Dangerous goods vehicle situation data visualization management method and system

Abstract

The application discloses a kind of in-transit dangerous chemical vehicle situation data visualization management method and system, it is related to data processing technical field, method includes: obtaining historical situation degradation sequence, combine cumulative fatigue with liquid sloshing dynamic correction reference evaluation period of dynamic load parameter, and according to actual operation interval, effective data is screened to calculate situation evolution trend value.Based on the difference of the trend value and the spatial distance of the predicted trajectory, the space-time coordination index is calculated, and the vehicles that meet the standard are classified into the cooperative management equipment set.Finally, combined with the basic fatigue, deterioration trend and the braking heat loss of the front elevation downhill, the cooperative calibration interval and the scheduling time sequence are generated.The application solves the technical problems of existing monitoring being disconnected from actual physical working conditions, lacking spatial constraints and terrain perception, leading to evaluation lag and scheduling difficulty in landing, and realizes preventive proactive maintenance and high operability cross-regional coordinated scheduling.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method and system for visual management of the status data of hazardous chemical vehicles en route. Background Technology

[0002] With the advancement of industrialization, the transportation of hazardous chemicals has become a crucial link in the operation of the chemical industry. Currently, most solutions for situational data management and pre-maintenance scheduling of transport vehicles draw on the conventional time-series prediction logic of fixed industrial IoT devices, relying on preset mileage or fixed time periods to statically set the maintenance intervals of the equipment, without fully considering the fatigue attenuation patterns of mobile vehicles under complex physical conditions.

[0003] First, in actual transportation of hazardous chemicals, due to the flow characteristics of the liquid medium, the acceleration, deceleration, or turning of the vehicle can cause the liquid half-loaded inside the tank to surge, generating dynamic impact forces from liquid sloshing. This physical impact, combined with the wear and tear on the underlying mechanical components, leads to a non-linear decay in the fatigue life of the vehicle's load-bearing structure and key sensors. Existing static time window assessment methods do not consider this physical scenario of fluid mechanics and mechanical fatigue coupling, which can easily result in some damaged vehicles not being identified by the system in a timely manner. Second, in long-distance and cross-regional transportation scenarios, existing clustering early warning methods based on pure data evolution trends lack topological constraints on the physical space. When multiple vehicles have similar deterioration values, they are grouped together for early warning, ignoring the reality that these vehicles are far apart in the actual road network and cannot enter the same maintenance service area for collaborative maintenance at the same time. This results in the lack of operability in the scheduling instructions generated by the system at the execution level, affecting the efficiency of cross-regional maintenance resource allocation. Finally, existing technologies mostly focus on post-event condition assessment and fail to integrate the static geographical elevation gradient parameters of the predetermined driving route ahead of the vehicle with the current dynamic deterioration data. When a hazardous chemical vehicle whose condition has begun to deteriorate is about to enter a long continuous downhill section, the existing system does not calculate the brake thermal fade damage caused by the conversion of gravitational potential energy into braking system thermal load. It is difficult to trigger preventive safety cut-off and scheduling in advance, which poses certain safety hazards. Summary of the Invention

[0004] To address the technical problems existing in the background art, the present invention provides a method and system for visual management of the status data of hazardous chemical vehicles en route. It solves the technical problems of existing hazardous chemical vehicle monitoring methods being detached from the vehicle's status during the driving process, lacking road network spatial topology constraints, and failing to integrate the elevation features of the terrain ahead, which leads to delayed vehicle status degradation assessment, lack of operability of cross-regional dispatch instructions, and difficulty in timely preventive pre-dispatch.

[0005] A method for visual management of the status data of hazardous chemical vehicles en route includes: acquiring status degradation sequences of multiple hazardous chemical vehicles en route within a historical record period; obtaining dynamic correction coefficients based on cumulative fatigue parameters and liquid sloshing dynamic load parameters, and using the dynamic correction coefficients to correct the benchmark assessment period to generate a target assessment interval; filtering effective status data based on the comparison results between the actual operating interval and the target assessment interval, and obtaining a status evolution trend value based on the time difference between adjacent effective status data; obtaining a spatiotemporal coordination index based on the difference in status evolution trend values ​​and the spatial distance of the predicted trajectory, and classifying vehicles with a spatiotemporal coordination index less than a set threshold into a collaborative management device set; generating a collaborative calibration interval based on the cumulative fatigue parameters, status evolution trend values, and elevation conversion braking heat loss parameters of the continuous downhill road section ahead, and generating a collaborative scheduling sequence including time windows and road network aggregation points accordingly.

[0006] Optionally, a dynamic correction coefficient is obtained based on the cumulative fatigue parameters and the liquid sloshing dynamic load parameters, including: multiplying the density of the hazardous chemical liquid, the tank volume, and the liquid level filling rate to obtain the liquid mass; multiplying the liquid mass by the statistical parameters of the vehicle's longitudinal acceleration to obtain the liquid sloshing dynamic load parameters; dividing the liquid sloshing dynamic load parameters by the structural rated yield stress limit to obtain a first damage ratio as dimensionless data; dividing the vehicle's accumulated mechanical load equivalent by the ultimate load threshold to obtain a second damage ratio as a cumulative fatigue parameter; and calculating the difference between the first damage ratio and the sum of the second damage ratio and the calculated value as a dynamic correction coefficient.

[0007] Optionally, effective situation data is selected based on the comparison between the actual operating interval and the target evaluation interval, and the situation evolution trend value is obtained based on the time difference between adjacent effective situation data. This includes: determining whether the actual operating interval of each recorded node in the situation degradation sequence is less than its corresponding target evaluation interval; if it is less, the corresponding actual operating interval is taken as effective situation data; obtaining the time difference between adjacent effective situation data, and algebraically summing all time differences to obtain the situation evolution trend value.

[0008] Optionally, the spatiotemporal coordination index is obtained based on the difference between the situation evolution trend values ​​and the spatial distance of the predicted trajectory, including: dividing the absolute value of the difference between the situation evolution trend values ​​of any two on-the-road hazardous chemical vehicles by the trend dispersion allowable threshold to obtain the time trend difference; obtaining the geographical coordinates of the two on-the-road hazardous chemical vehicles on their respective predicted trajectories within the prediction time window, calculating the physical distance between the two sets of geographical coordinates at the same time, and extracting the minimum value of the physical distance within the prediction time window as the spatial distance; dividing the spatial distance by the aggregation radius threshold to obtain the dimensionless spatial distance parameter; and linearly summing the time trend difference and the spatial distance parameter to obtain the spatiotemporal coordination index.

[0009] Optionally, a collaborative calibration interval is generated based on the cumulative fatigue parameters of vehicles within the collaborative management equipment set, the situation evolution trend value, and the elevation transition braking heat loss parameters of the continuous downhill section ahead. This includes: obtaining the minimum situation evolution trend value within the collaborative management equipment set; when the minimum situation evolution trend value is negative, multiplying the average total mass and gravitational acceleration constant of vehicles within the collaborative management equipment set by the cumulative elevation difference of the continuous downhill section ahead to obtain physical thermal energy; dividing the physical thermal energy by the heat dissipation limit of the vehicle braking system to obtain the thermal fade damage ratio as an elevation transition braking heat loss parameter; dividing the average mechanical load equivalent of vehicles within the collaborative management equipment set by the ultimate load threshold to obtain the basic wear ratio; dividing the absolute value of the minimum situation evolution trend value by the benchmark evaluation period to obtain the trend deterioration ratio; calculating the difference between the numerical value and the sum of the thermal fade damage ratio, the basic wear ratio, and the trend deterioration ratio to obtain the remaining ratio; extracting the maximum value between the remaining ratio and the preset compression lower limit ratio, and multiplying the maximum value by the benchmark evaluation period to obtain the collaborative calibration interval.

[0010] Optionally, a collaborative scheduling sequence containing time windows and road network aggregation points is generated accordingly, including: converting the collaborative calibration interval into a timestamp, generating aligned scheduling time points as time windows for all vehicles in the collaborative management device set; extracting the predicted geographic coordinates corresponding to the calculated spatial distance as road network aggregation points; and generating a collaborative scheduling sequence based on the time windows and road network aggregation points.

[0011] Optionally, the method further includes: triggering an emergency state when a step physical impact parameter or pressure drop parameter exceeding a set range is obtained; stopping the calculation of the situation evolution trend value and removing the time trend difference when calculating the spatiotemporal coordination index during the emergency state; setting the real-time geographic coordinates of the accident vehicle that triggered the emergency state as the spatial origin, and classifying the on-the-road hazardous chemical vehicles within a set radius around the spatial origin into the risk avoidance object set; establishing a gas diffusion equivalent coil layer based on the hazardous chemical leakage rate and meteorological wind speed, and generating an emergency dispatch instruction to guide the on-the-road hazardous chemical vehicles in the risk avoidance object set to evacuate away from the spatial origin along an upwind route that avoids areas above a set concentration threshold.

[0012] A data visualization management system for hazardous chemical vehicles en route is also provided. The system includes: a sequence acquisition module for acquiring the status degradation sequence of multiple hazardous chemical vehicles en route within a historical record period; an interval reconstruction module for obtaining dynamic correction coefficients based on cumulative fatigue parameters and liquid sloshing dynamic load parameters, and using the dynamic correction coefficients to correct the benchmark evaluation period to generate a target evaluation interval; a trend quantification module for filtering effective status data based on the comparison results between the actual running interval and the target evaluation interval, and obtaining the status evolution trend value based on the time difference between adjacent effective status data; a spatiotemporal coordination module for obtaining a spatiotemporal coordination index based on the difference between the status evolution trend value and the spatial distance of the predicted trajectory, and classifying vehicles with a spatiotemporal coordination index less than a set threshold into a collaborative management device set; and a scheduling generation module for generating a collaborative calibration interval based on the cumulative fatigue parameters, status evolution trend values, and elevation conversion braking heat loss parameters of the continuous downhill road section ahead, and generating a collaborative scheduling sequence containing time windows and road network aggregation points.

[0013] Optionally, the interval reconstruction module is also used to: multiply the density of the hazardous chemical liquid, the tank volume, and the liquid level filling rate to obtain the liquid mass; multiply the liquid mass by the statistical parameters of the vehicle's longitudinal acceleration to obtain the liquid sloshing dynamic load parameters; divide the liquid sloshing dynamic load parameters by the structural rated yield stress limit to obtain the first damage ratio as dimensionless data; divide the vehicle's accumulated mechanical load equivalent by the ultimate load threshold to obtain the second damage ratio as the accumulated fatigue parameter; and calculate the difference between the value and the sum of the first damage ratio and the second damage ratio as a dynamic correction coefficient.

