A kind of intelligent heavy-load AGV dispatching method

Abstract

The present invention discloses an intelligent heavy-load AGV scheduling method, which relates to the technical field of AGV scheduling, and is used to solve the defects of existing heavy-load AGV scheduling methods, such as slow response speed, inflexible path planning, insufficient optimization of task allocation, etc., in the face of increasingly complex industrial environments and growing transportation needs; the method comprises sensors that acquire the position and path data, task status data, traffic and environmental data, equipment status data, and collaborative operation data of the heavy-load AGV, analyzes the heavy-load AGV environmental scene, and analyzes the heavy-load AGV scene scheduling to obtain results such as the estimated task processing time; the intelligent scheduling of the present invention not only improves the operating efficiency of the heavy-load AGV system, but also can minimize task delays and safety hazards caused by equipment problems or improper scheduling, thereby significantly improving the reliability and stability of the entire transportation system.

Description

Technical Field

[0001] The present invention relates to the technical field of AGV scheduling, and in particular to an intelligent heavy-load AGV scheduling method. Background Art

[0002] Heavy-duty AGV, also known as automated guided transport vehicle, is an intelligent device that can autonomously navigate and carry heavy objects on a specific path. It can automatically complete the material handling task according to the predetermined guide path through computer control; heavy-duty AGV scheduling is a crucial link in the automated logistics system, which involves multiple aspects such as task allocation, path planning, traffic control, and abnormal situation handling of heavy-duty AGVs.

[0003] However, the existing heavy-duty AGV scheduling still has defects such as slow response speed, inflexible path planning, and insufficient optimization of task allocation when facing the increasingly complex industrial environment and growing transportation demand. With the advancement of technology and the continuous increase in application needs, the existing scheduling methods urgently need to be further improved to improve the overall efficiency and intelligence level of the AGV system. Summary of the invention

[0004] The purpose of the present invention is to propose an intelligent heavy-load AGV scheduling method in order to solve the problems of slow response speed, inflexible path planning, insufficient task allocation optimization, etc. in the existing heavy-load AGV scheduling.

[0005] The purpose of the present invention can be achieved by the following technical solution: an intelligent heavy-load AGV scheduling method, comprising:

[0006] S1: Obtain the location and path data, task status data, traffic and environment data, equipment status data and collaborative operation data of the heavy-duty AGV;

[0007] S2: Analyze the heavy-load AGV environment scenario. The specific analysis steps are as follows:

[0008] S201: Analyze the position and path data, which includes the position of the heavy-load AGV and the heavy-load AGV driving path, and analyze each heavy-load AGV driving path; obtain the length and minimum width of the heavy-load AGV driving path, the number of turns and the corresponding turning angles in the heavy-load AGV driving path, and the distance between two turns in the heavy-load AGV driving path; and obtain the speed coefficient of the heavy-load AGV driving path by calculation;

[0009] S202: Analyze the traffic and environmental data to obtain a traffic coefficient, an environmental coefficient and a comprehensive traffic and environmental value;

[0010] S3: Analyze the heavy-load AGV scenario scheduling. The specific analysis steps are as follows:

[0011] S301: Analyze the task status data to obtain the task specification safety value; compare the task specification safety value with the speed corresponding to the corresponding transportation scenario; if the task specification safety value is greater than the speed corresponding to the corresponding transportation scenario, the speed corresponding to the corresponding transportation scenario is adopted; otherwise, match the preset scenario factor corresponding to the corresponding transportation scenario with the task specification safety value GAZ, and then calculate the estimated task processing time;

[0012] S302: Analyze the equipment status data, where the equipment status data includes load data, energy consumption data, and maintenance data. The specific analysis is as follows:

[0013] S321: Analyze the load data to generate a load shipment abnormality signal;

[0014] S322: Analyze the energy consumption data to generate a charging signal, an available signal, and a maintenance signal;

[0015] S323: Analyze the maintenance data to generate a maintenance signal;

[0016] S303: Process the collaborative data to obtain collaborative processing based on the available number of heavy-duty AGVs, load shipment abnormality signals, charging signals, available signals, maintenance signals, maintenance signals and estimated task processing time.

