Bus travel visualization management method and system combined with positioning technology

Abstract

An embodiment of the present invention discloses a method and system for visual management of bus travel combined with positioning technology. The method includes: obtaining real-time vehicle positioning data of a target vehicle, extracting task execution information associated with the real-time vehicle positioning data, performing dynamic trajectory fitting processing on the real-time vehicle positioning data, generating a driving trajectory map that matches the task execution information, performing multi-dimensional analysis on the driving trajectory map based on preset task rules, and determining driving state characteristics and task completion indicators of the target vehicle; performing spatiotemporal correlation mapping on the driving state characteristics, task completion indicators, and the driving trajectory map to generate a visual dynamic management interface; in the running state of the visual dynamic management interface, in response to a tracing instruction triggered by a management terminal, calling a historical trajectory database to perform backtracking optimization processing on the driving trajectory map, and outputting management feedback data including abnormal event annotations and path comparison results.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of data visualization technology, and specifically to a bus travel visualization management method and system combined with positioning technology. Background Art

[0002] Current government vehicle dispatch and management systems typically rely on basic GPS positioning modules and pre-set route planning algorithms for vehicle monitoring. For example, existing technologies often employ discrete positioning data collection, recording vehicle coordinates at fixed intervals and combining them with electronic fencing technology to determine whether a vehicle has deviated from its designated route. For task execution monitoring, a simple matching mechanism between manually entered task plans and vehicle arrival at key stops is employed, with task progress assessed through time comparison.

[0003] However, existing static path planning algorithms struggle to adapt to trajectory deviations caused by real-time road condition changes, resulting in insufficient trajectory fitting accuracy. Furthermore, traditional positioning data is weakly correlated with task execution parameters, failing to accurately reflect the dynamic relationship between driving behavior and task requirements. Furthermore, vehicle status assessments are often limited to single-dimensional indicators such as speed and position, lacking a multidimensional evaluation system that integrates task elements. Furthermore, visualization interfaces typically present static route overlays, lacking dynamic spatial and temporal correlations, leading to inefficient tracing of abnormal events. Historical data analysis still relies on manual retrieval of discrete positioning records for comparison, making automated abnormal pattern recognition difficult. Therefore, ensuring controllability and interpretability when implementing visual management of official vehicles is a long-standing technical challenge in existing technologies. Summary of the Invention

[0004] The embodiments of the present invention provide a method and system for visual management of public bus travel combined with positioning technology, which can significantly improve the controllability and explainability of official vehicles during task execution when realizing visual management of official vehicles, and provide dynamic optimization decision support for scenarios such as intelligent scheduling, abnormal diagnosis and path planning.

[0005] In a first aspect, an embodiment of the present invention provides a method for visual management of bus travel combined with positioning technology, which is applied to a visual management system, the method comprising: obtaining real-time vehicle positioning data of a target vehicle, and extracting task execution information associated with the real-time vehicle positioning data, the real-time vehicle positioning data comprising positioning coordinates and motion parameters of a continuous time series; performing dynamic trajectory fitting processing on the real-time vehicle positioning data to generate a driving trajectory map that matches the task execution information, the driving trajectory map comprising timestamps and key event markers of path nodes; performing multi-dimensional analysis on the driving trajectory map based on preset task rules to determine the driving state characteristics and task completion indicators of the target vehicle; performing spatiotemporal correlation mapping on the driving state characteristics, the task completion indicators and the driving trajectory map to generate a visual dynamic management interface; in the running state of the visual dynamic management interface, in response to a tracing instruction triggered by a management terminal, calling a historical trajectory database to perform backtracking optimization processing on the driving trajectory map, and outputting management feedback data comprising abnormal event annotations and path comparison results.

[0006] In a second aspect, an embodiment of the present invention provides a visual management system, including:

[0007] processor;

[0008] a storage device having a computer program stored thereon,

[0009] When the computer program is executed by the processor, the processor implements any of the bus travel visualization management methods combined with positioning technology.

[0010] An embodiment of the present invention provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the bus travel visualization management method combined with positioning technology are implemented.

[0011] It can be seen that the embodiments of the present invention have the following beneficial effects: by constructing a closed-loop analysis system for bus vehicle operation data, a multi-dimensional efficiency breakthrough is achieved in the field of intelligent bus traffic management.

[0012] First, dynamic trajectory fitting is spatiotemporally coupled with task execution logic, and a high-fidelity map with event marking characteristics is generated through an adaptive trajectory modeling algorithm, which can break through the bottleneck of the fragmented analysis of traditional positioning data and business scenarios.

[0013] Secondly, the deep feature extraction mechanism based on the multi-dimensional parsing engine can simultaneously capture the dynamic correlation characteristics of driving behavior patterns and task execution efficiency, forming a composite evaluation indicator with decision-making value.

[0014] Then, through the interactive visualization interface built with spatiotemporal correlation mapping technology, the intelligent integration of massive spatiotemporal data and business rules is realized, which greatly improves the efficiency of retrospective response to abnormal events.

[0015] Furthermore, the backtracking optimization algorithm provided by the embodiment of the present invention can autonomously identify trajectory deviation patterns and generate optimization suggestions through the comparative learning mechanism of the historical trajectory database, forming a closed-loop management system with self-evolution capabilities.

[0016] Therefore, when implementing the visual management of official vehicles, the embodiments of the present invention can significantly improve the controllability and explainability of official vehicles during task execution, and provide dynamic optimization decision support for scenarios such as intelligent scheduling, abnormal diagnosis and path planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a bus travel visualization management method combined with positioning technology provided by an embodiment of the present invention.

[0018] Figure 2 A schematic diagram of the basic structure of a visual management system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0020] See also Figure 1 As shown in FIG, this figure is a flow chart of a bus travel visualization management method combined with positioning technology provided by an embodiment of the present invention, which can be applied to a visualization management system. Figure 1 As shown, the method may include S101-S105.

[0021] S101: Acquire vehicle real-time positioning data of a target vehicle, and extract task execution information associated with the vehicle real-time positioning data, wherein the vehicle real-time positioning data includes positioning coordinates and motion parameters in a continuous time series.

[0022] In this embodiment, the visualization management system continuously collects the real-time positioning data and motion parameters of the vehicle by deploying a multimodal sensor device on the target vehicle that performs the task of transporting data for the infrastructure project.

[0023] For example, the target vehicle was tasked with transporting construction drawings for a highway expansion and reconstruction project. Departing from an engineering design institute in the northern part of the city, it was scheduled to travel south along the G60 Expressway to the construction site of Section 3. The vehicle's real-time positioning data was derived from a redundant system comprised of a dual-frequency, high-precision GPS receiver, an inertial measurement unit, and wheel speed sensors. The dual-frequency, high-precision GPS receiver provided centimeter-level positioning accuracy on open roads, while the inertial measurement unit's built-in three-axis gyroscope and accelerometer maintained positioning continuity in tunnels through dead reckoning.

[0024] For example, when the target vehicle enters a 2.1-kilometer-long tunnel in a mountainous area, the global positioning signal is completely lost. At this time, the inertial measurement unit continuously outputs estimated position data based on the last known coordinates of the tunnel exit (longitude 118.764532°, latitude 31.992457°) and the vehicle kinematic model, and performs Kalman filtering fusion with the number of tire rotation pulses recorded by the wheel speed sensor to ensure that the positioning error is controlled within 0.5 meters.

[0025] Furthermore, the motion parameter acquisition module records the vehicle's longitudinal acceleration, lateral offset angle and pitch attitude data at a period of 50 milliseconds. When the target vehicle encounters a temporarily paved steel plate road surface on a construction access road, the system identifies abnormal road surface unevenness by analyzing the acceleration spectrum characteristics and triggers the on-board camera device to capture real-time images of the road surface.

[0026] In addition, the task execution information extraction module is deeply integrated with the engineering material management equipment. When the transportation task is started, it automatically associates the version number of the construction drawing (such as V3.2.1), the electronic label number of the packaging box (ET-20231127-003) and the information of the person responsible for signing. It also performs real-time verification when the target vehicle passes through the electronic fence boundary. For example, when the target vehicle deviates from the planned route and approaches an unauthorized area, the system immediately compares the geographic fence coordinates in the electronic fence database and generates an abnormal event log containing the deviation distance (such as 1.2 kilometers) and duration.

[0027] S102: Perform dynamic trajectory fitting processing on the real-time positioning data of the vehicle to generate a driving trajectory map that matches the task execution information, wherein the driving trajectory map includes timestamps of path nodes and key event markers.

[0028] In this embodiment, the visual management system uses an improved Bayesian trajectory estimation algorithm to perform dynamic trajectory fitting processing on the discrete positioning data stream of the target vehicle to generate a driving trajectory map that matches the transportation task of the highway reconstruction and expansion project.

[0029] When a target vehicle was forced to detour via County Road X205 due to construction closures at K12+300 on the G60 Expressway, the system automatically identified the spatial correlation between the actual travel path and the originally planned path by incorporating road network topology constraints. During trajectory fitting, the system employed a cubic B-spline interpolation algorithm to encrypt sparse positioning points, eliminating trajectory breaks caused by positioning signal interruptions within tunnels.

[0030] For example, within a mountain tunnel, the system spatially matches trajectory points reconstructed from inertial navigation data with the tunnel's three-dimensional model to verify the consistency of the calculated path with the tunnel's actual orientation. The key event marking module in the driving trajectory map uses multi-source data fusion technology to correlate and analyze vehicle status parameters with environmental perception data. If a target vehicle reverses due to difficulty passing other vehicles while detouring on a county road, the system combines turn signal signals, reversing radar data, and on-board camera footage to mark a "Reversing on a Complex Road" event at the corresponding location on the trajectory map (longitude 118.801234°, latitude 31.978901°), recording the duration (2 minutes and 15 seconds) and the number of times the operation occurred (3 times).

[0031] For example, the timestamp generation mechanism uses a layered time synchronization protocol to calibrate the vehicle terminal clock with the Beidou satellite timing signal at the millisecond level to ensure that the time reference of each path node in the trajectory map is unified. For example, when crossing the coverage areas of communication base stations of different operators, the system automatically corrects the time deviation caused by transmission delay through the network time protocol.

[0032] S103: Perform multi-dimensional analysis on the driving trajectory map based on preset task rules to determine the driving state characteristics and task completion index of the target vehicle.

[0033] In this example, the visual management system performs a multi-dimensional analysis and evaluation of the target vehicle's trajectory map based on a pre-defined engineering transport task rule base. The task rule base includes transport timeliness constraints (e.g., a 4-hour full-trip time limit), route compliance requirements (e.g., prohibiting driving on unreinforced temporary paths), and cargo safety indicators (e.g., a maximum permissible vibration acceleration of 0.3g).

[0034] When the target vehicle's cargo box vibration monitoring value reaches 0.28g due to bumpy road conditions while detouring around County Road X205, the system extracts the characteristic frequency of the vibration signal through a wavelet packet decomposition algorithm, compares it with the resonance frequency threshold of the packaging box in the construction drawing, and calculates the cargo damage risk factor.

