Logistics automation scheduling method and system based on data analysis

Abstract

The present invention relates to the technical field of logistics scheduling management, and in particular to a logistics automation scheduling method and system based on data analysis. The method comprises the following steps: obtaining and analyzing the facility data of a logistics transfer station to establish a transfer station transmission topology structure model; analyzing the facility data of the logistics transfer station to generate a logistics distribution container model set; simulating the transfer station transmission topology structure model and the distribution container model set to generate distribution container transmission data, and analyzing to formulate a mechanical arm loading control strategy; obtaining the data of the logistics cargo to be loaded and performing container allocation to generate a cargo logistics distribution container model; planning the container model to obtain cargo transmission path data; integrating the cargo distribution container model to obtain cargo loading space data; analyzing and formulating a logistics cargo scheduling management strategy based on the cargo transmission path data and loading space data. The present invention can improve the efficiency of logistics scheduling management.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics scheduling management, and in particular to a logistics automation scheduling method and system based on data analysis. Background Art

[0002] With the rapid development of globalization and e-commerce, the logistics industry faces unprecedented challenges, including efficient processing of large numbers of orders, reducing transportation costs, optimizing resource allocation, and improving customer satisfaction. Traditional logistics scheduling methods rely on human experience and manual intervention, which often leads to low efficiency and high error rates, and are difficult to cope with complex and dynamically changing logistics needs. The logistics industry faces the challenges of surging order volume, rising transportation costs, complex resource allocation, and increased customer expectations. Traditional methods are difficult to cope with these complex and dynamically changing demands, resulting in information delays, resource waste, and slow response speed in the scheduling process. Transportation cost is an important link in logistics management. Traditional methods have significant deficiencies in transportation route planning, vehicle scheduling, and cargo loading, making it difficult to achieve cost optimization. Manual intervention often fails to effectively integrate various factors, resulting in high transportation costs. Resource allocation involves multiple aspects such as warehousing, transportation tools, and personnel. Traditional scheduling methods lack a comprehensive perspective on resource allocation, and are prone to over- or under-allocation of resources. This not only affects logistics efficiency, but also leads to waste of resources. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a logistics automation scheduling method and system based on data analysis to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a logistics automation scheduling method based on data analysis includes the following steps:

[0005] Step S1: Obtaining logistics transfer station facility data, and extracting transfer station logistics structure features from the logistics transfer station facility data, thereby obtaining transfer station logistics structure data; performing transfer station transmission topology structure analysis based on the transfer station logistics structure data, thereby obtaining a transfer station transmission topology structure model;

[0006] Step S2: extracting the structural features of logistics distribution containers from the logistics transfer station facility data, thereby obtaining logistics distribution container data; performing three-dimensional physical finite element analysis on the logistics distribution container data, thereby obtaining a logistics distribution container model set;

[0007] Step S3: performing a distribution container transmission simulation on the transfer station transmission topology structure model and the logistics distribution container model set, thereby obtaining distribution container transmission simulation data, and performing a robot arm loading control strategy analysis on the distribution container transmission simulation data according to the logistics transfer station facility data, thereby obtaining a robot arm loading control strategy;

[0008] Step S4: obtaining the data of the logistics cargo to be loaded, and performing logistics distribution container allocation on the logistics cargo data to be loaded according to the logistics distribution container model set, thereby obtaining the cargo logistics distribution container model; performing cargo transmission path planning on the cargo logistics distribution container model according to the distribution container transmission simulation data, thereby obtaining the cargo transmission path data;

[0009] Step S5: matching the robot arm loading control strategy to the cargo logistics distribution container model according to the robot arm loading control strategy, thereby obtaining the robot arm loading cargo control data, and integrating the cargo loading space matrix according to the robot arm loading cargo control data, thereby obtaining the cargo loading space data;

[0010] Step S6: Analyze the logistics cargo scheduling management strategy based on the cargo transmission path data and the cargo loading space data, so as to obtain the logistics cargo scheduling management strategy, and upload it to the logistics transfer station control platform to execute the cargo scheduling task.

[0011] The present invention obtains the facility data of the logistics transfer station and extracts its structural features, which helps to fully understand the functions, layout and resource allocation of the transfer station. This can provide accurate basic data for subsequent analysis. By analyzing the transmission topological structure model of the transfer station, the bottlenecks and optimization points in the design of the transfer station can be identified. This helps to improve the layout of the transfer station and improve the efficiency of cargo transfer. Understanding the transmission topological structure of the transfer station can reduce the time for information to be transmitted between transfer stations, thereby speeding up the response speed. Structural feature extraction and three-dimensional physical analysis of logistics distribution containers can optimize the design and use of containers, reduce idle space, and improve the compactness of cargo loading. Through finite element analysis, the pressure and stress that the container may encounter during transportation can be predicted, thereby improving the container design and improving its durability and safety. Transmission simulation of distribution containers can predict problems in actual operations, such as collisions, obstructions, etc., so as to make adjustments and optimizations in advance. Analysis of the loading control strategy of the manipulator can improve the efficiency and accuracy of the manipulator during the loading process, reduce human intervention, and improve loading accuracy. Allocating goods according to the logistics distribution container model set helps to reasonably utilize container space, reduce the movement of goods during transportation, and reduce transportation costs. Cargo transmission path planning based on the distribution container transmission simulation data can select the optimal path, avoid unnecessary delays, and shorten transportation time. Through the matching of the robot arm loading control strategy, cargo loading can be carried out more efficiently, reduce manual intervention, and improve loading speed. The integration of the cargo loading space matrix can optimize the use of loading space, reduce gaps, and improve the stability of cargo transportation. Analyzing the logistics cargo scheduling management strategy based on the cargo transmission path data and loading space data can achieve more intelligent and efficient scheduling and reduce resource waste. Efficient scheduling management can reduce delays and errors, improve service quality, and meet customer needs for fast and accurate delivery. Upload the scheduling management strategy to the control platform to achieve real-time monitoring and adjustment to ensure smooth and timely logistics operations. Through the implementation of these steps, the logistics transfer station can improve efficiency, reduce costs, and achieve more efficient resource allocation and customer service in the face of complex and dynamically changing needs.

[0012] Optionally, step S1 specifically includes:

[0013] Step S11: acquiring logistics transfer station facility data, and classifying the logistics transfer station facility data into facility structure, thereby obtaining transportation tool structure data and loading facility structure data;

[0014] Step S12: performing loading process topology analysis according to the loading facility structure data, thereby obtaining a loading process topology model;

[0015] Step S13: performing transportation tool space structure modeling according to the transportation tool structure data, thereby obtaining a transportation tool space structure model;

[0016] Step S14: integrating the facility space connection relationship of the transportation tool structure data and the loading facility structure data, thereby obtaining the transfer station facility space connection data;

[0017] Step S15: Associating the transfer station transmission topology structure model with the loading process topology structure model and the transportation tool space structure model according to the transfer station facility space connection data, thereby obtaining the transfer station transmission topology structure model.

[0018] The present invention acquires and classifies the facility data of the logistics transfer station, which helps to systematically understand the functions and structures of each facility. This can accurately determine the needs of transportation tools and loading facilities, and ensure the rational allocation and use of resources. Through the loading process topological structure analysis, the efficiency and fluency of the loading process can be optimized. This helps to reduce loading time and avoid potential bottlenecks, and improve the overall operating efficiency of the transfer station. The spatial structure modeling of transportation tools can help design and arrange the internal space of transportation tools to better utilize space and improve the efficiency of cargo transportation. Integrating the spatial connection relationship of facilities can effectively identify and optimize the connection mode of each facility in the transfer station, thereby improving the synergy between facilities and the overall operating efficiency. By associating the spatial connection data of the transfer station facilities with the loading process topological structure model and the transportation tool space structure model, a comprehensive transfer station transmission topological structure model can be created, thereby realizing the comprehensive optimization and management of the transfer station transmission process, and improving the efficiency and reliability of the entire logistics system.

[0019] Optionally, step S12 is specifically:

[0020] Step S121: classifying the loading facility structure data into loading facility structure data, thereby obtaining loading facility connection structure data and loading facility function structure data;

[0021] Step S122: integrating the functional structure spatial distribution according to the loading facility functional structure data, thereby obtaining the loading facility functional structure spatial distribution data, and dividing the loading facility functional structure spatial distribution data into loading process units, thereby obtaining loading functional structure process unit data;

[0022] Step S123: performing connection structure similarity calculation according to the loading facility connection structure data, thereby obtaining loading facility connection structure similarity data, and performing connection structure connection relationship integration on the loading facility connection structure data according to the loading facility connection structure similarity data, thereby obtaining loading facility connection structure connection relationship data;

[0023] Step S124: performing spatial connection relationship association on the loading facility functional structure spatial distribution data and the loading facility connection structure connection relationship data, thereby obtaining loading facility connection relationship data;

[0024] Step S125: performing loading facility topology analysis according to the loading facility connection relationship data, thereby obtaining a loading facility topology model;

[0025] Step S126: mapping the loading process unit to the loading facility topology structure model according to the loading function structure process unit data, thereby obtaining a loading process topology structure model.

