A method and system for positioning and managing non-motorized vehicles on campus

Abstract

This invention provides a method and system for the location management of non-motorized vehicles on campus, relating to the field of campus non-motorized vehicle management systems. The method includes: collecting student vehicle usage data; identifying student behavior patterns based on the data using a clustering algorithm; determining student movement trends based on these behavior patterns using a long short-term memory network; predicting vehicle demand at different times and locations on campus using a time series prediction model based on these movement trends; and adjusting vehicle distribution locations based on the predicted vehicle demand at different times and locations on campus using a resource optimization algorithm. This invention can improve the efficiency and accuracy of dynamic vehicle allocation, ensure the security and privacy of user data, and achieve more efficient, economical, and secure campus non-motorized vehicle management.

Description

Technical Field

[0001] This invention relates to the technical field of campus non-motorized vehicle management systems, and in particular to a campus non-motorized vehicle positioning management method and system. Background Technology

[0002] Campus non-motorized vehicles refer to small vehicles used by students, faculty, and staff within the campus that do not rely on engine power or use light electric assistance. These vehicles are mainly used for short-distance commuting or daily movement within the campus and are characterized by being environmentally friendly, convenient, and economical. Campus non-motorized vehicle positioning refers to the process of using technological means to obtain the real-time location information of non-motorized vehicles (such as bicycles, electric vehicles, scooters, etc.) within the campus for effective management, scheduling, and monitoring.

[0003] With the increasing population density on campus and the growing requirements for environmental sustainability, non-motorized vehicles, especially bicycles and electric scooters, have become the main means of transportation for many students and faculty members on campus. Therefore, the management of non-motorized vehicles on campus is of great significance for the efficiency and accuracy of dynamic vehicle allocation, the security and privacy of user data, and the safe management of non-motorized vehicles on campus.

[0004] Currently, most campus non-motorized vehicle management relies primarily on traditional manual methods, such as setting fixed parking spots and manually registering vehicle usage. This approach is not only labor-intensive but also inefficient, struggling to cope with peak-hour demand. Furthermore, with technological advancements, some campuses have begun experimenting with RFID or GPS-based vehicle tracking systems to better monitor vehicle location and usage status. While these technologies have improved vehicle management to some extent, they still suffer from drawbacks such as untimely data processing, high costs, and privacy concerns. Even with the use of IoT, big data, and AI technologies, inaccurate demand forecasting remains, leading to a mismatch between vehicle distribution and actual demand, hindering optimal resource allocation. Moreover, many systems are slow enough to process data and respond to user requests, failing to meet users' immediate needs. Deploying high-tech vehicle management systems is often costly and involves collecting large amounts of user data, potentially causing privacy breaches. Summary of the Invention

[0005] To address the shortcomings of existing technologies such as IoT, big data, and AI, which still suffer from inaccurate demand forecasting leading to a mismatch between vehicle distribution and actual demand, thus hindering optimal resource allocation, and the fact that many systems are slow enough to process data and respond to user needs, failing to meet users' immediate requirements, and that deploying high-tech vehicle management systems is often costly and involves collecting large amounts of user data, potentially leading to privacy breaches, this invention provides a campus non-motorized vehicle positioning management method and system.

[0006] The technical solutions provided by the embodiments of the present invention are as follows:

[0007] First aspect

[0008] This invention provides a campus non-motorized vehicle positioning management method, comprising:

[0009] S1: Collect data related to student vehicle usage;

[0010] S2: Based on student vehicle usage data, clustering algorithms are used to identify student behavior patterns;

[0011] S3: Based on student behavior patterns, determine student movement trends using the Long Short-Term Memory network;

[0012] S4: Based on student movement trends, use a time series forecasting model to predict vehicle demand at different times and locations on campus.

[0013] S5: Based on the predicted vehicle demand at different times and locations on campus, adjust the distribution of vehicles using a resource optimization algorithm.

[0014] Second aspect

[0015] This invention provides a campus non-motorized vehicle positioning management system, comprising:

[0016] processor;

[0017] The memory stores computer-readable instructions, which, when executed by the processor, implement the campus non-motorized vehicle positioning management method as described in the first aspect.