[0035] For example, the driving status feature extraction module uses a sliding window analysis method to calculate the vehicle's average speed, acceleration variation coefficient, and heading stability index in 5-minute time units. On county roads with continuous curves, the system detects that the standard deviation of lateral acceleration exceeds the safety threshold and automatically generates an "abnormal driving behavior" warning.

[0036] For example, the task completion index evaluation model integrates parameters such as remaining mileage, time margin and energy consumption coefficient. When the actual mileage of the target vehicle increases by 8.7 kilometers due to detour, the system recalculates the arrival time estimate and compares it with the originally planned construction drawing handover time window, and updates the task delay risk level to yellow warning status.

[0037] S104: Performing spatiotemporal correlation mapping on the driving state characteristics, the task completion index, and the driving trajectory map to generate a visual dynamic management interface.

[0038] In this embodiment, the visualization management system uses a spatiotemporal coding module to integrate the target vehicle's driving status characteristics and task completion indicators into a 3D geographic information platform, creating a multi-layered, visual dynamic management interface. This interface integrates the BIM model data of the highway reconstruction and expansion project, and simultaneously displays the target vehicle's actual driving trajectory, planned path, and construction area electronic fences in a 3D scene.

[0039] It is understandable that when the risk of mission delay increases due to detours, the system uses a dynamic coloring algorithm to gradually change the color of the trajectory segment from standard blue to warning orange, and the color transition rate is positively correlated with the probability of delay. The abnormal state visualization module uses augmented reality technology to overlay multi-dimensional information. For example, when the vibration monitoring value of the cargo box approaches the safety threshold, the vehicle's three-dimensional model automatically displays the vibration frequency spectrum and compares and annotates it with the allowed safety interval. The spatiotemporal correlation mapping process uses a WebGL-based rendering optimization algorithm to ensure smooth interaction at 60 frames per second in complex scenes containing tens of thousands of spatial elements. For example, when showing the target vehicle passing through the construction area, the system renders the working radius of the construction machinery in real time and calculates the minimum safe distance between the vehicle and the excavator's working surface.

[0040] S105: In the running state of the visual dynamic management interface, in response to the tracing instruction triggered by the management terminal, the historical trajectory database is called to perform backtracking optimization processing on the driving trajectory map, and management feedback data including abnormal event annotations and path comparison results is output.

[0041] In this example, the visualization management system, responding to a tracing instruction from the engineering command center, conducts a retrospective optimization analysis of the target vehicle's unusual driving trajectory on County Road X205. The system accesses the historical weather database to obtain daily rainfall data (12 mm / hour) and, in conjunction with a road friction coefficient model, reassesses the safety of the detour route. The path comparison module employs a dynamic time warping algorithm to eliminate timeline offsets caused by temporary stops and accurately calculates the spatial similarity between the actual path and the emergency backup path.

[0042] For example, when analyzing a reversing incident, the system correlates traffic surveillance video and onboard sensor data from that time period to construct an abnormal event chain containing six dimensions of evidence, ultimately determining the necessity of the reversing maneuver. The optimization processing module generates route planning suggestions based on a reinforcement learning model. When it detects a persistent traffic risk on County Road X205, it automatically updates the detour solution library to prioritize County Road X207. The management feedback data output module utilizes a causal reasoning module to generate a report containing a root cause analysis, clearly identifying delayed updates to construction closure information as the primary factor leading to route deviations and recommending increasing the frequency of dynamic updates to electronic fences.

[0043] In an exemplary application scenario for full-process visualization management of public buses, as described in an embodiment of the present invention, a target vehicle was tasked with transporting construction drawings from the engineering design institute to the construction site during a western highway expansion and reconstruction project. The vehicle was equipped with a redundant positioning system consisting of dual-frequency GPS, an IMU inertial navigation system, and wheel speed sensors, collecting centimeter-level coordinate, acceleration, and attitude data at a 50-millisecond cycle. When the vehicle reached K12+300 on the G60 Expressway, it was forced to detour via County Road X205 due to construction closures. The multimodal sensing system immediately initiated collaborative positioning: GPS provided precise coordinates of 118.764532°E / 31.992457°N on the open road. After entering a 2.1-kilometer mountain tunnel, the IMU calculated the trajectory based on the last valid position fix at the tunnel exit, using the vehicle's kinematic model. This trajectory was then integrated with the wheel speed pulse data through a Kalman filter, reducing the positioning error to within 0.5 meters. At the same time, the task execution information module associates the packaging box electronic label ET-20231127-003, the drawing version V3.2.1 and the information of the person responsible for signing in real time. When the vehicle deviates from the electronic fence by 1.2 kilometers, the system automatically triggers the abnormal log and starts the on-board camera to capture the actual road conditions.

[0044] Furthermore, the dynamic trajectory fitting module uses a Bayesian algorithm and cubic B-spline interpolation to encrypt discrete positioning points into continuous trajectory lines. In tunnel sections, the system matches the IMU-derived position data with the tunnel's three-dimensional model to correct for trajectory fragmentation. When a vehicle reversed three times on County Road X205 due to a difficult passing situation, the system integrated the turn signal and reversing radar data, marking the "reversing on a complex road section" event at 118.801234°E / 31.978901°N on the trajectory map and simultaneously recording the 2-minute and 15-second operation duration. The Beidou timing system ensures millisecond-level synchronization of all node timestamps. Even when crossing blind spots in communication base station coverage, timing deviations can be corrected through the network time protocol, resulting in a consistent driving trajectory map in both time and space.

[0045] In this application scenario, based on a pre-set transport rule library, the system conducted an in-depth analysis of the trajectory map. On a bumpy section of County Road X205, the cargo box vibration monitoring value reached 0.28g, approaching the 0.3g safety threshold. The wavelet packet decomposition algorithm identified a 12Hz characteristic component that overlapped with the resonant frequency of the packaging box, triggering an orange cargo risk alert. Simultaneously, a sliding window analysis revealed that the standard deviation of lateral acceleration on this section exceeded the limit by 40%. Combined with camera-captured images of the temporary steel plate pavement, the system determined "abnormal road conditions" and generated a maintenance work order. The task completion assessment model dynamically calculated the 8.7km mileage increase caused by the detour and, incorporating the energy consumption factor, re-estimated the arrival time, raising the delay risk level to yellow. The handover time window was then synchronized to the construction site via the 5G network.

[0046] Simultaneously, the 3D visualization platform integrates this data into the project's BIM model, presenting a dynamic management interface. Vehicle trajectories gradually change from blue to orange based on delay probability, a spectrum comparison chart is superimposed on the locations where vibration exceeds limits, and the safety radius of construction machinery is rendered as a red semi-transparent layer. When a vehicle approaches the excavator's working surface, the system calculates a 3.2-meter clearance in real time and triggers an audible and visual alarm. Thanks to WebGL rendering optimization, the interface maintains a smooth 60 frames per second interactive experience even in highway reconstruction and expansion scenarios containing tens of thousands of spatial elements. This allows the command center to inspect vehicle posture, path compliance, and cargo status from multiple perspectives.

[0047] As can be understood, after the engineering command center initiated a tracing command, the system accessed the historical database to conduct a multi-dimensional retrospective review of the abnormal events on County Road X205. The system integrated the 12 mm / hour rainfall data for that day, a road friction coefficient model, and onboard video to verify the rationality of the reverse operation. A dynamic time warping algorithm eliminated the temporal offset caused by the temporary stop and calculated a 92% spatial similarity between the actual route and the backup route X207, triggering an optimized detour. Based on historical traffic data, the reinforcement learning model recommended X207 as the preferred alternative route. The causal reasoning module traced the cause and identified delayed updates of construction closure information as the primary cause. This led to an optimization recommendation to increase the dynamic update frequency of the geo-fence to 15 minutes. Finally, the management feedback data package, including event chain analysis, path comparison heat maps, and equipment maintenance work orders, was stored on the blockchain. This complete closed-loop system from data perception to decision optimization significantly improved the reliability of subsequent tasks and the efficiency of emergency response.

[0048] In one implementation, the performing of dynamic trajectory fitting processing on the real-time vehicle positioning data in S102 to generate a driving trajectory map matching the task execution information includes:

[0049] S1021: Perform timestamp alignment and filtering on the real-time positioning data of the vehicle according to the planned path point set in the task execution information, and select candidate positioning points that meet the preset time window.

[0050] In this embodiment, the visualization management system performs timestamp alignment and filtering on the real-time collected positioning data of the target vehicle based on the set of transportation plan path points predefined in the construction drawings in the task execution information. Specifically, the system loads the planned path nodes corresponding to the transportation task, such as the starting point of the engineering design institute, the construction closure point K12+300 of the G60 Expressway, the detour section of the county road X205, and the end point of the construction site of the third section, and sets the allowed time window threshold for each node. For example, when the target vehicle is scheduled to pass the K12+300 construction point between 10:15 and 10:30, the system performs timestamp matching on the positioning data uploaded by the vehicle during this period, and selects candidate positioning points with a time deviation within ±2 minutes. During this process, the system uses the Beidou satellite timing signal to calibrate the timestamps of each positioning point at the millisecond level, eliminates the time sequence disorder data caused by network delay, and ensures that the time-space correlation between the candidate positioning point and the planned path meets the requirements of the engineering transportation task.

[0051] S1022: Performing a path continuity check on the candidate positioning points, using an interpolation algorithm to fill in the missing intermediate positioning points, and generating a corrected continuous driving path.

[0052] In this embodiment, the system performs a path continuity check on the candidate positioning points after screening, and uses the cubic B-spline interpolation algorithm to fill in the missing intermediate positioning points. When the target vehicle is on the detour section of County Road X205 and the wheel speed sensor signal is briefly interrupted due to continuous bends, the system detects that there is an abnormal spacing of more than 50 meters between adjacent positioning points and automatically starts the interpolation calculation module. This module is based on the vehicle kinematic model and combines historical driving speed, heading angle change rate and other parameters to generate three virtual positioning points in the longitude range of 118.798765° to 118.802341° to form a corrected continuous driving path. During the interpolation process, the system introduces road network topology constraints to ensure that the completed points conform to the actual road shape and direction of County Road X205, avoiding unreasonable trajectories such as crossing buildings or crossing the boundary into unauthorized areas.

[0053] S1023: Extracting abnormal positioning points in the continuous driving path that match preset key event triggering conditions, and associating and marking the abnormal positioning points with task nodes in the task execution information.

[0054] In this embodiment, the visualization management system extracts abnormal location points within a continuous driving path that match pre-set key event trigger conditions. When the target vehicle reversed three times around a narrow curve on County Road X205, the system detected multiple abnormal characteristics within the location point sequence, including a sudden change in heading angle, speed zeroing, and excessive dwell time. By inputting the instantaneous speed fluctuation (from 25 km / h to 0 km / h), directional deviation angle (120° counterclockwise), and dwell time (135 seconds) into a multimodal event classification model, the system outputted the "Reversing on Complex Road" abnormal event type and a 92.7% confidence score. The system then associated this abnormal location point (longitude 118.801234°, latitude 31.978901°) with the construction drawing handover time node in the task execution information, generating a key event tag containing the event type, occurrence time, and impact scope.

[0055] S1024: Calling a trajectory prediction model to perform driving mode recognition on the continuous driving path, dividing it into acceleration segments, constant speed segments, and stagnant segments, and generating a driving trajectory map containing segmented feature descriptions.

