CN-121977593-A - Urban muck vehicle path dynamic planning system based on road topological relation

CN121977593ACN 121977593 ACN121977593 ACN 121977593ACN-121977593-A

Abstract

The invention discloses a road topology relation-based dynamic planning system for a path of an urban muck truck, which belongs to the technical field of intelligent transportation and vehicle scheduling, and aims at a dynamic path planning system for a heavy engineering vehicle, in particular for an urban muck transport vehicle, and the road topology relation-based dynamic planning system for the path of the urban muck truck is based on real-time road topology relation, multi-constraint condition fusion and multi-algorithm cooperation; the invention remarkably improves the adaptability and efficiency of the road planning of the muck truck through multi-level information fusion and algorithm iteration, effectively reduces the traffic jam and delay time, realizes the high-efficiency utilization of urban traffic resources and provides an innovative solution for the vehicle scheduling under complex road environment.

Inventors

Zhong pin
QU ZHE
WANG SHILE
CHEN HAO
ZHOU XI

Assignees

江苏舆图信息科技有限公司

Dates

Publication Date: 20260505
Application Date: 20251227

Claims (8)

1. The urban muck vehicle path dynamic planning system based on the road topological relation is characterized by comprising a real-time acquisition and preliminary path calculation module, a genetic algorithm optimization ordering module, a path matching judgment and resource optimization module, a grouping processing and dynamic updating module, a congestion monitoring and path correction module, an iteration adjustment and efficiency judgment module and a simulation verification and final determination module; the system comprises a real-time acquisition and preliminary path calculation module, a vehicle load and emergency degree information acquisition module and a vehicle load and emergency degree information acquisition module, wherein the real-time acquisition and preliminary path calculation module is used for acquiring a preliminary path set by acquiring road traffic capacity and temporary control data in real time and calculating an initial path option by adopting an A star algorithm; The genetic algorithm optimization sequencing module is used for optimizing the important sequencing through a genetic algorithm according to the congestion degree and delay time index of each path in the preliminary path set and determining an adjusted path priority sequence; The path matching judging and resource optimizing module is used for fusing real-time traffic flow data through an information processing link if the option which is not matched with the vehicle load exists in the adjusted path priority sequence, judging the utilization rate of path resources and obtaining an optimized path allocation scheme; The grouping processing and dynamic updating module is used for grouping a plurality of muck trucks by adopting the optimized path allocation scheme, acquiring road condition updating under dynamic change according to the emergency degree difference in grouping, and determining a final path resource allocation result; The congestion monitoring and path correcting module is used for monitoring the congestion degree change according to the final path resource allocation result, and if the change exceeds a preset threshold value, adopting an information processing link to recalculate delay time to obtain a corrected path set; The iterative adjustment and efficiency judgment module is used for fusing the load difference and the emergency degree data according to the corrected path set, iteratively adjusting the important sequences through an A star algorithm, judging the resource utilization efficiency and obtaining the enhanced path planning output; And the simulation verification and final determination module is used for adopting the enhanced path planning output to simulate and verify the whole traffic environment, and determining a final dynamic path scheme according to delay time and congestion degree feedback in simulation.
2. The dynamic planning system for the path of the urban muck truck based on the road topological relation according to claim 1, wherein the real-time acquisition and preliminary path calculation module is specifically configured to: determining the current road state by collecting road traffic capacity and temporary control data in real time; Judging the priority level of the vehicle according to the current road state and combining the vehicle load information and the emergency degree information; calculating an initial path under the current road state by adopting an A star algorithm to obtain a preliminary path set; Acquiring corresponding road traffic capacity and temporary control data change for each path in the preliminary path set; If the temporary control data change causes the interruption of a certain path, removing the path from the preliminary path set to obtain an updated path set; and determining optimal path options according to the updated path set and the priority level of the vehicle.
3. The dynamic planning system for the path of the urban muck truck based on the road topological relation according to claim 1, wherein the genetic algorithm optimization ordering module is specifically configured to: Acquiring real-time congestion data and historical delay records corresponding to each path according to the congestion degree and delay time indexes of each path in the preliminary path set; calculating the comprehensive delay weight of each path through real-time congestion data and historical delay records; According to the comprehensive delay weight, performing crossover and mutation operations on the preliminary path set by adopting a genetic algorithm to obtain a new generation of path sequencing candidates; Aiming at the new generation of path sequencing candidates, judging that if the path comprehensive delay weight is lower than that of the previous generation, reserving the path sequencing candidates, and determining an intermediate priority sequence; And according to the intermediate priority sequence, acquiring congestion change prediction data of the path in a peak time period, and recalculating a dynamic delay adjustment value to obtain an updated priority sequence serving as an adjusted path priority sequence.