[0026] The present invention can clearly understand the structural characteristics of different loading facilities by classifying the loading facility structure data, so as to more effectively carry out subsequent analysis. Classifying the loading facilities into different categories helps to accurately extract and process the connection structure data and functional structure data of the loading facilities, and provides basic data for subsequent steps. By classifying the structure of the loading facilities, the specific functional requirements of various types of facilities can be identified, and the design and configuration of the loading facilities can be optimized. The spatial distribution integration of the functional structure helps to clarify the spatial layout of different functional areas and improve the spatial utilization efficiency of the loading facilities. By dividing the loading process unit, each link in the loading process can be analyzed more carefully, and then the loading process can be optimized to improve the operation efficiency. Integrating the spatial distribution data of the functional structure can help design a more reasonable loading process, reduce conflicts and bottlenecks in the operation, and improve the overall operation efficiency. By calculating the similarity of the connection structure, the similarity between different loading facilities can be identified, so as to learn from and optimize similar connection structures in the design. Integrating the connection structure according to the similarity data helps to simplify the design of the loading facilities and improve the versatility and compatibility of the structure. Similarity calculation and integration help to improve the standardization of loading facilities and reduce design and maintenance costs. By associating the spatial distribution data of the functional structure and the connection relationship data of the connection structure, the overall layout and structural association of the loading facility can be understood more accurately. This association analysis can help identify and optimize the connection points in space and improve the space utilization and operation efficiency of the loading facility. It helps to systematically manage the spatial layout and connection relationship of the loading facility and improve the visualization and controllability of the overall operation. Topological structure analysis of the loading facility can clarify the relationship between each structural element and help understand the overall structure of the loading facility. By analyzing the topological structure model, potential problems and improvement points in the design can be identified, thereby optimizing the design of the loading facility. The topological structure model helps to improve the operation efficiency of the loading facility and reduce maintenance and operation costs. Mapping the loading functional structure process unit to the topological structure model helps to achieve efficient docking between the process unit and the structural model and optimize the overall smoothness of the loading process. Through mapping, it can ensure that each loading process unit is reasonably configured in the topological structure and improve the functional realization effect of the loading facility. It helps to better plan the loading process and improve the operation efficiency and reliability of the entire loading system.

[0027] Optionally, step S13 is specifically:

[0028] Step S131: classifying the transportation tool structure data into transportation tool structure data, thereby obtaining transportation tool power structure data and transportation tool loading structure data;

[0029] Step S132: performing three-dimensional structural modeling according to the power structure data of the transportation vehicle, thereby obtaining a three-dimensional power structure model of the transportation vehicle;

[0030] Step S133: Calculating the loading space capacity according to the transportation vehicle loading structure data, thereby obtaining the transportation vehicle loading space capacity data;

[0031] Step S134: performing three-dimensional structural modeling according to the transportation vehicle loading structure data, thereby obtaining a three-dimensional transportation vehicle loading structure model, and performing spatial capacity feature fusion on the three-dimensional transportation vehicle loading structure model according to the transportation vehicle loading space capacity data, thereby obtaining a three-dimensional space transportation vehicle loading structure model;

[0032] Step S135: integrating the three-dimensional transport vehicle power structure model and the three-dimensional space transport vehicle loading structure model to obtain a transport vehicle space structure model.

[0033] The present invention can classify the transport structure data into power structure data and loading structure data. This can help engineers understand the characteristics and requirements of each structure more clearly. Classifying the data helps to focus on specific structural features in the subsequent modeling process, reduces confusion, and improves the accuracy of modeling. Clear classification allows the design team to optimize the power system and loading system in a targeted manner, thereby improving the overall performance and efficiency of the transport. Through three-dimensional modeling, the data of the power structure can be visualized to help designers understand and analyze the structure of the power system more intuitively. The three-dimensional model can more easily discover potential design defects and optimization space, thereby improving the design of the power structure and improving its performance. The three-dimensional model can be used for virtual simulation and performance testing to reduce the cost and time of actual manufacturing and testing. Calculating the capacity of the loading space can ensure that the design meets specific transportation needs, optimize space utilization, and improve loading efficiency. Capacity calculation helps to ensure that the loading structure complies with relevant specifications and standards, and ensures the safety and compliance of the transport. Reasonable loading space design can enhance the market competitiveness of the transport and meet the needs of different users. By performing three-dimensional modeling on the loading structure data and integrating the capacity data, a more comprehensive loading structure model can be obtained, which helps to consider the practical application of space capacity in the design. By integrating spatial capacity features, designers can more effectively optimize loading structures, improve cargo loading efficiency, and reduce idle space. The fused three-dimensional model can more accurately reflect the actual use of the loading structure and improve the actual effect of the design. Integrating the power structure model with the loading structure model to form a complete spatial structure model of the transport tool will help evaluate the overall performance of the design. Integration can ensure the consistency and compatibility of the power structure and loading structure in design, reducing problems caused by structural mismatch. The integrated model can help with system-level optimization, such as coordinating between the power system and the loading system to improve the overall efficiency and reliability of the transport tool.

[0034] Optionally, step S2 specifically includes:

[0035] Step S21: extracting logistics distribution container structure features from the logistics transfer station facility data, thereby obtaining logistics distribution container data;

[0036] Step S22: extracting container structure features and container material features from the logistics distribution container data, thereby obtaining container structure data and container material data;

[0037] Step S23: analyzing the material characteristics of the container according to the container material data, thereby obtaining the material characteristic data of the delivery container;

[0038] Step S24: performing three-dimensional structure modeling according to the container structure data, thereby obtaining a three-dimensional logistics distribution container model set, and performing container material property mapping on the three-dimensional logistics distribution container model set according to the distribution container material property data, thereby obtaining a logistics distribution container property model set;

[0039] Step S25: setting physical simulation boundary conditions based on the material characteristic data of the distribution container, thereby obtaining physical simulation boundary conditions, and performing finite element analysis coupling on the logistics distribution container characteristic model set according to the physical simulation boundary conditions, thereby obtaining physical coupling data of the logistics distribution container;

[0040] Step S26: mapping the physical coupling characteristics of the logistics distribution container to the characteristic model set of the logistics distribution container according to the physical coupling data of the logistics distribution container, thereby obtaining the logistics distribution container model set.

[0041] The present invention extracts the container structure features from the logistics transfer station facility data, which helps to obtain preliminary container data, which provides a basis for subsequent analysis and modeling. Extracting the container structure and material features helps to understand the structure of the container and its material properties in detail, and provides comprehensive data for subsequent characteristic analysis. Container material property analysis can reveal the performance of the material under different conditions, thereby ensuring the reliability and durability of the container in practical applications. Three-dimensional structural modeling and material property mapping can create realistic container models, which are crucial for simulating and optimizing container design and help improve the performance and adaptability of the container. The physical simulation boundary condition setting and finite element analysis coupling can accurately evaluate the physical performance of the container in practical applications and ensure the stability and safety of the container under various operating conditions. By mapping the physical coupling characteristics, the final container model set obtained can better reflect the performance of the container in a real environment and provide a basis for design improvement and performance optimization.

[0042] Optionally, step S3 specifically includes:

[0043] Step S31: extracting container geometric features from the logistics distribution container model set, thereby obtaining distribution container geometric feature data;

[0044] Step S32: planning the transmission path of the delivery container according to the transfer station transmission topology model and the delivery container geometric feature data, thereby obtaining container delivery path data;

[0045] Step S33: constructing a transfer station transmission network model according to the transfer station transmission topology model and the logistics distribution container model set, thereby obtaining a transfer station transmission network model;

[0046] Step S34: performing a delivery container transmission simulation on the container delivery path data and the logistics delivery container model set through the transfer station transmission network model, thereby obtaining delivery container transmission simulation data;

[0047] Step S35: Analyze the robot loading control strategy of the distribution container transmission simulation data according to the logistics transfer station facility data, so as to obtain the robot loading control strategy.

[0048] The present invention can accurately understand the size, shape and capacity of each container by extracting the geometric features of logistics distribution containers. This helps to optimize the configuration and loading method of containers during the distribution process. Understanding the geometric features of the container can optimize the loading layout of the goods, thereby improving the utilization of the transportation space and reducing gaps and waste. Accurate geometric data helps to design a more effective loading plan, thereby reducing friction and damage during transportation and improving overall transportation efficiency. According to the transfer station transmission topology and container geometric feature data, the optimal distribution path can be planned to reduce transportation time and cost. Optimized path planning can reduce the load and delays of the transfer station and improve the overall distribution speed. Through scientific path planning, transportation resources can be effectively managed and scheduled, and unnecessary repeated transportation and resource waste can be reduced. Constructing a transfer station transmission network model can systematically manage the transfer station and help understand the workflow and network structure of the transfer station. It helps to optimize the configuration and scheduling of transfer station resources and improve the operating efficiency of the transfer station. The data support provided by the network model helps to make better decisions and improve the performance of the overall distribution system. Through simulation, the performance in the actual transportation process can be predicted and potential problems and bottlenecks can be identified. Simulation data can verify whether the previously planned path is effective and ensure the accuracy of path planning. By simulating the transportation process under different circumstances, the system's ability to cope with various uncertainties can be improved, and the robustness of the system can be improved. Analyzing the robot arm loading control strategy can improve the accuracy of the robot arm during the loading process and reduce loading errors. Appropriate control strategies can improve the work efficiency of the robot arm, speed up loading, and thus improve overall transportation efficiency. Through automated loading control strategies, the need for human intervention is reduced, reducing labor costs and operational risks.

