Engineering machinery collaborative operation control method and system based on multi-thread simulation
By combining multi-threaded simulation methods with resource allocation diagrams, the real-time and deadlock problems in multi-machine collaborative operations of construction machinery are solved, efficient and safe multi-machine collaborative operation control is achieved, and the system's dynamic obstacle avoidance capability and environmental adaptability are improved.
Patent Information
- Application Number
- CN202510784899.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-12
- Publication Date
- 2025-09-05
AI Technical Summary
Existing collaborative operations of multiple engineering machinery in dynamic environments have problems such as poor real-time performance, weak dynamic obstacle avoidance capabilities, difficulty in handling deadlocks, and circular waiting in priority preemption strategies. In particular, there are significant bottlenecks in centralized scheduling, distributed coordination, and intelligent optimization algorithms.
A multi-threaded simulation method is adopted to abstract construction machinery into independent threads. Through dynamic grid division and time window reservation, combined with resource allocation graph and improved banker algorithm, spatiotemporal joint planning is realized, deadlock is quickly detected and resolved, and the circular waiting of priority preemption strategy is avoided.
It achieves efficient and safe multi-machine collaborative operation in a dynamic environment, solves potential conflict problems in future time windows, improves the system's real-time performance and dynamic obstacle avoidance capabilities, and reduces communication delays and training costs.
Smart Images
Figure CN120595683A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of automatic driving of engineering machinery, and in particular relates to a collaborative operation control method and system for engineering machinery based on multi-threaded simulation. Background Art
[0002] In modern engineering construction, construction machinery has the demand for scale, efficiency, safety, intelligence and digitalization. The collaborative operation of multiple construction machinery such as excavators and bulldozers has become an inevitable choice to improve efficiency, ensure safety and optimize resources.
[0003] Current research on collaborative operations of multiple construction machines mainly focuses on three major directions: centralized scheduling, distributed coordination, and intelligent optimization algorithms. Among them, centralized scheduling relies on a central controller to collect status information of all construction machines and generates a fixed path for each machine through a global path planning algorithm. It has problems such as poor real-time performance and weak dynamic obstacle avoidance capabilities. Distributed coordination methods are based on local sensors to perceive the environment and autonomously avoid obstacles through a negotiation mechanism. Their obstacle avoidance decisions are short-sighted and lack a global deadlock detection mechanism. Intelligent optimization algorithms use reinforcement learning, genetic algorithms, etc. to optimize the efficiency of multi-machine collaboration. The training cost is high, requiring millions of data simulation trainings, and there is a lack of deadlock processing. There is also a circular waiting problem with the priority preemption strategy. Summary of the Invention
[0004] In order to solve the above problems, the present invention proposes a collaborative operation control method and system for engineering machinery based on multi-threaded simulation. The present invention extends traditional spatial obstacle avoidance to spatiotemporal joint planning through dynamic grid division and time window reservation, solving the potential conflict problem in the future time window; combining resource allocation graph loop detection and deadlock detection, the present invention realizes rapid detection and self-healing of deadlock in a dynamic environment, avoiding the circular waiting problem of traditional priority preemption strategy.
[0005] In order to achieve the above object, the present invention is implemented through the following technical solutions: In a first aspect, the present invention provides a method for controlling collaborative operations of construction machinery based on multi-threaded simulation, comprising: Each machine is abstracted as an independent thread, the path of each construction machine is planned, the time window occupied by map grid resources is pre-calculated, and dynamic grids are divided according to the complexity of the terrain; Determine resource contention and lock allocation based on time windows. In conflicting scenarios, allow high-priority engineering machines to take up resources first, while low-priority engineering machines adjust their time windows. Spatial resources are modeled as shared memory. A resource allocation graph is constructed based on the grid lock status after lock allocation, generating a graph of the occupation and request relationships between engineering machinery and the grid. Deadlocks in the resource allocation graph are detected, and for engineering machinery of different priorities, the low-priority machinery is forced to release its lock and replan.
[0006] Furthermore, when dividing the dynamic grid according to the complexity of the terrain, when the terrain complexity coefficient is less than or equal to the preset value, the grid side length is the first value, otherwise, the grid side length is the second value, wherein the first value is greater than the second value; the terrain complexity coefficient for: ; in, and is the weight coefficient; is the slope deviation; is the obstacle density.