[0017] As a preferred implementation of the present invention, the specific process of analyzing the traffic and environmental data to obtain the traffic coefficient is:

[0018] By acquiring traffic and environmental data, including heavy-duty AGV traffic data and transportation environment data, the heavy-duty AGV traffic data includes heavy-duty AGV traffic density, traffic speed and traffic path utilization, and the transportation environment data includes dynamic obstacle data and traffic signal and priority data; the heavy-duty AGV traffic density is obtained by using LiDAR or GPS to obtain the number of AGVs in the AGV working area, the traffic speed is recorded by speed sensors or GPS The driving speed of the AGV, the traffic path utilization is calculated by path monitoring and AGV task data to count the path usage frequency; the location of the moving obstacle is obtained. i and moving speed O V Traffic signal and priority data are recorded by the signal light system and the dispatching system to obtain signal changes and priority allocation; through the formula: Output traffic coefficient JTX, D t is the heavy-load AGV traffic density, D ref is the reference traffic density, k1 is the preset adjustment factor, and the traffic speed is V t , V max is the maximum permissible speed, U t is the traffic path utilization rate, Uref is the reference utilization rate, k2 is the preset adjustment factor, e is a natural number, and a1, a2 and a3 are their respective corresponding preset weight factors.

[0019] As a preferred implementation of the present invention, it is characterized in that the specific process of analyzing the traffic and environmental data to obtain the environmental coefficient and the comprehensive traffic and environmental value is:

[0020] By formula: Output environmental factor HJX, O t is the dynamic obstacle coefficient, O ref is the number of reference obstacles, k3 is the preset adjustment factor, S t is the traffic signal and priority value, S ref is a reference signal state or priority assignment value, k4 is a preset adjustment factor, b1, b2, a4 and a5 are preset weight factors; by the formula: Output the comprehensive loop value JHZ; then match the comprehensive loop value JHZ with the preset scenario interval. If the comprehensive loop value JHZ belongs to scenario interval one, the transportation scenario is a safe transportation scenario; if the comprehensive loop value JHZ belongs to scenario interval two, the transportation scenario is a low-speed transportation scenario; if the comprehensive loop value JHZ belongs to scenario interval three, the transportation scenario is a medium-speed transportation scenario; if the comprehensive loop value JHZ belongs to scenario interval four, the transportation scenario is a high-speed transportation scenario; the safe transportation scenario, low-speed transportation scenario, medium-speed transportation scenario or high-speed transportation scenario obtained by analysis will be stored.

[0021] As a preferred implementation of the present invention, the specific process of analyzing the load data to generate the load shipment abnormality signal is as follows:

[0022] The load sensor carried by the heavy-load AGV obtains the weight of the currently carried materials and the distribution of the materials on the AGV; the distribution of the materials on the AGV is determined by dividing the heavy-load AGV carrying area into several carrying distribution areas, and then dividing the several carrying distribution areas into central areas and edge areas. If the material weight in the edge area is greater than p times the material weight in the central area, the distribution is determined to be an edge area, and the material weight in the edge area is obtained. The distribution analysis is performed on several carrying edge areas, and the areas with the largest material weights in the first three edge areas are obtained. The interval distances between the first three edge areas are calculated. If one of the interval distances between the first three edge areas is less than the corresponding preset interval distance, a load shipment abnormality signal is generated.

[0023] As a preferred implementation of the present invention, the specific process of analyzing the energy consumption data to generate the charging signal, the available signal and the maintenance signal is as follows:

[0024] Obtain the current remaining battery power and battery energy consumption rate of the heavy-loaded AGV and the charging time of the heavy-loaded AGV; compare the current remaining battery power of the heavy-loaded AGV obtained with a set minimum remaining power threshold. If the remaining battery power is higher than the threshold, the AGV is marked as available; otherwise, a charging signal is generated, and the AGV that is being charged and whose power has not yet reached the working threshold is not counted in the available number. If the AGV whose power reaches the working threshold is counted in the available number, an available signal is generated; then compare the battery energy consumption rate with the corresponding energy consumption rate threshold. If the energy consumption rate of the heavy-loaded AGV is greater than the corresponding energy consumption rate threshold, the corresponding heavy-loaded AGV is excluded from the list of available AGVs, and a maintenance signal is generated.

