Campus multi-scene service reservation management system and method

Abstract

The invention discloses a campus multi-scene service reservation management system and method, relates to the technical field of campus service management, dynamically calculates the optimal transit time between scenes based on three-dimensional building topology and real-time environment data, and breaks through the limitation of static path planning. Personalized dynamic buffer time is generated by fusing user historical performance data, a fixed threshold value is replaced, and risk control and the resource utilization rate are both considered; and when detecting that the time exceeds the limit, starting a progressive response closed loop: tracking the position and the speed of the user in real time, re-estimating the path time through a dynamic compensation algorithm, and circularly deciding. If the risk continuously exists, an acceleration prompt is intelligently pushed or a reservation time period is adjusted; the cross-scene default risk is reduced through space-time conflict pre-judgment and personalized buffering, and the utilization rate of the venue is improved through a reservation time period dynamic adjustment mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of campus service management, and in particular to a campus multi-scenario service reservation management system and method. Background Art

[0002] With the advancement of smart campus construction, online reservations for multi-scenario services such as libraries, laboratories, and stadiums have become the basic infrastructure for campus management. However, existing online reservations have the following structural flaws: when making reservations for services in multiple scenarios, existing campus reservation systems (such as library management systems and laboratory reservation platforms) often have difficulty arriving at the reservation scene within the reservation time due to inaccurate control of travel time between multiple scenes. This results in the inability to complete subsequent scene reservations, resulting in subsequent scenes being vacant and causing a waste of scene resources. Specifically, the traditional system lacks the ability to dynamically plan the user's cross-scenario movement path and is unable to adjust the optimal path based on real-time environmental changes; it fails to consider individual user behavior differences and is unable to provide personalized time buffers based on the historical fulfillment of different users; when user movement is abnormal, there is a lack of effective dynamic early warning and reservation adjustment mechanisms. These problems lead to low utilization of multi-scenario reservation resources on campus and poor user experience in cross-scenario services. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the present invention provides a campus multi-scenario service reservation management system and method.

[0004] In order to achieve the above object, the technical solution of the present invention is as follows: In a first aspect, the present invention further provides a campus multi-scenario service reservation management method, comprising the following steps: Obtain at least two scene resource reservation records of the user arranged in chronological order, wherein the scene resource reservation records include a scene identifier, a reservation start time, and a reservation end time; Based on at least two scene resource reservation records arranged in chronological order and a pre-stored building node connectivity matrix, extracting the optimal path from the previous scene to the next scene and calculating the optimal path time; Obtaining historical performance deviation data of the user, and generating a dynamic buffer time based on statistical characteristics of the historical performance deviation data; Determine whether the sum of the optimal path time and the dynamic buffer time is greater than the difference between the start time of the next service scenario and the end time of the current service scenario; If the judgment result is no, move from the previous scene to the next scene according to the optimal path; If the result is yes, do the following: The user's real-time location data and user movement speed are obtained at predetermined intervals of a few seconds, and the path deviation is calculated based on the real-time location data and the optimal path; Determine whether the user's moving speed is lower than a preset speed threshold or whether the path deviation is greater than a preset deviation threshold; If so, update the optimal path time based on the real-time location data; Loop to determine whether the sum of the updated optimal path time and the dynamic buffer time is greater than the difference between the start time of the next service scenario and the current time; If the result of the cyclic judgment continues to be yes, it will trigger the push of acceleration prompts or reservation time adjustment to the user terminal.

[0005] In a second aspect, the present invention further provides a campus multi-scenario service reservation management system, which is applied with the above-mentioned campus multi-scenario service reservation management method, including: A multi-scenario reservation module is used to obtain at least two scenario resource reservation records arranged in chronological order by the user, wherein the scenario resource reservation record includes a scenario identifier, a reservation start time, and a reservation end time; A path planning calculation module is used to extract the optimal path from the previous scene to the next scene and calculate the optimal path time based on the resource reservation records of at least two scenes arranged in chronological order and the pre-stored building node connectivity matrix; A dynamic time prediction module is used to obtain the user's historical performance deviation data and generate a dynamic buffer time based on the statistical characteristics of the historical performance deviation data; A loop decision control module, configured to determine whether the sum of the optimal path time and the dynamic buffer time is greater than the difference between the start time of the next service scenario and the end time of the current service scenario; If the judgment result is no, move from the previous scene to the next scene according to the optimal path; If the result is yes, do the following: The user's real-time location data and user movement speed are obtained at predetermined intervals of a few seconds, and the path deviation is calculated based on the real-time location data and the optimal path; Determine whether the user's moving speed is lower than a preset speed threshold or whether the path deviation is greater than a preset deviation threshold; If so, update the optimal path time based on the real-time location data; Loop to determine whether the sum of the updated optimal path time and the dynamic buffer time is greater than the difference between the start time of the next service scenario and the current time; If the result of the cyclic judgment continues to be yes, it will trigger the push of acceleration prompts or reservation time adjustment to the user terminal.

