Automatic sample taking and sending scheduling method and system based on AGV

Abstract

The invention is suitable for the technical field of automatic scheduling, and provides an AGV-based automatic sample taking and sending scheduling method and system, and the method comprises the steps: obtaining the information of an automatic guided vehicle, and obtaining the information of a sample; acquiring factory map information, acquiring a sampling point and a sample sending point, calculating an optimal path according to the sampling point and the sample sending point through an A * algorithm, and sending the optimal path to the terminal; obtaining an optimal path of the plurality of automated guided vehicles, obtaining the importance of samples, dividing path priorities according to the importance, and re-planning the paths according to the priorities and the path overlap ratio; the method comprises the following steps: acquiring sample delivery time and delivery effect, scoring a plurality of sampled and delivered samples according to a scoring standard, acquiring a scoring result, and optimizing an A * algorithm according to the scoring result, thereby solving the problems that the optimal path of a single guide vehicle cannot be planned, priority division cannot be performed on a plurality of optimal paths, and the accuracy of the optimal paths cannot be improved. And re-planning the path of a single guide vehicle according to the divided priorities, and meanwhile, not optimizing the route of the optimal path.

Description

Technical Field

[0001] The present invention belongs to the technical field of automated scheduling, and in particular relates to an AGV-based automatic sample pickup and delivery scheduling method and system. Background Art

[0002] In the experiment of steel bar physical property testing, it is often necessary to manually move the material frame containing the steel bars to the experimental area for experiment. After the experiment, it is necessary to manually move the empty frame to the loading area and put the steel bars back into the material frame. It is also necessary to move the discarded material frame to the designated area and dump the waste sample. In this process, since the material frame is very heavy, it is difficult for operators with weak strength to move it. The material frame is very slippery and may slip easily. Manual transportation will cause deviations in the placement points and affect the experimental results. Therefore, an AGV automatic sample delivery scheduling system is required, which is connected to the automated experimental station. In addition, through the coordinated cooperation of software and hardware, a touch screen call system is provided on the sample delivery station to facilitate processing coordination and improve processing efficiency.

[0003] However, the existing AGV automatic sample delivery vehicle is unable to plan the optimal path for a single guide vehicle, and is unable to prioritize multiple optimal paths, re-plan the path of a single guide vehicle according to the divided priorities, and is unable to optimize the route of the optimal path. Summary of the invention

[0004] The purpose of the embodiment of the present invention is to provide an AGV-based automatic sample collection and delivery scheduling method, aiming to solve the problem raised in the third part of the background technology.

[0005] The embodiment of the present invention is implemented as follows: a method and system for scheduling automatic sample delivery based on AGV, the method comprising:

[0006] Acquire automatic guided vehicle information, the guided vehicle information including position, power and load, and acquire sample information, the sample information including sample position and sample protection measures;

[0007] Obtain factory map information, obtain sample collection and delivery points, calculate the optimal path based on the sampling and delivery points through the A* algorithm, and send the optimal path to the terminal;

[0008] Obtain the optimal paths for multiple automated guided vehicles, obtain sample importance, prioritize paths based on importance, and replan paths based on priority and route overlap;

[0009] Obtain sample delivery time and delivery effect, obtain scoring criteria, score multiple sample collection and delivery according to the scoring criteria, obtain scoring results, and optimize the A* algorithm according to the scoring results.

[0010] Preferably, the steps of obtaining factory map information, obtaining sample sampling points and sample delivery points, calculating the optimal path according to the sampling points and sample delivery points by using an A* algorithm, and sending the optimal path to the terminal specifically include:

[0011] Obtain order information, including sample type and duration, and obtain factory map information, including factory routes and area divisions;

[0012] Obtaining sample sampling points and sample delivery points, and calculating the optimal path according to the sampling points and sample delivery points by using the A* algorithm, wherein the A* algorithm adopts f(n)=g(n)+h(n);

[0013] Send the optimal path to the terminal and obtain the terminal's confirmation information.

[0014] Preferably, the steps of obtaining the optimal paths of multiple automated guided vehicles, obtaining the importance of samples, dividing the path priorities according to the importance, and replanning the paths according to the priorities and the route overlap specifically include:

[0015] Obtain the optimal paths of multiple automated guided vehicles, obtain the importance of samples, and divide the weights w according to the importance;

[0016] Divide the path priorities according to the importance, and calculate the comprehensive priority score S of each optimal path, where S=w1*P+w2*(1 / L)+w3*(1 / D)+w4*A;

[0017] The path is replanned based on the comprehensive priority score. The automated guided vehicles with high priority follow the original path, while the automated guided vehicles with low priority wait or replan the path to bypass.

[0018] Preferably, the steps of obtaining the sample delivery time and delivery effect, obtaining the scoring criteria, scoring the multiple sample collection and delivery according to the scoring criteria, obtaining the scoring results, and optimizing the A* algorithm according to the scoring results specifically include:

[0019] Obtain sample delivery time and delivery effect, obtain path efficiency, time efficiency, task completion status and resource consumption, and obtain corresponding weights of path efficiency, time efficiency, task completion status and resource consumption;

[0020] Calculate scores based on path efficiency, time efficiency, task completion, resource consumption and corresponding weights, obtain scoring criteria, and score multiple sample collection and delivery tasks based on the scoring criteria;

[0021] Get the scoring results and optimize the A* algorithm based on the scoring results.