[0025] As a preferred implementation of the present invention, the specific process of processing the collaborative data is as follows:

[0026] If a load handling abnormality signal is generated and processed by the corresponding processing personnel, the available number of heavy-loaded AGVs will be reduced, and the path that generates the load handling abnormality signal will be marked as abnormal, and the transport road path will be regenerated, and then the new available number of heavy-loaded AGVs and the path data of the transport road will be obtained, and the new estimated task processing time will be calculated through steps S2 and S301; if a charging signal or a maintenance signal or a maintenance signal is generated, the corresponding heavy-loaded AGV will be charged after completing the current task, and the available number of heavy-loaded AGVs will be reduced. By obtaining the new available number of heavy-loaded AGVs and the path data of the transport road, the new estimated task processing time will be calculated through steps S2 and S301; similarly, if an available signal is generated, the available number of heavy-loaded AGVs will increase, and by obtaining the new available number of heavy-loaded AGVs and the path data of the transport road, the new estimated task processing time will be calculated through steps S2 and S301.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] 1. By analyzing the environmental scenarios of heavy-loaded AGVs, the system can accurately identify and classify different transportation scenarios to ensure the safety and efficiency of transportation; by analyzing the path, traffic and environmental data, the system can evaluate the speed coefficient, traffic coefficient and environmental coefficient of the heavy-loaded AGV's driving path in real time, and finally output the comprehensive loop value JHZ to match the corresponding transportation scenarios; this process improves the autonomous decision-making ability of heavy-loaded AGVs in complex environments, and can also optimize the transportation path selection, effectively reduce transportation time and energy consumption, improve overall transportation efficiency, and at the same time ensure safety during transportation.

[0029] 2. Through the analysis of heavy-load AGV scene scheduling, the system can dynamically adjust and optimize the task allocation and path planning of heavy-load AGV. Through in-depth analysis of task status, equipment status and collaboration data, the system can effectively manage key factors such as AGV load, energy consumption, maintenance, etc. to ensure the efficient use of each AGV. When abnormal load, low power or maintenance needs are detected, the system can quickly make adjustments, reallocate tasks and paths, and ensure that transportation tasks are completed on time and safely; this intelligent scheduling not only improves the operating efficiency of the heavy-load AGV system, but also minimizes task delays and safety hazards caused by equipment problems or improper scheduling, thereby significantly improving the reliability and stability of the entire transportation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0031] Figure 1 It is a diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0032] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] It should be understood that the terms "include" and "comprising" used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0034] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and claims, the singular forms of "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations.

[0035] See also Figure 1 As shown, an intelligent heavy-load AGV scheduling method, the specific steps are:

[0036] S1: The position and path data, task status data, traffic and environment data, equipment status data and collaborative operation data of the heavy-duty AGV are obtained by sensors, specifically: the position and path data are obtained through GPS, laser navigation or visual navigation system; the traffic and environment data are obtained through ultrasonic sensors, infrared sensors, laser radar (LiDAR), etc., to perceive the road conditions and dynamic obstacle information in real time; the equipment status data is obtained through various sensors equipped inside the heavy-duty AGV, such as battery power sensors, temperature sensors, vibration sensors, etc., to monitor the operating status of the heavy-duty AGV in real time; the collaborative operation data is obtained by using wireless communication technology (such as Wi-Fi, Zigbee) and communication protocols, so that the heavy-duty AGVs can exchange collaborative data such as task status and path information to ensure the collaborative work of multiple AGV systems;

[0037] S2: Analyze the heavy-load AGV environment scenario. The specific analysis steps are as follows:

[0038] S201: Analyze the position and path data: The position and path data include the heavy-load AGV position and the heavy-load AGV driving path, and analyze each heavy-load AGV driving path; obtain the length and minimum width of the heavy-load AGV driving path, the number of turns and the corresponding turning angles in the heavy-load AGV driving path, and the distance between two turns in the heavy-load AGV driving path; and use the formula: Output heavy-load AGV travel path speed coefficient LJ β ; Where L is the total length of the path, T is the total time of the heavy-duty AGV driving path, and the minimum width of the path is W min , W ref is the reference path width, i is a position point on the path, α i is the angle of change in direction at position i, d ref is the reference turning distance; d i is the distance between position i and position i+1, and n is the number of turns in the heavy-load AGV driving path;