[0014] Optionally, the trend quantification module is also used to: determine whether the actual running interval of each recorded node in the situation degradation sequence is less than its corresponding target evaluation interval; if it is less, the corresponding actual running interval is taken as valid situation data; obtain the time difference between adjacent valid situation data, and algebraically accumulate all time differences to obtain the situation evolution trend value.

[0015] The beneficial effects of this invention are reflected in:

[0016] In the overall method for visualizing and managing the status data of hazardous chemical vehicles en route, firstly, cumulative fatigue parameters and liquid sloshing dynamic load parameters are used to obtain dynamic correction coefficients. This not only quantifies the basic mechanical wear of vehicles during daily operation but also reflects the impact stress on the tank caused by inertial surging of hazardous chemical liquids during acceleration and deceleration. By coupling multiple physical quantities, structural damage information of the vehicle is obtained, improving the problem of untimely maintenance caused by fixed assessment cycles. Furthermore, based on the quantification of differences in status evolution trends, spatial distance calculation of predicted trajectories is introduced, and a spatiotemporal coordination index is constructed. By combining the temporal degradation dimension and the spatial topology dimension, vehicles classified into the same collaborative management equipment set have similar attenuation rates and can converge at the same maintenance assembly point on the physical road network at a specific time in the future. This improves the practical operability of cross-regional dispatch instructions and facilitates the aggregation of wide-area maintenance resources and road network load balancing. Furthermore, by combining static digital elevation environmental parameters with dynamic degradation mechanisms, when a deterioration trend in vehicle condition is detected, the elevation conversion braking heat loss parameters of the continuous downhill section ahead are extracted. Based on the energy conservation logic, gravitational potential energy is converted into the braking heat fade damage ratio, and this is used as a reduction factor to compress the maintenance cycle of relevant vehicles when calculating the collaborative calibration interval. This safety cutoff mechanism based on terrain energy prediction prevents vehicles with degraded conditions from entering long downhill sections without maintenance, reduces the risk of accidents caused by braking heat fade, and improves the management level of hazardous chemical transport vehicles on the road. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0018] Figure 1 This is a schematic diagram illustrating the steps of the method for visualizing and managing the status data of hazardous chemical vehicles en route, as described in this invention.

[0019] Figure 2 This is a schematic diagram of a portion of step S1 in the method for visualizing and managing the status data of hazardous chemical vehicles en route according to the present invention;

[0020] Figure 3 This is a schematic diagram of part of step S2 in the method for visualizing and managing the status data of hazardous chemical vehicles en route according to the present invention;

[0021] Figure 4 This is a schematic diagram of part S3 in the method for visualizing and managing the status data of hazardous chemical vehicles en route according to the present invention;

[0022] Figure 5This is a schematic diagram of part of step S4 in the method for visualizing and managing the status data of hazardous chemical vehicles en route according to the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0024] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0025] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, the terms first, second, etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0026] This invention provides a method for visual management of the status data of hazardous chemical vehicles en route, such as... Figure 1 As shown, in one embodiment, the method includes:

[0027] S1. Obtain the status downgrade sequence of multiple hazardous chemical vehicles en route within the historical record period;

[0028] S2. Obtain dynamic correction coefficients based on cumulative fatigue parameters and liquid sloshing dynamic load parameters, and use dynamic correction coefficients to correct the benchmark evaluation cycle to generate the target evaluation interval. Filter effective situation data and obtain situation evolution trend values ​​based on the comparison results between the actual operating interval and the target evaluation interval.

[0029] S3. Obtain the spatiotemporal coordination index based on the difference between the trend value of the situation evolution and the spatial distance of the predicted trajectory, and classify vehicles with a spatiotemporal coordination index less than a set threshold into the set of collaborative management devices.

[0030] S4. Generate collaborative calibration intervals based on the cumulative fatigue parameters, situation evolution trend values, and elevation conversion braking heat loss parameters of vehicles in the collaborative management equipment set, and generate collaborative scheduling sequence containing time windows and road network aggregation points accordingly.

[0031] In this implementation, it should be noted that in S1, the past six months are first defined as the historical record period, and a search is initiated in the historical database to extract the status degradation sequence of heavy tanker truck fleets carrying the same hazardous chemicals, such as liquid acetic acid, within the same road network area. Taking the core transport vehicle A in the fleet as an example, multiple data nodes triggered by unplanned disturbances such as tire wear warnings, abnormal tire pressure, and brake pressure fluctuations were collected during this period. The actual operating intervals between these nodes were recorded as 75 days, 61 days, and 48 days, respectively. This extraction logic does not adopt the traditional periodic reporting method, but objectively records the status degradation nodes caused by various sudden disturbances under actual physical operating conditions. The technical means adopted is to complete the cleaning and time axis alignment of multi-source heterogeneous data through data aggregation and composite indexing. The beneficial effect is that it provides an objective and continuous data foundation for subsequent quantification of multiple mechanical fatigue of vehicles and dynamic evaluation, enabling quantitative analysis and prediction preparation based on objective physical degradation rhythm rather than artificially set fixed cycles.

[0032] In S2, the situation analysis processor performs a depth reduction calculation on the physical state of vehicle A in the most recent interval. The baseline evaluation period for this vehicle model is 90 days. Simultaneously, sensor data reveals a liquid density of 1050 kg / m³, a volume of 30 m³, and a fill rate of 0.5. From this, the liquid mass is derived as follows: kg. Combining the longitudinal acceleration standard deviation of 2.0 m / s² obtained from the accelerometer, the dynamic load parameters of the liquid sloshing generated during vehicle acceleration and deceleration are calculated as follows: N, divided by the structural rated yield stress limit N, representing the degree of liquid impact, yields a first damage ratio of 0.1; simultaneously, the accumulated mechanical load equivalent is... km·t divided by the ultimate load threshold The second damage ratio of the base wear was calculated to be 0.2 km·t. The dynamic correction coefficient was calculated as 0.7 by subtracting the sum of the two ratios from the value of 1, thus correcting the baseline period to the target evaluation interval of 63 days. Since the actual operating interval of vehicle A was 48 days, which is less than 63 days, it was determined to be valid situational data. The adjacent time differences (-14 days and -13 days) were calculated by combining the preceding data, and the accumulated values ​​yielded a situational evolution trend value of -27 days for the vehicle. This technique integrates fluid dynamics characteristics with mechanical fatigue parameters, overcoming the deficiency of static evaluation in not considering the impact of fluid surges during transit. Its beneficial effect lies in its ability to dynamically capture the accelerated decay characteristics of vehicles under high-risk operating conditions, and by quantifying the deterioration rate, it avoids the omission of damaged vehicles due to periodic solidification.

[0033] In S3, the collaborative management decision engine begins to assess the feasibility of joint scheduling between different vehicles, constructing a spatiotemporal collaboration index by comprehensively considering vehicle degradation differences and road network spatial accessibility. The state evolution trend value of vehicle A (-27d) and the state evolution trend value of vehicle B in the same road network (-17d) are extracted. The absolute value of the difference between the two is calculated to be 10d. Dividing this by the set trend dispersion allowable threshold of 20d yields a dimensionless time trend difference of 0.5. Subsequently, the road network planning trajectories of these two vehicles within the future prediction time window are retrieved. Extrapolation reveals that the two vehicles will reach the vicinity of a trunk service area in the 36th hour of the future, at which point their physical distance at the same time reaches a minimum of 15km. Dividing this value by the aggregation radius threshold of 50km yields a dimensionless spatial distance parameter of 0.3. The decision engine linearly sums the time trend difference of 0.5 and the spatial distance parameter of 0.3 to obtain a spatiotemporal collaboration index of 0.8. Since this value is less than the set index judgment threshold of 0.85, vehicle A and vehicle B are grouped into the same collaborative management device set. The technical approach adopted in this step is to simultaneously perform dimensionless fusion of the deterioration trend in the time dimension and the intersection of the trajectory in the spatial dimension. Its beneficial effect is to ensure that the grouped vehicles are not only in similar state degradation channels, but also can meet in physical space at a specific time in the future, thereby making the generated scheduling instructions truly executable.

[0034] In S4, the final scheduling sequence is generated and pre-intervention is performed for the grouped collaborative management equipment set. Since the minimum situational evolution trend value contained in this set is -27d, meeting the negative condition, the elevation conversion braking heat loss assessment mechanism for terrain energy is triggered. The average mechanical load equivalent of the vehicles in the group is obtained as follows: km·t, average total mass is kg, and combined with the cumulative elevation difference of 600m in the continuous downhill section ahead and the gravitational acceleration of 9.8m / s², the physical heat energy generated by the conversion of gravitational potential energy is calculated to be J. Divide this thermal energy by the braking heat dissipation limit. J yields a thermal decay damage ratio of 0.15; simultaneously, the average load equivalent is divided by the ultimate load threshold. The baseline wear ratio of 0.25 is calculated using km·t, and the absolute value of the minimum trend value is divided by the baseline period of 90 days to obtain the trend deterioration ratio of 0.3. Subtracting the sum of the above three ratios from the numerical value of 1 yields the remaining ratio of 0.3. This ratio is then multiplied by the maximum value of the preset compression lower limit of 0.2 and the baseline period of 90 days to generate a collaborative calibration interval of 27 days. This interval is then combined with the service area coordinates corresponding to the spatial minimum value to issue scheduling sequences. This technique transforms static terrain features into dynamic braking loads based on the energy conservation logic. Its beneficial effect lies in achieving preventative time compression before high-risk road sections through the quantification of physical thermal energy, reducing the safety hazards caused by vehicles in a degraded state entering continuous downhill sections.

