A wisdom vehicle comprehensive management system based on an instant communication platform and a mobile terminal webpage

Abstract

This invention provides a smart vehicle management system based on an instant messaging platform and mobile web page, comprising a management terminal, a first mobile interface, a second mobile interface, a server, and a database layer. The management terminal generates vehicle management configuration parameters and report query conditions; the first mobile interface receives user vehicle requests and generates vehicle application data containing coordinate sequences; the second mobile interface obtains user identity credentials through authorization via the instant messaging platform and generates a binding authentication token; the server includes a PC backend service, a mobile service, and an instant messaging binding service. The mobile service generates an approval task flow based on a multi-level approval state machine model, receives real-time positioning data from a satellite positioning platform, and matches it with the coordinate sequences to generate a real-time travel trajectory; the database layer includes a first business database storing business data and a second business database that interfaces with the satellite positioning platform. This invention can improve enterprise vehicle dispatch efficiency, approval transparency, and the accuracy of vehicle operation supervision.

Description

Technical Field

[0001] This invention relates to the field of vehicle management information technology, and more specifically, to a smart vehicle management system based on an instant messaging platform and a mobile web page. Background Technology

[0002] As businesses expand and the demand for official vehicles grows, traditional vehicle management models are no longer sufficient to meet the requirements of efficient and transparent management. Currently, most companies still rely on manual dispatching, paper-based approvals, and offline communication for vehicle management, resulting in lengthy approval processes, delayed information transmission, and difficulty in real-time monitoring of vehicle usage status. In recent years, some companies have begun to introduce information management systems, enabling vehicle application and approval through PC web pages or standalone mobile applications, which has improved management efficiency to some extent. Meanwhile, the widespread adoption of satellite positioning technology has made real-time vehicle location tracking possible, and some systems are attempting to link location data with vehicle usage records to monitor vehicle trajectories. Furthermore, instant messaging platforms (such as WeChat Work and DingTalk) are widely used for internal collaboration and message notifications and identity authentication, and some vehicle management systems are exploring integration with instant messaging platforms to automate approval reminders and identity binding.

[0003] However, in implementing the embodiments of the present invention, the prior art has at least the following problems or defects: Existing systems usually solidify the approval process, making it difficult to dynamically adjust the approval nodes and approval time limits according to the application type and department level, resulting in delays in emergency vehicle approval; trajectory matching mostly uses simple coordinate comparison without combining spherical distance algorithms and statistical difference analysis, resulting in insufficient matching accuracy; fuel consumption monitoring lacks a theoretical model that comprehensively considers factors such as road congestion and altitude changes, resulting in low accuracy in anomaly identification; system architecture is mostly designed with a single data source, with high coupling between real-time positioning data and business data, limiting scalability and stability; the binding mechanism of instant messaging platforms lacks security, and identity identifiers are easily forged or tampered with; the abnormal warning of vehicles deviating from the planned route does not make full use of road topology and hidden Markov models for accurate matching, resulting in a high false alarm rate; trip supplementation lacks a refined verification mechanism based on speed function integrals and fuel consumption characteristic coefficients, making it difficult to effectively identify cost anomalies; the data display dimensions on the management end are single, lacking a comprehensive efficiency evaluation system that integrates multiple indicators. Summary of the Invention

[0004] This invention provides a smart vehicle management system based on an instant messaging platform and a mobile web page, comprising: The management terminal is used to receive operation instructions from the administrator, generate vehicle management configuration parameters and report query conditions, and send them to the server. The first mobile interface is used to receive the user's car rental request, generate car rental application data containing a coordinate sequence based on the departure point, destination and multiple waypoints in the car rental request, and send it to the server. The second mobile interface is used to obtain the user's identity identifier through the authorization mechanism of the first instant messaging platform, generate a binding authentication token based on the user's identity identifier, and send it to the server. The server-side includes a PC backend service, a mobile terminal service, and an instant messaging binding service. The PC backend service receives the vehicle management configuration parameters and the report query conditions. The mobile terminal service receives the vehicle use application data. The instant messaging binding service receives the binding authentication token. The mobile terminal service generates an approval task flow based on a preset multi-level approval state machine model and sends the approval status data to the first mobile terminal interface or to the second mobile terminal interface through the instant messaging binding service. The mobile terminal service also receives real-time positioning data sequences from a satellite positioning platform through a secondary data source in the dual data source architecture, and performs matching calculations based on the real-time positioning data sequences and the coordinate sequences in the vehicle use application data to generate real-time travel trajectory data. The database layer includes a first business database and a second business database. The first business database is used to store the vehicle management configuration parameters and the vehicle application data. The second business database is used to interface with and obtain the real-time positioning data sequence of the satellite positioning platform.

