Warehouse and business scene operation method and system based on data carrier

Abstract

The invention relates to the technical field of intelligent warehouse logistics, in particular to a warehouse and business scene operation method and system based on a data carrier, and the method comprises the following steps: S1, constructing and adapting the data carrier of a flexible warehouse shelf and a business scene; s2, dynamically optimizing the layout of the warehouse by analyzing and processing the logistics demand, the shelf information, the inventory state and the work order information in the data carrier in real time; and S3, constructing a performance model of the flexible warehouse and the business scene for simulation and evaluation to optimize resource allocation of the warehouse, and establishing a sequence optimization model to optimize the operation flow of the flexible warehouse. The flexible warehouse system which is flexible, intelligent and optimized in real time is provided by combining the data carrier technology with the simulation environment and the optimization algorithm, the defects that in the prior art, the warehouse layout is rigid, the service scene adaptability is poor, and the service process is not flexible are overcome, and the efficiency and economical efficiency of warehouse management are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent warehousing and logistics technology, and in particular to an operating method and system for a warehouse and business scenario based on a data carrier. Background Art

[0002] Under the current e-commerce economic model, manufacturing companies need to respond quickly to changes in market demand, while the rigidity and limitations of traditional warehouse design hinder efficient and flexible logistics management. Existing warehouse management systems cannot fully adapt to diversified and personalized logistics needs, and there are problems such as low warehouse flexibility and lack of data drive. For example, CN115542857A flexible scheduling method and system flexibly plans the scheduling of carrier robots and various systems to complete services such as material distribution. However, the patent does not construct a production / logistics data carrier, and cannot achieve the adaptation of flexible warehouses to business scenarios, nor can it dynamically adjust the number, layout and size of shelves. For example, CN115271293A is a multi-agent flexible workshop autonomous scheduling method. The patent mainly considers the scheduling model inside the workshop. However, the patent does not involve business scenarios such as production and logistics connection, and it is difficult to achieve integrated management of production logistics. In order to meet these challenges, there is an urgent need for a flexible warehouse system that can respond in real time and dynamically adjust to improve operational efficiency and reduce costs. Summary of the invention

[0003] In order to solve the above technical problems existing in the prior art, the present invention proposes an operation method and system for warehouses and business scenarios based on data carriers, which realizes flexible adaptation of production / logistics data carriers through data carrier technology, reasonably configures the number of shelves, optimizes warehouse layout, optimizes picking modules, builds system performance models and simulates and evaluates them, optimizes flexible warehouse operation processes, and thus realizes efficient warehouse management and business process optimization. The specific technical solutions are as follows: A data carrier-based warehouse and business scenario operation method, comprising the following steps: S1: Build and adapt data carriers for flexible warehouse shelves and business scenarios; S2: Dynamically optimize warehouse layout by analyzing and processing logistics demand, shelf information, inventory status and work order information in data carriers in real time; S3: Build performance models of flexible warehouses and business scenarios for simulation and evaluation to optimize warehouse resource allocation, and establish sequential optimization models to optimize the operation process of flexible warehouses.

[0004] Furthermore, the S1 specifically includes: S1.1: Build data carriers for shelves and business scenarios; S1.2: Drive communication synchronization, data information synchronization, and data carrier node matching between production data carriers and logistics data carriers in business scenarios through low-code; S1.3: Collect the work order information and shelf information to be processed, design and adapt the work order and shelf data carriers based on the synchronous assignment model.

[0005] Furthermore, the S1.1 specifically includes: S1.1.1, Business scenario demand analysis and planning: Based on IoT technology and big data analysis, collect data from equipment at each node of warehouse and factory logistics to obtain warehouse and business scenario needs; S1.1.2, Data carrier system design and construction: Use cloud computing and edge computing technologies to design the data carrier system architecture, including warehouse management module, production planning module, and automation control module, and then design and develop the system, including front-end interface, back-end logic, and database. At the same time, connect to the WMS / ERP / MES system to reflect and manage the actual shelf layout and inventory status; S1.1.3, Data carrier system deployment and maintenance: Based on remote monitoring, the developed system is deployed to the actual production and storage environment, and hardware and software configuration and data migration are performed.

