Iot-based intelligent campus non-motor vehicle management method and system

Abstract

The application provides a kind of wisdom campus non-motor vehicle management method and system based on Internet of Things, it is related to Internet of Things technical field, first, obtain the real-time position information of campus non-motor vehicle basic information and Internet of Things perception, identity association information set is generated in association, determine target mobile area based on campus area function, user use purpose and vehicle type, then combine target area, path traffic state and real-time position, generate path guide instruction and send to user terminal, after vehicle arrives, collection parking area occupancy state, combine vehicle size and allocate parking area and guide parking, when user applies for leaving school, generate leaving school check information according to identity association information, parking allocation and vehicle current state, after being verified by the verification equipment of campus entrance Internet of Things, generate leaving school state record and store in management database, thus realize all-around intelligent management of campus non-motor vehicle, improve management efficiency and campus safety level.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and more specifically, to a smart campus non-motorized vehicle management method and system based on IoT. Background Technology

[0002] With the increasing number of non-motorized vehicles on campus, management issues are becoming increasingly prominent. Traditional campus non-motorized vehicle management methods have many drawbacks.

[0003] Currently, many schools mainly rely on manual registration and management of non-motorized vehicle information. This method is inefficient and prone to problems such as errors and loss of information records, making it difficult to accurately grasp the basic information of non-motorized vehicles on campus, such as vehicle model, color, and owner.

[0004] The management of non-motorized vehicle parking typically relies on designated parking areas, which lacks precise control over the real-time location of non-motorized vehicles and the occupancy status of these areas. This leads to chaotic parking on campus, with vehicles frequently parked haphazardly and blocking passageways. This not only affects the aesthetics of the campus environment but may also create safety hazards, such as blocking fire lanes.

[0005] There is a lack of effective route planning systems for guiding non-motorized vehicle users. The campus is densely populated with complex road conditions, making it difficult for non-motorized vehicle users to quickly find suitable routes, increasing travel time and inconvenience.

[0006] In addition, the lack of an effective verification mechanism for the management of non-motorized vehicles leaving the school makes it difficult to ensure that only legal non-motorized vehicles can leave the school, which poses certain risks to campus safety management. Summary of the Invention

[0007] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a smart campus non-motorized vehicle management method based on the Internet of Things, the method comprising: Obtain basic information of non-motorized vehicles on campus and real-time location information of non-motorized vehicles collected by IoT sensing devices, and associate the basic information of non-motorized vehicles on campus with the real-time location information of non-motorized vehicles to generate a set of non-motorized vehicle identity association information. Based on the pre-defined regional functional division standards within the campus and the usage purpose information provided by non-motorized vehicle users, combined with the non-motorized vehicle type information in the non-motorized vehicle identity association information set, the target movement area of ​​non-motorized vehicles is determined. Based on the target movement area of ​​non-motorized vehicles and the campus route access status information collected by IoT sensing devices, combined with the real-time location information of non-motorized vehicles in the non-motorized vehicle identity association information set, a campus route guidance instruction is generated and sent to the terminal device of the non-motorized vehicle user. After non-motorized vehicles arrive at the target movement area according to the campus route guidance instructions, the occupancy status information of each parking area in the target movement area is collected by the Internet of Things sensing device. Combined with the non-motorized vehicle size information in the non-motorized vehicle identity association information set, the parking area allocation result is determined and sent to the terminal device of the non-motorized vehicle user to guide the non-motorized vehicle to enter the designated parking location. After a non-motorized vehicle user triggers the application to leave campus, the system generates non-motorized vehicle departure verification information based on the non-motorized vehicle identity association information set, parking area allocation results, and current status information of non-motorized vehicles collected by IoT sensing devices. The non-motorized vehicle departure verification information is then verified by IoT verification devices at the campus entrances and exits. Once the verification is successful, a non-motorized vehicle departure status record is generated and stored in the campus non-motorized vehicle management database.

[0008] Furthermore, embodiments of the present invention also provide a smart campus non-motorized vehicle management system based on the Internet of Things, characterized in that it includes: A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the above-described IoT-based smart campus non-motorized vehicle management method by executing the machine-executable instructions.

[0009] In another aspect, embodiments of the present invention also provide a computer program product, the computer program product including machine-executable instructions, the machine-executable instructions being stored in a computer-readable storage medium, the processor of a computer device reading the machine-executable instructions from the computer-readable storage medium, the processor executing the machine-executable instructions, causing the computer device to execute the above-described smart campus non-motorized vehicle management method based on the Internet of Things.

[0010] Based on the above, by acquiring basic information about non-motorized vehicles on campus and real-time location information collected by IoT sensing devices, and performing correlation processing to generate a set of non-motorized vehicle identity association information, precise integration and management of non-motorized vehicle information is achieved. Then, based on the pre-set regional functional division standards within the campus and the user-provided usage purpose information, combined with non-motorized vehicle type information, the target movement area is determined, which can rationally plan the driving range of non-motorized vehicles, prevent them from entering unsuitable areas, and improve the orderliness of traffic on campus. Campus route guidance instructions are generated based on the target movement area and the campus route traffic status information and sent to the user terminal device, providing real-time and accurate route guidance for non-motorized vehicle users, helping them reach their destination quickly and safely, and improving travel efficiency. After a non-motorized vehicle arrives at the target movement area, by collecting parking area occupancy status information and combining it with non-motorized vehicle size information, the parking area allocation result is determined, achieving scientific management of non-motorized vehicle parking, effectively solving the problem of chaotic non-motorized vehicle parking on campus, and optimizing campus space utilization. After a non-motorized vehicle user triggers the application to leave campus, departure verification information is generated based on various information and verified through IoT verification devices. Once verified, a departure status record is generated and stored in the management database, which strengthens the management of non-motorized vehicles leaving campus, improves the level of campus safety management, and realizes comprehensive and intelligent management of non-motorized vehicles on campus from information management, travel guidance, parking management to departure management, effectively improving the efficiency and quality of campus non-motorized vehicle management. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of the execution flow of the smart campus non-motorized vehicle management method based on the Internet of Things provided in this embodiment of the invention.

[0012] Figure 2 This is a schematic diagram of exemplary hardware and software components of the IoT-based smart campus non-motorized vehicle management system provided in an embodiment of the present invention. Detailed Implementation

[0013] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating an embodiment of the IoT-based smart campus non-motorized vehicle management method provided by the present invention. The following is a detailed description of the IoT-based smart campus non-motorized vehicle management method.

[0014] Step S110: Obtain basic information of non-motorized vehicles on campus and real-time location information of non-motorized vehicles collected by IoT sensing devices. Associate the basic information of non-motorized vehicles on campus with the real-time location information of non-motorized vehicles to generate a set of non-motorized vehicle identity association information.

[0015] In this embodiment, to achieve effective management of non-motorized vehicles on campus, it is first necessary to comprehensively and accurately obtain and integrate relevant information. There are numerous and diverse non-motorized vehicles on campus; if basic information cannot be effectively linked with real-time location information, subsequent management will be difficult to carry out. Through the above steps, a unique identity and corresponding real-time location can be established for each non-motorized vehicle on campus.

[0016] Step S111: Collect basic information on campus non-motorized vehicles through the campus non-motorized vehicle registration system. The basic information on campus non-motorized vehicles includes brand information, model information, color information, frame identification information, and user identification information.

[0017] The campus non-motorized vehicle registration system is a platform specifically designed to collect and store information related to non-motorized vehicles on campus. During the data collection process, non-motorized vehicle users are required to accurately fill in relevant information in the system. Brand information helps distinguish vehicles produced by different manufacturers; for example, different brands of electric bicycles may differ in performance and maintenance requirements. Model information further refines the specific model of the vehicle; different models within the same brand may vary in size and function. Color information is an important feature of the vehicle's appearance, facilitating quick visual identification. Frame identification information is a unique identification code for each vehicle, similar to a vehicle's "ID number," and is crucial for achieving unique vehicle identification. The user's identification information links the vehicle to a specific user, facilitating accountability and management. For example, when a vehicle is illegally parked, the relevant user can be contacted promptly through the user's identification information.

[0018] Step S112: Collect real-time location information of non-motorized vehicles by using the positioning modules in the IoT sensing devices deployed in various areas of the campus. The positioning modules in the IoT sensing devices obtain location data by receiving signals sent by the positioning tags installed on the non-motorized vehicles.

[0019] IoT sensing devices are distributed across key areas on campus, such as under teaching buildings, at dormitory entrances, and along main campus roads. The positioning modules within these devices are continuously operational, constantly receiving signals from positioning tags installed on non-motorized vehicles. These tags periodically transmit signals containing their own identification information. Upon receiving these signals, the positioning modules of the IoT sensing devices perform preliminary processing to extract location-related data. Due to the complex campus environment, including building obstructions and electromagnetic interference, the positioning modules need to possess a certain level of interference resistance to ensure that the received signals accurately reflect the vehicle's location.

[0020] Step S1121: Deploy IoT sensing devices at key locations in the teaching area, office area, living area, sports area and logistics support area on campus. Each IoT sensing device is equipped with a positioning module and a signal receiving antenna.

[0021] Different areas of the campus have different functions and characteristics, so the specific conditions of each area need to be considered when deploying IoT sensing devices. Teaching areas are densely populated with many buildings, so devices should be deployed at key locations such as entrances and exits of teaching buildings and corners of surrounding roads. Office areas are relatively quiet with less vehicle traffic, so devices can be deployed around office buildings and near parking lots. Living areas, such as dormitories and the area around the canteen, have frequent vehicle and personnel activity, requiring increased device deployment density. Sports areas have concentrated personnel during activity periods, so devices should be deployed along roads surrounding sports fields. Logistics support areas involve the transportation of materials and have frequent vehicle traffic, so devices need to be deployed in locations such as logistics warehouses and maintenance centers. Each IoT sensing device is equipped with a positioning module responsible for processing received signals and calculating location, while the signal receiving antenna is used to capture signals sent by the positioning tag. The antenna selection will consider the signal reception range and sensitivity to adapt to the signal propagation environment of different areas.

[0022] Step S1122: Install a positioning tag on each registered non-motorized vehicle on campus. The positioning tag can periodically send a wireless signal containing the non-motorized vehicle frame identification information. The sending period is set according to the size of the campus area.

[0023] When installing location tags on non-motorized vehicles, it is essential to ensure that the tags are securely installed and not easily disassembled or damaged. They are typically installed in a concealed location on the vehicle frame. The location tags have a built-in battery and wireless communication module, enabling them to transmit wireless signals at set intervals. The transmission interval should be determined by considering the size of the campus area and management needs. For larger campuses, the transmission interval can be shortened to ensure real-time location information; conversely, for smaller campuses, the transmission interval can be lengthened to reduce battery consumption and signal interference. For example, for larger campuses, a shorter time interval can be set, allowing IoT sensing devices to receive vehicle location signals more frequently.

[0024] Step S1123: Activate the signal receiving mode of the positioning module in the IoT sensing device, and receive the wireless signals sent by the surrounding non-motorized vehicle positioning tags through the signal receiving antenna in the IoT sensing device.