[0022] Preferably, g(n) represents the actual cost from the starting point to the current node n, and h(n) represents the estimated cost from the current node n to the end point.

[0023] Another object of an embodiment of the present invention is to provide an AGV-based automatic sample delivery scheduling system, the system comprising:

[0024] The advance information module obtains the automatic guided vehicle information, including the position, power and load, and obtains the sample information, including the sample position and sample protection measures;

[0025] The optimal path module obtains factory map information, sample collection points and sample delivery points, calculates the optimal path based on the sampling points and sample delivery points through the A* algorithm, and sends the optimal path to the terminal;

[0026] The priority module obtains the optimal paths of multiple automated guided vehicles, obtains the importance of samples, divides the path priorities according to the importance, and replans the paths according to the priorities and route overlap;

[0027] The scoring module obtains the sample delivery time and delivery effect, obtains the scoring criteria, scores multiple sample collection and delivery according to the scoring criteria, obtains the scoring results, and optimizes the A* algorithm according to the scoring results.

[0028] Preferably, the optimal path module includes:

[0029] An information acquisition unit, which acquires order information, including sample type and duration, and obtains factory map information, including factory routes and regional divisions;

[0030] A* algorithm unit, obtains sample sampling points and sample delivery points, and calculates the optimal path according to the sampling points and sample delivery points through the A* algorithm, wherein the A* algorithm adopts f(n)=g(n)+h(n);

[0031] The path sending unit sends the optimal path to the terminal and obtains confirmation information from the terminal.

[0032] Preferably, the priority module comprises:

[0033] The multi-path unit obtains the optimal paths of multiple automated guided vehicles, obtains the importance of samples, and divides the weight w according to the importance;

[0034] A priority unit divides the path priorities according to the importance and calculates the comprehensive priority score S of each optimal path, where S=w1*P+w2*(1 / L)+w3*(1 / D)+w4*A;

[0035] The re-planning unit re-plans the path according to the comprehensive priority score. The high-priority automated guided vehicles follow the original path, while the low-priority automated guided vehicles wait or re-plan the path to bypass.

[0036] Preferably, the scoring module includes:

[0037] The delivery effect unit obtains the sample delivery time and delivery effect, obtains the path efficiency, time efficiency, task completion status and resource consumption status, and obtains the corresponding weights of the path efficiency, time efficiency, task completion status and resource consumption status;

[0038] The scoring unit calculates the scores according to the path efficiency, time efficiency, task completion status, resource consumption status and corresponding weights, obtains the scoring criteria, and scores the multiple samples according to the scoring criteria;

[0039] The algorithm optimization unit obtains the scoring results and optimizes the A* algorithm according to the scoring results.

[0040] Preferably, g(n) represents the actual cost from the starting point to the current node n, and h(n) represents the estimated cost from the current node n to the end point.

[0041] An AGV-based automatic sample collection and delivery scheduling method provided in an embodiment of the present invention obtains automatic guided vehicle information, the guided vehicle information includes location, power and load, obtains sample information, the sample information includes sample location and sample protection measures, obtains order information and factory map information, obtains sample sampling points and sample delivery points, calculates the optimal path according to the sampling points and sample delivery points through the A* algorithm, the A* algorithm adopts f(n)=g(n)+h(n), sends the optimal path to the terminal, obtains confirmation information of the terminal, obtains the optimal paths of multiple automatic guided vehicles, obtains sample importance, divides weights according to importance, divides path priorities according to importance, and calculates the comprehensive priority score S of each optimal path, wherein S=w1*P+ w2*(1 / L)+w3*(1 / D)+w4*A, re-plan the path according to the comprehensive priority score, obtain the sample delivery time and delivery effect, obtain the path efficiency, time efficiency, task completion status and resource consumption, obtain the corresponding weights of path efficiency, time efficiency, task completion status and resource consumption, obtain the scoring standard, score multiple sample collection and delivery according to the scoring standard, obtain the scoring results, optimize the A* algorithm according to the scoring results, solve the problem of not being able to plan the optimal path for a single guide vehicle, not being able to prioritize multiple optimal paths, re-plan the path of a single guide vehicle according to the divided priority, and not being able to optimize the route of the optimal path. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1A flowchart of an AGV-based automatic sample collection and delivery scheduling method provided in an embodiment of the present invention;

[0043] Figure 2 A flowchart of the steps of obtaining sample sampling points and sample delivery points, and calculating the optimal path according to the sampling points and sample delivery points by using the A* algorithm provided in an embodiment of the present invention;

[0044] Figure 3 A flowchart of the steps of dividing path priorities according to importance and replanning the paths according to priorities and route overlap provided in an embodiment of the present invention;

[0045] Figure 4 A flowchart of the steps of obtaining sample delivery time and delivery effect, obtaining scoring criteria, scoring multiple sample collection and delivery according to the scoring criteria, obtaining scoring results, and optimizing the A* algorithm according to the scoring results provided in an embodiment of the present invention;

[0046] Figure 5 An architecture diagram of an AGV-based automatic sample collection and delivery scheduling system provided in an embodiment of the present invention;

[0047] Figure 6 An architecture diagram of an optimal path module provided by an embodiment of the present invention;

[0048] Figure 7 An architectural diagram of a priority module provided in an embodiment of the present invention;

[0049] Figure 8 This is an architecture diagram of a scoring module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0051] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0052] like Figure 1 As shown, an AGV-based automatic sample delivery scheduling method is provided in an embodiment of the present invention, and the method includes:

[0053] S100, obtaining automatic guided vehicle information, the guided vehicle information including position, power and load, and obtaining sample information, the sample information including sample position and sample protection measures.