[0006] Compared with the prior art, the present invention has the following beneficial effects: 1. Calculate the optimal path time based on the building node connectivity matrix, generate personalized safety margins through dynamic buffer time, and detect and remind customers as they move between scenes, thereby ensuring the fulfillment rate of scheduled scenes and avoiding subsequent idle scene resources. 2. Solve the resource conflict problem caused by path time estimation deviation when users make consecutive reservations for multiple scenarios, and achieve seamless connection of the multi-scenario reservation chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The disclosure of the present invention is described with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of the present invention. In the drawings, the same reference numerals are used to refer to the same components. Among them: Figure 1 is a flow chart of the method of the present invention; Figure 2 It is a data flow diagram of the present invention; Figure 3 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0008] It is easy to understand that according to the technical solution of the present invention, without changing the essential spirit of the present invention, a person skilled in the art can propose a variety of interchangeable structural modes and implementation modes. Therefore, the following specific embodiments and drawings are only exemplary descriptions of the technical solution of the present invention and should not be regarded as the entire invention or as a limitation or restriction of the technical solution of the present invention.

[0009] Application Overview

[0010] In traditional campus multi-scenario reservation systems, the path planning module fails to account for dynamic factors such as real-time crowd density and device wait times, leading to significant discrepancies between predicted travel times and actual travel times. Furthermore, historical fulfillment data is not effectively modeled for time compensation, making the system unable to adapt to the diverse mobility characteristics of different users. This flaw can lead to inaccurate estimates of scene switching windows when users schedule multiple scenes consecutively, potentially causing vacancies in subsequent scene reservations or resource conflicts.

[0011] For example, when switching between the library and the laboratory, the system only calculated travel time based on the static distances on the building floor plan, ignoring the waiting time for the laboratory elevators during peak hours. After the user completed the library reservation, the system-generated route plan failed to account for the elevator wait time, preventing the scene switch from completing before the laboratory reservation period began. At this point, the laboratory reservation resources were locked but not actually used, and subsequent user reservation requests could not be responded to due to resource occupancy.

[0012] If these issues are not addressed, the multi-scenario reservation system will experience cascading resource scheduling failures, leading to increased vacancy rates for reservation slots and decreased resource utilization efficiency. Frequently experiencing reservation failures will reduce user willingness to use the system, impacting the overall operational efficiency of smart campus services. System maintainers will need to invest additional resources in manual time slot adjustments and conflict resolution, significantly increasing operational costs.

[0013] Faced with the above-mentioned problems, the present applicant first analyzed the core reason why existing systems cannot accurately predict scene switching time windows and found that the root cause lies in the fact that the path planning model relies solely on static building data. Therefore, the present applicant recognized the need to establish a dynamic environmental perception mechanism, the key of which lies in the integration of real-time crowd density and equipment waiting time data. Specifically, by deploying infrared sensors in building corridors to collect real-time crowd density, combined with dynamic monitoring of the number of people in elevator queues, a multidimensional time correction factor is formed. At the same time, the present applicant observed that individual user fulfillment deviations have statistical regularities and proposed converting historical fulfillment data into personalized buffer time to address the error accumulation problem caused by differences in user mobility characteristics. Furthermore, to address uncontrollable factors in the path execution phase, the present applicant designed a closed-loop feedback mechanism that dynamically updates path time predictions through real-time position deviation calculation and movement speed attenuation modeling. Ultimately, the present applicant forms a complete spatiotemporal constraint management solution by integrating static path planning, dynamic environmental perception, user behavior modeling, and real-time feedback control at the system level.

[0014] In this regard, Figure 1 As shown, this application proposes a campus multi-scenario service reservation management method, including the following steps: Obtain at least two scene resource reservation records arranged in chronological order by the user, where the scene resource reservation record includes the scene identifier, reservation start time, and reservation end time; a scene resource reservation record refers to multiple scene usage applications submitted in advance by the user, which include the scene identifier, reservation start time, and end time. This can be achieved by storing the reservation form submitted by the user in the database, which is used to determine the time window in which the user needs to move between different scenes.

[0015] Based on at least two chronologically ordered scene resource reservation records and a pre-stored building node connectivity matrix, the optimal path from one scene to the next is extracted and the optimal path time is calculated. The building node connectivity matrix is ​​a matrix data structure that describes the connection relationships between nodes within campus buildings. This can be achieved by generating a node network using 3D modeling tools and marking traffic status, and is used to calculate feasible movement paths between scenes. The optimal path time refers to the path travel duration calculated based on the building node connectivity matrix. This can be achieved by using the Dijkstra algorithm to select the shortest path and combining it with real-time pedestrian density to correct the travel time, ensuring the accuracy of travel time.

[0016] The user's historical fulfillment deviation data is obtained and dynamic buffer time is generated based on the statistical characteristics of this historical fulfillment deviation data. Historical fulfillment deviation data refers to the difference between the user's actual arrival time and the planned time. This data can be achieved by comparing the user's terminal positioning data with the appointment time, and is used to reflect the individual characteristics of the user's movement behavior. Dynamic buffer time refers to the elastic time interval generated based on historical fulfillment deviation. This data can be achieved by calculating the mean of historical deviations, adding the standard deviation and multiplying it by a safety factor to compensate for the uncertainty in the user's movement process.