[0025] After the system starts up, the positioning module in the IoT sensing device enters signal receiving mode. The signal receiving antenna searches for surrounding wireless signals in all directions. When it receives a signal from a non-motorized vehicle positioning tag, it transmits the signal to the positioning module. The positioning module performs preliminary filtering of the signal to determine whether it comes from a registered positioning tag within the campus, thus eliminating external interference signals. At the same time, the positioning module records information such as the time of signal reception and signal strength.

[0026] Step S1124: The positioning module in the IoT sensing device detects the signal strength of the received wireless signals and records the signal strength value of each wireless signal.

[0027] The positioning module's signal strength detection of received wireless signals is a crucial step in determining the vehicle's location. Signal strength varies at different distances; generally, the closer the distance, the stronger the signal, and the farther the distance, the weaker the signal. The positioning module uses a dedicated signal strength detection circuit to measure the signal and records the results in a specific numerical format. These signal strength values ​​are stored in a one-to-one correspondence with the received vehicle identification information, so that they can be combined with the detection results from multiple IoT sensing devices for subsequent location calculation.

[0028] Step S1125: Based on the signal strength values ​​of the wireless signals sent by the same non-motorized vehicle positioning tag received by multiple IoT sensing devices, and combined with the known geographical coordinates of each IoT sensing device, the location data of the non-motorized vehicle is calculated using the principle of triangulation algorithm.

[0029] When the signal transmitted by the location tag of the same non-motorized vehicle is received by multiple IoT sensing devices, the signal strength values ​​recorded by these devices and their respective known geographical coordinates can be collected. The principle of triangulation is based on the attenuation characteristics of signals propagating in space. The location of the transmitting device is calculated by the difference in signal strength received by receiving devices at different locations. Specifically, the approximate distance between the vehicle and each IoT sensing device can be estimated based on the signal strength value. Then, circles are drawn with the geographical coordinates of each IoT sensing device as vertices and the estimated distance as the radius. The intersection of these circles represents the approximate location of the vehicle. During the calculation process, the influence of buildings on signal propagation within the campus is also considered, and the estimated distance is corrected to improve the accuracy of the location calculation.

[0030] Step S1126: Determine the validity of the calculated non-motorized vehicle location data by comparing it with the coordinate range defined by the campus geographical area, and determine whether the calculated non-motorized vehicle location data falls within the coordinate range.

[0031] The calculated location data for non-motorized vehicles may contain errors and may even exceed the actual geographical area of ​​the campus. Therefore, it is necessary to determine its validity. The coordinate range defining the geographical area of ​​the campus is pre-defined in the system and includes the campus boundary coordinates. The horizontal and vertical coordinates of the calculated vehicle location data are compared with the horizontal and vertical boundaries of this coordinate range, respectively. If both the horizontal and vertical coordinates of the vehicle location data are within the corresponding boundary range, the location data is considered valid and reflects the vehicle's true location within the campus; otherwise, the location data is invalid, possibly due to signal interference or calculation errors.

[0032] Step S1127: If the calculated non-motorized vehicle location data falls within the coordinate range defined by the campus geographical area, then the calculated non-motorized vehicle location data shall be regarded as valid real-time non-motorized vehicle location data.

[0033] When the calculated non-motorized vehicle location data is determined to be valid, it can be marked as valid real-time location data and stored. This valid location data will be used for subsequent association processing with vehicle basic information and for generating route guidance instructions. Simultaneously, the data collection time can be recorded for later analysis of vehicle movement trajectories and dwell times.

[0034] Step S1128: If the calculated location data of the non-motorized vehicle exceeds the coordinate range defined by the geographical scope of the campus, discard the calculated location data of the non-motorized vehicle and wait for the next transmission cycle to receive the positioning tag signal before recalculating the location data of the non-motorized vehicle.

[0035] Location data outside the defined coordinate range of the campus can be discarded, as this data does not reflect the vehicle's true location within the campus. After discarding, the system waits for the next transmission cycle of the location tag to receive the signal again and perform location calculations. This ensures that the stored and used location data is valid, preventing invalid data from interfering with subsequent management work.

[0036] Step S1129: Bind the valid real-time location data of non-motorized vehicles calculated in each cycle with the corresponding non-motorized vehicle frame identification information to form a data pair containing non-motorized vehicle frame identification information and valid real-time location data of non-motorized vehicles.

[0037] After each location tag transmission cycle ends, the valid real-time location data of non-motorized vehicles calculated within that cycle can be bound to the corresponding vehicle identification information. The vehicle identification information is the unique identifier of a vehicle. By binding location data to the vehicle identification information, the specific vehicle corresponding to each piece of location data can be clearly identified. The resulting associated data pair contains both the vehicle's identity information and its real-time location information.

[0038] Step S11210: Transmit the associated data pair containing non-motorized vehicle frame identification information and valid non-motorized vehicle real-time location data to the database of the campus non-motorized vehicle management system. The associated data pair containing non-motorized vehicle frame identification information and valid non-motorized vehicle real-time location data is stored as non-motorized vehicle real-time location information.

[0039] Once the associated data pairs are formed, they can be transmitted via the campus network to the database of the campus non-motorized vehicle management system for storage. The database organizes and manages the data in a structured manner to facilitate quick querying and access. The stored data is arranged in chronological order, allowing administrators to easily view vehicle location changes at different times. Furthermore, the database has data backup and recovery functions to prevent data loss.

[0040] Step S113: The basic information of non-motorized vehicles on campus is formatted and processed, and records with missing information are removed, while records with complete information are retained.

[0041] The basic information on non-motorized vehicles collected on campus may come from different channels, and the data formats may differ. Format standardization involves converting this data into a unified standard format defined by the system. For example, different spellings of brand names are standardized to the official standard name, and model information is standardized to a specific coding rule. During format standardization, each record is checked, and if any key information is missing, such as missing frame identification or user identification information, that record is removed. Only records with complete information are retained to ensure the accuracy and effectiveness of subsequent correlation processing.

[0042] Step S114: Perform signal noise reduction processing on the real-time location information of non-motorized vehicles collected by IoT sensing devices, remove abnormal location data caused by signal interference in the real-time location information of non-motorized vehicles, and retain normal location data that conforms to the geographical range of the campus.

[0043] Real-time location information collected by IoT sensing devices may be affected by various factors, such as electromagnetic interference and multipath effects, leading to abnormal location data. Signal denoising aims to eliminate the influence of these interference factors and restore the authenticity of the data. Common signal denoising methods include moving average filtering and median filtering. For example, moving average filtering averages location data over a period of time to smooth data fluctuations; median filtering eliminates extreme outliers by selecting the median value of location data over a period of time. After denoising, the location data can be checked again to ensure it conforms to the campus geographical area, retaining the normal location data.

[0044] Step S115: Associate and match the complete basic information record of the non-motorized vehicle on campus with the normal location data that conforms to the geographical range of the campus according to the frame identification information, so that each frame identification information corresponds to a unique complete basic information record of the non-motorized vehicle on campus and normal location data that conforms to the geographical range of the campus.

[0045] The vehicle identification information (VIN) serves as a bridge connecting basic vehicle information and real-time location information. In this embodiment, all complete basic information records and normal location data can be traversed, and each VIN is matched against the corresponding information. For each VIN, a corresponding record can be found in the basic information records, and the latest location data corresponding to that VIN can be found in the location data. These are then linked together, thus ensuring that each VIN corresponds to a unique set of basic information and real-time location information, achieving precise binding between vehicle identity and location.

[0046] Step S116: Integrate the matched information into a structured data set, which serves as the non-motorized vehicle identity association information set.

[0047] The matched information includes various details such as the vehicle's brand, model, color, frame identification, user identification, and real-time location. In this embodiment, this information can be integrated according to a certain structure, such as using a database table to store different types of information in different fields. The resulting structured data set is the non-motorized vehicle identity association information set, which centralizes the key information of each campus non-motorized vehicle, facilitating quick querying and use in various management stages.

[0048] Step S120: Based on the pre-defined regional functional division standards within the campus and the usage purpose information provided by non-motorized vehicle users, combined with the non-motorized vehicle type information in the non-motorized vehicle identity association information set, determine the target movement area of ​​the non-motorized vehicle.

[0049] After obtaining the set of non-motorized vehicle identity association information, it is necessary to determine the target movement area for each non-motorized vehicle. The determination of the target movement area is based on a comprehensive consideration of factors such as the functional zoning of the campus, the user's purpose of use, and the vehicle type. This step guides non-motorized vehicle users to drive their vehicles to appropriate areas, avoiding disorderly driving and parking on campus, and improving the efficiency and safety of campus traffic management.

[0050] Step S121: Retrieve the preset regional functional division standards within the campus from the campus management system. The preset regional functional division standards within the campus include the geographical scope definition information of teaching areas, office areas, living areas, sports areas, and logistics support areas.

[0051] The campus management system stores overall campus planning information, including regional functional zoning standards. Retrieving these standards accesses the campus management system's database to obtain detailed geographical boundaries for teaching areas, office areas, living areas, sports areas, and logistical support areas. This information is typically presented as an electronic map, containing the boundary coordinates, size, and distribution of major buildings for each area. For example, the geographical boundaries of the teaching area clearly mark the boundaries of each teaching building, laboratory building, library, and other buildings.

[0052] Step S122: Receive the purpose information submitted by the non-motorized vehicle user through the terminal device. The purpose information includes a description of the functional type of the area that the non-motorized vehicle user plans to go to.

[0053] Before using a non-motorized vehicle, users need to submit their purpose of use information to the system through their terminal device (such as a mobile app). Users select or enter the function type of the area they plan to visit on their terminal device, such as "going to the teaching area for class" or "going to the living area for meals." The terminal device encrypts the user's submitted purpose information and transmits it to the campus non-motorized vehicle management system via the network. In this embodiment, the received information is decrypted and parsed to extract the function type description.

[0054] Step S123: Extract the model information corresponding to each non-motorized vehicle from the non-motorized vehicle identity association information set, and determine the type information of the non-motorized vehicle based on the model information corresponding to each non-motorized vehicle. The type information of the non-motorized vehicle includes electric bicycle type, ordinary bicycle type and electric scooter type.

[0055] The non-motorized vehicle identity association information set stores the model information of each non-motorized vehicle. In this embodiment, the set can be traversed to extract the model information of each vehicle one by one. Then, the vehicle type is determined according to a preset model-type correspondence rule. For example, if the model information contains keywords such as "electric" or "lithium battery," it is determined to be an electric bicycle; if the model information is only a regular bicycle model and has no electric-related identifier, it is determined to be a regular bicycle; if the model information matches the characteristics of an electric scooter, it is determined to be an electric scooter.

[0056] Step S124: Based on the preset regional functional division standards within the campus, determine the access rules for various types of non-motorized vehicles in different functional areas. The access rules include the types of non-motorized vehicles that are allowed to enter and the types of non-motorized vehicles that are prohibited from entering.

[0057] Step S1241: Extract usage time information for each functional area within the campus from the campus management system. The usage time information includes class hours in the teaching area, office hours in the office area, open hours in the living area, activity hours in the sports area, and work hours in the logistics support area.

[0058] The campus management system records detailed usage time information for each functional area. Class hours in the teaching area are determined according to the school's curriculum, such as 8:00 AM to 12:00 PM and 2:00 PM to 6:00 PM Monday through Friday; office hours in the office area are typically 9:00 AM to 5:00 PM on weekdays; living areas are open 24 hours a day, but different areas such as dormitories and canteens may have their own specific opening hours; activity hours in the sports area include student physical activities and sports meets; and working hours in the logistics support area are determined according to logistics work arrangements, such as the time for material transportation and facility maintenance.