[0054] In this step, the information of the automatic guided vehicle is obtained. The automatic guided vehicle is the AGV. The position is the coordinates of the current AGV, which is updated in real time to ensure that the system has an accurate grasp of the position of the AGV. The power is the power status of the current AGV, which needs to match the task requirements to avoid receiving tasks when the power is low and causing operation interruption. The load is the load status of the current AGV, and it is known whether the current AGV is empty or fully loaded, the load weight, and whether it carries special samples.

[0055] Obtain sample information, sample location is the current location of the sample, sample protection measures are special requirements or transportation conditions of the sample, including temperature control requirements, shockproof requirements and protection level, and efficiently dispatch AGV to complete the task of picking up and delivering samples. Combined with task scoring and path optimization, the operating efficiency and stability of the system can be further improved.

[0056] S200, obtain factory map information, obtain sample sampling points and sample delivery points, calculate the optimal path according to the sampling points and sample delivery points through the A* algorithm, and send the optimal path to the terminal.

[0057] In this step, the factory map information is obtained, and the factory is divided into grids of equal size. Each grid is represented by (x, y) coordinates. The grid attributes include passability, such as 0 for passability and 1 for obstacles. The sample sampling point and sample delivery point are obtained. The sampling point is the coordinate of the sample storage location, and the sample delivery point is the coordinate of the sample destination.

[0058] The A* algorithm calculates the optimal path based on the sampling points and the sample delivery points. The A* algorithm finds the optimal path by combining the path cost g(n) and the heuristic cost h(n):

[0059] f(n)=g(n)+h(n)

[0060] The optimal path is sent to the terminal, and the path is sent to the AGV terminal via wireless communication for task execution.

[0061] S300, obtaining optimal paths of multiple automated guided vehicles, obtaining sample importance, dividing path priorities according to the importance, and replanning the paths according to the priorities and route overlaps.

[0062] In this step, the optimal paths of multiple automated guided vehicles are obtained. The optimal path of each AGV includes the path point sequence, path length, and path estimation time. The optimal path of each AGV is calculated using the A* algorithm to ensure that it meets the shortest time or minimum cost under a single task.

[0063] Obtain the importance of samples. The importance of each sample task is determined by its urgency. According to the importance of the sample, the path priority is divided. Urgent or high-value tasks are high priority, general important tasks are medium priority, and tasks with loose time requirements are low priority;

[0064] The paths are replanned based on priority and route overlap. The path overlap is used to measure the number of nodes shared by two paths in space. The higher the overlap, the greater the possibility of conflict. If paths with different priorities pass through the same conflict point, the high-priority path should be prioritized.

[0065] S400, obtaining the sample delivery time and delivery effect, obtaining the scoring standard, scoring the multiple sample collection and delivery according to the scoring standard, obtaining the scoring result, and optimizing the A* algorithm according to the scoring result.

[0066] In this step, the sample delivery time and delivery effect are obtained. By obtaining the sample delivery time and delivery effect and combining the scoring criteria to score multiple sample delivery tasks, effective feedback can be provided for optimizing the A* algorithm, thereby improving the accuracy of path planning and system efficiency.

[0067] Obtain scoring criteria, including time deviation score and delivery effect score, score multiple pickup and delivery samples according to the scoring criteria, analyze the scoring results to find out the reasons for the low scores, and optimize the A* algorithm based on the scoring results.

[0068] like Figure 2 As shown, as a preferred embodiment of the present invention, the steps of obtaining factory map information, obtaining sample sampling points and sample delivery points, calculating the optimal path according to the sampling points and sample delivery points by using the A* algorithm, and sending the optimal path to the terminal specifically include:

[0069] S201, obtaining order information, the order information including sample type and duration, and obtaining factory map information, the factory map information including factory routes and area divisions.

[0070] In this step, order information is obtained. The order information includes sample type and time. Sample type refers to the specific type of sample, such as chemicals, biological samples, and electronic parts. The time requirements for sample transportation include sampling time limit and delivery time limit, for example, type: chemicals, sampling time: before 10:00 on November 20, 2024, delivery time: before 11:00 on November 20, 2024;

[0071] The route data within the factory is presented in the form of a grid diagram, showing the division and attributes of different functional areas within the factory, including sampling area, sample delivery area, charging area and prohibited area.

[0072] For example, a grid map:

[0073] 0 indicates a passable area, and 1 indicates an obstacle.

[0074] Sampling area: (0, 0) to (1, 0), sample delivery area: (4, 4) to (3, 4), prohibited area: (2, 3) to (3, 3).

[0075] S202, obtaining sample sampling points and sample delivery points, and calculating the optimal path according to the sampling points and sample delivery points by using the A* algorithm, wherein the A* algorithm adopts f(n)=g(n)+h(n).