[0006] Furthermore, the S1.2 specifically includes: S1.2.1, Communication synchronization: define the communication protocol and interface standards between the production data carrier and the logistics data carrier, including data format, message transmission method and communication frequency, use the low-code platform to configure the communication module to achieve real-time data transmission, and conduct communication synchronization testing; S1.2.2, Data Information Synchronization: Define data mapping relationships and conversion rules to keep data consistent between different data carriers, use low-code platforms to implement data synchronization and integration logic, including data extraction, conversion, and ETL loading processes, and adopt real-time monitoring mechanisms; S1.2.3, data carrier node matching: Use a low-code development platform to design integrated production data carriers and logistics data carriers, clarify their respective nodes and processes, determine the adaptation nodes and create adaptation applications. The nodes include production stations, warehouses, and shelves.

[0007] Furthermore, the S1.3 specifically includes: S1.3.1, collect data from data carriers, including interface information of orders and shelves, collect and organize data of all pending orders, including order quantity, order size, order deadline, collect shelf information, including shelf quantity, shelf location, shelf capacity, and information on the goods stored on each shelf; S1.3.2, based on the association information between the work order and the shelf, carry out the joint optimization algorithm to design the synchronous assignment model frame and obtain the joint synchronous assignment solution; S1.3.3, using the synchronous assignment model framework, simulate the allocation of work orders and shelves, evaluate performance indicators, and make optimization adjustments to find the best adaptation solution. The indicators include cost, efficiency, and order fulfillment time.

[0008] Furthermore, the S2 specifically includes: S2.1: Pick the work orders in batches according to their task requirements; S2.2: Based on the task requirements, define optimization indicators, including maximizing utilization, minimizing distance, and minimizing transportation costs. Based on the business scenario, determine the constraints of shelf quantity, size, distance, and location. Use a multi-objective optimization algorithm to establish a convex optimization mathematical model, convert the optimization objective function and constraints into mathematical expressions for solution, so as to optimize the shelf location layout and quantity configuration; S2.3: Perform finite element analysis and adjust the structure of the picking module in the warehouse, including the cabinet layout, SKU storage method, and the entry and exit mechanism of the picking items.

[0009] Furthermore, the S2.1 specifically includes: S2.1.1, design a picking work order aggregation method, analyze the work orders, find similarities, including the same or similar material requirements, and merge similar work orders into a task master order; S2.1.2, assign partition attributes to each work order, and select the most appropriate partition for each work order by using a heuristic method with the maximum regional similarity as the optimization goal; S2.1.3, establish a mixed integer programming model to optimize the picking process based on the picking area allocation, work order arrangement and constraints; S2.1.4, establish a cost function, taking into account the business data and constraints of each partition, including the number of shelf entries, the robot's movement distance, and the number of manual aisle crossings, to minimize the picking cost.

[0010] Furthermore, the performance model of the flexible warehouse and business scenarios of S3 is simulated and evaluated, including: S3.1.1: Define business scenarios and key indicators, including specific functions, operational processes and key indicators of flexible warehouses, determine the scope and objectives of the evaluation, and select key performance indicators, including warehouse throughput, processing time, and turnover rate; S3.1.2: Based on the collected data, use statistical models including regression analysis and time series analysis or machine learning models including neural networks and decision trees to establish performance models of flexible warehouses and business scenarios; S3.1.3: Based on the performance model, use discrete event simulation to simulate the warehouse operation process, including work order processing, goods movement, and personnel scheduling, or use agent-based simulation to simulate the behavior and interaction of different individuals in the warehouse, such as goods, personnel, and equipment; S3.1.4: Based on the simulation results, identify potential performance bottlenecks and room for improvement based on sensitivity analysis, optimization algorithms, or decision analysis methods, and propose optimization plans for resource allocation.