[0039] S202: Analyze the traffic and environment data; the traffic and environment data include heavy-duty AGV traffic data and transportation environment data, wherein the heavy-duty AGV traffic data includes heavy-duty AGV traffic density, traffic speed and traffic path utilization, and the transportation environment data includes dynamic obstacle data and traffic signal and priority data; the heavy-duty AGV traffic density is obtained by using LiDAR or GPS to obtain the number of AGVs in the AGV working area and calculate the number of AGVs per square meter; the traffic speed is recorded by a speed sensor or GPS to record the driving speed of the AGV and calculate the average value; the traffic path utilization rate is calculated by path monitoring and AGV task data to count the path usage frequency; the dynamic obstacle data (moving obstacles in the AGV path, such as pedestrians, vehicles, etc.) obtains the position of the moving obstacles. i and moving speed O V Traffic signal and priority data are recorded by the signal light system and the dispatching system to obtain signal changes and priority allocation; through the formula: Output traffic coefficient JTX, D t is the heavy-load AGV traffic density, D ref is the reference traffic density, k1 is the preset adjustment factor, and the traffic speed is V t , V max is the maximum permissible speed, U t is the traffic path utilization rate, U ref is the reference utilization rate, k2 is the preset adjustment factor, e is a natural number, a1, a2 and a3 are the corresponding preset weight factors; then through the formula: Output environmental factor HJX, O t is the dynamic obstacle coefficient, O ref is the number of reference obstacles, k3 is the preset adjustment factor, S t is the traffic signal and priority value, S ref is the reference signal state or priority allocation, k4 is the preset adjustment factor, b1, b2, a4 and a5 are the corresponding preset weight factors; finally, the formula is: Output the comprehensive cross-loop value JHZ; then match the comprehensive cross-loop value JHZ with the preset scene interval, the preset scene interval includes scene interval one, scene interval two, scene interval three and scene interval four; scene interval one corresponds to an interval (CF1, CF2], scene interval two corresponds to an interval (CF2, CF3], scene interval three corresponds to an interval (CF3, CF4], scene interval four corresponds to an interval (CF4, CF5]; CF1<CF2<CF3<CF4<CF5; if JHZ∈(CF1, CF2], it corresponds to the scene interval one, and its transportation scene is the safe transportation scene; if JHZ∈(CF2, CF3], it corresponds to the scene interval two, and its transportation scene is the low-speed transportation scene; if JHZ∈(CF3, CF4], it corresponds to the scene interval three, and its transportation scene is the medium-speed transportation scene; if JHZ∈(CF4, CF5], it corresponds to the scene interval three, and its transportation scene is the high-speed transportation scene; further, the safe transportation scene or the low-speed transportation scene or the medium-speed transportation scene or the high-speed transportation scene obtained by analysis is stored;

[0040] S3: Analyze the heavy-load AGV scenario scheduling. The specific analysis steps are as follows:

[0041] S301: Analyze the task status data; the task status data includes the task quantity, task specifications and task safety level (the task safety level indicates the safety requirement level of the task during execution. The task safety level obtains the urgency coefficient, irreplaceability coefficient and capital value of the goods transported by the corresponding task through the database. The urgency coefficient is determined by the length of time for transporting the goods of the corresponding task. The longer the time for transporting the goods of the corresponding task is, the smaller the urgency coefficient is; the irreplaceability coefficient is determined by the number of replaceable goods for the transport of the goods of the corresponding task. The more the number of replaceable goods is, the smaller the irreplaceability coefficient is; through the formula: Output the task safety value RAZ, where Jj is the emergency coefficient, θ is the irreplaceable coefficient, q is the amount of capital value, and h1, h2, and h3 are all preset weight factors; then set the task safety threshold one RAZ1 and the task safety threshold two RAZ2, and RAZ1 < RAZ2; if the task safety value RAZ is less than or equal to RAZ1, the safety level is low; if the task safety value RAZ is greater than RAZ1 and less than or equal to RAZ2, the safety level is medium; if the task safety value RAZ is greater than RAZ2, the safety level is high); first obtain the identity number of the heavy-duty AGV, then obtain the total number of tasks, number the total number, and record the allocation time, start time, and end time of the tasks corresponding to the identity number of each heavy-duty AGV; also obtain the weight, volume, and special processing requirements (such as temperature control, fragility, etc.) of the materials to be processed in the task; finally, obtain the corresponding task safety level (the safety level of each task, such as: high, medium, low, specific requirements related to safety) through the safety level database. If the safety level is high, then determine the corresponding task safety value RAZ as a fixed value A1, such as taking the value 100; if the safety level is medium, then determine the corresponding task safety value RAZ as a fixed value A2, such as taking the value 60; if the safety level is low, then determine the corresponding task safety value RAZ as a fixed value A3, such as taking the value 30, and A1 > A2 > A3; and calculate the maximum speed during transportation of the task status, specifically: through the formula: Output the task specification safety value GAZ, where e is a natural number, c1 and c2 are each corresponding preset weight factors, G1 is the weight of the materials to be processed in the task, K1 is the volume of the materials to be processed in the task, tη is the special processing requirement value. If there is no special processing requirement in the obtained task, then tη is 0, Aλ is the task safety level, and λ = 1 or 2 or 3;

