An intelligent generation method and system for vehicle usage regulations in enterprise vehicle usage scenarios

Abstract

The present invention discloses a method and system for intelligently generating a car use system in an enterprise car use scenario, wherein the method comprises: collecting enterprise car use orders of enterprise accounts, obtaining historical car use data of registered employees under the enterprise accounts; based on the historical car use data, respectively creating user profiles for the registered employees under the enterprise accounts; integrating the user profiles, extracting common features in the user profiles, and forming a common feature set; based on the common feature set and a car use system generation strategy, generating an overall enterprise car use system and a customized car use system for each registered employee; intelligently optimizing the car use system, continuously learning based on new car use data, and continuously optimizing the car use system. The present invention creates user profiles for the historical car use data of registered employees under the enterprise accounts, extracts common points, and automatically generates and optimizes a customized car use system, so that the enterprise car use system is more in line with the needs of the enterprise and employees.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle usage system management for corporate vehicles, and in particular to a method and system for intelligently generating a vehicle usage system in a corporate vehicle usage scenario. Background Art

[0002] Online ride-hailing, short for online taxi booking services, is a mode of transportation that has emerged thanks to the development and reach of internet technology. Typically, individual passengers make a reservation through an online ride-hailing platform, generating an online ride order and being assigned a pickup vehicle. The pickup vehicle then picks up the passenger and takes them to their destination. However, for some companies with extensive travel needs, such as those for overtime work, business trips, conferences, business visits, and other reimbursable vehicle usage, using the traditional online ride-hailing model significantly increases the number of employee advance payments, ticketing, and reimbursement processes, making the corporate vehicle usage process more complex.

[0003] To this end, dedicated accounts or applications specifically for corporate car use have appeared on the market, so that employees who use corporate cars do not need to advance payment, post tickets or reimbursements, making corporate car use easier, the overall process that passengers need to operate is simpler, and corporate employees have a better experience when using cars for company-related purposes.

[0004] However, in the existing technology, the application programs or dedicated accounts for corporate car use generally use preset standard templates for their car use systems. Registered employees must select and fill in the necessary information each time they use the corporate car service. Different corporate accounts and different registered employees all use the same car use system template, and there are unreasonable restrictions, which makes the corporate car use experience poor. Summary of the Invention

[0005] In order to overcome the above-mentioned technical defects of the existing poor enterprise car use experience, the present invention provides a method and system for intelligently generating a car use system in an enterprise car use scenario that satisfies the enterprise's diversified and customized car use scenarios through automatic optimization.

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

[0007] In a first aspect, the present invention discloses a method for intelligently generating a vehicle usage system in an enterprise vehicle usage scenario, specifically comprising the following steps:

[0008] Collect corporate car orders from corporate accounts and obtain historical car usage data of registered employees under corporate accounts;

[0009] Create user profiles for registered employees under the corporate account based on historical vehicle usage data;

[0010] Integrate user portraits, extract common features from user portraits, and form a common feature set;

[0011] Generate the overall enterprise car use policy and customized car use policy for each registered employee based on the common feature set and car use policy generation strategy;

[0012] Intelligently optimize the car usage system, constantly learn based on new car usage data, and continuously optimize the car usage system.

[0013] As a preferred embodiment of the present invention, the user profiling of registered employees under the enterprise account specifically includes:

[0014] Get the registered mobile phone numbers of the registered employees of the enterprise account respectively;

[0015] Obtain the historical car usage data of registered employees based on their registered mobile phone numbers;

[0016] Extract feature data from historical vehicle usage data, associate the feature data with registered mobile phone numbers, and store them in a feature database;

[0017] Convert feature data into computer language, define labels and build model features, and establish user classification models and user behavior models;

[0018] Based on the user classification model and user behavior model, and in accordance with data mapping rules and user portrait rules, user portrait models corresponding to registered employees are constructed to complete the user portraits of registered employees.

[0019] As a preferred embodiment of the present invention,

[0020] The characteristic data in the vehicle usage data include the starting point, the end point, the vehicle usage city, the order type, the vehicle usage time, the order amount, the fee details, the driver information, the payment method, the passengers, the vehicle usage scenario and the vehicle model.

