An asphalt pavement construction whole-process digital dispatching management system and method

Abstract

The present application relates to digital dispatch management technical field, disclose a kind of asphalt pavement construction whole-process digital dispatch management system and method, comprising: the first state parameter package of departure parameter generation is collected by field transaction processing module;In-transit state mapping module determines the second state parameter package of estimated arrival time generation;On-site material-receiving state sensing module determines material consumption rate;Coordinated scheduling decision module predicts paving temperature to construct temperature-queue state vector, calculates emptying time and temperature-queue interference factor, reorganizes virtual queue and distributes guide speed when timing overlap and temperature-queue interference factor is lower than specified threshold, while feedback adjustment departure step, the present application decouples the mutual relationship of logistics timing and heat attenuation, eliminates the material temperature segregation caused by material-receiving area transport vehicle disorder backlog, guarantees pavement paving quality continuity.

Description

Technical Field

[0001] This invention belongs to the field of digital scheduling and management technology, and in particular relates to a digital scheduling and management system and method for the entire process of asphalt pavement construction. Background Technology

[0002] Digital scheduling in asphalt pavement construction mostly adopts pre-set departure plans and controls vehicle queuing based on information from individual locations. The system typically collects the location data of transport vehicles, calculates the estimated arrival time, and then organizes material transportation according to fixed departure intervals. Asphalt mixtures continuously cool down during transportation and waiting. The paving site must maintain a continuous supply of materials while trying to keep the temperature of the mixture in the receiving area stable. When road traffic conditions fluctuate, the capacity of the mixing plant changes slightly, or the operating speed at the paving site is dynamically adjusted, the actual arrival time of vehicles can easily deviate from the original plan. Static scheduling cannot reflect the queuing after multiple transport vehicles arrive at the site in a timely manner. Changes in the number of vehicles and long waiting times in the material receiving area at the paving site can cause the mixture to continuously dissipate heat and produce local segregation. However, reducing the number of vehicles may cause the paver to stop working due to material shortages. Therefore, it is difficult to simultaneously achieve continuous construction and mixture insulation. Under long-distance transportation or complex road network conditions, the arrival time of vehicles is prone to random fluctuations, and the consumption rate of the mixture in the receiving bin will also change in real time. This can lead to discrepancies between the pre-set departure sequence and the actual unloading needs on site. In mountainous and other construction environments, communication signal blockage and location data loss may also occur, making it difficult for the dispatch server to continuously obtain the real-time location of vehicles, causing the estimated arrival time to deviate from the actual situation.

[0003] Simply increasing the number of backup vehicles or increasing the frequency of vehicle departures cannot reduce the heat loss caused by queuing after vehicles arrive at the site. On the contrary, it will occupy more road space on site. Relying solely on strengthening the insulation of individual vehicles or on-site manual command is also insufficient to achieve dynamic linkage of information in various stages of construction. For example, Chinese invention patent application CN122013628A discloses a construction method for asphalt macadam drainage base course for airport runways. It uses a distributed sensor network to monitor temperature. This scheme compares the measured temperature with a preset threshold, but fails to establish a dynamic prediction model for the material cooling process under different environmental thermal boundary conditions. Because it does not consider the coupling effect between the construction environment and the heat exchange mechanism, the control terminal is prone to misjudgment due to the lagging static threshold logic under extreme climate or complex working conditions. It is impossible to make process adjustments in advance before quality defects occur, thus affecting the stability of construction quality.

[0004] Therefore, the technical problem to be solved by this invention is how to construct a digital scheduling and management system for the entire asphalt pavement construction process that can coordinate the interaction between vehicle transportation timing and mixture temperature, and has the ability to automatically correct when communication conditions are unstable. Summary of the Invention

[0005] To address the problems in the background art, the technical solution of the present invention is as follows: A digital scheduling and management system for the entire process of asphalt pavement construction, comprising:

[0006] The outbound transaction processing module is used to collect the outbound departure time, outbound material temperature, and initial loading mass when the transport vehicle completes loading in order to generate the first state parameter package.

[0007] The on-the-way state mapping module, connected to the positioning module, is used to calculate the remaining travel time on the way based on the geographical coordinates of the transport vehicle, so as to generate a second state parameter package containing the expected arrival time.

[0008] The on-site material receiving status sensing module is used to collect the remaining material in the receiving hopper and the running speed to determine the consumption rate of the mixture;

[0009] The collaborative scheduling decision module, connected to all modules via a network, receives data and controls scheduling according to the following control steps:

[0010] Step S1: Predict the paving temperature based on ambient temperature and wind speed, and establish a temperature-queue state vector consisting of the predicted paving temperature and the expected arrival time.

[0011] Step S2: Calculate the emptying time based on the remaining material in the receiving bin, the running speed, and the current system time, and calculate the temperature-queue interference factor for each transport vehicle.

[0012] Step S3: When the estimated arrival times of two or more transport vehicles in transit overlap and the temperature-queue interference factor of the following vehicles is below a specified threshold, the virtual queue is reorganized and the guiding speed is calculated. The guiding speed is then distributed to the following vehicles through the in-transit state mapping module.

[0013] Preferably, the method for calculating the guiding vehicle speed in step S3 is as follows: the collaborative scheduling decision module calculates the quotient between the remaining road distance from the rear vehicle to the paving site and the time difference term. The time difference term is the difference obtained by subtracting the specified safety buffer time from the emptying time. The safety buffer time is fixed at 5 minutes. The collaborative scheduling decision module determines the quotient as the guiding vehicle speed and distributes it.

[0014] Preferably, the collaborative scheduling decision module calculates the corrected departure step length based on the average temperature-queue interference factor of the on-the-way transport vehicle group, and distributes the corrected departure step length to the departure transaction processing module; the departure transaction processing module adjusts the departure interval of subsequent vehicles according to the corrected departure step length; the corrected departure step length is the product of the baseline departure interval and the correction increment, and the correction increment is the sum of the number 1 and the average temperature-queue interference factor.

[0015] Preferably, the collaborative scheduling decision module also includes an abnormal data self-healing module, which is used to start a location trajectory self-calculation program when the communication link between the transport vehicle and the collaborative scheduling decision module is interrupted for more than 5 minutes. The location trajectory self-calculation program uses the last reported physical location of the transport vehicle and the historical average vehicle speed of the road segment to calculate the simulated trajectory minute by minute, and introduces a location confidence parameter. The location confidence parameter decreases exponentially with the increase of the interruption time. When the location confidence parameter is lower than the specified confidence threshold of 0.4, the collaborative scheduling decision module automatically removes the transport vehicle from the earliest arrival candidate queue and regenerates the capacity offset instruction.

[0016] Preferably, the in-transit status mapping module is also networked with meteorological sensor terminals deployed along the route to obtain the ambient temperature and wind speed along the route in real time; the collaborative scheduling decision module establishes an in-transit temperature decay prediction model for each vehicle based on the temperature of the outgoing material, the ambient temperature along the route, the wind speed, and the predicted travel time in the first status parameter package, and performs a secondary calibration on the predicted paving temperature when the transport vehicle travels to a decision threshold window of 2km away from the paving site.

[0017] Preferably, after the on-the-way status mapping module completes the secondary calibration within the decision threshold window, the collaborative scheduling decision module controls the scheduling according to the following control steps: Step S11, determine whether the predicted paving temperature obtained after the secondary calibration is lower than 135℃; Step S12, when the predicted paving temperature is lower than 135℃, rewrite the current unloading queue index to raise the receiving priority of the transport vehicle to the head of the queue, and issue a priority entry and allocation instruction to guide it to overtake other on-the-way transport vehicles to unload first.