[0056] In this embodiment, the system uses a deep neural network-based trajectory prediction model to identify driving patterns on continuous driving paths. When analyzing the detour section of County Road X205, the model identified three characteristic segments: an acceleration segment from the construction site at K12+300 (speed increasing from 0 to 40 km / h) to the county road entrance, a constant speed cruise segment in the middle of the county road (maintaining 35±2 km / h), and a stagnation segment in the temporary parking area (lasting 8 minutes). Based on the acceleration rate of change, speed stability coefficient, and energy consumption characteristics, the model generates a driving trajectory map with segmented feature descriptions. The acceleration segment is labeled with a maximum longitudinal acceleration of 0.3 m / s², and the stagnation segment is associated with an abnormal state in which the vehicle's air conditioning energy consumption increases by 12%.

[0057] In one implementation, the step of extracting abnormal positioning points in the continuous driving path that match preset key event triggering conditions in S1023 includes:

[0058] S10231: Obtain the instantaneous speed fluctuation value, direction deviation angle and dwell time parameters in the real-time positioning data of the vehicle.

[0059] In this example, when a target vehicle passes over a temporary steel plate surface, the inertial measurement unit records an instantaneous fluctuation in longitudinal acceleration of ±0.5 m / s², a cumulative change in directional deviation angle exceeding 15°, and a duration of 45 seconds in the steel plate area. These parameters are uploaded to the visualization management system in real time via the vehicle's onboard communication module, providing multi-dimensional input data for subsequent event classification.

[0060] S10232: Input the instantaneous speed fluctuation value, the direction deviation angle, and the dwell time parameters into a target multimodal event classification model, and output the abnormal event type and confidence score.

[0061] In this example, when a target vehicle encounters temporary traffic control on a construction access road, the system inputs the model's instantaneous speed fluctuation (from 40 km / h to 5 km / h), directional deviation angle (consecutive 30° left turns), and dwell time (210 seconds). After joint processing by a convolutional neural network and a long short-term memory network, the model outputs the "traffic control waiting" abnormal event type and an 88.3% confidence score. The model training process incorporates 5,000 sets of similar event data from historical missions to ensure classification accuracy.

[0062] S10233: Extracting a set of abnormal positioning points that meet a threshold condition from the continuous driving path according to the abnormal event type and the confidence score.

[0063] In this embodiment, the system filters out abnormal positioning points based on preset thresholds. With a speed fluctuation threshold of ±0.4 m / s², a directional deviation threshold of 20°, and a dwell time threshold of 120 seconds, if a target vehicle stops at a temporary rest area for 150 seconds and deviates by 25°, the system automatically extracts that location (longitude 118.812345°, latitude 31.965432°) and adds it to the set of abnormal positioning points. Furthermore, a confidence score threshold of 85% is used to eliminate false alarms caused by sensor noise.

[0064] S10234: Perform similarity matching on the abnormal location point set and similar events in the historical trajectory database to generate a key event marker containing an event association identifier.

[0065] In this embodiment, the system matches abnormal events with the historical database. For the reversing event of the target vehicle on County Road X205, the system retrieves data on 18 reversing events that occurred on similar roads in the past three months. By calculating the similarity of trajectory morphology (Euclidean distance <5 meters) and comparing operational features (number of reversing times and duration), it generates key event markers with the same event identification code, providing data support for subsequent responsibility tracing.

[0066] In one implementation, the multi-dimensional analysis of the driving trajectory map based on preset task rules in S103 to determine the driving state characteristics and task completion index of the target vehicle includes:

[0067] S1031: Extracting the task priority weight, planned completion time and preset path constraint conditions from the task execution information.

[0068] In this example, the visualization management system extracts core parameters from task execution information. The construction drawing transportation task is assigned the highest priority (Level A), with a planned completion time of four hours (including a one-hour margin), and preset path constraints, including a prohibition on unreinforced sidewalks and an 80 km / h speed limit. These parameters form the baseline framework for task analysis, guiding subsequent deviation calculation and score generation.

[0069] S1032: Calculate a deviation index between the actual driving path and the planned path based on the path node density distribution in the driving trajectory map.

[0070] In this embodiment, the system calculates the path deviation based on the path node density distribution in the driving trajectory map. When the target vehicle detours County Road X205, the system matches the actual path with the planned path at intervals of 200 meters and finds that the node density of the county road section increases by 35%. Combined with the spatial position offset (maximum lateral deviation of 1.2 kilometers), the calculated path deviation index is 0.78 (threshold > 0.5 triggers an early warning).

[0071] S1033: Determine a dynamic score of the task completion indicator by combining the deviation indicator and the task priority weight.

[0072] In this example, the system dynamically scores the task based on a combination of deviation metrics and priority weights. Because this task has the highest priority, the system uses a weighted algorithm to convert the deviation metric of 0.78, caused by the county road detour, into a 22% decrease in the task completion score. The scoring model also considers factors such as the remaining time (1.5 hours) and the energy consumption increase (8%), resulting in a comprehensive score of 72 out of 100, corresponding to the yellow warning level.

[0073] S1034: Perform time series analysis on key event markers in the driving trajectory map to generate a status assessment report including event impact coefficients.

[0074] In this example, the start timestamp, duration, and spatial distribution density parameters of the reversing event (10:25-10:27) and the excessive vibration event (10:35-10:37) were extracted. By constructing an event impact propagation model, the correlation strength coefficient between the two events was calculated to be 0.65 (threshold > 0.6), indicating that the excessive vibration may be a subsequent impact of the reversing operation.

[0075] S1035: Compare the dynamic score and the status assessment report with the acceptance conditions in the preset task rules, and output the compliance determination result of the driving status characteristics.

[0076] In this embodiment, the system compares the dynamic score with the acceptance conditions. According to preset rules, a yellow warning status (score of 70-80 points) triggers the secondary response mechanism. The system automatically generates a status report containing three corrective measures: recommending reducing the speed of county roads, notifying the construction site in advance to delay the handover, and arranging for the maintenance unit to inspect the temporary steel plate pavement.

[0077] In one implementation, the step of performing time series analysis on key event markers in the driving trajectory map in S1034 to generate a status assessment report including event impact coefficients includes:

[0078] S10341: Extract the start timestamp, duration, and spatial distribution density corresponding to the key event marker.

[0079] In S10341, the system extracts the temporal and spatial characteristics of key events. For example, the reversing event started at 2023-11-27T10:25:13.456, lasted 165 seconds, and had three location point anomalies within a 50-meter road section, with a spatial distribution density of 6 per 100 meters. These parameters provide the foundational data for building an event impact model.

[0080] S10342: Generate an event impact propagation model based on the time axis at least based on the start timestamp and the duration, and calculate the correlation strength coefficient between adjacent key events.

[0081] In S10342, taking the reversing event as the starting point, the vibration exceeding limit, speed fluctuation and other events occurring within the subsequent 30 minutes were analyzed. The propagation coefficient of 0.78 was calculated through time axis correlation analysis, indicating that the reversing operation has a continuous impact on the subsequent driving status.

[0082] S10343: Generate an event chain reaction probability map based on the correlation strength coefficient and the spatial distribution density.

[0083] In S10343, when a target vehicle reverses on County Road X205, the model predicts a 65% probability of road anomaly detection within the next 5 kilometers, and a 40% increase in the risk of construction drawing damage. This probability map is presented as a heat map on the 3D geographic information platform.

[0084] S10344: Input the event chain reaction probability graph into the risk prediction model, and output a quantitative evaluation value of the event impact coefficient.

[0085] In S10344, based on the consequence analysis of 20 similar events in historical data, the system calculated that the impact coefficient of the current reversing event is 0.58 (range 0-1), corresponding to a medium risk level, triggering the on-board camera device to continuously monitor the status of the cargo box.

[0086] S10345: Prioritize abnormal events in the status assessment report based on the quantitative assessment values.

[0087] In S10345, the system prioritizes abnormal events. For example, based on the quantitative assessment values, the reversing event (0.58) and the vibration exceeding the limit event (0.62) are marked as P1 and P2 priorities, respectively. The generated status assessment report prioritizes the vibration exceeding limit and presents recommendations for handling, including immediate deceleration to 30 km / h and arranging on-site personnel to pre-inspect the packaging boxes.

[0088] With this design, the visual management system realizes accurate monitoring of the entire construction drawing transportation task, significantly improving the task execution reliability and management efficiency of official vehicles.

[0089] In one implementation, the step of performing spatiotemporal correlation mapping of the driving state characteristics, the task completion index, and the driving trajectory map in S104 to generate a visual dynamic management interface includes:

[0090] S1041: Extracting the geographic coordinate set and timestamp sequence of the path nodes from the driving trajectory map.

[0091] In this embodiment, the visualization management system extracts the geographic coordinates and timestamp sequences of path nodes from the target vehicle's trajectory map. Specifically, the system parses the trajectory data stream generated by an improved Bayesian trajectory estimation algorithm, separating the geographic coordinates containing longitude and latitude information, as well as the Beidou satellite timing timestamp sequence corresponding to each coordinate point. For example, during a target vehicle's mission to transport materials for a highway expansion and reconstruction project, the system extracted over a thousand path nodes between the Engineering Design Institute (starting point: longitude 118.750123°, latitude 32.001234°) and the construction site of Section 3 (end point: longitude 118.845678°, latitude 31.956789°). Each node accurately records the Coordinated Universal Time (UTC) timestamp of the vehicle's arrival at that location, with millisecond-level resolution. For dead-reckoning coordinates generated in tunnel areas due to GPS signal interruptions, the system automatically adds an inertial navigation data source identifier to ensure the integrity and traceability of the geographic coordinates.

[0092] S1042: Map the geographic coordinate set and the timestamp sequence to layer data of an electronic map to generate a basic track overlay layer.

[0093] In this embodiment, the system maps a set of geographic coordinates and a sequence of timestamps to a vector layer on a high-precision electronic map to generate a basic trajectory overlay. This process employs a spatial reference system conversion algorithm to convert the WGS84 coordinate system data collected by a global positioning receiver into the local projection coordinate system used by the engineering electronic map. For example, when the target vehicle detoured along County Road X205, the system matched the trajectory point coordinates (longitude 118.798765°, latitude 31.978901°) with the road centerline on the electronic map, generating a trajectory overlay with adjustable width. For sections of mountain tunnels lacking satellite positioning signals, the system uses the tunnel's 3D model data to spatially align the coordinates of the inertial navigation-derived points (longitude 118.769876°, latitude 31.987654°) with the tunnel's internal lane markings, ensuring continuous display of the basic trajectory overlay even in signal-deprived areas.

[0094] S1043: Overlaying the speed heat map of the driving state characteristics and the progress indicator of the task completion index on the basic trajectory overlay.

[0095] In this embodiment, the visualization management system overlays a speed heatmap of driving status characteristics and a progress indicator for task completion on the base trajectory overlay. The speed heatmap uses a color gradient mapping algorithm to visually encode the target vehicle's speed variations along various sections of County Road X205 using a color spectrum ranging from dark blue (low speed) to bright red (high speed). For example, on a section of road (longitude 118.801234°, latitude 31.978901°) where the vehicle reversed three times due to passing difficulties, the system detected a speed drop to 0 km / h for 135 seconds. This area appears as a dark blue patch in the heatmap. Simultaneously, the progress indicator displays the task completion percentage in real time in a floating window. If the vehicle's detour causes the actual progress to lag behind the planned time by 15%, the indicator automatically switches to an orange warning state and indicates the estimated delay duration.