[0049] Optionally, step S35 is specifically:

[0050] Step S351: extracting loading robot arm features from the logistics transfer station facility data, thereby obtaining loading robot arm data;

[0051] Step S352: extracting cargo loading unit simulation data and delivery container simulation weight data from the delivery container transmission simulation data, thereby obtaining cargo loading unit simulation data and delivery container simulation weight data;

[0052] Step S353: performing statistics on the movement trajectory of the cargo to be loaded according to the cargo loading unit simulation data, thereby obtaining simulated movement trajectory data of the cargo to be loaded, and performing statistics on the spatial distribution of the cargo to be loaded according to the simulated movement trajectory data of the cargo to be loaded, thereby obtaining spatial distribution data of the cargo to be loaded;

[0053] Step S354: integrating the working conditions of the robot arm according to the simulated weight data of the delivery container and the spatial distribution data of the cargo to be loaded, thereby obtaining the working condition data of the robot arm;

[0054] Step S355: performing three-dimensional motion dynamics modeling according to the loading robot arm data, thereby obtaining a loading robot arm power simulation model, and performing robot arm cargo grabbing simulation on the loading robot arm power simulation model according to the robot arm working condition data, thereby obtaining robot arm cargo grabbing simulation data;

[0055] Step S356: performing a combination of robot arm control parameters selection on the robot arm cargo grabbing simulation data, thereby obtaining a robot arm loading control strategy.

[0056] By extracting the characteristic data of the loading robot arm, the present invention can more accurately understand the performance and configuration of the robot arm, laying the foundation for subsequent simulation and optimization. Extracting the weight data of the cargo loading unit and the container from the distribution container transmission simulation data helps to more realistically reflect the actual logistics situation in the simulation and improve the accuracy of the model. Statistics on the motion trajectory and spatial distribution of the goods to be loaded can optimize the layout and loading strategy of the goods and reduce conflicts and inefficiencies during the loading process. Integrating the working conditions of the robot arm in combination with the simulated weight of the distribution container and the spatial distribution data of the goods helps to optimize the operation of the robot arm under different conditions and improve loading efficiency and accuracy. Three-dimensional motion dynamics modeling can simulate the movements of the loading robot arm, and adjust the simulation parameters through the working condition data, thereby enhancing the performance and reliability of the robot arm in actual operation. Selecting appropriate robot arm control parameters based on simulation data can formulate more effective loading control strategies and improve the operating efficiency and accuracy of the robot arm.

[0057] Optionally, step S4 specifically includes:

[0058] Step S41: acquiring data of the logistics cargo to be loaded, and extracting the three-dimensional structural features of the logistics cargo from the data of the logistics cargo to be loaded, thereby obtaining the three-dimensional structural data of the logistics cargo;

[0059] Step S42: performing three-dimensional structural similarity calculation based on the three-dimensional structural data of the logistics goods and the logistics distribution container model set, thereby obtaining the three-dimensional similarity data of the logistics goods;

[0060] Step S43: selecting the logistics distribution container with the highest similarity from the logistics distribution container model set according to the three-dimensional similarity data of the logistics goods, thereby obtaining the goods logistics distribution container model;

[0061] Step S44: Cargo transmission path planning is performed on the cargo logistics distribution container model according to the distribution container transmission simulation data, thereby obtaining cargo transmission path data.

[0062] The present invention can obtain and extract the three-dimensional structural characteristics of logistics goods, so as to understand the space occupation and shape characteristics of the goods in detail. This makes the subsequent container selection more accurate and reduces the logistics problems caused by size mismatch. The three-dimensional structural similarity between the logistics goods and the distribution container model is calculated to help find the most suitable container model. This improves the utilization efficiency of the container, reduces the empty space, and thus reduces the transportation cost. The best container model is selected according to the similarity data to ensure that the goods are properly protected and supported during transportation. This reduces the risk of damage to the goods and improves the safety of the transportation process. By optimizing the distribution path through cargo transmission path planning, it is possible to reduce transportation time and cost and improve logistics efficiency. In addition, reasonable path planning can also reduce energy consumption during transportation.

[0063] Optionally, step S5 specifically includes:

[0064] Step S51: using a cargo logistics distribution container model to perform container loading simulation on the cargo data to be loaded, thereby obtaining cargo loading container simulation data;

[0065] Step S52: integrating the cargo loading container weight characteristics with respect to the cargo loading container simulation data, thereby obtaining loading container weight data;

[0066] Step S53: predicting the cargo loading motion trajectory according to the cargo loading container simulation data, thereby obtaining cargo loading motion trajectory data;

[0067] Step S54: performing robot arm loading control parameter combination matching on the loading container weight data and the cargo loading motion trajectory data according to the robot arm loading control strategy, thereby obtaining robot arm cargo loading control data;

[0068] Step S55: performing stress-balanced distribution of the transport vehicle loading space on the loading container weight data through the transfer station transmission topology structure model, thereby obtaining stress-balanced loading space data;

[0069] Step S56: integrating the stress-balanced loading space data into a cargo loading space matrix according to the cargo loading control data of the robot arm, thereby obtaining the cargo loading space data.

[0070] The present invention can predict the actual loading situation through container loading simulation, ensure that the cargo can adapt to the space and shape of the container, and improve loading efficiency and accuracy. Integrate the weight characteristic data of the loading container to help optimize the load configuration of the container, reduce possible uneven loads, and enhance transportation stability. Predicting the motion trajectory of cargo loading can identify and resolve possible conflicts or unstable factors in advance, and optimize the loading and transportation process. Matching the loading control strategy with the weight data and motion trajectory data ensures that the robotic arm can complete cargo loading efficiently and accurately, improving operational efficiency. Through stress balancing, ensure that the load of the transport vehicle is evenly distributed, reduce structural pressure during transportation, and reduce the risk of damage to the transport vehicle. Integrating the cargo loading space matrix helps to optimize space usage and loading strategies, and improve the overall efficiency of cargo storage and transportation.

[0071] Optionally, the present invention further provides a logistics automation scheduling system based on data analysis, which is used to execute the logistics automation scheduling method based on data analysis as described above, and the logistics automation scheduling system based on data analysis includes:

[0072] The transmission topology structure analysis module is used to obtain the facility data of the logistics transfer station, and extract the logistics structure features of the transfer station from the facility data of the logistics transfer station, so as to obtain the logistics structure data of the transfer station; perform the transmission topology structure analysis of the transfer station according to the logistics structure data of the transfer station, so as to obtain the transmission topology structure model of the transfer station;

[0073] The three-dimensional physical finite element analysis module is used to extract the structural features of the logistics distribution container from the logistics transfer station facility data, thereby obtaining the logistics distribution container data; and to perform three-dimensional physical finite element analysis based on the logistics distribution container data, thereby obtaining the logistics distribution container model set;

[0074] A robot arm control strategy analysis module is used to simulate the transmission of distribution containers on the transfer station transmission topology model and the logistics distribution container model set, thereby obtaining the distribution container transmission simulation data, and to analyze the robot arm loading control strategy on the distribution container transmission simulation data according to the logistics transfer station facility data, thereby obtaining the robot arm loading control strategy;

[0075] The logistics distribution container allocation module is used to obtain the data of logistics goods to be loaded, and to allocate logistics distribution containers to the data of logistics goods to be loaded according to the logistics distribution container model set, so as to obtain the goods logistics distribution container model; and to plan the goods transmission path for the goods logistics distribution container model according to the distribution container transmission simulation data, so as to obtain the goods transmission path data;

[0076] The cargo loading space matrix integration module is used to match the robot loading control strategy to the cargo logistics distribution container model according to the robot loading control strategy, so as to obtain the robot loading cargo control data, and to integrate the cargo loading space matrix according to the robot loading cargo control data, so as to obtain the cargo loading space data;

[0077] The scheduling management strategy analysis module is used to analyze the logistics cargo scheduling management strategy based on the cargo transmission path data and the cargo loading space data, so as to obtain the logistics cargo scheduling management strategy and upload it to the logistics transfer station control platform to execute the cargo scheduling task.

[0078] The data analysis-based logistics automation scheduling system of the present invention can implement any one of the data analysis-based logistics automation scheduling methods of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the data analysis-based logistics automation scheduling method. The internal modules of the system cooperate with each other, thereby improving the efficiency and accuracy of logistics scheduling management. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0080] Figure 1 It is a schematic diagram of the steps of the logistics automation scheduling method based on data analysis of the present invention;

[0081] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0082] Figure 3 Detailed step flow diagram of step S2 in the present invention;

[0083] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0084] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0085] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0086] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0087] To achieve this, please refer to Figures 1 to 3 The present invention provides a logistics automation scheduling method based on data analysis, the method comprising the following steps:

[0088] Step S1: Obtaining logistics transfer station facility data, and extracting transfer station logistics structure features from the logistics transfer station facility data, thereby obtaining transfer station logistics structure data; performing transfer station transmission topology structure analysis based on the transfer station logistics structure data, thereby obtaining a transfer station transmission topology structure model;

[0089] In this embodiment, laser scanners and high-definition cameras are used to obtain the spatial layout data of the logistics transfer station, including the shelves, conveyor belts, entrances and exits. By performing image processing and structural analysis on these data, the structural features of the transfer station, such as shelf height, conveyor belt length and transfer area, are extracted. Using these feature data, a transmission topology model of the transfer station is constructed to describe the flow path and mode of materials inside the transfer station. For example, a three-dimensional model is created to show how the goods pass through different conveyor belts and transfer areas to reach the final exit point.