[0059] Step S1242: Combining the spatial size and pedestrian density data of each functional area, analyze the carrying capacity of each functional area for non-motorized vehicle passage during different time periods. The carrying capacity analysis is based on the correspondence between the spatial size of the functional area and the number of non-motorized vehicles that can be accommodated per unit space, and the correspondence between pedestrian density and the space demand for non-motorized vehicle passage.

[0060] The spatial size of each functional area is fixed and can be measured using a campus electronic map. Pedestrian density data is collected through video surveillance equipment or infrared sensors deployed in each area. This data can be statistically analyzed to determine the pedestrian density in each area at different times. For carrying capacity analysis, firstly, based on the correspondence between area size and the number of non-motorized vehicles that can be accommodated per unit space, the theoretical maximum number of non-motorized vehicles that the area can accommodate is calculated. Then, combining the correspondence between pedestrian density and the demand for non-motorized vehicle passage space, the size of the space available for non-motorized vehicle passage within the area is analyzed under different pedestrian densities, thus determining the actual number of non-motorized vehicles allowed to enter. For example, when the pedestrian density in an area is high, the space available for non-motorized vehicle passage decreases, and the carrying capacity is correspondingly reduced.

[0061] Step S1243: Based on the carrying capacity analysis results, determine the upper limit of the number of non-motorized vehicles allowed to enter each functional area during different time periods.

[0062] After completing the carrying capacity analysis, the maximum number of non-motorized vehicles allowed to enter each functional area can be set at different times based on the analysis results. For example, in teaching areas, the pedestrian density is high and the carrying capacity is low during class hours, so the maximum number of non-motorized vehicles allowed to enter will be set lower; while during non-class hours, the pedestrian density is low and the carrying capacity is high, so the maximum number of non-motorized vehicles allowed to enter will be increased accordingly. This can avoid traffic congestion caused by too many vehicles in the area, and ensure pedestrian safety and regional order.

[0063] Step S1244: For electric bicycles, ordinary bicycles, and electric scooters, analyze the impact of each type of non-motorized vehicle on pedestrian safety and regional order when passing through the functional area. The impact analysis is based on vehicle speed, vehicle size, and interference factors generated during vehicle operation.

[0064] Different types of non-motorized vehicles have varying degrees of impact on pedestrian safety and area order. Electric bicycles have relatively high speeds and larger sizes, and may generate noise and electromagnetic interference during operation; regular bicycles have slower speeds and moderate sizes, causing less disturbance to the surrounding environment; electric scooters are smaller but more agile, and may be more prone to collisions in densely populated areas. In this embodiment, the impact of each type of non-motorized vehicle in different functional areas can be assessed and scored based on these factors, resulting in an impact assessment result.

[0065] Step S1245: For the teaching area, during class hours, based on the characteristics of dense crowds, it is determined that electric bicycles and electric scooters are prohibited from entering the teaching area, while ordinary bicycles are allowed to pass through designated passages in the teaching area.

[0066] During class hours, the teaching area experiences frequent movement of students and teachers, resulting in a high density of people. Electric bicycles and electric scooters travel at relatively high speeds and are difficult to control in such crowded environments, increasing the risk of traffic accidents and posing a significant threat to pedestrian safety. Therefore, these two types of vehicles are prohibited from entering the teaching area during class hours. Regular bicycles, which travel at slower speeds and are relatively stable, are permitted to use designated pathways within the teaching area. These pathways typically avoid areas of high pedestrian traffic to minimize disruption to the teaching process.

[0067] Step S1246: For office areas, during office hours, electric bicycles and regular bicycles are allowed to enter the office area, and they must be parked in the designated parking area after entering the office area. Electric scooters are prohibited from entering the office area.

[0068] During office hours, staff must conduct their work activities within the designated office area. Electric bicycles and regular bicycles are commonly used commuting tools and are permitted in the office area; however, to maintain cleanliness and order, they must be parked in designated areas and not randomly. Electric scooters are prohibited from entering the office area due to their small size, difficulty in management, and potential disruption to work order caused by their rapid movement.

[0069] Step S1247: For the residential area, during the open hours, it is determined that non-motorized vehicles of electric bicycle, ordinary bicycle and electric scooter are allowed to enter the residential area, and after entering the residential area, they must follow the designated path of the residential area and park in the designated parking area of ​​the residential area.

[0070] The living area is the main place for the daily lives of students and faculty, and there is frequent activity during opening hours, resulting in a high demand for non-motorized vehicles. Therefore, all types of non-motorized vehicles are allowed to enter the living area. However, in order to regulate traffic order, non-motorized vehicles entering the living area must follow designated routes and must not travel against the flow of traffic or exceed the speed limit. At the same time, vehicles must be parked in designated parking areas within the living area to avoid haphazard parking that affects the living environment.

[0071] Step S1248: For the sports area, during the activity period, electric bicycles, regular bicycles and electric scooters are prohibited from entering the sports area; during the non-activity period, regular bicycles are allowed to enter the sports area.

[0072] During activity periods, the sports area is primarily for the use of athletes and staff. Allowing non-motorized vehicles into the area during these times could severely disrupt the safety and order of activities; therefore, all types of non-motorized vehicles are prohibited. During off-peak hours, when fewer people are present, regular bicycles are permitted to move around the campus, but must still adhere to traffic rules and are prohibited from speeding or parking within the area.

[0073] Step S1249: For the logistics support area, during working hours, based on the logistics material transportation needs, determine whether electric bicycles and ordinary bicycles are allowed to enter the logistics support area, and whether electric scooters are prohibited from entering the logistics support area.

[0074] During working hours, the logistics support area requires the transportation of supplies and the maintenance of facilities. Electric bicycles and regular bicycles can be used as transportation tools for logistics personnel to improve work efficiency. However, electric scooters, due to their limited carrying capacity, cannot meet the needs of logistics supply transportation and are prone to conflicts with other transport vehicles in the transportation area. Therefore, they are prohibited from entering the logistics support area.

[0075] Step S12410: Integrate the types of non-motorized vehicles allowed to enter different functional areas at different times, the types of non-motorized vehicles prohibited from entering, the maximum number of non-motorized vehicles allowed to enter, and the passage requirements to form a complete access rule.

[0076] The above-mentioned permitted entry types, prohibited entry types, quantity limits, and access requirements for different functional areas at different times are integrated. Based on the combination of area functional type and time period, the rules are systematically organized to form a complete access rule system. For example, the access rules for the teaching area from 8:00 AM to 12:00 PM, Monday to Friday, are: ordinary bicycles are allowed to pass through designated lanes, electric bicycles and electric scooters are prohibited, and the maximum number of vehicles allowed is X, etc.

[0077] Step S12411: Store the complete access rules in the rule database of the campus non-motorized vehicle management system. The complete access rules are used to determine the target movement area of ​​non-motorized vehicles in the future.

[0078] Complete access rules are stored in the rule database of the campus non-motorized vehicle management system. The rule database uses an efficient indexing structure to quickly query and match relevant rules when determining target movement areas. Simultaneously, the rule database supports rule updates and maintenance; when campus area functional divisions or management needs change, administrators can modify the access rules accordingly through the system backend.

[0079] Step S125: Compare the target area function type described in the usage purpose information provided by the non-motorized vehicle user with the access rules, and filter out candidate areas that meet the access rules.

[0080] In this embodiment, based on the target area function type in the usage purpose information provided by the non-motorized vehicle user, the corresponding area type is searched in the access rules. Then, the access rules for that area type in the current time period are checked, and areas that allow the user's vehicle type to enter are filtered out. These areas are candidate areas that meet the access rules. For example, if the user's purpose is "to go to the teaching area for class", the current time period is the class period, and the user's vehicle type is a regular bicycle, then the teaching area is filtered out as a candidate area.

[0081] Step S126: Combine the non-motor vehicle type information in the non-motor vehicle identity association information set to further filter the candidate areas that meet the access rules and exclude candidate areas that do not allow the current type of non-motor vehicle to enter.

[0082] After selecting candidate areas in step S125, the access rules for these candidate areas regarding restrictions on the current non-motorized vehicle type can be checked again. If a candidate area prohibits the current type of non-motorized vehicle from entering during the current time period, it is excluded from the candidate area list. For example, if a candidate area includes a sports area, and the current time period is the activity period for the sports area, and the user's vehicle type is an electric bicycle, then according to the access rules, the sports area prohibits all types of non-motorized vehicles from entering during the activity period, and the sports area is excluded.

[0083] Step S127: Based on the remaining candidate areas after filtering, and combined with the location distribution of the department or college to which the non-motorized vehicle user belongs according to the identity information of the non-motorized vehicle user, calculate the distance between each remaining candidate area after filtering and the department or college to which the non-motorized vehicle user belongs, and select the remaining candidate area after filtering with the closest distance as the target movement area of ​​the non-motorized vehicle.

[0084] The identification information of non-motorized vehicle users includes information about their department or faculty. In this embodiment, the location distribution information of the user's department or faculty is retrieved from the campus management system to determine their geographical coordinates. Then, the distance between the geometric center coordinates of each remaining candidate area after filtering and the location coordinates of the user's department or faculty is calculated. The distance calculation can use the distance formula in the Cartesian coordinate system. By comparing the distances between each candidate area and the user's department or faculty, the closest candidate area is selected as the target movement area for the non-motorized vehicle, thereby reducing the user's travel distance and improving travel efficiency.

[0085] Step S130: Based on the target movement area of ​​the non-motorized vehicle and the campus route access status information collected by the IoT sensing device, combined with the real-time location information of the non-motorized vehicle in the non-motorized vehicle identity association information set, generate a campus route guidance instruction and send the campus route guidance instruction to the terminal device of the non-motorized vehicle user.

[0086] After determining the target movement area for non-motorized vehicles, it is necessary to plan an optimal route from the user's current location to the target movement area and generate corresponding guidance instructions. This step comprehensively considers the traffic conditions of the paths within the campus and the real-time location of the vehicles to ensure that the generated route guidance instructions can guide users to the target area efficiently and safely.

[0087] Step S131: Collect campus path traffic status information through the traffic detection module in the IoT sensing device deployed on each path in the campus. The campus path traffic status information includes the current number of vehicles, vehicle speed and whether there is any temporary blockage in each path segment.

[0088] IoT sensing devices are deployed along various paths on campus. The traffic detection modules in these devices can collect real-time traffic status information. The current number of vehicles is detected using video image recognition technology or infrared sensing technology, and the devices count the vehicles in a designated area. The vehicle speed is calculated by the time difference between two fixed detection points. Whether there are temporary blockages on the path is obtained through manual reporting or sensor detection (such as vibration sensors detecting road construction).

[0089] Step S132: Extract the real-time location information of the current non-motorized vehicle from the non-motorized vehicle identity association information set, and determine the starting point of the path where the current non-motorized vehicle is located.

[0090] In this embodiment, the vehicle record corresponding to the current user is searched from the non-motorized vehicle identity association information set, and its real-time location information is extracted. Based on the coordinates of this real-time location information on the campus electronic map, the starting point of the vehicle's current path is determined. The starting point of the path is usually a point on the specific path segment where the vehicle is currently located.