[0017] Determine whether the sum of the optimal path time and the dynamic buffer time is greater than the difference between the start time of the next service scenario and the end time of the current service scenario; If the judgment result is no, move from the previous scene to the next scene according to the optimal path; If the result is yes, do the following: The user's real-time location data and movement speed are collected every predetermined number of seconds. Path deviation is calculated based on the real-time location data and the optimal path. Path deviation refers to the degree of deviation between the user's real-time location and the planned path. This is achieved by calculating the ratio of the vertical distance from the real-time location to the nearest path segment to the tolerance radius, and is used to dynamically monitor movement trajectory anomalies. Real-time location data refers to user coordinate information collected via GPS or indoor positioning systems, specifically using Bluetooth beacons or WiFi fingerprint positioning technology, and is used to track user movement status in real time.

[0018] Determine whether the user's movement speed is lower than the preset speed threshold or whether the path deviation is greater than the preset deviation threshold; the preset speed threshold refers to the critical movement speed that triggers path recalculation. It can be achieved by setting the threshold range according to the average walking speed of pedestrians on campus, which is used to determine whether the user has travel delays.

[0019] If so, update the optimal path time based on the real-time location data; Loop to determine whether the sum of the updated optimal path time and the dynamic buffer time is greater than the difference between the start time of the next service scenario and the current time; If the result of the cyclic judgment continues to be yes, it will trigger the push of acceleration prompts or reservation time adjustment to the user terminal.

[0020] The core innovation of this application lies in combining dynamic buffer time with real-time path deviation monitoring, dynamically updating the optimal path time through a cyclic decision-making mechanism. This method generates personalized buffer time based on historical fulfillment deviations, while continuously evaluating path deviation status based on real-time positioning data. It triggers path time recalculation when speed anomalies or trajectory deviations are detected, ultimately avoiding idle scene resources through acceleration prompts or reservation time adjustments. This mechanism effectively solves the problem of resource waste caused by inaccurate travel time estimates in multiple scenarios.

[0021] like Figure 2 As shown, it is a data flow diagram of the present application; as a preferred embodiment, the solution of the present application is specifically implemented as follows:

[0022] The user has made reservations for two scenarios: the library and the laboratory. First, obtain the two reservation records, including the scenario identifier, and the reservation start and end times.

[0023] Based on the pre-stored campus building node connectivity matrix, the optimal path and estimated time from the library to the laboratory are calculated. Simultaneously, the user's fulfillment data from the past month is analyzed to calculate an average lateness of 5 minutes as a dynamic buffer time.

[0024] The system determines whether the optimal route time (15 minutes) plus the 5-minute buffer is greater than the 25-minute difference between the lab appointment start time and the library appointment end time. If this is not the case, the system instructs the user to follow the optimal route.

[0025] If the optimal path time is 25 minutes, then the optimal path time of 25 minutes plus the buffer time of 5 minutes is greater than the difference of 25 minutes mentioned above, and the subsequent steps are performed:

[0026] After a user leaves the library, the system obtains their location and speed every 30 seconds. If the user's speed drops below a preset threshold of 1 meter per second or deviates from the optimal path by more than 10 meters, the system recalculates the path time.

[0027] If the updated route time plus the buffer time is greater than the remaining time, the system will push a speed-up prompt to the user. If the situation persists, the system will try to extend the laboratory reservation time or suggest that the user end the library reservation early.

[0028] Through the above solution, this application can dynamically perceive user mobility status and environmental changes, and promptly adjust path planning and time prediction. This method effectively reduces appointment conflicts caused by path deviations or unexpected delays, and improves the punctuality rate of multi-scenario appointments. At the same time, through real-time monitoring and proactive intervention, it reduces resource vacancy rates and improves the overall utilization efficiency of campus facilities. In addition, the personalized time buffer mechanism adapts to the behavioral characteristics of different users, further optimizing the appointment experience.

[0029] This application further proposes a solution for calculating path deviation based on real-time location data and the optimal path.

[0030] Specifically: Get the coordinate sequence of the user's actual movement trajectory ; Calculate real-time location To the nearest path segment Vertical distance ; Generate path deviation ,in is the preset tolerance radius.

[0031] Calculating path deviation involves three key steps: acquiring the actual trajectory coordinate sequence, calculating the vertical distance from the real-time location to the nearest path segment, and normalizing the tolerance radius. The trajectory coordinate sequence is achieved by continuously collecting the user's latitude and longitude or three-dimensional coordinates during movement. The vertical distance uses a vector projection algorithm to determine the closest point between the current location and a preset optimal path geometric segment, calculating the Euclidean distance between the two. The tolerance radius is dynamically set based on building channel width or navigation accuracy requirements to eliminate interference from measurement errors in deviation determination.

[0032] During user movement, the optimal path is first decomposed into continuous line segments, and the nearest path segment corresponding to the current position is determined through spatial geometric operations. Subsequently, the vertical distance between the current position and the line segment is calculated and normalized using a preset tolerance radius. For example, when the tolerance radius is set to 2 meters, if the vertical distance of the real-time position is 3 meters, the path deviation calculation result is (3-2) / 2=0.5. This numerical indicator can intuitively reflect whether the user's deviation exceeds the safety boundary and provide a quantitative basis for subsequent path time updates. By comparing the path deviation with the preset threshold, the system can accurately identify whether the user has experienced a substantial path deviation, avoiding misjudgment due to device positioning errors.