[0018] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0019] In this invention, the method collects vehicle usage data, analyzes student behavior patterns using clustering algorithms and Long Short-Term Memory (LSTM) networks to predict student movement trends, then uses the ARIMA time-series prediction model to predict vehicle demand at different times and locations on campus. Finally, combined with resource optimization algorithms, the distribution of vehicles and vehicle demand are adjusted to provide decision support for vehicle scheduling, ensuring a high degree of match between vehicle distribution and student demand, optimizing resource utilization, and reducing vehicle idle time. This invention integrates machine learning and data analysis techniques to optimize prediction and scheduling algorithms, improving the efficiency and accuracy of dynamic vehicle allocation, achieving optimal resource allocation, meeting users' immediate needs, reducing system deployment and maintenance costs, improving system scalability and flexibility, and enhancing user experience and data security. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating a campus non-motorized vehicle positioning management method provided in an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of a campus non-motorized vehicle positioning management method provided by an embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of a campus non-motorized vehicle positioning management system provided in an embodiment of the present invention. Detailed Implementation

[0024] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0025] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0026] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0027] Reference manual attached Figure 1 The diagram shows a flowchart of a campus non-motorized vehicle positioning management method provided by an embodiment of the present invention.

[0028] This invention provides a method for managing the location of non-motorized vehicles on campus. This method can be implemented using a campus non-motorized vehicle location management device, which can be a terminal or a server. The processing flow of the campus non-motorized vehicle location management method may include the following steps:

[0029] S1: Collect data related to student vehicle usage.

[0030] Among them, student vehicle usage data includes various information about students' use of non-motorized vehicles on campus. By comprehensively collecting student vehicle usage data, high-quality input is provided for subsequent behavior modeling and demand prediction. The diversity of this data (such as time, location, behavior, etc.) can accurately reflect students' actual usage habits and demand trends, thereby improving the accuracy of prediction.

[0031] In one possible implementation, student vehicle usage data includes: student class schedules, historical location data, vehicle usage records, weather conditions, and special campus events.

[0032] Reference manual attached Figure 2 The diagram shows a structural schematic of a campus non-motorized vehicle positioning management method provided by an embodiment of the present invention.

[0033] Figure 2 In this process, collecting data is the first step in building the model. This includes students' class schedules, historical location data, vehicle usage records, and other relevant information (such as weather conditions, special campus events, etc.). This data provides the necessary input for the model, helping it to more accurately capture the dynamic characteristics of student behavior.

[0034] Establishing a student behavior model is a fundamental step, providing core data support for the entire system. This model predicts student movement trends and vehicle demand by analyzing students' class schedules, historical location data, and vehicle usage habits. The output of this model directly affects the second step—dynamically adjusting vehicle distribution, because the optimization of vehicle allocation needs to be based on the predicted behavior and demand of students. In addition, the data from the student behavior model can also assist the third step, the real-time feedback and early warning system, by predicting and warning of possible abnormal or irregular behaviors through pattern recognition.

[0035] Dynamically adjusting vehicle distribution, as a direct action in response to the student behavior model, requires optimizing the geographical distribution of vehicles based on predicted data. The effectiveness of this step directly impacts the utility of the interactive intelligent interface in the fourth step, as the interface needs to display the latest and most accurate vehicle distribution information to the user. At the same time, dynamic adjustment also relies on the real-time feedback and early warning system in the third step to adjust its strategy, such as quickly adjusting vehicle positions based on real-time data to cope with emergencies.

[0036] The real-time feedback and early warning system monitors behavioral patterns derived from the student behavior model in the first step and uses this information to detect anomalies in real time. The output of this system helps in the dynamic adjustment of vehicle distribution in the second step, especially in handling emergencies and immediate needs. In addition, the feedback from the early warning system can also provide feedback to the behavior correction and incentive mechanism in the fifth step to adjust incentive measures and ensure that the incentive measures can address the problem in a targeted manner. The interactive intelligent interface is the front end that users directly interact with. It not only displays the results of the dynamic adjustment of vehicle distribution in the second step, but also provides user feedback and behavioral data to the system. This user data is crucial for refining the student behavior model in the first step. Furthermore, user interaction and feedback on the interface can serve as input for the behavior correction and incentive mechanism in the fifth step, helping the system better understand which incentive measures are most effective. The behavior correction and incentive mechanism directly affects student behavior. This influence is fed back to the system through user activities on the interactive intelligent interface. Successful behavior correction will optimize the accuracy of the student behavior model and improve the efficiency of dynamic vehicle distribution. At the same time, the results of behavior correction will also be monitored through the real-time feedback and early warning system to ensure that incentive measures can be adjusted and optimized in real time.