[0035] In summary, the entire method for visualizing and managing the status quo data of hazardous chemical vehicles en route firstly employs cumulative fatigue parameters and liquid sloshing dynamic load parameters to obtain dynamic correction coefficients. This not only quantifies the basic mechanical wear of vehicles during daily operation but also reflects the impact stress on the tank caused by inertial surging of hazardous chemical liquids during acceleration and deceleration. By coupling multiple physical quantities, structural damage information of the vehicle is obtained, improving the problem of untimely maintenance caused by fixed assessment cycles. Furthermore, based on the quantification of differences in status quo evolution trends, spatial distance calculation of predicted trajectories is introduced, and a spatiotemporal coordination index is constructed. By combining the temporal degradation dimension and the spatial topology dimension, vehicles classified into the same collaborative management device set have similar attenuation rates and can converge at the same maintenance assembly point on the physical road network at a specific time in the future. This improves the practical operability of cross-regional dispatch instructions and facilitates the aggregation of wide-area maintenance resources and road network load balancing. Furthermore, by combining static digital elevation environmental parameters with dynamic degradation mechanisms, when a deterioration trend in vehicle condition is detected, the elevation conversion braking heat loss parameters of the continuous downhill section ahead are extracted. Based on the energy conservation logic, gravitational potential energy is converted into the braking heat fade damage ratio, and this is used as a reduction factor to compress the maintenance cycle of relevant vehicles when calculating the collaborative calibration interval. This safety cutoff mechanism based on terrain energy prediction prevents vehicles with degraded conditions from entering long downhill sections without maintenance, reduces the risk of accidents caused by braking heat fade, and improves the management level of hazardous chemical transport vehicles on the road.

[0036] like Figure 2 As shown, in one specific embodiment, S1 includes: S11, obtaining the current time and tracing back to the past timeline according to a preset time span, thereby locking a complete historical record period.

[0037] S12. The situation analysis processor sends a retrieval request to the historical database server and filters out the set of target vehicles currently deployed in the same preset road network area that carry the same type of hazardous chemical medium from the database.

[0038] S13. The situation analysis processor extracts all situation degradation sequences of the target vehicle set within the historical record period from the time series database.

[0039] In a technical context, the situation degradation sequence here refers to a time-series set of data recording nodes triggered by unplanned sudden disturbances (such as sensor parameter exceeding limits, structural stress anomalies caused by unstable driving, or temporary roadside inspections) during the operation of a hazardous chemical vehicle en route. These nodes are arranged in chronological order of their timestamps. Physically, this sequence quantifies the abnormal health decline or performance degradation process of the vehicle during transit, replacing the traditional assumption of periodic maintenance. The situation degradation sequence consists of the timestamps of multiple event nodes and the actual operating intervals between adjacent event nodes; the actual operating interval is the continuous driving time between two consecutive uncertain disturbance events.

[0040] In this embodiment, it should be noted that in S11, the current time is acquired, and based on a preset time span, the data is traced back to the past timeline to lock in a complete historical record period. Specifically, for a fleet of heavy tanker trucks operated by a provincial logistics company specifically for transporting liquid glacial acetic acid, the time window is set to the past six months, and this six-month time span is used as the boundary range for data analysis. The technical means adopted in this stage is to establish a unified and unchangeable time reference system by comparing the built-in hardware clock with the database timestamp, so that all subsequently extracted sensor signals and alarm records are measured in the same time dimension. This step does not involve complex numerical conversion, but its calculation logic is to provide a statistically significant data sampling period. The beneficial effect of this operation is that it eliminates data noise interference caused by single, occasional events, establishes an objective and continuous basic timeline for subsequent analysis of the multiple mechanical fatigue and dynamic performance evolution patterns of hazardous chemical vehicles in transit, and ensures the data integrity of the entire situation analysis process.

[0041] In S12, the situation analysis processor initiates a retrieval request to the historical database server, filtering out a set of target vehicles currently deployed within the same preset road network area and carrying the same type of hazardous chemical medium. Based on the actual application scenario, multiple heavy-duty tank trucks carrying liquid acetic acid, including core transport vehicle A and its group of vehicles B, are separated from the regional transportation road network database. This step utilizes a distributed database's composite indexing mechanism, performing an intersection comparison query based on the vehicle registration type code and the transport medium identifier to ensure a high degree of consistency in the mechanical structure and load characteristics of the extracted analysis objects. This query logic avoids mixing different types of hazardous chemicals into the same evaluation model. The beneficial effects of this step are that by narrowing the data processing scope and unifying the physical baseline attributes of the evaluated objects, it reduces computational bias caused by heterogeneous data, improving the targeting and data flow efficiency of subsequent situation degradation sequence construction and spatiotemporal coordination index calculation.

[0042] In S13, the situation analysis processor extracts all situation degradation sequences of the target vehicle set within the historical record period from the time-series database. Taking core transport vehicle A as an example, over the past six months, data nodes triggered by unplanned disturbances such as tire wear warnings, abnormal tire pressure, and brake pressure fluctuations were extracted. The actual operating intervals between these event nodes were 75 days, 61 days, and 48 days, respectively. The technical approach adopted here is to identify unplanned maintenance records and sensor anomaly threshold alarms, and then serialize and concatenate them on the timeline. This data extraction logic does not use traditional fixed-period reporting data, but rather defines the vehicle's degradation stage by capturing real physical state fluctuations. The beneficial effect is that it quantifies and extracts the real process of state damage caused by various sudden disturbances to hazardous chemical vehicles in transit under actual physical operating conditions, making up for the limitations of static prediction based on theoretical lifespan, and providing source data with real physical meaning for subsequent quantification of deterioration rate and calculation of historical situation evolution trend values.

[0043] like Figure 3 As shown, in one specific embodiment, S2 includes: S21, the situation analysis processor obtains the legally mandated benchmark assessment cycle from the configuration database based on the currently extracted mechanical structure type of the hazardous chemical vehicle in transit and the physical hazard level of the hazardous chemicals it carries, as the time benchmark for standard preventive inspection.

[0044] S22. For each recording node in the situation degradation sequence, the situation analysis processor extracts the average liquid level filling rate and longitudinal acceleration characteristic values ​​reported by the vehicle-mounted sensors within its operating range, and calculates the dynamic correction coefficient specific to each recording node.

[0045] The dynamic correction factor introduced here is a dimensionless scaling factor that reduces the basic assessment time based on the current total structural damage (including impact damage from liquid sloshing and fatigue wear of the vehicle foundation), in order to reflect the accelerated degradation effect caused by the superposition of multi-source stresses in real time.

[0046] The physical fatigue of hazardous chemical tank trucks during operation is not solely linearly related to mileage. When the tank is partially loaded, the vehicle's acceleration and deceleration trigger violent surging and impact of the hazardous liquid inside, generating liquid sloshing dynamic load parameters. Liquid sloshing dynamic load parameters refer to parameters used to quantify the dynamic physical impact stress load generated by the inertial surging of the partially loaded liquid inside the tank on the tank's interior and load-bearing structure during vehicle acceleration, deceleration, or turning.

[0047] Furthermore, the impact load and the basic fatigue load are converted into structural damage proportions and superimposed, with a dynamic correction coefficient λ. i The acquisition logic can be expressed as:

[0048]

[0049] Where, λ i This represents the dynamic correction coefficient corresponding to the i-th record node, which is a dimensionless real number. This indicates the accumulated mechanical load equivalent of the vehicle at the start time of this record node, expressed in kilometers of standard load equivalent. Mechanical load equivalent is a cumulative structural stress index obtained by combining the vehicle's mileage with its actual load weight, used to more objectively quantify the total mechanical wear and tear on the vehicle. This indicates the ultimate load threshold, expressed in kilometers of standard load equivalent (kilometers). ); ρ represents the density of the hazardous liquid in the tank, in kilograms per cubic meter (kilograms per cubic meter). ); This indicates the tank volume, in cubic meters (m³). ); σ represents the average liquid level filling rate, dimensionless; α This represents the statistical parameters of the vehicle's longitudinal acceleration collected and analyzed by the onboard triaxial accelerometer, specifically the standard deviation of the longitudinal acceleration, expressed in meters per second squared (m²). ); This indicates the rated yield stress limit of the structure, expressed in Newtons (N).

[0050] The ultimate load threshold is obtained based on the statistical analysis of the entire life-cycle maintenance data of the same type of hazardous chemical tanker truck under various historical typical road conditions. It is calculated by extracting the cumulative fatigue load values ​​before major repairs or fatigue cracks occur and taking 80% of the average as the safety threshold. For example, by statistically analyzing the historical life-cycle data of 20 tanker trucks of the same type, the average mechanical load equivalent when severe mechanical fatigue degradation occurs is 1.25 × 10⁻⁶. 6 After multiplying km·t by a safety margin reduction factor of 0.2, the final ultimate load threshold is determined to be 1.0 × 10⁻⁶. 6 km·t.

[0051] In computational logic, fractional terms within a formula This constitutes the second damage ratio, which is the cumulative fatigue parameter in the instruction manual, representing the dimensionless basic mechanical fatigue wear degree of the vehicle due to daily driving. The numerator... The dynamic load parameters of liquid sloshing are derived, which are then divided by the structural rated yield stress limit. Subsequently, the first damage ratio, representing the dimensionless microscopic damage caused by liquid sloshing to the vehicle's load-bearing structure, was obtained. The first and second damage ratios were linearly superimposed to obtain the total damage ratio. Finally, the dynamic correction coefficient was obtained by subtracting the total damage ratio from the numerical value. .

[0052] S23. The situation analysis processor multiplies the acquired baseline evaluation period with the calculated dynamic correction coefficient to correct the baseline evaluation period, thereby dynamically generating a unique target evaluation interval for each recording node.

[0053] The target evaluation interval here refers to the exclusive time judgment threshold generated for each recording node after dynamically correcting the benchmark evaluation period using dynamic correction coefficients. This threshold serves as the dynamic judgment boundary for measuring whether the vehicle state degradation exceeds the standard at that stage.

[0054] S24. The situation analysis processor traverses each record node in the situation degradation sequence, reads the actual running interval of the node, and compares its value with the corresponding target evaluation interval.

[0055] S25. If the actual operating interval of a node is less than its corresponding target evaluation interval, it indicates that the vehicle's state decay rate is faster than theoretically expected at this stage. Based on this, the system determines it as a valid degradation event and extracts and retains the actual operating interval as valid situational data.

[0056] The valid situational data here refers to high-risk abnormal time interval data representing the accelerated decline of vehicle physical state, filtered out when the actual operating interval of the recorded node is less than the target assessment interval. This data is used to eliminate routine planned reset interference. If the actual operating interval is greater than or equal to the target assessment interval, it is removed as a routine event.

[0057] S26. The situation analysis processor acquires the time difference between adjacent valid situation data, and algebraically accumulates all the time differences within the entire historical record period to finally calculate the situation evolution trend value of the vehicle.