[0042] Furthermore, compare the task specification safety value GAZ with the corresponding speed of the transportation scenario. If the task specification safety value GAZ is greater than the corresponding speed of the transportation scenario, then adopt the corresponding speed of the transportation scenario; otherwise, match the corresponding preset scenario factor of the transportation scenario with the task specification safety value GAZ. The preset scenario factors corresponding to the safe transportation scenario, low-speed transportation scenario, medium-speed transportation scenario, and high-speed transportation scenario are CJZ1, CJZ2, CJZ3, and CJZ4 respectively, and through the formula: Output the expected task processing time YSZ, where L fis the path length of scenario Jf (safe transportation scenario, low-speed transportation scenario, medium-speed transportation scenario and high-speed transportation scenario are marked as J1, J2, J3 and J4 respectively); Q is the number of tasks; CJZf is the collective name of the preset scenario factors CJZ1, CJZ2, CJZ3 and CJZ4 respectively, and f=1, 2, 3, 4; C is the number of heavy-load AGVs, and C' is the transportation volume correction factor corresponding to the heavy-load AGV; compare the expected task processing time YSZ with the preset processing time threshold. If the expected task processing time YSZ is greater than the preset processing time threshold, analyze the state of the heavy-load AGV and execute S302;

[0043] S302: Analyze the equipment status data, which includes load data, energy consumption data and maintenance data, specifically:

[0044] S321: Analyze the load data, and obtain the weight of the currently carried materials and the distribution of the materials on the AGV through the load sensor carried by the heavy-duty AGV; the distribution of the materials on the AGV is determined by dividing the heavy-duty AGV carrying area into several carrying distribution areas, and then dividing the several carrying distribution areas into a central area and an edge area. If the weight of the materials in the edge area is greater than p times the weight of the materials in the central area (the value of p is determined by the AGV of the corresponding specification), the distribution is determined to be an edge area, and the weight of the materials in the edge area is obtained. The distribution analysis of several carrying edge areas is performed to obtain the areas with the largest material weights in the first three edge areas, and the interval distances between the first three edge areas are calculated. If one of the interval distances between the first three edge areas is less than the corresponding preset interval distance, a load shipment abnormality signal is generated; it should be noted that: the material weights of the first three edge areas all have a certain difference. If the interval distances between the first three edge areas are less than the corresponding preset interval distance, and the difference in the material weights of the first three edge areas is less than the corresponding preset difference, it is determined to be invalid data;

[0045] S322: Analyze the energy consumption data to obtain the current remaining battery power and battery energy consumption rate (energy consumption rate = consumed power ÷ driving distance) of the heavy-loaded AGV and the charging time of the heavy-loaded AGV; compare the current remaining battery power of the heavy-loaded AGV obtained with a set minimum remaining power threshold (such as 20%). Only AGVs with a remaining battery power higher than the threshold are considered available; otherwise, a charging signal is generated, and AGVs that are being charged and whose power has not yet reached the working threshold are not counted in the available number. AGVs whose power has reached the working threshold are counted in the available number and an available signal is generated; then compare the battery energy consumption rate with the corresponding energy consumption rate threshold. If the energy consumption rate of the heavy-loaded AGV is greater than the corresponding energy consumption rate threshold, the corresponding heavy-loaded AGV is excluded from the list of available AGVs, and a maintenance signal is generated;