[0037] It should be noted that by collecting students' class schedules, historical location data, and vehicle usage habits, a personalized student behavior model is established. This model uses clustering algorithms and Long Short-Term Memory (LSTM) networks to analyze student behavior patterns, predict student movement trends and vehicle demand, and provide decision support for vehicle scheduling. Secondly, based on the output of the behavior model, the system dynamically adjusts the distribution of vehicles to meet real-time and predicted demand. The adjustment of vehicle distribution relies on time series forecasting and resource optimization algorithms to ensure efficient vehicle utilization. Furthermore, the real-time feedback and early warning system monitors vehicle usage status and student behavior through anomaly detection algorithms, promptly identifying and handling abnormal situations, improving the system's response speed and security. An interactive intelligent interface allows students to view nearby available vehicles, reserve vehicles, and view recommended routes, enhancing user experience and interactivity. Finally, behavior correction and incentive mechanisms encourage students to comply with vehicle usage rules, such as proper parking, through reward systems such as points rewards and coupons, thereby optimizing student behavior patterns. These measures, combined with social functions and gamification design, effectively stimulate student participation, improve usage habits, and effectively solve the problems of vehicle resource waste and inadequate management.

[0038] S2: Based on student vehicle usage data, clustering algorithms are used to identify student behavior patterns.

[0039] Clustering algorithms are a data analysis method used to group data points with similar characteristics. Here, it refers to using the weighted K-means algorithm to divide student behavior data into different pattern categories. Student behavior patterns reflect students' activity patterns and vehicle usage habits on campus, such as commuting during fixed time periods, frequent vehicle borrowing and returning, and usage preferences in specific activity areas.

[0040] It should be noted that clustering algorithms can quickly and accurately identify students' behavioral patterns, providing an important basis for subsequent movement trend prediction and vehicle demand analysis. Through clustering algorithms, complex behavioral data can be simplified into clear pattern categories, thereby helping the system understand students' usage habits and travel patterns. This classification can capture the characteristics of different groups (such as morning and evening commuting peaks and demand in special areas), improving the system's demand prediction capabilities.

[0041] In one possible implementation, S2 specifically refers to:

[0042] Identify student behavior patterns using the weighted K-means clustering algorithm:

[0043]

[0044] Where S represents the objective function value, k represents the total number of behavioral patterns, and C i w represents the i-th behavior pattern category. i μ represents the weight associated with the data point x contained in the i-th behavior pattern category. i Let || represent the weighted average position of all points in the i-th behavior pattern category, and || denotes the norm operation.

[0045] S3: Based on student behavior patterns, determine student movement trends using long short-term memory networks.

[0046] Among them, Long Short-Term Memory (LSTM) networks are a special type of recurrent neural network that solves the problem of long-term dependency through gating mechanisms (such as forget gate, input gate, and output gate) and are used for prediction of time series data. Student mobility trends are the potential movement patterns of students in non-motorized vehicles on campus and their possible future travel needs, such as the frequency of vehicle use on frequently used routes or at activity locations during a certain period.

[0047] Specifically, this network learns students' behavioral patterns through historical data, considering long-term dependencies in time series to predict students' future locations and vehicle usage needs. Each unit of the Long Short-Term Memory (LSTM) network contains multiple gating mechanisms (forget gate, input gate, output gate), which determine what information the network retains, discards, and outputs at each time step. In this way, the LTM network can effectively extract features from complex input sequences and perform accurate time series predictions.

[0048] It should be noted that by using Long Short-Term Memory (LSTM) networks, the long and short-term characteristics of time series data can be fully utilized to accurately predict students' future movement trends. Compared with traditional prediction methods, LSTM networks can effectively capture long-term dependencies in students' behavior, such as regular travel patterns, as well as the impact of short-term emergencies (such as temporary activities). By combining multiple input data (timetables, locations, weather, etc.), this method not only improves prediction accuracy but also dynamically adapts to changes in students' behavior, providing a scientific basis for subsequent vehicle distribution adjustments and resource scheduling.

[0049] In one possible implementation, S3 specifically refers to:

[0050] Based on different behavioral patterns, student mobility trends are determined using long short-term memory networks:

[0051] X t =[C t ;P t-1 E t-1 ]

[0052] f t =σ(W f [h t-1 ,X t ]+b f (Forgotten Gate)

[0053] i t =σ(W i [h t-1 ,X t ]+b i (Input Gate)

[0054] (Candidate cell status)

[0055] o t =σ(W o [h t-1 ,X t ]+b o (Output Gate)

[0056] (Unit status update)

[0057] h t =o t ⊙tanh(c t (Output status)

[0058] Among them, X t C represents the input at time t. t P represents the timetable data input at time t. t-1 E represents the position data input at time t-1. t-1 f represents additional features that influence student behavior and location selection based on the input at time t-1. t This represents the forget gate, σ represents the sigmoid activation function, and h represents the h-value. t-1 Represents the hidden state at time t-1, i t Indicates the input gate. Let tanh represent the possible new state at time t, and let o represent the hyperbolic tangent function. t Indicates the output gate, c t C represents the cell state retained after considering past states and the current candidate state. t-1 h represents the timetable data input at time t-1. t The output of the LSTM network at time t is used for student movement trend prediction. W represents the dot product operation. f W i W o and W c Each represents a weight matrix used to control the inputs of each gate and the influence of past states on the current state, b f b i b o and b c Both represent bias terms used to adjust the gating and candidate state activation levels.