[0076] In this step, the sample sampling point and sample delivery point are obtained, g(n) represents the actual cost from the starting point to the current node n, h(n) represents the estimated cost from the current node n to the end point, f(n) represents the total cost from the starting point to the target point through node n, h(n) is an estimation function used to estimate the distance from node n to the target point, and Manhattan distance is used for the grid graph;

[0077] Put the starting point into the open list, where the f and g values ​​are initialized to 0, create a closed list to store the processed nodes, and take the node with the smallest f(n value) from the open list as the current node. If the current node is the target point, the algorithm ends and the path is generated.

[0078] Otherwise, move the current node into the closed list and process its neighbor nodes. For each neighbor node, calculate g, h, and f. If the neighbor node is not in the open list, add it. If the neighbor node is in the open list and the new g value is smaller, update its value.

[0079] Starting from the target point, trace back to the starting point through the recorded parent node to generate the optimal path.

[0080] S203: Send the optimal path to the terminal and obtain confirmation information from the terminal.

[0081] In this step, sending the optimal path to the terminal and obtaining the terminal confirmation information are the key links in task allocation in the intelligent logistics system. The timestamp, status, path and other information of task sending and confirmation are recorded to facilitate subsequent tracking and debugging. If successful, the task reception time is recorded; if failed, the cause of the error is analyzed and retry is tried.

[0082] like Figure 3 As shown, as a preferred embodiment of the present invention, the steps of obtaining the optimal paths of multiple automated guided vehicles, obtaining the importance of samples, dividing the path priorities according to the importance, and replanning the paths according to the priorities and the route overlap specifically include:

[0083] S301, obtaining optimal paths of multiple automated guided vehicles, obtaining sample importance, and dividing weights w according to the importance.

[0084] In this step, the optimal paths of multiple automated guided vehicles are obtained. The optimal path of each AGV is calculated using the A* algorithm, and the node coordinates from the starting point to the end point are listed in order to obtain the total length of the path. The estimated time to complete the path is calculated based on the speed of the AGV;

[0085] Obtain the importance of samples. The task importance of each sample is determined by its urgency, including time requirements, sample characteristics and task priority. The importance of the sample is assigned a weight w, with high priority w=3, medium priority w=2, and low priority w=1.

[0086] S302, divide the path priorities according to the importance, and calculate the comprehensive priority score S of each optimal path, where S=w1*P+w2*(1 / L)+w3*(1 / D)+w4*A.

[0087] In this step, the path priority is divided according to the importance. In order to comprehensively evaluate the optimal path of each automated guided vehicle (AGV), the comprehensive priority score S can be calculated based on the importance of the sample, the path attributes and the AGV status. Through the calculation of S, the priority of the path can be determined more scientifically, thereby optimizing task allocation and path planning;

[0088] S=w1*P+w2*(1 / L)+w3*(1 / D)+w4*A

[0089] P is the task priority, which indicates the importance of the sample or the urgency of the task. A high-priority task corresponds to a larger P value, for example, a high-priority task: P = 3, a medium-priority task: P = 2, and a low-priority task: P = 1;

[0090] L is the path length, which indicates the total length of the current optimal path, expressed in grid points or meters. The shorter the path, the higher the priority score.

[0091] D is the time distance, which indicates the remaining time from the current task to the deadline, in minutes. The shorter the time distance, the higher the score;

[0092] A is the AGV status score, which indicates the current status score of the AGV;

[0093] w1, w2, w3, and w4 represent weight parameters, which are used to balance the impact of various factors on the comprehensive priority score.

[0094] S303, re-planning the path according to the comprehensive priority score, the automatic guided vehicle with a high priority follows the original path, and the automatic guided vehicle with a low priority waits or re-plans a detour.

[0095] In this step, the path is replanned according to the comprehensive priority score. In the multi-automatic guided vehicle (AGV) task scenario, the path is replanned according to the comprehensive priority score SSS, which can effectively avoid path conflicts. The AGV with high priority follows the original path, while the AGV with low priority waits or replans the path to ensure the execution efficiency of high-priority tasks;

[0096] According to the comprehensive priority score S, priorities are assigned to the paths of all AGVs. High priority (high S value): the original path remains unchanged to ensure that high-priority tasks are not interfered with; medium and low priority (low S value): depending on the conflict situation, wait and give priority to high-priority AGVs at the conflict point, or re-plan the path: bypass the conflict point and generate a new path.

[0097] like Figure 4 As shown, as a preferred embodiment of the present invention, the steps of obtaining sample delivery time and delivery effect, obtaining scoring criteria, scoring multiple sample collection and delivery according to the scoring criteria, obtaining scoring results, and optimizing the A* algorithm according to the scoring results specifically include:

[0098] S401, obtain the sample delivery time and delivery effect, obtain the path efficiency, time efficiency, task completion status and resource consumption, and obtain the corresponding weights of the path efficiency, time efficiency, task completion status and resource consumption.

[0099] In this step, the sample delivery time and delivery effect are obtained. The delivery time includes the actual time of transporting the sample from the starting point to the target point. The delivery effect measures the transportation quality, including integrity, environmental conditions and accuracy, and obtains the path efficiency, time efficiency, task completion and resource consumption.