[0018] Preferably, the on-site material receiving status sensing module delineates a virtual electronic fence with a width of 100m in the entrance area of ​​the unloading area to monitor the real-time number of vehicles backed up within the virtual electronic fence; when the number of backed up vehicles exceeds 3, the collaborative scheduling decision module triggers a congestion warning for the unloading area and issues speed adjustment and capacity reduction instructions to on-the-road transport vehicles located 5km away from the paving site, guiding them to decelerate in a step-by-step manner in the safety parking lane.

[0019] Preferably, when the unloading area is under congestion warning, the collaborative scheduling decision module simultaneously distributes adjustment parameters to the outgoing transaction processing module to increase the departure step length, thereby limiting the continuous loading rate of the mixing plant and reducing the peak transport capacity from the source; until the number of backlogged vehicles in the virtual electronic fence is less than 3, the collaborative scheduling decision module lifts the congestion warning.

[0020] Preferably, the collaborative scheduling decision module adopts a distributed resource-constrained queuing network model composed of multi-node state machine coupled logic. It performs matrix-based correlation and solution of the data flow status of the outgoing transaction processing module, the in-transit status mapping module, and the on-site material receiving status perception module, and updates the virtual queue and corrects the departure step size.

[0021] A digital scheduling and management method for the entire process of asphalt pavement construction, implemented through a digital scheduling and management system for the entire asphalt pavement construction process, includes the following steps:

[0022] Step S101: When the transport vehicle finishes loading, the departure time, the temperature of the material leaving the vehicle, and the initial loading mass are collected by the departure transaction processing module to generate the first state parameter package.

[0023] Step S102: The geographical coordinates of the transport vehicle are collected by the positioning module connected to the on-the-way state mapping module, and the remaining travel time on the way is calculated based on the average travel speed of the road network, so as to generate a second state parameter package containing the expected arrival time.

[0024] Step S103: Collect the remaining material quantity and operating speed of the receiving hopper through the on-site material receiving status sensing module to determine the consumption rate of the mixture;

[0025] Step S104: The collaborative scheduling decision module connects to the outgoing transaction processing module, the in-transit status mapping module, and the on-site material receiving status sensing module via a network and receives data. The scheduling is controlled according to the following control sequence:

[0026] Step S1041: Based on the collected ambient temperature and wind speed, predict the paving temperature and establish a temperature-queue state vector consisting of the predicted paving temperature and the expected arrival time.

[0027] Step S1042: Calculate the emptying time based on the remaining material in the receiving bin, the running speed, and the current system time, and calculate the temperature-queue interference factor for each transport vehicle.

[0028] Step S1043: When the estimated arrival times of two or more transport vehicles in transit overlap and the temperature-queue interference factor of the following vehicle is lower than a specified threshold, the virtual queue is reorganized and the guiding speed is calculated. The guiding speed is then distributed to the following vehicle through the in-transit state mapping module.

[0029] Compared with existing technologies, the digital scheduling and management system and method for the entire process of asphalt pavement construction of this invention has the following advantages:

[0030] 1. In the digital scheduling and management of the entire asphalt pavement construction process, by comprehensively considering the expected arrival time of transport vehicles, the predicted paving temperature of the mixture, and the emptying time of the receiving bin, the virtual queue and guiding vehicle speed can be adjusted in a timely manner according to the on-site material supply demand and the temperature change of the mixture. This ensures that the rhythm of vehicle arrival is coordinated with the rhythm of material consumption at the paving site. As a result, disorderly accumulation of transport vehicles in the receiving area and the paving machine waiting for materials to stop work can be reduced. The risk of segregation of the mixture due to continuous cooling during the queuing process can be reduced, and the possibility of cold joints in the pavement due to interruption of material supply can be reduced. This is conducive to maintaining continuous paving of asphalt pavement and the stability of construction quality.

[0031] 2. By performing a secondary calibration of the predicted paving temperature for transport vehicles near the paving site and dynamically adjusting the unloading sequence based on the calibration results, the vehicle's receiving priority can be promptly increased when the temperature of the mixture is low due to traffic delays or environmental changes. Compared to unloading according to the order of arrival, this method allows the unloading arrangement to take into account both the arrival time of vehicles and the temperature of the mixture, reducing the situation where the temperature of the mixture continues to drop due to excessive waiting time and reducing the risk of mixture waste.

[0032] 3. By monitoring the number of vehicles accumulating at the entrance of the unloading area and adjusting the speed of remote transport vehicles and the departure pace of the mixing plant, when congestion occurs in the unloading area, the concentrated arrival of vehicles can be slowed down from both the en route transportation and vehicle departure stages. This reduces the large-scale occupation of road space in the unloading area, maintains on-site vehicle traffic and unloading order, and avoids scheduling delays caused by manual guidance after vehicles arrive on site, thereby improving the collaborative scheduling efficiency between transport vehicles, mixing plants, and paving sites. Attached Figure Description

[0033] Figure 1 This is the system control principle and data flow diagram of the present invention;

[0034] Figure 2 This is a fishbone diagram illustrating the system functional architecture and related element analysis of the present invention. Detailed Implementation

[0035] The technical solutions in the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0036] A digital scheduling and management system for the entire asphalt pavement construction process includes:

[0037] The outbound transaction processing module is used to collect the outbound departure time, outbound material temperature, and initial loading mass when the transport vehicle completes loading in order to generate the first state parameter package.

[0038] The on-the-way state mapping module, connected to the positioning module, is used to calculate the remaining travel time on the way based on the geographical coordinates of the transport vehicle, so as to generate a second state parameter package containing the expected arrival time.

[0039] The on-site material receiving status sensing module is used to collect the remaining material in the receiving hopper and the running speed to determine the consumption rate of the mixture;

[0040] The collaborative scheduling decision module, connected to all modules via a network, receives data and controls scheduling according to the following control steps:

[0041] Step S1: Predict the paving temperature based on ambient temperature and wind speed, and establish a temperature-queue state vector consisting of the predicted paving temperature and the expected arrival time.

[0042] Step S2: Calculate the emptying time based on the remaining material in the receiving bin, the running speed, and the current system time, and calculate the temperature-queue interference factor for each transport vehicle.

[0043] Step S3: When the estimated arrival times of two or more transport vehicles in transit overlap and the temperature-queue interference factor of the following vehicles is below a specified threshold, the virtual queue is reorganized and the guiding speed is calculated. The guiding speed is then distributed to the following vehicles through the in-transit state mapping module.

[0044] Preferably, the method for calculating the guiding vehicle speed in step S3 is as follows: the collaborative scheduling decision module calculates the quotient between the remaining road distance from the rear vehicle to the paving site and the time difference term. The time difference term is the difference obtained by subtracting the specified safety buffer time from the emptying time. The safety buffer time is fixed at 5 minutes. The collaborative scheduling decision module determines the quotient as the guiding vehicle speed and distributes it.

[0045] Preferably, the collaborative scheduling decision module calculates the corrected departure step length based on the average temperature-queue interference factor of the on-the-way transport vehicle group, and distributes the corrected departure step length to the departure transaction processing module; the departure transaction processing module adjusts the departure interval of subsequent vehicles according to the corrected departure step length; the corrected departure step length is the product of the baseline departure interval and the correction increment, and the correction increment is the sum of the number 1 and the average temperature-queue interference factor.

[0046] Preferably, the collaborative scheduling decision module also includes an abnormal data self-healing module, which is used to start a location trajectory self-calculation program when the communication link between the transport vehicle and the collaborative scheduling decision module is interrupted for more than 5 minutes. The location trajectory self-calculation program uses the last reported physical location of the transport vehicle and the historical average vehicle speed of the road segment to calculate the simulated trajectory minute by minute, and introduces a location confidence parameter. The location confidence parameter decreases exponentially with the increase of the interruption time. When the location confidence parameter is lower than the specified confidence threshold of 0.4, the collaborative scheduling decision module automatically removes the transport vehicle from the earliest arrival candidate queue and regenerates the capacity offset instruction.