[0007] Furthermore, the path grid sequence and time window are pre-calculated by the spatiotemporal A* algorithm to predict conflicts; each thread includes five states: idle, application, execution, blocking and release.
[0008] Furthermore, resource competition is determined by a time window overlap algorithm. For conflict scenarios, a dynamic priority negotiation mechanism is introduced to allow high-priority engineering machinery to occupy resources first and low-priority engineering machinery to adjust the time window. If there is still a conflict after adjusting the time window or replanning the path more than the preset number of times, the engineering machinery is forced to replan the path and the conflict is resolved. The detection algorithm is:
[0009] in, Representing the The start and end time of a construction machinery time window.
[0010] Furthermore, based on the banker's algorithm, the spatiotemporal dimensions and dynamic weights are introduced to adapt to dynamic engineering scenarios. The resource allocation graph is periodically traversed to identify deadlocks. Combined with dynamic priority calculation, low-priority machines are forced to release some resources, the source allocation graph is updated, and the loop is re-detected until the deadlock is resolved.
[0011] Furthermore, sudden obstacles are detected, a point cloud map is generated, a detour path is calculated, and a new grid pre-occupancy request is determined; if the new path conflicts with the original path, the time window of the relevant construction machinery is fine-tuned; if the density of construction machinery in a certain area is higher than the preset value, some construction machinery is guided to migrate.
[0012] In a second aspect, the present invention further provides an engineering machinery collaborative operation control system based on multi-threaded simulation, comprising: The time window determination module is configured to: abstract each machine into an independent thread, plan the path of each construction machine, pre-calculate the time window occupied by map grid resources, and divide the dynamic grid according to the complexity of the terrain; The time window adjustment module is configured to determine resource contention and lock allocation based on the time window. In case of conflict, high-priority engineering machines are allowed to occupy resources first, while low-priority engineering machines adjust their time windows. The time window adjustment module is configured to: model spatial resources as shared memory, construct a resource allocation graph based on the grid lock status after lock allocation, and generate a graph of the occupation and request relationship between engineering machinery and the grid; detect deadlocks in the source allocation graph, and for engineering machinery of different priorities, force the low-priority machinery to release the lock and replan.
[0013] In a third aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the engineering machinery collaborative operation control method based on multi-threaded simulation described in the first aspect.
[0014] In a fourth aspect, the present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the program, the steps of the engineering machinery collaborative operation control method based on multi-threaded simulation described in the first aspect are implemented.
[0015] In a fifth aspect, the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the engineering machinery collaborative operation control method based on multi-threaded simulation described in the first aspect.
[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention first abstracts each machine into an independent thread, plans the path of each engineering machine, pre-calculates the time window occupied by map grid resources, and divides the dynamic grid according to the complexity of the terrain; then, resource competition and lock allocation are determined according to the time window. For conflict scenarios, high-priority engineering machines are allowed to occupy resources first, and low-priority engineering machines adjust the time window; finally, spatial resources are modeled as shared memory, and a resource allocation graph is constructed according to the grid lock state after lock allocation, and an occupation and request relationship graph between engineering machines and grids is generated; deadlocks in the source allocation graph are detected, and for engineering machines of different priorities, low-priority machines are forced to release the lock and re-plan; through dynamic grid division and time window reservation, traditional spatial obstacle avoidance is extended to spatiotemporal joint planning, which solves the potential conflict problem in the future time window; combined with resource allocation graph loop detection and deadlock detection, rapid detection and self-healing of deadlocks are achieved in a dynamic environment, avoiding the cyclic waiting problem of traditional priority preemption strategy. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] The drawings constituting a part of the specification of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments and descriptions of this embodiment are used to explain this embodiment and do not constitute an improper limitation on this embodiment.
[0018] Figure 1 This is a system schematic diagram of Example 1 of the present invention; Figure 2 This is a schematic diagram of a resource allocation graph (RAG) according to embodiment 1 of the present invention. DETAILED DESCRIPTION
[0019] The present invention will be further described below with reference to the accompanying drawings and embodiments.
[0020] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present application belongs.
[0021] Example 1: In modern engineering construction, construction machinery has the demand for scale, efficiency, safety, intelligence and digitalization. The collaborative operation of multiple construction machinery has become an inevitable choice to improve efficiency, ensure safety and optimize resources.