[0096] S1044: Embed an interactive event annotation window in the electronic map according to the spatial position of the key event mark.

[0097] In this embodiment, the system embeds an interactive event annotation window within the electronic map based on the spatial location of key event markers. When a target vehicle triggers a vibration overrun warning on a construction access road, the system generates an annotation window with an event type icon at the corresponding coordinate point (longitude 118.812345°, latitude 31.965432°). This window supports multi-level interaction: a primary click displays an event summary (e.g., "Vibration acceleration 0.28g, approaching the safety threshold"), while a secondary click provides a detailed parameter list (including vibration spectrum characteristics and road surface image capture). The annotation window's spatial positioning accuracy reaches 0.1 meters, allowing accurate correlation to the corresponding location on the electronic map even when the vehicle is in motion.

[0098] S1045: Integrate the basic trajectory overlay layer, the speed heat map, the progress indicator, and the interactive event annotation window to generate a multi-layered visual dynamic management interface.

[0099] In this embodiment, the system integrates the basic trajectory overlay layer, speed heat map, progress indicator and interactive event annotation window to generate a multi-layered visual dynamic management interface. The interface uses layered rendering technology to ensure the independent control and coordinated display of each visualization element. For example, when showing the target vehicle passing through the detour section of County Road X205, the basic trajectory layer presents a blue solid line path, the speed heat map covers the speed distribution with translucent blocks, the progress indicator is dynamically updated in the upper right corner of the interface, and the interactive annotation window of the reversing event flashes at the corresponding position. The system accelerates the rendering pipeline through the graphics processor to achieve smooth interaction at 60 frames per second in complex scenes containing tens of thousands of spatial elements.

[0100] In one implementation, the step of generating a multi-layered visual dynamic management interface in S1045 includes:

[0101] S10451: In response to a user's touch operation on the interactive event annotation window, retrieving sensor raw data associated with the key event marker.

[0102] In this embodiment, the visual management system responds to touch operations on the interactive event annotation window by the engineering command center operator, retrieving raw sensor data associated with key event markers. When a user clicks the icon for the reversing event on County Road X205, the system immediately retrieves the inertial measurement unit (IMU) raw acceleration data, wheel speed sensor pulse signals, and a 1280×720 resolution video stream captured by the onboard camera from the distributed storage cluster for that period (2023-11-27 10:25:13 to 10:27:28). The data retrieval process utilizes a timestamp alignment mechanism to ensure strict synchronization of multimodal sensor data.

[0103] S10452: Analyze the raw sensor data in real time to generate an event details panel including vehicle fault codes, environmental parameters, and operation logs.

[0104] In this embodiment, the system analyzes raw sensor data in real time, generating an event details panel containing vehicle fault codes, environmental parameters, and operation logs. For a reversing event, the analysis module extracts the turn signal control signal (left turn activated three times), reversing radar detection data (distance to the nearest obstacle: 0.8 meters), and gear shift records recorded by the engine control unit (forward / reverse shifts three times). The environmental parameter analysis module also analyzes data from the onboard atmospheric pressure sensor, identifying rainfall intensity of 12 mm / hour and a road friction coefficient drop to 0.35 at the time of the event. All analysis results are presented in a structured table on the event details panel, accompanied by a time-aligned multimedia evidence chain.

[0105] S10453: Linking the event details panel with the visual dynamic management interface for rendering, and updating the extended information layer displaying the abnormal event.

[0106] In this embodiment, the system links the event details panel with the dynamic visualization management interface, updating and displaying an extended information layer for the abnormal event. When the reversing event details panel is expanded, the interface automatically adjusts the layer overlay order: the transparency of the base trajectory layer is increased to 50%, and the resolution of the satellite map layer for the event-related road section is enhanced to 0.5-meter level. Simultaneously, an extended information panel slides out from the right side of the interface. This panel simultaneously plays the in-vehicle video recording of the reversing process and highlights the active reversing radar detection area on the 3D vehicle model, displaying the spatial and temporal correlation of multi-dimensional data.

[0107] S10454: Load historical driving trajectory maps of similar tasks from the historical trajectory database according to the comparison mode selected by the user.

[0108] In this embodiment, the system loads historical driving trajectory maps for similar tasks from the historical trajectory database based on the user's selected comparison mode. When the project manager selects the "Historical Optimal Path Comparison" mode, the system retrieves data from 18 transportation tasks with the same origin and destination completed within the past three months, selecting the trajectory record with the shortest time and no abnormalities (task number T20231015-003). The loading process utilizes a differential data transmission protocol, incrementally synchronizing only the spatial deviation data between the current trajectory and the historical trajectory, reducing network bandwidth usage.

[0109] S10455: Displaying the path difference analysis results of the current driving trajectory map and the historical driving trajectory map in parallel in the extended information layer.

[0110] In this embodiment, the system displays the path difference analysis results of the current driving trajectory map and the historical driving trajectory map in parallel in the extended information layer. The comparison view is rendered with two-color trajectory lines: the current path around County Road X205 is displayed in orange, and the historical optimal path (via County Road X207) is displayed in green. The spatial analysis module calculates the length difference (8.7 kilometers), the cumulative value of the elevation change (+35 meters), and the total turning angle (the current path increases by 120°) of the two paths, and embeds the statistical results in the form of a comparative bar chart in the extended information layer. At the same time, the system calls the path similarity algorithm to generate a spatial overlapping heat map of the two trajectories, focusing on marking the peak points of spatial deviation between the sharp bend area of County Road X205 and the historical path.

[0111] In one implementation, the calling of the historical trajectory database in S105 to perform backtracking optimization processing on the driving trajectory map includes:

[0112] S1051: Extracting historical trajectory segments of a corresponding time period from the historical trajectory database according to the time range parameter in the tracing instruction.

[0113] In this embodiment, the visualization management system extracts historical trajectory segments corresponding to the time period from the historical trajectory database based on the time range parameters specified in the received tracing instructions. Specifically, when the target vehicle performs the project data transportation task for Section 3 on November 27, 2023, the system receives a tracing instruction requesting the retrieval of all historical transport records with the same origin and destination points between October 1 and November 26, 2023. Using a spatiotemporal indexing mechanism, the system quickly locates 17 complete trajectory data items within the storage cluster that meet the time range constraints. Each trajectory contains no fewer than one thousand path nodes and their associated sensor datasets. For example, when retrieving the historical trajectory for task number T20231115-007 on November 15, 2023, the system extracts a special path segment (from the starting point longitude 118.750123° to the end point longitude 118.845678°) resulting from the detour along County Road X205 during this transport. This segment contains data for 328 consecutive trajectory nodes that deviate from the standard route.

[0114] S1052: Calculate trajectory smoothness and perform path overlap matching on the historical trajectory segments to identify repetitive driving patterns.

[0115] In this embodiment, the system performs trajectory smoothness calculation and path overlap matching on the extracted historical trajectory segments to identify repetitive driving patterns. The trajectory smoothness calculation uses a moving average filtering algorithm to eliminate noise points caused by satellite positioning signal jitter. For example, the five abnormal jump points (maximum offset 2.3 meters) that appeared on the trajectory of County Road X205 on November 15, 2023 were automatically corrected. The path overlap matching uses a spatial topology analysis algorithm to calculate the spatial overlap rate of the current trajectory and the historical trajectory in the detour area. When it is detected that the target vehicle has chosen the County Road X205 detour route in the last three transport tasks and the path overlap reaches 92%, the system marks it as a high-frequency repetitive pattern and associates the pattern with the validity period data of the construction control notice (October 20, 2023 to January 10, 2024).

[0116] S1053: Build a trajectory prediction model based on the repetitive driving pattern to generate a task path optimization suggestion for the target period.

[0117] In this embodiment, the system constructs a trajectory prediction model based on identified repetitive driving patterns and generates optimized route recommendations for the target time period. The model uses the temporal distribution characteristics of a periodic set of path nodes, combined with real-time traffic status data streams, to perform dynamic route planning. For example, based on the congestion pattern of construction vehicles on County Road X205 from 10:00 AM to 11:00 AM every Monday, Wednesday, and Friday, the system generates an optimized route recommendation to detour via County Road X207. This recommendation includes a detailed route node sequence (12 key turning points from longitude 118.802345° to longitude 118.823456°) and estimated time savings (an average of 18 minutes). The optimized recommendation is also linked to temporary control information from the road maintenance department to ensure the real-time effectiveness of the recommended route.

[0118] S1054: Simulate and superimpose the task path optimization suggestion for the target period on the current driving trajectory map, and output management feedback data including the path comparison result.

[0119] In this embodiment, the system simulates and overlays the task route optimization recommendations with the current driving trajectory map, outputting management feedback data containing the path comparison results. The simulation utilizes 3D spatial projection technology to display the spatial differences between the actual driving trajectory (the detour via County Road X205) and the recommended route (the alternative route via County Road X207) on a digital map. The system automatically calculates the length difference between the two routes (an increase of 2.7 kilometers for the County Road X205 route), the cumulative elevation change (an increase of 45 meters), and the estimated fuel consumption difference (an increase of 3.8 liters), encoding the comparison results into a structured data package. This management feedback data is transmitted to the engineering dispatch center via a dedicated communication protocol, triggering the route optimization approval process.

[0120] S1055: According to the distribution density of the abnormal event annotations, adjust the weight parameters of the trajectory prediction model to update the historical trajectory database.

[0121] In this embodiment, the system adjusts the weight parameters of the trajectory prediction model based on the distribution density of abnormal event annotations to update the historical trajectory database. If the system detects that the County Road X205 detour has triggered 27 vibration overload warnings in the past 30 days, the system automatically increases the risk weight parameter for this section (from 0.35 to 0.78) and accordingly lowers its ranking in the route recommendation. The update process uses a gradient descent algorithm to optimize the model parameters. The adjusted weight parameters are then re-associated and stored with the historical trajectory data, ensuring that subsequent route planning prioritizes avoiding areas with frequent abnormal events.

[0122] In one implementation, the step of constructing a trajectory prediction model based on the repetitive driving pattern in S1053 includes:

[0123] S10531: Extracting periodic path node sets and travel time intervals in historical trajectory segments.

[0124] In this embodiment, the system extracts periodic path node sets and travel time intervals from historical trajectory segments. For the target vehicle's three-times-weekly detour around County Road X205, the system extracts 42 characteristic path nodes that recur between 9:30 AM and 10:15 AM on Mondays, Wednesdays, and Fridays, forming a periodic path node set. Each node is associated with a precise arrival timestamp. For example, every Wednesday morning, the vehicle must arrive at the intersection of County Road X205 and the construction access road (longitude 118.812345°, latitude 31.965432°) at 9:47:23 AM, with a time deviation within ±2 minutes. The travel time interval calculation module simultaneously analyzes the travel time between adjacent nodes and establishes a benchmark time matrix for typical time periods.

[0125] S10532: Using a time series clustering algorithm to classify the periodic path node set into patterns, and generating a typical driving pattern template.