[0021] As a preferred embodiment of the present invention, the integration of user portraits, extraction of common features in the user portraits, and formation of a common feature set specifically include:

[0022] Obtain user portraits of registered employees separately and integrate them to obtain user portrait integration data;

[0023] Based on user portrait integration data, feature extraction is performed to obtain relevant behavioral feature data of common corporate behaviors and common employee historical behaviors;

[0024] Establish behavioral labels based on the frequency of common corporate behaviors and employee historical behaviors, and assign weights to different behavioral labels.

[0025] The common points are calculated according to the weight value and the frequency of occurrence, and sorted based on the score to form a common feature set.

[0026] As a preferred embodiment of the present invention, the generation of the enterprise's overall car use system and the customized car use system for each registered employee based on the common feature set and the car use system generation strategy specifically includes:

[0027] Start the car use system decision engine and input the common feature set into the car use system decision engine;

[0028] The vehicle utilization decision engine analyzes the common feature set and matches the analyzed common feature set with the company's overall vehicle utilization strategy.

[0029] The car use policy decision engine matches common feature sets with employees' personal car use strategies;

[0030] Based on the hit results of scenario matching, the enterprise's overall car use system and the registered employees' personal car use system are generated respectively.

[0031] As a preferred embodiment of the present invention, the intelligent optimization vehicle utilization system continuously learns and optimizes the vehicle utilization system based on new vehicle utilization data, specifically including:

[0032] The overall enterprise vehicle use system and the registered employee personal vehicle use system are transmitted to the intelligent learning module respectively, and the user portrait is transmitted to the intelligent learning module. The intelligent learning module integrates the historical vehicle use system and the final vehicle use selection in the database, and conducts learning, optimization and automatic correction to continuously optimize the vehicle use system. The overall enterprise vehicle use system is updated to the enterprise account. When a registered employee uses the enterprise vehicle, a query is performed based on the registered mobile phone number, and the corresponding customized vehicle use system is recommended to the registered employee.

[0033] In a second aspect, the present invention further discloses an intelligent generation system for a vehicle use policy in an enterprise vehicle use scenario, comprising a data collection module, a user profiling module, a feature extraction module, a policy generation module, and an intelligent optimization module, wherein:

[0034] The data collection module is used to collect corporate car orders from corporate accounts and obtain historical car usage data of registered employees under the corporate accounts;

[0035] The user profiling module is used to create user profiles for registered employees under the enterprise account based on historical vehicle usage data;

[0036] The feature extraction module is used to integrate user profiles, extract common features in user profiles, and form a common feature set;

[0037] The system generation module is used to generate the overall enterprise car use system and customized car use systems for each registered employee based on the common feature set and car use system generation strategy;

[0038] The intelligent optimization module is used to intelligently optimize the vehicle usage system, continuously learn based on new vehicle usage data, and continuously optimize the vehicle usage system.

[0039] As a preferred embodiment of the present invention, when the user portrait module is running, it specifically performs the following steps:

[0040] Get the registered mobile phone numbers of the registered employees of the enterprise account respectively;

[0041] Obtain the historical car usage data of registered employees based on their registered mobile phone numbers;

[0042] Extract feature data from historical vehicle usage data, associate the feature data with registered mobile phone numbers, and store them in a feature database;

[0043] Convert feature data into computer language, define labels and build model features, and establish user classification models and user behavior models;

[0044] Based on the user classification model and user behavior model, and in accordance with the data mapping rules and user portrait rules, user portrait models corresponding to registered employees are constructed to complete the user portraits of registered employees;

[0045] Among them, the characteristic data in the vehicle usage data include the starting point, the end point, the vehicle usage city, the order type, the vehicle usage time, the order amount, the fee details, the driver information, the payment method, the passengers, the vehicle usage scenario and the vehicle model.

[0046] As a preferred embodiment of the present invention, when the feature extraction module is running, it specifically performs the following steps:

[0047] Obtain user portraits of registered employees separately and integrate them to obtain user portrait integration data;

[0048] Based on user portrait integration data, feature extraction is performed to obtain relevant behavioral feature data of common corporate behaviors and common employee historical behaviors;

[0049] Establish behavioral labels based on the frequency of common corporate behaviors and employee historical behaviors, and assign weights to different behavioral labels.