[0058] The situation evolution trend value here refers to the algebraic index obtained by algebraically summing the time differences of all adjacent valid situation data within the entire historical record period. It is used to quantify the rate of change of the frequency of vehicle abnormal degradation events from a macro time axis.

[0059] In this embodiment, it should be noted that in S21, the situation analysis processor obtains the legally mandated baseline assessment period from the configuration database based on the currently extracted mechanical structure type of the hazardous chemical vehicles en route and the physical hazard level of the hazardous chemicals they carry. For the liquid acetic acid heavy tanker fleet to which vehicle A belongs, the baseline assessment period obtained by table lookup matching is 90 days. The logic for setting this parameter is based on industry-standard safety operation and maintenance guidelines and the static mechanical performance assessment report of the vehicle at the time of manufacture, using them as the time benchmark for standard preventive inspections. The technical means adopted in this step is to establish a localized mapping dictionary to automatically bind the vehicle's static attributes with the theoretical life parameters at the time of manufacture, providing an initial reference base for the subsequent introduction of dynamic attenuation factors. Although the data at this time is a static theoretical value, the beneficial effect is that it establishes the basic judgment standard for vehicle situation management, ensuring that subsequent dynamic corrections based on liquid sloshing loads and mechanical fatigue parameters are all carried out within a safe framework that complies with regulatory standards, avoiding scheduling failures caused by calculation results deviating from basic mechanical common sense.

[0060] In S22, the situation analysis processor calculates a dynamic correction coefficient specific to each recording node. The calculation expression is as follows: This process calculates a dynamic correction coefficient specific to each record node. The design of this calculation procedure aims to address the technical problem in traditional hazardous materials vehicle management, which relies solely on static assessments based on fixed mileage or time periods, thus neglecting the nonlinear fatigue damage to the vehicle's load-bearing structure caused by the complex hydrodynamic impacts of the liquid medium during transportation. In the specific calculation logic, the part within the parentheses on the right side of the expression represents the proportion of total structural damage suffered by the vehicle at the current node. This total proportion is a linear superposition of basic mechanical wear and fluid dynamic impact. First, the fluid dynamic impact part, i.e., the numerator, is processed. This part is constructed based on Newton's second law in physics, extracting the density of the hazardous liquid inside the tank. Tank volume and average liquid level filling rate Multiplying these three factors together, the actual physical mass of the liquid inside the tank under the current half-load condition is calculated to be 15750 kg. Subsequently, this liquid mass is compared with the statistical parameters of longitudinal acceleration obtained from the vehicle-mounted triaxial accelerometer. Multiply to obtain the quantized value The parameter, known as the liquid sloshing dynamic load parameter, objectively quantifies the physical impact force generated by the inertial surging of the internal liquid on the tank wall and chassis during vehicle acceleration and deceleration. To convert this force-dimensional physical quantity into a dimensionless parameter usable for proportional calculations, it is divided by the structural rated yield stress limit. The rated yield stress limit of the structure here is calculated through multiple rounds of cyclic tensile and impact fatigue tests on the special alloy steel used in the chassis and tank of this vehicle model using a universal testing machine. This aims to define the stress boundary at which the material undergoes irreversible plastic deformation. For example, by applying dynamic loads gradually increasing from 100,000 N to samples of high-strength steel from the same batch, recording the critical point where the stress-strain curve deviates from the linear elastic stage, and combining this with a safety factor reduction under full vehicle load conditions, the rated yield stress limit of the structure of this vehicle model is finally physically calibrated. The value is 315,000 N. In the unit derivation, dividing Newton by Newton eliminates the dimension, resulting in a first damage ratio of 0.1, a dimensionless value representing ten percent of the structure's yield strength due to the current liquid impact force. Next, the basic mechanical wear is addressed, specifically the equivalent of the vehicle's accumulated mechanical load. Divide by the ultimate load threshold Dividing the kilometer standard load equivalent by the kilometer standard load equivalent also eliminates dimensions, yielding a second damage ratio of 0.2, which serves as a cumulative fatigue parameter. This indicates that the vehicle's basic structural lifespan has been consumed by 20% due to daily driving. Based on the fatigue cumulative damage theory, the first damage ratio of 0.1 and the second damage ratio of 0.2 are added together to obtain a total damage ratio of 0.3. Finally, subtracting this total damage ratio from the first value yields the dynamic correction coefficient. This computational process integrates heterogeneous physical quantities (density, volume, filling rate, acceleration) into a unified dimensionless damage ratio by reducing their dimensionality. This allows for a proportional reduction of the legally mandated assessment cycle based on the vehicle's current actual load-bearing state and the intensity of driving. This overcomes the limitations of traditional on-time maintenance in handling severe conditions such as high-frequency acceleration and deceleration under half-load conditions, ensuring the early identification of vehicles subjected to high-intensity physical damage and providing a dynamic threshold with a realistic physical background for subsequent accurate screening of effective situational data.

[0061] In S23, the situation analysis processor multiplies the acquired baseline assessment period with the calculated dynamic correction coefficient to correct the baseline assessment period, thereby dynamically generating a unique target assessment interval for each recording node. Continuing with the calculation data for vehicle A, the baseline assessment period of 90d is directly multiplied by the dynamic correction coefficient of 0.7 calculated in the previous sub-step, resulting in a target assessment interval of 63d for this recording node. The core of this calculation logic lies in scaling the theoretical healthy operating time of the vehicle proportionally using a dimensionless attenuation factor between zero and one, transforming the originally fixed time baseline into a dynamic judgment boundary that fluctuates with the actual load borne by the vehicle. The technical means adopted in this step is to linearly map the physical stress conversion result with the progress of time. The beneficial effect is that it provides a time judgment threshold with physical environment support for subsequent identification of abnormal state degradation, changes the extensive management mode of performing uniform maintenance cycles for all vehicles, and improves the targeting of resource scheduling.

[0062] In S24, the situation analysis processor traverses each recording node in the situation degradation sequence, reads the actual operating interval of that node, and compares it numerically with the corresponding target evaluation interval. In the application scenario of vehicle A, its actual operating interval at the most recent recording node is read as 48d. This 48d is then compared with the calculated target evaluation interval of 63d in the same comparator. The calculation logic of this step is to construct a time-dimensional funnel filtering mechanism to determine whether the vehicle's mechanical state degradation exceeds the tolerance limit predicted based on actual operating conditions. The technical means adopted is basic numerical comparison calculation, which plays a crucial threshold switching role in the entire data flow process. The beneficial effect is that, through automated data comparison, potentially high-risk operating ranges can be screened in real time, data noise within the normal wear range can be filtered out, and clear data processing boundaries and pre-filtering conditions are provided for focusing on truly occurring accelerated degradation events.

[0063] In S25, if the actual operating interval of a node is less than its corresponding target evaluation interval, it is judged as a valid degradation event, and the actual operating interval is extracted and retained as valid situational data. Based on the comparison results of vehicle A, since its actual operating interval of 48 days is less than the target evaluation interval of 63 days, it is determined that the physical state decay rate of the vehicle in this stage is faster than theoretically expected, so these 48 days are recorded as valid situational data. The judgment logic here is that if a disturbance alarm occurs before the actual continuous operating time of the vehicle reaches the corrected allowable duration, it indicates that the vehicle has an abnormal fault of accelerated deterioration. The technical means adopted is to separate the time series segments that satisfy the inequality conditions from the original degradation sequence and store them in the high-priority analysis queue. The beneficial effect is that it can capture the accelerated fatigue signal hidden in the normal operation process, providing high-risk data samples for subsequent calculation of time difference, and avoiding over-analysis of normal reset data.

[0064] In S26, the situation analysis processor acquires the time difference between adjacent valid situation data and algebraically accumulates all time differences within the entire historical record period to finally calculate the situation evolution trend value of the vehicle. In the data sequence of vehicle A, three valid situation data points (75d, 61d, and 48d) have been identified. The difference between the previous and subsequent data points is calculated sequentially, yielding -14d for 61d minus 75d and -13d for 48d minus 61d. The algebraic summation of -14d and -13d yields the situation evolution trend value of vehicle A as -27d. This calculation logic quantifies the evolution of fault occurrence frequency by capturing the temporal gradient changes between consecutive degradation events; the negative result objectively indicates that the interval between degradation events is continuously shortening. The technical approach employed is the difference and summation operation of time-series data. The beneficial effect is that a single macroscopic algebraic index quantifies the rate of state deterioration of a single vehicle, providing crucial input parameters for subsequent cross-vehicle comparisons and the introduction of an elevation penalty mechanism.

[0065] like Figure 4 As shown, in one specific implementation, S3 includes: S31, the collaborative control decision engine extracts the absolute value of the difference between the trend values ​​of the situation evolution of any two hazardous chemical vehicles on the road, divides it by the trend dispersion allowable threshold, and performs dimensionless processing to obtain the time trend difference.

[0066] The time trend difference here refers to the ratio of the absolute value of the difference in the trend value of the situation evolution between the two vehicles to the allowable threshold of trend dispersion, representing the dimensionless difference between the two vehicles in the dimension of physical deterioration rate.

[0067] S32. Retrieve the geographic coordinate sequences of the predicted trajectories of two hazardous chemical transport vehicles en route within the future prediction time window. Maintain strict synchronization on the time axis and calculate the physical distance between the two sets of geographic coordinates at the same time, specifically the road network travel distance calculated based on the Earth ellipsoid model. Traverse the prediction time window and extract the minimum value of the road network physical travel distance within the prediction time window as the spatial distance required for evaluation.

[0068] S33. The collaborative management and control decision engine divides the extracted spatial distance by the preset aggregation radius threshold and performs dimensionless processing to obtain the dimensionless spatial distance parameter.

[0069] The spatial distance parameter here refers to the ratio of the minimum physical travel distance (spatial distance) between two vehicles within the prediction time window to the aggregation radius threshold. It represents the dimensionless proximity of the two vehicles in the spatial geometric dimension and is used as a basis for evaluating the feasibility of multi-vehicle spatial intersection.