[0047] Preferably, the in-transit status mapping module is also networked with meteorological sensor terminals deployed along the route to obtain the ambient temperature and wind speed along the route in real time; the collaborative scheduling decision module establishes an in-transit temperature decay prediction model for each vehicle based on the temperature of the outgoing material, the ambient temperature along the route, the wind speed, and the predicted travel time in the first status parameter package, and performs a secondary calibration on the predicted paving temperature when the transport vehicle travels to a decision threshold window of 2km away from the paving site.

[0048] Preferably, after the on-the-way status mapping module completes the secondary calibration within the decision threshold window, the collaborative scheduling decision module controls the scheduling according to the following control steps: Step S11, determine whether the predicted paving temperature obtained after the secondary calibration is lower than 135℃; Step S12, when the predicted paving temperature is lower than 135℃, rewrite the current unloading queue index to raise the receiving priority of the transport vehicle to the head of the queue, and issue a priority entry and allocation instruction to guide it to overtake other on-the-way transport vehicles to unload first.

[0049] Preferably, the on-site material receiving status sensing module delineates a virtual electronic fence with a width of 100m in the entrance area of ​​the unloading area to monitor the real-time number of vehicles backed up within the virtual electronic fence; when the number of backed up vehicles exceeds 3, the collaborative scheduling decision module triggers a congestion warning for the unloading area and issues speed adjustment and capacity reduction instructions to on-the-road transport vehicles located 5km away from the paving site, guiding them to decelerate in a step-by-step manner in the safety parking lane.

[0050] Preferably, when the unloading area is under congestion warning, the collaborative scheduling decision module simultaneously distributes adjustment parameters to the outgoing transaction processing module to increase the departure step length, thereby limiting the continuous loading rate of the mixing plant and reducing the peak transport capacity from the source; until the number of backlogged vehicles in the virtual electronic fence is less than 3, the collaborative scheduling decision module lifts the congestion warning.

[0051] Preferably, the collaborative scheduling decision module adopts a distributed resource-constrained queuing network model composed of multi-node state machine coupled logic. It performs matrix-based correlation and solution of the data flow status of the outgoing transaction processing module, the in-transit status mapping module, and the on-site material receiving status perception module, and updates the virtual queue and corrects the departure step size.

[0052] A digital scheduling and management method for the entire process of asphalt pavement construction, implemented through a digital scheduling and management system for the entire asphalt pavement construction process, includes the following steps:

[0053] Step S101: When the transport vehicle finishes loading, the departure time, the temperature of the material leaving the vehicle, and the initial loading mass are collected by the departure transaction processing module to generate the first state parameter package.

[0054] Step S102: The geographical coordinates of the transport vehicle are collected by the positioning module connected to the on-the-way state mapping module, and the remaining travel time on the way is calculated based on the average travel speed of the road network, so as to generate a second state parameter package containing the expected arrival time.

[0055] Step S103: Collect the remaining material quantity and operating speed of the receiving hopper through the on-site material receiving status sensing module to determine the consumption rate of the mixture;

[0056] Step S104: The collaborative scheduling decision module connects to the outgoing transaction processing module, the in-transit status mapping module, and the on-site material receiving status sensing module via a network and receives data. The scheduling is controlled according to the following control sequence:

[0057] Step S1041: Based on the collected ambient temperature and wind speed, predict the paving temperature and establish a temperature-queue state vector consisting of the predicted paving temperature and the expected arrival time.

[0058] Step S1042: Calculate the emptying time based on the remaining material in the receiving bin, the running speed, and the current system time, and calculate the temperature-queue interference factor for each transport vehicle.

[0059] Step S1043: When the estimated arrival times of two or more transport vehicles in transit overlap and the temperature-queue interference factor of the following vehicle is lower than a specified threshold, the virtual queue is reorganized and the guiding speed is calculated. The guiding speed is then distributed to the following vehicle through the in-transit state mapping module.

[0060] Example 1: The current digital dispatch and management system is deployed at an asphalt pavement construction site. The construction site includes a mixing plant, transport vehicles, pavers, and rollers. The mixing plant produces asphalt mixture, and the transport vehicles deliver the asphalt mixture to the paving site. The pavers complete the paving at a set speed, and the rollers compact the paved asphalt pavement within a set temperature window. When the transport route is congested, the time interval between vehicles arriving at the paving site fluctuates, which may cause the paver to stop due to lack of material, or it may cause multiple transport vehicles to arrive at the same time and queue for a long time, resulting in the temperature of the asphalt mixture falling below the set compaction temperature limit, affecting the compaction quality of the pavement and causing the equipment to be idle.

[0061] The digital dispatch and management system includes an outbound transaction processing module, an in-transit status mapping module, an on-site material receiving status sensing module, and a collaborative dispatch decision module. After the transport vehicle completes loading, the outbound transaction processing module collects the vehicle's outbound departure time, outbound material temperature, and initial loading quality. According to the vehicle identification, the above data is combined into a first status parameter package and sent to the collaborative dispatch decision module.

[0062] The in-transit state mapping module connects to the positioning sensor and infrared temperature sensor installed on the transport vehicle to collect the geographical coordinates of the transport vehicle and the real-time temperature of the asphalt mixture. Based on the geographical coordinates, the module calls the map path planning interface to obtain the remaining road distance from the vehicle's current location to the paving site and the average driving speed of each road segment. Then, based on the remaining distance of each road segment and the average driving speed, it calculates the remaining travel time in transit. The remaining travel time in transit is added to the current system time to obtain the estimated arrival time, and a second state parameter package containing the estimated arrival time is generated.

[0063] Meteorological sensor terminals are deployed along the transportation route to collect ambient temperature and wind speed. The in-transit status mapping module transmits the ambient temperature, wind speed and predicted travel time along the route to the collaborative scheduling decision module. The collaborative scheduling decision module establishes an in-transit temperature decay prediction model based on the material temperature at the exit in the first status parameter package and the above data, and calculates the predicted paving temperature when the vehicle arrives at the paving site.

[0064] In this embodiment, the collaborative scheduling decision module uses the following temperature prediction formula to calculate the predicted paving temperature: ,in, To predict paving temperature, The temperature of the material leaving the mixing plant after the transport vehicle has finished loading and leaves. This represents the average cooling rate of the asphalt mixture during transportation. To predict travel time, subscript Indicates the state of the vehicle upon arrival at the paving site, subscript This indicates the state of the vehicle when it has finished loading and left the mixing plant. (Subscript) Indicates the cooling status during transportation, subscript Indicates the transportation status.

[0065] Average cooling rate Through transportation calibration, the following parameters were recorded during calibration: the temperature of the material leaving the paving site, the actual temperature upon arrival, the transportation time, the ambient temperature along the route, and the wind speed. The average cooling rate was determined based on calibration results under the same ambient temperature and wind speed conditions. In one calibration test, the ambient temperature was 25℃, the average vehicle speed was 11 m / s, and the temperature of the material leaving the paving site was... The initial temperature was 160℃. After 1800 seconds of transportation, the actual temperature of the vehicle upon arrival at the paving site was measured to be 145℃. The average cooling rate under this condition was then calculated. The predicted travel time is 0.0083℃ / s. The on-the-go status mapping module calculates the real-time vehicle location coordinates, the road path returned by the map path planning interface, and the average driving speed of each road segment.

[0066] The on-site material receiving status sensing module collects the remaining material in the receiving bin and the paver's operating speed. This module determines the material consumption per unit time based on the change in the remaining material in the receiving bin between adjacent sampling times, and updates the mixture consumption rate in combination with the change in the paver's operating speed. The collaborative scheduling decision module divides the remaining material in the receiving bin by the mixture consumption rate to obtain the time required for the receiving bin to empty from its current state, and then adds it to the current system time to obtain the emptying time.