[0090] Step S2: extracting the structural features of logistics distribution containers from the logistics transfer station facility data, thereby obtaining logistics distribution container data; performing three-dimensional physical finite element analysis on the logistics distribution container data, thereby obtaining a logistics distribution container model set;

[0091] In this example, the structural features of the distribution containers used in the logistics transfer station are extracted, including the size, shape, load-bearing capacity, and material properties of the containers. The geometry of the container is captured using 3D scanning technology, and the container is physically analyzed using finite element analysis (FEA) software to evaluate its performance under different load conditions. A container model set is created that covers different types of containers (such as standard pallets, custom boxes, etc.), and strength and stability assessments are performed. For example, the structural stability of a standard pallet when fully loaded is analyzed to ensure its safety and durability during transportation.

[0092] Step S3: performing a distribution container transmission simulation on the transfer station transmission topology structure model and the logistics distribution container model set, thereby obtaining distribution container transmission simulation data, and performing a robot arm loading control strategy analysis on the distribution container transmission simulation data according to the logistics transfer station facility data, thereby obtaining a robot arm loading control strategy;

[0093] In this embodiment, the transfer station transmission topology model and the distribution container model set are input into the simulation software to simulate the container transmission process inside the transfer station, including the behavior under different conveyor belts and robot arm operations. Based on the simulation data, the loading control strategy of the robot arm is analyzed, such as the robot arm's grasping method, movement path, and loading speed. For example, the optimal action sequence when using a robot arm to transfer goods from one conveyor belt to another in the transfer station is analyzed to reduce the transit time and improve efficiency.

[0094] Step S4: obtaining the data of the logistics cargo to be loaded, and performing logistics distribution container allocation on the logistics cargo data to be loaded according to the logistics distribution container model set, thereby obtaining the cargo logistics distribution container model; performing cargo transmission path planning on the cargo logistics distribution container model according to the distribution container transmission simulation data, thereby obtaining the cargo transmission path data;

[0095] In this embodiment, detailed data of the goods to be loaded, including volume, weight, and packaging characteristics, are obtained. Based on these data, the most suitable container is selected from the predefined logistics distribution container model set. For example, if the size of the goods to be loaded matches a certain type of standard pallet, the pallet is selected as the container. At the same time, based on the container transmission simulation data, the optimal path for the goods from loading to unloading is planned to reduce transmission time and avoid congestion. For example, a transmission path is designed so that the goods can pass through each link of the transfer station efficiently and reduce cross interference with other goods.

[0096] Step S5: matching the robot arm loading control strategy to the cargo logistics distribution container model according to the robot arm loading control strategy, thereby obtaining the robot arm loading cargo control data, and integrating the cargo loading space matrix according to the robot arm loading cargo control data, thereby obtaining the cargo loading space data;

[0097] In this embodiment, the robot loading control strategy is used and applied to the selected cargo logistics distribution container model to ensure that the robot can perform accurate loading operations according to the specific characteristics of the container (such as container size and weight distribution). According to the loading control data, the cargo loading space matrix is ​​integrated to optimize the layout of the cargo in the container. For example, large and small cargoes are reasonably arranged on the pallet to maximize the use of space and avoid the movement of cargoes during transportation.

[0098] Step S6: Analyze the logistics cargo scheduling management strategy based on the cargo transmission path data and the cargo loading space data, so as to obtain the logistics cargo scheduling management strategy, and upload it to the logistics transfer station control platform to execute the cargo scheduling task.

[0099] In this embodiment, the logistics cargo dispatch management strategy is analyzed by combining the cargo transmission path data and the loading space data to formulate an efficient dispatch plan. For example, the priority loading order and transportation route of the cargo are arranged according to the cargo priority, destination and the operation capacity of the transfer station. The formulated dispatch strategy is uploaded to the transfer station control platform, and the operation plan of the transfer station is automatically adjusted to perform the cargo dispatch task. For example, the operation of the robot arm and the running speed of the conveyor belt are adjusted through system instructions to achieve the optimized dispatch task.

[0100] Optionally, step S1 specifically includes:

[0101] Step S11: acquiring logistics transfer station facility data, and classifying the logistics transfer station facility data into facility structure, thereby obtaining transportation tool structure data and loading facility structure data;

[0102] In this embodiment, laser scanners and high-definition cameras are used to obtain facility data of logistics transfer stations, including warehouses, loading and unloading platforms, and transportation tools. For example, the size, shape, and storage capacity of warehouses; the size, location, and carrying capacity of loading and unloading platforms; and the specifications and quantity of transportation tools such as trucks and containers are collected. These facilities are structurally classified into "storage facilities", "loading facilities", and "transportation tools", and corresponding data sets are generated based on the classification.

[0103] Step S12: performing loading process topology analysis according to the loading facility structure data, thereby obtaining a loading process topology model;

[0104] In this embodiment, the structural data of the loading facilities are analyzed to determine the topology of the loading process. For example, the flow path of the goods from the warehouse entry to the warehouse exit is determined, and each loading point and the connection relationship between them are identified. A topological structure model of the loading process is constructed, and each loading point and connection channel are marked to ensure a smooth and efficient process.

[0105] Step S13: performing transportation tool space structure modeling according to the transportation tool structure data, thereby obtaining a transportation tool space structure model;

[0106] In this embodiment, a spatial structure model is created based on the structural data of the transport vehicle. For example, the spatial layout of trucks and containers is modeled using the size and shape data of the transport vehicle. The model can show the spatial distribution of different types of transport vehicles, taking into account loading capacity and space utilization efficiency.

[0107] Step S14: integrating the facility space connection relationship of the transportation tool structure data and the loading facility structure data, thereby obtaining the transfer station facility space connection data;

[0108] In this embodiment, the data of transportation tools and loading facilities are integrated to establish the spatial connection relationship of the facilities. For example, a data model is created to integrate the spatial connection information of transportation tools and loading facilities into a unified data structure. This includes determining the physical location of each facility and how they are connected to each other through roads and channels.

[0109] Step S15: Associating the transfer station transmission topology structure model with the loading process topology structure model and the transportation tool space structure model according to the transfer station facility space connection data, thereby obtaining the transfer station transmission topology structure model.

[0110] In this embodiment, the spatial connection data of the transfer station facilities is used to associate the loading process topology model with the transportation tool space structure model to form a transfer station transmission topology model. For example, each point in the loading process is matched with the corresponding transportation tool space layout to ensure that the flow of goods in the transfer station and the spatial configuration of the transportation tool can work together to optimize the overall transportation efficiency.

[0111] Optionally, step S12 is specifically:

[0112] Step S121: classifying the loading facility structure data into loading facility structure data, thereby obtaining loading facility connection structure data and loading facility function structure data;

[0113] In this embodiment, detailed structural data of the loading facilities are obtained, which can be obtained by laser scanning, CAD drawings, sensor data, etc. Then, these data are structurally classified, for example, by marking and classifying the functions and physical characteristics of different components in the loading facilities (such as conveyor belts, elevators, stackers, etc.). Specific techniques can use machine learning algorithms, such as cluster analysis or decision tree classification, to identify the main components of the loading facilities and their functional relationships. For example, the K-means algorithm is used to classify all loading facility components into several categories such as "transportation", "stacking", and "sorting".

[0114] Step S122: integrating the functional structure spatial distribution according to the loading facility functional structure data, thereby obtaining the loading facility functional structure spatial distribution data, and dividing the loading facility functional structure spatial distribution data into loading process units, thereby obtaining loading functional structure process unit data;

[0115] In this embodiment, the obtained loading facility functional structure data is input into a spatial analysis tool, and the spatial data analysis method (such as GIS tool) is used to integrate these functional data. The spatial distribution of each functional module is optimized through an algorithm to improve loading efficiency. Next, the functional structure spatial distribution data is divided into loading process units, which can be achieved through the community detection algorithm in graph theory. For example, the Girvan-Newman algorithm is used to identify the boundaries of different functional areas and divide them into units such as "loading area" and "sorting area".

[0116] Step S123: performing connection structure similarity calculation according to the loading facility connection structure data, thereby obtaining loading facility connection structure similarity data, and performing connection structure connection relationship integration on the loading facility connection structure data according to the loading facility connection structure similarity data, thereby obtaining loading facility connection structure connection relationship data;

[0117] In this embodiment, a graph matching algorithm can be used to calculate the connection structure similarity between loading facility components. First, a connection structure graph of the loading facilities is constructed, where nodes represent components and edges represent connection relationships. Then, a similarity metric (such as Jaccard similarity, cosine similarity, etc.) is used to evaluate the connection structure similarity between different facilities. For example, a graph isomorphism algorithm is used to compare the connection graphs of two loading facilities to determine their similarity in connection structure.