[0033] Through the above technical solution, this application can accurately quantify the degree of deviation between the user's actual movement trajectory and the planned path. This can promptly detect whether the user has deviated from the planned path, providing a basis for subsequent path planning and time prediction, and helping to improve the accuracy and reliability of multi-scenario service reservations.

[0034] This application further proposes that the optimal path time be updated based on real-time location data, including: Calculate the initial path time to move to the next scene based on the current position ; According to the user's movement speed Calculate the velocity attenuation coefficient , the calculation formula is: ,in Plan the initial speed for the preset path; speed attenuation coefficient By user movement speed The initial speed of the preset path planning The ratio reflects the user's current mobility efficiency. When the user's moving speed is lower than the initial speed, the coefficient is less than 1, resulting in a longer initial path time. be magnified; According to the path deviation Calculate deviation compensation time , the calculation formula is: ,in is the path deviation, is the preset unit deviation time increment, is the maximum compensation threshold; deviation compensation time By path deviation Deviating from unit time increment The product of quantifies the extra time consumed by the user when they deviate from the path, and sets the maximum compensation threshold. Avoid extreme deviations that may affect the calculation results.

[0035] Updated optimal path time The calculation formula is: ,in is the deviation weight. Deviation weight Used to adjust the proportion of deviation compensation time in the total time. Its value is dynamically adjusted according to the user's movement speed and scene identification.

[0036] Specifically, the current position is obtained in real time through the positioning device and mapped to the building node coordinates, and the initial path time Determined based on the benchmark travel time of the shortest path between nodes. Speed ​​attenuation coefficient During calculation, the preset path planning initial speed Taken from the median of the user's historical movement speed or the average movement speed between scenes. Deviation compensation time When calculating, the path deviation Dynamically generated based on the vertical distance between the user's actual position and the optimal path, the unit deviation time increment The experimental data is set to increase the deviation by 0.5 seconds for each meter. Exceeding the maximum compensation threshold When the time is set to 180 seconds, for example, the system only calculates the compensation time based on the threshold. By introducing a dual correction mechanism for speed decay and path deviation, it effectively solves the problem of performance time prediction deviation caused by reduced user mobility efficiency or path deviation.

[0037] As a preferred embodiment, the solution of this application is specifically implemented as follows: Re-updating the optimal path time based on real-time location data includes the following steps:

[0038] First, the initial path time to the next scene is calculated based on the current location. For example, using campus map data and navigation algorithms, the shortest path from the current location to the next scheduled scene and its corresponding initial path time are calculated.

[0039] According to the user's movement speed Calculate the velocity attenuation coefficient , the calculation formula is: ,in Plan the initial speed for the preset path; Among them, the preset path planning initial speed It can be set to 1.2 m / s. If the user's current moving speed is detected is 0.8 m / s, then the velocity attenuation coefficient is It is 0.67.

[0040] Then, the deviation compensation time is calculated based on the path deviation. The calculation formula is: ,in is the path deviation, is the preset unit deviation time increment, is the maximum compensation threshold; Preset unit deviation time increment Can be set to 30 seconds, maximum compensation threshold It can be set to 180 seconds. Assume that the calculated path deviation is 2.5, then the deviation compensation time 75 seconds.

[0041] Finally, the updated optimal path time calculation formula is: ,in is the deviation weight.

[0042] Among them, the deviation weight can be set to 0.8.

[0043] Assuming the initial path time is 300 seconds, the updated optimal path time is: 300 / 0.67 + 75*0.8=508 seconds.

[0044] Through the above technical solution, this application can dynamically adjust the optimal path time based on the user's real-time location and mobility status. By introducing a speed attenuation coefficient, the impact of the user's actual movement speed on the path time can be more accurately reflected. At the same time, by calculating the deviation compensation time, reasonable compensation can be made for the user's deviation from the planned path. This dynamic update mechanism improves the accuracy of path time prediction, helps the system make more reasonable decisions, and thus improves the reliability and user experience of multi-scenario service appointments.

[0045] This application further proposes that the calculation process of the deviation weight is: Obtain scene identifiers of at least two scene resource reservation records; Query the preset scene identifier-weight mapping table to extract the basic weight value ; The scene identifier-weight mapping table stores the basic weight values ​​corresponding to different scenes, for example, the weight value of the laboratory scene is 0.8, and the weight value of the gymnasium scene is 0.5.

[0046] According to the user's movement speed , preset speed threshold Calculate the speed correction factor : ; The speed correction coefficient is calculated by the ratio of the actual moving speed to the preset speed threshold. When the user's moving speed is 1.2 m / s and the preset threshold is 1.5 m / s, the speed correction coefficient is 0.8.

[0047] Deviation weight The calculation formula is: The deviation weight dynamically associates the scenario weight with the speed factor through a linear combination. For example, the deviation weight of the laboratory scenario is 0.64 under a speed correction factor of 0.8.