[0067] The collaborative scheduling decision module writes the predicted paving temperature and expected arrival time of each transport vehicle into the temperature-queue state vector according to the vehicle identifier, and arranges each vehicle according to the expected arrival time. For each transport vehicle in transit, the collaborative scheduling decision module calculates the temperature-queue interference factor based on the vehicle's expected arrival time, the emptying time of the receiving bin, and the predicted cooling of the asphalt mixture.

[0068] When calculating the temperature-queue interference factor, the estimated arrival time of the following transport vehicles is first obtained, and the absolute difference between the estimated arrival time and the emptying time of the receiving silo is calculated. This difference is then used as the queuing waiting time. Next, the queuing waiting time is multiplied by the preset average cooling rate of the mixture during transit to obtain the predicted queuing cooling increment. Subsequently, the predicted attenuation temperature during transportation and the predicted queuing cooling increment are subtracted sequentially from the material temperature of the following vehicles at the exit to obtain the predicted final paving temperature of the vehicle at the actual unloading time. Finally, the absolute difference between the predicted final paving temperature and the standard paving temperature of 140℃ is calculated, and this absolute difference is divided by 140℃. The resulting dimensionless ratio is the temperature-queue interference factor. The lower the value of this factor, the closer the predicted final paving temperature at the actual unloading time of the vehicle is to the standard paving temperature. The specified threshold is calibrated according to the allowable paving temperature deviation adopted in the construction.

[0069] When two or more transport vehicles are expected to arrive before the emptying time of the same receiving bin, the collaborative scheduling decision module determines that their expected arrival times overlap. If the temperature-queue interference factor of the following vehicle is lower than the specified threshold, the current unloading queue index is rewritten, the virtual queue is reorganized, and the guiding speed of the following vehicle is calculated.

[0070] When calculating the guiding speed, the collaborative scheduling decision module first converts the emptying time into the remaining time relative to the current system time, and then subtracts a 5-minute safety buffer time from the remaining time to obtain the time difference term. Subsequently, it calculates the quotient between the remaining road distance from the following vehicle to the paving site and the time difference term, and determines the quotient as the guiding speed. The guiding speed is sent to the vehicle navigation terminal of the following vehicle through the on-the-go status mapping module so that the driver can adjust the driving speed.

[0071] When the difference in estimated arrival times between two adjacent transport vehicles exceeds the consumption time of the asphalt mixture transported by the previous vehicle, the collaborative scheduling decision module adjusts the paver's operating speed to 80% of the original set speed via the control interface and adjusts the benchmark departure interval to 85% of the original departure interval. At the same time, the collaborative scheduling decision module calculates the average temperature-queue interference factor of the current transport vehicle group, uses the sum of the number 1 and the average value as the correction increment, and then determines the correction departure step size by multiplying the benchmark departure interval by the correction increment. The correction departure step size is sent to the exit transaction processing module, which adjusts the actual exit interval of subsequent vehicles accordingly.

[0072] After the transport vehicle enters the decision threshold window 2km from the paving site, the collaborative scheduling decision module controls the on-the-way status mapping module to re-collect the vehicle's location, ambient temperature along the route, and wind speed. It then uses the updated predicted travel time to perform a secondary calibration of the predicted paving temperature. If the predicted paving temperature after the secondary calibration is lower than 135℃, the collaborative scheduling decision module rewrites the current unloading queue index, elevates the receiving priority of the transport vehicle to the head of the virtual queue, and issues a priority entry and allocation instruction to guide it to enter the site and unload materials before other on-the-way transport vehicles.

[0073] When the difference in the estimated arrival time of two adjacent transport vehicles is less than 500 seconds, the collaborative scheduling decision module includes them in the time-overlapping candidate vehicle group and continues to make scheduling judgments based on the emptying time and temperature-queue interference factor. When the predicted paving temperature of the preceding vehicle after secondary calibration is lower than 135℃, the vehicle will enter the paving position to unload materials first. For the following vehicles, the collaborative scheduling decision module distributes guiding speeds. When the current road cannot meet the guiding speed, the on-road status mapping module sends adjustment instructions to the vehicle navigation terminal of the following vehicle to guide it to switch to the preset backup route, so that the road surface compaction operation temperature is maintained between 120℃ and 150℃.

[0074] When the number of vehicles queuing on site exceeds 5, the collaborative scheduling decision module continues to update the virtual queue vehicle by vehicle according to the aforementioned time sequence overlap conditions and temperature-queue interference factor. It also adjusts the unloading priority based on the real-time temperature of the asphalt mixture in each transport vehicle. Transport vehicles with asphalt mixture temperatures between 120°C and 130°C are given priority to enter the paving position for unloading, while transport vehicles with asphalt mixture temperatures above 140°C wait in the queuing area. This is to take advantage of the temperature drop during the waiting period to reduce the risk of low-temperature mixtures continuing to queue and causing scrapping.

[0075] The collaborative scheduling decision module also includes an abnormal data self-healing module. When the communication link between the transport vehicle and the collaborative scheduling decision module is interrupted for more than 5 minutes, the abnormal data self-healing module starts the on-the-way trajectory self-calculation program. This program reads the last reported physical location of the transport vehicle and the historical average vehicle speed of the road segment, and calculates the vehicle's movement distance and simulated position minute by minute according to the vehicle's predetermined driving route, forming a simulated trajectory from the continuous simulated positions.

[0076] The location trajectory self-calculation program starts with the confidence level corresponding to the last valid positioning result before the communication interruption. The location confidence level parameter decreases according to a pre-calibrated exponential law as the communication interruption duration increases. The exponential decay relationship is calibrated based on the simulated location deviation during the historical communication interruption period of this road segment. When the location confidence level parameter is lower than the specified confidence level threshold of 0.4, the collaborative scheduling decision module removes the transport vehicle from the earliest arrival candidate queue. The earliest arrival candidate queue is arranged according to the expected arrival time of the currently en route transport vehicles. After the vehicle is removed, the collaborative scheduling decision module re-determines the expected earliest arrival vehicle from the remaining vehicles with a location confidence level parameter of not less than 0.4, generates a capacity offset instruction, recalculates the virtual queue, and corrects the departure step size to compensate for the material supply timing gap caused by the removed vehicle.

[0077] During the operation of the digital dispatch and management system, positioning sensors, infrared temperature sensors, meteorological sensors, and on-site material receiving status perception modules continuously transmit operational data to the collaborative dispatch and decision-making module. The system adjusts the logistics and distribution rhythm based on vehicle location, mixture temperature, remaining material in the receiving bin, and paver operating speed. The paver's single continuous operation time is extended from 1800s to 7200s. The arrival temperature of the asphalt mixture is maintained above 120℃, and the compaction degree of the compacted road surface is above 98%. No local cracking or uneven compaction of the road surface caused by excessively low asphalt mixture temperature occurs.

[0078] Example 2: In a highway paving project, a digital scheduling and management system for the entire process of asphalt pavement construction was deployed at the construction site. The system was tested for vehicle scheduling status and pavement construction results during continuous operation. The system includes a departure transaction processing module, an in-transit status mapping module, a site material receiving status perception module, and a collaborative scheduling decision module.

[0079] The outgoing transaction processing module is connected to the temperature measuring device at the outlet of the asphalt mixture mixing plant to collect the outlet temperature of the asphalt mixture. and discharge time When the transport vehicle finishes loading and departs, this module records the departure time and records the temperature collected by the outlet temperature measuring device at the end of the loading process for that vehicle. The temperature of the outgoing material is used as the departure temperature, and the departure time, the temperature of the outgoing material, and the initial loading mass are written into the first state parameter package.

[0080] The on-the-go status mapping module uses a GPS positioning terminal installed on the transport vehicle to collect the vehicle's current location coordinates. and driving speed The remaining travel time is calculated based on the remaining road distance from the vehicle's current location to the paving site and the average travel speed of the road network, and a second state parameter package containing the expected arrival time is generated.