[0091] Step S133: Based on the current starting point location of the non-motorized vehicle and the geographical scope of the target movement area of ​​the non-motorized vehicle, mark the starting point coordinates and target area coordinates on the campus electronic map.

[0092] In the campus electronic map, the coordinates of the starting point and the geographical boundaries of the target area can be marked based on the coordinates of the starting point and the geographical boundaries of the target area. The target area coordinates are usually the geometric center coordinates or the coordinates of the main entrances / exits of the target area, to facilitate route planning. The marked electronic map serves as the visual basis for route planning.

[0093] Step S134: Based on the path network data in the campus electronic map, generate multiple alternative paths from the starting coordinates of the path to the coordinates of the target area. Each alternative path contains multiple consecutive path segments.

[0094] Step S1341: Retrieve the campus electronic map from the map database of the campus non-motorized vehicle management system. The campus electronic map contains geographic coordinate data, path width data, path material data, and regional node information of all paths within the campus.

[0095] The campus non-motorized vehicle management system's map database stores detailed campus electronic map data. In this embodiment, the campus electronic map is retrieved through a database query operation. This campus electronic map includes not only the geographic coordinate data of the paths, but also the path width data (such as the width of main roads and side roads), path material data (such as asphalt roads, cement roads, and stone slab roads), and information on regional nodes connecting the paths (such as intersection nodes and building entrance / exit nodes).

[0096] Step S1342: Mark the starting coordinates of the path where the non-motorized vehicle is located and the target area coordinates corresponding to the target movement area of ​​the non-motorized vehicle on the campus electronic map. The target area coordinates are the geometric center coordinates of the target movement area of ​​the non-motorized vehicle.

[0097] In this embodiment, specific markers (such as red dots representing the starting point and green dots representing the target area) are used on the retrieved campus electronic map to mark the coordinates of the starting point and the target area, respectively. The target area coordinates are selected as the geometric center coordinates of the target movement area. This ensures that the endpoint of the route planning is representative, making it easier for users to find their specific destination after entering the target area.

[0098] Step S1343: Based on the path connection relationship in the campus electronic map, identify all area nodes that can be directly reached from the starting coordinates of the path, and use all area nodes that can be directly reached from the starting coordinates of the path as first-level path nodes.

[0099] In the campus electronic map, path connections are represented by nodes and edges. Each regional node represents a connection point on the path, and an edge represents a path segment connecting two nodes. In this embodiment, based on the coordinates of the path's starting point, regional nodes directly connected to it are found in the electronic map. These nodes are the first-level path nodes that can be directly reached from the starting point. For example, if the starting point is located on a main road that connects to multiple branch roads, then the regional nodes at the connection points are all first-level path nodes.

[0100] Step S1344: For each first-level path node, continue to identify the next-level region node that can be directly reached from that first-level path node, and so on, until a region node containing the target region coordinates is identified that can be directly reached.

[0101] Based on the first-level path nodes, the same processing can be performed on each first-level path node to identify the next-level region node that can be directly reached from that node, i.e., the second-level path node. By recursively following the above method, the next-level nodes are continuously identified until a region node containing the coordinates of the target region is found that can be directly reached, thus forming a node connection chain from the starting point to the target region.

[0102] Step S1345: Analyze all possible path combinations from the starting point coordinates of the path through each level of path nodes to the target area coordinates, identify invalid path combinations that are duplicated or detours, and remove them.

[0103] After obtaining all possible node connection chains, these chains can be converted into specific path combinations. Then, these path combinations are analyzed to check for path duplication (i.e., the same path segment is traversed multiple times) or detours (i.e., paths that significantly deviate from the straight line from the starting point to the target region). Invalid path combinations exhibiting these characteristics can be removed to reduce the workload of subsequent calculations.

[0104] Step S1346: Divide each valid path combination into multiple continuous path segments according to the intersections and turns of the path.

[0105] For an effective path combination, it can be divided into multiple continuous path segments based on feature points such as intersections and turns. Each path segment has a clear start and end point, corresponding to the path between two adjacent area nodes. The divided path segments facilitate subsequent analysis and calculation of the traffic status of each segment.

[0106] Step S1347: Assign a unique path segment number to each path segment, and record the starting coordinates, ending coordinates, length data and corresponding travel direction of each path segment.

[0107] In this embodiment, each path segment is assigned a unique number for easy identification and management. Simultaneously, detailed information for each path segment is recorded, including start-point coordinates, end-point coordinates, length (calculated from the start-point and end-point coordinates), and direction of travel (one-way or two-way). This information is stored in the system's path database.

[0108] Step S1348: Combine each valid path containing multiple consecutive path segments into a candidate path. Each candidate path contains a path segment number sequence, the length data of each path segment, and the total path length.

[0109] The multiple consecutive path segments, after being split, are combined sequentially to form a candidate path. Each candidate path contains a sequence of path segment numbers, which reflects the order in which the path segments are ordered. The length data of each path segment is used to calculate the total path length; the total path length is the sum of the length data of each path segment. Using this information, in this embodiment, the lengths of different candidate paths can be compared.

[0110] Step S1349: Perform an integrity check on all generated candidate paths to determine whether each candidate path can reach the target area coordinates completely from the path start coordinates, and whether all path segments in the candidate path are within the campus geographical area; if the candidate path can reach the target area coordinates completely from the path start coordinates and all path segments in the candidate path are within the campus geographical area, then retain the candidate path; if not, regenerate the candidate path.

[0111] Completeness checks are a crucial step in ensuring the validity of alternative routes. In this embodiment, a simulation can be performed starting from the path's starting coordinates and proceeding sequentially according to the path segment numbers of the alternative routes to check if the target area coordinates can be reached. Simultaneously, it is checked whether the starting and ending coordinates of each path segment in the alternative routes are within the coordinate range defined by the campus's geographical boundaries. Only alternative routes that simultaneously meet both conditions are retained; otherwise, the alternative route can be regenerated.

[0112] Step S13410: Store the alternative routes that pass the integrity check into the path database of the campus non-motorized vehicle management system.

[0113] Alternative routes that pass the integrity check are stored in the route database of the campus non-motorized vehicle management system. The route database categorizes and indexes these alternative routes for quick querying and access during subsequent selection of the optimal guidance route. Stored alternative route information includes route segment number sequences, the length of each route segment, and the total route length.

[0114] Step S135: Analyze the campus route traffic status information corresponding to each route segment in each candidate route, and calculate the average traffic speed and estimated travel time for each candidate route. The average traffic speed is calculated by weighting the vehicle traffic speeds of each route segment, and the estimated travel time is calculated based on the total length of the candidate route and the average traffic speed.

[0115] In this embodiment, the current vehicle speed of each segment in each candidate path is obtained from the path traffic status information. Since the lengths of each segment differ, their impact on the average speed of the entire candidate path also varies. Therefore, a weighted average method is used to calculate the average speed, with the length of each segment as the weight. For example, if a candidate path includes segment A (length L1, speed V1) and segment B (length L2, speed V2), then the average speed V = (L1 × V1 + L2 × V2) / (L1 + L2). The estimated travel time T is calculated by dividing the total length S of the candidate path by the average speed V, i.e., T = S / V.

[0116] Step S136: Combining the non-motorized vehicle type information in the non-motorized vehicle identity association information set, determine the upper limit of the permitted passage speed for different types of non-motorized vehicles on each path segment. Compare the calculated estimated passage time with the maximum permitted passage time corresponding to the upper limit of the permitted passage speed for that type of non-motorized vehicle on each path segment. If the calculated estimated passage time is less than or equal to the maximum permitted passage time, retain the calculated estimated passage time; if the calculated estimated passage time is greater than the maximum permitted passage time, recalculate the estimated passage time for the alternative route.

[0117] Different types of non-motorized vehicles have different performance characteristics, and different allowable speed limits are set for each path segment within the campus based on its road conditions and surrounding environment. In this embodiment, the corresponding allowable speed limit is found based on the type information of the non-motorized vehicle and the path segment information. Then, the maximum allowable travel time for each path segment is calculated based on its length and the allowable speed limit, and then the maximum allowable total travel time for the alternative paths is calculated. The estimated travel time calculated in step S135 is compared with the maximum allowable total time. If the estimated travel time is less than or equal to the maximum allowable time, it indicates that the alternative path is feasible in terms of speed, and the estimated travel time is retained; otherwise, it indicates that the alternative path may have congestion or other conditions affecting the travel speed, and the estimated travel time needs to be recalculated, which may require considering reducing the travel speed assumptions for some path segments.

[0118] Step S137: Select the path with the shortest estimated travel time from the revised alternative paths as the optimal guiding path.

[0119] After revising the estimated travel time for all alternative routes, these estimated travel times can be compared to select the alternative route with the shortest estimated travel time. This alternative route is the optimal guidance route, enabling non-motorized vehicle users to reach their target movement area as quickly as possible.

[0120] Step S138: Generate a campus route guidance instruction containing turning prompts, distance prompts, and passage precautions for each route segment according to the order of each route segment in the optimal guidance path.

[0121] In this embodiment, guidance information is generated sequentially for each path segment in the optimal guidance path. Turning prompts inform the user of the required turning direction at the end of the path segment, such as "left turn," "right turn," or "straight ahead." Distance prompts inform the user of the length of the current path segment and the distance to the next turning point. Traffic precautions include whether there are densely populated pedestrian areas, construction zones, or whether slowing down is necessary. Integrating this information forms the campus path guidance instructions.

[0122] Step S139: Send campus route guidance instructions to the non-motorized vehicle user's terminal device through the communication module between the campus non-motorized vehicle management system and the non-motorized vehicle user's terminal device.

[0123] The communication module of the campus non-motorized vehicle management system establishes a stable communication connection with the terminal devices of non-motorized vehicle users. In this embodiment, the generated campus route guidance instructions are encoded and encrypted, and then sent to the user's terminal device through the communication module. After receiving the instructions, the terminal device decrypts and decodes them, displaying them to the user in the form of text, voice, or map markers, guiding the user to travel along the optimal guidance route.

[0124] Step S140: After the non-motorized vehicle arrives at the target movement area according to the campus route guidance instructions, the occupancy status information of each parking area in the target movement area is collected by the Internet of Things sensing device. Combined with the non-motorized vehicle size information in the non-motorized vehicle identity association information set, the parking area allocation result is determined and sent to the terminal device of the non-motorized vehicle user to guide the non-motorized vehicle to enter the designated parking location.

[0125] Once non-motorized vehicle users arrive at their target movement area following the route guidance instructions, a suitable parking space needs to be assigned to them. This step involves collecting information on the occupancy status of the parking area and the vehicle's dimensions to ensure reasonable parking, improve the utilization rate of the parking area, and prevent haphazard parking.

[0126] Step S141: When a non-motorized vehicle arrives at the target movement area according to the campus route guidance instructions, the identification module in the IoT sensing device at the entrance of the target movement area collects the frame identification information of the non-motorized vehicle. The collected frame identification information of the non-motorized vehicle is matched with the non-motorized vehicle identity association information set. If the match is successful, it is confirmed that the non-motorized vehicle has arrived at the target movement area; if the match fails, the frame identification information of the non-motorized vehicle is collected again and matched with the non-motorized vehicle identity association information set again.