[0011] Furthermore, the S3 establishes a sequential optimization model to optimize the operation process of the flexible warehouse, specifically including: S3.2.1: Considering the efficiency of the picking area and buffer area in the warehouse, the existing inventory information and the work order set information itself, the work order picking sequence optimization model is established with the goal of minimizing the total work order processing time; S3.2.2: Use the improved adaptive genetic algorithm to solve the work order picking sequence optimization model and obtain a reasonably planned work order picking sequence.

[0012] A data carrier-based warehouse and business scenario operation system, including: Production / logistics data carrier design module: used to build and manage data carriers for work orders, shelf information, inventory status, and logistics needs inside and outside the flexible warehouse, collect real-time data inside and outside the warehouse through IoT technology, and perform data mapping and synchronization on the low-code platform; Shelf and business scenario adaptation module: Dynamically match and configure shelves and business scenarios based on data carriers; Task demand and shelf configuration optimization algorithm module: Use multi-objective optimization algorithm and dynamic path planning algorithm to analyze the real-time data in the data carrier, and dynamically adjust the storage layout and optimal logistics path of the flexible warehouse; Performance evaluation and operation performance optimization module: Build performance models of flexible warehouses and business scenarios for simulation and evaluation to optimize warehouse resource allocation, and establish sequential optimization models to optimize the operation process of flexible warehouses.

[0013] The present invention provides a flexible, intelligent, and real-time optimized flexible warehouse system through data carrier technology combined with simulation environment and optimization algorithm, which overcomes the shortcomings of rigid warehouse layout, poor adaptability to business scenarios, inflexible business processes and other shortcomings in the prior art, significantly improves the efficiency and economy of warehouse management, and can realize flexible low-code configuration and modular system orchestration of multi-type and multi-vendor equipment, facilitates user independent management, significantly improves the flexibility and responsiveness of warehouse management, reduces logistics delivery time, and reduces operating costs. It is suitable for manufacturing companies facing complex and changeable market demands. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a schematic diagram of an operation method of a warehouse and business scenario based on a data carrier in this embodiment; Figure 2 is a flow chart of flexible warehouse shelves and business adaptation based on data carriers in this embodiment; Figure 3 It is a schematic diagram of the two-stage problem decomposition strategy; Figure 4 is a flowchart of task requirements and shelf configuration optimization based on data carriers in this embodiment; Figure 5 is a schematic diagram of task requirement merging and batch optimization based on heuristic and mixed integer programming modeling in this embodiment; Figure 6 is a schematic diagram of the shelf position layout and quantity configuration of this embodiment; Figure 7 is a schematic diagram of the structural optimization of the picking module based on finite element and ergonomics of this embodiment; Figure 8 is a flow chart of performance evaluation and operation performance optimization based on data carriers of this embodiment; Fig. 9 is a technical flow chart of flexible warehouse operation sequence optimization of this embodiment; In the figure: 1. Shelf storage area, 2. Picking area, 3. Shelves to be picked. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical scheme and technical effect of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0016] The present embodiment provides an intelligent production flexible warehouse and business scenario operation system based on a data carrier, including: Production / logistics data carrier design module: used to build and manage data carriers such as work orders, shelf information, inventory status, and production logistics requirements inside and outside the flexible warehouse; the production / logistics data carrier design module collects real-time data inside and outside the warehouse through Internet of Things technology, and performs data mapping and synchronization on the low-code platform.

[0017] Shelf and business scenario adaptation module: based on the data carrier, dynamic matching and configuration of shelves and business scenarios are realized; the shelf and business scenario adaptation module can adjust the shelf configuration and task allocation of the warehouse in real time according to changes in business needs, so as to realize flexible adaptation of warehouse shelves and business scenarios.

[0018] Task demand and shelf configuration optimization algorithm module: adopts multi-objective optimization algorithm and dynamic path planning algorithm to analyze the real-time data in the data carrier, and dynamically adjusts the storage layout and optimal logistics path of the flexible warehouse; the task demand and shelf configuration optimization algorithm module dynamically optimizes the layout of the flexible warehouse by real-time analysis of the logistics demand, inventory status and work order information in the data carrier, improves logistics efficiency and reduces operating costs.