[0081] The on-site material receiving status sensing module is connected to an infrared temperature imager and a speed sensor installed on the paver to collect data on the remaining material in the receiving bin and the paver's operating speed. Number of vehicles waiting for materials before paving and the real-time paving temperature of the paved sections This module determines the mixture consumption rate based on the change in the remaining material in the receiving bin and the paver's operating speed at adjacent sampling times. The collaborative scheduling decision module calculates the emptying time based on the remaining material in the receiving bin, the mixture consumption rate, and the current system time.

[0082] The collaborative scheduling decision-making module wirelessly connects to the outgoing transaction processing module, the in-transit status mapping module, and the on-site material receiving status sensing module to receive the outgoing temperature. Discharge time Location coordinates Driving speed Paver operating speed Number of vehicles awaiting materials In addition to the remaining material in the receiving bin, the system's preset target range for the number of vehicles waiting for material is... The value of this interval is [3, 5 vehicles].

[0083] Meteorological sensor terminals deployed along the transportation route collect ambient temperature and wind speed data. The collaborative scheduling decision module establishes an in-transit temperature decay prediction model based on the material temperature at the point of departure, ambient temperature along the route, wind speed, and predicted travel time, and calculates the temperature decay value of the asphalt mixture during the transportation vehicle's journey. : ,in, This represents the temperature decay value of the asphalt mixture. The temperature attenuation coefficient was selected based on the current ambient temperature and wind speed; the value used in this test was 0.00015 / s. This refers to the discharge temperature of the asphalt mixture. The ambient temperature was collected by the on-site weather station. The estimated transport time for the transport vehicle to travel from its current location to the paver's location.

[0084] Expected shipping time Calculate according to the following formula: ,in, The remaining transport distance from the transport vehicle to the paver's current position is calculated based on the transport vehicle's current position coordinates. The current position coordinates of the paver are determined; This represents the current speed of the transport vehicle.

[0085] The collaborative scheduling decision module writes the predicted paving temperature and expected arrival time of each transport vehicle into the temperature-queue state vector, and arranges the transport vehicles on the way according to the expected arrival time. For vehicles with overlapping expected arrival times, the system calculates the temperature-queue interference factor in combination with the emptying time. When the temperature-queue interference factor of the following vehicle is lower than the specified threshold, the collaborative scheduling decision module reorganizes the virtual queue and calculates the guiding speed of the vehicle.

[0086] When the number of vehicles waiting for materials When there are fewer than 3 vehicles, the collaborative scheduling decision module first calculates the material supply matching speed. : ,in, For the paver's operating speed, The paved road length of the asphalt mixture carried by a single vehicle is set at 15m. The material supply matching speed is used to determine whether the remote transport vehicle can replenish the material before the receiving bin is emptied. For the rear vehicles included in the virtual queue reorganization, the collaborative scheduling decision module converts the emptying time into the remaining time relative to the current system time and subtracts a 5-minute safety buffer time to obtain the time difference term. Then, it calculates the quotient between the remaining road distance from the vehicle to the paving site and the time difference term, determines the obtained quotient as the guiding speed, and sends it to the display terminal of the corresponding vehicle through the on-the-way status mapping module to prompt the driver to adjust the driving speed.

[0087] When the number of vehicles waiting for materials When there are more than 5 vehicles, the collaborative scheduling decision module sends a speed reduction command to the transport vehicles on the way to slow down the concentrated arrival of vehicles. When the number of vehicles backed up at the entrance of the unloading area exceeds 3, the system enters the unloading area congestion warning state and issues speed adjustment and capacity reduction commands to transport vehicles more than 5km away from the paving site to guide vehicles in the safe parking zone to slow down in stages.

[0088] The speed adjustment and capacity reduction command is used to adjust the number of vehicles arriving per unit time in the entire on-road transport vehicle group without changing the loading capacity of a single transport vehicle. When executing this command, the collaborative scheduling decision module selects vehicles with a high degree of overlap in expected arrival times and a long distance from the paving site from the on-road transport vehicle queue. In addition to guiding them to temporarily park in the safety parking lane, it can also temporarily adjust their delivery destination to the backup storage point, or send them a route replanning command to increase the arrival time interval between vehicles.

[0089] To test the system's operational effectiveness, both the test and control groups used 20 transport vehicles. The test group used a digital dispatch management system for scheduling, while the control group used manual dispatch based on experience. Both groups operated on the same construction road sections and under the same environmental conditions, including the ambient temperature. The discharge temperature of asphalt mixture is 30℃. The temperature is 165℃, and the total transportation distance is... The paver's operating speed is 25km / h. The speed is set to 0.05 m / s, and the continuous construction time is 10 hours.

[0090] During construction, infrared temperature imagers collect the lateral temperature distribution of the mixture when each transport vehicle unloads, and the ratio of the lateral temperature standard deviation to the average value is used as the temperature segregation rate of the asphalt mixture arriving at the site. After the paving is completed and the road surface cools, a continuous smoothness meter is used to detect the road surface smoothness IRI.

[0091] Under the same number of transport vehicles and construction conditions, the average number of vehicles waiting for materials in the test group was 3.8, which is within the target range of [3, 5] vehicles; the average number of vehicles waiting for materials in the control group was 6.2, which exceeds the upper limit of the range. After the vehicle arrival interval corresponds to the material consumption rate of the receiving warehouse, the average temperature decay during transportation in the test group was 12.4℃, while that in the control group was 19.8℃, a difference of 7.4℃.

[0092] Based on the lateral temperature distribution at the time of unloading of each transport vehicle, the temperature segregation rate of the asphalt mixture arriving at the test site was 4.2%, while that of the control group was 11.5%. During 10 hours of continuous construction, the paving continuity rate of the test group was 98.2%, while that of the control group was 85.3%, with the test group showing an improvement of 12.9%. After the road surface cooled, the road surface smoothness IRI of the test group was 0.65 m / km, while that of the control group was 1.12 m / km.

[0093] Example 3: This example provides a digital scheduling and management method for the entire process of asphalt pavement construction. During the construction process, the departure affairs processing module, the in-transit status mapping module, the on-site material receiving status perception module, and the collaborative scheduling decision module collect the operation data of the mixing plant, transport vehicles, and paving site, respectively, and adjust the vehicle arrival order according to the vehicle transportation sequence and the temperature change of the asphalt mixture.

[0094] During the asphalt mixture production stage, the outgoing transaction processing module collects real-time raw material weighing data for each batch of asphalt mixture via a data acquisition cable connected to the weighing sensors at the mixing plant. This raw material weighing data includes the weight of aggregates, powder, and asphalt. The outgoing transaction processing module transmits the collected data to the collaborative scheduling decision module, which calculates the actual asphalt-aggregate ratio of the current production batch of asphalt mixture using the following formula: ,in, This represents the actual asphalt-aggregate ratio of the asphalt mixture produced in the current batch. This represents the total weight of asphalt for the current train trip. This represents the total weight of aggregates of all grades for the current train. This represents the total weight of the mineral powder in the current train.

[0095] The collaborative scheduling decision module reads the target oil-stone ratio from the memory. and the actual oil-stone ratio Compared with target oilstone ratio A comparison is performed, and when the actual asphalt-aggregate ratio meets the following constraints, the asphalt mixture production quality of the current production batch is determined to be abnormal, and a gradation warning signal is sent to the mixing plant control terminal: ,in, The allowable deviation threshold for the oil-stone ratio is set to 0.2% in this embodiment.

[0096] When a transport vehicle completes loading, the departure processing module collects the vehicle's departure time, material temperature, and initial loading mass, and generates a first status parameter package according to the vehicle identification. The initial loading mass is obtained from the mass data of the weighing sensor when the loading of this vehicle is completed, and the material temperature is obtained from the temperature measurement device at the discharge port of the mixing plant during the loading process of this vehicle.