[0048] For example: When a user moves from the library to the laboratory, the system obtains the corresponding basic weight value of the laboratory through the scene identifier-weight mapping table At the same time, the user's moving speed is measured through real-time positioning data. The preset speed threshold is 1.0 m / s is 1.2 m / s, and the speed correction factor is calculated is 0.83. Multiply 0.8 by 0.83 to generate the final deviation weight The weight value is 0.664. The optimal path time after the update The calculation of the path deviation in the laboratory scene gives a higher weight in the time prediction. When the speed is reduced to 0.8 m / s, the speed correction factor Reduced to 0.67, the deviation weight Adjusted to 0.536, reducing the impact of speed on the weight. By adjusting both scene identification and movement speed, we achieve differentiated processing of deviation compensation time weights in different scenarios, improving the environmental adaptability of the time prediction model.

[0049] Through the above technical solution, this application achieves dynamic adjustment of deviation weights based on different scenarios and the user's real-time movement status. This method considers the importance of the scenario and the user's actual movement, making path planning more flexible and accurate. This improves the accuracy and efficiency of multi-scenario service appointment management and reduces the problem of appointment failures caused by path deviation.

[0050] This application further proposes a method for correcting the benchmark travel time of each feasible path based on real-time crowd density data and device waiting data, including: A feasible path set is extracted from the building node connectivity matrix; the feasible path set is extracted based on the relationship between adjacent nodes marked as connected in the building node connectivity matrix to form all possible movement routes from the starting point to the end point.

[0051] The crowd density data of building passages is collected through infrared sensors, and the equipment waiting data is obtained by combining the historical average waiting time of the elevator with the product of the real-time queue number. The correction of the benchmark passage time is achieved by superimposing the real-time crowd density coefficient and the equipment waiting coefficient. Specifically, when the infrared sensor of a certain channel detects that the crowd density exceeds the threshold, the benchmark passage time of the channel is increased by a preset proportion; when the real-time queue number of the elevator exceeds twice the historical average, the equipment waiting time is calculated as the product of the queue number and the historical average time.

[0052] Using this real-time data, the baseline travel time for each feasible path is revised; the path with the shortest revised travel time is selected as the optimal path. The path selection phase uses a dynamic programming algorithm to compare the revised travel times of all feasible paths and select the path with the shortest total travel time.

[0053] For example, when a user needs to move from a laboratory to a gymnasium, all connected path nodes in the building model are first retrieved. For each feasible path, infrared sensor data from the corridor on the third floor of the current teaching building is obtained, and it is detected that the crowd density in this area reaches 0.8 people per square meter, triggering a 20% increase in the baseline travel time. At the same time, the elevator waiting time is calculated, and it is found that there are currently 6 people in the queue. Combined with the historical average waiting time of 30 seconds, the device waiting time is generated as 180 seconds. The corrected travel time is compared with the stair path time, and the stair path with the shorter total time is finally selected as the optimal path. This correction mechanism ensures that the path time calculation fits the actual environmental changes, effectively increasing the probability that the user arrives at the next scene on time.

[0054] Through the above technical solution, this application can dynamically adjust path planning based on real-time crowd density and device waiting conditions, avoiding delays caused by choosing congested paths and improving the accuracy and timeliness of path planning. At the same time, by comprehensively considering multiple factors to select the optimal path, it can better meet the user's needs for efficient movement between multiple scenarios and reduce resource waste.

[0055] This application further proposes that real-time crowd density data is obtained through infrared sensors deployed in building passages, and equipment waiting data includes the product of the historical average waiting time of the elevator and the real-time number of people in queue.

[0056] Infrared sensors are deployed in arrays at key corridors, stairwell entrances, and exits. They detect human heat signals, triggering counts to generate a real-time heat map of the number of people passing through a designated area per unit time. The elevator control system retrieves the average elevator waiting time data from the past 30 days. The real-time queue count is calculated based on the number of times the elevator car's weight sensors or floor button triggers are triggered, dynamically calculating a correction to the current elevator waiting time. The infrared sensor's crowd density data and elevator waiting data are wirelessly transmitted to the path planning and calculation module, where they serve as correction factors for the baseline travel time of feasible paths.

[0057] Specifically, when a user schedules multiple scenarios, the system extracts a set of feasible paths from the building node connectivity matrix. Infrared sensors deployed in building corridors continuously collect pedestrian density data for each corridor segment. For example, if a corridor detects 15 heat signals within 10 seconds, the pedestrian density level in that section is determined to be high, triggering a 20% increase in the baseline travel time. Elevator waiting data is also combined with historical average wait times (e.g., 2 minutes) and the current number of people in queue (e.g., 5 people currently in queue) to calculate the real-time wait time as 2 minutes x 5 people = 10 minutes. The path planning calculation module incorporates this data into a revised model of feasible paths. For example, if a route requires passing through a high-density corridor and taking an elevator, the baseline travel time is revised from 5 minutes to 5 x 120% + 10 = 16 minutes. The path with the shortest revised travel time is selected as the optimal path, ensuring over 30% improvement in time prediction accuracy and effectively preventing booking failures due to sudden changes in passenger flow or elevator congestion.