[0100] Path efficiency is a measure of whether the transport path is close to the optimal path, time efficiency is a measure of the time performance of the transport task, task completion is a measure of whether the task is successful, and resource consumption is a measure of the energy efficiency of the transport process through resource consumption;

[0101] Obtain the corresponding weights of path efficiency, time efficiency, task completion status and resource consumption status to balance the importance of each indicator in the comprehensive score. Path efficiency weight = 0.3, time efficiency weight = 0.4, task completion weight = 0.2, resource consumption weight = 0.1.

[0102] S402, calculating scores according to path efficiency, time efficiency, task completion status, resource consumption status and corresponding weights, obtaining scoring standards, and scoring multiple sample collection and delivery according to the scoring standards.

[0103] In this step, the score is calculated based on the path efficiency, time efficiency, task completion, resource consumption and corresponding weights. The closer the path efficiency is to 1, the better the path selection is. The closer the time efficiency is to 1, the better the path selection is. The closer the task completion is to 1, the better the path selection is. The closer the resource consumption is to 1, the better the path selection is.

[0104] Calculate the score = path efficiency * path efficiency weight + time efficiency * time efficiency weight + task completion * task completion weight + resource consumption * resource consumption weight, obtain the scoring criteria, and score multiple sample collection and delivery according to the scoring criteria. The higher the score, the higher the priority.

[0105] S403, obtaining a scoring result, and optimizing the A* algorithm according to the scoring result.

[0106] In this step, the scoring results are obtained and the A* algorithm is optimized based on the scoring results. The purpose is to analyze the performance in the scoring, discover and improve potential problems in path planning, and thus improve the task execution effect of the automatic guided vehicle (AGV). The scoring indicators include path efficiency, time efficiency, task completion, and resource consumption. The parameters and strategies of the A* algorithm can be adjusted based on these scoring results to make it more in line with actual task requirements.

[0107] like Figure 5 As shown, an AGV-based automatic sample delivery scheduling system is provided in an embodiment of the present invention, and the system includes:

[0108] The advance information module 100 is used to obtain automatic guided vehicle information, the guided vehicle information includes position, power and load, and obtain sample information, the sample information includes sample position and sample protection measures.

[0109] In this system, the advance information module 100 obtains the information of the automatic guided vehicle, which is the AGV. The position is the coordinates of the current AGV, which is updated in real time to ensure that the system has an accurate grasp of the position of the AGV. The power is the power status of the current AGV, which needs to match the task requirements to avoid receiving tasks when the power is low and causing operation interruption. The load is the load status of the current AGV, and it knows whether the current AGV is empty or fully loaded, the load weight, and whether it carries special samples.

[0110] Obtain sample information, sample location is the current location of the sample, sample protection measures are special requirements or transportation conditions of the sample, including temperature control requirements, shockproof requirements and protection level, and efficiently dispatch AGV to complete the task of picking up and delivering samples. Combined with task scoring and path optimization, the operating efficiency and stability of the system can be further improved.

[0111] The optimal path module 200 is used to obtain factory map information, obtain sample sampling points and sample delivery points, calculate the optimal path according to the sampling points and sample delivery points through the A* algorithm, and send the optimal path to the terminal.

[0112] In this system, the optimal path module 200 obtains factory map information, divides the factory into grids of equal size, and each grid is represented by (x, y) coordinates. The grid attributes include passability, such as 0 for passability and 1 for obstacles. The sample sampling point and sample delivery point are obtained. The sampling point is the coordinate of the sample storage location, and the sample delivery point is the coordinate of the sample destination.

[0113] The A* algorithm calculates the optimal path based on the sampling points and the sample delivery points. The A* algorithm finds the optimal path by combining the path cost g(n) and the heuristic cost h(n):

[0114] f(n)=g(n)+h(n)

[0115] The optimal path is sent to the terminal, and the path is sent to the AGV terminal via wireless communication for task execution.

[0116] The priority module 300 is used to obtain the optimal paths of multiple automated guided vehicles, obtain the importance of samples, divide the path priorities according to the importance, and re-plan the paths according to the priorities and route overlap.

[0117] In this system, the priority module 300 obtains the optimal paths of multiple automated guided vehicles. The optimal path of each AGV includes the path point sequence, path length and path estimation time. The optimal path of each AGV is calculated by the A* algorithm to ensure that it meets the shortest time or minimum cost under the single task condition.

[0118] Obtain the importance of samples. The importance of each sample task is determined by its urgency. According to the importance of the sample, the path priority is divided. Urgent or high-value tasks are high priority, general important tasks are medium priority, and tasks with loose time requirements are low priority;

[0119] The paths are replanned based on priority and route overlap. The path overlap is used to measure the number of nodes shared by two paths in space. The higher the overlap, the greater the possibility of conflict. If paths with different priorities pass through the same conflict point, the high-priority path should be prioritized.

[0120] The scoring module 400 is used to obtain the sample delivery time and delivery effect, obtain the scoring criteria, score multiple sample collection and delivery according to the scoring criteria, obtain the scoring results, and optimize the A* algorithm according to the scoring results.

[0121] In this system, the scoring module 400 obtains the sample delivery time and delivery effect. By obtaining the sample delivery time and delivery effect and combining the scoring criteria to score multiple sample collection and delivery tasks, effective feedback can be provided for optimizing the A* algorithm, thereby improving the accuracy of path planning and system efficiency.