[0050] The common points are calculated according to the weight value and the frequency of occurrence, and sorted based on the score to form a common feature set.

[0051] As a preferred embodiment of the present invention, the system generation module and the intelligent optimization module, when running, specifically perform the following:

[0052] The car use policy decision engine is started and the common feature set is input into the car use policy decision engine. The car use policy decision engine analyzes the common feature set and matches the analyzed common feature set with the overall enterprise car use strategy. The car use policy decision engine matches the common feature set with the individual employee car use strategies. Based on the matching results, the overall enterprise car use policy and the registered employee individual car use policy are generated.

[0053] The overall enterprise vehicle use system and the registered employee personal vehicle use system are transmitted to the intelligent learning module respectively, and the user portrait is transmitted to the intelligent learning module. The intelligent learning module integrates the historical vehicle use system and the final vehicle use selection in the database, and conducts learning, optimization and automatic correction to continuously optimize the vehicle use system. The overall enterprise vehicle use system is updated to the enterprise account. When a registered employee uses the enterprise vehicle, a query is performed based on the registered mobile phone number, and the corresponding customized vehicle use system is recommended to the registered employee.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] The present invention provides a method and system for intelligently generating a car use system in an enterprise car use scenario. The method creatively creates user profiles based on the historical car use data of enterprise employees, extracts common features, and forms a common feature set. Based on the common feature set, a customized enterprise car use system that meets the car use needs of enterprise employees is intelligently generated. The car use system is optimized according to the employee car use profiles to meet the diversified and customized car use scenario needs of the enterprise, so that registered employees of the enterprise account can obtain a car use system that is closer to the registered employee when requesting enterprise car use. The intelligently generated enterprise car use system can restrict and manage enterprise car use accordingly based on the characteristics of the enterprise account itself, and can effectively improve the convenience and user experience of enterprise car use. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings, wherein:

[0057] Figure 1 This is a flow chart of the method for intelligently generating a vehicle usage system in an enterprise vehicle usage scenario according to the present invention;

[0058] Figure 2 This is a schematic diagram of the structure of the intelligent generation system of the vehicle use system in the enterprise vehicle use scenario of the present invention;

[0059] The specific contents in the drawings are explained in detail in the following specific embodiments. DETAILED DESCRIPTION

[0060] The preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although preferred embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0061] As used herein, the term "including" and its variations represent open inclusion, i.e., "including but not limited to." Unless otherwise stated, the term "or" means "and / or." The term "based on" means "based at least in part on." The terms "an example embodiment" and "an embodiment" mean "at least one example embodiment." The term "another embodiment" means "at least one additional embodiment." The terms "first," "second," etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0062] The access device and server can be connected directly or indirectly via wired or wireless communication. The access device can be a terminal or a server. The target application is running on the access device. The target application is an application program capable of initiating data requests to the server, such as a social networking application, a payment application, or a gaming application. The server can be an application server providing services for the target application, or a proxy server distinct from the application server corresponding to the target application. The server is responsible for identifying whether each access device is a malicious device and intercepting data packets from malicious devices. When the server is a proxy server, the proxy server forwards data packets that are not from malicious devices to the application server. The terminal can be a desktop terminal or a mobile terminal. Mobile terminals can be, but are not limited to, smartphones, tablets, laptops, desktop computers, smart speakers, smart watches, etc. The server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

[0063] Example 1

[0064] like Figure 1 As shown, in a first aspect, the present invention discloses a method for intelligently generating a vehicle use system in an enterprise vehicle use scenario, specifically comprising the following steps:

[0065] Step S1: Collect corporate car orders from corporate accounts and obtain historical car usage data of registered employees under the corporate accounts.

[0066] Specifically, when a company account is registered, it is recorded in the server's database. The registered employees under the company account are then registered in sequence. Once completed, the registered employees' historical corporate car usage orders are stored in the database. Upon receiving instructions to optimize the car usage system, the server collects the company account's corporate car usage orders from the database, specifically by obtaining the historical car usage data for each registered employee.

[0067] Step S2: Based on historical vehicle usage data, create user profiles for each registered employee under the enterprise account.