4. The dynamic planning system for the path of the urban muck truck based on the road topological relation according to claim 1, wherein the path matching judgment and resource optimization module is specifically configured to: Acquiring traffic flow data from a real-time traffic system, and analyzing the flow conditions of each path in the adjusted path priority sequence to obtain a flow distribution result; According to the flow distribution result, combining with vehicle load data, judging that if the resource utilization rate of a certain path is lower than a preset threshold value, marking the path as a non-matching path, and determining a path list to be adjusted; Aiming at the path list to be adjusted, acquiring related constraint conditions of path matching, and adopting a pre-established path allocation rule to obtain a preliminarily adjusted allocation scheme; And analyzing the feasibility of dynamic adjustment according to the distribution scheme after preliminary adjustment, and if the matching degree of the priority of the path after adjustment and the load of the vehicle is higher than that of the original scheme, reserving the distribution scheme as an optimized path distribution scheme.
5. The urban muck vehicle path dynamic planning system based on road topology according to claim 1, wherein the grouping processing and dynamic updating module is specifically configured to: grouping a plurality of muck trucks according to the load and the transportation aging requirement of the truck to obtain a muck truck grouping list; aiming at the emergency degree difference of each group in the slag car grouping list, acquiring current road condition updating data from a real-time road monitoring system; Analyzing the predicted value of the passing time length of each path according to the road condition data set, judging that if the predicted value of the passing time length of a certain path exceeds the time limit corresponding to the emergency degree of the grouping, marking the path as a limited path, and determining a limited path set; Screening alternative paths by adopting a pre-established path standby rule aiming at the limited path set to obtain a candidate path set; And according to the matching traffic capacity of the candidate path set and the residue soil vehicle grouping list, sequencing the candidate paths by adopting a genetic algorithm to obtain a sequenced path sequence and distributing the sequenced path sequence to each residue soil vehicle grouping.
6. The dynamic planning system for the path of the urban muck truck based on the road topological relation according to claim 1, wherein the congestion monitoring and path correcting module is specifically configured to: acquiring the latest traffic flow data through a real-time monitoring system, judging that if the traffic flow data exceeds a preset threshold value, marking the relevant path as a high-congestion path, and obtaining a high-congestion path list; according to the high-congestion path list, analyzing the real-time traffic condition of each path, and determining the affected path resource range to obtain a limited resource list; Screening alternative paths meeting traffic conditions from a pre-established standby path database aiming at the limited resource list to obtain a candidate alternative path set; Analyzing the predicted passing time length of each alternative path by combining the dynamically updated traffic data, judging that if the predicted passing time length of a certain path exceeds the delay time length limit, removing the path to obtain an optimized path set; And carrying out priority ranking on the optimized path set by adopting a genetic algorithm to obtain a corrected path set.
7. The dynamic planning system for the path of the urban muck truck based on the road topological relation according to claim 1, wherein the iterative adjustment and efficiency judgment module is specifically configured to: According to the correction path set, vehicle load data and task emergency degree data are fused, and comprehensive priority scores are obtained; Calculating heuristic cost of each path by adopting an A star algorithm through the comprehensive priority score, and determining an initial path sequencing sequence; Judging that if the resource utilization rate of a certain path is lower than a preset threshold value according to the initial path sequencing sequence, marking the path as an inefficient path, obtaining an inefficient path list, removing the inefficient path list from a corrected path set, and obtaining a filtered path set; iteratively adjusting the important ranking of the residual paths through the filtering path set to determine an optimized ranking path sequence; and acquiring real-time vehicle load distribution conditions, judging that if load distribution exceeds the path capacity limit, recalculating the path cost to obtain a path set with the adjustment cost, and generating enhanced path planning output.
8. The dynamic planning system for the path of the urban muck truck based on the road topological relation according to claim 1, wherein the simulation verification and final determination module is specifically configured to: acquiring the primary distribution condition and traffic flow data of the whole traffic environment, and obtaining a primary flow distribution map; screening the key road sections by adopting a preset threshold according to the preliminary flow distribution map, and determining a high-load road section list; Acquiring delay influence weights of all road sections by combining delay time and time feedback data, and marking the road sections as priority adjustment road sections if the delay influence weights exceed a preset threshold value to obtain a priority adjustment list; Aiming at the priority adjustment list, acquiring real-time traffic flow update information, judging whether a continuous congestion phenomenon exists, and if so, generating a temporary shunting path to obtain a shunting path set; Sequencing the feasibility of the shunt path set, and determining an optimal shunt path sequence; Obtaining the simulation feedback data after the path execution, obtaining a final path execution scheme, and determining a final dynamic path scheme according to the optimized distribution data of the traffic environment.