[0097] During the asphalt mixture transportation phase, the in-transit status mapping module uses an onboard positioning terminal and an onboard infrared thermometer to collect the geographical coordinates of the transport vehicle and the surface temperature of the mixture in the hopper at 10-second intervals. The collected results are then transmitted wirelessly to the collaborative scheduling decision module. Based on the geographical coordinates of the transport vehicle and the coordinate range of the construction section, the in-transit status mapping module calculates the remaining transportation distance from the vehicle to the current paving location. Then, based on the vehicle's average speed over the past 5 minutes on the road network... Calculate the remaining travel time, add it to the current system time to obtain the estimated arrival time, and generate a second state parameter packet containing the estimated arrival time.

[0098] The collaborative scheduling decision module receives the material temperature at the point of departure from the first state parameter packet and acquires the ambient temperature and wind speed collected along the transportation route. Using the predicted travel time as a time parameter, it establishes an in-transit temperature decay prediction model. During vehicle travel, the surface temperature of the mixture collected by the onboard infrared thermometer is used to update the current temperature state in the model. The predicted paving temperature for the remaining road section is calculated according to the following formula: ,in, The predicted paving temperature when the transport vehicles arrive at the paving site. This is the current surface temperature of the mixture. The temperature decay coefficient is determined based on historical transportation temperature drop data. For the remaining transportation distance, In this embodiment, the average speed of the transport vehicle over the past 5 minutes is used. The setting is 0.05℃ / km.

[0099] The collaborative scheduling decision module will predict the paving temperature. The temperature-queue state vector is written with the expected arrival time, and the emptying time is calculated by combining the remaining material in the receiving bin, the paver running speed and the mixture consumption rate provided by the on-site material receiving status sensing module. For transport vehicles with overlapping expected arrival times, the collaborative scheduling decision module calculates the temperature-queue interference factor for each vehicle. When the temperature-queue interference factor of the following vehicle is lower than the specified threshold, the unloading queue index is rewritten, the virtual queue is reorganized, and a guide speed is distributed to the vehicle.

[0100] After the transport vehicle enters the decision threshold window 2km from the paving site, the in-transit status mapping module re-collects the vehicle's geographical coordinates, mixture surface temperature, ambient temperature along the route, and wind speed. The collaborative scheduling decision module performs a secondary calibration of the predicted paving temperature based on the updated predicted travel time. The predicted paving temperature after the secondary calibration is compared with the lower limit of the paving opening temperature. Compare them. Set to 135℃, when the conditions are met At that time, the collaborative scheduling decision module will raise the material receiving priority of the transport vehicle to the head of the virtual queue and send a priority entry and allocation instruction to the vehicle. At the same time, it will send a material temperature warning instruction to the digital receiving terminal at the paving site to guide the vehicle to enter the site first to unload the material.

[0101] During the pavement paving phase, the on-site material receiving status sensing module uses non-contact ultrasonic ranging sensors fixed to both sides of the paver's screed and angle sensors mounted on the screed to collect real-time data on the relative height of the screed relative to the top surface of the base course and the screed's elevation angle. This data is then transmitted to the collaborative scheduling decision-making module. Based on the relative height and elevation angle values, and combined with a preset asphalt mixture loose paving coefficient, the collaborative scheduling decision-making module calculates the predicted loose paving thickness at the current construction location. .

[0102] After the paver passes the current position, the pre-embedded electromagnetic induction thickness gauge embedded in the paving base surface measures the initial value of the actual paving layer thickness. The measurement results are then transmitted to the collaborative scheduling decision module, which reads the standard value of the design layer thickness from the memory. And determine whether the current paving layer thickness exceeds the tolerance according to the following conditions: ,in, The allowable deviation threshold for layer thickness is set to 5mm in this embodiment. When the above conditions are met, the collaborative scheduling decision module sends a leveling cylinder stroke correction command to the paver hydraulic leveling system according to the direction of thickness deviation, controls the extension and retraction of the leveling cylinder and adjusts the screed elevation angle until the actual paving layer thickness meets the design requirements.

[0103] The on-site material receiving status sensing module also collects the remaining material in the receiving bin and the paver's operating speed, and determines the mixture consumption rate and the initial value of the actual paving layer thickness based on this. and standard value of design layer thickness To correct the mixture consumption rate, when the actual paving layer thickness is consistently greater than the standard value of the design layer thickness and the thickness deviation exceeds 5mm, the collaborative scheduling decision module synchronously increases the mixture consumption rate according to the increase ratio of the actual paving layer thickness to the design layer thickness, and recalculates the emptying time based on the corrected mixture consumption rate; when the actual paving layer thickness is less than the standard value of the design layer thickness, the mixture consumption rate is reduced according to the corresponding ratio. Thus, the emptying time is updated according to the actual material usage at the paving site.

[0104] During the road compaction phase, the compaction monitoring module uses a high-precision differential GPS receiver, infrared temperature sensor, and acceleration sensor installed on the roller to collect real-time data on the roller's compaction trajectory coordinates, road surface compaction temperature, and roller vibration acceleration. The collected results are transmitted to the collaborative scheduling and decision-making module, which matches the compaction trajectory coordinates with a pre-set construction area grid map and calculates the cumulative number of compaction passes for each grid cell. Then calculate the compaction evaluation value of each grid cell according to the following formula: ,in, The compaction assessment value is expressed as a percentage. The average compaction temperature of the grid cells is collected by an infrared temperature sensor. The effective value of vibration acceleration collected by the accelerometer; , and In this embodiment, the weighted results of the cumulative number of compaction passes, average compaction temperature, and effective value of vibration acceleration are uniformly converted into percentage values ​​to include the corresponding unit conversion relationships in the evaluation weighting coefficients. , , .

[0105] The collaborative scheduling decision module will use the compaction assessment value With acceptable compaction threshold Perform a comparison, when the conditions are met If the compaction degree of the corresponding grid unit is determined to be below the set value, the grid unit is marked as a red under-compaction area on the digital construction map. The digital construction map is then sent to the display terminal in the roller's cab, prompting the driver to re-compact the red under-compaction area until the compaction degree assessment value is greater than or equal to the qualified compaction degree threshold. In this embodiment, The standard compaction test data is set at 98%.

[0106] Example 4: This example combines Figures 1 to 2 This paper describes a digital scheduling and management system and method for the entire process of asphalt pavement construction, such as... Figure 1 As shown, the departure transaction processing module collects the departure time, material temperature, and initial loading mass to generate a first state parameter package. This first state parameter package carries the vehicle's initial load and heat data and is input to the collaborative scheduling decision module. The collaborative scheduling decision module provides feedback to correct the departure step size and adjusts the interval based on interference factors, sending this information to the departure transaction processing module. The meteorological sensor terminal acquires real-time ambient temperature and wind speed along the route and inputs this data to the in-transit state mapping module. The positioning module collects geographic coordinates, calculates the remaining time, and inputs it to the in-transit state mapping module. Based on the geographic coordinates and meteorological data, the in-transit state mapping module calculates the remaining travel time and generates a second state parameter package. This second state parameter package carries time-series data including the estimated arrival time and is input to the collaborative scheduling decision module. The collaborative scheduling decision module then distributes guidance to the in-transit state mapping module. Vehicle speed, speed reduction and priority allocation instructions; virtual electronic fence monitors the real-time number of vehicles backlogged in the unloading area and inputs it to the on-site material receiving status perception module. The on-site material receiving status perception module collects the remaining material in the receiving bin and the paver's operating speed to determine on-site consumption data. The on-site consumption data determines the mixture consumption rate and the number of backlogged vehicles and inputs it to the collaborative scheduling decision module. The collaborative scheduling decision module executes: 1. Constructing a temperature-queue state vector, predicting paving temperature and performing secondary calibration; 2. Calculating the emptying time and temperature-queue interference factor, and reorganizing the virtual queue; 3. Matrix-based correlation solution of the distributed resource constraint queuing network model of material leaving, en route and on-site material receiving status; communication interruption triggers the abnormal data self-healing module, which starts the location en route trajectory self-calculation program and automatically updates the queue based on confidence to feed back to the collaborative scheduling decision module.