[0046] S323: Analyze the maintenance data; obtain the maintenance record of each heavy-load AGV to obtain the corresponding maintenance date. If the maintenance date of a heavy-load AGV matches the collected real-time date, exclude the corresponding heavy-load AGV from the available AGV list and generate a maintenance signal; then count the available number of heavy-load AGVs;

[0047] S303: Process the collaborative data to obtain the available number of heavy-loaded AGVs, load handling abnormality signals, charging signals, available signals, maintenance signals, maintenance signals and estimated task processing time; if a load handling abnormality signal is generated, it is processed by the corresponding processing personnel, then the available number of heavy-loaded AGVs is reduced, and the path that generates the load handling abnormality signal is marked as abnormal, the transport road path is regenerated, and then the new available number of heavy-loaded AGVs and the path data of the transport road are obtained, etc., and the new estimated task processing time is calculated based on S2 and S301; if a charging signal or maintenance signal or maintenance signal is generated, the corresponding heavy-loaded AGV is charged after completing the current task, then the available number of heavy-loaded AGVs is reduced, and the new estimated task processing time is calculated based on S2 and S301 by obtaining the new available number of heavy-loaded AGVs and the path data of the transport road; similarly, if an available signal is generated, the available number of heavy-loaded AGVs increases, and the new estimated task processing time is calculated based on S2 and S301 by obtaining the new available number of heavy-loaded AGVs and the path data of the transport road.

[0048] The preset factors involved in this application are all customized by those skilled in the art during use.

[0049] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent heavy-load AGV scheduling method, characterized in that: include: S1: Obtain the location and path data, task status data, traffic and environment data, equipment status data and collaborative operation data of the heavy-duty AGV; S2: Analyze the heavy-load AGV environment scenario. The specific analysis steps are as follows: S201: Analyze the position and path data, which include the heavy-load AGV position and the heavy-load AGV driving path, and analyze each heavy-load AGV driving path; obtain the length and minimum width of the heavy-load AGV driving path, the number of turns and the corresponding turning angles in the heavy-load AGV driving path, and the distance between two turns in the heavy-load AGV driving path; and obtain the speed coefficient of the heavy-load AGV driving path by calculation, through the formula: Output heavy-load AGV travel path speed coefficient LJ β ; Where L is the total length of the path, T is the total time of the heavy-duty AGV driving path, and the minimum width of the path is W min , W ref is the reference path width, i is a position point on the path, α i is the angle of change in direction at position i, d ref is the reference turning distance; d i is the distance between the i position point and the i+1 position point; S202: The specific process of analyzing the traffic and environmental data to obtain the traffic coefficient, environmental coefficient and comprehensive traffic and environmental value is as follows: By acquiring traffic and environmental data, including heavy-duty AGV traffic data and transportation environment data, the heavy-duty AGV traffic data includes heavy-duty AGV traffic density, traffic speed and traffic path utilization, and the transportation environment data includes dynamic obstacle data and traffic signal and priority data; the heavy-duty AGV traffic density is obtained by using LiDAR or GPS to obtain the number of AGVs in the AGV working area, the traffic speed is recorded by speed sensors or GPS The driving speed of the AGV, the traffic path utilization is calculated by path monitoring and AGV task data to count the path usage frequency; the location of dynamic obstacles is obtained. i and moving speed O V ; Obtain traffic signal changes and priority allocation through the records of the signal light system and the dispatching system; Through the formula: Output traffic coefficient JTX, D t is the heavy-load AGV traffic density, D ref is the reference traffic density, k1 is the preset adjustment factor, and the traffic speed is V t , V max is the maximum permissible speed, U t is the traffic path utilization rate, U ref is the reference utilization rate, k2 is the preset adjustment factor, e is the natural constant, and a1, a2 and a3 are the corresponding preset weight factors; By formula: Output environmental factor HJX, O t is the dynamic obstacle coefficient, O ref is the number of reference obstacles, k3 is the preset adjustment factor, O i is the position of the dynamic obstacle, O V is the moving speed of the dynamic obstacle, S t is the priority value corresponding to the traffic signal, e is a natural constant, S ref is the reference signal state, k4 is the preset adjustment factor, b1, b2, a4 and a5 are all preset weight factors; according to the formula: Output comprehensive cross-loop value JHZ; S3: Analyze the heavy-load AGV scenario scheduling. The specific analysis steps are as follows: S301: Analyze the task status data to obtain the task specification safety value, according to the formula: Output the task specification safety value GAZ, where e is a natural constant, c1 and c2 are the corresponding preset weight factors, G1 is the weight of the material to be processed in the task, K1 is the volume of the material to be processed in the task, tη is the special processing requirement value, if there is no special processing requirement in the task, tη is 0, Aλ is the task safety level, and λ=1 or 2 or 3; and compare the task specification safety value with the speed corresponding to the corresponding transportation scenario. If the task specification safety value is greater than the speed corresponding to the corresponding transportation scenario, the speed corresponding to the corresponding transportation scenario is adopted; otherwise, the preset scenario factor corresponding to the corresponding transportation scenario is matched with the task specification safety value GAZ, and then the estimated task processing time is calculated; S302: Analyze the equipment status data, where the equipment status data includes load data, energy consumption data, and maintenance data. The specific analysis is as follows: S321: Analyze the load data to generate a load shipment abnormality signal; S322: Analyze the energy consumption data to generate a charging signal, an available signal, and a maintenance signal; S323: Analyze the maintenance data to generate a maintenance signal; S303: Process the collaborative data to obtain comprehensive results of corresponding factors, including: the available number of heavy-load AGVs, load transportation abnormality signals, charging signals, available signals, maintenance signals, maintenance signals, and estimated task processing time, and perform collaborative processing.

