AGV automatic conveying management method and system based on cloud computing

Abstract

The invention discloses an AGV automatic conveying management method and system based on cloud computing, and relates to the technical field of AGV conveying management, and the method comprises the steps: integrating a cross-warehouse task and AGV resources through constructing a cloud resource constraint model, and achieving the global optimization scheduling through combining an order attribute rule, a cargo attribute rule and a dynamic constraint rule; generating a dynamic avoidance path by using a sliding linear tracing algorithm, quantitatively evaluating a collision risk through an obstacle thermodynamic diagram mark and a safety area model, and introducing an auction algorithm to realize dynamic score distribution of a task matching degree and electric quantity information; the integrated sensor collects environment data in real time, and an obstacle avoidance model containing multi-dimensional information such as a safety boundary and an emergent obstacle is constructed. According to the technology, the AGV scheduling efficiency and the path safety are remarkably improved, the method can be widely applied to the fields of intelligent manufacturing, warehouse logistics and the like, and an efficient and reliable solution is provided for intelligent conveying management in a complex industrial scene.

Description

Technical Field

[0001] The present invention relates to the technical field of AGV transportation management, and specifically relates to a method and system for automatic AGV transportation management based on cloud computing. Background Art

[0002] In the context of the intelligent transformation of the manufacturing industry, traditional AGV transportation management has problems such as low efficiency and difficult scheduling. Cloud computing has powerful data processing and storage capabilities, and a method and system for automatic AGV transportation management based on cloud computing have emerged as the times require. Its significance lies in optimizing AGV scheduling through cloud computing, improving transportation efficiency and accuracy, and realizing intelligent allocation of resources; reducing management costs, enhancing system flexibility and scalability, helping enterprises achieve production automation and intelligent upgrading, and enhancing market competitiveness.

[0003] Existing methods and systems for automatic AGV transportation management based on cloud computing lack a cross-warehouse resource integration mechanism, cannot dynamically coordinate the AGV task allocation of multiple warehouses, and do not fully consider dynamic scenarios in the workshop (such as equipment movement, temporary obstacles), resulting in frequent path conflicts. Therefore, a method and system for automatic AGV transportation management based on cloud computing are needed to solve the above problems. Summary of the Invention

[0004] To solve the above technical problems, a method and system for automatic AGV transportation management based on cloud computing are provided. The present technical solution solves the problems in the above background art that existing methods and systems for automatic AGV transportation management based on cloud computing lack a cross-warehouse resource integration mechanism, cannot dynamically coordinate the AGV task allocation of multiple warehouses, and do not fully consider dynamic scenarios in the workshop, resulting in frequent path conflicts.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for automatic AGV transportation management based on cloud computing, including: S1. Obtain the location information of multiple warehouses and import the location information of multiple warehouses into the cloud platform. Then, receive the AGV task request information of multiple warehouses through the cloud platform, and use the AGV task request information and location information of multiple warehouses to construct a resource constraint model; S2. Based on the AGV task request information of multiple warehouses, determine the candidate AGV information. Then, according to the candidate AGV information and the resource constraint model, screen the candidate AGVs to determine the optimal AGV; S3. Download the internal workshop scene information and production line scene information from the cloud platform, arbitrarily select a preferred AGV from the preferred AGVs to obtain the first preferred AGV, and then simulate and generate the first initial conveying route information of the first preferred AGV through the AGV task request information, internal workshop scene information, and production line scene information; S4. Determine the conveying focus position information in the first initial conveying route information according to the first initial conveying route information, internal workshop scene information, and production line scene information, and obtain the second initial conveying route information according to the conveying focus position information in the first initial conveying route information; S5. Select at least one preferred AGV from the preferred AGVs, import the second initial conveying route information into the selected preferred AGVs, and then simulate the conveying of the preferred AGVs based on the first initial conveying route information and the second initial conveying route information to obtain the AGV conveying safety constraint model; S6. Import the AGV conveying safety constraint model into all the preferred AGVs to obtain the conveying safety AGV information, then import the AGV task request information into the conveying safety AGVs, and use the sensors deployed on the AGVs to collect real-time environmental data to generate an obstacle avoidance model to realize the automatic conveying management of the AGVs.

[0006] In an alternative embodiment, step S1 specifically includes: Collect the longitude and latitude information and regional range information of each warehouse through a GPS device to obtain the location information of multiple warehouses, and import the location information of multiple warehouses into the cloud platform; Receive the AGV task request information of each warehouse through the cloud platform, where the AGV task request information includes a conveying start point, a conveying end point, order attribute information, cargo attribute information, and time information; Define order attribute rules and cargo attribute rules based on the order attribute information and the cargo attribute information; Obtain the AGV load information and warehouse busyness from the cloud platform, and define resource constraint rules based on the AGV load information and warehouse busyness; Define dynamic constraint rules based on the conveying start point, the conveying end point, the time information, and the location information of multiple warehouses; Construct a resource constraint model according to the order attribute rules, the cargo attribute rules, the resource constraint rules, and the dynamic constraint rules.