[0068] Specifically, it includes: obtaining the registered mobile phone numbers of the registered employees of the enterprise account respectively; obtaining the historical vehicle usage data of the registered employees based on the registered mobile phone numbers; extracting feature data from the historical vehicle usage data, associating the feature data with the registered mobile phone numbers and storing them in the feature database; converting the feature data into computer language, and performing label definition and model feature construction to establish a user classification model and a user behavior model; based on the user classification model and the user behavior model, according to the data mapping rules and user portrait rules, respectively constructing the user portrait models corresponding to the registered employees to complete the user portraits of the registered employees.

[0069] The characteristic data in the vehicle usage data include the starting point, the end point, the vehicle usage city, the order type, the vehicle usage time, the order amount, the fee details, the driver information, the payment method, the passengers, the vehicle usage scenario and the vehicle model.

[0070] Through historical vehicle usage data, we can obtain the specific characteristics of registered employees in their previous corporate vehicle usage, in order to infer the registered employees' corporate vehicle usage habits and the main purposes of corporate vehicle use, so as to create an accurate user profile.

[0071] Step S3: Integrate the user portraits, extract common features from the user portraits, and form a common feature set.

[0072] Specifically, this process involves obtaining user profiles of registered employees and integrating them to generate aggregated user profile data; extracting features based on the aggregated user profile data to obtain behavioral feature data related to common corporate and employee historical behaviors; establishing behavioral labels based on the frequency of occurrence of common corporate and employee historical behaviors, assigning weights to different behavioral labels; calculating commonalities based on weights and frequency of occurrence, and sorting them based on scores to form a common feature set. This integrated and sorted common feature set enables better matching and computation, accelerating overall efficiency.

[0073] Step S4: Based on the common feature set and the car use system generation strategy, generate the overall car use system of the enterprise and the customized car use system for each registered employee.

[0074] Specifically, it includes: starting the car use system decision engine and inputting the common feature set into the car use system decision engine; the car use system decision engine parses the common feature set and matches the parsed common feature set with the company's overall car use strategy; the car use system decision engine matches the common feature set with the employees' personal car use strategy; based on the hit results of the scenario matching, the company's overall car use system and the registered employees' personal car use system are generated respectively.

[0075] The present invention analyzes the common feature set and calculates based on the weight and frequency of occurrence of the data to obtain the overall enterprise car use system and the registered employee personal car use system. The overall enterprise car use system is derived from the common features of all registered employees under the enterprise account, which can basically reflect the overall commuting time period, main travel needs and travel time period of the enterprise, and serves as an overall framework for overall optimization and restriction of enterprise car services. The registered employee personal car use system is closely related to the position and work content of the registered employee. For example, registered employees who need to travel frequently, work overtime, pick up and drop off customers, attend meetings, etc. can be intuitively known through user portraits, and the frequency of travel plans is inferred through the common feature set to obtain the corresponding registered employee personal car use system for each registered employee, and make corresponding recommendations when the registered employee uses the enterprise car.

[0076] Step S5: Intelligently optimize the vehicle usage system, continuously learn based on new vehicle usage data, and continuously optimize the vehicle usage system.

[0077] Specifically, the system transfers the company's overall vehicle usage policy and registered employee's individual vehicle usage policy to the intelligent learning module, as well as user profiles. The intelligent learning module then integrates historical vehicle usage policies and final vehicle selections in the database to learn, optimize, and automatically correct them. This allows for continuous optimization of the vehicle usage policy, updates the company's overall vehicle usage policy to the company account, and queries registered employees based on their registered mobile phone numbers when they use the company's vehicle. The module then recommends a customized vehicle usage policy to each registered employee. Through automated intelligent learning, optimization, and correction, the vehicle usage policy becomes more aligned with big data and user profiles, resulting in more accurate recommendations and a better user experience.

[0078] To sum up, the intelligent generation method of a car use system in an enterprise car use scenario described in an embodiment of the present invention creatively creates user profiles through the historical car use data of enterprise employees, and extracts common features to form a common feature set. Based on the common feature set, a customized enterprise car use system that meets the car use needs of enterprise employees is intelligently generated. The car use system is optimized according to the employee car use profile to meet the diversified and customized car use scenario needs of the enterprise, so that registered employees of the enterprise account can obtain a car use system that is closer to the registered employees when requesting enterprise cars. The intelligently generated enterprise car use system can restrict and manage the corresponding enterprise cars based on the characteristics of the enterprise account itself, which can effectively improve the convenience and usage experience of enterprise cars.