[0127] The identification modules in the IoT sensing devices deployed at the entrance of the target movement area typically employ radio frequency identification (RFID) or image recognition technology. When a non-motorized vehicle enters the entrance, the identification module automatically collects the vehicle's frame identification information. The collected frame identification information is transmitted to the system, where, in this embodiment, it is compared with the frame identification information in the non-motorized vehicle identity association information set. If a matching record is found, it is confirmed that the vehicle has arrived at the target movement area; if no matching record is found, it may be due to collection errors or the vehicle not being registered, and the identification module can be controlled to re-collect the frame identification information and perform matching again. If multiple matching attempts fail, an alarm will be issued and management personnel will be notified for handling.

[0128] Step S142: Activate the occupancy detection module in the IoT sensing device deployed in each parking area within the target mobile area, and collect the occupancy status information of each parking area. The occupancy status information includes whether a non-motorized vehicle has been parked in the parking area, the frame identification information of the parked non-motorized vehicle, and the remaining space size of the parking area.

[0129] After confirming that a non-motorized vehicle has arrived at the target mobile area, a command can be sent to the IoT sensing devices in each parking area within the target mobile area to activate the occupancy detection module. The occupancy detection module can detect the occupancy status of the parking area through infrared sensors, ultrasonic sensors, or video image analysis. If a vehicle is detected in the area, the frame identification information of the parked non-motorized vehicle is recorded (obtained by reading the positioning tag on the vehicle or license plate recognition), and the remaining space size of the parking area is calculated; if no vehicle is detected, it is recorded as an vacant state, and the remaining space size is recorded as the total space size of the parking area.

[0130] Step S143: Extract the model information of the current non-motorized vehicle from the non-motorized vehicle identity association information set, and determine the size information of the non-motorized vehicle based on the model information of the current non-motorized vehicle. The size information of the non-motorized vehicle includes the vehicle length information, vehicle width information and vehicle height information.

[0131] In this embodiment, the model information of the current non-motorized vehicle is extracted from the non-motorized vehicle identity association information set. Then, according to a preset model-to-size correspondence database, the vehicle's body length, width, and height information are queried. The model-to-size correspondence database stores standard size data for various common non-motorized vehicle models. If the size information for a particular model is not found in the database, it can be obtained by prompting the management personnel to manually input it or by on-site measurement through image recognition or other methods.

[0132] Step S144: Calculate the required parking area for non-motorized vehicles as the space requirement based on the vehicle length and width information in the non-motorized vehicle size information.

[0133] The required parking area for non-motorized vehicles is calculated by multiplying the vehicle's length and width. Considering the need for operational space when parking, a margin can be added to the calculated area as the final space requirement. For example, if the vehicle length is L, the width is W, and the margin coefficient is K (K is greater than 1), then the space requirement S = L × W × K.

[0134] Step S145: Analyze the remaining space size of the parking areas in the occupancy status information of each parking area within the target mobile area, compare the remaining space size of the parking areas with the calculated space requirements, and filter out the vacant parking areas whose remaining space size is greater than or equal to the space requirements.

[0135] In this embodiment, the occupancy status information of all parking areas within the target movement area is traversed to extract the remaining space size of each parking area. Then, the remaining space size of each parking area is compared with the current space demand of non-motorized vehicles. If the remaining space size is greater than or equal to the space demand, the parking area is considered an available parking area for the current vehicle and is added to the candidate parking area list.

[0136] Step S146: Sort the selected vacant parking areas by location, measure the distance between each selected vacant parking area and the entrance of the target mobile area, and sort the selected vacant parking areas by priority in order of distance from near to far.

[0137] In this embodiment, the straight-line distance or actual path distance between each candidate parking area and the entrance to the target mobile area is measured using a campus electronic map. Then, the candidate parking areas are prioritized according to their distance from nearest to farthest, with parking areas closer to the entrance having higher priority, thereby reducing the walking distance for users to park their vehicles.

[0138] Step S147: Retrieve the daily parking preference record corresponding to the identity information of non-motorized vehicle users from the campus non-motorized vehicle management database. The daily parking preference record includes the parking area location type previously selected by the non-motorized vehicle users. Based on the daily parking preference record, the priority of the vacant parking areas after priority sorting is adjusted again, and the priority of the vacant parking areas that are consistent with the parking area location type previously selected by the non-motorized vehicle users is increased.

[0139] The campus non-motorized vehicle management database stores the daily parking preferences of each non-motorized vehicle user, recording the types of parking areas the user frequently chooses, such as parking areas near dormitory buildings or near teaching building entrances. In this embodiment, this record is retrieved based on the user's identification information, and then it is checked whether the location type of the vacant parking areas after priority sorting matches the user's preference type. If they match, the priority of that parking area is appropriately increased to improve user satisfaction.

[0140] Step S148: Select the highest priority vacant parking area from the adjusted priority sorting results as the designated parking location for the current non-motorized vehicle, and generate a parking area allocation result containing the geographic coordinates, area number, and entrance location of the designated parking location.

[0141] In this embodiment, based on the adjusted priority ranking results, the highest priority vacant parking area is selected as the designated parking location for the current non-motorized vehicle. Then, a parking area allocation result is generated, which includes the geographical coordinates of the designated parking location (to facilitate users to find it on an electronic map), the area number (for internal system management and identification), and the parking area entrance location (to guide users into the parking area).

[0142] Step S149: The parking area allocation results are sent to the terminal devices of non-motorized vehicle users through the communication module of the campus non-motorized vehicle management system. At the same time, the guide indicator lights near the designated parking locations are activated to guide non-motorized vehicles into the designated parking locations.

[0143] For example, step S1491: Initiate a wireless communication connection request between the communication module of the campus non-motorized vehicle management system and the non-motorized vehicle user terminal equipment. After the terminal equipment responds to the request, a wireless communication connection is established. The wireless communication connection uses a dedicated communication frequency band within the campus.

[0144] The communication module of the campus non-motorized vehicle management system proactively initiates a wireless communication connection request to the terminal device of the non-motorized vehicle user. The request includes the system's identity identifier and encryption information to ensure connection security. Upon receiving the request, the user confirms their agreement to the connection, and the terminal device sends a response signal to the system. In this embodiment, a wireless communication connection is established between the terminal device and the system. To avoid interference with other wireless signals, the communication connection uses a dedicated communication frequency band applied for within the campus, which offers high communication quality and security.

[0145] Step S1492: Extract the geographic coordinates of the specified parking location, the area number of the specified parking location, and the entrance location of the parking area of ​​the specified parking location from the parking area allocation result. Convert the geographic coordinates of the specified parking location, the area number of the specified parking location, and the entrance location of the parking area of ​​the specified parking location from the parking area allocation result into a format that can be recognized by the terminal device. The format includes a text description format and a map coordinate format.

[0146] In this embodiment, the geographic coordinates, area number, and entrance location of the designated parking location are extracted from the parking area allocation results. Then, this information is converted into a format recognizable by the terminal device. The text description format is such as "Your designated parking location is parking area 12 in residential area A, with the entrance located east of the dormitory building in area A"; the map coordinate format converts the geographic coordinates into a coordinate format supported by the terminal device's map application, such as latitude and longitude coordinates or Cartesian coordinates.

[0147] Step S1493: The converted parking area allocation result is encapsulated into a data packet through the communication module of the campus non-motorized vehicle management system. A check code is added to the data packet, and the data packet is sent to the terminal device of the non-motorized vehicle user.

[0148] In this embodiment, the converted parking area allocation results are encapsulated into data packets according to a specific protocol format. To ensure the accuracy of data transmission, a checksum is added to the data packets, which is calculated using a specific algorithm based on the data packet content. The encapsulated data packets are then sent to the non-motorized vehicle user's terminal device via an established wireless communication connection.

[0149] Step S1494: After the non-motorized vehicle user's terminal device receives the data packet, it runs the built-in verification program to verify the check code in the data packet; if the verification passes, it parses the parking area allocation result information in the data packet; if the verification fails, it sends a retransmission request to the communication module of the campus non-motorized vehicle management system, and performs verification again after receiving the retransmitted data packet.

[0150] After receiving the data packet, the non-motorized vehicle user's terminal device can automatically run the built-in verification program. The verification program uses the same algorithm as the system to calculate the data packet content, obtain a checksum, and compare it with the checksum in the data packet. If they match, the verification passes, and the terminal device parses the data packet to extract the parking area allocation result information. If they do not match, the verification fails, and the terminal device sends a retransmission request to the system's communication module. In this embodiment, after receiving the request, the data packet is retransmitted, and the terminal device performs verification again. If multiple verifications fail, the user is prompted that they cannot obtain parking area information and is advised to contact the management personnel.

[0151] Step S1495: The parsed parking area allocation results are displayed to non-motorized vehicle users in the form of a pop-up prompt through the terminal device. At the same time, the location of the designated parking location is marked in the campus map application of the terminal device. Based on the current location information of the terminal device and the location of the designated parking location, a short-distance guidance route from the current location of the terminal device to the designated parking location is generated.

[0152] After the terminal device parses the parking area allocation results, it can display them on the screen as a pop-up window to remind the user to check. Simultaneously, the campus map application on the terminal device automatically opens, marking the specific location of the designated parking spot on the map and displaying the area number and entrance location. Then, based on the terminal device's current location information (obtained via mobile phone GPS, etc.) and the location of the designated parking spot, a short guidance route is generated in the map application. This short guidance route includes detailed turning prompts and distance information, guiding the user from their current location to the designated parking spot.

[0153] Step S1496: Send a control signal to the guidance and control system within the target movement area, the control signal including the area number of the designated parking location and guidance instructions.

[0154] In this embodiment, while sending the parking area allocation result to the user terminal device, a control signal can also be sent to the guidance and control system within the target movement area. The control signal includes the area number specifying the parking location, so that the guidance and control system can determine the guidance indicator light that needs to be controlled; the guidance command instructs the guidance and control system to activate the guidance indicator light.

[0155] Step S1497: After receiving the control signal, the guidance control system determines the guide indicator corresponding to the designated parking location based on the area number of the designated parking location. The guide indicator is installed at the entrance of the parking area and at key turning positions within the parking area.

[0156] After receiving the control signal, the guidance control system parses the area number of the designated parking location. Then, according to the correspondence table between area numbers and guidance indicator lights, it determines all the guidance indicator lights corresponding to that area number. These guidance indicator lights are distributed at the entrance of the parking area and at key turning points within the area, forming a guidance path.

[0157] Step S1498: Send a start signal to the designated guide indicator. After the guide indicator receives the start signal, it switches its own state to flashing mode and flashes a different color than the indicator lights in other areas.

[0158] The guidance control system sends a start signal to the designated guide indicator light. This start signal contains flashing frequency and color control information. Upon receiving the start signal, the guide indicator light's internal control circuit switches to flashing mode and flashes at the set frequency. To distinguish it from indicator lights in other areas, a specific color, such as blue, is used for flashing, while indicator lights in other areas may be green or red.

[0159] Step S1499: The non-motorized vehicle is parked in real time by using the position detection module in the IoT sensing device deployed at the designated parking location. If the non-motorized vehicle is detected to be parked in place, a position signal is sent to the campus non-motorized vehicle management system; if it is not detected, the detection continues.