[0007] In an alternative embodiment, step S2 specifically includes: Obtain the location information of all AGVs within the range of the conveying start point and the conveying end point according to the AGV task request information of multiple warehouses; Based on the position information of all AGVs within the range of the transportation starting point and the transportation ending point, a pool of candidate AGVs is constructed to determine the candidate AGV information, where the candidate AGV information includes candidate AGV position information, candidate AGV power information, and candidate AGV type information; According to the candidate AGV type information, the maximum load capacity of the candidate AGV is obtained, and according to the cargo attribute information, the cargo weight is obtained. Then, the ratio of the maximum load capacity of the candidate AGV to the cargo weight is used as the task matching degree; Based on the resource constraint model, an AGV score threshold is obtained. Then, according to the task matching degree and the candidate AGV power information, the candidate AGV score is obtained; The candidate AGVs are screened through the candidate AGV score and the AGV score threshold. The candidate AGVs with a candidate AGV score greater than or equal to the AGV score threshold are extracted from the candidate AGV pool, and a preferred AGV pool is constructed; The auction algorithm is used to allocate preferred AGVs to the AGV task request information of multiple warehouses. Then, the AGV corresponding to the AGV request information of each warehouse is designated as the preferred AGV.

[0008] In an alternative embodiment, step S3 specifically includes: Download the internal workshop scene information and production line scene information from the MES system of the cloud platform. The internal workshop scene information includes the workshop layout diagram, and the production line scene information includes the shelf position and equipment coordinates; Using the workshop layout diagram, shelf position, and equipment coordinates, a two-dimensional production line map is constructed, and the equipment occupied environment information is obtained using the equipment coordinates. The obstacle heat map is marked with the equipment occupied environment information; Based on the two-dimensional production line map and the obstacle heat map, the obstacle heat map is deleted from the two-dimensional production line map, and the remaining area is used as the preferred AGV transportable area map; Obtain the edge points of the limited AGV transportable area map and determine the center point of the two-dimensional production line map; With the center point of the two-dimensional production line map, a sliding straight line f(x)=h is constructed, where h is a dynamic sliding value, and the unit value of the dynamic sliding value is the distance value between the two nearest edge points of the limited AGV transportable area map; The sliding straight line is used to traverse all edge points in the limited AGV transportable area map, and when the dynamic sliding values are the same, the transport path tracing straight line f(x)=j with at least two edge points existing on the sliding straight line is obtained; Obtain the coordinates of the two edge points with the farthest distance in the transport path tracing straight line, and construct the first transport path tracing line segment with the coordinates of the two edge points with the farthest distance in the transport path tracing straight line; Determine the center point of the first conveying path tracing line segment, and obtain the two edge points closest to the center point of the first conveying path tracing line segment; Construct a second conveying path tracing line segment with the two edge points closest to the center point of the conveying path tracing line segment; Starting from the conveying starting point, sequentially connect the center points of all the second conveying path tracing line segments to obtain an irregular line segment from the conveying starting point to the center point of the second conveying path tracing line segment to the conveying ending point; Utilize the irregular line segment to construct a conveying path tracing function, and based on the conveying path tracing function, simulate and generate the first initial conveying route information of the first preferred AGV corresponding to all AGV task request messages; Wherein the expression formula of the conveying path tracing function is: ; In the formula, is any point n on the irregular line segment, is the distance value from any point n on the irregular line segment to the conveying ending point, is the conveying path tracing reference value.

[0009] In an alternative embodiment, step S4 specifically includes: According to the first initial conveying route information, the internal workshop scene information, and the production line scene information, determine the conveying focus position information in the first initial conveying route information, where the conveying focus position information includes the AGV conveying turning point and the AGV conveying intersection point; Randomly select a first initial conveying route from the first initial conveying route information of the first preferred AGV corresponding to all simulated AGV task request messages, and designate the remaining first initial conveying routes as the third initial conveying routes; Traverse the third initial conveying routes, and designate the third initial conveying routes that include the conveying focus positions in the first initial conveying route information as the second initial conveying route information.

[0010] In an alternative embodiment, step S5 specifically includes: Based on the first initial conveying route information and the second initial conveying route information, perform simulated conveyance on the preferred AGV, and obtain the second initial conveying route information where the preferred AGV collides during the simulated conveyance; Obtain the total number of the second initial conveying routes where collisions occur, then based on the information of the type of AGV to be selected, determine the floor area of the preferred AGV, and multiply the floor area of the preferred AGV by the total number of the second initial conveying routes where collisions occur to obtain the AGV conveying safety area information; Use the AGV conveying safety area information as the AGV conveying safety constraint model.

[0011] In an optional embodiment, step S6 specifically includes: Import the AGV transportation safety constraint model into all the selected AGVs to obtain the transportation safety AGV information. Then, import the AGV task request information into the transportation safety AGVs, and use the sensors deployed on the AGVs to collect real-time environmental data to generate an obstacle avoidance model, so as to realize the automatic transportation management of AGVs; Import the AGV transportation safety constraint model into all the selected AGVs, and use the edge of the AGV transportation safety constraint model as the safety boundary of the selected AGVs, so as to obtain the transportation safety AGV information. The transportation safety AGV information includes the safety boundary of the selected AGVs, the position information of the selected AGVs, the power information of the selected AGVs, and the type information of the selected AGVs; Use the sensors deployed on the AGVs to collect real-time environmental data. The real-time environmental data includes the positions of sudden obstacles, the positions of working AGVs, and the edges of production equipment; When the position of a sudden obstacle, the position of a working AGV, or the edge of production equipment coincides with the safety boundary of the selected AGVs, an obstacle avoidance model is generated according to the position information of the selected AGVs, the type information of the selected AGVs, and the real-time environmental data, and the automatic transportation management of AGVs is realized according to the obstacle avoidance model.