[0070] S34. The collaborative control decision engine linearly sums the time trend difference with the spatial distance parameter to obtain a spatiotemporal coordination index, which quantifies the degree of spatiotemporal correlation between the two vehicles. The spatiotemporal coordination index is a dimensionless characteristic indicator calculated by comprehensively considering the difference in the situational evolution trend values ​​(time dimension) between the two vehicles and their geographic spatial accessibility (spatial dimension) on their future predicted trajectory sequences. It is used to evaluate the comprehensive economic efficiency of centralized joint maintenance calibration of multiple vehicles. The logic for obtaining the combined expression can be expressed as:

[0071]

[0072] in, The spatiotemporal coordination index between vehicle u and vehicle v is a dimensionless real number. and ΔS represents the trend values ​​of the situational evolution of vehicle u and vehicle v, respectively, in days (d); max This represents the allowable threshold for trend dispersion, in days (d); T now ΔT represents the current time; ΔT represents the prediction time window; and These represent the predicted geographic coordinates of vehicle u and vehicle v at time t along their respective routes; D represents the physical distance between two sets of geographic coordinates at the same time, expressed in kilometers (km); max The threshold value represents the aggregation radius, in kilometers (km); min is the minimum value operator.

[0073] Set an index judgment threshold, and use the solver to perform a traversal comparison to determine the spatiotemporal co-existence index. Vehicles with a spatiotemporal coordination index below the set threshold are grouped into the same collaborative management equipment set. The collaborative management equipment set refers to a dynamically reorganized collaborative regulatory set composed of hazardous chemical vehicles whose spatiotemporal coordination index is below the set threshold, which are both located in similar high-risk deterioration channels and have actual geographical spatial intersections in the future road network.

[0074] In this embodiment, it should be noted that in S31, the collaborative management and control decision engine extracts the absolute value of the difference between the situation evolution trend values ​​of any two hazardous chemical transport vehicles en route, and divides it by a preset trend dispersion allowable threshold to obtain the time trend difference. Based on specific data, the situation evolution trend value of vehicle A is -27d, and the trend value of vehicle B in the same road network is -17d. The absolute value of the difference between the two vehicles is calculated as... The allowable threshold for trend dispersion here is determined through cluster analysis of the abnormal state degradation rates of historical maintenance vehicles within the road network. This reflects the maximum difference in the allowed time degradation frequency for collaborative calibration between two vehicle groups. For example, by statistically analyzing data on shortened operating intervals due to performance degradation in similar historical fleets over a six-month period, the standard deviation of the degradation rate difference between vehicles is calculated to be 15 days. This is then rounded up based on the time tolerance of vehicle scheduling, ultimately setting the allowable threshold for trend dispersion at 20 days. Dividing this by the set threshold yields a dimensionless time trend difference of 0.5. The calculation logic here involves normalizing degradation indicators with time units to measure the similarity of the degradation rates between two vehicles. The technical approach used is basic distance measurement and proportional calculation, mapping the absolute time difference to a relative range of zero to one. The beneficial effect is that it eliminates the dimensional influence caused by different initial states of different vehicles, enabling a unified scale to measure the synchronicity of the physical degradation trends of different vehicles, providing a time-dimensional measurement parameter for constructing a comprehensive spatiotemporal coordination index.

[0075] In S32, the collaborative management and control decision engine retrieves the geographic coordinate sequences of the predicted trajectories of two hazardous chemical vehicles en route from the positioning module within the future prediction time window, and extracts the minimum physical travel distance of the road network as the spatial distance. Retrieving the road network planning trajectories of vehicles A and B for the next 48 hours, under strict time synchronization, it is found that the two vehicles will reach the vicinity of a trunk service area in the 36th hour of the future. At this point, the physical distance between the two sets of geographic coordinates reaches a minimum of 15km. The processing logic here is to find the proximity points where multiple vehicles intersect in a constantly changing dynamic space, in order to determine the physical feasibility boundary for their joint scheduling. The technical approach adopted is to discretize the predicted vehicle trajectories and solve the distance matrix based on the Earth ellipsoid model. The beneficial effect is that it changes the limitation of grouping based solely on data similarity, ensuring that the vehicles subsequently grouped have the prerequisite of spatial intersection in the objective physical world, thus giving the generation of scheduling instructions operability and a road network implementation basis.

[0076] In S33, the collaborative management and control decision engine divides the extracted spatial distance by a preset assembly radius threshold and performs dimensionless processing to obtain a dimensionless spatial distance parameter. Continuing from the previous step's calculated minimum spatial distance of 15km, this assembly radius threshold is determined based on the average physical road network spacing of maintenance centers or high-level service areas within the transportation region and the limit of vehicle empty-run detour costs. For example, statistics show that the actual driving distance between service areas within the provincial logistics network capable of handling hazardous chemical repairs is between 80km and 120km. To ensure that the additional assembly detour increment when two vehicles intersect on planned routes does not exceed half of the regular repair distance, this value is halved and combined with historical joint scheduling success rates, ultimately determining the preset assembly radius threshold to be 50km. This value is then divided by the preset assembly radius threshold of 50km to calculate a spatial distance parameter of 0.3. The initial intention of this calculation logic is to transform the actual physical distance into a proportional factor representing the degree of spatial proximity, allowing the spatial dimension value to be integrated and added with the temporal dimension value on a unified scale. The technical approach employed is linear normalization using constant division, which defines the spatial tolerance for scheduling costs. The beneficial effect is that by setting a clear aggregation radius limit, the wasted running costs resulting from forcibly including vehicles that are too far apart in the same maintenance batch are avoided. The impact of road network topology constraints on joint scheduling is quantified, ensuring that the final scheduling scheme is within a relatively reasonable range in physical space.

[0077] In S34, the collaborative management and control decision engine linearly sums the time trend difference with the spatial distance parameter to obtain a spatiotemporal collaboration index, and categorizes vehicles with an index less than a set threshold into the collaborative management device set. Specifically, it executes the calculation expression. This method aims to obtain a spatiotemporal coordination index that quantifies the overall correlation between two vehicles. This computational process addresses the technical problem in wide-area road network transportation scenarios where traditional data-driven clustering algorithms focus solely on the similarity of equipment condition degradation values, neglecting the geographical dispersion of moving vehicles in physical space. This leads to dispatch instructions requiring vehicles hundreds of kilometers apart to travel to the same station for maintenance, resulting in instructions lacking physical executability and increasing dispatch costs. The expression's computational logic employs a multi-objective cost function, performing dimensionless processing on the degradation differences in the time dimension and the trajectory intersection in the spatial dimension, followed by linear summation. In the time dimension calculation, the trend value of vehicle A's situational evolution is extracted. The trend value of the situation evolution of vehicle B By calculating the absolute value of the difference between the two vehicles This is used to measure the objective difference in the physical degradation rate between the two vehicles. To eliminate the dimension of days and limit its numerical range, the absolute value is divided by a preset trend discrepancy allowable threshold ΔS. max =20d, the dimensionless time trend difference is calculated to be 0.5. In the spatial dimension calculation, the numerator in the formula uses the minimum operator min to solve for the dynamic distance within the future prediction time window. Based on the predicted geographical coordinates of the two vehicles... and Driven by the time variable t, the physical driving distance between two sets of geographical coordinates at the same time is continuously calculated. Based on actual data, the minimum physical distance between the two vehicles near a highway service area is deduced at the 36th hour in the future. This value represents the extracted spatial distance. Similarly, to eliminate the dimension of kilometers, this spatial distance is divided by a preset aggregation radius threshold. The dimensionless spatial distance parameter was calculated to be 0.3. After normalization of both dimensions, the time trend difference of 0.5 was added to the spatial distance parameter of 0.3 to obtain the spatiotemporal coordination index. The calculation process of dividing physical quantities of different dimensions by their maximum tolerance limits and then adding them together effectively prevents distance parameters with larger absolute values ​​from masking time parameters with smaller values, ensuring that the two evaluation dimensions have a balanced influence in multi-objective decision-making. Through this calculation process, a comprehensive judgment scale is established that can simultaneously screen whether vehicles are in similar states and whether they can spatially intersect in the future. The index judgment threshold here is obtained by simulation fitting based on the economic benefits and spatiotemporal coupling curves of historical multi-vehicle joint maintenance events. It aims to ensure that the resource aggregation benefits of grouped centralized maintenance can fully compensate for the empty running costs of multi-vehicle detours. For example, by extracting data from 100 historical collaborative cases for sensitivity analysis, the results show that when the index is below 0.85, the fleet can achieve a 15% reduction in comprehensive maintenance costs. However, when it exceeds 0.85, the benefits drop sharply due to excessive detour distances. Therefore, only when the spatiotemporal collaboration index is less than the set judgment threshold of 0.85 will vehicles be grouped into the same collaborative management device set, thereby ensuring that the subsequently issued centralized dispatch instructions have real spatial accessibility and economic rationality in the actual road network.

[0078] like Figure 5 As shown, in one specific implementation, S4 includes: S41, when the minimum situational evolution trend value within the collaborative management device set is negative (indicating the presence of vehicles in the group whose status is rapidly deteriorating), the collaborative control decision engine retrieves a high-precision digital elevation model (DEM) and calculates the cumulative elevation difference of the continuous downhill section based on the planned paths of the vehicles in the group ahead within the prediction time window. The scheduling engine multiplies the average total mass of the vehicles, the gravitational acceleration constant, and the cumulative elevation difference to calculate the physical heat energy that needs to be absorbed by the vehicle braking due to gravitational potential energy conversion; the physical heat energy is divided by the heat dissipation limit of the vehicle braking to obtain the heat fade damage ratio as a parameter of the heat loss during elevation conversion braking.

[0079] The elevation transition braking heat loss parameter (i.e., the calculated heat fade damage ratio) for the continuous downhill section ahead refers to the dimensionless proportion of physical damage caused by braking, obtained by dividing the heat energy that the vehicle needs to absorb and dissipate due to continuous gravitational potential energy conversion during a long downhill slope by its rated heat dissipation limit. This parameter is used to quantify the potential heat fade loss of vehicles on the road due to terrain features.

[0080] Meanwhile, the average mechanical load equivalent of vehicles in the group is divided by the ultimate load threshold to obtain the basic wear ratio, which is used to quantify the degree of aging of the fleet's basic machinery; the absolute value of the minimum situation evolution trend value is divided by the benchmark assessment period to obtain the trend deterioration ratio, which is used to quantify the deterioration signal of high-risk individuals.

[0081] S42. The collaborative control decision engine calculates the difference between the calculated value and the sum of the thermal decay damage ratio, the basic wear ratio, and the trend deterioration ratio to obtain the remaining ratio. To ensure the safety and feasibility of the control strategy in the physical world, the maximum value between the remaining ratio and the preset compression lower limit ratio is extracted as the scaling ratio, and the maximum value is multiplied by the benchmark evaluation period to obtain the final adjusted collaborative calibration interval.