[0022] Current research on collaborative operations of multiple construction machinery mainly focuses on three major directions: centralized scheduling, distributed coordination, and intelligent optimization algorithms. However, there are significant bottlenecks in dynamic environment adaptability and deadlock processing. Centralized scheduling relies on a central controller (such as a cloud server) to collect the status information (position, speed, task progress) of all construction machinery, and generates a fixed path for each machine through a global path planning algorithm (such as A* and Dijkstra). This has problems such as poor real-time performance and weak dynamic obstacle avoidance capabilities. Distributed coordination methods perceive the environment based on local sensors and autonomously avoid obstacles through a negotiation mechanism. Their obstacle avoidance decisions are short-sighted and lack a global deadlock detection mechanism. Intelligent optimization algorithms use reinforcement learning, genetic algorithms, etc. to optimize the efficiency of multi-machine collaboration. The training cost is high, requiring millions of data simulation training times, and there is a lack of deadlock processing.
[0023] In order to solve at least one of the above problems, Figure 1 As shown, this embodiment provides an engineering machinery collaborative operation control system based on multi-threaded simulation. First, through dynamic grid division and time window reservation, traditional spatial obstacle avoidance is extended to spatiotemporal joint planning, which solves the potential conflict problem in the future time window; second, combined with resource allocation graph (RAG) loop detection and improved banker algorithm, fast deadlock detection and self-healing are achieved in a dynamic environment, avoiding the circular waiting problem of traditional priority preemption strategy; in addition, through the combination of centralized scheduling and distributed edge computing, system reliability is guaranteed while reducing communication delay.
[0024] It's important to note that the traditional Banker's Algorithm is a classic deadlock avoidance algorithm in operating systems. Its core features include a fixed total resource requirement, static resource allocation, and known global state. However, in construction machinery scenarios, the resource requirements of machinery change in real time along the route, making it impossible to predetermine the total amount. Grid resources are dynamically divided based on terrain, making the number of resources variable. Furthermore, the position, speed, and task status of machinery change dynamically, leading to high latency in global state synchronization. Therefore, the banker's algorithm was improved: to address the problem of fixed resource demand, the machine's spatiotemporal path planner is used to predict future resource demand. When the machine submits a path, a grid sequence and an occupied time window are automatically generated, and the scheduler converts the time window into a dynamic resource demand vector. To address the problem of static resource allocation, two-dimensional spatiotemporal resource conflict detection is integrated. In the spatial dimension, grid resources are dynamically divided according to the terrain. In the temporal dimension, resource occupancy is decomposed into time slots. The occupancy status of each grid on the time axis is managed independently, avoiding the defect that traditional algorithms only support static resources. To address the problem of global state synchronization delay, the improved algorithm introduces dynamic priority-driven deadlock recovery. When a loop deadlock is detected, the machine with the lowest priority is selected. When resources are released, only the machine with the lowest priority is forced to release the grid lock of the conflicting time slot.
[0025] The control system in this embodiment includes a mechanical control module, a resource management module, a deadlock processing module, a communication module, a perception and execution module, and a central scheduling module.
[0026] The mechanical control module abstracts each construction machine into an independent thread and manages the state machine of each construction machine. Optionally, each construction machine has five states, including idle, request, execute, block, and release. The spatiotemporal A* algorithm is used to plan the path of each construction machine, pre-calculate the time window occupied by map grid resources, and send movement instructions such as speed and steering to the construction machine to control the movement of the construction machine. When pre-calculating the time window occupied by map grid resources, the time window occupied by map grid resources can be calculated as follows:
[0027]
[0028]
[0029]
[0030] in, is the start time of the time window; is the current time; is the arrival delay time; The end time of the time window; To travel through time; is the side length of the map grid; is the maximum speed of the machine; is the maximum acceleration; is the safe braking distance; T is the time window.
[0031] The resource management module adaptively adjusts the map grid size based on operational needs and terrain complexity, typically to 10 cm or 5 cm. It receives grid pre-occupancy requests from the machine control module, identifies whether the path time windows of different engineering machines overlap, and thus determines grid occupancy conflicts. It also manages the grid lock allocation status table, obtains the grid lock status, and issues grid occupancy notifications and lock allocation results to engineering machines.
[0032] The deadlock processing module constructs a resource allocation diagram based on the grid lock status of the resource management module, generates a real-time occupation and request relationship diagram between engineering machinery and the grid, and periodically runs the banker's algorithm to detect deadlocks, returning loop detection results. For engineering machinery of different priorities, it forces low-priority machinery to release locks and replan.