[0012] Furthermore, an AGV automatic transportation management system based on cloud computing is proposed for implementing the management method as described in any one of the above, including: An acquisition module, which is used to obtain the position information of multiple warehouses and import the position information of multiple warehouses into the cloud platform, and is also used to collect real-time environmental data by using the sensors deployed on the AGVs; A main control module, which is used to receive the AGV task request information of multiple warehouses through the cloud platform, and is also used to download the internal workshop scene information and the production line scene information from the cloud platform, and is used to construct a resource constraint model by using the AGV task request information and the position information of multiple warehouses, and is used to determine the candidate AGV information based on the AGV task request information of multiple warehouses. Then, according to the candidate AGV information and the resource constraint model, the candidate AGVs are screened to determine the selected AGVs; A management module, which is used to arbitrarily select a preferred AGV from the preferred AGVs to obtain a first preferred AGV. Then, based on the AGV task request information, the internal workshop scene information, and the production line scene information, it simulates and generates the first initial conveying route information of the first preferred AGV. According to the first initial conveying route information, the internal workshop scene information, and the production line scene information, it determines the conveying focus position information in the first initial conveying route information, and based on the conveying focus position information in the first initial conveying route information, it obtains the second initial conveying route information, which is used to select at least one preferred AGV from the preferred AGVs and import the second initial conveying route information into the selected preferred AGV. Then, based on the first initial conveying route information and the second initial conveying route information, it conducts a simulated conveyance of the preferred AGV to obtain an AGV conveyance safety constraint model, which is used to import the AGV conveyance safety constraint model into all the preferred AGVs to obtain the conveyance safety AGV information. Then, it imports the AGV task request information into the conveyance safety AGV and generates an obstacle avoidance model to realize the automatic conveyance management of the AGV; A display module, which is used to display the acquisition process and results of the acquisition module, the data processing process and results of the main control module, and the management process and results of the management module.

[0013] In an alternative embodiment, the main control module includes: A receiving unit, which is used to receive the AGV task request information of multiple warehouses through the cloud platform and is also used to download the internal workshop scene information and the production line scene information from the cloud platform; A processing unit, which is used to construct a resource constraint model by using the AGV task request information and the location information of multiple warehouses; An AGV determination unit, which is used to determine the candidate AGV information based on the AGV task request information of multiple warehouses, and then screen the candidate AGV according to the candidate AGV information and the resource constraint model to determine the preferred AGV.

[0014] In an alternative embodiment, the management module includes: A path determination unit, which is used to arbitrarily select a preferred AGV from the preferred AGVs to obtain a first preferred AGV. Then, based on the AGV task request information, the internal workshop scene information, and the production line scene information, it simulates and generates the first initial conveying route information of the first preferred AGV. According to the first initial conveying route information, the internal workshop scene information, and the production line scene information, it determines the conveying focus position information in the first initial conveying route information, and based on the conveying focus position information in the first initial conveying route information, it obtains the second initial conveying route information; Safety constraint unit, which is used to select at least one preferred AGV from the preferred AGVs, import the second initial conveying route information into the selected preferred AGV, and then simulate the conveying of the preferred AGV based on the first initial conveying route information and the second initial conveying route information to obtain an AGV conveying safety constraint model; Obstacle avoidance management unit, which is used to import the AGV conveying safety constraint model into all the preferred AGVs to obtain the conveying safety AGV information, and then import the AGV task request information into the conveying safety AGVs and generate an obstacle avoidance model to realize the automatic conveying management of AGVs.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. A method and system for automatic conveying management of AGV based on cloud computing proposed by this solution can unify the management of multi-warehouse task requests and AGV resources by constructing a cloud resource constraint model, realize global optimal scheduling, and dynamically coordinate the AGV task allocation of multiple warehouses; 2. A method and system for automatic conveying management of AGV based on cloud computing proposed by this solution can generate a dynamic avoidance path and reduce the path intersection points through the construction of a two-dimensional map of the production line, the marking of the obstacle heat map and the sliding straight line tracing algorithm; 3. A method and system for automatic conveying management of AGV based on cloud computing proposed by this solution can collect environmental data in real time through sensors, dynamically update the safety boundary model, and trigger the obstacle avoidance mechanism when it is detected that the obstacle coincides with the AGV safety boundary, so as to avoid the lag in response of AGV when encountering sudden obstacles. Description of the Drawings

[0016] Figure 1 It is a flowchart of a method for automatic conveying management of AGV based on cloud computing proposed by the present invention; Figure 2 It is a flowchart for obtaining the preferred AGV in the present invention; Figure 3 It is a flowchart for obtaining the first initial conveying route information in the present invention; Figure 4 It is a system framework diagram of a system for automatic conveying management of AGV based on cloud computing proposed by the present invention. Detailed Embodiments

[0017] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations.