[0059] It should be noted that this network structure allows the model to use past information (such as previous location and additional environmental factors) to predict students' future locations on campus. This is crucial for understanding students' movement patterns, predicting high-demand locations, and optimizing the distribution of non-motorized vehicles. By learning patterns extracted from historical behavior, this model can dynamically adapt to changes in student behavior and effectively predict future behavioral trends, thereby achieving more accurate and effective campus management.

[0060] S4: Based on student movement trends, use a time series forecasting model to predict vehicle demand at different times and locations on campus.

[0061] Among them, the time series forecasting model is a mathematical model used to process and predict time series data. The main model used here is ARIMA (AutoRegressive Integrated Moving Average), which is used to predict future vehicle demand based on historical data. Vehicle demand refers to the number of non-motorized vehicles needed at various locations and time periods on campus. It involves predicting how many non-motorized vehicles will be needed at a specific time and location to meet the students' needs.

[0062] It should be noted that time series forecasting models (such as ARIMA) can transform complex historical data and student movement trends into specific vehicle demand forecasts, thereby effectively planning vehicle resources. Compared to relying solely on experience or simple statistical analysis, time series models can consider seasonal and trend changes in the data, generating more accurate demand forecasts. This ARIMA model combines the trends and random fluctuations of historical demand data, using comprehensive historical data to predict future demand. The parameters of this model are usually obtained through statistical analysis of historical data to ensure the accuracy of the forecast.

[0063] In one possible implementation, S4 specifically refers to:

[0064] By integrating the ARIMA model, vehicle demand at different times and locations on campus can be predicted:

[0065] Y t =α+φ1Y t-1 +φ2Y t-2 +…+φ p Y t-p +θ1∈ t-1 +θ2∈ t-2 +…+θ q ∈ t-q +∈ t

[0066] Among them, Y t Y represents the predicted vehicle demand at time t. t-1 Y represents the predicted vehicle demand at time t-1. t-2 Let α represent the predicted vehicle demand at time t-2, and let φ1,…,φ2 represent the constants of the ensemble ARIMA model. p Denotes the autoregressive parameters, θ1,…,θ p Represents the moving average parameter, ∈ t-1 ,…,∈ t-q Represents the error term for the first q time points, ∈ t This represents the prediction error at the current time t.

[0067] Specifically, by using an integrated ARIMA model, the system can accurately predict vehicle demand at various times and locations in the future. This prediction takes into account historical demand trends, seasonal factors, and random fluctuations, providing a scientific basis for vehicle scheduling. Through the autoregressive and moving average parameters obtained from historical data analysis, the model can effectively capture dynamic changes in demand and predict future demand peaks and troughs.

[0068] S5: Based on the predicted vehicle demand at different times and locations on campus, adjust the distribution of vehicles using a resource optimization algorithm.

[0069] Among them, resource optimization algorithms refer to algorithms used to adjust and optimize resource allocation. Here, it specifically refers to algorithms used in campus non-motorized vehicle management to adjust the distribution of vehicles, such as linear programming or non-linear programming, to ensure that the distribution of vehicles matches the actual needs. The distribution of vehicles refers to the specific placement location of each non-motorized vehicle on campus.

[0070] It should be noted that this method of dynamically adjusting vehicle distribution not only allows for rapid response based on real-time data, but also enables proactive resource adjustments. This increases vehicle supply during peak demand periods and facilitates vehicle recycling and maintenance during off-peak periods. This strategy significantly improves the overall operational efficiency of non-motorized vehicles on campus and enhances student satisfaction.

[0071] In one possible implementation, S5 specifically includes:

[0072] Based on the prediction results, the distribution of vehicles is dynamically adjusted using a nonlinear programming algorithm:

[0073]

[0074] Among them, c ij Let x represent the scheduling cost from the i-th vehicle to the j-th demand point, where i = 1, ..., n, n represents the total number of vehicles, j = 1, ..., m, m represents the total number of demand points, and x represents the total number of demand points. ij Let x represent the decision variable for whether to dispatch the i-th vehicle to the j-th demand point. ij =0 indicates no scheduling, x ij =1 indicates scheduling. This indicates that the demand constraints are met. b represents the supply constraint condition. j Let d represent the vehicle demand at the j-th demand point during the forecast period. i This represents the number of vehicles available at the current time point for the i-th vehicle.