[0005] The embodiments of the present invention have at least the following beneficial effects: 1. This invention receives vehicle application data containing the origin, destination, and multiple transit points on a first mobile terminal interface, and deploys a mobile terminal service based on a multi-level approval state machine model on the server side. It can dynamically generate an approval node sequence based on the type identifier of the vehicle application data and the department level identifier of the submitting user. At the same time, it calculates the approval time limit value of each approval node by combining the basic approval time limit constant, node complexity level, and priority weight, realizing flexible configuration and dynamic control of the approval process. It effectively solves the problems of rigid approval process and delayed response to emergency vehicle use in the prior art. Furthermore, it pushes the approval status data and overdue reminder messages to the second mobile terminal interface in real time through instant messaging binding service, improving approval efficiency and timeliness of information delivery.

[0006] 2. This invention adopts a dual-data source architecture. It independently receives real-time positioning data sequences from a satellite positioning platform through a secondary data source. The trip trajectory matching module in the server calculates the distance difference between each adjacent point in the coordinate sequence and the real-time positioning data sequence based on the spherical distance algorithm. The matching degree is determined by the mean squared difference. At the same time, the anomaly warning unit matches the real-time positioning point with candidate road nodes in the road topology based on the hidden Markov model. The Viterbi algorithm is used to solve the optimal hidden state sequence to determine the actual road segment where the vehicle is located. This achieves high-precision matching of the trip trajectory and accurate identification of vehicle position anomalies, overcoming the shortcomings of insufficient trajectory matching accuracy and high false alarm rate of route deviation in the prior art, and ensuring the consistency of vehicle use application and actual trip monitoring.

[0007] 3. This invention utilizes a fuel consumption anomaly warning module in the server to comprehensively acquire multiple parameters such as total trip mileage, congestion duration and average speed of each road segment, and altitude difference between the start and end points of the trip. Based on multiple linear regression coefficients, a weighted summation is performed to obtain a theoretical fuel consumption estimate, which is then compared with the actual fuel consumption value to generate a warning message. Simultaneously, during the supplementary data entry application stage, an instantaneous speed function is fitted based on the real-time speed sequence, and an integral calculation is performed using the fuel consumption characteristic coefficients preset for the vehicle model to obtain a fuel consumption estimate. The fuel consumption deviation rate is calculated to mark abnormal trips, forming a full-cycle fuel consumption management system covering real-time monitoring during the trip and post-trip supplementary data entry verification. This solves the problems of single fuel consumption monitoring models and low accuracy in anomaly identification in existing technologies, improving the refined control capability of vehicle operating costs. Attached Figure Description

[0008] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated in the drawings by way of example and not limitation, wherein: Figure 1 This is a schematic diagram of the structure of a smart vehicle management system based on an instant messaging platform and a mobile webpage, provided in an embodiment of the present invention. Detailed Implementation

[0009] The principles and spirit of the invention will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided merely to enable those skilled in the art to better understand and implement the invention, and are not intended to limit the scope of the invention in any way. Rather, these embodiments are provided to make the invention more thorough and complete, and to fully convey the scope of the invention to those skilled in the art.

[0010] Those skilled in the art will recognize that embodiments of the present invention can be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present invention can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0011] It should be noted that the number of any elements in the accompanying drawings is for illustrative purposes only and not as a limitation, and any naming is for distinction only and has no limiting meaning.

[0012] The following is for reference. Figure 1 , Figure 1 This is a schematic diagram of the structure of a smart vehicle management system based on an instant messaging platform and a mobile webpage, provided in an embodiment of the present invention.

[0013] like Figure 1 As shown, the management interface is deployed on a personal computer, developed using Angular 15 and the Ng-Zorro-Antd component library, and is used by vehicle management administrators. Administrators input operation commands into the management interface using a mouse and keyboard, such as adding vehicle information, configuring approval processes, and querying statistical reports. The management interface generates vehicle management configuration parameters or report query conditions based on these commands, and then sends this data to the PC backend service on the server side via an HTTP POST request. Vehicle management configuration parameters include vehicle brand, model, license plate number, passenger capacity, insurance information, maintenance mileage threshold, etc.; report query conditions include time range, department identifier, vehicle type, etc.

[0014] The first mobile interface is an H5 webpage running in the mobile browser of the driver or ride requester, developed based on the Ionic 7 and Angular 17 frameworks. On this interface, the user fills in a ride request, including the departure point name, destination name, multiple optional waypoint names, number of passengers, and ride notes. The first mobile interface calls the reverse geocoding function of the map application programming interface to convert the departure point name, destination name, and each waypoint name into corresponding latitude and longitude coordinates, thereby generating ride request data containing a sequence of departure point coordinates, destination coordinates, and waypoint coordinates. This coordinate sequence is arranged in driving order; for example, first the departure point coordinates, then the coordinates of waypoint 1, waypoint 2, etc., and finally the destination coordinates. The first mobile interface sends the above ride request data to the mobile service on the server via an HTTP POST request.

[0015] The second mobile interface is an H5 webpage integrated within an instant messaging platform. In this embodiment, the instant messaging platform is the WeChat Official Accounts Platform, and the second mobile interface is developed based on Vue 3 and TypeScript. After the user opens the second mobile interface, the front end calls the OAuth 2.0 authorization interface provided by the instant messaging platform to guide the user through the authorization process, thereby obtaining the user's unique identity identifier on the instant messaging platform, namely OpenID.