[0018] Refer to Figure 1 - Figure 4As shown in the figure, a cloud computing-based AGV automatic conveying management method includes: S1. Obtain the location information of multiple warehouses and import the location information of multiple warehouses into the cloud platform. Then, receive the AGV task request information of multiple warehouses through the cloud platform, and use the AGV task request information and location information of multiple warehouses to construct a resource constraint model; S2. Based on the AGV task request information of multiple warehouses, determine the candidate AGV information. Then, according to the candidate AGV information and the resource constraint model, screen the candidate AGVs to determine the optimal AGV; S3. Download the internal workshop scene information and production line scene information from the cloud platform. Arbitrarily select an optimal AGV from the optimal AGVs to obtain the first optimal AGV. Then, through the AGV task request information, internal workshop scene information and production line scene information, simulate and generate the first initial conveying route information of the first optimal AGV; S4. According to the first initial conveying route information, internal workshop scene information and production line scene information, determine the conveying focus position information in the first initial conveying route information, and obtain the second initial conveying route information according to the conveying focus position information in the first initial conveying route information; S5. Select at least one optimal AGV from the optimal AGVs, and import the second initial conveying route information into the selected optimal AGVs. Then, based on the first initial conveying route information and the second initial conveying route information, simulate the conveying of the optimal AGVs to obtain an AGV conveying safety constraint model; S6. Import the AGV conveying safety constraint model into all the optimal AGVs to obtain the conveying safety AGV information. Then, import the AGV task request information into the conveying safety AGVs, and use the sensors deployed on the AGVs to collect real-time environmental data to generate an obstacle avoidance model, realizing the automatic conveying management of AGVs.

[0019] Further, step S1 specifically includes: Collect the longitude and latitude information and regional range information of each warehouse through a GPS device to obtain the location information of multiple warehouses, and import the location information of multiple warehouses into the cloud platform; Receive the AGV task request information of each warehouse through the cloud platform. The AGV task request information includes the conveying starting point, conveying ending point, order attribute information, cargo attribute information and time information; Define order attribute rules and cargo attribute rules based on the order attribute information and cargo attribute information; Obtain the AGV load information and warehouse busy degree from the cloud platform, and define resource constraint rules based on the AGV load information and warehouse busy degree; Define dynamic constraint rules based on the delivery starting point, delivery ending point, time information, and the location information of multiple warehouses; Construct a resource constraint model according to the order attribute rules, cargo attribute rules, resource constraint rules, and dynamic constraint rules.

[0020] Specifically, for GPS data collection, a high-precision GPS receiver (such as a device supporting GNSS multi-mode positioning) can be used to collect latitude and longitude coordinates at the warehouse boundary and key points (entrance / exit, shelf area), and the RTK (Real-Time Kinematic) technology can be combined to improve the positioning accuracy to the centimeter level. The warehouse outline can be scanned by lidar or drone aerial survey to generate polygon area range data. When integrating and uploading data, the geographical coordinates and area range data can be integrated into a JSON format file and uploaded to the cloud database (such as AWS IoT Core or Alibaba Cloud IoT platform) through the RESTful API interface. A spatial index can be established in PostgreSQL using the PostGIS extension to support subsequent geospatial queries.

[0021] Receive the AGV task request information of each warehouse through the cloud platform. The AGV task request information includes the delivery starting point, delivery ending point, order attribute information, cargo attribute information, and time information. Deploy an MQTT message queue (such as EMQX) in the cloud to receive the task request stream from the ERP and WMS systems. The delivery starting point / ending point in the AGV task request information is the coordinate point inside the warehouse (such as loading and unloading area A1, production line B3); the order attribute is the urgency level (priority code), cargo value level; the cargo attribute is used to describe the volume (length, width, height), weight, and temperature control requirements (if there is a cold chain). The time information is the expected delivery time interval and the latest deadline.

[0022] For the resource constraint rules, the AGV vehicle sensor data (such as weighing sensor, battery SOC value) can be collected in real time through the OPCUA protocol and updated to the time series database (such as InfluxDB). For the warehouse busyness, the warehouse busyness model can be used for calculation: When the score exceeds the threshold (such as 85%), a resource scheduling warning is triggered.

[0023] For the dynamic constraint rules, constraint programming (CP-SAT solver) can be used to handle time conflicts.

[0024] Then it can be understood that the resource constraint model is: , where i represents the number of the task (or order) (such as the transportation task that the AGV needs to execute), and j represents the number of the AGV (such as the vehicle resources participating in the task). Denote the time required for task i to be executed by AGV j, including transportation time, loading and unloading time, etc. Is a binary variable (0 or 1), indicating whether task i is assigned to AGV j. : Task i is executed by AGV j. : Task i is not executed by AGV j. Is a weight parameter. Is an overload penalty coefficient, which is the cost of AGV k being overloaded or the weight of the capacity limit. Is an overload indication variable, which is also a binary variable (0 or 1), indicating whether AGV k is overloaded. : The actual load of AGV k exceeds its capacity limit. : The load of AGV k is within the safe range.

