown in the content, the lease also includes: an information acquisition unit, a difference calculation unit and an optimization unit.

[0223] The information acquisition unit is used to obtain real-time construction data of the charging station; the real-time construction data includes at least key data, image data and video data; the key data includes at least construction progress, equipment status and environmental conditions.

[0224] The difference calculation unit is used to calculate the difference between the real-time construction data and the preset construction data in the charging station construction information to obtain deviation information.

[0225] The optimization unit is used to optimize the preset construction data in the charging station construction information according to the deviation information, obtain the optimized charging station construction information, and feed back the optimized charging station construction information.

[0226] Preferably, combined Figure 10 As shown in the content, the lease further includes: an information generating unit, a first input unit, a second input unit, an information optimizing unit, a comparing unit and an information storing unit.

[0227] The information generating unit is used to generate construction progress information based on the image data and the video data.

[0228] The first input unit is used to input the construction progress information into the audit model to obtain the audit results; the audit model is pre-built based on the machine learning algorithm.

[0229] The second input unit is used to input the audit results into the analysis model to obtain the analysis results; the analysis model is a deep learning model.

[0230] The information optimization unit is used to optimize the construction progress information according to the analysis results to obtain the optimized construction progress information.

[0231] The comparison unit is used to compare the construction progress information with the optimized construction progress information to obtain a comparison result.

[0232] The information storage unit is used to store the comparison results, construction progress information and optimized construction progress information in a database.

[0233] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein 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 they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Ordinary technicians in this field can understand and implement it without expending creative work.

[0234] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0235] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for leasing an electric vehicle charging station, characterized in that: include: When a charging station construction request is received, project information is obtained from the charging station construction request and converted into vector data; the project information includes at least charging station site selection information data, equipment list data, construction plan data, budget details data and expected revenue data; Generate feasibility evaluation rules based on the vector data and a pre-constructed final charging rental process; the final charging rental process is obtained by optimizing the charging rental process in advance; generating charging station construction information based on the feasibility assessment rule, and feeding back the charging station construction information so as to construct a charging station according to the charging station construction information; When the charging station is built and a charging station rental request is received, the historical usage data and current usage demand of the charging station are obtained; Based on the historical usage data and the current usage demand, a mode feature vector is generated; the mode feature vector indicates a feature vector of a usage mode of the charging station; A recommendation algorithm is used to filter out a leasing plan corresponding to the pattern feature vector from all leasing plans, and the leasing plan corresponding to the pattern feature vector is fed back.

2. The method according to claim 1, characterized in that After receiving the charging station construction request, obtaining project information from the charging station construction request and converting the project information into vector data, the method further includes: The amount of external financing for obtaining the project information; Calculate the difference between the budget detail data and the external financing amount to obtain a target financing amount, and feed back the target financing amount to the user; After receiving the financing request carrying the target financing amount, reviewing the target financing amount and the project information to obtain a review result; An electronic archive file is generated based on the project information and the audit result, and the electronic archive file is stored in a database.

3. The method according to claim 1, characterized in that The process of optimizing the charging rental process in advance to obtain the final charging rental process includes: Obtaining a charging station leasing process, execution rules of each task node in the charging station leasing process, and real-time data; the real-time data includes at least customer behavior data, external environment data, device status data, and event data; According to the execution rules of each of the task nodes, the overall structure matrix of the charging station leasing process is adjusted to obtain an adjusted charging station leasing process; Optimizing the adjusted charging station leasing process according to the real-time data to obtain an optimized charging station leasing process; The optimized charging station leasing process is optimized using an optimization function to obtain a final charging station leasing process.

4. The method according to claim 1, characterized in that: Also includes: Acquire real-time construction data of the charging station; the real-time construction data includes at least key data, image data and video data; the key data includes at least construction progress, equipment status and environmental conditions; Calculating the difference between the real-time construction data and the preset construction data in the charging station construction information to obtain deviation information; The preset construction data in the charging station construction information is optimized according to the deviation information to obtain optimized charging station construction information, and the optimized charging station construction information is fed back.

5. The method according to claim 4, characterized in that Also includes: generating construction progress information based on the image data and the video data; Inputting the construction progress information into the audit model to obtain the audit result; The audit model is pre-built based on a machine learning algorithm; Inputting the audit results into the analysis model to obtain analysis results; The analysis model is a deep learning model; Optimizing the construction progress information according to the analysis result to obtain optimized construction progress information; Comparing the construction progress information with the optimized construction progress information to obtain a comparison result; The comparison result, the construction progress information and the optimized construction progress information are stored in a database.

6. The method according to claim 1, characterized in that The generating charging station construction information based on the feasibility assessment rule includes: The feasibility assessment rules are converted using augmented reality technology to obtain a virtual charging station model; Acquire and use real-time environmental data and the virtual charging station model to adjust the final charging station leasing process to obtain a final planning model; the real-time environmental data at least includes real-time traffic data and real-time power demand data; The final planning model and the feasibility assessment rules are integrated to obtain charging station construction information.

7. A rental device for an electric vehicle charging station, characterized in that: include: A conversion unit, configured to, upon receiving a charging station construction request, obtain project information from the charging station construction request and convert the project information into vector data; the project information at least includes charging station site selection information data, equipment list data, construction plan data, budget detail data and expected revenue data; A first generating unit is used to generate a feasibility evaluation rule based on the vector data and a pre-constructed final charging rental process; the final charging rental process is obtained by optimizing the charging rental process in advance; a second generating unit, configured to generate charging station construction information based on the feasibility evaluation rule, and feed back the charging station construction information, so as to construct a charging station according to the charging station construction information; An acquisition unit, used to acquire historical usage data and current usage demand of the charging station after the construction of the charging station is completed and a charging station rental request is received; The third generating unit is used to generate a mode feature vector based on the historical usage data and the current usage demand; Mode feature vector: a feature vector indicating a usage mode of the charging station; The screening unit is used to screen out the rental scheme corresponding to the pattern feature vector from all the rental schemes by using a recommendation algorithm, and to feed back the rental scheme corresponding to the pattern feature vector.

8. The device according to claim 7, characterized in that Also includes: An acquisition unit, used for acquiring the external financing amount of the project information; A calculation unit, used to calculate the difference between the budget detail data and the external financing amount, obtain a target financing amount, and feed back the target financing amount to the user; An audit unit, configured to audit the target financing amount and the project information after receiving the financing request carrying the target financing amount, and obtain an audit result; The storage unit is used to generate an electronic archive file based on the project information and the audit result, and store the electronic archive file in a database.

9. The device according to claim 7, characterized in that The first generating unit is specifically used for: Obtaining a charging station leasing process, execution rules of each task node in the charging station leasing process, and real-time data; the real-time data includes at least customer behavior data, external environment data, device status data, and event data; According to the execution rules of each of the task nodes, the overall structure matrix of the charging station leasing process is adjusted to obtain an adjusted charging station leasing process; Optimizing the adjusted charging station leasing process according to the real-time data to obtain an optimized charging station leasing process; The optimized charging station leasing process is optimized using an optimization function to obtain a final charging station leasing process.

10. The device according to claim 7, characterized in that Also includes: An information acquisition unit, used to acquire real-time construction data of the charging station; the real-time construction data includes at least key data, image data and video data; the key data includes at least construction progress, equipment status and environmental conditions; a difference calculation unit, used to calculate the difference between the real-time construction data and the preset construction data in the charging station construction information to obtain deviation information; The optimization unit is used to optimize the preset construction data in the charging station construction information according to the deviation information, obtain the optimized charging station construction information, and feed back the optimized charging station construction information.

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