[0075] Specifically, a nonlinear programming method is used to optimize vehicle distribution. This optimization process not only considers cost minimization but also ensures that the supply of vehicles can meet the predicted demand. By setting specific constraints, such as vehicle supply limits and demand satisfaction criteria, the optimization algorithm can minimize scheduling costs while ensuring that all demand points are met.

[0076] In this optimization model, the goal is to achieve the optimal allocation of vehicle demand and supply by minimizing the total cost of vehicle scheduling. This model takes into account the cost of each vehicle to the demand point and the availability of vehicles, ensuring that the demand of all demand points is met while minimizing scheduling costs. During the optimization process, factors such as vehicle maintenance time and battery charging status may also be considered, as these will affect the actual availability of vehicles. The key to resource optimization algorithms is their ability to dynamically adjust vehicle distribution and respond to real-time changes in demand. For example, if a large demand for vehicles is predicted for a certain area, the system will schedule enough vehicles to go to that area in advance, while also considering recycling and charging plans. This intelligent scheduling not only reduces vehicle idle time but also improves vehicle utilization.

[0077] In one possible implementation, it also includes:

[0078] S6: Anomaly detection and early warning are performed using the isolated forest algorithm.

[0079] Among them, the Isolation Forest algorithm is particularly suitable for processing high-dimensional data and large-scale datasets. It can efficiently identify outlier data points. Isolation Forest "isolates" each observation point by constructing multiple random decision trees. Outlier points are usually isolated in the tree earlier due to their different attributes. Therefore, their path length is shorter than that of normal points. This path length is used to calculate the outlier score of each point. The higher the score, the more likely the point is to be an outlier.

[0080] It should be noted that the Isolation Forest algorithm can quickly and accurately identify abnormal situations in vehicle use and distribution, such as abnormal vehicle movement, abuse, or excessive concentration. The Isolation Forest algorithm is particularly suitable for processing high-dimensional data and large-scale datasets. It is highly efficient, requires no supervised learning, and can analyze vehicle status in real time and trigger warnings.

[0081] In one possible implementation, S6 specifically includes:

[0082] S601: Determine the outlier score for each data point using the Isolation Forest algorithm:

[0083]

[0084] Where score(x) represents the outlier score of data point x, E[h(x)] represents the average path length of data point x in the isolated forest, the shorter the path length, the more likely the data point is to be an anomaly, and c(n) represents the average path length of normal data when the sample size is n.

[0085] S602: Determine whether the abnormal score of each data point is greater than the dynamic threshold; if so, determine it as abnormal data and trigger an alarm; otherwise, continue monitoring.

[0086] Among them, data points refer to specific information records obtained through various types of data (such as location, vehicle usage behavior, etc.). Each data point represents specific information at a specific time and state. The anomaly score is a value calculated by the Isolation Forest algorithm to measure the degree of anomaly of a data point. The higher the score, the greater the difference in behavioral characteristics between the data point and other data, which may be an anomaly. The dynamic threshold is a standard that is adjusted in real time to determine whether a data point is an anomaly.

[0087] It should be noted that by comparing the anomaly score with the dynamic threshold, it is possible to flexibly and accurately determine which data points are abnormal, ensuring that anomalies are detected and responded to in a timely manner. Unlike fixed thresholds, dynamic thresholds can be automatically adjusted according to changes in historical data and the current environment, significantly reducing the probability of false alarms and missed alarms, thereby improving the robustness and practicality of the system. The alarm triggering mechanism can promptly notify management personnel to take measures to prevent problems from escalating, such as theft of non-motorized vehicles, improper parking, or system failures.

[0088] In one possible implementation, the dynamic threshold is specifically:

[0089]

[0090] Where θ(t) represents the dynamic threshold at time t, θ0 represents the standard threshold determined based on historical data during the initial system setup, i.e., the baseline threshold, α represents the adjustment coefficient used to control the impact of historical anomalies on the current threshold, and δ i This represents the abnormal change at time i, where i = 1, ..., t, and t represents the total number of times.

[0091] The warning threshold is dynamically adjusted based on recent abnormal activity and the current state of the system to avoid excessive false alarms or missed alarms.

[0092] It should be noted that dynamic threshold adjustment is key in real-time feedback and early warning systems. This mechanism enables the system to adaptively adjust the early warning threshold based on recent abnormal activities to reflect changes in the current environment or system state. For example, if the abnormal score is observed to gradually increase over several consecutive time points, the system will automatically increase the threshold to reduce the possibility of false alarms. Conversely, if the environment is stable, the system may lower the threshold to increase its sensitivity to anomalies.