[0025] Furthermore, step S2 specifically includes: According to the AGV task request information of multiple warehouses, obtain the location information of all AGVs within the range of the transportation starting point and the transportation ending point. Based on the location information of all AGVs within the range of the transportation starting point and the transportation ending point, construct a pool of candidate AGVs, thereby determining the candidate AGV information. The candidate AGV information includes candidate AGV location information, candidate AGV power information, and candidate AGV type information. According to the candidate AGV type information, obtain the maximum load of the candidate AGV, and according to the cargo attribute information, obtain the cargo weight. Then, use the ratio of the maximum load of the candidate AGV to the cargo weight as the task matching degree. Based on the resource constraint model, obtain the AGV score threshold. Then, according to the task matching degree and the candidate AGV power information, obtain the candidate AGV score. Through the candidate AGV score and the AGV score threshold, screen the candidate AGVs, extract the candidate AGVs with candidate AGV scores greater than or equal to the AGV score threshold from the candidate AGV pool, and construct a preferred AGV pool. Adopt an auction algorithm to allocate preferred AGVs for the AGV task request information of multiple warehouses. Then, the AGV corresponding to the AGV request information of each warehouse is defined as the preferred AGV.

[0026] Specifically, , , Are all weight coefficients and are adjusted according to business requirements.

[0027] Furthermore, step S3 specifically includes: Download the internal workshop scene information and production line scene information from the MES system of the cloud platform. The internal workshop scene information includes the workshop layout diagram, and the production line scene information includes the shelf location and equipment coordinates. Use the workshop layout diagram, shelf location, and equipment coordinates to construct a two-dimensional production line map, and obtain the equipment occupation environment information using the equipment coordinates, and mark the obstacle heat map with the equipment occupation environment information. Based on the two-dimensional production line map and the obstacle heat map, delete the obstacle heat map from the two-dimensional production line map, and thus use the remaining area as the preferred AGV transportable area map. Obtain the edge points of the limited AGV transportable area map, and determine the center point of the two-dimensional production line map. With the center point of the two-dimensional production line map, construct a sliding straight line f(x)=h, where h is a dynamic sliding value, and the unit value of the dynamic sliding value is the distance value between the two nearest edge points of the limited AGV transportable area map. Use the sliding straight line to traverse all edge points in the limited AGV transportable area map, and obtain the transport path tracing straight line f(x)=j where there are at least two edge points on the sliding straight line when the dynamic sliding values are the same. Obtain the coordinates of the two edge points with the farthest distance in the transport path tracing straight line, and construct the first transport path tracing line segment with the coordinates of the two edge points with the farthest distance in the transport path tracing straight line. Determine the center point of the first transport path tracing line segment, and obtain the two edge points closest to the center point of the first transport path tracing line segment. Construct the second transport path tracing line segment with the two edge points closest to the center point of the transport path tracing line segment. Starting from the transport starting point, connect the center points of all the second transport path tracing line segments in sequence to obtain an irregular line segment from the transport starting point to the center point of the second transport path tracing line segment to the transport ending point. Use the irregular line segment to construct a transport path tracing function, and according to the transport path tracing function, simulate and generate the first initial transport route information of the first preferred AGV corresponding to all AGV task request messages. Among them, the expression formula of the transport path tracing function is: ; In the formula, is any point n in the irregular line segment, is the distance value from any point n in the irregular line segment to the transport ending point, is the transport path tracing reference value.

[0028] Specifically, according to the conveying path tracing function, the first initial conveying route information of the first preferred AGV corresponding to all AGV task request information is simulated and generated, which means that a point n in the irregular line segment corresponding to the minimum conveying path tracing reference value is a point on the first initial conveying route, and the points n corresponding to the minimum conveying path tracing reference value are connected in sequence to obtain the first initial conveying route.

[0029] Further, step S4 specifically includes: According to the first initial conveying route information, the internal workshop scene information, and the production line scene information, determine the conveying focus position information in the first initial conveying route information, where the conveying focus position information includes the AGV conveying turning point and the AGV conveying intersection point; Randomly select a first initial conveying route from the first initial conveying route information of the first preferred AGV corresponding to all simulated AGV task request information, and designate the remaining first initial conveying routes as the third initial conveying routes; Traverse the third initial conveying routes, and designate the third initial conveying routes that contain the conveying focus positions in the first initial conveying route information as the second initial conveying route information.

[0030] Further, step S5 specifically includes: Based on the first initial conveying route information and the second initial conveying route information, simulate the conveying of the preferred AGV, and obtain the second initial conveying route information where the preferred AGV collides during the simulated conveying; Obtain the total number of the second initial conveying routes where collisions occur, and then based on the information of the candidate AGV types, determine the floor area of the preferred AGV, and multiply the floor area of the preferred AGV by the total number of the second initial conveying routes where collisions occur to obtain the AGV conveying safety area information; Use the AGV conveying safety area information as the AGV conveying safety constraint model.

[0031] Further, step S6 specifically includes: Import the AGV conveying safety constraint model into all the preferred AGVs to obtain the conveying safety AGV information, and then import the AGV task request information into the conveying safety AGVs, and use the sensors deployed on the AGVs to collect real-time environmental data to generate an obstacle avoidance model to realize the automatic conveying management of the AGVs; Import the AGV conveying safety constraint model into all the preferred AGVs, and use the edge of the AGV conveying safety constraint model as the safety boundary of the preferred AGV, so as to obtain the conveying safety AGV information, where the conveying safety AGV information includes the safety boundary of the preferred AGV, the preferred AGV position information, the preferred AGV power information, and the preferred AGV type information; Collect real-time environmental data using sensors deployed on the AGV. The real-time environmental data includes the positions of sudden obstacles, the positions of working AGVs, and the positions of the edges of production equipment. When the position of a sudden obstacle, the position of a working AGV, or the position of the edge of production equipment coincides with the safety boundary of the preferred AGV, an obstacle avoidance model is generated based on the preferred AGV position information, the preferred AGV type information, and the real-time environmental data, and AGV automatic conveying management is realized according to the obstacle avoidance model.