[0079] For other steps of the intelligent generation method of the vehicle usage system in the enterprise vehicle usage scenario described in this embodiment, please refer to the existing technology.

[0080] Example 2

[0081] like Figure 2 As shown, in a second aspect, the present invention further discloses an intelligent generation system for a vehicle use system in an enterprise vehicle use scenario, comprising a data collection module M1, a user portrait module M2, a feature extraction module M3, a system generation module M4 and an intelligent optimization module M5, wherein:

[0082] The data collection module M1 is used to collect corporate car orders from corporate accounts and obtain historical car usage data of registered employees under the corporate accounts;

[0083] The user portrait module M2 is used to create user portraits for registered employees under the enterprise account based on historical vehicle usage data;

[0084] The feature extraction module M3 is used to integrate user portraits, extract common features in user portraits, and form a common feature set;

[0085] The system generation module M4 is used to generate the overall enterprise car use system and the customized car use system for each registered employee based on the common feature set and the car use system generation strategy;

[0086] The intelligent optimization module M5 is used to intelligently optimize the vehicle usage system. It continuously learns and optimizes the vehicle usage system based on new vehicle usage data.

[0087] When the user portrait module M2 is running, it specifically performs the following operations:

[0088] Obtain the registered mobile phone numbers of the registered employees of the enterprise account respectively; obtain the historical vehicle usage data of the registered employees based on the registered mobile phone numbers; extract feature data from the historical vehicle usage data, associate the feature data with the registered mobile phone numbers respectively, and store them in the feature database; convert the feature data into computer language, and perform label definition and model feature construction to establish a user classification model and a user behavior model; based on the user classification model and the user behavior model, according to data mapping rules and user portrait rules, respectively construct user portrait models corresponding to the registered employees to complete the user portraits of the registered employees; wherein, the feature data in the vehicle usage data includes the starting point, the end point, the vehicle usage city, the order type, the vehicle usage time, the order amount, the fee details, the driver information, the payment method, the passenger, the vehicle usage scenario and the vehicle model.

[0089] When the feature extraction module M3 is running, it specifically performs the following steps:

[0090] Obtain user portraits of registered employees separately and integrate them to obtain user portrait integration data; perform feature extraction based on the user portrait integration data to obtain relevant behavioral feature data of common corporate behavior points and common employee historical behavior points; establish behavioral labels based on the frequency of occurrence of common corporate behavior points and common employee historical behavior points, and set weights for different behavioral labels; calculate the common points separately according to the weight value and frequency of occurrence, and sort them based on the score to form a common feature set.

[0091] When the system generation module M4 and the intelligent optimization module M5 are running, they specifically perform the following operations:

[0092] The car use policy decision engine is started and the common feature set is input into the car use policy decision engine. The car use policy decision engine analyzes the common feature set and matches the analyzed common feature set with the overall enterprise car use strategy. The car use policy decision engine matches the common feature set with the individual employee car use strategies. Based on the matching results, the overall enterprise car use policy and the registered employee individual car use policy are generated.

[0093] The overall enterprise vehicle use system and the registered employee personal vehicle use system are transmitted to the intelligent learning module respectively, and the user portrait is transmitted to the intelligent learning module. The intelligent learning module integrates the historical vehicle use system and the final vehicle use selection in the database, and conducts learning, optimization and automatic correction to continuously optimize the vehicle use system. The overall enterprise vehicle use system is updated to the enterprise account. When a registered employee uses the enterprise vehicle, a query is performed based on the registered mobile phone number, and the corresponding customized vehicle use system is recommended to the registered employee.

[0094] To sum up, when the intelligent generation system of the vehicle usage system in the enterprise vehicle usage scenario described in the embodiment of the present invention is running, it can implement all the steps of the intelligent generation method of the vehicle usage system in the enterprise vehicle usage scenario described in Example 1 and achieve the corresponding technical effects.

[0095] For other structures of the intelligent generation system of the vehicle usage system in the enterprise vehicle usage scenario described in this embodiment, please refer to the existing technology.