[0093] In this invention, by monitoring data streams in real time and using isolated forests for anomaly detection, the system can quickly identify unusual vehicle usage patterns or student behaviors, such as unauthorized vehicle use or parking in unintended locations. Once these anomalies are detected, the system dynamically adjusts through an early warning mechanism, promptly alerting administrators and allowing them to react quickly to resolve potential problems. Furthermore, the implementation of this system makes significant contributions to maintaining campus safety, optimizing vehicle utilization, and improving student satisfaction. By reducing vehicle damage and misuse, schools can manage their resources more effectively while protecting students from potential safety risks. In summary, the real-time feedback and early warning system, through advanced data analysis technology and intelligent threshold adjustment strategies, provides a powerful tool to enhance the efficiency and safety of campus non-motorized vehicle management.

[0094] In practical applications, the interactive intelligent interface plays a crucial role in the campus non-motorized vehicle positioning management system. It provides a user-friendly platform that allows students and administrators to view and manage vehicles in real time. This interface not only needs to display real-time data but also needs to support user interaction, such as reserving vehicles and reporting problems.

[0095] Specifically, interactive intelligent interface design includes:

[0096] Use React as the front-end framework and Redux for state management:

[0097] state t =reducer(state) t-1 action t )

[0098] Where, state t This represents the application state at time t, where reducer represents the core function in the Redux architecture, and action represents the application state at time t. t This represents an action generated by the user or by the system at time t.

[0099] It's worth noting that using React as a front-end framework makes it possible to develop complex single-page applications (SPAs) due to its efficient update mechanism and component-based architecture. React allows developers to control component rendering through state management, making the interface responsive to user interactions. Redux, as a state management library, helps manage the state of the application, enabling multiple components to share data while maintaining state consistency and predictability. WebSocket is used for real-time communication, maintaining a continuous connection between the client and server, allowing the application to receive and send data in real time, such as vehicle location updates and reservation status.

[0100] Vehicle location and status information are obtained from the server via WebSocket.

[0101] Specifically, the system uses a WebSocket connection to obtain vehicle location and status information from the server in real time, such as the number of available vehicles and their current lending status, and dynamically updates the map and vehicle information list to display the real-time location and availability of vehicles.

[0102] Design a user interface based on vehicle location and status information.

[0103] It's worth noting that the system provides an intuitive user interface, allowing users to easily book and manage vehicles. Responsive design ensures a good user experience across various devices. Modern UI elements such as modal windows and sliding menus enhance the user experience, allowing users to book vehicles directly by clicking on a vehicle icon on the map. The system design also prioritizes data security and privacy, ensuring all communications are encrypted. The interface design allows for future additions of new features, such as vehicle fault reporting and a user rating system, to adapt to evolving management needs and user expectations.

[0104] In summary, the interactive intelligent interface not only enhances user convenience and satisfaction, but also greatly improves the efficiency and effectiveness of the campus non-motorized vehicle management system through real-time data processing and advanced user interaction technology. Through such a system, schools can better serve the campus community, optimize vehicle utilization, and reduce management costs and time.

[0105] In this invention, behavior correction and incentive mechanisms play a key role in the campus non-motorized vehicle positioning management system. The goal is to promote students' compliance with vehicle use rules through rewards and incentives, thereby improving the overall efficiency of vehicle use and the sustainability of the system. This mechanism aims to change students' behavioral habits through positive incentives, prompting them to use vehicle resources more responsibly.

[0106] Specifically, the incentive mechanism is as follows:

[0107] Rewardt =Reward t-1 +f(actions,compliance)

[0108] Among them, Reward t Reward represents the student's total reward score at time t. t-1 Let f(actions, compliance) represent the student's total reward score at time t-1, and let f(actions, compliance) represent the reward function that determines the reward based on the student's specific behavior and compliance with the rules.

[0109] It should be noted that this reward function is the core of the incentive system. It determines the reward score based on the user's specific behavior (such as returning the vehicle on time, parking the vehicle correctly, etc.) and compliance with the rules (such as complying with vehicle usage time and area restrictions). In this way, the system not only encourages positive behavior, but also provides specific rewards such as discounts and priority through the accumulation of points. The reward mechanism dynamically calculates the reward score generated by each behavior and updates the student's total reward amount in real time.

[0110] In this invention, the system monitors students' vehicle usage in real time using integrated sensors and data tracking technology. This data is used to evaluate each student's behavioral patterns, including vehicle usage frequency, accuracy of parking locations, and compliance with vehicle usage rules. The system regularly provides users with feedback on their behavior scores and reward points, encouraging them to self-adjust by reviewing their performance. This feedback can be delivered through in-app notifications, emails, or other communication channels. Furthermore, the system increases user engagement and incentives through gamification elements (such as leaderboards, badges, and achievement systems), making rule compliance and good behavior more attractive and competitive. By establishing community features, users can share their experiences and achievements, which not only enhances interaction among users but also encourages more students to comply with the rules through community pressure and positive incentives. Community interactions can include sharing stories about vehicle usage, exchanging best practice tips, and praising rule-followers.