[0016] After obtaining the identity identifier, the second mobile interface combines it with the current timestamp to generate a binding authentication token, which is then sent to the instant messaging binding service on the server via an HTTP request. This binding authentication token is used to securely associate the user's identity on the instant messaging platform with the user's identity in the enterprise's internal personnel database.

[0017] The server-side consists of three independently deployed Spring Boot microservices: a PC backend service, a mobile application service, and an instant messaging binding service, all developed using Java 15. The PC backend service receives vehicle management configuration parameters and report query conditions from the management terminal and stores them in the first business database of the database layer. Simultaneously, it retrieves data from the database based on the query conditions and returns it to the management terminal.

[0018] The mobile service receives vehicle application data from the first mobile interface and generates an approval task flow based on an internally preset multi-level approval state machine model. This state machine model is a finite state automaton, with states including pending review, under review, pending dispatch, pending driver confirmation, in progress, completed, rejected, and withdrawn. The transitions between states are driven by pre-configured multi-level approval nodes.

[0019] The mobile service sends the current status data of the approval task flow to the first mobile interface, allowing vehicle applicants to view the approval progress in real time. Simultaneously, the mobile service transmits notification data to the instant messaging binding service, which then pushes the approval status data to the second mobile interface via the instant messaging platform's template message interface, enabling WeChat message notifications. Furthermore, the mobile service receives real-time location data sequences from the satellite positioning platform through the secondary data source in the dual-data source architecture. The secondary data source refers to a second database connection configured on the server, dedicated to connecting to the SQL Server database of the BeiDou GPS platform. The mobile service reads the latest vehicle location coordinates, speed, direction angle, and positioning timestamp from this database every second or every two seconds in a polling manner. The mobile service matches the real-time location data sequence with the coordinate sequences in the previously stored vehicle application data to generate real-time travel trajectory data, which is then dynamically displayed on a map by the management end.

[0020] The instant messaging binding service is specifically responsible for handling authentication and message push related to the instant messaging platform. This service receives a binding authentication token from the second mobile interface, verifies the token's validity, and then establishes a mapping relationship between the instant messaging platform user's identity and the user identifier in the enterprise's internal personnel database, storing this mapping relationship in the first business database. When an approval notification needs to be pushed to a user, the mobile service calls the instant messaging binding service's interface, passing in the target user's internal identifier and the message content. The instant messaging binding service then finds the corresponding instant messaging platform identity identifier based on the mapping relationship and calls the instant messaging platform's message interface to complete the push.

[0021] The database layer comprises a first business database and a second business database. The first business database uses a MySQL relational database to store core business data such as vehicle management configuration parameters, vehicle application data, approval process records, driver information, maintenance management data, and insurance management data. The second business database uses an SQL Server database, which is the original system database shared with the BeiDou GPS positioning platform. This system only connects to this database in read-only mode to obtain real-time vehicle positioning data sequences without writing any data to it, thus achieving non-intrusive integration with the existing BeiDou GPS platform.

[0022] The server-side also includes a trip trajectory matching module. This module is implemented as a Java service class within the mobile service and is responsible for accurately matching the real-time received BeiDou GPS positioning points with the coordinate sequence of the predetermined route in the car rental application.

[0023] In practice, the module first reads the coordinate sequence corresponding to a single vehicle request from the database. This sequence, arranged in the order of travel, includes the coordinates of the departure point, the coordinates of each waypoint, and the coordinates of the destination, denoted as P1, P2, ..., Pn, where n is the total number of coordinate points. Then, the module retrieves the real-time location data sequence corresponding to the same trip from the second business database, sorts it in ascending order by location timestamp, and extracts the coordinates of each location point, denoted as Q1, Q2, ..., Qn, where Qi is the location coordinate of the point closest to Pi in time.

[0024] The module calls a preset spherical distance algorithm to calculate the first distance sequence between adjacent points in the coordinate sequence. The spherical distance algorithm uses the Haversine formula, which calculates the arc distance between two geographic coordinates based on the Earth's radius and the latitude and longitude of the two points. Specifically, for each pair of adjacent coordinate points... and +1, the module calculates their spherical distance. Thus, the first distance sequence is obtained. Next, the module performs a process for each pair of corresponding positioning points. and The Haversine formula is also used to calculate the spherical distance. The second distance sequence is obtained. Then, the module calculates the square of the difference between corresponding elements in the first distance sequence and the second distance sequence, sums them, and finally divides by . The mean squared difference M is obtained. If M is less than a preset threshold, such as 0.1 square kilometers, then the actual driving trajectory of the vehicle is determined to be a successful match with the route requested for vehicle use.

[0025] Upon successful matching, the module generates a matching success identifier, which is a JSON-formatted string containing a matching score and the judgment result. This identifier is sent to the first mobile interface via WebSocket or HTTP long polling. The driver's first mobile interface will display a message indicating that the route matching is normal, and the identifier is also recorded in the database for subsequent auditing.