[0118] Step S124: performing spatial connection relationship association on the loading facility functional structure spatial distribution data and the loading facility connection structure connection relationship data, thereby obtaining loading facility connection relationship data;

[0119] In this embodiment, the spatial distribution data of the functional structure of the loading facilities is combined with the connection structure connection relationship data to associate the spatial connection relationship. Spatial association analysis technology (such as spatial linkage algorithm) can be used to map the spatial data and the connection data into a comprehensive model. For example, a spatial database management system (such as PostGIS) is used to associate the spatial data with the connection structure data to obtain a new loading facility connection relationship data model.

[0120] Step S125: performing loading facility topology analysis according to the loading facility connection relationship data, thereby obtaining a loading facility topology model;

[0121] In this embodiment, a topological structure analysis is performed based on the obtained loading facility connection relationship data. This process can be completed by a topological analysis tool, for example, using a network analysis tool to perform topological modeling on the connection relationship data. Specifically, algorithms in graph theory (such as the shortest path algorithm and network flow analysis) can be used to establish a topological structure model of the loading facility. For example, the Dijkstra algorithm is used to analyze the shortest path between nodes in the loading facility, thereby determining the topological structure of the facility.

[0122] Step S126: mapping the loading process unit to the loading facility topology structure model according to the loading function structure process unit data, thereby obtaining a loading process topology structure model.

[0123] In this embodiment, the loading function structure flow unit data is mapped to the obtained topological structure model. This process can be implemented using a mapping algorithm (such as Boolean mapping or geometric mapping). The specific method is to correspond the function structure flow unit to the node or edge in the topological structure to form a loading process topological structure model. For example, a linear programming algorithm is used to optimize the distribution of the loading process unit in the topological structure, thereby forming an efficient loading process topological structure model.

[0124] Optionally, step S13 is specifically:

[0125] Step S131: classifying the transportation tool structure data into transportation tool structure data, thereby obtaining transportation tool power structure data and transportation tool loading structure data;

[0126] In this embodiment, the structural data of the transportation vehicle (such as a truck) is classified into power structure and loading structure. The power structure includes the engine, transmission, etc.; the loading structure includes the carriage, shelf, etc. The data set is analyzed using data mining technology to extract representative structural features, such as engine power, carriage size, etc., to generate classified power structure data and loading structure data.

[0127] Step S132: performing three-dimensional structural modeling according to the power structure data of the transportation vehicle, thereby obtaining a three-dimensional power structure model of the transportation vehicle;

[0128] In this embodiment, the power structure data is used to perform three-dimensional modeling using computer-aided design (CAD) software. Taking a truck as an example, a three-dimensional model is created including an engine and a transmission, and their positions, sizes and relationships are set to generate a three-dimensional power structure model.

[0129] Step S133: Calculating the loading space capacity according to the transportation vehicle loading structure data, thereby obtaining the transportation vehicle loading space capacity data;

[0130] In this embodiment, the capacity of the loading space is calculated based on the loading structure data, such as the internal dimensions of the compartment, the shelf design, etc. The total loading capacity of the truck, for example, 10 cubic meters, is obtained using mathematical formulas and calculation tools.

[0131] Step S134: performing three-dimensional structural modeling according to the transportation vehicle loading structure data, thereby obtaining a three-dimensional transportation vehicle loading structure model, and performing spatial capacity feature fusion on the three-dimensional transportation vehicle loading structure model according to the transportation vehicle loading space capacity data, thereby obtaining a three-dimensional space transportation vehicle loading structure model;

[0132] In this embodiment, CAD software is used to perform three-dimensional modeling on the loading structure data to construct a truck compartment model. The calculated loading space capacity is integrated into the three-dimensional model, for example, by adjusting the compartment layout in the model to match the actual capacity requirements, thereby generating a three-dimensional space transportation vehicle loading structure model.

[0133] Step S135: integrating the three-dimensional transport vehicle power structure model and the three-dimensional space transport vehicle loading structure model to obtain a transport vehicle space structure model.

[0134] In this embodiment, the three-dimensional power structure model is integrated with the three-dimensional space loading structure model. In the CAD software, the two are superimposed to ensure that the space arrangement of the power system and the loading system is reasonable. Through structural integration, a complete transportation tool space structure model is obtained, including a comprehensive view of the power system and the loading space, for further analysis and optimization.

[0135] Optionally, step S2 specifically includes:

[0136] Step S21: extracting logistics distribution container structure features from the logistics transfer station facility data, thereby obtaining logistics distribution container data;

[0137] In this embodiment, the design drawings and physical data of all logistics distribution containers are first collected. These data include the container's external dimensions, internal partition layout, opening method, etc. Each container is scanned using laser scanning technology to obtain its three-dimensional point cloud data. The point cloud data is processed using a computer vision algorithm to extract the container's geometric features, such as length, width, height, partition position and shape, etc. These features will be converted into a digital data format and stored in a database for subsequent analysis.

[0138] Step S22: extracting container structure features and container material features from the logistics distribution container data, thereby obtaining container structure data and container material data;

[0139] In this embodiment, after the structural feature extraction is completed, samples are taken from each container for material analysis. These samples will be tested for chemical composition by a spectrometer to determine the main material of the container (such as plastic, metal, composite material, etc.) and its physical properties (such as density, elastic modulus). Then, the mechanical properties of the material, such as compressive strength, tensile strength, etc., are measured by a material tester. These data will be combined with the structural feature data of the container to form container structure data and container material data, which will be stored in a unified data management system.

[0140] Step S23: analyzing the material characteristics of the container according to the container material data, thereby obtaining the material characteristic data of the delivery container;

[0141] In this embodiment, based on the container material data, the material performance test standards (such as ASTM or ISO standards) are used to conduct in-depth analysis of the characteristics of each material. For example, for plastic containers, temperature resistance, chemical corrosion resistance, and impact strength tests are performed. The test results are organized into a detailed material property report, including the elastic modulus, thermal expansion coefficient, friction coefficient, etc. of the material. These property data are used for subsequent 3D modeling and physical simulation to ensure that the model can accurately reflect the behavior of the actual material.

[0142] Step S24: performing three-dimensional structure modeling according to the container structure data, thereby obtaining a three-dimensional logistics distribution container model set, and performing container material property mapping on the three-dimensional logistics distribution container model set according to the distribution container material property data, thereby obtaining a logistics distribution container property model set;

[0143] In this embodiment, CAD software (such as AutoCAD or SolidWorks) is used to convert the extracted container structure data into a three-dimensional model. The model will include all the details of the container, such as partitions, handles, openings, etc. Next, the container material property data is mapped to the three-dimensional model to generate a property model set containing material properties. It also includes assigning actual mechanical properties to the model, such as assigning thermal expansion coefficients to plastic parts and elastic modulus to metal parts. The final generated logistics distribution container property model set will be used for subsequent physical simulation analysis.

[0144] Step S25: setting physical simulation boundary conditions based on the material characteristic data of the distribution container, thereby obtaining physical simulation boundary conditions, and performing finite element analysis coupling on the logistics distribution container characteristic model set according to the physical simulation boundary conditions, thereby obtaining physical coupling data of the logistics distribution container;

[0145] In this embodiment, the boundary conditions of the physical simulation are set according to the material property data of the distribution container, such as the external force applied to the container, temperature change, humidity effect, etc. The finite element analysis software (such as ANSYS or ABAQUS) is used to perform physical coupling analysis on the three-dimensional logistics distribution container model set. After setting the boundary conditions, simulation is performed to analyze the behavior of the container under different conditions, such as stress distribution, deformation, etc. The simulation results will provide the performance data of the container in actual use and generate physical coupling data of the logistics distribution container.

[0146] Step S26: mapping the physical coupling characteristics of the logistics distribution container to the characteristic model set of the logistics distribution container according to the physical coupling data of the logistics distribution container, thereby obtaining the logistics distribution container model set.

[0147] In this embodiment, the obtained physical coupling data of the logistics distribution container is used to map the physical coupling characteristics of the three-dimensional logistics distribution container model set. This includes mapping the physical coupling data such as stress, strain, deformation, etc. into the model to accurately display the performance of the container under different working conditions. These mapping data will form the final logistics distribution container model set, providing a comprehensive understanding of the performance of the container in the actual logistics environment, and supporting further design optimization and material selection.

[0148] Optionally, step S3 specifically includes:

[0149] Step S31: extracting container geometric features from the logistics distribution container model set, thereby obtaining distribution container geometric feature data;

[0150] In this embodiment, the logistics distribution container model set is 3D scanned to extract the specific geometric features of the container, including size, shape, volume and weight distribution. These data are accurately modeled by combining computer vision technology with laser ranging. A laser scanner can be used to obtain detailed point cloud data of the container, and combined with CAD software to analyze and extract geometric features to generate a detailed geometric feature data file for each container.

[0151] Step S32: planning the transmission path of the delivery container according to the transfer station transmission topology model and the delivery container geometric feature data, thereby obtaining container delivery path data;

[0152] In this embodiment, based on the transmission topology model of the transfer station and the container geometric feature data, a path planning algorithm (such as Dijkstra or A* algorithm) is used to plan the transmission path of the distribution container. This requires considering the impact of the volume and weight of the container on the path selection, and optimizing the path to reduce transportation time and cost. Path planning software can be used for simulation to ensure that the selected path meets the actual layout of the transfer station and the physical limitations of the container, and finally generates optimized container distribution path data.