[0122] Obtain scoring criteria, including time deviation score and delivery effect score, score multiple pickup and delivery samples according to the scoring criteria, analyze the scoring results to find out the reasons for the low scores, and optimize the A* algorithm based on the scoring results.

[0123] like Figure 6 As shown, as a preferred embodiment of the present invention, the optimal path module 200 includes:

[0124] The information acquisition unit 201 is used to acquire order information, the order information includes sample type and duration, and acquire factory map information, the factory map information includes factory routes and area divisions.

[0125] In this module, the information acquisition unit 201 acquires order information, which includes sample type and time. Sample type is the specific type of sample, such as chemicals, biological samples and electronic parts. The time requirements for sample transportation include sampling time limit and delivery time limit, for example, type: chemicals, sampling time: before 10:00 on November 20, 2024, delivery time: before 11:00 on November 20, 2024;

[0126] The route data within the factory is presented in the form of a grid diagram, showing the division and attributes of different functional areas within the factory, including sampling area, sample delivery area, charging area and prohibited area.

[0127] For example, a grid map:

[0128] 0 indicates a passable area, and 1 indicates an obstacle.

[0129] Sampling area: (0, 0) to (1, 0), sample delivery area: (4, 4) to (3, 4), prohibited area: (2, 3) to (3, 3).

[0130] The A* algorithm unit 202 is used to obtain sample sampling points and sample delivery points, and calculate the optimal path according to the sampling points and sample delivery points through the A* algorithm. The A* algorithm adopts f(n)=g(n)+h(n).

[0131] In this module, the A* algorithm unit 202 obtains the sample sampling point and the sample delivery point, g(n) represents the actual cost from the starting point to the current node n, h(n) represents the estimated cost from the current node n to the end point, f(n) represents the total cost from the starting point to the target point through the node n, h(n) is an estimation function used to estimate the distance from the node n to the target point, and the Manhattan distance is used for the grid graph;

[0132] Put the starting point into the open list, where the f and g values ​​are initialized to 0, create a closed list to store the processed nodes, and take the node with the smallest f(n value) from the open list as the current node. If the current node is the target point, the algorithm ends and the path is generated.

[0133] Otherwise, move the current node into the closed list and process its neighbor nodes. For each neighbor node, calculate g, h, and f. If the neighbor node is not in the open list, add it. If the neighbor node is in the open list and the new g value is smaller, update its value.

[0134] Starting from the target point, trace back to the starting point through the recorded parent node to generate the optimal path.

[0135] The path sending unit 203 is used to send the optimal path to the terminal and obtain confirmation information from the terminal.

[0136] In this module, the path sending unit 203 sends the optimal path to the terminal. Sending the optimal path to the terminal and obtaining the terminal confirmation information is a key link in task allocation in the intelligent logistics system. It records the timestamp, status, path and other information of task sending and confirmation to facilitate subsequent tracking and debugging. If successful, the task reception time is recorded; if failed, the cause of the error is analyzed and retry is performed.

[0137] like Figure 7 As shown, as a preferred embodiment of the present invention, the priority module 300 includes:

[0138] The multi-path unit 301 is used to obtain the optimal paths of multiple automated guided vehicles, obtain the importance of samples, and divide the weights w according to the importance.

[0139] In this module, the multi-path unit 301 obtains the optimal paths of multiple automated guided vehicles. The optimal path of each AGV is calculated by the A* algorithm, and the node coordinates from the starting point to the end point are listed in order to obtain the total length of the path, and the estimated time to complete the path is calculated based on the speed of the AGV;

[0140] Obtain the importance of samples. The task importance of each sample is determined by its urgency, including time requirements, sample characteristics and task priority. The importance of the sample is assigned a weight w, with high priority w=3, medium priority w=2, and low priority w=1.

[0141] The priority unit 302 is used to divide the path priorities according to the importance and calculate the comprehensive priority score S of each optimal path, where S=w1*P+w2*(1 / L)+w3*(1 / D)+w4*A.

[0142] In this module, the priority unit 302 divides the path priority according to the importance. In order to comprehensively evaluate the optimal path of each automated guided vehicle (AGV), the comprehensive priority score S can be calculated according to the importance of the sample, the path attribute and the AGV status. Through the calculation of S, the priority of the path can be determined more scientifically, thereby optimizing task allocation and path planning;

[0143] S=w1*P+w2*(1 / L)+w3*(1 / D)+w4*A

[0144] P is the task priority, which indicates the importance of the sample or the urgency of the task. A high-priority task corresponds to a larger P value, for example, a high-priority task: P = 3, a medium-priority task: P = 2, and a low-priority task: P = 1;

[0145] L is the path length, which indicates the total length of the current optimal path, expressed in grid points or meters. The shorter the path, the higher the priority score.

[0146] D is the time distance, which indicates the remaining time from the current task to the deadline, in minutes. The shorter the time distance, the higher the score;

[0147] A is the AGV status score, which indicates the current status score of the AGV;

[0148] w1, w2, w3, and w4 represent weight parameters, which are used to balance the impact of various factors on the comprehensive priority score.

[0149] The re-planning unit 303 is used to re-plan the path according to the comprehensive priority score, and the automatic guided vehicles with high priority travel along the original path, and the automatic guided vehicles with low priority wait or re-plan the path to bypass.