[0026] The server-side also includes a fuel consumption anomaly warning module. This module is implemented internally as an event listener on the mobile service, acquiring fuel consumption data sequences by subscribing to controller area network bus data reported by the vehicle's OBD interface. Specifically, the module obtains the total trip mileage L from the trip records, in kilometers, accumulated from the BeiDou GPS trajectory.

[0027] At the same time, the trip is divided into several segments based on real-time traffic data, and the congestion duration of each segment is obtained. and the corresponding average speed Where j ranges from 1 to m, and m is the number of road segments. The module also obtains the altitude of the starting and ending points by calling the digital elevation model application programming interface (DEM), and calculates the altitude difference between the start and end points of the journey. .

[0028] During implementation, a set of multiple linear regression coefficients is trained in advance using machine learning. The training method involves collecting the vehicle's mileage data under normal operating conditions over the past three months, including the total mileage L, the sum of the products of congestion duration and average speed on each road segment, and the total distance traveled. altitude difference And the corresponding actual fuel consumption value, which was obtained by fitting using the least squares method. The specific numerical value. In this embodiment, for a certain type of sedan, Take 0.08 liters per kilometer. Take 0.02 liters per kilometer, and γ take 0.005 liters per meter.

[0029] The theoretical fuel consumption estimate is calculated as shown in the following formula; .

[0030] Read the actual fuel consumption value at the end of the trip from the real-time data reported by the fuel level sensor. Calculate the absolute value of the deviation If the absolute value of the deviation exceeds a preset deviation threshold, for example, 3 liters for a small car and 5 liters for a medium-sized bus, a fuel consumption anomaly warning message will be generated. This message includes the vehicle's license plate number, trip start and end times, theoretical fuel consumption value, actual fuel consumption value, and deviation percentage, and will be sent to the vehicle management administrator and driver as a WeChat template message via instant messaging service.

[0031] The multi-level approval state machine model in the mobile service is a database-driven workflow engine. Its core data tables are the approval process definition table and the approval node detail table. When a user submits a car rental application through the first or second mobile interface, the mobile service parses the type identifier and department level identifier in the application data. The type identifier includes four types: ordinary car rental application, one-click travel, official travel, and supplementary registration application. The department level identifier includes, for example, section-level, division-level, and department-level.

[0032] The mobile service reads the corresponding multi-level approval node configuration information from a pre-configured process template library based on two identifiers. This configuration information is stored in the process template table of the first business database in JSON format. Dynamically generated approval node sequences include, for example, the department head approval node, the department supervisor approval node, the vehicle management dispatch node, and the driver confirmation node. Each node corresponds to an approval role. The mobile service generates a globally unique process instance identifier, a universally unique identifier (UUID), for this application. Following the order of the approval node sequence, the mobile service activates each approval node sequentially.

[0033] Upon activation, the module retrieves the WeChat ID of the approver for the current node from the approver role mapping table, which is pre-maintained by the administrator. The mobile service calls the template message interface of the WeChat Official Accounts Platform via the instant messaging binding service to push a pending review notification to the approver's WeChat. After clicking the notification, the approver enters a second mobile interface where they can perform operations such as approving, rejecting, or forwarding the review. The operation result is returned to the mobile service via an HTTP request.

[0034] After all review nodes return an approved status, the mobile service sequentially activates the dispatch node and the driver confirmation node. The dispatch node is handled by the vehicle management department dispatcher, and the driver confirmation node is where the assigned driver confirms whether they can accept the order. When the dispatch node returns an approved status and the driver confirmation node returns a confirmation to accept the order, the mobile service updates the process instance status to pending execution and generates approval information.

[0035] For the approval time limit at each approval node, the mobile service uses the following calculation method: Obtain the preset basic approval time limit constant. , in this embodiment, 24 hours is taken. Node complexity level takes an integer value from 1 to 5, which is configured by the administrator in the process template. Priority weight of vehicle use application takes a value from 0 to 10, and the user selects normal, urgent or extremely urgent when submitting the application, corresponding to different values.

[0036] The time scaling factor k is set to 0.5 hours per level. Then the approval time limit for each approval node is shown in the following formula; .

[0037] For example, for a certain node = 3, priority weight = 5, then = 24 + 0.5×(3 + 5) = 28 hours. After an approval node is assigned to a certain approver, the mobile service enables a timed task based on the XXL-JOB scheduling framework. After the duration, it checks whether the node has completed the approval operation. If not, it generates a timeout reminder message, which contains information such as the application order number, node name, and timeout duration, and pushes a WeChat template message to the approver again through the instant messaging binding service, and simultaneously copies it to the superior administrator.

[0038] The maintenance management module in the first mobile interface is a functional block integrated in the driver's H5 page, covering functions such as maintenance application, maintenance factory management, maintenance order, spare parts management, and maintenance evaluation.