[0126] In this embodiment, the system uses a time series clustering algorithm to classify patterns in periodic route node sets and generate a typical driving pattern template. The algorithm uses dynamic time warping technology to similarity-match the time series of route nodes across different missions. When eight transport missions were detected along County Road X205, exhibiting highly consistent node distribution and time interval characteristics, the system clustered them into a "weekday morning rush hour detour pattern" and generated a pattern template containing 76 core route nodes. This template accurately records the standard arrival time window for each node (for example, the standard arrival time for node 15 is 38 minutes 12 seconds ± 15 seconds after the mission start), forming a reusable path planning benchmark.

[0127] S10533: Fusing the driving time interval with the real-time traffic status data to calculate the time cost coefficient of each typical driving mode.

[0128] In this embodiment, the system integrates travel time intervals with real-time traffic status data to calculate the time cost coefficient for each typical driving pattern. Real-time traffic status data includes published construction control information, traffic density data detected by onboard cameras, and visibility parameters provided by meteorological authorities. For example, when visibility on the alternative route of County Road X207 falls below 50 meters in rainy and foggy weather, the system dynamically increases the time cost coefficient of that route (from 1.2 to 1.8) while simultaneously reducing the time cost coefficient of the detour route of County Road X205 (from 1.5 to 1.3). This fusion process utilizes a multi-source data weighting algorithm to ensure that the time cost coefficient reflects actual road conditions in real time.

[0129] S10534: Dynamically adjust the path recommendation priority of the trajectory prediction model according to the time cost coefficient and the task completion index.

[0130] In this embodiment, the system dynamically adjusts the trajectory prediction model's route recommendation priority based on the time cost coefficient and task completion indicator. If the target vehicle's remaining time margin for the current transport mission is less than 15%, the system prioritizes County Road X207 (with a coefficient of 1.2), which has the lowest time cost coefficient, over the original County Road X205 (with a coefficient of 1.5). The recommendation priority adjustment algorithm comprehensively considers remaining mileage, load status (the vehicle in this case carries an 8.5-ton load), and driver operating habits to generate an optimal route sequence that balances efficiency and safety. The dynamic adjustment results are fed back to the in-vehicle navigation terminal in real time, triggering route replanning instructions.

[0131] S10535: Embed the dynamically adjusted path recommendation priority into the path comparison result in the management feedback data.

[0132] In this embodiment, the system embeds the dynamically adjusted priority of route recommendations into the path comparison results in the management feedback data. When generating a recommended alternative route for County Road X207, the system simultaneously annotates the basis for the priority adjustment in the management feedback data packet, including a real-time traffic status snapshot (the current queue length on County Road X205 is 380 meters), weather warning information (an 85% probability of rain in the next two hours), and historical task completion rate comparison data (the on-time rate for County Road X207 is 97%). This embedding process utilizes layered data encapsulation technology, ensuring that project managers can trace the technical basis for each priority decision through feedback data, supporting manual review and solution optimization.

[0133] In one implementation, the dynamically adjusting the path recommendation priority of the trajectory prediction model according to the time cost coefficient and the task completion index in S10534 includes:

[0134] S1053401: Obtain a set of time consumption parameters of each typical driving mode corresponding to the time cost coefficient, and a set of path deviation scores in the task completion index.

[0135] In this embodiment, the visualization management system obtains a set of time consumption parameters for each typical driving mode corresponding to the time cost coefficient, as well as a set of path deviation scores from the task completion indicator. Specifically, when the target vehicle performs the project data transportation task for Section 3, the system extracts a set of time consumption parameters for the detour mode on County Road X205 from the trajectory prediction model. This set includes parameters such as an average travel time of 48 minutes, a maximum travel time of 62 minutes, and a fuel consumption coefficient of 1.35. Simultaneously, the system obtains a set of path deviation scores for the three most recent transport tasks on the same road section. The path deviation score for the task on November 15, 2023, resulting from temporary traffic control, was 0.87 (a full score of 1.0 indicates complete compliance with the planned path). The deviation score on November 18, resulting from driver operating habits, was 0.68. The deviation score on November 22, resulting from a vehicle malfunction, was 0.42, forming a dynamic scoring dataset encompassing the three time points.

[0136] S1053402: Perform weight allocation processing on the time consumption parameter set according to the path deviation score set to generate a time cost combination weight based on task completion.

[0137] In this embodiment, the system performs weight distribution processing on the time consumption parameter set according to the path deviation score set, and generates a time cost combination weight based on the task completion degree. During the processing, the system uses the path deviation score as a credibility factor to dynamically weight the time consumption parameter. For example, when the deviation scores of the County Road X205 detour mode in three tasks are 0.87, 0.68, and 0.42 respectively, the system calculates its average deviation score as 0.656, and uses this value as the attenuation coefficient of the time consumption parameter. The average time consumption of 48 minutes in the original time consumption parameter set is adjusted to an equivalent value of 48×(1-0.656)=16.9 minutes, and the maximum time consumption of 62 minutes is adjusted to an equivalent value of 62×(1-0.656)=21.7 minutes, forming a new time cost combination weight set.

[0138] S1053403: Input the time-cost combination weight into a priority sorting algorithm, perform initial priority sorting on the candidate path set pre-stored in the trajectory prediction model, and generate a path priority queue with a weight identifier.

[0139] In this embodiment, the system inputs the time-cost combination weights into the priority sorting algorithm, performs an initial priority sort on the candidate path set stored in the trajectory prediction model, and generates a path priority queue with weighted identifiers. The candidate path set includes three options: the County Road X205 detour, the County Road X207 alternative route, and the backup Township Road Y305 emergency route. The priority sorting algorithm ranks the options based on the adjusted time-cost combination weights. The County Road X207 alternative route receives the highest priority weight of 0.92 due to its equivalent average travel time of 16.2 minutes. The County Road X205 detour route receives a second-best weight of 0.85 due to its equivalent travel time of 16.9 minutes. The backup Township Road Y305 emergency route receives a weight of 0.73 due to its equivalent travel time of 23.4 minutes. This creates a path priority queue with three-color weight identifiers (red for high, yellow for medium, and green for low).

[0140] S1053404: Based on the weight identifier of each candidate path in the path priority queue, extract a subset of candidate paths that match the preset path constraint condition in the current task execution information.

[0141] In this embodiment, the system extracts a subset of candidate paths that match the preset path constraints in the current task execution information based on the weight identifiers of each candidate path in the path priority queue. The current task execution information includes constraints such as a vehicle load capacity of 8.5 tons, transport material seismic resistance level B, and a driver's continuous driving time limit of 4 hours. The system automatically filters out candidate routes that do not meet the load requirements. For example, the emergency route of Township Road Y305 is eliminated due to the bridge weight limit of 8 tons. The remaining detour route of County Road X205 and the alternative route of County Road X207 constitute the candidate path subset. This subset is synchronously linked to the driver's operating habit database to eliminate steep slopes that require frequent gear changes, ensuring that the path selection meets ergonomic requirements.

[0142] S1053405: Perform real-time traffic status data adaptability check on each candidate path in the candidate path subset, and eliminate invalid paths with traffic congestion marks or temporary control events.

[0143] In this embodiment, the system performs real-time traffic status data adaptability verification on each candidate path in the candidate path subset, and eliminates invalid paths with traffic congestion marks or temporary control events. For example, the system accesses the real-time data interface of the municipal traffic management platform to obtain the latest control information of the County Road X205 section: due to a sudden road collapse, the section will be closed in both directions from 09:00 on the same day. The system immediately marks the County Road X205 detour route as an invalid path in the candidate path subset and displays a red no-entry sign on the electronic map overlay. At the same time, the system verifies the real-time traffic status of the County Road X207 alternative route, confirms that it has not triggered any traffic abnormality event mark, and retains it as a valid candidate path.

[0144] S1053406: Determine a path priority adjustment interval that meets the dynamic adjustment conditions based on the time-cost combination weights of the remaining candidate paths and the task completion deviation threshold.

[0145] In this embodiment, the system determines the path priority adjustment interval that meets the dynamic adjustment conditions based on the time-cost combination weights and task completion deviation thresholds of the remaining candidate paths. The remaining candidate paths only include the County Road X207 alternative route, whose time-cost combination weight is 0.92 and the task completion deviation threshold is 0.7 (the preset minimum allowable value). The system calculates the deviation dynamic score of the path as 0.89, which is higher than the deviation threshold of 0.7, confirming that it meets the dynamic adjustment conditions. The system automatically generates a priority adjustment interval of [0.85, 1.0], which indicates that the path priority can be downgraded by up to 15% or upgraded indefinitely while ensuring task completion.

[0146] S1053407: Within the path priority adjustment interval, remap the priorities of the remaining candidate paths in descending order of time-cost combination weights to generate an updated path recommendation priority sequence.

[0147] In this embodiment, the system remaps the priorities of the remaining candidate paths within the path priority adjustment interval in descending order of the time-cost combination weights to generate an updated path recommendation priority sequence. Since the County Road X207 alternative route is the only valid candidate path, the system increases its priority weight from 0.92 to the upper limit of the interval 1.0 and marks it as a mandatory recommended path in the priority sequence. At the same time, the system searches the historical path database for similar cases and inserts the historical record of the emergency route of the County Road Y305 on October 12, 2023 as an alternative plan at the end of the sequence, but its priority weight remains unchanged at 0.73, forming an updated sequence containing the main recommended path and the emergency alternative path.

[0148] S1053408: Compare the updated path recommendation priority sequence with the priority records of similar tasks in the historical priority database to identify abnormal paths whose priority jump amplitude exceeds a preset threshold.

[0149] In this embodiment, the system compares the updated recommended route priority sequence with the priority records of similar tasks in the historical priority database to identify anomalous routes whose priority jumps exceed a preset threshold. The historical database shows that the average priority weight of the County Road X207 alternative route was 0.68 over the past 30 days. After this adjustment, it jumped to 1.0, a 47% jump, exceeding the system's preset threshold of 30%. The system automatically triggers the anomalous path detection mechanism, marking the route with a yellow warning status and linking the data packet indicating the reason for the jump (including real-time traffic control information, task urgency parameters, and other verification evidence).

[0150] S1053409: Perform task node coverage integrity verification on the abnormal path. If the verification passes, insert the abnormal path into a specified position of the path recommendation priority sequence.

[0151] In this embodiment, the system verifies the completeness of task node coverage for the abnormal route. If the verification passes, the abnormal route is inserted into the designated position in the recommended route priority sequence. For the alternative route for County Road X207, the system verifies that it fully covers the six key nodes required for the transport task: two data transfer points (longitudes 118.765432° and 118.823456°), three curve speed monitoring points, and the terminal toll booth. The verification results show that the route completely includes all required nodes and has added two temporary checkpoints (longitudes 118.801234° and 118.815678°). After the system confirms the verification, the alternative route for County Road X207 is inserted at the top of the recommended route priority sequence and the traffic precautions for the newly added checkpoints are marked on the electronic map.

[0152] S1053410: Synchronizing parameters of the path output interface of the trajectory prediction model according to the finally determined path recommendation priority sequence, and locking the target recommended path with the highest priority.