[0058] Through the above technical solution, this application can accurately capture the density of pedestrians and equipment waiting times within a building in real time, providing more precise input data for route planning. This helps to more accurately estimate the actual travel time of different routes, thereby selecting the optimal route and providing more reasonable time estimates. This in turn improves the accuracy and reliability of multi-scenario service reservations and reduces reservation conflicts and resource waste caused by inaccurate arrival time estimates.

[0059] This application further proposes: The building node connectivity matrix is ​​generated by converting the campus modeling data into a node network. Each node is labeled with three-dimensional coordinates. When the traffic status between adjacent nodes is connected, it is marked as 1, and when the status is blocked, it is marked as 0. The benchmark travel time is obtained through historical path data statistics.

[0060] By converting the three-dimensional model data of campus buildings into a node network containing floor information, the three-dimensional coordinate annotations between nodes can distinguish the vertical position differences between different floors, avoiding the underestimation of path time caused by ignoring floor transition times in traditional two-dimensional path planning. The connectivity marks between adjacent nodes dynamically reflect the actual traffic status. For example, when an elevator is out of service, the relevant nodes are automatically marked as blocked, ensuring the real-time accuracy of path planning. The benchmark travel time analyzes historical user mobility data and calculates the average time spent walking, taking stairs, or taking the elevator between nodes in different time periods. It further combines real-time crowd density and equipment waiting data to form a multi-dimensional travel time correction model, effectively improving the accuracy of optimal path time prediction and reducing the risk of user fulfillment failure due to path time errors.

[0061] Through the above technical solution, this application realizes the digital expression of campus spatial information, providing accurate spatial and temporal references for multi-scenario service reservation management. This method can more accurately calculate the travel time between different scenarios, helping to improve the accuracy of reservation times and reduce the waste of resources caused by inaccurate arrival time estimates. At the same time, due to the use of three-dimensional coordinates, this method can better handle vertical movement in multi-story buildings, improving the efficiency of path planning in complex campus environments.

[0062] This application further proposes generating a dynamic buffer time based on the statistical characteristics of historical performance deviation data, including: Extract the deviation data set between the user's historical actual arrival time and planned arrival time based on the historical fulfillment deviation data; Calculate the mean of the deviation data set and standard deviation ; Generate dynamic buffer time ,in is the preset safety factor.

[0063] The deviation data set is constructed by the difference between the actual arrival time and the scheduled arrival time in the user's historical fulfillment records, and contains multiple independent time deviation samples; the mean Reflects the average level and standard deviation of user performance deviation Characterizes the degree of fluctuation in performance time; safety factor Dynamically adjust according to the importance of scene resources to expand or shrink the coverage of the buffer time.

[0064] Each sample in the deviation data set corresponds to the actual deviation value of a fulfillment event. By counting the mean and standard deviation of multiple samples, a calculation model for the dynamic buffer time is established. The safety factor is pre-set by the administrator. For example, when it is set to 1.5, it can cover about 93% of the normal distribution deviation range. When generating the dynamic buffer time, by taking the mean as the basic buffer amount and superimposing the product of the standard deviation and the safety factor as the risk compensation amount, it is possible to balance the user's average fulfillment ability and the impact of sudden abnormal fluctuations. The dynamic buffer time generated in this way is dynamically updated with the user's fulfillment behavior data. Compared with the fixed buffer time, it can reduce the estimation error caused by individual differences between users or temporary environmental changes, and improve the fulfillment success rate of subsequent scene appointments.

[0065] For example:

[0066] The user extracts the deviation data between the actual arrival time and the planned arrival time of the most recent 30 scene transitions from the historical fulfillment records, where the deviation dataset contains 30 time difference samples.

[0067] The mean parameter is obtained by calculating the arithmetic mean of the deviation data set, and the fluctuation parameter is obtained by calculating the sample standard deviation.

[0068] Set the preset safety factor to 1.5. When the mean parameter is 120 seconds and the standard deviation is 40 seconds, the dynamic buffer time is 120+40×1.5=180 seconds.

[0069] Through the above technical solution, this application can dynamically generate adaptive buffer time based on historical user behavior data, effectively covering the average delays and sudden fluctuations that may occur during user movement. Mean calculation can reflect the user's normal movement efficiency, standard deviation calculation can quantify travel uncertainty, and the introduction of a safety factor further enhances the fault tolerance of time estimation. The resulting dynamic buffer time mechanism can significantly reduce the vacancy rate of subsequent scenario resources caused by travel delays, improving the fulfillment reliability of the multi-scenario reservation system.

[0070] This application further proposes adjustments to appointment time slots, including: Sending a time period extension request to the next scene resource database, the request including the length of time to be extended; If the subsequent free time slot of the next scene is not occupied, the extended reservation time slot will be automatically locked; If the extension fails, a prompt instruction to leave the current scene early will be pushed to the user terminal.

[0071] As a preferred embodiment, the solution of this application is specifically implemented as follows: When the user's path time from the laboratory to the gymnasium exceeds expectations, the system sends a time extension request containing a 15-minute extension to the gymnasium resource management system.