[0039] Specifically, when a vehicle fails, the driver clicks the maintenance application on the first mobile interface, and a text input box pops up. The driver enters the fault description text, such as engine shaking, acceleration weakness, and fault light on. After the module receives this text, it calls the Chinese word segmentation library HanLP for word segmentation processing, removes stop words such as "yes", "of", etc., and obtains the fault word set: Q = {engine, shaking, acceleration, weakness, fault light, on}.

[0040] Furthermore, access the historical maintenance database, which stores all vehicle maintenance records in the past two years. Each maintenance record contains fields such as maintenance description text, maintenance factory identifier, maintenance cost, and evaluation score.

[0041] For each maintenance record D, its maintenance description text is also segmented to obtain a set of maintenance terms. Then, the word frequency similarity between Q and D is calculated. The word frequency weights are pre-calculated using the entire historical maintenance corpus. The weight of each word w is the word frequency multiplied by the logarithm to the base 10, where the argument of the logarithm is the total number of documents in the corpus divided by the number of documents containing w. The similarity Sim(Q,D) is equal to the sum of the products of the weights of all words in the intersection of Q and D, divided by the product of the square root of the sum of the squares of the weights of all words in Q and the square root of the sum of the squares of the weights of all words in D.

[0042] In this embodiment, the three repair shops with the highest similarity scores are selected. The names, addresses, contact information, and historical ratings of these three shops are retrieved from the repair shop management table to generate a recommended repair shop list, which is displayed as a card list on the first mobile interface. Drivers can then select and submit a repair request. After submission, the system generates a repair order and proceeds to subsequent stages such as repair supervision, parts replacement, expense reimbursement, and final evaluation.

[0043] The insurance expiration warning module on the server side runs as a separate scheduled task in the PC backend service, managed using the XXL-JOB distributed task scheduling framework. The scheduling center is configured to execute the task at 1:00 AM every day. When the task is triggered, the insurance expiration warning module reads all insurance records from the insurance policy table in the main MySQL database. Each record contains the policy number, vehicle identifier, insurance expiration date, and last warning time.

[0044] For each record, the module performs the following calculations: the current scan time is the current date, the insurance end date is the end date in the database, and the remaining valid days. This equals the insurance expiration date minus the current scan time. The number of days between the last warning time and the current scan time. It equals the current scan time minus the last warning time.

[0045] Preset warning threshold number of days In this embodiment, 30 days are used. The risk factor R is calculated according to... Divide by 30 and then multiply by the negative of e Divide by The calculation is performed using the power of 1, where e is the base of the natural logarithm. For example, if an insurance policy has 15 days remaining and the interval between the last warning time and the current scan time is 10 days, then the risk coefficient is as shown in the following formula; .

[0046] The preset risk threshold is 0.15. Therefore, if R exceeds the threshold, an alert is triggered. The module generates an insurance expiration alert message, including the insurance policy number, vehicle identifier, and remaining valid days. This message is pushed to the vehicle's administrator via a WeChat template message interface using the instant messaging binding service. After the alert is issued, the module updates the last alert time for the insurance record to the current scan time to avoid duplicate sending.

[0047] The data dashboard display unit in the management terminal is a front-end page component of the PC management terminal. It is developed based on ECharts 5.4.2 and Highcharts 11.1.0 chart library and is used to display the vehicle utilization efficiency indicators of each department to the management.

[0048] In practice, the management system first sends an HTTP request to the PC backend service to obtain vehicle usage statistics. The PC backend service then uses a structured query language (SQL) aggregate query to calculate the number of completed trips for each department from the MySQL database. Total number of applications Excess costs Budget costs Average single-bike mileage and target mileage The number of completed trips refers to the number of application forms with a status of "completed". The total number of applications refers to the total number of all application forms. Excess cost. The difference between the actual vehicle usage cost and the budgeted cost is recorded as zero if it is negative. Budgeted cost For annual or monthly budgets.

[0049] Average bike mileage Target mileage is the total mileage traveled by all vehicles in the department divided by the number of vehicles in the department. The performance department sets a preset threshold, for example, 500 kilometers per vehicle per month. After receiving this data, the data dashboard displays it and uses JavaScript in the browser frontend to calculate the department's vehicle usage efficiency index. .

[0050] Calculation of departmental vehicle efficiency indicators The mileage efficiency coefficient is calculated by multiplying the ratio of completed trips to total applications by one, subtracting the difference between excess cost and budgeted cost, and then multiplying this difference by the ratio of average vehicle mileage to target mileage. For example, if a department completes 80 trips, has 100 total applications (completion rate 0.8), excess cost of 2000 yuan, budgeted cost of 10000 yuan (cost control coefficient 0.8), average vehicle mileage of 450 kilometers, target mileage of 500 kilometers (mileage efficiency coefficient 0.9), then... .

[0051] All departments The values ​​are placed into an array and sorted in descending order of numerical value. Finally, the bar chart component of ECharts is used to generate a visual ranking chart, with the horizontal axis representing the department name and the vertical axis representing the department name. The data is displayed as a value, with the specific value shown at the top of each column. The chart is displayed in real time on a large screen in the management console, and administrators can switch data ranges by year, quarter, or month.