[0032] Furthermore, an AGV automatic conveying management system based on cloud computing is proposed for implementing the management method as described in any one of the above, including: A collection module, which is used to obtain the position information of multiple warehouses and import the position information of multiple warehouses into the cloud platform, and is also used to collect real-time environmental data using sensors deployed on the AGV. A main control module, which is used to receive the AGV task request information of multiple warehouses through the cloud platform, and is also used to download the internal workshop scene information and the production line scene information from the cloud platform, to construct a resource constraint model using the AGV task request information and the position information of multiple warehouses, to determine the candidate AGV information based on the AGV task request information of multiple warehouses, and then to screen the candidate AGVs according to the candidate AGV information and the resource constraint model to determine the preferred AGV. A management module, which is used to arbitrarily select a preferred AGV from the preferred AGVs to obtain the first preferred AGV, and then to simulate and generate the first initial conveying route information of the first preferred AGV through the AGV task request information, the internal workshop scene information, and the production line scene information, to determine the conveying focus position information in the first initial conveying route information according to the first initial conveying route information, the internal workshop scene information, and the production line scene information, and to obtain the second initial conveying route information according to the conveying focus position information in the first initial conveying route information, to select at least one preferred AGV from the preferred AGVs, and to import the second initial conveying route information into the selected preferred AGVs, and then to simulate the conveying of the preferred AGVs based on the first initial conveying route information and the second initial conveying route information to obtain an AGV conveying safety constraint model, to import the AGV conveying safety constraint model into all the preferred AGVs to obtain the conveying safety AGV information, and then to import the AGV task request information into the conveying safety AGVs and generate an obstacle avoidance model to realize AGV automatic conveying management. A display module, which is used to display the collection process and results of the collection module, to display the data processing process and results of the main control module, and to display the management process and results of the management module.

[0033] Furthermore, the main control module includes: A receiving unit, which is used to receive AGV task request information of multiple warehouses through a cloud platform, and is also used to download workshop internal scene information and production line scene information from the cloud platform; A processing unit, which is used to construct a resource constraint model by using AGV task request information and location information of multiple warehouses; An AGV determination unit, which is used to determine candidate AGV information based on AGV task request information of multiple warehouses, and then screen the candidate AGVs according to the candidate AGV information and the resource constraint model to determine the optimal AGV.

[0034] Further, the management module includes: A path determination unit, which is used to arbitrarily select an optimal AGV from the optimal AGVs to obtain a first optimal AGV, and then simulate and generate first initial conveying route information of the first optimal AGV through the AGV task request information, workshop internal scene information and production line scene information, determine the conveying focus position information in the first initial conveying route information according to the first initial conveying route information, workshop internal scene information and production line scene information, and obtain second initial conveying route information according to the conveying focus position information in the first initial conveying route information; A safety constraint unit, which is used to select at least one optimal AGV from the optimal AGVs, import the second initial conveying route information into the selected optimal AGVs, and then simulate the conveying of the optimal AGVs based on the first initial conveying route information and the second initial conveying route information to obtain an AGV conveying safety constraint model; An obstacle avoidance management unit, which is used to import the AGV conveying safety constraint model into all the optimal AGVs to obtain conveying safety AGV information, and then import the AGV task request information into the conveying safety AGVs and generate an obstacle avoidance model to realize automatic AGV conveying management.

[0035] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for automatic conveying management of AGV based on cloud computing, characterized in that, Including: S1. Obtain the location information of multiple warehouses and import the location information of the multiple warehouses into the cloud platform. Then, receive the AGV task request information of the multiple warehouses through the cloud platform, and use the AGV task request information and location information of the multiple warehouses to construct a resource constraint model; S2. Based on the AGV task request information of the multiple warehouses, determine the candidate AGV information. Then, according to the candidate AGV information and the resource constraint model, screen the candidate AGVs to determine the optimal AGV; S3. Download the internal workshop scene information and production line scene information from the cloud platform. Arbitrarily select one optimal AGV from the optimal AGVs to obtain the first optimal AGV. Then, through the AGV task request information, internal workshop scene information and production line scene information, simulate and generate the first initial conveying route information of the first optimal AGV; S4. According to the first initial conveying route information, internal workshop scene information and production line scene information, determine the conveying focus position information in the first initial conveying route information, and obtain the second initial conveying route information according to the conveying focus position information in the first initial conveying route information; S5. Select at least one optimal AGV from the optimal AGVs, and import the second initial conveying route information into the selected optimal AGVs. Then, based on the first initial conveying route information and the second initial conveying route information, perform simulated conveying on the optimal AGVs to obtain an AGV conveying safety constraint model; S6. Import the AGV conveying safety constraint model into all the optimal AGVs to obtain the conveying safety AGV information. Then, import the AGV task request information into the conveying safety AGVs, and use the sensors deployed on the AGVs to collect real-time environment data to generate an obstacle avoidance model to realize automatic AGV conveying management.