[0111] Through behavior correction and incentive mechanisms, the management system can effectively improve students' compliance with vehicle management rules, reduce vehicle damage and management costs. In the long run, this mechanism helps cultivate students' sense of responsibility and awareness of sharing public resources, thereby improving the overall utilization efficiency and sustainability of campus vehicle resources. Overall, the behavior correction and incentive mechanisms, through systematic reward procedures and real-time feedback, not only enhance the user experience but also strengthen the long-term effects and influence of the campus non-motorized vehicle management system.

[0112] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0113] In this invention, the method collects vehicle usage data, analyzes student behavior patterns using clustering algorithms and Long Short-Term Memory (LSTM) networks to predict student movement trends, then uses the ARIMA time-series prediction model to predict vehicle demand at different times and locations on campus. Finally, combined with resource optimization algorithms, the distribution of vehicles and vehicle demand are adjusted to provide decision support for vehicle scheduling, ensuring a high degree of match between vehicle distribution and student demand, optimizing resource utilization, and reducing vehicle idle time. This invention integrates machine learning and data analysis techniques to optimize prediction and scheduling algorithms, improving the efficiency and accuracy of dynamic vehicle allocation, achieving optimal resource allocation, meeting users' immediate needs, reducing system deployment and maintenance costs, improving system scalability and flexibility, and enhancing user experience and data security.

[0114] Reference manual attached Figure 3 The diagram shows a structural schematic of a campus non-motorized vehicle positioning management system provided by the present invention.

[0115] The present invention also provides a campus non-motorized vehicle positioning management system 20, applied to the above-mentioned campus non-motorized vehicle positioning management method, comprising:

[0116] Processor 201.

[0117] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201, the campus non-motorized vehicle positioning management method as described in the method embodiment is implemented.

[0118] The campus non-motorized vehicle positioning management system 20 provided by the present invention can execute the above-mentioned campus non-motorized vehicle positioning management method and achieve the same or similar technical effects. To avoid duplication, the present invention will not elaborate further.

[0119] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0120] In this invention, the method collects vehicle usage data, analyzes student behavior patterns using clustering algorithms and Long Short-Term Memory (LSTM) networks to predict student movement trends, then uses the ARIMA time-series prediction model to predict vehicle demand at different times and locations on campus. Finally, combined with resource optimization algorithms, the distribution of vehicles and vehicle demand are adjusted to provide decision support for vehicle scheduling, ensuring a high degree of match between vehicle distribution and student demand, optimizing resource utilization, and reducing vehicle idle time. This invention integrates machine learning and data analysis techniques to optimize prediction and scheduling algorithms, improving the efficiency and accuracy of dynamic vehicle allocation, achieving optimal resource allocation, meeting users' immediate needs, reducing system deployment and maintenance costs, improving system scalability and flexibility, and enhancing user experience and data security.

[0121] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0122] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0123] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0124] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0125] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0126] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0127] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0128] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0129] In the embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0130] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0131] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0132] If the functionality is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0133] This invention provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the campus non-motorized vehicle positioning management method as described in the method embodiment.

[0134] The computer-readable storage medium provided by this invention can implement the steps and effects of the campus non-motorized vehicle positioning management method of the above-described method embodiments. To avoid repetition, this invention will not repeat them.

[0135] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0136] In this invention, the method collects vehicle usage data, analyzes student behavior patterns using clustering algorithms and Long Short-Term Memory (LSTM) networks to predict student movement trends, then uses the ARIMA time-series prediction model to predict vehicle demand at different times and locations on campus. Finally, combined with resource optimization algorithms, the distribution of vehicles and vehicle demand are adjusted to provide decision support for vehicle scheduling, ensuring a high degree of match between vehicle distribution and student demand, optimizing resource utilization, and reducing vehicle idle time. This invention integrates machine learning and data analysis techniques to optimize prediction and scheduling algorithms, improving the efficiency and accuracy of dynamic vehicle allocation, achieving optimal resource allocation, meeting users' immediate needs, reducing system deployment and maintenance costs, improving system scalability and flexibility, and enhancing user experience and data security.

[0137] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0138] The following points need to be explained:

[0139] (1) The accompanying drawings of the embodiments of the present invention only involve the structures involved in the embodiments of the present invention. Other structures can refer to the general design.