[0052] The user binding unit in the instant messaging binding service is responsible for securely binding the OpenID of an instant messaging platform user with a user identifier in the enterprise's internal personnel database, enabling login via QR code scanning. In practice, the user first clicks the "Bind Account" button on a second mobile interface. The front-end calls the WeChat JS-SDK's `getLocalUserInfo` method to obtain the user's OpenID and the current timestamp (in Unix seconds). The front-end concatenates the OpenID and timestamp into a string formatted as OpenID followed by two vertical bars and then the timestamp, and then sends it to the binding interface of the instant messaging binding service. Upon receiving the request, the instant messaging binding service reads the pre-stored secret key from the configuration file. This secret key is a 32-byte random string visible only to the server.

[0053] The server uses the HMAC-SHA256 hash message authentication code algorithm to generate an encrypted binding token. The token is equal to the result of HMAC-SHA256 calculation, where the concatenated string is the message and the secret is the key. HMAC-SHA256 is a keyed hash function that is irreversible and collision-resistant. After generating the token, the server establishes a mapping relationship between the token and a user identifier (e.g., employee ID) in the enterprise personnel database. The mapping record is stored in the WeChat binding table of the first business database, including the token field, OpenID field, employee identifier field, and binding time field. After successful binding, when the user subsequently initiates a car rental application through the second mobile interface, the front end will carry the token stored in WeChat's local storage. Upon receiving the application request, the mobile service retrieves the token and calls the verification interface of the instant messaging binding service, passing the token as input.

[0054] The instant messaging binding service recalculates the HMAC-SHA256 using the same secret key and compares it with the received token. Since the server-side database stores a mapping between tokens and employee identifiers, if the token is valid and not expired, the server returns the corresponding employee identifier. The mobile service then uses this to complete identity authentication, allowing the user to submit a ride request.

[0055] The anomaly warning unit on the server side is used to detect whether vehicles deviate from the planned route, employing a map matching algorithm based on a Hidden Markov Model (HMM) and road topology. In specific implementation, the anomaly warning unit first receives real-time vehicle positioning data sequences from the second business database, namely the SQL Server BeiDou database. Each positioning point includes longitude, latitude, speed, azimuth, and positioning time. Simultaneously, the unit obtains road topology data from an external road network database. In this embodiment, off-line network data from OpenStreetMap is pre-imported into a MySQL road network table. This data includes road node coordinates and adjacency relationships between nodes. Each road node has a unique identifier and coordinates, and adjacency relationships are stored as edges, including the start node identifier, end node identifier, road length, and road level. The HMM is constructed as follows: each real-time positioning point is considered an observation state, and the possible real road nodes where the vehicle is located are considered hidden states. The observation probability is defined as the probability of observing positioning point g given a candidate road node r. This probability is equal to the probability density function value of a Gaussian distribution, where the mean of the Gaussian distribution is the vertical distance from g to the road segment where r is located, and the standard deviation is the GPS standard deviation, which is 10 meters in this embodiment. The transition probability is defined as the hidden state at the previous time step. Transition to the hidden state at the current moment The probability is equal to the result of an exponential function divided by a scaling factor, where the exponent of the exponential function is negative. arrive The shortest path length along the road network minus two location points and The absolute value of the great circle distance between them is divided by the scaling factor of 50 meters.

[0056] The shortest path length is pre-calculated using Dijkstra's algorithm. Then, the Viterbi algorithm is used to solve for the optimal hidden state sequence. The Viterbi algorithm is a dynamic programming algorithm that recursively calculates the maximum probability path for each candidate node at each time step, and finally backtracks to obtain the globally optimal road node sequence. After obtaining the actual road segment where the vehicle is located at the current time, the anomaly warning unit calculates the offset distance between the actual road segment and the planned route in the vehicle use application information. The planned route is also represented as a set of road segments, and the offset distance is defined as the straight-line distance from the current vehicle position along the vertical line to the nearest point on the planned route. If the offset distance exceeds a preset threshold of 50 meters for three consecutive positioning points, an abnormal vehicle position warning message is generated. This message is simultaneously sent to the management terminal to flash a red warning on the monitoring map, and to the first mobile terminal interface to pop up a prompt message informing the driver that the vehicle has deviated from the route.

[0057] The supplementary registration application unit in the first mobile interface is used to handle scenarios where a trip cannot be requested through the system in time due to special circumstances and the trip needs to be registered retroactively. It also automatically performs cost anomaly detection. In practice, after the trip, the driver opens the supplementary registration application function on the first mobile interface and fills in the actual departure time. Actual arrival time And the real-time speed sequence recorded by the vehicle's OBD interface during the journey, with one speed value per second in kilometers per hour, recorded as a speed array. And actual fuel consumption value The unit is liters. The frontend encapsulates this data as JSON and sends it to the mobile service. After receiving the data, the mobile service first... and Determine the integration interval, and then fit the velocity sequence.