[0019] Performance evaluation and operation performance optimization module: Build a system performance model, evaluate the operation performance of the system through simulation technology, and optimize the warehouse's operational efficiency and resource allocation based on the evaluation results. This module uses discrete event simulation and agent-based simulation technology to simulate and evaluate the system's operation performance under different business scenarios, and optimize the flexible warehouse's operation process based on the simulation results. There is also an iterative optimization module, which performs iterative optimization of the system based on the simulation results to ensure the continuous and efficient operation of the warehouse.

[0020] The production / logistics data carrier design module, shelf and business scenario adaptation module, optimization algorithm module and performance evaluation module work together to realize the intelligence, flexibility and efficiency of the warehouse system and adapt to the complex and changing production and logistics needs.

[0021] like Figure 1 As shown, this embodiment proposes an operation method for a smart production flexible warehouse and business scenario based on a data carrier. By building a data carrier system, data such as work orders, shelf information, inventory status, and logistics demand are dynamically managed to achieve data carrier adaptation. The system is developed using a low-code platform, combined with a multi-objective optimization algorithm to optimize task requirements and shelf configuration, make real-time adjustments to warehouse layout and picking modules, and evaluate and optimize system performance through simulation technology to achieve adaptation of flexible warehouses and business scenarios. The method specifically includes the following steps: S1, flexible warehouse shelves and business adaptation based on data carriers: Figure 2 As shown in the figure, a simulation environment for shelves and business scenarios is constructed, data carriers for shelves and business scenarios are constructed, data collection, transmission and analysis are realized, low-code programming technology is used to drive the adaptation of production / logistics data carriers, and the adaptation of work orders and shelves in data carriers is completed, including: S1.1, Construction of shelf and business scenario data carriers: Use IoT and cloud computing technologies to conduct demand analysis and system planning, system design and development, and finally deploy the system to the actual environment and perform regular maintenance, including: S1.1.1, Business scenario demand analysis and planning: Based on IoT technology and big data analysis, through demand analysis and system planning, collect data from equipment at each node of warehousing and factory logistics, understand the needs and challenges of flexible warehouses and business scenarios, design system architecture and functional modules, determine the technology stack, database structure and user interface, and ensure the system is integrated with production equipment, warehousing equipment and sensors.

[0022] S1.1.2, Data carrier system design and construction: Use cloud computing and edge computing technologies to design and develop the system, design the system architecture, including modules such as warehouse management, production planning, and automated control, to ensure that the system is scalable. Develop the system according to the system design, including the front-end interface, back-end logic, database, etc. At the same time, connect to the existing WMS / ERP / MES system to reflect and manage the actual shelf layout and inventory status, ensure that the various components of the system can be effectively integrated, and conduct system testing to fix vulnerabilities.

[0023] S1.1.3, Data carrier system deployment and maintenance: Based on remote monitoring, deploy the developed system to the actual production and storage environment, and perform tasks such as hardware and software configuration and data migration. Provide training for system users to ensure their effective use. Regularly monitor system performance, perform maintenance work, and continuously improve the system, add new functions, and optimize existing functions based on user feedback and business needs.

[0024] S1.2, low-code driven adaptation of production / logistics data carriers: communication synchronization, data information synchronization and node matching between production data carriers and logistics data carriers are achieved through the low-code platform to achieve data interoperability and functional integration between the two systems, including: S1.2.1, Communication synchronization: Study the message queue system and define the communication protocol and interface standards between production and logistics data carriers, including data format, message delivery method and communication frequency. Use the low-code platform to configure the communication module to achieve real-time data transmission, and conduct communication synchronization tests to ensure transmission accuracy and timeliness.

[0025] S1.2.2, Data information synchronization: Study data information synchronization methods, define data mapping relationships and conversion rules, and set necessary data conversion rules to ensure data consistency between different data carriers. Use low-code platforms to implement data synchronization and integration logic, including data extraction, conversion, and ETL loading processes. Establish a real-time monitoring mechanism to ensure data consistency, accuracy, and availability.