[0160] The IoT sensing devices deployed at designated parking locations employ position detection modules using pressure sensors or infrared beam sensors. When a non-motorized vehicle is parked in the designated location, the pressure sensor detects a pressure change, or the infrared beam sensor is blocked, allowing the position detection module to determine that the vehicle is parked correctly. It then sends a position signal to the campus non-motorized vehicle management system, which includes the vehicle's frame identification information and parking location number. If the vehicle is not detected as parked correctly, the position detection module continues to monitor until the vehicle is parked correctly or the timeout period expires (after which management personnel will be notified).

[0161] Step S14910: After receiving the position signal, the campus non-motorized vehicle management system sends a stop signal to the guidance control system. After receiving the stop signal, the guidance control system sends a close signal to the corresponding guidance indicator light. After receiving the close signal, the guidance indicator light turns off.

[0162] After receiving the parking signal, the campus non-motorized vehicle management system confirms that the vehicle has been successfully parked. Then, it sends a stop signal to the guidance control system, instructing it to cease guidance. Upon receiving the stop signal, the guidance control system sends a turn-off signal to the previously activated guidance indicator lights. Upon receiving the turn-off signal, the guidance indicator lights stop flashing and turn off, awaiting the next guidance instruction.

[0163] Step S150: After a non-motorized vehicle user triggers the application to leave school, based on the non-motorized vehicle identity association information set, the parking area allocation result, and the current status information of the non-motorized vehicle collected by the IoT sensing device, non-motorized vehicle departure verification information is generated. The non-motorized vehicle departure verification information is verified by the IoT verification device at the campus entrance and exit. After the verification is successful, a non-motorized vehicle departure status record is generated and stored in the campus non-motorized vehicle management database.

[0164] When non-motorized vehicle users need to leave the campus, they need to submit a leave application and undergo verification. This step ensures that the vehicle is in good condition when leaving the campus, preventing theft or damage, and also records the vehicle's departure information for campus management purposes.

[0165] Step S151: After the non-motorized vehicle user triggers the leave-from-school application operation through the terminal device, the leave-from-school application signal is received. The leave-from-school application signal contains the non-motorized vehicle user's identity information and application time information.

[0166] Non-motorized vehicle users can find the "Leave Campus Application" function on their terminal devices and click to trigger the application. The terminal device automatically obtains the user's identification information (such as student ID, employee ID, etc.) and the current application time information, encrypts the above information, generates a leave campus application signal, and sends it to the campus non-motorized vehicle management system. In this embodiment, after receiving the leave campus application signal, it is decrypted and parsed to extract the user's identification information and application time information.

[0167] Step S152: Based on the identity information of the non-motorized vehicle user, retrieve the corresponding basic information of the non-motorized vehicle, the real-time location information of the non-motorized vehicle, and the frame identification information of the non-motorized vehicle from the non-motorized vehicle identity association information set.

[0168] In this embodiment, the corresponding vehicle record is searched in the non-motorized vehicle identity association information set based on the user's identity identification information. Then, the basic information of the non-motorized vehicle (such as brand, model, color, etc.), real-time location information (to confirm whether the vehicle is currently on campus), and frame identification information are retrieved from the record.

[0169] Step S153: Based on the non-motorized vehicle frame identification information, retrieve the designated parking location information corresponding to the non-motorized vehicle from the parking area allocation results. The designated parking location information includes the geographical coordinates and area number of the designated parking location.

[0170] In this embodiment, the retrieved non-motorized vehicle frame identification information is used to query the parking area allocation results to find the designated parking location information corresponding to the frame identification. The geographical coordinates in the designated parking location information are used to confirm whether the vehicle is in the correct parking location, and the area number is used to locate the specific parking area.

[0171] Step S154: Collect the current status information of non-motorized vehicles through the status detection module in the IoT sensing device deployed at the designated parking location. For electric non-motorized vehicles, collect the battery status, whether the non-motorized vehicle is in the designated parking location, and whether there is obvious damage to the non-motorized vehicle body. For non-electric non-motorized vehicles, collect whether the non-motorized vehicle is in the designated parking location and whether there is obvious damage to the non-motorized vehicle body.

[0172] The status detection module in the IoT sensing device at the designated parking location collects different status data based on the vehicle type. For electric non-motorized vehicles, the status detection module collects the battery status by connecting to the vehicle's charging port or through wireless sensing; it determines whether the vehicle is in the designated parking location through location tag signals or a position detection module; and it captures images of the vehicle's exterior using an image sensor, performs image analysis, and determines whether there is any obvious damage to the vehicle body, such as scratches or dents. For non-electric non-motorized vehicles, the status detection module mainly determines whether the vehicle is in the designated parking location through a position detection module and determines whether there is any obvious damage to the vehicle body through image analysis.

[0173] Step S155: Integrate the retrieved basic information of the non-motorized vehicle, real-time location information of the non-motorized vehicle, frame identification information of the non-motorized vehicle, designated parking location information, and current status information of the non-motorized vehicle to generate non-motorized vehicle departure verification information containing all the above information. During the integration process, the information can be organized according to a preset format to ensure the completeness and accuracy of the information. For example, the brand, model, color, etc. in the basic information of the non-motorized vehicle can be associated with the frame identification information, the real-time location information can be matched with the designated parking location information in terms of spatial coordinates, and the current status information such as the battery status, parking location status, and vehicle damage status can be recorded accordingly.

[0174] Step S156: Send the non-motorized vehicle leaving school verification information to the IoT verification device at the school entrance and exit. The IoT verification device includes an information reading module and a comparison module.

[0175] In this embodiment, the integrated non-motorized vehicle departure verification information is encrypted and sent to IoT verification devices deployed at various campus entrances and exits via the campus's secure communication network. These IoT verification devices are dedicated terminal devices for vehicle departure verification, integrating information reading and comparison modules to read and verify vehicle information.

[0176] Step S157: Read the frame identification information from the positioning tag installed on the non-motorized vehicle through the information reading module of the IoT verification device, and compare the read frame identification information with the non-motorized vehicle frame identification information in the non-motorized vehicle leaving school verification information.

[0177] The information reading module of the IoT verification device uses signal receiving technology compatible with the positioning tag, such as RFID reading technology. When a non-motorized vehicle approaches the IoT verification device at the entrance / exit, the information reading module actively scans and reads the frame identification information stored in the positioning tag on the vehicle. After reading, the frame identification information is immediately transmitted to the comparison module, which compares it bit by bit with the frame identification information in the received non-motorized vehicle departure verification information to determine whether the two are completely consistent.

[0178] Step S158: The comparison module of the IoT verification device retrieves the corresponding basic information of non-motorized vehicles from the campus non-motorized vehicle management database based on the read vehicle frame identification information, and performs a second comparison between the retrieved basic information of non-motorized vehicles and the basic information of non-motorized vehicles in the non-motorized vehicle departure verification information.

[0179] If the initial comparison (frame identification information comparison) passes, the comparison module will access the campus non-motorized vehicle management database via the network based on the retrieved frame identification information to retrieve the complete basic information of the non-motorized vehicle corresponding to that frame identification, including detailed data such as brand, model, and color. Subsequently, the retrieved basic information will be compared item by item with the basic information carried in the non-motorized vehicle departure verification information to ensure that the basic characteristics of the vehicle are consistent and to prevent the misuse or tampering with of the frame identification.

[0180] Step S159: If the two comparison results are consistent, and the current status information of the non-motorized vehicle shows that the non-motorized vehicle is in the designated parking location and there is no abnormality, then the verification of the non-motorized vehicle leaving school is deemed to have passed; if any comparison result is inconsistent, or the current status information of the non-motorized vehicle shows that the non-motorized vehicle is not in the designated parking location or there is an abnormality, then the verification of the non-motorized vehicle leaving school is deemed to have failed, and the relevant information is collected again and verified again.

[0181] In this embodiment, the results of the two comparisons and the current status information of the non-motorized vehicle are comprehensively judged. If the results of the two comparisons are consistent, it indicates that the vehicle identity information is accurate. At the same time, the current status information of the non-motorized vehicle must show that the vehicle is in the designated parking location (confirmed by comparing the real-time location information with the geographical coordinates of the designated parking location), and the vehicle body has no obvious damage (image analysis results show no abnormalities), and the battery status of the electric non-motorized vehicle is normal (the battery value is within the preset normal range). Only when all these conditions are met is the verification considered successful. If any comparison is inconsistent, or the vehicle is not in the designated location, the vehicle body is damaged, or the electric vehicle has an abnormal battery, the verification is considered unsuccessful. At this time, the IoT verification device will issue a prompt signal to notify the user of the verification failure and automatically trigger the information re-collection process, reread the frame identification information, retrieve basic information and compare it, and re-acquire the vehicle's current status information. If the verification still fails after multiple attempts, the manual verification process is initiated, and the entrance and exit management personnel intervene.

[0182] Step S1510: After the non-motorized vehicle leaving school verification information is verified, a non-motorized vehicle leaving school status record is generated, which includes the non-motorized vehicle leaving school time, leaving school entrance / exit number, non-motorized vehicle frame identification information and non-motorized vehicle user identification information.

[0183] Upon successful verification, in this embodiment, the current system time is immediately recorded as the non-motorized vehicle's departure time. The campus entrance / exit number where the IoT verification device is located is obtained. Combined with the non-motorized vehicle frame identification information and the corresponding non-motorized vehicle user identification information, a complete non-motorized vehicle departure status record is generated according to a preset data format. This non-motorized vehicle departure status record clearly reflects the key information of the vehicle's departure, facilitating subsequent traceability and management.

[0184] Step S1511: Store the non-motorized vehicle departure status records in the campus non-motorized vehicle management database in chronological order, find the record corresponding to the non-motorized vehicle from the non-motorized vehicle identity association information set, and update the non-motorized vehicle status in the record to "departed from school".

[0185] In this embodiment, the generated non-motorized vehicle departure status records are transmitted to the campus non-motorized vehicle management database. The database stores the records in the order they were generated to ensure the timeliness of the data. Simultaneously, in this embodiment, the corresponding record for the non-motorized vehicle is retrieved from the non-motorized vehicle identity association information set, and the "non-motorized vehicle status" field in the record is updated from "on campus" or other statuses to "departed from campus." Thus, the campus non-motorized vehicle management system can monitor the dynamic status of vehicles in real time.

[0186] Through the detailed description of the above embodiments, the complete implementation process of the IoT-based smart campus non-motorized vehicle management method can be clearly seen. From the collection and association of basic vehicle information and real-time location information, to the determination of target movement areas and the generation of route guidance instructions, to the allocation of parking areas and the verification and recording upon leaving the school, each step is closely linked, forming a closed-loop management system. It fully utilizes IoT technology, database technology, and communication technology to achieve intelligent and refined management of non-motorized vehicles on campus, effectively improving the safety and order of campus traffic and providing teachers and students with a convenient and efficient non-motorized vehicle usage experience. Simultaneously, during data collection and processing, relevant laws and regulations are strictly followed, and sensitive data involving user identity and other privacy are encrypted during storage and transmission to ensure data security and privacy. For example, encryption algorithms are used during the transmission of user identity information, and sensitive fields are de-identified during database storage to prevent privacy data leakage. The design and implementation of the entire system fully considers the actual needs and application scenarios of the campus, possessing strong practicality and operability. Those skilled in the art can successfully implement the solution of this invention by following the content described in the above embodiments.