[0033] The communication module, as a message bus, is used for message transmission between engineering machines and between modules, and transmits engineering machine status, lock allocation notifications, deadlock alarms, etc.
[0034] The perception and execution module uses sensors such as LiDAR (Light Laser Detection and Ranging) and cameras to monitor obstacle information in real time, generate local point cloud maps, collect data such as the position, speed, and oil pressure of construction machinery, and convert received control instructions into mechanical actuator actions.
[0035] The central scheduling module manages the allocation of global tasks, negotiates and arbitrates time conflicts among multiple engineering machines, receives the task completion status of engineering machines, monitors the entire system, and reallocates the task queue if a task is interrupted.
[0036] This implementation also provides a collaborative operation control method for construction machinery based on multi-threaded simulation. By simulating the multi-threaded scheduling mechanism in the operating system, the construction machinery is abstracted into independent threads, and ground space resources are modeled as shared memory. This method combines spatiotemporal conflict prediction, dynamic priority deadlock recovery, and distributed communication technology to achieve efficient multi-machine collaborative operation. The specific implementation steps are as follows: S1. Each machine is abstracted as an independent thread, given a unique thread ID, and bound to the local planner (improved spatiotemporal A* algorithm) and resource manager. The loading task coordinates are initialized and the dynamic grid is divided according to the terrain complexity coefficient. When the terrain complexity coefficient is less than or equal to the preset value, the grid side length is the first value, otherwise, the grid side length is the second value, where the first value is greater than the second value, thus achieving dynamic grid division; for example, when the terrain complexity coefficient is less than or equal to 0.3, the grid side length is 10cm, and when the terrain complexity coefficient is greater than 0.3, the grid side length is 3cm. Terrain complexity coefficient for: ; in, and is the weight coefficient; is the slope deviation; is the obstacle density.
[0037] Optionally, the weight coefficient is determined in two stages: multi-objective optimization experimental calibration and runtime feedback adjustment. The experimental calibration stage is based on typical terrain test data to obtain the optimal weight; in the dynamic adjustment stage, the weight is fine-tuned online according to the actual traffic performance of the machine; this design significantly improves the environmental adaptability and operational safety of the collaborative system. , refers to the algebraic difference between the slopes of two adjacent slope sections on the road longitudinal section. Along the direction of the route, the slope of the uphill section is positive and the slope of the downhill section is negative. When the slope deviation is positive, it means that the road longitudinal section is convex and the slope is changed. A convex vertical curve is set; conversely, a concave vertical curve is set if the slope is changed concavely. Obstacle density , refers to the coverage ratio of obstacles that affect the safe passage of machinery within a unit area. Its calculation needs to be combined with the physical size of the obstacle and the safety margin. The calculation formula is: ; Among them, the projected area is the area of the circumscribed rectangle of the obstacle in the horizontal plane; the safety margin area is the mechanical safety radius extended outside the obstacle; the statistical area is the circular area centered on the current position of the machine; and the radius is adjusted according to the mechanical braking distance.
[0038] It should be noted that the traditional A* algorithm only considers two-dimensional geometric paths, ignoring the machine's volume, which can lead to collisions. Secondly, the traditional A* algorithm uses discrete time step planning, which does not reserve safe braking time. Unexpected obstacles can result in response delays, leading to accidents. Furthermore, the traditional A* algorithm independently plans for a single machine and does not anticipate the spatiotemporal occupancy of other machines. This significantly increases the collision rate when multiple machine paths intersect. To address these issues, this embodiment simplifies the machine into a moving envelope in the A* algorithm. The grids covered by the envelope are marked as occupied, and a safety margin is added to the occupied time window of each grid, thereby reserving reaction time for unexpected obstacles. During the planning process, the central dispatcher's grid lock status table is accessed in real time to detect future time window conflicts. If a grid conflict is detected, an attempt is made to delay the travel time window or redirect the path.
[0039] Optionally, when dividing the dynamic grid according to the terrain complexity coefficient, when the terrain complexity coefficient is less than or equal to 0.3, the grid side length is 10 cm, and when the terrain complexity coefficient is greater than 0.3, the grid side length is 3 cm; incorporating mechanical performance parameters into the grid division decision can allow the machine to dynamically adapt to different operating environments, thereby improving operating efficiency and universality.