[0026] S1.2.3, Data carrier node matching: Use low-code development platforms to research node matching technology and achieve seamless integration between production data carriers and logistics data carriers. Identify the respective nodes and processes, determine the adaptation nodes, such as production stations, warehouses, shelves, etc., and create adaptation applications. Finally, integrate the new application with the existing system to ensure smooth data and event transmission.

[0027] S1.3, Synchronous assignment of work orders and shelf data carrier adaptation: Collect pending work order information and shelf information, design a synchronous assignment model framework, simulate work order and shelf allocation based on the synchronous assignment model, find the best adaptation solution, and continuously optimize the synchronous assignment model and algorithm based on actual operation data and feedback, including: S1.3.1, Work order-shelf data carrier model building: Collect data from data carriers, including interface information between work orders and shelves, collect and organize data on all pending work orders, including work order quantity, work order size, work order deadline, etc. Collect shelf information, including shelf quantity, shelf location, shelf capacity, and information on the goods stored on each shelf.

[0028] S1.3.2, Joint Optimization Synchronous Assignment Model: Carry out the design of the joint optimization algorithm to determine the synchronous assignment model framework, such as Figure 3 The two-stage problem decomposition strategy is shown. An algorithm is designed to compress shelf information, reduce the problem scale, study the information association between work orders and shelves, and design a joint synchronization assignment solution.

[0029] S1.3.3, Optimize algorithms to adapt to changing environments and goals: Use the synchronous assignment model framework to simulate work order and shelf allocation plans, evaluate performance indicators such as cost, efficiency, work order fulfillment time, etc., and make optimization adjustments to find the best adaptation plan. Update data in real time and continuously optimize models and algorithms based on actual operational feedback.

[0030] S2, optimization of task requirements and shelf configuration based on data carriers: reference Figures 4 to 7 ,in Figure 6 The buffer area shown is the shelf storage area 1, the picking area 2, and the picking shelf and the waiting-to-pick shelf 3 moved to the picking area 2 by the AGV; configure the number of shelves and optimize the warehouse location layout, use the task demand merging, batch optimization and other technologies to optimize the picking module structure, improve the flexibility and efficiency of the flexible warehouse, reduce the logistics distribution time, and reduce the operating cost, including: S2.1, task demand merging and batch optimization: Improve picking efficiency and reduce costs by aggregating work orders and optimizing the picking process, including: S2.1.1: Design a picking work order aggregation method to analyze the work orders and find similarities, including the same or similar material requirements, and merge similar work orders into a total task order to improve picking efficiency.

[0031] S2.1.2: Assign partition attributes to each work order. Use heuristic methods to maximize regional similarity and select the most appropriate partition for each work order. Optimize partition allocation so that work orders within the total task order are picked in the same area as much as possible to reduce cross-area operations.

[0032] S2.1.3: Establish a mixed integer programming model to optimize the picking process and minimize the picking cost based on the picking area allocation, work order arrangement and related constraints.

[0033] S2.1.4: Establish a cost function that takes into account the business data and constraints of each partition, such as the number of shelf entries, the robot's movement distance, the number of manual aisle crossings, and other factors to minimize the picking cost.

[0034] S2.2, Shelf location layout and quantity configuration optimization: Use a multi-objective optimization algorithm to optimize shelf location layout and quantity configuration, including: S2.2.1: Based on the task requirements, define optimization indicators, including maximizing utilization, minimizing distance, minimizing transportation costs, etc.; based on the business scenario, determine the constraints such as shelf quantity, size, distance, and location. Establish a convex optimization mathematical model and convert the optimization objective function and constraints into mathematical expressions. According to the complexity of the problem, select the appropriate solution algorithm, including interior point method, gradient projection method, sequential quadratic programming method, etc.

[0035] S2.2.2: Verify the optimization solution to ensure that the constraints are met and the goal is achieved. Based on the evaluation results, adjust the objective function, constraints or algorithm, and iterate the optimization until the requirements are met.