[0153] Step S33: constructing a transfer station transmission network model according to the transfer station transmission topology model and the logistics distribution container model set, thereby obtaining a transfer station transmission network model;

[0154] In this embodiment, when constructing the transfer station transmission network model, it is necessary to comprehensively consider the actual layout, equipment configuration, and transmission process of the transfer station. By integrating the facility data of the transfer station with the container model set, a detailed network topology model is created to describe the connection relationship between each transfer station and its processing capacity. A network model can be created using a network modeling tool (such as the NetworkX library), taking into account the capacity, transmission efficiency, and possible bottlenecks of the equipment to obtain a transfer station transmission network model that reflects the actual transmission environment.

[0155] Step S34: performing a delivery container transmission simulation on the container delivery path data and the logistics delivery container model set through the transfer station transmission network model, thereby obtaining delivery container transmission simulation data;

[0156] In this embodiment, the transfer station transmission network model is used to simulate the transmission of container distribution path data. The simulation process involves the actual transmission process of the container in the network, evaluating the transmission time, path stability, and network load. Discrete event simulation tools (such as AnyLogic) can be used for simulation to analyze possible delays or congestion in the container transmission process and generate detailed distribution container transmission simulation data.

[0157] Step S35: Analyze the robot loading control strategy of the distribution container transmission simulation data according to the logistics transfer station facility data, so as to obtain the robot loading control strategy.

[0158] In this embodiment, according to the actual needs of the robot arm loading, the distribution container transmission simulation data is analyzed to formulate an optimized loading control strategy. This includes the robot arm's motion trajectory planning, gripping force adjustment, and loading sequence optimization. For example, the strategy analysis can be combined with the robot arm's control system (such as ABB or KUKA's programming environment), and the effectiveness of the strategy can be verified using simulation software, and finally the robot arm loading control strategy is generated to ensure an efficient and accurate loading process.

[0159] Optionally, step S35 is specifically:

[0160] Step S351: extracting loading robot arm features from the logistics transfer station facility data, thereby obtaining loading robot arm data;

[0161] In this embodiment, the data of the logistics transfer station facilities are input into the loading robot arm feature extraction system, which extracts the key features of the robot arm such as range of motion, flexibility, and grasping ability by analyzing the sensor data, the motion parameters of the robot arm, load capacity and other information. This process can be optimized by machine learning algorithms to ensure that the extracted data can accurately reflect the actual operating capabilities of the robot arm. During implementation, the input data should include detailed information such as the model, specifications, and working environment of the robot arm to ensure the accuracy of feature extraction.

[0162] Step S352: extracting cargo loading unit simulation data and delivery container simulation weight data from the delivery container transmission simulation data, thereby obtaining cargo loading unit simulation data and delivery container simulation weight data;

[0163] In this embodiment, cargo loading unit simulation data and delivery container simulation weight data are extracted from the delivery container transmission simulation data. In specific implementation, cargo loading unit simulation data is generated by transmitting the parameters such as the position, volume and weight of the loading unit recorded in the simulation data. At the same time, the delivery container simulation weight data is calculated according to the load condition in the container simulation data. Data processing can use data analysis software to ensure the accuracy and practicality of the simulation data.

[0164] Step S353: performing statistics on the movement trajectory of the cargo to be loaded according to the cargo loading unit simulation data, thereby obtaining simulated movement trajectory data of the cargo to be loaded, and performing statistics on the spatial distribution of the cargo to be loaded according to the simulated movement trajectory data of the cargo to be loaded, thereby obtaining spatial distribution data of the cargo to be loaded;

[0165] In this embodiment, the movement trajectory of the goods to be loaded is counted based on the simulation data of the cargo loading unit. The path of the goods during transportation is analyzed through the movement trajectory information in the simulation data to generate the simulated movement trajectory data of the goods to be loaded. Then, the spatial distribution information of the goods, such as the density and distribution of the goods at different locations, is counted based on these trajectory data to generate the spatial distribution data of the goods to be loaded. This process can be analyzed and optimized with the help of data visualization tools.

[0166] Step S354: integrating the working conditions of the robot arm according to the simulated weight data of the delivery container and the spatial distribution data of the cargo to be loaded, thereby obtaining the working condition data of the robot arm;

[0167] In this embodiment, the simulated weight data of the delivery container and the spatial distribution data of the goods to be loaded are combined to integrate the working conditions of the robot arm. The relationship between the simulated weight data and the spatial distribution data can be considered to adjust the working parameters of the robot arm, such as load capacity and range of motion, to adapt to the actual working conditions. This can be done by using simulation software to integrate the working conditions to ensure the performance optimization of the robot arm under different working conditions.

[0168] Step S355: performing three-dimensional motion dynamics modeling according to the loading robot arm data, thereby obtaining a loading robot arm power simulation model, and performing robot arm cargo grabbing simulation on the loading robot arm power simulation model according to the robot arm working condition data, thereby obtaining robot arm cargo grabbing simulation data;

[0169] In this embodiment, the data of the loading robot arm is used to perform three-dimensional motion dynamics modeling. For example, computer-aided design software (CAD) and kinematics simulation tools are used to establish a three-dimensional motion dynamics model of the robot arm based on the extracted characteristic data of the robot arm, including joint motion, load distribution, etc. Then, based on the working condition data of the robot arm, the model is simulated for cargo grabbing to generate simulation data of cargo grabbing by the robot arm.

[0170] Step S356: performing a combination of robot arm control parameters selection on the robot arm cargo grabbing simulation data, thereby obtaining a robot arm loading control strategy.

[0171] In this embodiment, the control parameter combination is selected for the robot arm cargo grabbing simulation data, thereby forming a loading control strategy for the robot arm. For example, by analyzing various grabbing parameters (such as grabbing force, speed, angle, etc.) in the simulation data, the optimal control parameter combination is selected and a corresponding control strategy is formulated. This process can be carried out using optimization algorithms and control theory to ensure that the robot arm can efficiently and safely complete the cargo loading task in actual operation.

[0172] Optionally, step S4 specifically includes:

[0173] Step S41: acquiring data of the logistics cargo to be loaded, and extracting the three-dimensional structural features of the logistics cargo from the data of the logistics cargo to be loaded, thereby obtaining the three-dimensional structural data of the logistics cargo;

[0174] In this embodiment, the goods to be loaded are scanned by a laser scanner or a 3D camera to obtain detailed three-dimensional point cloud data. These devices can accurately capture the geometric shape, size and surface features of the goods. Then, these point cloud data are processed using computer vision algorithms (such as convolutional neural networks) to extract the three-dimensional structural features of the goods, such as volume, shape contour, boundary curve, etc. Assuming there are a batch of boxes of different sizes, the scanning results will contain the three-dimensional coordinates and surface features of each box. After processing, the system can output the three-dimensional geometric feature data of each box, such as length, width, height and shape complexity.

[0175] Step S42: performing three-dimensional structural similarity calculation based on the three-dimensional structural data of the logistics goods and the logistics distribution container model set, thereby obtaining the three-dimensional similarity data of the logistics goods;

[0176] In this embodiment, a three-dimensional model database is established based on the logistics distribution container model, which includes various logistics distribution container models. The three-dimensional structural feature data of each cargo to be loaded is compared with the container model in the database through a feature matching algorithm, such as a three-dimensional shape context algorithm or a three-dimensional point cloud registration algorithm. The similarity calculation can be based on a distance metric (such as Euclidean distance) or a matching score. For example, for a rectangular cargo, by comparing it with container models of different sizes, the similarity score with each container model is calculated, and finally the similarity data of each cargo with all container models is obtained.

[0177] Step S43: selecting the logistics distribution container with the highest similarity from the logistics distribution container model set according to the three-dimensional similarity data of the logistics goods, thereby obtaining the goods logistics distribution container model;

[0178] In this embodiment, a sorting algorithm (such as quick sort or merge sort) is applied to sort all the distribution container models according to the calculated three-dimensional similarity data, and the container model with the highest similarity to the goods to be loaded is selected. Assuming that a specific model of container is found to have the highest similarity score with the three-dimensional structural features of a certain cargo through calculation, the container will be selected as the most suitable logistics distribution container. Additional conditions, such as the container's load capacity and transportation safety standards, can also be combined to further optimize the selection results.

[0179] Step S44: Cargo transmission path planning is performed on the cargo logistics distribution container model according to the distribution container transmission simulation data, thereby obtaining cargo transmission path data.

[0180] In this embodiment, the most suitable distribution container model is selected to plan the cargo transmission path. By establishing a simulation environment model containing a transportation network (including roads, bridges, traffic rules, etc.), a path planning algorithm (such as A* algorithm or Dijkstra algorithm) is used to calculate the optimal transportation path. Assuming that the cargo in the container needs to be transported from the warehouse to the final distribution center, the simulation system will calculate the shortest or fastest path from the warehouse to the distribution center based on the size and loading restrictions of the transport container, and take into account the actual traffic conditions, and output the final cargo transmission path data.

[0181] Optionally, step S5 specifically includes:

[0182] Step S51: using a cargo logistics distribution container model to perform container loading simulation on the cargo data to be loaded, thereby obtaining cargo loading container simulation data;

[0183] In this embodiment, a cargo logistics distribution container model is used to simulate the logistics cargo to be loaded. First, the size, weight and shape data of the cargo are input, and the model is used to calculate the effects of different loading schemes. During the simulation, the stacking method of the cargo, the space utilization rate in the container, and the contact points between the cargo and the container wall are considered to generate detailed loading simulation data. This data includes the position of each cargo in the container, the relative spatial distribution and its impact on the mechanical properties of the container, which helps optimize the loading strategy and reduce the risk of damage during transportation.