[0150] In this module, the re-planning unit 303 re-plans the path according to the comprehensive priority score. In the multi-automatic guided vehicle (AGV) task scenario, the path is re-planned according to the comprehensive priority score SSS, which can effectively avoid path conflicts. The AGV with high priority follows the original path, while the AGV with low priority waits or re-plans the path to bypass, so as to ensure the execution efficiency of the high priority task;

[0151] According to the comprehensive priority score S, priorities are assigned to the paths of all AGVs. High priority (high S value): the original path remains unchanged to ensure that high-priority tasks are not interfered with; medium and low priority (low S value): depending on the conflict situation, wait and give priority to high-priority AGVs at the conflict point, or re-plan the path: bypass the conflict point and generate a new path.

[0152] like Figure 8 As shown, as a preferred embodiment of the present invention, the scoring module 400 includes:

[0153] The delivery effect unit 401 is used to obtain the sample delivery time and delivery effect, obtain the path efficiency, time efficiency, task completion status and resource consumption, and obtain the corresponding weights of the path efficiency, time efficiency, task completion status and resource consumption.

[0154] In this module, the delivery effect unit 401 obtains the sample delivery time and delivery effect. The delivery time includes the actual time for the sample to be transported from the starting point to the target point. The delivery effect measures the transportation quality, including integrity, environmental conditions and accuracy, and obtains the path efficiency, time efficiency, task completion and resource consumption.

[0155] Path efficiency is a measure of whether the transport path is close to the optimal path, time efficiency is a measure of the time performance of the transport task, task completion is a measure of whether the task is successful, and resource consumption is a measure of the energy efficiency of the transport process through resource consumption;

[0156] Obtain the corresponding weights of path efficiency, time efficiency, task completion status and resource consumption status to balance the importance of each indicator in the comprehensive score. Path efficiency weight = 0.3, time efficiency weight = 0.4, task completion weight = 0.2, resource consumption weight = 0.1.

[0157] The scoring unit 402 is used to calculate the score according to the path efficiency, time efficiency, task completion status and resource consumption status and the corresponding weights, obtain the scoring standard, and score the multiple sample collection and delivery according to the scoring standard.

[0158] In this module, the scoring unit 402 calculates the score according to the path efficiency, time efficiency, task completion status and resource consumption status and the corresponding weights. The closer the path efficiency is to 1, the better the path selection is. The closer the time efficiency is to 1, the better the path selection is. The closer the task completion status is to 1, the better the path selection is. The closer the resource consumption status is to 1, the better the path selection is.

[0159] Calculate the score = path efficiency * path efficiency weight + time efficiency * time efficiency weight + task completion * task completion weight + resource consumption * resource consumption weight, obtain the scoring criteria, and score multiple sample collection and delivery according to the scoring criteria. The higher the score, the higher the priority.

[0160] The algorithm optimization unit 403 is used to obtain the scoring result and optimize the A* algorithm according to the scoring result.

[0161] In this module, the algorithm optimization unit 403 obtains the scoring results and optimizes the A* algorithm based on the scoring results. The purpose is to discover and improve potential problems in path planning by analyzing the performance in the scoring, thereby improving the task execution effect of the automatic guided vehicle (AGV). The scoring indicators include path efficiency, time efficiency, task completion status and resource consumption, etc. The parameters and strategies of the A* algorithm can be adjusted according to these scoring results to make it more in line with actual task requirements.

[0162] In one embodiment, a computer device is provided, the computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are implemented:

[0163] Acquire automatic guided vehicle information, the guided vehicle information including position, power and load, and acquire sample information, the sample information including sample position and sample protection measures;

[0164] Obtain factory map information, obtain sample collection and delivery points, calculate the optimal path based on the sampling and delivery points through the A* algorithm, and send the optimal path to the terminal;

[0165] Obtain the optimal paths for multiple automated guided vehicles, obtain sample importance, prioritize paths based on importance, and replan paths based on priority and route overlap;

[0166] Obtain sample delivery time and delivery effect, obtain scoring criteria, score multiple sample collection and delivery according to the scoring criteria, obtain scoring results, and optimize the A* algorithm according to the scoring results.

[0167] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the processor performs the following steps:

[0168] Acquire automatic guided vehicle information, the guided vehicle information including position, power and load, and acquire sample information, the sample information including sample position and sample protection measures;

[0169] Obtain factory map information, obtain sample collection and delivery points, calculate the optimal path based on the sampling and delivery points through the A* algorithm, and send the optimal path to the terminal;

[0170] Obtain the optimal paths for multiple automated guided vehicles, obtain sample importance, prioritize paths based on importance, and replan paths based on priority and route overlap;

[0171] Obtain sample delivery time and delivery effect, obtain scoring criteria, score multiple sample collection and delivery according to the scoring criteria, obtain scoring results, and optimize the A* algorithm according to the scoring results.

[0172] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0173] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0174] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0175] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0176] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for scheduling automatic sample delivery based on AGV, characterized in that: The method comprises: Acquire automatic guided vehicle information, the guided vehicle information including position, power and load, and acquire sample information, the sample information including sample position and sample protection measures; Obtain factory map information, obtain sample collection and delivery points, calculate the optimal path based on the sampling and delivery points through the A* algorithm, and send the optimal path to the terminal; Obtain the optimal paths for multiple automated guided vehicles, obtain sample importance, prioritize paths based on importance, and replan paths based on priority and route overlap; Obtain sample delivery time and delivery effect, obtain scoring criteria, score multiple sample collection and delivery according to the scoring criteria, obtain scoring results, and optimize the A* algorithm according to the scoring results.