[0036] S2.3, Optimization of the picking function module structure: Simulate and analyze the container layout, SKU storage method, and entry and exit mechanism; adjust the container size, storage method, or adopt an efficient storage solution; evaluate the effectiveness of the optimization strategy through simulation experiments, data analysis, or actual testing, including: finite element analysis of the structure of the picking module, including the container layout, SKU storage method, and the entry and exit mechanism of the picking parts, optimize the picking module structure, and improve efficiency. Determine the optimization strategy for special situations, such as analyzing high-frequency commodities and large-volume work orders. Increase the number of SKUs stored in the container, adjust the container SKU size, storage method, or efficient storage solution, and improve the efficiency of a single picking. Evaluate the effectiveness of the optimization strategy through simulation experiments, data analysis, or actual testing. Based on the evaluation results, summarize the improvement strategy, including adjusting the container size, optimizing the storage method, or improving the entry and exit mechanism, to provide a reference for the actual picking module design.

[0037] S3, performance evaluation and optimization of operation performance based on data carriers: e.g. Figure 8 As shown in the figure, the system performance model of flexible warehouse and business scenarios is constructed and simulated and evaluated to optimize system performance; a sequential optimization model is established to optimize the flexible warehouse operation process, achieve the optimal allocation of resources in warehouse and logistics management, improve warehouse operation efficiency and reduce costs. Specifically include: S3.1: Establish performance models based on business scenarios and key indicators, verify the models and simulate operations, and apply algorithm improvements based on simulation results. Specifically include: S3.1.1: Define business scenarios and key indicators. Clarify the business scenarios for evaluation, including the specific functions, operating processes, and key indicators of the flexible warehouse, determine the scope and objectives of the evaluation, and select key performance indicators such as warehouse throughput, processing time, and turnover rate.

[0038] S3.1.2: Based on the collected data, use statistical models such as regression analysis and time series analysis, or use machine learning models such as neural networks and decision trees to establish performance models for flexible warehouses and business scenarios.

[0039] S3.1.3: Based on the model, use discrete event simulation to simulate the operation process of the warehouse, including work order processing, goods movement, personnel scheduling, etc., or use agent-based simulation to simulate the behavior and interaction of different individuals in the warehouse such as goods, personnel, equipment, etc.

[0040] S3.1.4: Based on the simulation results, identify potential performance bottlenecks and room for improvement based on sensitivity analysis, optimization algorithms, or decision analysis methods, and propose optimization plans for resource allocation.

[0041] S3.2: If Fig. 9 As shown in the figure, a sequential optimization model is established and solved using an improved adaptive genetic algorithm to obtain a reasonable work order picking sequence, including: S3.2.1: Considering various factors such as the efficiency of each zone, existing inventory information, and the work order set’s own information, the sequential optimization model is established with the goal of minimizing the total work order processing time, taking into account factors such as delay time.

[0042] S3.2.2: Use the improved adaptive genetic algorithm to solve the work order picking sequence optimization model. By solving the model, a well-planned work order picking sequence is obtained, the operation of picking area 2 and buffer area is optimized, and the total work order processing time is reduced.

[0043] The above is only a preferred implementation case of the present invention and does not limit the present invention in any form. Although the implementation process of the present invention is described in detail above, for those familiar with the art, they can still modify the technical solutions recorded in the above examples, or replace some of the technical features therein with equivalents. All modifications, equivalent replacements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A data carrier-based warehouse and business scenario operation method, characterized in that: The following steps are involved: S1: Build and adapt data carriers for flexible warehouse shelves and business scenarios; S2: Dynamically optimize warehouse layout by analyzing and processing logistics demand, shelf information, inventory status and work order information in data carriers in real time; S3: Build performance models of flexible warehouses and business scenarios for simulation and evaluation to optimize warehouse resource allocation, and establish sequential optimization models to optimize the operation process of flexible warehouses.

2. The data carrier-based warehouse and business scenario operation method according to claim 1 is characterized in that: The S1 specifically includes: S1.1: Build data carriers for shelves and business scenarios; S1.2: Drive communication synchronization, data information synchronization, and data carrier node matching between production data carriers and logistics data carriers in business scenarios through low-code; S1.3: Collect the work order information and shelf information to be processed, design and adapt the work order and shelf data carriers based on the synchronous assignment model.