[0082] The collaborative calibration interval here refers to the time interval for the next centralized maintenance calibration, ultimately determined by the collaborative management equipment set and aligned with the time of all vehicles in the group, after multi-source dynamic compression of three physical dimensions: vehicle base wear, the rate of deterioration of high-risk individual vehicles, and the potential energy heat loss from the continuous downhill sections ahead. The acquisition logic can be expressed as:

[0083]

[0084] in, This represents the calculated target collaborative calibration interval, in days (d). β represents the baseline assessment period, in days (d); β represents the lower limit of compression, which is dimensionless. This represents the average mechanical load equivalent of vehicles within the collaborative management equipment set, expressed in kilometers of standard load equivalent. ); This indicates the ultimate load threshold, expressed in kilometers of standard load equivalent (kilometers). ); M represents the minimum trend value of situational evolution within the collaborative management device set, expressed in days (d); avg The average total mass of vehicles within the collaboratively managed equipment set is expressed in kilograms (kg); g is the gravitational acceleration constant, expressed in meters per second squared (m²). ); ΔH down E represents the cumulative elevation difference of the continuous downhill section ahead, in meters (m); brake This represents the heat dissipation limit of vehicle braking, in joules (J); max is the maximum value operator.

[0085] S43, the collaborative management and control decision engine converts the calculated collaborative calibration interval into a timestamp, generating a scheduling time point aligned on the future timeline for all vehicles within the collaborative management device set, serving as a unified time window; simultaneously, it extracts the predicted geographical coordinates corresponding to the two vehicles at the minimum spatial distance calculated in S32, as recommended road network aggregation points. Based on the time window and road network aggregation points, the final collaborative scheduling sequence is generated and distributed to each vehicle terminal for execution.

[0086] The coordinated scheduling sequence here refers to the closed-loop maintenance command sequence output by the solution, which includes strictly time-aligned future scheduling time windows and recommended spatial geometric coordinates of road network assembly points. This sequence guides vehicle fleets to achieve grouped coordinated maintenance. The situation visualization platform displays the coordinated scheduling sequence on a GIS base map layer using color classification and highlighting rendering.

[0087] S44. This method also integrates an emergency response mechanism for sudden accidents. When a step physical impact parameter (captured by the step response of the vehicle-mounted triaxial accelerometer) or a pressure drop parameter (captured by a sudden leak from the pressure sensor) exceeding the set range is obtained, the normal process is interrupted, triggering an emergency state. To accurately distinguish between the instantaneous pressure fluctuations caused by complex bumps on normal road surfaces or normal valve opening, the aforementioned set range is determined based on the tanker's physical extreme collision test data and the safety red line of the normal operation fluctuation of the safety valve. For example, the triaxial acceleration response amplitude caused by a normal speed bump or pothole impact is within ±5g, while the step response during a physical impact or rollover usually increases sharply and exceeds ±20g; at the same time, the normal pressure regulation fluctuation of the main pipeline is within ±5%, but a major rupture and leak can cause an instantaneous pressure drop exceeding 40%. Therefore, by defining an acceleration exceeding ±20g or a sudden pressure drop exceeding 40% as the aforementioned safety red line exceeding the set range, the emergency state is accurately triggered. In the emergency state, the calculation of the situation evolution trend value is stopped, and the time trend difference term is removed when calculating the spatiotemporal coordination index. The real-time geographic coordinates of the accident vehicle that triggered the emergency are set as the spatial origin. Hazardous chemical vehicles en route within a defined radius around the spatial origin are dynamically categorized into a risk avoidance target set. This defined radius is calculated based on statistical data of the effective damage range of historical hazardous chemical tanker accidents involving severe leaks, explosions, or large-scale toxic gas diffusion of similar volumes. For example, extreme values ​​are extracted from the farthest lethal distance of rapid downwind diffusion of toxic gas within the first 5 minutes of 50 similar high-pressure tanker leak accidents in the past decade. Combined with Gaussian plume hydrodynamic simulation results, the range of the highly vulnerable red alert zone is determined, thus scientifically defining the defined radius as 5 kilometers around the spatial origin. Local meteorological wind speeds and hazardous chemical leakage rates are used to establish a gas diffusion isoplethora layer, generating emergency dispatch instructions to guide en route hazardous chemical vehicles within the risk avoidance target set to evacuate away from the spatial origin along upwind routes that avoid areas above the defined concentration threshold.

[0088] The concentration threshold is set based on the toxicological limit for immediate danger to life or health (IDLH) specified in the Material Safety Data Sheet (MSDS) of the specific hazardous chemical being transported. Taking liquid glacial acetic acid as an example, by consulting national occupational health standards, the critical exposure concentration parameter (such as 50 ppm) that would cause severe respiratory burns or direct coma in the air is obtained. The system directly uses this critical safe concentration as the set concentration threshold and converts it into the contour physical boundary corresponding to the diffusion model, ensuring that the generated evacuation route avoids the lethal concentration zone.

[0089] In this embodiment, it should be noted that in S41, when the minimum situational evolution trend value within the collaborative management device set is negative, the elevation transition braking heat loss parameter is calculated based on parameters such as the cumulative elevation difference of the road section ahead. This calculation is triggered because the minimum value for this group is -27d. The average total mass of vehicles within the group... for The acceleration due to gravity g is The cumulative elevation difference of a long downhill slope The length is 600m. Multiplying the three together, we get the physical heat energy as follows: Divide it by the braking heat dissipation limit. Value The thermal fade damage ratio, used as a parameter for heat loss during elevation transition braking, was found to be 0.15. Simultaneously, the average mechanical load equivalent... Divide by the ultimate load threshold The basic wear ratio is 0.25; Dividing by the baseline period of 90 days yields a trend deterioration rate of 0.3. This logic is based on energy conservation, converting terrain energy into a braking heat load ratio. Employing physical energy equations for derivation, the beneficial effect is the quantification of the potential thermal fade loss caused by terrain on the braking of high-risk vehicles.

[0090] In S42, the collaborative management and control decision engine calculates the final collaborative calibration interval. The expression used is: This process generates collaborative calibration intervals for the entire collaborative management equipment set. The construction of this computational process aims to address the problem that existing hazardous chemical vehicle monitoring technologies primarily focus on static assessments of existing conditions, failing to consider the complex geographical and elevation environments the vehicle will encounter ahead. This results in the inability to pre-quantify the risk of brake fade caused by prolonged high-load friction when a heavily loaded vehicle, whose condition has already deteriorated, enters a long, continuous downhill section, making it difficult to take timely safety measures to prevent brake failure accidents. The core of this expression's computation lies in the transformation of the law of conservation of energy into a dimensionless damage ratio. First, the braking heat loss during elevation transitions is processed, and the average total mass of vehicles within the collaborative management equipment set is extracted. Gravitational acceleration constant and the cumulative elevation difference of the continuous downhill section ahead. Multiplying these three terms together yields... In physics, the product of mass, acceleration, and height yields gravitational potential energy. This value represents the physical heat energy that a vehicle needs to absorb and dissipate through frictional heat generation when braking downhill due to the conversion of potential energy. Dividing this physical heat energy by the vehicle's braking heat dissipation limit... Dividing by joules achieves dimensional cancellation, resulting in a calculated heat fade damage ratio of 0.15, which serves as a parameter for heat dissipation during elevation transition braking. Here, the vehicle braking heat dissipation limit is obtained based on the specific heat capacity of the brake drum and friction pad materials, as well as calibration through continuous braking temperature rise tests on a test bench. It reflects the maximum heat energy the braking system can absorb and convert before severe heat fade failure. For example, by simulating a fully loaded heavy tanker truck continuously braking for half an hour on a long downhill slope on a chassis dynamometer, when the brake temperature reaches the critical temperature (e.g., 450℃) where brake fluid boils or the coefficient of friction drops precipitously, the total braking friction work consumed during this test cycle is calculated integrally. Based on this, the heat dissipation limit Ebrake for a single continuous braking of this type of vehicle is ultimately determined to be 1.4896 × 10⁻⁶. 9 J.

[0091] Secondly, calculate the conventional loss terms separately: the average mechanical load equivalent of the vehicles in the set. Divide by the ultimate load threshold The baseline wear ratio was found to be 0.25; the minimum trend value of the situation evolution within the set was extracted. Take its absolute value and divide it by the benchmark evaluation period. The trend deterioration ratio, representing the recent rate of deterioration, is 0.3. The thermal decay damage ratio (0.15), the basic wear ratio (0.25), and the trend deterioration ratio (0.3) are linearly summed to obtain a total reduction ratio of 0.7. Subtracting this sum from the given value yields a remaining ratio of 0.3. To prevent the calculated remaining ratio from being negative or approaching zero due to the superposition of multiple high-risk factors, thus generating scheduling instructions exceeding physical execution capabilities, the formula introduces a maximum value operator `max` and a preset lower limit ratio for compression. The compression lower limit ratio is determined based on the minimum permissible technical inspection cycle constraint in the safety management regulations for hazardous chemical transport enterprises, and the safety boundary value to avoid excessive maintenance and waste of transport capacity resources. For example, the regulations limit the time interval between two consecutive safety technical calibrations of heavy tank trucks to a maximum of 20% of the original statutory period, regardless of the extreme high-risk operating conditions, in order to control high-frequency oscillations. Based on this, the preset compression lower limit ratio β is determined to be 0.2. The calculated remaining ratio of 0.3 is compared with the lower limit ratio of 0.2. Since 0.3 is greater than 0.2, the maximum value of 0.3 is taken as the final reduction factor. Finally, this factor is compared with the benchmark evaluation cycle. Multiply to calculate the final collaborative calibration interval. This computational process, which incorporates terrain energy, enables proactive intervention by shortening maintenance cycles before the physical pressure from the environment and terrain exceeds the braking limit, thus achieving preventative safety scheduling for high-risk road sections.