[0072] After receiving the request, the gymnasium resource management system automatically checks the availability of the user's original reservation time within the next 30 minutes. If it detects that the subsequent 15 minutes are not occupied by other users, the original reservation end time is automatically extended from 16:00 to 16:15 and marked as locked in the database. If it detects that the subsequent time period is occupied, it generates a prompt containing the message "Recommend leaving the lab 5 minutes early" and pushes this prompt to the user's mobile terminal application interface via the message queue, simultaneously triggering the lab reservation system's early departure reminder function.

[0073] Through the above technical solution, this application realizes the ability to dynamically adjust reservation time periods in scenarios with multi-scenario mobile time errors. By real-time detecting the occupancy status of subsequent time periods in the target scene, the time period extension operation is automatically completed when the resources are not occupied, avoiding invalid reservations due to user movement delays; when the extension operation is blocked, a departure prompt is generated in a timely manner to effectively prevent the current scene resources from being invalidly occupied. This forms a closed-loop dynamic regulation mechanism for reservation time periods, which not only ensures the continuity of users' cross-scene reservations, but also avoids the invalid reservation of scene resources, significantly improving the comprehensive utilization rate of multi-scene resources on campus.

[0074] like Figure 3 As shown, the present application further proposes a campus multi-scenario service reservation management system, including a multi-scenario reservation module, a path planning calculation module, a dynamic time prediction module, and a cyclic decision control module.

[0075] The multi-scene reservation module is used to obtain the user's scene resource reservation records arranged in chronological order, receive serialized data including scene identification and reservation time through the interface, and store it as a structured reservation record table.

[0076] The path planning calculation module calculates the optimal path time for moving between scenes based on the building node connectivity matrix. By calling the building node connectivity matrix, which marks the traffic status between nodes with three-dimensional coordinates, the path travel time is corrected by combining real-time crowd density data and equipment waiting data.

[0077] The dynamic time prediction module generates dynamic buffer time based on the user's historical fulfillment deviation and calculates the dynamic buffer time using the mean-standard deviation model.

[0078] The cycle decision control module triggers path time updates and appointment adjustments through real-time position data and speed monitoring.

[0079] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0080] The multi-scene reservation module obtains scene resource reservation records arranged in chronological order through the user terminal. The records include scene identifiers, reservation start time and end time.

[0081] The path planning calculation module extracts the optimal path between adjacent scenes and calculates the optimal path time based on the reservation records and the pre-stored building node connectivity matrix. The building node connectivity matrix is ​​generated through three-dimensional modeling data, and the connectivity status between nodes is marked as 0 or 1. The benchmark travel time is based on historical path data statistics.

[0082] The dynamic time prediction module calculates the mean and standard deviation through the user's historical fulfillment deviation data set, and generates a dynamic buffer time as the product of the mean plus the standard deviation and the safety factor.

[0083] The loop decision control module determines whether the sum of the optimal path time and the dynamic buffer time exceeds the difference between the start time of the next scene and the end time of the current scene. If so, the real-time monitoring process is started:

[0084] The user's real-time location data and moving speed are obtained every predetermined number of seconds, and the path deviation is calculated as the vertical distance from the real-time location to the optimal path segment minus the preset tolerance radius divided by the tolerance radius; when the moving speed is lower than the preset threshold or the deviation exceeds the preset threshold, the initial path time is recalculated and the deviation compensation time is superimposed, and a loop is used to determine whether the updated total time is still out of tolerance; if the deviation continues, an acceleration prompt is pushed to the user terminal or a reservation time adjustment request is initiated, where the time adjustment request automatically extends the lock when the subsequent idle time period of the next scene is not occupied, otherwise an early departure prompt is triggered.

[0085] Through the above technical solutions, this application effectively solves the problem of idle resources caused by inaccurate control of travel time between multiple scenes. Through the collaborative mechanism of dynamic buffer time and real-time path deviation monitoring, the deviation of the user's actual arrival time is accurately predicted, and secondary decisions are triggered in time when the path is abnormal, reducing the probability of appointment failure caused by movement delays. Through closed-loop cyclic judgment and automatic adjustment strategies, the system ensures the timeliness of users' cross-scene movement in complex building environments and reduces the idle waste of scene resources due to failed fulfillment.

[0086] The technical scope of the present invention is not limited to the contents of the above description. Those skilled in the art can make various deformations and modifications to the above embodiments without departing from the technical idea of ​​the present invention, and these deformations and modifications should all fall within the protection scope of the present invention.

Claims

1. A campus multi-scenario service reservation management method, characterized by: The steps include: Obtain at least two scene resource reservation records of the user arranged in chronological order, wherein the scene resource reservation records include a scene identifier, a reservation start time, and a reservation end time; Based on at least two scene resource reservation records arranged in chronological order and a pre-stored building node connectivity matrix, extracting the optimal path from the previous scene to the next scene and calculating the optimal path time; Obtaining historical performance deviation data of the user, and generating a dynamic buffer time based on statistical characteristics of the historical performance deviation data; Determine whether the sum of the optimal path time and the dynamic buffer time is greater than the difference between the start time of the next service scenario and the end time of the current service scenario; If the judgment result is no, move from the previous scene to the next scene according to the optimal path; If the result is yes, do the following: The user's real-time location data and user movement speed are obtained at predetermined intervals of a few seconds, and the path deviation is calculated based on the real-time location data and the optimal path; Determine whether the user's moving speed is lower than a preset speed threshold or whether the path deviation is greater than a preset deviation threshold; If so, update the optimal path time based on the real-time location data; Loop to determine whether the sum of the updated optimal path time and the dynamic buffer time is greater than the difference between the start time of the next service scenario and the current time; If the result of the cyclic judgment continues to be yes, it will trigger the push of acceleration prompts or reservation time adjustment to the user terminal.