[0107] like Figure 2As shown, the departure transaction processing module corresponds to the upper-level input branch of the digital scheduling and management system for the entire asphalt pavement construction process by collecting departure time and material temperature, initial loading mass, and generating a first state parameter package. The collaborative scheduling decision module corresponds to the core decision branch of the digital scheduling and management system for the entire asphalt pavement construction process by constructing a temperature-queue state vector, calculating emptying time and temperature-queue interference factor, reorganizing virtual queues, and calculating guiding vehicle speed. The in-transit status mapping module corresponds to the middle-level input branch of the digital scheduling and management system for the entire asphalt pavement construction process by generating a second state parameter package containing the expected arrival time, calculating the remaining travel time based on geographical coordinates, and obtaining real-time ambient temperature and wind speed along the route. The on-site material receiving status perception module corresponds to the lower-level input branch of the digital scheduling and management system for the entire asphalt pavement construction process by determining the mixture consumption rate, collecting the remaining material quantity and operating speed in the receiving bin, and defining virtual electronic fences to monitor the number of backlogged vehicles.

[0108] Example 5: Before the system is put into production operation, the weighing sensors of the mixing plant, the positioning terminal of the road roller, and the in-transit temperature decay prediction model are calibrated on-site to determine the system initialization parameters.

[0109] When calibrating the weighing sensors at the mixing plant, the calibration personnel collect historical weighing data of 10 consecutive batches of asphalt mixture, calculate the average absolute deviation of the weighing results of aggregate, powder and asphalt respectively, and form the initial drift vector of the sensor from the average absolute deviation. When the outgoing transaction processing module collects raw material weighing data in the future, it uses the initial drift vector of the sensor as the basis for correcting the weighing data.

[0110] When calibrating the high-precision differential GPS receiver on the road roller, the receiver is placed on a standard reference point with known coordinates, and positioning data is continuously collected for 30 minutes. The collaborative scheduling decision module calculates the Euclidean distance between each collected coordinate and the standard coordinate, thereby obtaining the static positioning deviation value. Based on the sensor's initial drift vector and the static positioning deviation value, the collaborative scheduling decision module performs matrix addition correction on the subsequently collected raw material weighing data and compaction trajectory coordinates to reduce the measurement deviation caused by different hardware initialization states.

[0111] After the digital receiving terminal is deployed at the construction site, the temperature attenuation coefficient is fine-tuned on-site according to the current construction environment temperature. Before the formal paving, the on-the-way status mapping module records the actual transportation time, transportation distance, and surface temperature value of the first three batches of asphalt mixture from the mixing plant to the paving site. The collaborative scheduling decision module determines the actual temperature drop based on the difference between the temperature of the material leaving the plant and the surface temperature upon arrival. Then, the ratio of the actual temperature drop to the corresponding transportation distance is calculated to obtain the actual temperature attenuation rate under the current environment.

[0112] Calculate the temperature decay coefficient after on-site calibration using the following formula: ,in, The initial temperature decay coefficient is set based on historical transportation temperature drop data. The actual temperature decay rate was obtained based on the actual transportation data of the first three trips. and The first correction weight and the second correction weight are respectively set to 0.35 and 0.65 in this embodiment.

[0113] Calculated temperature decay coefficient The data is written into the memory of the collaborative scheduling decision module as input parameters for the subsequent calculation of the predicted paving temperature of transport vehicles. The on-the-way status mapping module continues to collect vehicle location, transport distance and mixture temperature. The collaborative scheduling decision module calls the calibrated temperature decay coefficient to update the predicted paving temperature and generates a material temperature warning command accordingly.

[0114] Example 6: The current collaborative scheduling decision module adopts a distributed resource constraint queuing network model composed of multi-node state machine coupled logic. It performs correlation processing on the data flow status of the outgoing transaction processing module, the in-transit status mapping module, and the on-site material receiving status perception module. Each module maintains its current status and sends the changed status data to the collaborative scheduling decision module after collecting data, generating status parameter packages, or executing scheduling instructions.

[0115] The departure transaction processing module corresponds to the vehicle loading and departure nodes. After the transport vehicle is loaded, this node determines whether the vehicle is in a waiting-to-departure state or a departed state based on whether the first state parameter package has been generated, whether the vehicle meets the departure conditions, and the current corrected departure step size. The in-transit status mapping module corresponds to the vehicle transport node. Based on the second state parameter package, the estimated arrival time, the predicted paving temperature, and the communication link status, it determines whether the vehicle is in a normal in-transit state, a candidate arrival state, or an abnormal pending confirmation state. The on-site material receiving status perception module corresponds to the on-site unloading node. Based on the remaining material in the receiving bin, the running speed, the mixed material consumption rate, the emptying time, and the number of vehicles backed up in the virtual electronic fence, it determines whether the site is in a material receiving state, an unloading occupied state, or a congestion warning state.

[0116] Each node establishes a state transition relationship according to the sequence of vehicle loading, departure, in-transit transportation, candidate arrival, queuing for unloading, and unloading completion. A vehicle only enters the next state when the data of the current node has been generated and the resource constraints corresponding to the next node are met. For example, after the departure transaction processing module generates the first state parameter package and reaches the current departure interval, the vehicle transitions from the waiting-to-departure state to the departed state; after the in-transit state mapping module generates the second state parameter package, the vehicle enters the normal in-transit state; after the estimated arrival time enters the field scheduling range, the vehicle enters the candidate arrival state; after the receiving bin has the conditions for receiving materials and the vehicle obtains the current unloading queue index, the vehicle enters the queuing for unloading state.

[0117] The collaborative scheduling decision module writes the node status, estimated arrival time, predicted paving temperature, temperature-queue interference factor, emptying time, and current unloading queue index of each transport vehicle into the association matrix. Each row in the association matrix corresponds to a transport vehicle, and each column corresponds to a data flow status or resource constraint. When the module status changes, only the matrix row and matrix column corresponding to that status are updated to keep the latest transport status, temperature status, and on-site material receiving status of the vehicle consistent.

[0118] When performing matrix-based correlation solving, the collaborative scheduling decision module first arranges each transport vehicle according to its expected arrival time, and then judges whether there is a time overlap between the expected arrival times of adjacent vehicles based on the emptying time. For vehicles with time overlap, the module continues to compare the temperature-queue interference factor of the following vehicles with a specified threshold. If the temperature-queue interference factor of the following vehicle is lower than the specified threshold, the module modifies the unloading queue index in the corresponding matrix row of the vehicle, adjusts it to a position that meets the on-site material receiving conditions, and reorganizes the virtual queue accordingly.

[0119] When the receiving hopper is in an unloading occupied state, vehicles that arrive before the emptying time remain in the virtual queue. When the on-site receiving status perception module detects that the number of backlogged vehicles reaches the congestion warning condition, the on-site access status of the corresponding vehicle in the matrix changes to a restricted state. The collaborative scheduling decision module stops moving remote vehicles into the on-site candidate queue and issues speed adjustment and capacity reduction instructions to the corresponding vehicles. For vehicles whose communication links are interrupted and whose location confidence parameters are lower than the specified confidence threshold, their corresponding matrix rows change to an abnormal pending confirmation state and are removed from the earliest arriving candidate queue.