[0092] In S43, the collaborative management and control decision engine converts the calculated collaborative calibration interval into a timestamp, combines it with the recommended road network assembly point to generate and issue a collaborative scheduling sequence. The calculated 27 days is converted into a specific future date and time, generating a maintenance window that is forcibly aligned on the timeline for vehicles A and B within the collaborative management device set. Simultaneously, the predicted geographical coordinates of the two vehicles at the 36th hour (i.e., the location near the aforementioned highway service area) when the spatial distance is calculated to be a minimum of 15km are extracted and used as the road network assembly point. This scheduling logic, combining time and space, transforms abstract computational parameters into executable guidance trajectories and time limits in the physical world. The technical means adopted is to encapsulate the dispatch instructions through time sequence conversion and spatial location mapping. The beneficial effects achieved are that it streamlines the process from data analysis to maintenance execution, prevents hazardous chemical vehicles with deteriorating conditions from continuing to enter high-risk sections such as long downhill slopes, and realizes a closed loop of pre-emptive defense and road network entity control.

[0093] S44 integrates an emergency response mechanism to handle unsteady extreme events such as physical collisions or high-pressure gas leaks. When the onboard acceleration sensor detects a step impact exceeding a set safety range, a hardware interrupt is activated, switching to emergency mode. At this point, long-term historical evolution calculations are immediately paused, and time trend difference terms are removed from the spatiotemporal coordination index. The current latitude and longitude of the accident vehicle are set as the spatial origin, surrounding emergency vehicles are dynamically extracted and incorporated into the coordination object set, and a gas diffusion isopleth coil layer is constructed based on wind speed and leakage rate. This logic suspends long-term preventative assessments and prioritizes responses to transient physical damage and chemical diffusion. The technical means employed include interrupt control, real-time wind direction diffusion model calculation, and command issuance. The beneficial effect achieved is a shift from normal time-aligned prediction to sudden spatial emergency mobilization, guiding rescue vehicles to evacuate and assemble along upwind routes, thus compressing the spread space of secondary disasters from hazardous chemical accidents.

[0094] A data visualization management system for the status of hazardous chemical vehicles en route is also provided. The system includes:

[0095] The sequence acquisition module is used to acquire the status degradation sequences of multiple hazardous chemical vehicles en route within a historical record period;

[0096] The interval reconstruction module is used to obtain dynamic correction coefficients based on cumulative fatigue parameters and liquid sloshing dynamic load parameters, and to use the dynamic correction coefficients to correct the benchmark evaluation cycle to generate the target evaluation interval.

[0097] The trend quantification module is used to filter effective situation data based on the comparison results between the actual operating interval and the target evaluation interval, and to obtain the situation evolution trend value based on the time difference between adjacent effective situation data.

[0098] The spatiotemporal coordination module is used to obtain the spatiotemporal coordination index based on the difference between the trend value of the situation evolution and the spatial distance of the predicted trajectory, and to classify vehicles with a spatiotemporal coordination index less than a set threshold into the collaborative management device set.

[0099] The scheduling generation module is used to generate collaborative calibration intervals based on the cumulative fatigue parameters of vehicles in the collaborative management equipment set, the trend value of situation evolution, and the elevation conversion braking heat loss parameters of the continuous downhill road section ahead, and accordingly generate collaborative scheduling sequence containing time windows and road network aggregation points.

[0100] In one specific implementation, the interval reconstruction module is further configured to: multiply the density of the hazardous chemical liquid, the tank volume, and the liquid level filling rate to obtain the liquid mass; multiply the liquid mass by the statistical parameters of the vehicle's longitudinal acceleration to obtain the liquid sloshing dynamic load parameters; divide the liquid sloshing dynamic load parameters by the structural rated yield stress limit to obtain a first damage ratio as dimensionless data; divide the vehicle's accumulated mechanical load equivalent by the ultimate load threshold to obtain a second damage ratio as a cumulative fatigue parameter; and calculate the difference between the value and the sum of the first and second damage ratios as a dynamic correction coefficient.

[0101] In one specific implementation, the trend quantification module is also used to: determine whether the actual running interval of each recorded node in the situation degradation sequence is less than its corresponding target evaluation interval; if it is less, the corresponding actual running interval is taken as valid situation data; obtain the time difference between adjacent valid situation data, and algebraically accumulate all time differences to obtain the situation evolution trend value.

[0102] In one specific implementation, the system is deployed within a monitoring network scenario of hazardous chemical vehicles transporting goods in transit within a specific area. The hazardous chemical vehicle status data visualization management system is vertically divided into three physical layers at the underlying physical architecture: a data acquisition layer, a data processing layer, and an application display layer. Each layer communicates with the others through standardized industrial communication interfaces and network protocols to achieve full-duplex time-series data interaction and function calls.

[0103] Furthermore, the data acquisition layer is deployed on hazardous chemical vehicles en route and in static monitoring infrastructure along the transportation network. The vehicle-mounted unit is equipped with a BeiDou positioning module to continuously output location latitude and longitude trajectory coordinates and timestamp data; it also features a three-axis accelerometer with a physical range set to ±200g, specifically designed to capture longitudinal and lateral dynamic impact loads during vehicle operation in real time; simultaneously, an ultrasonic level sensor is deployed on the upper part of the vehicle tank to monitor the real-time height of the hazardous chemical liquid inside the tank and convert it into a liquid level filling rate; temperature and pressure sensors are also installed at the tank pipelines. The physical signals collected by these sensors are encapsulated through an onboard industrial gateway and converted into a unified structured time-series data stream. The roadside monitoring unit includes meteorological monitoring stations distributed at key traffic nodes (collecting meteorological environmental data such as wind speed and direction parameters) and high-definition video stream capture nodes.

[0104] Furthermore, the data processing layer hardware is located in the server room of the regional monitoring center, specifically including a data aggregation server, a historical database server, a situation analysis processor, and a collaborative management and control decision engine. The data aggregation server receives raw time-series data from the vehicle-mounted gateway, performs protocol parsing and data cleaning and transformation, and then writes it to the distributed message queue system in real time. The historical database server adopts a cluster architecture and enables time-series database extensions to support high-concurrency, low-latency read and write of massive amounts of location and sensor time-series data. The table structure uses a composite index based on the vehicle's unique identifier and the collection timestamp. The situation analysis processor is equipped with a GPU-accelerated computing architecture to perform parallel computations of large-scale spatiotemporal matrices. The collaborative management and control decision engine is used to run business rule determination and quantification formula calculations to provide key parameter outputs in adaptive computation.

[0105] Furthermore, the application presentation layer is set up in the monitoring workstation of the monitoring center, including a situational visualization platform, an early warning management system, and a dispatch and command terminal built on the Web front-end technology stack, which are used to intuitively present multi-dimensional security risk information, historical trajectory evolution curves, and collaborative dispatch timing on the electronic map tiles.

[0106] It should be noted that the sequence acquisition module in the on-the-go hazardous chemical vehicle situation data visualization management system is mainly deployed and executed on the data aggregation server and historical database server; the interval reconstruction module and trend quantification module are mainly deployed and executed on the situation analysis processor; and the spatiotemporal coordination module and scheduling generation module are mainly deployed and executed on the collaborative management and control decision engine.

[0107] To enable those skilled in the art to fully understand and implement the technical solutions described in this specification, the following section, using a control scenario containing specific data, provides a detailed full-process deduction and data analysis of the implementation principle of a method and system for visualizing the status data of hazardous chemical vehicles en route.

[0108] A provincial logistics company is operating a fleet of heavy-duty tanker trucks dedicated to transporting liquid glacial acetic acid. The system first enters S1, where the time base module locks in the past six months as the historical record period. The situation analysis processor then extracts the complete situation degradation sequence of the target vehicle set from the time-series database. Taking the core transport vehicle A in the fleet as an example, the system extracts multiple record nodes generated over a period of time due to disturbances such as tire wear warnings, abnormal tire pressure, and brake pressure fluctuations. The actual operating intervals between these nodes are 75 days, 61 days, and 48 days, respectively, constituting the initial situation degradation sequence for vehicle A and establishing the basic timeline for subsequent quantitative analysis.

[0109] Subsequently, the system enters S2, performing a deep reduction calculation on the physical state of vehicle A within the interval of the most recent recording node. The situation analysis processor retrieves the baseline assessment period for this type of hazardous chemical tanker truck from the configuration database. Vehicle A's onboard sensor data within the operating area showed the density of the liquid glacial acetic acid in the tank. Tank volume Average liquid level filling rate (Half-load conditions can lead to significant inertial hydraulic sloshing impact). The standard deviation of longitudinal acceleration in this range is statistically obtained from the onboard triaxial accelerometer. At the same time, the vehicle chassis database provides feedback on the structural rated yield stress limit of this model. The accumulated mechanical load equivalent at the starting point of this record node And the ultimate load threshold .

[0110] In the same S2, the situation analysis processor executes the first formula in the embodiment based on the above physical parameters. The calculation begins with determining the dynamic load parameters for liquid sloshing: the liquid mass is... , and Multiplying them, we obtain the dynamic load parameters for liquid sloshing as follows: Divide the dynamic load parameter by The dimensionless first damage ratio was calculated to be 0.1. Next, the mechanical load equivalent was... Divide by the ultimate load threshold The dimensionless second damage ratio was obtained as 0.2. The system subtracts the sum of the two ratios from the numerical value, i.e. The dynamic correction coefficient is calculated to be 0.7. Multiplying this coefficient by the baseline evaluation period of 90 days, the system dynamically generates a dedicated target evaluation interval of 63 days for vehicle A at this record node.

[0111] Since vehicle A's actual operating interval at the most recent node was 48 days, which is significantly less than the calculated target evaluation interval of 63 days, the system determined that a valid degradation event had occurred during this period and retained 48 days as valid situational data. Combining the data above, vehicle A's three consecutive valid situational data points within the historical record period were 75 days, 61 days, and 48 days, respectively. The system calculated the time difference between adjacent data points, which were as follows: as well as By algebraically summing all the differences, we obtain the trend value of the situational evolution of vehicle A. The negative results clearly indicate that the interval between degrade events is continuously shortening. Simultaneously, the system extracts the situational evolution trend value of vehicle B, another vehicle in the same convoy located on the same road network. .

[0112] Next, the system enters S3, using the second formula in the embodiment. Calculate the spatiotemporal coordination index between the two vehicles. The system's preset trend dispersion allowable threshold... Cluster radius threshold In the time dimension, the system calculates the absolute value of the difference between the trend values ​​of the two vehicles as... , divided by The dimensionless time trend difference was obtained as 0.5. Spatially, the system extrapolated the geographical coordinates of the two vehicles over the next 48 hours based on the predicted time window, finding that in the 36th hour, the two vehicles would reach their minimum physical distance near a highway service area. Divide it by The dimensionless spatial distance parameter 0.3 is obtained. The spatiotemporal coordination index is obtained by linearly summing the two parameters. Because the set threshold for the index is 0.85, and The system dynamically groups vehicle A and vehicle B into the same collaborative management device set.