Description

Urban muck vehicle path dynamic planning system based on road topological relation Technical Field The invention relates to the technical field of intelligent transportation and vehicle scheduling, in particular to a dynamic planning system for an urban muck vehicle path based on a road topological relation. Background Urban muck vehicle path planning is used as an important research field of urban traffic management and environmental protection, and carries key mission for improving urban operation efficiency and reducing traffic jam. With the acceleration of the urban process, the transportation demands of the dregs car are increased increasingly, and the path planning directly influences the road safety, the life quality of residents and the overall effect of urban management. The road route of the slag car is reasonably planned, so that the risk of traffic accidents can be reduced, and the pollution to urban environment can be effectively reduced, and therefore, the research in the field is particularly urgent and important. However, current methods for road planning for earth-moving vehicles tend to be difficult to adapt to complex urban traffic environments. Many existing solutions lack flexible coping ability in the face of dynamic changes in road conditions, especially when dealing with multiple constraints, often fail to balance the importance of the various factors. For example, conditions such as traffic capacity, temporary control, vehicle load and the like of a road can affect each other in actual running, but the existing method is difficult to comprehensively consider the conditions according to real-time conditions, so that a planned path often deviates from actual requirements, and even a new traffic problem is caused. Under the background, the core technical difficulties are gradually revealed, and the importance ranking of how to dynamically adjust the road conditions is focused. Urban road environments are complex and variable, conditions such as construction, regulation or peak hours can change the trafficability of roads at any time, and the change requires a planning system to timely identify and re-evaluate the importance of each road. If this cannot be done, it is further difficult to reasonably allocate path resources according to specific situations of each vehicle, such as different loads or different emergency degrees of destinations, when the system runs simultaneously in the face of a plurality of muck vehicles. For a specific example, in a certain transportation task, a vehicle full of dregs needs to reach a designated place as soon as possible, but the system fails to recognize temporary road control information in time, so that the vehicle is arranged to bypass a congestion road section, the task time is delayed finally, and the traffic pressure of surrounding roads is increased. For example, in reality, a heavy vehicle full of dregs may be guided to a certain road section because the navigation system does not recognize the weight limit information of the bridge of the road section, causing a safety hazard, or the whole transportation fleet is blocked in a construction area because the temporary traffic control road section is not avoided, so that the engineering progress is seriously affected and the surrounding road network congestion is aggravated. Therefore, an intelligent dynamic path planning system which can deeply integrate multi-source real-time data, fully consider the operation characteristics of the muck trucks, has the capability of multi-truck collaborative scheduling and can verify the reliability of a guarantee scheme through simulation is needed. Disclosure of Invention The invention aims to solve the technical problem of providing a dynamic planning system for the path of an urban muck truck based on a road topological relation aiming at the defects of the background art, so as to realize efficient, safe and reliable intelligent scheduling of a muck truck team and adapt to a dynamic traffic environment. The invention adopts the following technical scheme for solving the technical problems: A dynamic planning system for urban muck vehicle path based on road topological relation comprises real-time acquisition and preliminary path calculation The system comprises a module, a genetic algorithm optimization sequencing module, a path matching judgment and resource optimization module, a grouping processing and dynamic updating module, a congestion monitoring and path correction module, an iteration adjustment and efficiency judgment module and a simulation verification and final determination module; the system comprises a real-time acquisition and preliminary path calculation module, a vehicle load and emergency degree information acquisition module and a vehicle load and emergency degree information acquisition module, wherein the real-time acquisition and preliminary path calculation module is used for acquiring a preliminary path set by acquiring road traf