2. The method for automatically transporting and managing an AGV based on cloud computing according to claim 1, wherein Step S1 specifically includes: Collect the longitude and latitude information and regional range information of each warehouse through a GPS device to obtain the location information of the multiple warehouses, and import the location information of the multiple warehouses into the cloud platform; Receive the AGV task request information of each warehouse through the cloud platform, and the AGV task request information includes a conveying starting point, a conveying ending point, order attribute information, cargo attribute information, and time information; Define order attribute rules and cargo attribute rules based on the order attribute information and the cargo attribute information; Obtain the AGV load information and warehouse busyness degree from the cloud platform, and define resource constraint rules based on the AGV load information and the warehouse busyness degree; Define dynamic constraint rules based on the conveying starting point, the conveying ending point, the time information, and the location information of the multiple warehouses; Construct a resource constraint model according to the order attribute rules, the cargo attribute rules, the resource constraint rules, and the dynamic constraint rules.

3. A method for automatically transporting and managing AGV based on cloud computing according to claim 2, characterized in that, Step S2 specifically includes: According to the AGV task request information of the multiple warehouses, obtain the location information of all AGVs within the range of the conveying starting point and the conveying ending point; Based on the location information of all AGVs within the range of the conveying starting point and the conveying ending point, construct a candidate AGV pool to determine the candidate AGV information, and the candidate AGV information includes candidate AGV location information, candidate AGV battery power information, and candidate AGV type information; According to the information of the AGV type to be selected, obtain the maximum load of the AGV to be selected, and according to the cargo attribute information, obtain the cargo weight. Then, use the ratio of the maximum load of the AGV to be selected and the cargo weight as the task matching degree. Based on the resource constraint model, obtain the AGV score threshold. Then, according to the task matching degree and the power information of the AGV to be selected, obtain the score of the AGV to be selected. Filter the AGV to be selected through the score of the AGV to be selected and the AGV score threshold. Extract the AGV to be selected whose score is greater than or equal to the AGV score threshold from the pool of AGVs to be selected, and construct a pool of preferred AGVs. Adopt the auction algorithm to allocate the preferred AGVs for the AGV task request information of multiple warehouses. Then, the AGV corresponding to the AGV request information of each warehouse is defined as the preferred AGV.

4. A method for automatic conveying management of AGV based on cloud computing according to claim 3, characterized in that, Step S3 specifically includes: Download the internal workshop scene information and production line scene information from the MES system of the cloud platform. The internal workshop scene information includes the workshop layout diagram, and the production line scene information includes the shelf position and equipment coordinates. Utilize the workshop layout diagram, shelf position and equipment coordinates to construct a two-dimensional production line map, and use the equipment coordinates to obtain the equipment occupation environment information, and mark the obstacle heat map with the equipment occupation environment information. Based on the two-dimensional production line map and the obstacle heat map, delete the obstacle heat map from the two-dimensional production line map, so as to use the remaining area as the preferred AGV transportable area map. Obtain the edge points of the limited AGV transportable area map, and determine the center point of the two-dimensional production line map. Taking the center point of the two-dimensional production line map, construct a sliding straight line f(x)=h, where h is a dynamic sliding value, and the unit value of the dynamic sliding value is the distance value between the two nearest edge points of the limited AGV transportable area map. Use the sliding straight line to traverse all the edge points in the limited AGV transportable area map, and obtain the transport path tracing straight line f(x)=j when there are at least two edge points on the sliding straight line at the same time when the dynamic sliding values are the same. Obtain the coordinates of the two edge points with the farthest distance in the transport path tracing straight line, and construct the first transport path tracing line segment with the coordinates of the two edge points with the farthest distance in the transport path tracing straight line. Determine the center point of the first transport path tracing line segment, and obtain the two edge points closest to the center point of the first transport path tracing line segment. Construct the second transport path tracing line segment with the two edge points closest to the center point of the transport path tracing line segment. Starting from the transport starting point, connect the center points of all the second transport path tracing line segments in sequence to obtain an irregular line segment from the transport starting point to the center point of the second transport path tracing line segment to the transport end point. Utilize the irregular line segment to construct a transport path tracing function, and according to the transport path tracing function, simulate and generate the first initial transport route information of the first preferred AGV corresponding to all AGV task request information. Where the expression formula of the transport path tracing function is: ； In the formula, is any point n on the irregular line segment, is the distance value from any point n on the irregular line segment to the conveying end point, is the conveying path tracing reference value.

5. A method for automatically transporting and managing AGV based on cloud computing according to claim 4, characterized in that, Step S4 specifically includes: Based on the first initial transportation route information, the internal workshop scene information, and the production line scene information, determine the transportation focus position information in the first initial transportation route information, where the transportation focus position information includes the AGV transportation turning point and the AGV transportation intersection point; Randomly select one first initial transportation route from the first initial transportation route information of the first preferred AGV corresponding to all AGV task request information generated by simulation, and designate the remaining first initial transportation routes as the third initial transportation routes; Traverse the third initial transportation routes, and designate the third initial transportation routes that contain the transportation focus positions in the first initial transportation route information as the second initial transportation route information.