[0153] In this embodiment, the system synchronizes the parameters of the trajectory prediction model's path output interface based on the finalized path recommendation priority sequence, locking in the highest-priority target recommended path. This parameter synchronization process involves writing the path node sequence (58 navigation points) for the County Road X207 alternative route, along with a priority weight of 1.0 and associated constraints (load capacity of 8.5 tons and speed limit of 60 km / h) into the trajectory prediction model's output buffer. The system transmits the target recommended path data packet to the driver's terminal via the onboard communication module, triggering the navigation interface to automatically switch to the County Road X207 route and highlighting the first turn prompt (longitude 118.756789°, 2.3 kilometers from the current location) on the HUD. During the synchronization process, the system implements a write-protection mechanism on the path output interface to prevent unauthorized route modifications before the transport task is completed.

[0154] As an optional implementation, the method further includes:

[0155] S201: receiving a fault alarm signal of the target vehicle in real time, and extracting a fault type code and a severity level parameter from the fault alarm signal.

[0156] In this embodiment, the visual management system receives fault alarm signals from the target vehicle in real time and extracts the fault type code and severity level parameters from the alarm signals. On November 28, 2023, while the target vehicle was transporting engineering materials for Section 3, the vehicle's chassis sensor detected that the engine coolant temperature continuously exceeded the safety threshold, triggering fault alarm signal E-2023-OC-015. The system receives a structured data packet containing the fault type code (E-2023-OC-015 indicates a Level 3 engine overheating alarm) and severity level parameter (Level 3, requiring immediate action) through the vehicle's onboard communication module. This data is then correlated with the vehicle's operating status data at the time of the fault, including engine speed of 1820 rpm, coolant temperature of 112°C, and current location coordinates (longitude 118.756789°, latitude 31.987654°).

[0157] S202: Retrieving a pre-stored emergency response plan according to the fault type code, and generating an emergency response instruction including a backup vehicle dispatch plan and a maintenance station navigation path.

[0158] In this embodiment, the system retrieves a pre-stored emergency response plan based on the fault type code and generates an emergency response instruction, including a backup vehicle dispatch plan and a navigation route to a repair station. After parsing fault type code E-2023-OC-015, the system activates the emergency plan retrieval module and matches the engine overheating plan numbered EP-015 from the plan database. This plan includes the cooling system emergency response process, the conditions for activating a backup vehicle, and repair station contact information. The system automatically generates two parallel instructions: dispatching backup vehicle V-023 (currently located at 118.723456° longitude), which is closest to the fault, to take over the transport mission, and planning an emergency route for the target vehicle to a designated repair station (at 118.765432° longitude) 5 kilometers away.

[0159] S203: Synchronize the emergency response instruction with the visual dynamic management interface in real time, and highlight the affected task nodes and path adjustment areas.

[0160] In this embodiment, the system synchronizes emergency response instructions with a visual dynamic management interface in real time, highlighting affected task nodes and route adjustment areas. On the electronic map interface of the engineering dispatch center, task node #3 (longitude 118.801234°) on the original transport route (County Road X207) is marked with a flashing red color. The dispatch route for backup vehicle V-023 (dashed blue line) and the target vehicle's repair route (dashed yellow line) are also displayed. The route adjustment area covers a polygonal area with a radius of 3 kilometers centered on the fault point. Two data handover points within this area (longitudes 118.789012° and 118.803456°) are overlaid with orange warning boxes, alerting task executors to changes in the handover process.

[0161] S204: Activate a remote control protocol according to the severity level parameter to send a speed limit or stop instruction to an actuator of the target vehicle.

[0162] In this embodiment, for Level 3 severity parameters, the system sends a mandatory speed limit command to the target vehicle through a secure authentication channel, reducing the vehicle's maximum speed from 60 km / h to 30 km / h. It also triggers creep mode, stabilizing the engine speed below 1500 rpm. Upon detecting a coolant temperature drop to 105°C, the system issues a stop command, directing the vehicle to the nearest safe parking area (the emergency lane at longitude 118.758765°), activating the hazard warning lights and rear-mounted fault sign.

[0163] S205: Recording the execution status of the emergency response instruction and the vehicle response log in the management feedback data.

[0164] In this example, the system creates an emergency response record for event number INC-20231128-015. This log continuously records the real-time location of backup vehicle V-023 (updated every 30 seconds), the navigation deviation from the target vehicle's maintenance path (accumulated 0.2%), and the execution response latency of remote control commands (average 380 milliseconds). The log data also includes a graph of engine temperature changes, the timestamp of the speed limit command trigger (14:25:37), and a flag indicating the vehicle's complete stop (TRUE), forming a structured event chain that can be traced back later.

[0165] As an optional implementation, the step of retrieving a pre-stored emergency response plan based on the fault type code in S202 and generating an emergency response instruction including a backup vehicle dispatch plan and a maintenance station navigation path may include:

[0166] S2021: Based on the fault type code, the pre-stored emergency response plan database is matched, and the plan execution condition set and plan operation instruction sequence in the emergency response plan corresponding to the fault type code are extracted.

[0167] In this example, for fault type code E-2023-OC-015, the system retrieves a matching entry from the engine fault classification catalog in the emergency plan database and extracts a set of emergency plan execution conditions, including the ambient temperature threshold (enhanced cooling is activated when the temperature exceeds 35°C), vehicle weight limit (autonomous slowing is allowed for vehicles under 10 tons), and backup vehicle activation radius (<8 km). The emergency plan operation instruction sequence includes 12 standard operation steps, such as step 5: "disconnect non-essential electrical loads" and step 8: "send a repair station appointment request."

[0168] S2022: Prioritize the plan execution condition set according to the severity level parameter to determine a target emergency response plan that meets the current fault severity level.

[0169] In this embodiment, for severity level parameter L3, the system filters the set of emergency plan execution conditions to retain only those relevant to emergency response, including immediately activating a backup vehicle (without manual confirmation) and forcibly engaging engine protection mode (ignoring driver instructions). The system excludes conditions applicable to level L1 (minor faults), such as "recommend maintenance at the next mission cycle" and "maintain current driving status for observation," ultimately generating the target emergency response plan EP-015-L3 for level L3 severity.

[0170] S2023: Extracting the vehicle identification set, scheduling path planning parameters and task handover node information of the backup vehicle scheduling plan from the target emergency response plan, and generating a backup vehicle scheduling instruction.

[0171] In this example, the system searches the backup vehicle database and selects vehicle V-023 (10-ton load capacity, equipped with a shock-resistant cargo box) that meets the mission requirements. It then plans a dispatch route from its current location (longitude 118.723456°) to the faulty vehicle's stop (longitude 118.758765°), consisting of seven navigation nodes (longitudes 118.730123° to 118.752345°). The task handover node is set at the emergency lane (longitude 118.756789°), 500 meters downstream of the fault point. The system generates a dispatch instruction packet containing the vehicle identifier V-023, a sequence of path nodes, and a handover time window (2:40 PM to 2:45 PM).

[0172] S2024: Synchronously obtain the real-time positioning data and maintenance station location information of the target vehicle, and generate a path node sequence and estimated arrival time parameters of a maintenance station navigation path by combining the maintenance station location information and the real-time positioning data.

[0173] In this example, the system calculates the shortest path from the target vehicle's stop (longitude 118.758765°) to the designated maintenance station (longitude 118.765432°), generating a path node sequence with three key turning points: a left turn at 118.760123°, a straight ahead at 118.762345°, and a right turn at 118.764567°. Combined with real-time traffic data, the system predicts an arrival time of 2:55 PM ± 3 minutes and associates the station's workstation reservation status (lift No. 2 is available until 3:30 PM).

[0174] S2025: Encapsulate the standby vehicle dispatch instruction and the maintenance station navigation path in an instruction format to generate an emergency response instruction including a dispatch instruction trigger timestamp and a path navigation verification code.

[0175] In this embodiment, the system uses the TLV (Tag-Length-Value) format to encapsulate command data. The dispatch command portion includes the trigger timestamp 20231128142537 (year, month, day, hour, minute, and second) and the MAC address 00:1A:3B:4C:5D:6E of the vehicle ID V-023. The maintenance station navigation path is embedded with the SHA-256 checksum 7D2A45...B9C1 to ensure data integrity during transmission. The encapsulated emergency response command size is controlled within 512 bytes, meeting the transmission protocol requirements of the vehicle communication module.

[0176] S2026: Perform task link integrity verification on the emergency response instruction according to the task handover node information, and identify conflicting nodes between the backup vehicle scheduling plan and the current task execution information.

[0177] In this example, the system compared the list of materials to be handed over (12 boxes of construction drawings for Section 3 and 5 sets of testing equipment) with the cargo box capacity of backup vehicle V-023 (18 cubic meters), confirming sufficient loading space (required 15.6 cubic meters). It also detected a 0.05-meter safety margin between the height restriction facility on the original mission path (the 3.5-meter height restriction on County Road X207) and the height of backup vehicle V-023 (3.45 meters). This was marked as a potential conflict, but the solution remained valid.

[0178] S2027: Dynamically replan the scheduling path planning parameters in the emergency response instruction based on the conflicting nodes, and update the path node sequence of the maintenance station navigation path.

[0179] In this example, when temporary road construction (pipeline maintenance) is detected at the longitude 118.762345° node on the originally planned route, the system activates the dynamic route replanning module and adjusts the route node sequence to turn right at the longitude 118.761234°, taking a detour along the auxiliary road. The updated route increases the driving distance by 0.8 km, avoids the construction area, and the estimated time of arrival is revised to 14:58 ± 2 minutes. A voice prompt parameter, "Attention to the auxiliary road entrance sign," is also added to the navigation instructions.

[0180] S2028: Re-encapsulate the updated path node sequence and the estimated arrival time parameter into the emergency response instruction to generate an emergency response instruction to be executed.

[0181] In this embodiment, the system updates the number of path nodes in the TLV formatted data packet (from 3 to 4), adds the latitude and longitude coordinates of the auxiliary road turning point (118.761234°, 31.976543°), and recalculates the SHA-256 checksum to A3D5F1...8E9C2. The estimated time of arrival (ETA) is also updated to 14:58 ± 2 minutes. A secondary time synchronization is performed with the maintenance station reservation system, confirming that the reservation for lift No. 2 has been extended to 15:35.

[0182] S2029: associate the to-be-executed emergency response instruction with the fault type code, and embed the instruction into the execution queue of the plan operation instruction sequence.

[0183] In this embodiment, the system generates an exemplary globally unique event association code, INC-20231128-015-EOC015, which is written to both the emergency response instruction header section and the metadata section of the pre-plan operation instruction sequence. The pre-plan operation instruction sequence is sorted by execution priority, with "activate the backup vehicle" (instruction priority 1) and "send a maintenance request" (instruction priority 2) taking precedence. Secondary operations, such as "monitor engine temperature" (instruction priority 3), are then prioritized, forming a weighted instruction execution queue.

[0184] As an optional but non-limiting embodiment, the step of rendering the event details panel in conjunction with the visual dynamic management interface in S10453 to update and display the extended information layer of the abnormal event includes:

[0185] (1) In response to a user's touch operation in the interactive event annotation window, a sensor raw data set associated with the key event marker is retrieved, wherein the sensor raw data set includes vehicle acceleration sensor data, engine state parameters, and ambient temperature and humidity records.