[0120] After the virtual queue is updated, the collaborative scheduling decision module calculates the average temperature-queue interference factor of the current group of transport vehicles on the road. The sum of the number 1 and the average value is used as the correction increment. The baseline departure interval is then multiplied by the correction increment to obtain the correction departure step size. The correction departure step size is distributed to the departure transaction processing module to adjust the departure interval of subsequent vehicles.

[0121] During continuous system operation, the departure of vehicles, vehicle location updates, changes in material quantity in receiving bins, changes in paver running speed, and completion of vehicle unloading will all trigger state updates of the corresponding nodes. The collaborative scheduling decision module re-solves the virtual queue and corrects the departure step size based on the updated correlation matrix, so that the three links of vehicle departure, in-transit transportation, and on-site unloading are continuously connected according to the same set of state constraints.

[0122] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. A digital scheduling and management system for the entire asphalt pavement construction process, characterized in that, include: The outbound transaction processing module is used to collect the outbound departure time, outbound material temperature, and initial loading mass when the transport vehicle completes loading in order to generate the first state parameter package. The on-the-way state mapping module, connected to the positioning module, is used to calculate the remaining travel time on the way based on the geographical coordinates of the transport vehicle, so as to generate a second state parameter package containing the expected arrival time. The on-site material receiving status sensing module is used to collect the remaining material in the receiving hopper and the running speed to determine the consumption rate of the mixture; The collaborative scheduling decision module, connected to all modules via a network, receives data and controls scheduling according to the following control steps: Step S1: Predict the paving temperature based on ambient temperature and wind speed, and establish a temperature-queue state vector consisting of the predicted paving temperature and the expected arrival time. Step S2: Calculate the emptying time based on the remaining material in the receiving bin, the running speed, and the current system time, and calculate the temperature-queue interference factor for each transport vehicle. Step S3: When the estimated arrival times of two or more transport vehicles in transit overlap and the temperature-queue interference factor of the following vehicles is below a specified threshold, the virtual queue is reorganized and the guiding speed is calculated. The guiding speed is then distributed to the following vehicles through the in-transit state mapping module.

2. The digital scheduling and management system for the entire asphalt pavement construction process according to claim 1, characterized in that, The method for calculating the guiding vehicle speed in step S3 is as follows: the collaborative scheduling decision module calculates the quotient between the remaining road distance from the rear vehicle to the paving site and the time difference term. The time difference term is the difference obtained by subtracting the specified safety buffer time from the emptying time. The safety buffer time is fixed at 5 minutes. The collaborative scheduling decision module determines the quotient as the guiding speed and distributes it.

3. The digital scheduling and management system for the entire asphalt pavement construction process according to claim 1, characterized in that, The collaborative scheduling decision module calculates the corrected departure step size based on the average temperature-queue interference factor of the on-the-way transport vehicle group and distributes the corrected departure step size to the departure transaction processing module. The departure transaction processing module adjusts the departure interval of subsequent vehicles based on the corrected departure step size. The corrected departure step size is the product of the baseline departure interval and the correction increment, and the correction increment is the sum of the number 1 and the average temperature-queue interference factor.

4. The digital scheduling and management system for the entire asphalt pavement construction process according to claim 1, characterized in that, The collaborative scheduling decision module also includes an abnormal data self-healing module, which is used to start a location trajectory self-calculation program when the communication link between the transport vehicle and the collaborative scheduling decision module is interrupted for more than 5 minutes. The location trajectory self-calculation program uses the last reported physical location of the transport vehicle and the historical average vehicle speed of the road segment to calculate the simulated trajectory minute by minute, and introduces a location confidence parameter. The location confidence parameter decreases exponentially with the increase of the interruption time. When the location confidence parameter is lower than the specified confidence threshold of 0.4, the collaborative scheduling decision module automatically removes the transport vehicle from the earliest arrival candidate queue and regenerates the capacity offset instruction.

5. The digital scheduling and management system for the entire asphalt pavement construction process according to claim 1, characterized in that, The on-route status mapping module also connects to the weather sensor terminals deployed along the route to obtain real-time ambient temperature and wind speed along the route. The collaborative scheduling decision module establishes an in-transit temperature decay prediction model for each vehicle based on the temperature of the outgoing material, the ambient temperature along the route, the wind speed, and the predicted travel time in the first state parameter package. When the transport vehicle travels to within a decision threshold window of 2km from the paving site, the predicted paving temperature is calibrated a second time.

6. The digital scheduling and management system for the entire asphalt pavement construction process according to claim 5, characterized in that, After the on-the-way state mapping module completes the secondary calibration within the decision threshold window, the collaborative scheduling decision module controls the scheduling according to the following control steps: Step S11, determine whether the predicted paving temperature obtained after the secondary calibration is lower than 135℃; Step S12, when the predicted paving temperature is lower than 135℃, rewrite the current unloading queue index to raise the material receiving priority of the transport vehicle to the head of the queue, and issue a priority entry and allocation instruction to guide it to overtake other on-the-way transport vehicles to unload first.

7. The digital scheduling and management system for the entire asphalt pavement construction process according to claim 1, characterized in that, The on-site material receiving status sensing module delineates a 100m wide virtual electronic fence at the entrance of the unloading area to monitor the real-time number of vehicles backed up within the virtual electronic fence. When the number of backed up vehicles exceeds 3, the collaborative scheduling decision module triggers a congestion warning for the unloading area and issues speed adjustment and capacity reduction instructions to transport vehicles on the road 5km away from the paving site, guiding them to decelerate in a step-by-step manner in the safety parking lane.

8. The digital scheduling and management system for the entire asphalt pavement construction process according to claim 7, characterized in that, When the unloading area is under congestion warning, the collaborative scheduling decision module simultaneously distributes adjustment parameters to the outgoing transaction processing module to increase the departure step length, thereby limiting the continuous loading rate of the mixing plant and reducing the peak transport capacity from the source. The collaborative scheduling decision module lifts the congestion warning when the number of vehicles backed up in the virtual electronic fence is less than 3.

9. The digital scheduling and management system for the entire asphalt pavement construction process according to claim 1, characterized in that, The collaborative scheduling decision module adopts a distributed resource-constrained queuing network model composed of multi-node state machine coupled logic. It performs matrix-based correlation and solution of the data flow status of the outgoing transaction processing module, the in-transit status mapping module, and the on-site material receiving status perception module, and updates the virtual queue and corrects the departure step size.

10. A digital scheduling and management method for the entire process of asphalt pavement construction, implemented through the digital scheduling and management system for the entire process of asphalt pavement construction as described in claim 1, characterized in that... Includes the following steps: Step S101: When the transport vehicle finishes loading, the departure time, the temperature of the material leaving the vehicle, and the initial loading mass are collected by the departure transaction processing module to generate the first state parameter package. Step S102: The geographical coordinates of the transport vehicle are collected by the positioning module connected to the on-the-way state mapping module, and the remaining travel time on the way is calculated based on the average travel speed of the road network, so as to generate a second state parameter package containing the expected arrival time. Step S103: Collect the remaining material quantity and operating speed of the receiving hopper through the on-site material receiving status sensing module to determine the consumption rate of the mixture; Step S104: The collaborative scheduling decision module connects to the outgoing transaction processing module, the in-transit status mapping module, and the on-site material receiving status sensing module via a network and receives data. The scheduling is controlled according to the following control sequence: Step S1041: Based on the collected ambient temperature and wind speed, predict the paving temperature and establish a temperature-queue state vector consisting of the predicted paving temperature and the expected arrival time. Step S1042: Calculate the emptying time based on the remaining material in the receiving bin, the running speed, and the current system time, and calculate the temperature-queue interference factor for each transport vehicle. Step S1043: When the estimated arrival times of two or more transport vehicles in transit overlap and the temperature-queue interference factor of the following vehicle is lower than a specified threshold, the virtual queue is reorganized and the guiding speed is calculated. The guiding speed is then distributed to the following vehicle through the in-transit state mapping module.

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