[0184] Step S52: integrating the cargo loading container weight characteristics with respect to the cargo loading container simulation data, thereby obtaining loading container weight data;

[0185] In this embodiment, the weight feature integration is performed on the loading container simulation data. By analyzing the distribution of cargo in each loading scheme, the total weight of each area in the container is calculated. This includes combining the weight information in the simulation data with the actual cargo weight data to form the overall loading container weight data. This data will help determine the maximum carrying capacity of the container, optimize cargo distribution to prevent overweight and unbalanced loads, and ensure transportation safety and efficiency.

[0186] Step S53: predicting the cargo loading motion trajectory according to the cargo loading container simulation data, thereby obtaining cargo loading motion trajectory data;

[0187] In this embodiment, the movement trajectory of the cargo is predicted based on the loading container simulation data. The kinematic and dynamic models are used to predict the movement trajectory of the cargo during transportation using the cargo position and dynamic characteristics in the loading simulation data. This includes considering the possible vibration, collision and displacement of the cargo during transportation to generate accurate movement trajectory data. These data are helpful in formulating corresponding protection measures to ensure that the cargo remains stable and reduces damage during transportation.

[0188] Step S54: performing robot arm loading control parameter combination matching on the loading container weight data and the cargo loading motion trajectory data according to the robot arm loading control strategy, thereby obtaining robot arm cargo loading control data;

[0189] In this embodiment, the weight data of the loading container and the data of the cargo motion trajectory are combined and matched according to the robot loading control strategy. First, the loading path and motion parameters of the robot are formulated by combining the weight data and the motion trajectory data through a combination of multiple parameters in the robot loading control strategy. The control strategy includes setting different gripping forces and moving speeds for cargo of different weights, as well as dynamic adjustment of the cargo motion trajectory. The robot loading cargo control data finally obtained ensures that the robot can effectively handle cargo of different weights and positions during the loading process, and achieves precise loading.

[0190] Step S55: performing stress-balanced distribution of the transport vehicle loading space on the loading container weight data through the transfer station transmission topology structure model, thereby obtaining stress-balanced loading space data;

[0191] In this embodiment, the transfer station transmission topology structure model is used to distribute stress evenly to the weight data of the loading container. The stress distribution inside the container is calculated based on the spatial configuration of the transfer station and the load distribution of the transportation vehicle. The stress of each area is evenly distributed through the optimization algorithm to generate stress-balanced loading space data. This process ensures that the transportation vehicle can withstand uniform stress load during loading, avoiding structural damage or safety hazards caused by local overload. The specific approach includes calculating the stress distribution of different areas and adjusting the loading plan through the optimization algorithm so that the stress in each area is balanced, thereby reducing potential problems caused by stress imbalance during transportation.

[0192] Step S56: integrating the stress-balanced loading space data into a cargo loading space matrix according to the cargo loading control data of the robot arm, thereby obtaining the cargo loading space data.

[0193] In this embodiment, the cargo loading control data of the robot arm is integrated with the stress-balanced loading space data to form a cargo loading space matrix. The cargo loading space data is generated by applying the control data to the optimized stress distribution space. These data include the final position of the cargo in the container, the loading method, and the spatial configuration after stress balance, which helps to accurately achieve the best cargo layout in the actual loading process and improve loading efficiency and safety. For example, the cargo loading control data (such as the grasping path and grasping force) of the robot arm is integrated with the stress-balanced loading space data (such as the stress distribution in the container). Ensure that the two data sets are aligned in the same coordinate system for effective fusion. Construct a three-dimensional space matrix model to map the stress-balanced loading space data to the cargo control data of the robot arm. This can be achieved by interpolation methods or space conversion algorithms. Mathematical and computational methods (such as linear programming or optimization algorithms) are applied to integrate the robot arm control data with the stress-balanced data, and adjust the spatial layout of the cargo in the container to optimize the loading effect and stress distribution. The integrated cargo loading space data is verified to check whether it meets the actual loading requirements and stress balance standards. Adjust according to the actual situation to ensure that the final cargo loading space data meets all design requirements.

[0194] Optionally, the present invention further provides a logistics automation scheduling system based on data analysis, which is used to execute the logistics automation scheduling method based on data analysis as described above, and the logistics automation scheduling system based on data analysis includes:

[0195] The transmission topology structure analysis module is used to obtain the facility data of the logistics transfer station, and extract the logistics structure features of the transfer station from the facility data of the logistics transfer station, so as to obtain the logistics structure data of the transfer station; perform the transmission topology structure analysis of the transfer station according to the logistics structure data of the transfer station, so as to obtain the transmission topology structure model of the transfer station;

[0196] The three-dimensional physical finite element analysis module is used to extract the structural features of the logistics distribution container from the logistics transfer station facility data, thereby obtaining the logistics distribution container data; and to perform three-dimensional physical finite element analysis based on the logistics distribution container data, thereby obtaining the logistics distribution container model set;

[0197] A robot arm control strategy analysis module is used to simulate the transmission of distribution containers on the transfer station transmission topology model and the logistics distribution container model set, thereby obtaining the distribution container transmission simulation data, and to analyze the robot arm loading control strategy on the distribution container transmission simulation data according to the logistics transfer station facility data, thereby obtaining the robot arm loading control strategy;

[0198] The logistics distribution container allocation module is used to obtain the data of logistics goods to be loaded, and to allocate logistics distribution containers to the data of logistics goods to be loaded according to the logistics distribution container model set, so as to obtain the goods logistics distribution container model; and to plan the goods transmission path for the goods logistics distribution container model according to the distribution container transmission simulation data, so as to obtain the goods transmission path data;

[0199] The cargo loading space matrix integration module is used to match the robot loading control strategy to the cargo logistics distribution container model according to the robot loading control strategy, so as to obtain the robot loading cargo control data, and to integrate the cargo loading space matrix according to the robot loading cargo control data, so as to obtain the cargo loading space data;

[0200] The scheduling management strategy analysis module is used to analyze the logistics cargo scheduling management strategy based on the cargo transmission path data and the cargo loading space data, so as to obtain the logistics cargo scheduling management strategy and upload it to the logistics transfer station control platform to execute the cargo scheduling task.

[0201] The data analysis-based logistics automation scheduling system of the present invention can implement any one of the data analysis-based logistics automation scheduling methods of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the data analysis-based logistics automation scheduling method. The internal modules of the system cooperate with each other, thereby improving the efficiency and accuracy of logistics scheduling management.