6. The method for automatically transporting and managing an AGV based on cloud computing according to claim 5, wherein, Step S5 specifically includes: Based on the first initial transportation route information and the second initial transportation route information, conduct simulated transportation on the preferred AGV, and obtain the second initial transportation route information where the preferred AGV collides during the simulated transportation; Obtain the total number of the second initial transportation routes where collisions occur. Then, based on the information of the AGV types to be selected, determine the floor area of the preferred AGV, and multiply the floor area of the preferred AGV by the total number of the second initial transportation routes where collisions occur to obtain the AGV transportation safety area information; Use the AGV transportation safety area information as the AGV transportation safety constraint model.

7. An AGV automatic conveying management method based on cloud computing according to claim 6, characterized in that, Step S6 specifically includes: Import the AGV transportation safety constraint model into all the preferred AGVs to obtain the transportation safety AGV information. Then, import the AGV task request information into the transportation safety AGVs, and use the sensors deployed on the AGVs to collect real-time environmental data to generate an obstacle avoidance model to achieve automatic AGV transportation management; Import the AGV transportation safety constraint model into all the preferred AGVs, and use the edge of the AGV transportation safety constraint model as the safety boundary of the preferred AGVs, thereby obtaining the transportation safety AGV information, where the transportation safety AGV information includes the safety boundary of the preferred AGVs, the preferred AGV position information, the preferred AGV power information, and the preferred AGV type information; Use the sensors deployed on the AGVs to collect real-time environmental data, where the real-time environmental data includes the position of sudden obstacles, the position of the working AGV, and the edge position of production equipment; When the position of sudden obstacles, the position of the working AGV, or the edge position of production equipment coincides with the safety boundary of the preferred AGVs, generate an obstacle avoidance model according to the preferred AGV position information, the preferred AGV type information, and the real-time environmental data, and achieve automatic AGV transportation management according to the obstacle avoidance model.

8. A cloud computing-based AGV automatic conveying management system for implementing the management method according to any one of claims 1-7, characterized in that, Including: A collection module, where the collection module is used to obtain the position information of multiple warehouses and import the position information of multiple warehouses into the cloud platform, and is also used to use the sensors deployed on the AGVs to collect real-time environmental data; The main control module is used to receive AGV task request information of multiple warehouses through the cloud platform, and is also used to download the internal workshop scene information and production line scene information from the cloud platform. It is used to construct a resource constraint model by using the AGV task request information and location information of multiple warehouses, and is used to determine candidate AGV information based on the AGV task request information of multiple warehouses. Then, according to the candidate AGV information and the resource constraint model, the candidate AGVs are screened to determine the optimal AGV; The management module is used to arbitrarily select an optimal AGV from the optimal AGVs to obtain the first optimal AGV. Then, through the AGV task request information, internal workshop scene information and production line scene information, the first initial conveying route information of the first optimal AGV is simulated and generated. According to the first initial conveying route information, internal workshop scene information and production line scene information, the conveying focus position information in the first initial conveying route information is determined, and according to the conveying focus position information in the first initial conveying route information, the second initial conveying route information is obtained. It is used to select at least one optimal AGV from the optimal AGVs and import the second initial conveying route information into the selected optimal AGVs. Then, based on the first initial conveying route information and the second initial conveying route information, the optimal AGVs are simulated for conveying to obtain the AGV conveying safety constraint model. It is used to import the AGV conveying safety constraint model into all the optimal AGVs to obtain the conveying safety AGV information. Then, the AGV task request information is imported into the conveying safety AGVs, and an obstacle avoidance model is generated to realize the automatic conveying management of AGVs; The display module is used to display the acquisition process and results of the acquisition module, the data processing process and results of the main control module, and the management process and results of the management module.

9. The AGV automatic conveying management system based on cloud computing according to claim 8, characterized in that, The main control module includes: A receiving unit, which is used to receive AGV task request information of multiple warehouses through the cloud platform, and is also used to download the internal workshop scene information and production line scene information from the cloud platform; A processing unit, which is used to construct a resource constraint model by using the AGV task request information and location information of multiple warehouses; An AGV determination unit, which is used to determine candidate AGV information based on the AGV task request information of multiple warehouses. Then, according to the candidate AGV information and the resource constraint model, the candidate AGVs are screened to determine the optimal AGV.

10. A cloud computing-based AGV automatic conveying management system according to claim 8, characterized in that, The management module includes: A path determination unit, which is used to arbitrarily select an optimal AGV from the optimal AGVs to obtain the first optimal AGV. Then, through the AGV task request information, internal workshop scene information and production line scene information, the first initial conveying route information of the first optimal AGV is simulated and generated. According to the first initial conveying route information, internal workshop scene information and production line scene information, the conveying focus position information in the first initial conveying route information is determined, and according to the conveying focus position information in the first initial conveying route information, the second initial conveying route information is obtained; Safety constraint unit, which is used to select at least one preferred AGV from the preferred AGVs, import the second initial conveying route information into the selected preferred AGV, and then simulate the conveying of the preferred AGV based on the first initial conveying route information and the second initial conveying route information to obtain an AGV conveying safety constraint model; Obstacle avoidance management unit, which is used to import the AGV conveying safety constraint model into all the preferred AGVs to obtain the conveying safety AGV information, then import the AGV task request information into the conveying safety AGVs, and generate an obstacle avoidance model to realize the automatic conveying management of AGVs.

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