[0040] The path grid sequence and time window are pre-calculated using the spatiotemporal A* algorithm and submitted to the central scheduler for conflict prediction. Each thread has five states: Idle: Listen to the central scheduler task queue and receive the initial job coordinates. Apply: Discretize the planned path into a grid sequence and calculate the occupied time window of each grid. Execute: Move according to the planned path and send a data packet containing real-time position, speed and remaining path to the scheduler every 100ms. Blocking: If a sudden obstacle is detected or a deadlock instruction is received, suspend movement and trigger local re-planning, and choose to detour if necessary. Release: After the task is completed, release all occupied grid locks and notify the scheduler to update the resource table.
[0041] S2. The scheduler uses a two-phase protocol to process lock requests: Optional: During the pre-occupancy phase, the path grid sequence and time window requirements sent by the machine are collected; during the conflict detection phase, resource contention is determined using a time window overlap algorithm. For conflict scenarios, a dynamic priority negotiation mechanism is introduced, allowing high-priority engineering machines to take priority over resources, while low-priority engineering machines adjust their time windows or re-plan their paths, thus avoiding the "starvation" problem caused by static priority allocation. If conflicts persist after three fine-tuning steps, the engineering machine is forced to re-plan its path. The conflict detection algorithm is as follows:
[0042] in, Representing the The start and end time of a construction machinery time window.
[0043] S3. A resource allocation graph (RAG) is constructed based on the grid lock status. This algorithm, based on the operating system's banker's algorithm, introduces spatiotemporal dimensions and dynamic weights to adapt to dynamic engineering scenarios. The RAG is periodically traversed, enabling the system to quickly identify deadlocks. Combined with dynamic priority calculation (combining task urgency, machine motion status, and environmental complexity), low-priority machines are forced to release some resources. The RAG is then updated and loops rechecked until the deadlock is resolved, triggering real-time replanning if necessary.
[0044] Optionally, the introduction of spatiotemporal dimensions and dynamic weights, as well as the calculation of dynamic priorities, include: the spatiotemporal dimension, which introduces a grid occupation time window in time, pre-calculates it during path planning and sends it to the scheduler to avoid collisions caused by time window conflicts; spatially, the machine is simplified into a moving envelope, such as an excavator = a cuboid + a rotating cylinder, which solves the problem of the machine volume being ignored in traditional methods. Dynamic weights, which divide grids of different sizes according to the complexity of different terrains, dynamically adapt to different operating environments. The dynamic priority calculation formula is: ; in, is a dynamic priority; is the remaining time of the task, corresponding to the comprehensive task urgency; is the total task time; is the maximum speed, corresponding to the mechanical motion state; is the current speed; is the distance from the emergency task point, corresponding to the complexity of the environment; is the total distance.
[0045] If an unexpected obstacle occurs, the machine detects it using LiDAR and generates a point cloud map. The local planner immediately calculates a detour and submits a new grid reservation request to the scheduler. If the new path conflicts with the original path, the scheduler coordinates the relevant machines to fine-tune the time window, ultimately updating global resources and broadcasting the new lock allocation results. If the density of machines in a certain area is too high, the central scheduler dynamically allocates task queues to guide some machines to move to lower-load areas.
[0046] Example 2: This embodiment provides a construction machinery collaborative operation control system based on multi-threaded simulation, including: The time window determination module is configured to: abstract each machine into an independent thread, plan the path of each construction machine, pre-calculate the time window occupied by map grid resources, and divide the dynamic grid according to the complexity of the terrain; The time window adjustment module is configured to determine resource contention and lock allocation based on the time window. In case of conflict, high-priority engineering machines are allowed to occupy resources first, while low-priority engineering machines adjust their time windows. The time window adjustment module is configured to: model spatial resources as shared memory, construct a resource allocation graph based on the grid lock status after lock allocation, and generate a graph of the occupation and request relationship between engineering machinery and the grid; detect deadlocks in the source allocation graph, and for engineering machinery of different priorities, force the low-priority machinery to release the lock and replan.
[0047] The working method of the system is the same as the engineering machinery collaborative operation control method based on multi-threaded simulation in Example 1, and will not be repeated here.
[0048] Example 3: This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the engineering machinery collaborative operation control method based on multi-threaded simulation described in Example 1 are implemented.
[0049] Example 4: This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the program, the steps of the engineering machinery collaborative operation control method based on multi-threaded simulation described in Example 1 are implemented.