2. A campus multi-scenario service reservation management method according to claim 1, characterized in that: Calculating the path deviation based on the real-time location data and the optimal path includes: Get the coordinate sequence of the user's actual movement trajectory ; Calculate real-time location To the nearest path segment Vertical distance ; Generate path deviation ,in is the preset tolerance radius.

3. A campus multi-scenario service reservation management method according to claim 1, characterized in that: The re-updating of the optimal path time according to the real-time location data includes: Calculate the initial path time to move to the next scene based on the current position ; According to the user's movement speed Calculate the velocity attenuation coefficient , the calculation formula is: ,in Plan the initial speed for the preset path; According to the path deviation Calculate deviation compensation time , the calculation formula is: ,in is the path deviation, is the preset unit deviation time increment, is the maximum compensation threshold; Updated optimal path time The calculation formula is: ,in is the deviation weight.

4. A campus multi-scenario service reservation management method according to claim 3, characterized in that: The deviation weight The calculation process is: Obtain scene identifiers of at least two scene resource reservation records; Query the preset scene identifier-weight mapping table to extract the basic weight value ; According to the user's movement speed , preset speed threshold Calculate the speed correction factor : ； Deviation weight The calculation formula is: .

5. A campus multi-scenario service reservation management method according to claim 1, characterized in that: The extracting the optimal path from the previous scene to the next scene and calculating the optimal path time comprises: Extracting a set of feasible paths from the previous scene to the next scene from the building node connectivity matrix; Based on real-time crowd density data and equipment waiting data, the benchmark travel time of each feasible path is revised; The path with the shortest corrected travel time is selected as the optimal path, and its travel time is the optimal path time.

6. A campus multi-scenario service reservation management method according to claim 1, characterized in that: The real-time crowd density data is obtained through infrared sensors deployed in building passages, and the equipment waiting data includes the product of the historical average waiting time of the elevator and the real-time number of people in queue.

7. A campus multi-scenario service reservation management method according to claim 1, characterized in that: The building node connectivity matrix is ​​generated by converting the campus modeling data into a node network. Each node is marked with a three-dimensional coordinate. When the traffic status between adjacent nodes is connected, it is marked as 1, and when the status is usually blocked, it is marked as 0. The benchmark traffic time is obtained through historical path data statistics.

8. A campus multi-scenario service reservation management method according to claim 1, characterized in that: Generating a dynamic buffer time based on the statistical characteristics of the historical performance deviation data includes: Extract the deviation data set between the user's historical actual arrival time and planned arrival time based on the historical fulfillment deviation data; Calculate the mean of the deviation data set and standard deviation ; Generate dynamic buffer time ,in is the preset safety factor.

9. A campus multi-scenario service reservation management method according to claim 1, characterized in that: The adjustment of the appointment time period includes: Sending a time period extension request to the next scene resource database, the request including the length of time to be extended; If the subsequent free time slot of the next scene is not occupied, the extended reservation time slot will be automatically locked; If the extension fails, a prompt instruction to leave the current scene early will be pushed to the user terminal.

10. A campus multi-scenario service reservation management system, characterized by: A campus multi-scenario service reservation management method according to any one of claims 1 to 9 is applied, comprising: A multi-scenario reservation module is used to obtain at least two scenario resource reservation records arranged in chronological order by the user, wherein the scenario resource reservation record includes a scenario identifier, a reservation start time, and a reservation end time; A path planning calculation module is used to extract the optimal path from the previous scene to the next scene and calculate the optimal path time based on the resource reservation records of at least two scenes arranged in chronological order and the pre-stored building node connectivity matrix; A dynamic time prediction module is used to obtain the user's historical performance deviation data and generate a dynamic buffer time based on the statistical characteristics of the historical performance deviation data; A loop decision control module, configured to determine whether the sum of the optimal path time and the dynamic buffer time is greater than the difference between the start time of the next service scenario and the end time of the current service scenario; If the judgment result is no, move from the previous scene to the next scene according to the optimal path; If the result is yes, do the following: The user's real-time location data and user movement speed are obtained at predetermined intervals of a few seconds, and the path deviation is calculated based on the real-time location data and the optimal path; Determine whether the user's moving speed is lower than a preset speed threshold or whether the path deviation is greater than a preset deviation threshold; If so, update the optimal path time based on the real-time location data; Loop to determine whether the sum of the updated optimal path time and the dynamic buffer time is greater than the difference between the start time of the next service scenario and the current time; If the result of the cyclic judgment continues to be yes, it will trigger the push of acceleration prompts or reservation time adjustment to the user terminal.

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