[0096] Example 3

[0097] The present invention also discloses an electronic device, at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor. When the at least one processor executes the instructions, the following steps are specifically implemented: collecting corporate car orders from corporate accounts and obtaining historical car usage data of registered employees under the corporate accounts; based on the historical car usage data, creating user profiles for the registered employees under the corporate accounts; integrating the user profiles, extracting common features from the user profiles, and forming a common feature set; based on the common feature set and the car usage system generation strategy, generating an overall corporate car usage system and a customized car usage system for each registered employee; intelligently optimizing the car usage system, continuously learning based on new car usage data, and continuously optimizing the car usage system.

[0098] Example 4

[0099] The present invention also discloses a storage medium storing a computer program. When the computer program is executed by a processor, the following steps are specifically implemented: collecting corporate car use orders of corporate accounts and obtaining historical car use data of registered employees under the corporate accounts; based on the historical car use data, creating user profiles for the registered employees under the corporate accounts; integrating the user profiles, extracting common features from the user profiles, and forming a common feature set; based on the common feature set and a car use system generation strategy, generating an overall corporate car use system and a customized car use system for each registered employee; intelligently optimizing the car use system, continuously learning based on new car use data, and continuously optimizing the car use system.

[0100] The present disclosure may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present disclosure.

[0101] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0102] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0103] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, Java, and conventional procedural programming languages ​​such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., utilizing an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions. The electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0104] Various aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0105] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine such that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0106] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0107] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the above-mentioned module, program segment or instruction includes one or more executable instructions for realizing the prescribed logical function. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a special hardware-based system that performs the prescribed function or action, or can be implemented with a combination of special hardware and computer instructions.

[0108] The embodiments of the present disclosure have been described above. The above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for intelligently generating a vehicle usage system in an enterprise vehicle usage scenario, characterized in that: include: Collect corporate car orders from corporate accounts and obtain historical car usage data of registered employees under corporate accounts; Create user profiles for registered employees under the corporate account based on historical vehicle usage data; Integrate user profiles, extract common features from them, and form a common feature set. This common feature set establishes behavior labels based on the frequency of common points in corporate behavior and common points in employee historical behavior, and assigns weights to different behavior labels. Calculate the common points based on their weights and frequency of occurrence, and sort them based on their scores to form a common feature set. Generate the overall enterprise car use policy and customized car use policy for each registered employee based on the common feature set and car use policy generation strategy; The generation of the overall enterprise car use system and the customized car use system for each registered employee based on the common feature set and the car use system generation strategy specifically includes: Start the car use system decision engine and input the common feature set into the car use system decision engine; The vehicle utilization decision engine analyzes the common feature set and matches the analyzed common feature set with the company's overall vehicle utilization strategy. The car use policy decision engine matches common feature sets with employees' personal car use strategies; Based on the hit results of scenario matching, the overall enterprise car use system and the registered employee personal car use system are generated respectively; Intelligently optimize the car usage system, constantly learn based on new car usage data, and continuously optimize the car usage system.

2. The intelligent generation method of the vehicle use system in the enterprise vehicle use scenario according to claim 1 is characterized in that: The user profiling of registered employees under the enterprise account specifically includes: Get the registered mobile phone numbers of the registered employees of the enterprise account respectively; Obtain the historical car usage data of registered employees based on their registered mobile phone numbers; Extract feature data from historical vehicle usage data, associate the feature data with registered mobile phone numbers, and store them in a feature database; Convert feature data into computer language, define labels and build model features, and establish user classification models and user behavior models; Based on the user classification model and user behavior model, and in accordance with data mapping rules and user portrait rules, user portrait models corresponding to registered employees are constructed to complete the user portraits of registered employees.

3. The intelligent generation method of a vehicle use system in an enterprise vehicle use scenario according to claim 2 is characterized by: The characteristic data in the vehicle usage data include the starting point, the end point, the vehicle usage city, the order type, the vehicle usage time, the order amount, the fee details, the driver information, the payment method, the number of passengers, the vehicle usage scenario and the vehicle model.

4. The intelligent generation method of the vehicle use system in the enterprise vehicle use scenario according to claim 3 is characterized in that: The integration of user portraits, extraction of common features in the user portraits, and formation of a common feature set specifically includes: Obtain user portraits of registered employees separately and integrate them to obtain user portrait integration data; Based on the user portrait integration data, feature extraction is performed to obtain relevant behavioral feature data of common corporate behaviors and common historical behaviors of employees.