[0140] (2) For clarity, the thickness of layers or regions is enlarged or reduced in the drawings used to describe embodiments of the present invention; that is, these drawings are not drawn to actual scale. It is understood that when an element such as a layer, film, region, or substrate is referred to as being “above” or “below” another element, the element may be “directly” located “above” or “below” the other element, or there may be intermediate elements.

[0141] (3) Where there is no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0142] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for positioning and managing non-motorized vehicles on campus, characterized in that, include: S1: Collect data related to student vehicle usage; S2: Based on the student vehicle usage data, identify student behavior patterns using a clustering algorithm; The student vehicle usage data includes: student class schedules, historical location data, vehicle usage records, weather conditions, and special campus events. S3: Based on the student behavior patterns, determine student movement trends using the Long Short-Term Memory network; Specifically, S3 is: ； in, X t Indicates time t Input time, C t Indicates time t The timetable data entered at that time P t-1 Indicates time t -1 indicates the location data entered. E t-1 Indicates time t Additional features of input at -1 that influence student behavior and location selection f t Represents the Gate of Oblivion This represents the sigmoid activation function. h t-1 express t The hidden state at time -1 i t Indicates the input gate. Indicates the possible new states at time t. tanh Represents the hyperbolic tangent function. o t Indicates the output gate. c t This represents the cell state retained after considering past states and the current candidate state. C t-1 express t The timetable data entered at time -1. h t Indicates that the LSTM network is in t The output at any given time is used to predict student movement trends. This represents the dot product operation. W f , W i , W o and W c Each represents a weight matrix used to control the inputs of each gate and the influence of past states on the current state. b f , b i , b o and b c Both represent bias terms used to adjust the gating and candidate state activation levels; S4: Based on the student movement trends, predict vehicle demand at different times and locations on campus by integrating the ARIMA model; Specifically, S4 is: ； in, Y t Indicates in t Real-time forecasting of vehicle demand, Y t-1 Indicates in t Predicted vehicle demand at time -1 Y t-2 Indicates in t Predicted vehicle demand at time -2 This represents the constant term of the ensemble ARIMA model. Represents the autoregressive parameters. Represents the moving average parameter. Indicates the preceding q Error term at each time point, Indicates the current time t The prediction error; S5: Based on the predicted vehicle demand at different times and locations on campus, adjust the distribution of vehicles using a nonlinear programming algorithm. Specifically, S5 is: ； ； ； in, c ij Indicates the first i The vehicle arrived at the j The scheduling cost of each demand point , n This indicates the total number of vehicles. , m This represents the total number of demand points. x ij Indicate whether to... i The vehicle was dispatched to the first j Decision variables for each demand point x ij =0 indicates no scheduling. x ij =1 indicates scheduling. This indicates that the demand constraints are met. Indicates supply constraints. b j Indicates the first j The vehicle demand at each demand point during the forecast period. d i Indicates the first i The number of vehicles available at the current time.

2. The campus non-motorized vehicle positioning management method according to claim 1, characterized in that, Specifically, S2 is: Identify student behavior patterns using the weighted K-means clustering algorithm: ； in, S Represents the objective function value. k This represents the total number of categories of behavioral patterns. C i Indicates the first i Each behavioral pattern category w i Indicates the relationship with the first i Data points contained in each behavioral pattern category x Associated weights x Indicates the first i Data points in each behavioral pattern category Indicates the first i The weighted average position of all points in each behavioral pattern category Represents norm operations.

3. The campus non-motorized vehicle positioning management method according to claim 1, characterized in that, Also includes: S6: Anomaly detection and early warning are performed using the isolated forest algorithm.

4. The campus non-motorized vehicle positioning management method according to claim 3, characterized in that, S6 specifically includes: S601: Determine the outlier score for each data point using the Isolation Forest algorithm: ； in, score ( x ) represents a data point x Abnormal scores, E [ h ( x )] represents a data point x In an isolated forest, the average path length is considered; the shorter the path length, the more likely the data point is to be an anomaly. c ( n ) indicates that the sample size is n The average path length of normal data; S602: Determine whether the abnormal score of each data point is greater than the dynamic threshold; if so, determine it as abnormal data and trigger an alarm; otherwise, continue monitoring.

5. The campus non-motorized vehicle positioning management method according to claim 4, characterized in that, The dynamic threshold is specifically: ； in, express t The dynamic threshold at time t, This refers to the standard threshold determined based on historical data during the initial system setup, i.e., the baseline threshold. This represents the adjustment factor used to control the impact of historical anomalies on the current threshold. express i Abnormal changes at time t. , t Indicates the total number of moments.

6. A campus non-motorized vehicle positioning management system, characterized in that, include: processor; A memory storing computer-readable instructions, which, when executed by the processor, implement the campus non-motorized vehicle positioning management method as described in any one of claims 1 to 5.

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