[0113] Subsequently, the system enters S4 to prepare for calculating the collaborative calibration interval of this set. This is based on the minimum situational evolution trend value within this collaboratively managed device set. A negative value directly triggers the dynamic penalty mechanism for braking heat loss during elevation transitions. The system extracts the average mechanical load equivalent of the two vehicles in the group. average total mass Retrieving the high-precision digital elevation model revealed a long, dangerous downhill section within the planned road ahead of the two vehicles, with a cumulative elevation difference. Given the gravitational acceleration constant. And the heat dissipation limit of the vehicle braking system As specified by the manufacturer The system calculates the physical heat energy converted from gravitational potential energy on this long downhill slope as follows: Divide the physical thermal energy by the heat dissipation limit. The calculated thermal decay damage ratio, which serves as a parameter for heat loss during elevation transition braking, is 0.15.

[0114] At the same time, continue through Calculate the final collaborative calibration interval. First, calculate the base wear ratio. Secondly, the proportion of the trend deterioration is calculated as follows: Introducing the previously calculated thermal degradation damage ratio of 0.15, the sum of the above three ratios is: Calculate the remaining proportion by subtracting the sum from the first value. The system's preset compression lower limit ratio. The maximum value is then taken as 0.3. Finally, the system multiplies the baseline evaluation period of 90 days by 0.3 to obtain the collaborative calibration interval. The system then converts this interval data into a specific future date and timestamp, and combines it with the service area coordinates corresponding to the predicted minimum distance. It then simultaneously sends a coordinated scheduling sequence containing time windows and road network aggregation points to vehicle A and vehicle B, preventing vehicles from entering high-risk road sections while their performance is compromised.

[0115] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the specific details of the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the protection scope of the present invention.

[0116] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the present invention will not describe the various possible combinations separately.

[0117] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed by the present invention.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for visual management of the status data of hazardous chemical vehicles en route, characterized in that, The methods include: Obtain the status downgrade sequence of multiple hazardous chemical vehicles en route within the historical record period; The dynamic correction coefficient is obtained based on the cumulative fatigue parameters and the dynamic load parameters of the liquid sloshing, and the benchmark evaluation period is corrected using the dynamic correction coefficient to generate the target evaluation interval. Valid situation data are selected based on the comparison between the actual operating interval and the target assessment interval, and the situation evolution trend value is obtained based on the time difference between adjacent valid situation data. The spatiotemporal coordination index is obtained by comparing the difference between the trend value of the situation evolution and the spatial distance of the predicted trajectory, and vehicles with a spatiotemporal coordination index less than a set threshold are classified into the set of collaborative management devices. Based on the cumulative fatigue parameters, situation evolution trend values, and elevation conversion braking heat loss parameters of vehicles within the collaborative management equipment set, a collaborative calibration interval is generated, and a collaborative scheduling sequence including time windows and road network aggregation points is generated accordingly.

2. The method for visual management of the status data of hazardous chemical vehicles en route, as described in claim 1, is characterized in that, The process of obtaining the dynamic correction coefficient based on the cumulative fatigue parameters and the dynamic load parameters of the liquid sloshing includes: The density of the hazardous chemical liquid, the tank volume, and the liquid level filling rate are multiplied to obtain the liquid mass. Multiplying the liquid mass by the statistical parameters of the vehicle's longitudinal acceleration yields the dynamic load parameters of the liquid sloshing. Divide the liquid sloshing dynamic load parameters by the structural rated yield stress limit to obtain the first damage ratio as dimensionless data. Divide the vehicle's accumulated mechanical load equivalent by the ultimate load threshold to obtain the second damage ratio as a cumulative fatigue parameter. The difference between the first damage ratio and the sum of the second damage ratios is calculated and used as a dynamic correction coefficient.

3. The method for visual management of the status data of hazardous chemical vehicles en route, as described in claim 1, is characterized in that... The process of filtering effective situation data based on the comparison between the actual operating interval and the target assessment interval, and obtaining the situation evolution trend value based on the time difference between adjacent effective situation data, includes: Determine whether the actual running interval of each recorded node in the situation degradation sequence is less than its corresponding target evaluation interval; If it is less than, then the corresponding actual operating interval will be taken as valid situation data; The time difference between adjacent valid situation data is obtained, and all time differences are algebraically summed to obtain the situation evolution trend value.

4. The method for visual management of the status data of hazardous chemical vehicles en route, as described in claim 1, is characterized in that... The process of obtaining the spatiotemporal coordination index based on the difference between the trend values ​​of situational evolution and the spatial distance of the predicted trajectory includes: Divide the absolute value of the difference between the trend values ​​of any two hazardous chemical vehicles on the road by the allowable threshold for trend dispersion to obtain the time trend difference. Obtain the geographic coordinates of the predicted trajectories of two hazardous chemical vehicles in transit within the prediction time window, calculate the physical distance between the two sets of geographic coordinates at the same time, and extract the minimum value of the physical distance within the prediction time window as the spatial distance. Divide the spatial distance by the aggregation radius threshold to obtain the dimensionless spatial distance parameter; The spatiotemporal synergy index is obtained by linearly summing the time trend difference with the spatial distance parameter.

5. The method for visual management of the status data of hazardous chemical vehicles en route, as described in claim 1, is characterized in that... The process of generating collaborative calibration intervals based on the cumulative fatigue parameters, situational evolution trend values, and elevation transition braking heat loss parameters of the continuous downhill road section ahead, within the collaborative management equipment set, includes: Obtain the minimum situational evolution trend value within the collaborative management equipment set. When the minimum situational evolution trend value is negative, multiply the average total mass of vehicles within the collaborative management equipment set, the gravitational acceleration constant, and the cumulative elevation difference of the continuous downhill section ahead to obtain the physical thermal energy. Divide the physical heat energy by the heat dissipation limit of the vehicle braking system to obtain the heat fade damage ratio, which is used as a parameter for heat loss during elevation transition braking. The basic wear ratio is obtained by dividing the average mechanical load equivalent of vehicles in the collaborative management equipment set by the ultimate load threshold; and the trend deterioration ratio is obtained by dividing the absolute value of the minimum situation evolution trend value by the benchmark assessment period. The remaining percentage is obtained by subtracting the sum of the thermal decay damage ratio, the base wear ratio, and the trend deterioration ratio from the calculated value. Extract the maximum value between the remaining ratio and the preset compression lower limit ratio, and multiply the maximum value by the benchmark evaluation period to obtain the collaborative calibration interval.

6. The method for visual management of the status data of hazardous chemical vehicles en route, as described in claim 5, is characterized in that... The generation of the coordinated scheduling sequence containing time windows and road network aggregation points includes: The collaborative calibration interval is converted into a timestamp, and an aligned scheduling time point is generated as a time window for all vehicles in the collaborative management device set; Extract the predicted geographic coordinates corresponding to the calculated spatial distance as the road network aggregation points; The coordinated scheduling sequence is generated based on the time window and the road network aggregation point.

7. The method for visual management of the status data of hazardous chemical vehicles en route, as described in claim 1, is characterized in that, Also includes: An emergency state is triggered when a step physical impact parameter or pressure drop parameter that exceeds the set range is obtained. In an emergency, the calculation of the situation evolution trend value is stopped, and the time trend difference is removed when calculating the spatiotemporal coordination index; Set the real-time geographic coordinates of the accident vehicle that triggered the emergency state as the spatial origin, and classify the on-the-road hazardous chemical vehicles within a set radius around the spatial origin into the risk avoidance object set. Based on the leakage rate of hazardous chemicals and the meteorological wind speed, an equivalent gas diffusion coil layer is established, and an emergency dispatch instruction is generated to guide on-the-go hazardous chemical vehicles within the risk avoidance target set to evacuate away from the spatial origin along an upwind route that avoids areas above the set concentration threshold.

8. A visualized management system for the status data of hazardous chemical vehicles en route, characterized in that, The system includes: The sequence acquisition module is used to acquire the status degradation sequences of multiple hazardous chemical vehicles en route within a historical record period; The interval reconstruction module is used to obtain dynamic correction coefficients based on cumulative fatigue parameters and liquid sloshing dynamic load parameters, and to use the dynamic correction coefficients to correct the benchmark evaluation cycle to generate the target evaluation interval. The trend quantification module is used to filter effective situation data based on the comparison results between the actual operating interval and the target evaluation interval, and to obtain the situation evolution trend value based on the time difference between adjacent effective situation data. The spatiotemporal coordination module is used to obtain the spatiotemporal coordination index based on the difference between the trend value of the situation evolution and the spatial distance of the predicted trajectory, and to classify vehicles with a spatiotemporal coordination index less than a set threshold into the collaborative management device set. The scheduling generation module is used to generate collaborative calibration intervals based on the cumulative fatigue parameters of vehicles in the collaborative management equipment set, the trend value of situation evolution, and the elevation conversion braking heat loss parameters of the continuous downhill road section ahead, and accordingly generate collaborative scheduling sequence containing time windows and road network aggregation points.

9. The on-the-go hazardous chemical vehicle status data visualization management system according to claim 8, characterized in that, The interval reconstruction module is also used for: The density of the hazardous chemical liquid, the tank volume, and the liquid level filling rate are multiplied to obtain the liquid mass. Multiplying the liquid mass by the statistical parameters of the vehicle's longitudinal acceleration yields the dynamic load parameters of the liquid sloshing. Divide the liquid sloshing dynamic load parameters by the structural rated yield stress limit to obtain the first damage ratio as dimensionless data. Divide the vehicle's accumulated mechanical load equivalent by the ultimate load threshold to obtain the second damage ratio as a cumulative fatigue parameter. The difference between the first damage ratio and the sum of the second damage ratios is calculated and used as a dynamic correction coefficient.

10. The on-the-go hazardous chemical vehicle status data visualization management system according to claim 8, characterized in that, The trend quantification module is also used for: Determine whether the actual running interval of each recorded node in the situation degradation sequence is less than its corresponding target evaluation interval; If it is less than, then the corresponding actual operating interval will be taken as valid situation data; The time difference between adjacent valid situation data is obtained, and all time differences are algebraically summed to obtain the situation evolution trend value.

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