5. The intelligent generation method of the vehicle use system in the enterprise vehicle use scenario according to claim 1 is characterized in that: The intelligent optimization vehicle utilization system continuously learns and optimizes the vehicle utilization system based on new vehicle utilization data, specifically including: The overall enterprise vehicle use system and the registered employee personal vehicle use system are transmitted to the intelligent learning module respectively, and the user portrait is transmitted to the intelligent learning module. The intelligent learning module integrates the historical vehicle use system and the final vehicle use selection in the database, and conducts learning, optimization and automatic correction to continuously optimize the vehicle use system. The overall enterprise vehicle use system is updated to the enterprise account. When a registered employee uses the enterprise vehicle, a query is performed based on the registered mobile phone number, and the corresponding customized vehicle use system is recommended to the registered employee.

6. An intelligent generation system for vehicle use regulations in enterprise vehicle use scenarios, characterized by: include: The data collection module is used to collect corporate car orders from corporate accounts and obtain historical car usage data of registered employees under the corporate accounts; The user profiling module is used to create user profiles for registered employees under the enterprise account based on historical vehicle usage data; A feature extraction module is used to integrate user profiles, extract common features from the user profiles, and form a common feature set. The common feature set establishes behavior labels based on the frequency of common points in corporate behavior and common points in employee historical behavior, and assigns weights to different behavior labels. The common points are calculated based on the weight value and frequency of occurrence, and are sorted based on the scores to form a common feature set. The system generation module is used to generate the overall enterprise car use system and customized car use systems for each registered employee based on the common feature set and car use system generation strategy; The system generation module, when running, specifically performs: The car use policy decision engine is started and the common feature set is input into the car use policy decision engine. The car use policy decision engine analyzes the common feature set and matches the analyzed common feature set with the overall enterprise car use strategy. The car use policy decision engine matches the common feature set with the individual employee car use strategies. Based on the matching results, the overall enterprise car use policy and the registered employee individual car use policy are generated. The intelligent optimization module is used to intelligently optimize the vehicle usage system, continuously learn based on new vehicle usage data, and continuously optimize the vehicle usage system.

7. The intelligent generation system of the vehicle use system in the enterprise vehicle use scenario according to claim 6 is characterized in that: When the user portrait module is running, it specifically performs the following: Get the registered mobile phone numbers of the registered employees of the enterprise account respectively; Obtain the historical car usage data of registered employees based on their registered mobile phone numbers; Extract feature data from historical vehicle usage data, associate the feature data with registered mobile phone numbers, and store them in a feature database; Convert feature data into computer language, define labels and build model features, and establish user classification models and user behavior models; Based on the user classification model and user behavior model, and in accordance with the data mapping rules and user portrait rules, user portrait models corresponding to registered employees are constructed to complete the user portraits of registered employees; Among them, the characteristic data in the vehicle usage data include the starting point, the end point, the vehicle usage city, the order type, the vehicle usage time, the order amount, the fee details, the driver information, the payment method, the number of passengers, the vehicle usage scenario and the vehicle model.

8. The intelligent generation system of the vehicle use system in the enterprise vehicle use scenario according to claim 7 is characterized in that: When the feature extraction module is running, it specifically performs: Obtain user portraits of registered employees separately and integrate them to obtain user portrait integration data; Based on the user portrait integration data, feature extraction is performed to obtain relevant behavioral feature data of common corporate behaviors and common historical behaviors of employees.

9. The intelligent generation system of the vehicle use system in the enterprise vehicle use scenario according to claim 8 is characterized in that: When the intelligent optimization module is running, it specifically performs the following operations: The overall enterprise vehicle use system and the registered employee personal vehicle use system are transmitted to the intelligent learning module respectively, and the user portrait is transmitted to the intelligent learning module. The intelligent learning module integrates the historical vehicle use system and the final vehicle use selection in the database, and conducts learning, optimization and automatic correction to continuously optimize the vehicle use system. The overall enterprise vehicle use system is updated to the enterprise account. When a registered employee uses the enterprise vehicle, a query is performed based on the registered mobile phone number, and the corresponding customized vehicle use system is recommended to the registered employee.

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