[0202] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0203] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A logistics automation scheduling method based on data analysis, characterized in that: The following steps are involved: Step S1: Obtaining logistics transfer station facility data, and extracting transfer station logistics structure features from the logistics transfer station facility data, thereby obtaining transfer station logistics structure data; According to the logistics structure data of the transfer station, the transfer station transmission topology structure analysis is carried out to obtain the transfer station transmission topology structure model; Step S2: extracting the structural features of logistics distribution containers from the logistics transfer station facility data, thereby obtaining logistics distribution container data; performing three-dimensional physical finite element analysis on the logistics distribution container data, thereby obtaining a logistics distribution container model set; Step S3: Performing a distribution container transmission simulation on the transfer station transmission topology model and the logistics distribution container model set to obtain distribution container transmission simulation data, and performing a manipulator loading control strategy analysis on the distribution container transmission simulation data according to the logistics transfer station facility data to obtain the manipulator loading control strategy. Step S3 is specifically as follows: Step S31: extracting container geometric features from the logistics distribution container model set, thereby obtaining distribution container geometric feature data; Step S32: planning the transmission path of the delivery container according to the transfer station transmission topology model and the delivery container geometric feature data, thereby obtaining container delivery path data; Step S33: constructing a transfer station transmission network model according to the transfer station transmission topology model and the logistics distribution container model set, thereby obtaining a transfer station transmission network model; Step S34: performing a delivery container transmission simulation on the container delivery path data and the logistics delivery container model set through the transfer station transmission network model, thereby obtaining delivery container transmission simulation data; Step S35: Performing a robot arm loading control strategy analysis on the distribution container transmission simulation data according to the logistics transfer station facility data, thereby obtaining a robot arm loading control strategy. Step S35 is specifically as follows: Step S351: extracting loading robot arm features from the logistics transfer station facility data, thereby obtaining loading robot arm data; Step S352: extracting cargo loading unit simulation data and delivery container simulation weight data from the delivery container transmission simulation data, thereby obtaining cargo loading unit simulation data and delivery container simulation weight data; Step S353: performing statistics on the movement trajectory of the cargo to be loaded according to the cargo loading unit simulation data, thereby obtaining simulated movement trajectory data of the cargo to be loaded, and performing statistics on the spatial distribution of the cargo to be loaded according to the simulated movement trajectory data of the cargo to be loaded, thereby obtaining spatial distribution data of the cargo to be loaded; Step S354: integrating the working conditions of the robot arm according to the simulated weight data of the delivery container and the spatial distribution data of the cargo to be loaded, thereby obtaining the working condition data of the robot arm; Step S355: performing three-dimensional motion dynamics modeling according to the loading robot arm data, thereby obtaining a loading robot arm power simulation model, and performing robot arm cargo grabbing simulation on the loading robot arm power simulation model according to the robot arm working condition data, thereby obtaining robot arm cargo grabbing simulation data; Step S356: performing a combination of robot arm control parameters selection on the robot arm cargo grabbing simulation data, thereby obtaining a robot arm loading control strategy; Step S4: obtaining the data of the logistics cargo to be loaded, and performing logistics distribution container allocation on the logistics cargo data to be loaded according to the logistics distribution container model set, thereby obtaining the cargo logistics distribution container model; performing cargo transmission path planning on the cargo logistics distribution container model according to the distribution container transmission simulation data, thereby obtaining the cargo transmission path data. Step S4 is specifically as follows: Step S41: acquiring data of the logistics cargo to be loaded, and extracting the three-dimensional structural features of the logistics cargo from the data of the logistics cargo to be loaded, thereby obtaining the three-dimensional structural data of the logistics cargo; Step S42: performing three-dimensional structural similarity calculation based on the three-dimensional structural data of the logistics goods and the logistics distribution container model set, thereby obtaining the three-dimensional similarity data of the logistics goods; Step S43: selecting the logistics distribution container with the highest similarity from the logistics distribution container model set according to the three-dimensional similarity data of the logistics goods, thereby obtaining the goods logistics distribution container model; Step S44: planning a cargo transmission path for the cargo logistics distribution container model according to the distribution container transmission simulation data, thereby obtaining cargo transmission path data; Step S5: According to the robot loading control strategy, the robot loading control strategy is matched to the cargo logistics distribution container model, so as to obtain the robot loading cargo control data, and the cargo loading space matrix is ​​integrated according to the robot loading cargo control data, so as to obtain the cargo loading space data. Step S5 is specifically as follows: Step S51: using a cargo logistics distribution container model to perform container loading simulation on the cargo data to be loaded, thereby obtaining cargo loading container simulation data; Step S52: integrating the cargo loading container weight characteristics with respect to the cargo loading container simulation data, thereby obtaining loading container weight data; Step S53: predicting the cargo loading motion trajectory according to the cargo loading container simulation data, thereby obtaining cargo loading motion trajectory data; Step S54: performing robot arm loading control parameter combination matching on the loading container weight data and the cargo loading motion trajectory data according to the robot arm loading control strategy, thereby obtaining robot arm cargo loading control data; Step S55: performing stress-balanced distribution of the transport vehicle loading space on the loading container weight data through the transfer station transmission topology structure model, thereby obtaining stress-balanced loading space data; Step S56: integrating the stress-balanced loading space data into a cargo loading space matrix according to the cargo loading control data of the robot arm, thereby obtaining the cargo loading space data; Step S6: Analyze the logistics cargo scheduling management strategy based on the cargo transmission path data and the cargo loading space data, so as to obtain the logistics cargo scheduling management strategy, and upload it to the logistics transfer station control platform to execute the cargo scheduling task.

2. The logistics automation scheduling method based on data analysis according to claim 1 is characterized in that: Step S1 is specifically as follows: Step S11: acquiring logistics transfer station facility data, and classifying the logistics transfer station facility data into facility structure, thereby obtaining transportation tool structure data and loading facility structure data; Step S12: performing loading process topology analysis according to the loading facility structure data, thereby obtaining a loading process topology model; Step S13: performing transportation tool space structure modeling according to the transportation tool structure data, thereby obtaining a transportation tool space structure model; Step S14: integrating the facility space connection relationship of the transportation tool structure data and the loading facility structure data, thereby obtaining the transfer station facility space connection data; Step S15: Associating the transfer station transmission topology structure model with the loading process topology structure model and the transportation tool space structure model according to the transfer station facility space connection data, thereby obtaining the transfer station transmission topology structure model.

3. The logistics automation scheduling method based on data analysis according to claim 2 is characterized in that: Step S12 is specifically as follows: Step S121: classifying the loading facility structure data into loading facility structure data, thereby obtaining loading facility connection structure data and loading facility function structure data; Step S122: integrating the functional structure spatial distribution according to the loading facility functional structure data, thereby obtaining the loading facility functional structure spatial distribution data, and dividing the loading facility functional structure spatial distribution data into loading process units, thereby obtaining loading functional structure process unit data; Step S123: performing connection structure similarity calculation according to the loading facility connection structure data, thereby obtaining loading facility connection structure similarity data, and performing connection structure connection relationship integration on the loading facility connection structure data according to the loading facility connection structure similarity data, thereby obtaining loading facility connection structure connection relationship data; Step S124: performing spatial connection relationship association on the loading facility functional structure spatial distribution data and the loading facility connection structure connection relationship data, thereby obtaining loading facility connection relationship data; Step S125: performing loading facility topology analysis according to the loading facility connection relationship data, thereby obtaining a loading facility topology model; Step S126: mapping the loading process unit to the loading facility topology structure model according to the loading function structure process unit data, thereby obtaining a loading process topology structure model.

4. The logistics automation scheduling method based on data analysis according to claim 2 is characterized in that: Step S13 is specifically as follows: Step S131: classifying the transportation tool structure data into transportation tool structure data, thereby obtaining transportation tool power structure data and transportation tool loading structure data; Step S132: performing three-dimensional structural modeling according to the power structure data of the transportation vehicle, thereby obtaining a three-dimensional power structure model of the transportation vehicle; Step S133: Calculating the loading space capacity according to the transportation vehicle loading structure data, thereby obtaining the transportation vehicle loading space capacity data; Step S134: performing three-dimensional structural modeling according to the transportation vehicle loading structure data, thereby obtaining a three-dimensional transportation vehicle loading structure model, and performing spatial capacity feature fusion on the three-dimensional transportation vehicle loading structure model according to the transportation vehicle loading space capacity data, thereby obtaining a three-dimensional space transportation vehicle loading structure model; Step S135: integrating the three-dimensional transport vehicle power structure model and the three-dimensional space transport vehicle loading structure model to obtain a transport vehicle space structure model.

5. The logistics automation scheduling method based on data analysis according to claim 1 is characterized in that: Step S2 is specifically as follows: Step S21: extracting logistics distribution container structure features from the logistics transfer station facility data, thereby obtaining logistics distribution container data; Step S22: extracting container structure features and container material features from the logistics distribution container data, thereby obtaining container structure data and container material data; Step S23: analyzing the material characteristics of the container according to the container material data, thereby obtaining the material characteristic data of the delivery container; Step S24: performing three-dimensional structure modeling according to the container structure data, thereby obtaining a three-dimensional logistics distribution container model set, and performing container material property mapping on the three-dimensional logistics distribution container model set according to the distribution container material property data, thereby obtaining a logistics distribution container property model set; Step S25: setting physical simulation boundary conditions based on the material characteristic data of the distribution container, thereby obtaining physical simulation boundary conditions, and performing finite element analysis coupling on the logistics distribution container characteristic model set according to the physical simulation boundary conditions, thereby obtaining physical coupling data of the logistics distribution container; Step S26: mapping the physical coupling characteristics of the logistics distribution container to the characteristic model set of the logistics distribution container according to the physical coupling data of the logistics distribution container, thereby obtaining the logistics distribution container model set.

6. A logistics automation scheduling system based on data analysis, characterized in that: Used to execute the logistics automation scheduling method based on data analysis as claimed in claim 1, the logistics automation scheduling system based on data analysis includes: The transmission topology structure analysis module is used to obtain the facility data of the logistics transfer station, and extract the logistics structure features of the transfer station from the facility data of the logistics transfer station, so as to obtain the logistics structure data of the transfer station; perform the transmission topology structure analysis of the transfer station according to the logistics structure data of the transfer station, so as to obtain the transmission topology structure model of the transfer station; The three-dimensional physical finite element analysis module is used to extract the structural features of the logistics distribution container from the logistics transfer station facility data, thereby obtaining the logistics distribution container data; and to perform three-dimensional physical finite element analysis based on the logistics distribution container data, thereby obtaining the logistics distribution container model set; A robot arm control strategy analysis module is used to simulate the transmission of distribution containers on the transfer station transmission topology model and the logistics distribution container model set, thereby obtaining the distribution container transmission simulation data, and to analyze the robot arm loading control strategy on the distribution container transmission simulation data according to the logistics transfer station facility data, thereby obtaining the robot arm loading control strategy; The logistics distribution container allocation module is used to obtain the data of logistics goods to be loaded, and to allocate logistics distribution containers to the data of logistics goods to be loaded according to the logistics distribution container model set, so as to obtain the goods logistics distribution container model; and to plan the goods transmission path for the goods logistics distribution container model according to the distribution container transmission simulation data, so as to obtain the goods transmission path data; The cargo loading space matrix integration module is used to match the robot loading control strategy to the cargo logistics distribution container model according to the robot loading control strategy, so as to obtain the robot loading cargo control data, and to integrate the cargo loading space matrix according to the robot loading cargo control data, so as to obtain the cargo loading space data; The scheduling management strategy analysis module is used to analyze the logistics cargo scheduling management strategy based on the cargo transmission path data and the cargo loading space data, so as to obtain the logistics cargo scheduling management strategy and upload it to the logistics transfer station control platform to execute the cargo scheduling task.

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