[0058] This embodiment employs cubic spline interpolation, using the SplineInterpolator class from the Apache Commons Math library to fit discrete velocity points to a continuous function v(t), where t is in hours. Next, the server retrieves the vehicle model from the vehicle file table and then reads the corresponding fuel consumption characteristic coefficients a, b, and c from a pre-set parameter table. Coefficient a is the quadratic coefficient related to air resistance and rolling resistance, b is the linear coefficient related to engine efficiency, and c is the idle fuel consumption constant. These coefficients are obtained through bench testing or large-scale data regression; for example, for a certain type of sedan, a=0.003, b=0.15, and c=1.2. Fuel consumption estimate. Through the exist arrive The result is obtained by integrating over the interval. The integration calculation uses Simpson's rule from numerical integration, dividing the time interval into 1000 equal parts for approximate calculation. The fuel consumption deviation rate is then calculated as shown in the following formula; .

[0059] For example, if the actual fuel consumption is 15 liters and the estimated value is 12 liters, then... =25%. The preset deviation percentage threshold is 20%, which is adjustable. Since 25% is greater than 20%, this supplementary trip is marked as a cost anomaly. The mobile service generates an anomaly record, including vehicle identifier, driver identifier, trip start and end times, actual fuel consumption, estimated fuel consumption, deviation rate, and marking time, stored in the cost anomaly record table in MySQL. Administrators can view all cost anomaly records on the management console for subsequent auditing and driver performance evaluation.

[0060] The above description is merely an explanation of some preferred embodiments of the present invention and the technical principles employed. Those skilled in the art should understand that the scope of the invention as described in the embodiments of the present invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A smart vehicle management system based on an instant messaging platform and mobile web pages, characterized in that, include: The management terminal is used to receive operation instructions from the administrator, generate vehicle management configuration parameters and report query conditions, and send them to the server. The first mobile interface is used to receive the user's car rental request, generate car rental application data containing a coordinate sequence based on the departure point, destination and multiple waypoints in the car rental request, and send it to the server. The second mobile interface is used to obtain the user's identity identifier through the authorization mechanism of the first instant messaging platform, generate a binding authentication token based on the user's identity identifier, and send it to the server. The server includes a PC backend service, a mobile terminal service, and an instant messaging binding service. The PC backend service is used to receive the vehicle management configuration parameters and the report query conditions. The mobile terminal service is used to receive the vehicle application data. The instant messaging binding service is used to receive the binding authentication token. The mobile service generates an approval task flow based on a preset multi-level approval state machine model and sends the approval status data to the first mobile interface or to the second mobile interface through the instant messaging binding service; the mobile service is also used to receive real-time positioning data sequences from a satellite positioning platform through the secondary data source in the dual data source architecture, and perform matching calculations based on the real-time positioning data sequences and the coordinate sequences in the vehicle application data to generate real-time travel trajectory data; The database layer includes a first business database and a second business database. The first business database is used to store the vehicle management configuration parameters and the vehicle application data. The second business database is used to interface with and obtain the real-time positioning data sequence of the satellite positioning platform.

2. The intelligent vehicle management system based on an instant messaging platform and mobile webpage as described in claim 1, characterized in that, The server also includes a travel trajectory matching module, which is configured to: calculate a first distance sequence between adjacent points in the coordinate sequence and a second distance sequence between the corresponding position coordinates of each adjacent point in the real-time positioning data sequence according to a preset spherical distance algorithm; calculate the mean squared difference between the first distance sequence and the second distance sequence to obtain a matching degree determination result; When the matching degree determination result is less than the preset threshold, it is determined that the trip trajectory and the car use application route are successfully matched, a matching success mark is generated and sent to the first mobile terminal interface.

3. The intelligent vehicle management system based on an instant messaging platform and mobile webpage as described in claim 1, characterized in that, The server also includes a fuel consumption anomaly warning module, which is configured to: receive the fuel consumption data sequence reported by the vehicle during the trip; obtain the total trip mileage, the congestion duration of each road segment and the corresponding average speed, and the altitude difference between the start and end points of the trip; Based on preset multiple linear regression coefficients, the total mileage, the product of congestion time and average speed on each road segment, and the altitude difference are weighted and summed to obtain the theoretical fuel consumption estimate. Calculate the absolute value of the deviation between the actual fuel consumption value and the theoretical fuel consumption estimate. When the absolute value of the deviation exceeds a preset deviation threshold, generate a fuel consumption anomaly warning message.

4. The intelligent vehicle management system based on an instant messaging platform and mobile webpage as described in claim 1, characterized in that, The mobile service includes a multi-level approval state machine model, which is configured as follows: based on the type identifier of the vehicle application data and the department level identifier of the submitting user, a sequence of approval nodes is dynamically generated, with each approval node corresponding to an approval role; a preset basic approval time limit constant, node complexity level, and priority weight of the vehicle application are obtained; the basic approval time limit constant and the time scaling factor are multiplied by the sum of the node complexity level and the priority weight, and then added together to obtain the approval time limit value of each approval node; When the approval node is assigned and the approval operation is not completed within the approval time limit, an overdue reminder message is generated and pushed to the approval role corresponding to the current approval node through the instant messaging binding service and the second mobile terminal interface.