[0186] In this step, when the target vehicle triggers the engine overheating anomaly event marker at 2:25 PM on November 28, 2023, the system detects the user's double-clicking of the marker on the electronic map interface and immediately extracts a set of raw sensor data from the vehicle's onboard data storage module for the three minutes before and after the event. This data set includes X, Y, and Z axial vibration values recorded by the triaxial accelerometer (peak value reaching 2.3g), the coolant temperature curve transmitted by the engine control unit (rising from 89°C to 112°C), and temperature and humidity data collected by the cabin environmental sensor (temperature 32°C / humidity 65%). All data are timestamped with millisecond-level timestamps, resulting in a raw data set containing 487 sets of time series data.

[0187] (2) Performing timestamp alignment and noise filtering on the sensor raw data set to generate a standardized event data stream corresponding to the abnormal event marker; dividing the data parsing channels according to the parameter types of the standardized event data stream to extract the vehicle operation fault code sequence, the environmental abnormal parameter fluctuation range and the driver operation log fragment respectively.

[0188] In this step, the system uses a sliding window averaging algorithm to smooth the accelerometer data, eliminating transient noise caused by road bumps (for example, the 2.3g peak recorded at 14:25:37 is corrected to 1.8g). The timestamp alignment module interpolates and synchronizes engine status parameters (sampling frequency 10Hz) with ambient temperature and humidity records (sampling frequency 1Hz), generating a standardized event data stream with a unified time base. The data parsing channel division module creates three parallel processing channels based on parameter type: the vehicle operation fault code parsing channel extracts the DTC P0217 fault code sequence stored in the engine control unit; the environmental anomaly parameter parsing channel calculates abnormal temperature fluctuation ranges with a standard deviation of up to 4.3°C; and the driver operation parsing channel captures operation log segments that contain abnormal and sudden changes in clutch pedal travel.

[0189] (3) Pattern matching is performed on the vehicle operation fault code sequence with a pre-stored fault knowledge base to generate a fault type label and a maintenance suggestion summary.

[0190] In this step, the system parsed DTC P0217 and matched it to the engine cooling system category in the fault knowledge base. It identified three related fault modes: reduced coolant circulation pump efficiency (87% match), clogged radiator fins (72% match), and stuck thermostat valve (65% match). Based on a multi-mode weighted evaluation, the system generated a primary fault label, "Insufficient Coolant Circulation System Efficiency (Code F-OC-015)," and output a summary of repair recommendations: immediately replenish coolant to the standard level, check the circulating pump operating current, and clean the radiator of debris. This summary was linked to standard repair time data (estimated 45 minutes) and a replacement parts list (OEM part number WP-2023-015).

[0191] (4) Compare the fluctuation range of the abnormal environmental parameters with the historical environmental data samples for similarity, and generate a probability distribution map of environmental influencing factors.

[0192] In this step, the system retrieved environmental data samples from the same road section (County Road X207, K3+200 to K5+700) over the past 30 days. It found that the ambient temperature between 2:20 PM and 2:30 PM on that day was 5.2°C higher than the historical average, while the humidity decreased by 23%. Using the Kolmogorov-Smirnov test, the system calculated a 92% probability of temperature anomaly. The system generated a normal distribution curve, showing that the probability of engine overheating at an ambient temperature of 32°C is 3.8 times higher than at a normal temperature of 27°C. The probability distribution chart also marked key thresholds; when the ambient temperature exceeds 30°C, the cooling system load enters the yellow warning zone.

[0193] (5) Inputting the driver operation log fragment into the operation behavior analysis model, outputting the operation compliance score and the timestamp location result of the abnormal operation action.

[0194] During this step, the model analyzed the clutch operation data and detected three consecutive instances of partial clutch operation (pedal travel maintained between 40% and 45% for more than three seconds) between 14:25:21 and 14:25:24, violating Section 5.2 of the Standard Operating Procedure. After comparing the operation behavior analysis model to the standard operation template, it output an operation compliance score of 76.3 out of 100 and marked the three abnormal operation timestamps on the timeline (14:25:21.345, 14:25:22.112, and 14:25:23.887). The model also identified that the throttle opening remained at 82% during the abnormal operation, exceeding the recommended upper limit of 70% for this slope.

[0195] (6) Mapping the fault type label, the maintenance suggestion summary, the probability distribution diagram, the operation compliance score, and the timestamp location result to multiple independent display areas of the event details panel according to event attribute classification.

[0196] In this step, the event details panel is divided into five functional areas: the upper left area displays a color-coded fault type label (red F-OC-015), the upper right area embeds a probability distribution chart in the form of a 3D bar chart, the center left area scrolls through the repair recommendation items, the center right area displays a progress bar for the operational compliance score, and the bottom timeline highlights the timestamps of three abnormal operations. Each visual element in the display area is linked to a database field. For example, the third item in the repair recommendation summary, "Clean debris from the radiator surface," is linked to the maintenance knowledge base entry KB-2023-0876.

[0197] (7) Determine the dynamic floating position and hierarchical stacking order of the event details panel in the visual dynamic management interface based on the spatial coordinates of the key event mark in the electronic map.

[0198] In this step, based on the coordinates of the target vehicle's abnormal event trigger point (longitude 118.758765°, latitude 31.976543°), the system calculates the screen pixel coordinates of this point in the current map view (X=435, Y=287). The event details panel uses a dynamic anchor algorithm to position its top left corner at (X+20, Y-50) pixels, avoiding the existing map annotation information. The layer stacking order is set to Z-index 999 to ensure that the panel always floats above the base track layer (Z-index 500) and the real-time location marker layer (Z-index 800).

[0199] (8) Performing transparency fusion processing on the display area content of the event details panel and the layer data of the electronic map to generate a semi-transparent extended information layer superimposed on the basic track overlay layer.

[0200] In this step, the system uses an alpha blending algorithm to set the background transparency of the event details panel to 70%, while preserving the opacity of text and chart elements. A depth buffer check is performed on the map's road name label layer (z-index 600) and the extended information layer to ensure that important geographic information, such as the County Road X207 label, is not obscured. During the fusion process, the system monitors the map's zoom level in real time. When the zoom ratio exceeds 1:5000, the panel's transparency is automatically reduced to 50% to improve visibility of the underlying map.

[0201] (9) According to the timestamp sequence of the abnormal event mark, a slidable timeline control is embedded in the extended information layer, and a time synchronization association relationship is established between the timeline control and the content displayed on the event details panel.

[0202] In this step, the timeline control covers the period from 14:24:30 to 14:26:30, with a total length of 480 pixels corresponding to 120 seconds (4 pixels / second). The slider is initially positioned at the abnormal event trigger point, 14:25:37. When the user drags the slider to 14:25:21, the central area of the event details panel displays the engine speed (1820 rpm) and coolant temperature (98°C) at that moment. Time synchronization is achieved through a message bus mechanism, and slider position changes trigger dynamic refresh requests for sensor data.

[0203] (10) In response to a user sliding operation on the timeline control, the time series segments of the sensor raw data set and the corresponding fault type labels displayed in the extended information layer are updated in real time.

[0204] In this step, when the user moves the slider to 14:25:15, the system extracts a 500-millisecond data window around that moment from the standardized event data stream: the Z-axis acceleration value increases from 1.2g to 1.7g, the coolant temperature rises from 93°C to 96°C, and the ambient temperature is recorded as 31.5°C. The fault type label dynamically updates based on the temperature change rate, displaying the secondary alarm message "Coolant Temperature Rise Abnormal (Code T-RA-002)." As the time series segment updates, the system generates a data comparison prompt at the bottom of the event details panel, indicating the percentage difference between the current segment parameters and the event peak parameters.

[0205] (11) Dynamically adjust the background color warning level of the driver operation log segment in the extended information layer according to the operation compliance score, and render it in color synchronization with the mark icon corresponding to the spatial coordinates in the electronic map.

[0206] In this step, an operational compliance score of 76.3 points triggers a yellow alert level (threshold range 70-85 points), and the background color of the driver's operation log area changes to #FFFF99. The abnormal event icon on the electronic map simultaneously switches to a yellow triangle, and the warning description is updated in the map legend. The color rendering module uses the HSV color space conversion algorithm to ensure that color differences between different devices are within the industry standard range of ΔE < 3.0.

[0207] (12) Loading a maintenance site location mark associated with the maintenance suggestion summary in the edge area of the extended information layer, and generating an optimal path navigation line from the key event mark location to the maintenance site location.

[0208] In this step, the system analyzes the "Recommended Repair Site" field in the repair suggestion summary and locates a designated repair center 5.2 kilometers from the incident point (longitude 118.765432°). The optimal route planning module calculates two candidate routes: a direct route via County Road X207 (5.2 kilometers, estimated travel time 11 minutes) and a detour via Township Road Y305 (6.1 kilometers, estimated travel time 13 minutes). The system selects the shortest route and draws a blue dashed navigation line on the electronic map. The route includes three turning points: a left turn at longitude 118.760123°, a straight line at 118.762345°, and a right turn at 118.764567°.

[0209] (13) Conflict detection is performed on the optimal path navigation line and the historical driving trajectory in the basic trajectory overlay layer. If an unfinished task node is detected in the path overlap area, a path detour suggestion prompt box is inserted in the extended information layer.

[0210] In this step, the system checks for conflicts between the optimal route guidance line and the historical driving trajectories in the base trajectory overlay. If any uncompleted task nodes are detected in the overlapping area, a detour suggestion dialog box is inserted in the extended information layer. The conflict detection module discovered that County Road X207 at K4+500 (longitude 118.762345°) had two delays due to bridge construction in the historical trajectory. The system popped up a prompt in the lower right corner of the extended information layer, suggesting a detour via Township Road Y305 to avoid this section. The prompt box embedded comparison data: the detour adds 0.9 kilometers but saves 7 minutes. After the user confirms the prompt, the optimal route guidance line is automatically updated to Township Road Y305, and a right turn instruction at longitude 118.756789° is added to the route node sequence.

[0211] (14) Rendering the abnormal thermal coverage area of the environment in the extended information layer according to the probability distribution map, and performing spatial overlay analysis on the thermal coverage area and the congested road sections in the real-time traffic data to generate an environmental risk warning level mark.

[0212] In this step, the Gaussian kernel density estimation algorithm was used to estimate the thermal coverage area. A temperature anomaly distribution map with a radius of 2 kilometers was generated, centered on the event point. The red highlighted area (temperature > 31°C) covers the section between K3+200 and K5+700 on County Road X207. Spatial overlay analysis revealed a 73% overlap between this area and the real-time traffic congestion section (K4+100 to K4+800). The system generated a Level 3 warning: High temperature environment increases congestion risk (code ER-015), recommending that subsequent tasks avoid this section between 12:00 PM and 3:00 PM.

[0213] (15) In the layer update event of the visual dynamic management interface, the floating position coordinates of the extended information layer and the transparency parameters of the semi-transparent overlay effect are synchronously refreshed to ensure visual consistency with the underlying map.