[0187] Based on the same inventive concept, please refer to Figure 2 This paper shows a schematic block diagram of the structure of an IoT-based smart campus non-motorized vehicle management system 100 for executing the above-described IoT-based smart campus non-motorized vehicle management method, provided in an embodiment of this application. The IoT-based smart campus non-motorized vehicle management system 100 may include a communication unit 110, a machine-readable storage medium 120, and a processor 130.

[0188] In this embodiment, both the machine-readable storage medium 120 and the processor 130 are located within the IoT-based smart campus non-motorized vehicle management system 100 and are separately configured. However, it should be understood that the machine-readable storage medium 120 may also be independent of the IoT-based smart campus non-motorized vehicle management system 100 and may be accessed by the processor 130 via a bus interface. Alternatively, the machine-readable storage medium 120 may be integrated into the processor 130 and may communicate and interact with external systems through the communication unit 110.

[0189] The processor 130 is the control center of the IoT-based smart campus non-motorized vehicle management system 100. It connects various parts of the system via various interfaces and lines, and performs various functions and processes data by running or executing software programs and / or modules stored in the machine-readable storage medium 120 and calling data stored in the machine-readable storage medium 120, thereby providing overall monitoring of the IoT-based smart campus non-motorized vehicle management system 100. Optionally, the processor 130 may include one or more processing cores; for example, the processor 130 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. In this embodiment, it is understood that the modem processor may not be integrated into the processor. The machine-readable storage medium 120 is used to store machine-executable instructions for executing the scheme of this application, and the processor 130 is used to execute the machine-executable instructions stored in the machine-readable storage medium 120 to implement the IoT-based smart campus non-motorized vehicle management method provided in the aforementioned method embodiments.

[0190] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A smart campus non-motorized vehicle management method based on the Internet of Things, characterized in that, The method includes: Obtain basic information of non-motorized vehicles on campus and real-time location information of non-motorized vehicles collected by IoT sensing devices, and associate the basic information of non-motorized vehicles on campus with the real-time location information of non-motorized vehicles to generate a set of non-motorized vehicle identity association information. Based on the pre-defined regional functional division standards within the campus and the usage purpose information provided by non-motorized vehicle users, combined with the non-motorized vehicle type information in the non-motorized vehicle identity association information set, the target movement area of ​​non-motorized vehicles is determined. Based on the target movement area of ​​non-motorized vehicles and the campus route access status information collected by IoT sensing devices, combined with the real-time location information of non-motorized vehicles in the non-motorized vehicle identity association information set, a campus route guidance instruction is generated and sent to the terminal device of the non-motorized vehicle user. After non-motorized vehicles arrive at the target movement area according to the campus route guidance instructions, the occupancy status information of each parking area in the target movement area is collected by the Internet of Things sensing device. Combined with the non-motorized vehicle size information in the non-motorized vehicle identity association information set, the parking area allocation result is determined and sent to the terminal device of the non-motorized vehicle user to guide the non-motorized vehicle to enter the designated parking location. After a non-motorized vehicle user triggers the application to leave campus, the system generates non-motorized vehicle departure verification information based on the non-motorized vehicle identity association information set, parking area allocation results, and current status information of non-motorized vehicles collected by IoT sensing devices. The non-motorized vehicle departure verification information is then verified by IoT verification devices at the campus entrances and exits. Once the verification is successful, a non-motorized vehicle departure status record is generated and stored in the campus non-motorized vehicle management database.

2. The smart campus non-motorized vehicle management method based on the Internet of Things according to claim 1, characterized in that, The process involves acquiring basic information about non-motorized vehicles on campus and real-time location information collected by IoT sensing devices, then associating the basic information with the real-time location information to generate a set of non-motorized vehicle identity association information, including: The campus non-motorized vehicle registration system collects basic information about campus non-motorized vehicles, including brand information, model information, color information, frame identification information, and user identification information. The real-time location information of non-motorized vehicles is collected by the positioning module in the IoT sensing device deployed in various areas of the campus. The positioning module in the IoT sensing device obtains location data by receiving signals sent by the positioning tag installed on the non-motorized vehicle. The basic information of non-motorized vehicles on campus is formatted and processed, and records with missing information are removed, while records with complete information are retained. Signal noise reduction processing is performed on the real-time location information of non-motorized vehicles collected by IoT sensing devices to remove abnormal location data caused by signal interference and retain normal location data that conforms to the geographical range of the campus. The complete basic information records of non-motorized vehicles on campus are matched with normal location data that conforms to the geographical range of the campus according to the frame identification information, so that each frame identification information corresponds to a unique complete basic information record of non-motorized vehicles on campus and normal location data that conforms to the geographical range of the campus. The matched information is integrated into a structured data set, which serves as the non-motorized vehicle identity association information set.

3. The smart campus non-motorized vehicle management method based on the Internet of Things according to claim 1, characterized in that, The method for determining the target movement area of ​​a non-motorized vehicle based on the pre-defined regional functional division standards within the campus and the usage purpose information provided by non-motorized vehicle users, combined with the non-motorized vehicle type information in the non-motorized vehicle identity association information set, includes: The preset regional functional division standards within the campus are retrieved from the campus management system. These preset regional functional division standards include the geographical boundaries of teaching areas, office areas, living areas, sports areas, and logistics support areas. Receive usage purpose information submitted by non-motorized vehicle users through terminal devices, wherein the usage purpose information includes a description of the functional type of the area that the non-motorized vehicle user plans to visit; Extract the model information corresponding to each non-motorized vehicle from the non-motorized vehicle identity association information set, and determine the type information of the non-motorized vehicle based on the model information corresponding to each non-motorized vehicle. The type information of the non-motorized vehicle includes electric bicycle type, ordinary bicycle type and electric scooter type. Based on the pre-set functional zoning standards within the campus, the access rules for various types of non-motorized vehicles are determined for different functional areas. The access rules include the types of non-motorized vehicles that are allowed to enter and the types of non-motorized vehicles that are prohibited from entering. The target area function type described in the usage purpose information provided by non-motorized vehicle users is compared with the access rules to filter out candidate areas that meet the access rules. By combining the non-motor vehicle type information in the non-motor vehicle identity association information set, the candidate areas that meet the access rules are further filtered, and candidate areas that do not allow the current type of non-motor vehicle to enter are excluded. Based on the remaining candidate areas after filtering, and combined with the location distribution of the department or college corresponding to the identity information of the non-motorized vehicle user, the distance between each remaining candidate area after filtering and the department or college to which the non-motorized vehicle user belongs is calculated, and the remaining candidate area after filtering with the closest distance is selected as the target movement area of ​​the non-motorized vehicle.

4. The smart campus non-motorized vehicle management method based on the Internet of Things according to claim 1, characterized in that, The process of generating campus route guidance instructions based on the target movement area of ​​non-motorized vehicles and the campus route access status information collected by IoT sensing devices, combined with the real-time location information of non-motorized vehicles in the non-motorized vehicle identity association information set, and sending the campus route guidance instructions to the terminal device of non-motorized vehicle users includes: Traffic flow detection modules in IoT sensing devices deployed on various paths within the campus collect information on the traffic status of paths within the campus. This information includes the current number of vehicles on each path segment, vehicle speed, and whether there are any temporary blockages on the path. Extract the real-time location information of the current non-motorized vehicle from the non-motorized vehicle identity association information set to determine the starting point of the current non-motorized vehicle's path; Based on the current starting point location of the non-motorized vehicle and the geographical scope of the non-motorized vehicle's target movement area, mark the starting point coordinates and target area coordinates on the campus electronic map; Based on the path network data in the campus electronic map, multiple alternative paths are generated from the coordinates of the starting point of the path to the coordinates of the target area. Each alternative path contains multiple consecutive path segments. The campus route traffic status information corresponding to each route segment in each alternative route is analyzed, and the average traffic speed and estimated travel time of each alternative route are calculated. The average traffic speed is calculated by weighting the vehicle traffic speed of each route segment, and the estimated travel time is calculated based on the total length of the alternative route and the average speed. By combining the non-motorized vehicle type information in the non-motorized vehicle identity association information set, the upper limit of the permitted travel speed for different types of non-motorized vehicles on each route segment is determined. The calculated estimated travel time is compared with the maximum permitted travel time corresponding to the upper limit of the permitted travel speed for that type of non-motorized vehicle on each route segment. If the calculated estimated travel time is less than or equal to the maximum permitted travel time, the calculated estimated travel time is retained; if the calculated estimated travel time is greater than the maximum permitted travel time, the estimated travel time for the alternative route is recalculated. The path with the shortest estimated travel time is selected from the revised alternative paths as the optimal guidance path; Based on the order of each path segment in the optimal guidance path, generate campus path guidance instructions that include turning prompts, distance prompts, and passage precautions for each path segment; The campus non-motorized vehicle management system sends campus route guidance instructions to the non-motorized vehicle user's terminal device in the form of text and map markers through the communication module between the campus non-motorized vehicle management system and the non-motorized vehicle user's terminal device.

5. The smart campus non-motorized vehicle management method based on the Internet of Things according to claim 1, characterized in that, After a non-motorized vehicle arrives at the target movement area according to the campus route guidance instructions, the occupancy status information of each parking area within the target movement area is collected through IoT sensing devices. This information, combined with the non-motorized vehicle size information in the non-motorized vehicle identity association information set, determines the parking area allocation result and sends the parking area allocation result to the non-motorized vehicle user's terminal device to guide the non-motorized vehicle to the designated parking location. This includes: When a non-motorized vehicle arrives at the target movement area according to the campus route guidance instructions, the identification module in the IoT sensing device at the entrance of the target movement area collects the frame identification information of the non-motorized vehicle. The collected frame identification information of the non-motorized vehicle is matched with the non-motorized vehicle identity association information set. If the match is successful, it is confirmed that the non-motorized vehicle has arrived at the target movement area; if the match fails, the frame identification information of the non-motorized vehicle is collected again and matched with the non-motorized vehicle identity association information set again. The occupancy detection module in the IoT sensing device deployed in each parking area within the target mobile area is activated to collect occupancy status information of each parking area. The occupancy status information includes whether a non-motorized vehicle is parked in the parking area, the frame identification information of the parked non-motorized vehicle, and the remaining space size of the parking area. Extract the model information of the current non-motorized vehicle from the non-motorized vehicle identity association information set, and determine the size information of the non-motorized vehicle based on the model information of the current non-motorized vehicle. The size information of the non-motorized vehicle includes the vehicle length information, vehicle width information, and vehicle height information. Based on the body length and body width information in the non-motorized vehicle size information, the required parking area for non-motorized vehicles is calculated as the space requirement. Analyze the remaining space size of each parking area in the occupancy status information of the target mobile area, compare the remaining space size of the parking area with the calculated space requirement, and filter out the vacant parking areas whose remaining space size is greater than or equal to the space requirement. The selected vacant parking areas are sorted by location, and the distance between each selected vacant parking area and the entrance of the target mobile area is measured. The selected vacant parking areas are then prioritized according to the distance from closest to furthest. Retrieve daily parking preference records corresponding to the identity information of non-motorized vehicle users from the campus non-motorized vehicle management database. These daily parking preference records include the parking area location types previously selected by non-motorized vehicle users. Based on these daily parking preference records, the priority of the vacant parking areas after priority sorting is adjusted again, and the priority of vacant parking areas that are consistent with the parking area location types previously selected by non-motorized vehicle users is increased. Select the highest priority vacant parking area from the adjusted priority sorting results as the designated parking location for the current non-motorized vehicle, and generate a parking area allocation result containing the geographic coordinates, area number, and entrance location of the designated parking location; The campus non-motorized vehicle management system uses its communication module to send the parking area allocation results to the terminal devices of non-motorized vehicle users. At the same time, it activates the guide lights near the designated parking locations to guide non-motorized vehicles into the designated parking areas.