5. The intelligent vehicle management system based on an instant messaging platform and mobile webpage as described in claim 1, characterized in that, The first mobile interface includes a maintenance management module, which is configured to: receive vehicle fault description text input by the driver; perform word segmentation on the fault description text to obtain a fault word set; Calculate the word frequency similarity between the fault term set and the maintenance term set corresponding to each maintenance record in the historical maintenance database; Select a preset number of repair shops with the highest similarity values, generate a list of recommended repair shops, and display it on the first mobile terminal interface.

6. The intelligent vehicle management system based on an instant messaging platform and mobile webpage as described in claim 1, characterized in that, The server also includes an insurance expiration warning module, which is configured to: read the insurance expiration date and the last warning time of each insurance policy record from the first business database according to a preset timed scanning cycle; calculate the remaining valid days based on the current scanning time and the insurance expiration date; calculate the interval days based on the current scanning time and the last warning time; and calculate the risk coefficient using an exponential decay model based on the ratio of the interval days to the remaining valid days. When the risk coefficient is greater than the preset risk threshold, an insurance expiration warning message containing the insurance policy number and the remaining valid days is generated and pushed to the corresponding vehicle administrator through the instant messaging binding service and the second mobile terminal interface.

7. The intelligent vehicle management system based on an instant messaging platform and mobile webpage as described in claim 1, characterized in that, The management terminal includes a data dashboard display unit, which is configured to: receive vehicle usage statistics from the server, the vehicle usage statistics including the number of completed trips, total number of applications, excess cost, budgeted cost, average vehicle mileage, and target mileage for each department; calculate a completion rate indicator based on the ratio of the number of completed trips to the total number of applications; calculate a cost control indicator based on the ratio of the difference between the excess cost and the budgeted cost; calculate a mileage efficiency indicator based on the ratio of the average vehicle mileage to the target mileage; multiply the completion rate indicator, the cost control indicator, and the mileage efficiency indicator to obtain a departmental vehicle usage efficiency indicator; and sort the departmental vehicle usage efficiency indicators of each department in descending order of value, generate a visual ranking chart, and display it on the display interface of the management terminal.

8. The intelligent vehicle management system based on an instant messaging platform and mobile webpage as described in claim 1, characterized in that, The instant messaging binding service includes a user binding unit, which is configured to receive a first instant messaging platform identity identifier and a current timestamp from the second mobile terminal interface. Based on the hash message authentication code algorithm, an encrypted binding token is generated by concatenating the identity identifier of the first instant messaging platform with the current timestamp as the message and the key pre-stored on the server as the key. Establish a mapping relationship between the encrypted binding token and the user ID in the enterprise personnel database, and store the mapping relationship in the first business database; When a user subsequently initiates a car rental request through the second mobile terminal interface, the mobile terminal service receives the encrypted binding token, decrypts and restores the first instant messaging platform identity identifier based on the encrypted binding token and the key, and obtains the corresponding user ID based on the mapping relationship to complete identity authentication.

9. The intelligent vehicle management system based on an instant messaging platform and mobile webpage as described in claim 1, characterized in that, The server also includes an anomaly warning unit, which is configured to receive vehicle positioning data sequences from the second business database and road topology data from an external road network database in real time. The road topology data includes road node coordinates and adjacency relationships between nodes. Based on the Hidden Markov Model, each real-time location point is matched with candidate road nodes in the road topology. According to the Euclidean distance probability between the location point and the candidate node and the shortest path probability along the road network between adjacent location points, the Viterbi algorithm is used to solve the optimal hidden state sequence to obtain the actual road segment where the vehicle is currently located. According to the offset distance between the actual road segment and the planned route in the vehicle use application information, when the cumulative offset distance exceeds a preset threshold, a vehicle location anomaly warning message is generated.

10. The intelligent vehicle management system based on an instant messaging platform and mobile webpage as described in claim 1, characterized in that, The first mobile interface also includes a data supplement application unit, which is configured to: receive the actual departure time, actual arrival time, real-time speed sequence, and actual fuel consumption value submitted by the user after the trip; after receiving the above data, the mobile service fits a function of instantaneous speed changing with time based on the real-time speed sequence; obtains the fuel consumption characteristic coefficient preset for the vehicle model, and performs an integral operation on the function of instantaneous speed changing with time over the time interval formed by the actual departure time and the actual arrival time to obtain a fuel consumption estimate; calculates the fuel consumption deviation rate by dividing the difference between the actual fuel consumption value and the fuel consumption estimate by the fuel consumption estimate and then multiplying by 100%; when the fuel consumption deviation rate exceeds a preset deviation percentage threshold, the trip data supplement is marked as a cost anomaly, and an anomaly record is generated and stored in the first business database.

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