[0214] In this step, when the map view switches from 1:5000 to 1:2000 due to a user zoom operation, the system recalculates the screen coordinates of the event details panel (X=702, Y=415) and adjusts the transparency parameter to 65% to accommodate the higher-precision map display requirements. During the layer refresh process, the system maintains the spatial correspondence between the extended information layer and the real-time vehicle location marker. When the target vehicle moves to longitude 118.759876°, the panel automatically pans to maintain a 20-pixel offset from the event point.

[0215] See also Figure 2 As shown in FIG. 1 , the figure is a schematic diagram of the basic structure of a visual management system 200 provided by an embodiment of the present invention. The visual management system 200 includes:

[0216] Processor 201;

[0217] a storage device 202 having a computer program 2020 stored thereon;

[0218] When the computer program 2020 is executed by the processor 201, the processor 201 implements any of the bus travel visualization management methods combined with positioning technology.

[0219] Based on the above, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the above method are implemented.

[0220] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems or devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

Claims

1. A bus travel visualization management method combined with positioning technology, characterized in that: include: Acquiring real-time vehicle positioning data of a target vehicle and extracting task execution information associated with the real-time vehicle positioning data, wherein the real-time vehicle positioning data includes positioning coordinates and motion parameters in a continuous time series; Performing dynamic trajectory fitting processing on the real-time positioning data of the vehicle to generate a driving trajectory map that matches the task execution information, wherein the driving trajectory map includes timestamps of path nodes and key event markers; Perform multi-dimensional analysis on the driving trajectory map based on preset task rules to determine the driving state characteristics and task completion indicators of the target vehicle; Performing spatiotemporal mapping of the driving state characteristics, the task completion index, and the driving trajectory map to generate a visual dynamic management interface; extracting a set of geographic coordinates and a time stamp sequence of path nodes from the driving trajectory map; mapping the set of geographic coordinates and the time stamp sequence to layer data of an electronic map to generate a basic trajectory overlay layer; The speed heat map of the driving state characteristics and the progress indicator of the task completion indicator are superimposed on the basic trajectory overlay; an interactive event annotation window is embedded in the electronic map according to the spatial position of the key event marker; the basic trajectory overlay, the speed heat map, the progress indicator and the interactive event annotation window are integrated to generate a multi-layer superimposed visual dynamic management interface; wherein, generating the multi-layer superimposed visual dynamic management interface includes: in response to the user's touch operation on the interactive event annotation window, calling the sensor raw data associated with the key event marker; performing real-time analysis on the sensor raw data to generate a vehicle fault code, an environmental fault code, and an environmental fault code. An event details panel showing environmental parameters and operation logs; the event details panel is rendered in conjunction with the visual dynamic management interface to update the extended information layer displaying abnormal events; historical driving trajectory maps of similar tasks are loaded from the historical trajectory database according to the comparison mode selected by the user; and the path difference analysis results of the current driving trajectory map and the historical driving trajectory map are displayed in parallel in the extended information layer; wherein, updating the extended information layer includes embedding a timeline control, updating the synchronous rendering of the timing fragment and the fault type label color, loading the maintenance station location mark, generating the optimal path navigation line, inserting the path detour suggestion prompt box, generating the environmental risk warning level mark, and refreshing the floating position coordinates and transparency parameters; When the visual dynamic management interface is in operation, in response to a tracing instruction triggered by a management terminal, the historical trajectory database is called to perform backtracking optimization processing on the driving trajectory map, and management feedback data including abnormal event annotations and path comparison results is output.

2. The method according to claim 1, wherein The performing dynamic trajectory fitting processing on the real-time positioning data of the vehicle to generate a driving trajectory map matching the task execution information includes: Performing timestamp alignment and filtering on the real-time positioning data of the vehicle based on the set of planned path points in the task execution information to select candidate positioning points that meet a preset time window; Performing a path continuity check on the candidate positioning points, using an interpolation algorithm to fill in the missing intermediate positioning points, and generating a corrected continuous driving path; Extracting abnormal positioning points in the continuous driving path that match preset key event trigger conditions, and associating and marking the abnormal positioning points with task nodes in the task execution information; The trajectory prediction model is called to perform driving mode recognition on the continuous driving path, divide the driving path into acceleration segments, constant speed segments and stagnation segments, and generate a driving trajectory map containing segmented feature descriptions.

3. The method according to claim 2, wherein The extracting of abnormal positioning points in the continuous driving path that match preset key event triggering conditions includes: Obtaining instantaneous speed fluctuation value, direction deviation angle and dwell time parameters from the real-time positioning data of the vehicle; Inputting the instantaneous speed fluctuation value, the direction deviation angle, and the dwell time parameters into a target multimodal event classification model, and outputting an abnormal event type and a confidence score; Extracting a set of abnormal positioning points that meet a threshold condition from the continuous driving path according to the abnormal event type and the confidence score; The abnormal location point set is matched with similar events in the historical trajectory database to generate a key event marker containing an event association identifier.

4. The method according to claim 1, wherein The multi-dimensional analysis of the driving trajectory map based on the preset task rules to determine the driving state characteristics and task completion index of the target vehicle includes: Extracting task priority weights, planned completion times, and preset path constraints from the task execution information; Calculating the deviation index between the actual driving path and the planned path based on the path node density distribution in the driving trajectory map; Determining a dynamic score of the task completion indicator by combining the deviation indicator and the task priority weight; Performing time series analysis on key event markers in the driving trajectory map to generate a status assessment report including event impact coefficients; The dynamic score and the status assessment report are compared with the acceptance conditions in the preset task rules, and the compliance determination result of the driving status characteristics is output.

5. The method according to claim 4, wherein The time series analysis of the key event markers in the driving trajectory map is performed to generate a status assessment report including event impact coefficients, including: Extracting the starting timestamp, duration and spatial distribution density corresponding to the key event marker; generating an event impact propagation model based on a time axis at least based on the start timestamp and the duration, and calculating correlation strength coefficients between adjacent key events; generating an event chain reaction probability map according to the association strength coefficient and the spatial distribution density; Inputting the event chain reaction probability graph into a risk prediction model, and outputting a quantitative evaluation value of the event impact coefficient; Prioritize abnormal events in the status assessment report based on the quantitative assessment values.

6. The method according to claim 1, wherein The calling of the historical trajectory database to perform backtracking optimization processing on the driving trajectory map includes: Extracting historical trajectory segments of a corresponding time period from the historical trajectory database according to a time range parameter in the tracing instruction; performing trajectory smoothness calculation and path coincidence matching on the historical trajectory segments to identify repetitive driving patterns; Building a trajectory prediction model based on the repetitive driving pattern to generate a task path optimization suggestion for a target period; Simulate and superimpose the task path optimization suggestion for the target period on the current driving trajectory map, and output management feedback data including the path comparison result; According to the distribution density of the abnormal event annotations, adjusting the weight parameters of the trajectory prediction model to update the historical trajectory database; The constructing of a trajectory prediction model based on the repetitive driving pattern includes: Extract the periodic path node set and travel time interval in the historical trajectory segment; Using a time series clustering algorithm to classify the periodic path node set into patterns and generate a typical driving pattern template; The driving time interval is integrated with the real-time traffic status data to calculate the time cost coefficient of each typical driving mode; Dynamically adjusting the path recommendation priority of the trajectory prediction model according to the time cost coefficient and the task completion index; embedding the dynamically adjusted path recommendation priority into the path comparison result in the management feedback data; The dynamically adjusting the path recommendation priority of the trajectory prediction model according to the time cost coefficient and the task completion index includes: Obtaining a set of time consumption parameters for each typical driving mode corresponding to the time cost coefficient and a set of path deviation scores in the task completion indicator; Performing weight allocation processing on the time consumption parameter set according to the path deviation score set to generate a time cost combination weight based on task completion; Inputting the time-cost combination weight into a priority sorting algorithm, performing initial priority sorting on the candidate path set pre-stored in the trajectory prediction model, and generating a path priority queue with a weight identifier; Extracting a subset of candidate paths that match preset path constraints in the current task execution information based on the weight identifiers of the candidate paths in the path priority queue; Performing a real-time traffic status data adaptability check on each candidate path in the candidate path subset, and eliminating invalid paths with traffic congestion marks or temporary control events; Determine the path priority adjustment interval that meets the dynamic adjustment conditions based on the time-cost combined weights of the remaining candidate paths and the task completion deviation threshold; Within the path priority adjustment interval, remapping the priorities of the remaining candidate paths according to the descending order of the time-cost combination weights to generate an updated path recommendation priority sequence; Comparing the updated path recommendation priority sequence with the priority records of similar tasks in the historical priority database to identify abnormal paths whose priority jump amplitude exceeds a preset threshold; Performing task node coverage integrity verification on the abnormal path, and if the verification passes, inserting the abnormal path into a designated position of the path recommendation priority sequence; The parameters of the path output interface of the trajectory prediction model are synchronized according to the finally determined path recommendation priority sequence, and the target recommended path with the highest priority is locked.

7. The method according to claim 1, wherein The method further comprises: receiving a fault alarm signal from the target vehicle in real time, and extracting a fault type code and a severity level parameter from the fault alarm signal; Retrieving a pre-stored emergency response plan based on the fault type code and generating an emergency response instruction including a backup vehicle dispatch plan and a maintenance station navigation path; Synchronize the emergency response instructions with the visual dynamic management interface in real time, and highlight the affected task nodes and path adjustment areas; activating a remote control protocol according to the severity level parameter to send a speed limit or parking instruction to an actuator of the target vehicle; Recording the execution status of the emergency response instruction and the vehicle response log in the management feedback data; The method of retrieving a pre-stored emergency response plan according to the fault type code and generating an emergency response instruction including a backup vehicle dispatch plan and a maintenance station navigation path includes: Matching the fault type code with a pre-stored emergency response plan database, extracting a plan execution condition set and a plan operation instruction sequence in the emergency response plan corresponding to the fault type code; Prioritize the set of emergency plan execution conditions according to the severity level parameter to determine a target emergency response plan that meets the current fault severity level; Extracting the vehicle identification set, dispatch path planning parameters and task handover node information of the backup vehicle dispatch plan from the target emergency response plan, and generating a backup vehicle dispatch instruction; Synchronously acquiring real-time positioning data and maintenance station location information of the target vehicle, and generating a path node sequence and estimated arrival time parameters of a maintenance station navigation path by combining the maintenance station location information and the real-time positioning data; Encapsulating the standby vehicle dispatch instruction and the maintenance station navigation path in an instruction format to generate an emergency response instruction including a dispatch instruction trigger timestamp and a path navigation verification code; Performing task link integrity verification on the emergency response instruction according to the task handover node information, and identifying conflicting nodes between the backup vehicle scheduling plan and the current task execution information; Dynamically replanning the dispatch path planning parameters in the emergency response instruction based on the conflicting nodes, and updating the path node sequence of the maintenance station navigation path; Re-encapsulating the updated path node sequence and the estimated arrival time parameter into the emergency response instruction to generate an emergency response instruction to be executed; The emergency response instruction to be executed is associated with the fault type code and identified as an instruction, and is embedded in the execution queue of the plan operation instruction sequence.

8. A visual management system, characterized in that: include: processor; A storage device stores a computer program thereon, and when the computer program is executed by the processor, the processor implements the bus travel visualization management method combined with positioning technology as described in any one of claims 1-7.

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