2. The method for scheduling automatic sample pickup and delivery based on AGV according to claim 1, characterized in that: The steps of obtaining factory map information, obtaining sample sampling points and sample delivery points, calculating the optimal path according to the sampling points and sample delivery points by using the A* algorithm, and sending the optimal path to the terminal specifically include: Obtain order information, including sample type and duration, and obtain factory map information, including factory routes and area divisions; Obtaining sample sampling points and sample delivery points, and calculating the optimal path according to the sampling points and sample delivery points by using the A* algorithm, wherein the A* algorithm adopts f(n)=g(n)+h(n); Send the optimal path to the terminal and obtain the terminal's confirmation information.

3. The method for scheduling automatic sample delivery based on AGV according to claim 2 is characterized in that: The steps of obtaining the optimal paths of multiple automated guided vehicles, obtaining the importance of samples, dividing the path priorities according to the importance, and replanning the paths according to the priorities and the route overlap specifically include: Obtain the optimal paths of multiple automated guided vehicles, obtain the importance of samples, and divide the weights w according to the importance; Divide the path priorities according to the importance, and calculate the comprehensive priority score S of each optimal path, where S=w1*P+w2*(1 / L)+w3*(1 / D)+w4*A; The path is replanned based on the comprehensive priority score. The automated guided vehicles with high priority follow the original path, while the automated guided vehicles with low priority wait or replan the path to bypass.

4. The method for scheduling automatic sample delivery based on AGV according to claim 3 is characterized in that: The steps of obtaining the sample delivery time and delivery effect, obtaining the scoring criteria, scoring the multiple sample collection and delivery according to the scoring criteria, obtaining the scoring results, and optimizing the A* algorithm according to the scoring results specifically include: Obtain sample delivery time and delivery effect, obtain path efficiency, time efficiency, task completion status and resource consumption, and obtain the corresponding weights of path efficiency, time efficiency, task completion status and resource consumption; Calculate scores based on path efficiency, time efficiency, task completion, resource consumption and corresponding weights, obtain scoring criteria, and score multiple sample collection and delivery tasks based on the scoring criteria; Get the scoring results and optimize the A* algorithm based on the scoring results.

5. The method for scheduling automatic sample delivery based on AGV according to claim 2, characterized in that: The g(n) represents the actual cost from the starting point to the current node n, and h(n) represents the estimated cost from the current node n to the end point.

6. An AGV-based automatic sample delivery scheduling system, characterized in that: The system comprises: The advance information module obtains the automatic guided vehicle information, including the position, power and load, and obtains the sample information, including the sample position and sample protection measures; The optimal path module obtains factory map information, sample collection points and sample delivery points, calculates the optimal path based on the sampling points and sample delivery points through the A* algorithm, and sends the optimal path to the terminal; The priority module obtains the optimal paths of multiple automated guided vehicles, obtains the importance of samples, divides the path priorities according to the importance, and replans the paths according to the priorities and route overlap; The scoring module obtains the sample delivery time and delivery effect, obtains the scoring criteria, scores multiple sample collection and delivery according to the scoring criteria, obtains the scoring results, and optimizes the A* algorithm according to the scoring results.

7. The AGV-based automatic sample delivery scheduling system according to claim 6 is characterized in that: The optimal path module includes: An information acquisition unit, which acquires order information, including sample type and duration, and obtains factory map information, including factory routes and regional divisions; A* algorithm unit, obtains sample sampling points and sample delivery points, and calculates the optimal path according to the sampling points and sample delivery points through the A* algorithm, wherein the A* algorithm adopts f(n)=g(n)+h(n); The path sending unit sends the optimal path to the terminal and obtains confirmation information from the terminal.

8. The AGV-based automatic sample delivery scheduling system according to claim 7 is characterized in that: The priority module comprises: The multi-path unit obtains the optimal paths of multiple automated guided vehicles, obtains the importance of samples, and divides the weight w according to the importance; A priority unit divides the path priorities according to the importance and calculates the comprehensive priority score S of each optimal path, where S=w1*P+w2*(1 / L)+w3*(1 / D)+w4*A; The re-planning unit re-plans the path according to the comprehensive priority score. The high-priority automated guided vehicles follow the original path, while the low-priority automated guided vehicles wait or re-plan the path to bypass.

9. The AGV-based automatic sample delivery scheduling system according to claim 8, characterized in that: The scoring module includes: The delivery effect unit obtains the sample delivery time and delivery effect, obtains the path efficiency, time efficiency, task completion status and resource consumption status, and obtains the corresponding weights of the path efficiency, time efficiency, task completion status and resource consumption status; The scoring unit calculates the scores according to the path efficiency, time efficiency, task completion status, resource consumption status and corresponding weights, obtains the scoring criteria, and scores the multiple samples according to the scoring criteria; The algorithm optimization unit obtains the scoring results and optimizes the A* algorithm according to the scoring results.

10. The AGV-based automatic sample delivery scheduling system according to claim 9, characterized in that: The g(n) represents the actual cost from the starting point to the current node n, and h(n) represents the estimated cost from the current node n to the end point.

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