2. The intelligent heavy-load AGV scheduling method according to claim 1 is characterized in that: The specific process of analyzing the load data to generate the load shipment abnormality signal is as follows: The load sensor carried by the heavy-load AGV obtains the weight of the currently carried materials and the distribution of the materials on the AGV; the distribution of the materials on the AGV is determined by dividing the heavy-load AGV carrying area into several carrying distribution areas, and then dividing the several carrying distribution areas into central areas and edge areas. If the material weight in the edge area is greater than p times the material weight in the central area, the distribution is determined to be an edge area, and the material weight in the edge area is obtained. The distribution analysis is performed on several carrying edge areas, and the areas with the largest material weights in the first three edge areas are obtained. The interval distances between the first three edge areas are compared. If one of the intervals is less than the corresponding preset interval distance, a load shipment abnormality signal is generated.

3. The intelligent heavy-load AGV scheduling method according to claim 1 is characterized in that: The specific process of analyzing the energy consumption data to generate the charging signal, the available signal and the maintenance signal is as follows: Obtain the current remaining battery power and battery energy consumption rate of the heavy-loaded AGV and the charging time of the heavy-loaded AGV; compare the current remaining battery power of the heavy-loaded AGV obtained with a set minimum remaining power threshold. If the remaining battery power is higher than the threshold, the AGV is marked as available; otherwise, a charging signal is generated, and the AGV that is being charged and whose power has not yet reached the working threshold is not counted in the available number. If the AGV whose power reaches the working threshold is counted in the available number, an available signal is generated; then compare the battery energy consumption rate with the corresponding energy consumption rate threshold. If the energy consumption rate of the heavy-loaded AGV is greater than the corresponding energy consumption rate threshold, the corresponding heavy-loaded AGV is excluded from the list of available AGVs, and a maintenance signal is generated.

4. The intelligent heavy-load AGV scheduling method according to claim 1 is characterized in that: The specific process of processing the collaborative data is as follows: If a load handling abnormality signal is generated and processed by the corresponding processing personnel, the available number of heavy-loaded AGVs will be reduced, and the path that generates the load handling abnormality signal will be marked as abnormal, and the transport road path will be regenerated, and then the new available number of heavy-loaded AGVs and the path data of the transport road will be obtained, and the new estimated task processing time will be calculated through steps S2 and S301; if a charging signal or a maintenance signal or a maintenance signal is generated, the corresponding heavy-loaded AGV will be charged after completing the current task, and the available number of heavy-loaded AGVs will be reduced. By obtaining the new available number of heavy-loaded AGVs and the path data of the transport road, the new estimated task processing time will be calculated through steps S2 and S301; similarly, if an available signal is generated, the available number of heavy-loaded AGVs will increase, and by obtaining the new available number of heavy-loaded AGVs and the path data of the transport road, the new estimated task processing time will be calculated through steps S2 and S301.

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