6. The smart campus non-motorized vehicle management method based on the Internet of Things according to claim 1, characterized in that, After a non-motorized vehicle user triggers a leave-from-school application, based on the non-motorized vehicle identity association information set, parking area allocation results, and current status information of the non-motorized vehicle collected by IoT sensing devices, non-motorized vehicle leave-from-school verification information is generated. This information is then verified by IoT verification devices at campus entrances and exits. Upon successful verification, a non-motorized vehicle leave-from-school status record is generated and stored in the campus non-motorized vehicle management database, including: After a non-motorized vehicle user triggers a leave-from-school application through a terminal device, a leave-from-school application signal is received. The leave-from-school application signal contains the non-motorized vehicle user's identity information and application time information. Based on the identity information of the non-motorized vehicle user, retrieve the corresponding basic information of the non-motorized vehicle, the real-time location information of the non-motorized vehicle, and the frame identification information of the non-motorized vehicle from the non-motorized vehicle identity association information set; Based on the non-motorized vehicle frame identification information, the designated parking location information corresponding to the non-motorized vehicle is retrieved from the parking area allocation results. The designated parking location information includes the geographical coordinates and area number of the designated parking location. The status detection module in the IoT sensing device deployed at the designated parking location collects the current status information of the non-motorized vehicle. For electric non-motorized vehicles, it collects the battery status, whether the non-motorized vehicle is in the designated parking location, and whether there is any obvious damage to the non-motorized vehicle body. For non-electric non-motorized vehicles, it collects whether the non-motorized vehicle is in the designated parking location and whether there is any obvious damage to the non-motorized vehicle body. The retrieved basic information of non-motorized vehicles, real-time location information of non-motorized vehicles, frame identification information of non-motorized vehicles, designated parking location information and current status information of non-motorized vehicles are integrated with the collected information to generate non-motorized vehicle departure verification information that includes the retrieved basic information of non-motorized vehicles, real-time location information of non-motorized vehicles, frame identification information of non-motorized vehicles, designated parking location information and current status information of non-motorized vehicles. The non-motorized vehicle leaving school verification information is sent to the Internet of Things (IoT) verification device at the school entrance and exit. The IoT verification device includes an information reading module and a comparison module. The information reading module of the IoT verification device reads the frame identification information from the positioning tag installed on the non-motorized vehicle, and compares the read frame identification information with the non-motorized vehicle frame identification information in the non-motorized vehicle leaving school verification information. The comparison module of the IoT verification device retrieves the corresponding basic information of non-motorized vehicles from the campus non-motorized vehicle management database based on the read vehicle frame identification information, and then compares the retrieved basic information of non-motorized vehicles with the basic information of non-motorized vehicles in the non-motorized vehicle leaving campus verification information. If the two comparison results are consistent, and the current status information of the non-motorized vehicle shows that the non-motorized vehicle is in the designated parking location and there is no abnormality, then the verification of the non-motorized vehicle leaving school is deemed to have passed; if any comparison result is inconsistent, or the current status information of the non-motorized vehicle shows that the non-motorized vehicle is not in the designated parking location or there is an abnormality, then the verification of the non-motorized vehicle leaving school is deemed to have failed, and the relevant information is collected again and verified again. After the non-motorized vehicle leaving school verification information is verified, a non-motorized vehicle leaving school status record is generated, which includes the non-motorized vehicle leaving school time, leaving school entrance and exit number, non-motorized vehicle frame identification information and non-motorized vehicle user identification information; The non-motorized vehicle departure status records are stored in the campus non-motorized vehicle management database in chronological order. The record corresponding to the non-motorized vehicle is found from the non-motorized vehicle identity association information set, and the non-motorized vehicle status in the record is updated to "departed from school".

7. The IoT-based smart campus non-motorized vehicle management method according to claim 2, characterized in that, The method involves collecting real-time location information of non-motorized vehicles through positioning modules in IoT sensing devices deployed in various areas of the campus. The positioning modules in these IoT sensing devices acquire location data by receiving signals from positioning tags installed on the non-motorized vehicles, including: IoT sensing devices are deployed at key locations in the teaching, office, living, sports and logistics support areas on campus. Each IoT sensing device is equipped with a positioning module and a signal receiving antenna. A positioning tag is installed on each registered non-motorized vehicle on campus. The positioning tag can periodically send wireless signals containing the non-motorized vehicle frame identification information. The sending period is set according to the size of the campus area. The signal receiving mode of the positioning module in the IoT sensing device is activated, and the wireless signals sent by the surrounding non-motorized vehicle positioning tags are received through the signal receiving antenna in the IoT sensing device. The signal strength of the received wireless signals is detected by the positioning module in the IoT sensing device, and the signal strength value of each wireless signal is recorded. Based on the signal strength values ​​of the wireless signals sent by the same non-motorized vehicle positioning tag received by multiple IoT sensing devices, and combined with the known geographical coordinates of each IoT sensing device, the location data of the non-motorized vehicle is calculated using the principle of triangulation algorithm. The validity of the calculated non-motorized vehicle location data is determined by comparing the calculated non-motorized vehicle location data with the coordinate interval defined by the campus geographical scope, and determining whether the calculated non-motorized vehicle location data falls within the coordinate interval. If the calculated location data of non-motorized vehicles falls within the coordinate range defined by the geographical scope of the campus, then the calculated location data of non-motorized vehicles shall be regarded as valid real-time location data of non-motorized vehicles. If the calculated location data of the non-motorized vehicle exceeds the coordinate range defined by the geographical scope of the campus, the calculated location data of the non-motorized vehicle will be discarded, and the location data of the non-motorized vehicle will be recalculated after receiving the positioning tag signal in the next transmission cycle. The effective real-time location data of non-motorized vehicles calculated in each cycle is bound with the corresponding non-motorized vehicle frame identification information to form a data pair containing non-motorized vehicle frame identification information and effective real-time location data of non-motorized vehicles. The associated data pair containing non-motorized vehicle frame identification information and valid real-time location data of non-motorized vehicles is transmitted to the database of the campus non-motorized vehicle management system. This associated data pair containing non-motorized vehicle frame identification information and valid real-time location data of non-motorized vehicles is stored as real-time location information of non-motorized vehicles.

8. The IoT-based smart campus non-motorized vehicle management method according to claim 3, characterized in that, The rules for accessing various types of non-motorized vehicles are determined based on the pre-defined functional zoning standards within the campus. These access rules include permitted and prohibited types of non-motorized vehicles, including: The usage time information of various functional areas within the campus is extracted from the campus management system. The usage time information includes class hours in the teaching area, office hours in the office area, open hours in the living area, activity hours in the sports area, and work hours in the logistics support area. By combining the spatial size and pedestrian density data of each functional area, the carrying capacity of each functional area for non-motorized vehicle passage is analyzed at different time periods. The carrying capacity analysis is based on the correspondence between the spatial size of the functional area and the number of non-motorized vehicles that can be accommodated per unit space, and the correspondence between pedestrian density and the space demand for non-motorized vehicle passage. Based on the carrying capacity analysis results, the upper limit of the number of non-motorized vehicles allowed to enter each functional area during different time periods is determined; For electric bicycles, ordinary bicycles and electric scooters, the impact of each type of non-motorized vehicle on pedestrian safety and regional order when passing through different functional areas is analyzed. The impact analysis is based on vehicle speed, vehicle size and interference factors generated during vehicle operation. For the teaching area, during class hours, based on the characteristics of dense crowds, electric bicycles and electric scooters are prohibited from entering the teaching area, while ordinary bicycles are allowed to pass through designated passages in the teaching area. For office areas, during office hours, electric bicycles and regular bicycles are allowed to enter the office area, and they must be parked in the designated parking areas. Electric scooters are prohibited from entering the office area. For residential areas, during open hours, electric bicycles, regular bicycles, and electric scooters are permitted to enter the residential areas. Once inside, they must follow the designated routes and park in the designated parking areas. For the sports area, during the activity period, electric bicycles, regular bicycles, and electric scooters are prohibited from entering the sports area; during non-activity periods, regular bicycles are allowed to enter the sports area. For the logistics support area, during working hours, based on the needs of logistics material transportation, it is determined that electric bicycles and ordinary bicycles are allowed to enter the logistics support area, while electric scooters are prohibited from entering the logistics support area. The rules for accessing different functional areas at different times, including the types of non-motorized vehicles allowed to enter, the types of non-motorized vehicles prohibited from entering, the maximum number of non-motorized vehicles allowed to enter, and the traffic requirements, are integrated to form a complete set of access rules. The complete access rules are stored in the rule database of the campus non-motorized vehicle management system.

9. The IoT-based smart campus non-motorized vehicle management method according to claim 4, characterized in that, Based on the path network data in the campus electronic map, multiple alternative paths are generated from the coordinates of the path's starting point to the coordinates of the target area. Each alternative path contains multiple consecutive path segments, including: The campus electronic map is retrieved from the map database of the campus non-motorized vehicle management system. The campus electronic map contains the geographic coordinate data, path width data, path material data, and regional node information of all paths within the campus. Mark the starting coordinates of the current non-motorized vehicle's path and the target area coordinates corresponding to the non-motorized vehicle's target movement area on the campus electronic map. The target area coordinates are the geometric center coordinates of the non-motorized vehicle's target movement area. Based on the path connection relationship in the campus electronic map, identify all area nodes that can be directly reached from the starting coordinates of the path, and use all area nodes that can be directly reached from the starting coordinates of the path as the first-level path nodes. For each first-level path node, continue to identify the next-level region node that can be directly reached from that first-level path node, and so on, until a region node containing the coordinates of the target region is identified. Analyze all possible path combinations from the starting point coordinates through each level of path nodes to the target area coordinates, identify invalid path combinations that are duplicated or detours, and remove them. Each valid path combination is broken down into multiple continuous path segments according to the intersections and turns of the path; Assign a unique path segment number to each path segment, and record the starting coordinates, ending coordinates, length data, and corresponding travel direction of each path segment; Each valid path containing multiple consecutive path segments is combined into a candidate path. Each candidate path contains a sequence of path segment numbers, the length data of each path segment, and the total path length. Perform an integrity check on all generated candidate paths to determine whether each candidate path can reach the target area coordinates completely from the path's starting coordinates, and whether all path segments in the candidate path are within the campus geographical area. If a candidate path can reach the target area coordinates completely from the path's starting coordinates and all path segments in the candidate path are within the campus geographical area, then the candidate path is retained; otherwise, the candidate path is regenerated. Alternative routes that pass the integrity check are stored in the route database of the campus non-motorized vehicle management system.

10. A smart campus non-motorized vehicle management system based on the Internet of Things, characterized in that, include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; The processor is configured to execute the IoT-based smart campus non-motorized vehicle management method according to any one of claims 1 to 9 by executing the machine-executable instructions.

Images