[0050] Example 5: This embodiment provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the engineering machinery collaborative operation control method based on multi-threaded simulation described in Example 1 are implemented.
[0051] The above description is merely a preferred embodiment of this embodiment and is not intended to limit this embodiment. Those skilled in the art will readily appreciate that this embodiment may be modified and varied in various ways. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this embodiment shall be within the scope of protection of this embodiment.
Claims
1. A collaborative operation control method for construction machinery based on multi-threaded simulation, characterized in that: include: Each machine is abstracted as an independent thread, the path of each construction machine is planned, the time window occupied by map grid resources is pre-calculated, and dynamic grids are divided according to the complexity of the terrain; Determine resource contention and lock allocation based on time windows. In conflicting scenarios, allow high-priority engineering machines to take up resources first, while low-priority engineering machines adjust their time windows. Spatial resources are modeled as shared memory. A resource allocation graph is constructed based on the grid lock status after lock allocation, generating a graph of the occupation and request relationships between engineering machinery and the grid. Deadlocks in the resource allocation graph are detected, and for engineering machinery of different priorities, the low-priority machinery is forced to release its lock and replan.
2. The engineering machinery collaborative operation control method based on multi-threaded simulation according to claim 1 is characterized in that: When dividing dynamic grids according to the complexity of the terrain, when the terrain complexity coefficient is less than or equal to the preset value, the grid side length is the first value, otherwise, the grid side length is the second value, wherein the first value is greater than the second value; the terrain complexity coefficient for: ； in, and is the weight coefficient; is the slope deviation; is the obstacle density.
3. The engineering machinery collaborative operation control method based on multi-threaded simulation according to claim 2 is characterized in that: The path grid sequence and time window are pre-calculated by the spatiotemporal A* algorithm to predict conflicts; each thread includes five states: idle, application, execution, blocking and release.
4. The engineering machinery collaborative operation control method based on multi-threaded simulation according to claim 1, characterized in that: Resource competition is determined through a time window overlap algorithm. For conflict scenarios, a dynamic priority negotiation mechanism is introduced to allow high-priority engineering machinery to occupy resources first, while low-priority engineering machinery adjusts its time window. If the conflict still exists after adjusting the time window or re-planning the path for more than the preset number of times, the construction machinery will be forced to re-plan the path and the conflict will be resolved. The detection algorithm is: in, Representing the The start and end time of a construction machinery time window.
5. The engineering machinery collaborative operation control method based on multi-threaded simulation according to claim 1 is characterized in that: Based on the banker's algorithm, the spatiotemporal dimension and dynamic weights are introduced to adapt to dynamic engineering scenarios. The resource allocation graph is periodically traversed to identify deadlocks. Combined with dynamic priority calculation, low-priority machines are forced to release some resources, the source allocation graph is updated, and loops are rechecked until the deadlock is resolved.
6. The engineering machinery collaborative operation control method based on multi-threaded simulation according to claim 1, characterized in that: Detect sudden obstacles, generate a point cloud map, calculate a detour path, and determine a new grid pre-occupancy request; if the new path conflicts with the original path, fine-tune the time window of the relevant construction machinery; if the density of construction machinery in a certain area is higher than the preset value, guide some construction machinery to relocate.
7. The engineering machinery collaborative operation control system based on multi-threaded simulation is characterized by: include: The time window determination module is configured to: abstract each machine into an independent thread, plan the path of each construction machine, pre-calculate the time window occupied by map grid resources, and divide the dynamic grid according to the complexity of the terrain; The time window adjustment module is configured to determine resource contention and lock allocation based on the time window. In case of conflict, high-priority engineering machines are allowed to occupy resources first, while low-priority engineering machines adjust their time windows. The time window adjustment module is configured to: model spatial resources as shared memory, construct a resource allocation graph based on the grid lock status after lock allocation, and generate a graph of the occupation and request relationship between engineering machinery and the grid; detect deadlocks in the source allocation graph, and for engineering machinery of different priorities, force the low-priority machinery to release the lock and replan.
8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the engineering machinery collaborative operation control method based on multi-threaded simulation as described in any one of claims 1 to 6 are implemented.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the program, the steps of the engineering machinery collaborative operation control method based on multi-threaded simulation as described in any one of claims 1 to 6 are implemented.
10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the engineering machinery collaborative operation control method based on multi-threaded simulation as described in any one of claims 1 to 6 are implemented.