3. The operation method of the warehouse and business scenario based on the data carrier according to claim 2 is characterized in that: The S1.1 specifically includes: S1.1.1, Business scenario demand analysis and planning: Based on IoT technology and big data analysis, collect data from equipment at each node of warehouse and factory logistics to obtain warehouse and business scenario needs; S1.1.2, Data carrier system design and construction: Use cloud computing and edge computing technologies to design the data carrier system architecture, including warehouse management module, production planning module, and automation control module, and then design and develop the system, including the front-end interface, back-end logic, and database, while connecting to the WMS / ERP / MES system to reflect and manage the actual shelf layout and inventory status; S1.1.3, Data carrier system deployment and maintenance: Based on remote monitoring, the developed system is deployed to the actual production and storage environment, and hardware and software configuration and data migration are performed.

4. The operation method of the warehouse and business scenario based on the data carrier according to claim 2 is characterized in that: The S1.2 specifically includes: S1.2.1, Communication synchronization: define the communication protocol and interface standards between the production data carrier and the logistics data carrier, including data format, message transmission method and communication frequency, use the low-code platform to configure the communication module to achieve real-time data transmission, and conduct communication synchronization testing; S1.2.2, Data Information Synchronization: Define data mapping relationships and conversion rules to keep data consistent between different data carriers, use low-code platforms to implement data synchronization and integration logic, including data extraction, conversion, and ETL loading processes, and adopt real-time monitoring mechanisms; S1.2.3, data carrier node matching: Use a low-code development platform to design integrated production data carriers and logistics data carriers, clarify their respective nodes and processes, determine the adaptation nodes and create adaptation applications. The nodes include production stations, warehouses, and shelves.

5. The operation method of warehouse and business scenario based on data carrier according to claim 2 is characterized in that: The S1.3 specifically includes: S1.3.1, collect data from data carriers, including interface information of orders and shelves, collect and organize data of all pending orders, including order quantity, order size, order deadline, collect shelf information, including shelf quantity, shelf location, shelf capacity, and information on the goods stored on each shelf; S1.3.2, based on the association information between the work order and the shelf, carry out the joint optimization algorithm to design the synchronous assignment model frame and obtain the joint synchronous assignment solution; S1.3.3, using the synchronous assignment model framework, simulate the allocation of work orders and shelves, evaluate performance indicators, and make optimization adjustments to find the best adaptation solution. The indicators include cost, efficiency, and order fulfillment time.

6. The data carrier-based warehouse and business scenario operation method according to claim 1, characterized in that: The S2 specifically includes: S2.1: Pick the work orders in batches according to their task requirements; S2.2: Based on the task requirements, define optimization indicators, including maximizing utilization, minimizing distance, and minimizing transportation costs. Based on the business scenario, determine the constraints of shelf quantity, size, distance, and location. Use a multi-objective optimization algorithm to establish a convex optimization mathematical model, convert the optimization objective function and constraints into mathematical expressions for solution, so as to optimize the shelf location layout and quantity configuration; S2.3: Perform finite element analysis and real-time adjustments to the structure of the picking modules in the warehouse, including the layout of the shelves, the storage method of SKUs, and the entry and exit mechanism of the picking items.

7. The data carrier-based warehouse and business scenario operation method according to claim 6 is characterized in that: The S2.1 specifically includes: S2.1.1, design a picking work order aggregation method, analyze the work orders, find similarities, including the same or similar material requirements, and merge similar work orders into a task master order; S2.1.2, assign partition attributes to each work order, and select the most appropriate partition for each work order by using a heuristic method with the maximum regional similarity as the optimization goal; S2.1.3, establish a mixed integer programming model to optimize the picking process based on the picking area allocation, work order arrangement and constraints; S2.1.4, establish a cost function, taking into account the business data and constraints of each partition, including the number of shelf entries, the robot's movement distance, and the number of manual aisle crossings, to minimize the picking cost.

8. The data carrier-based warehouse and business scenario operation method according to claim 1, characterized in that: The performance model of the S3 flexible warehouse and business scenarios is simulated and evaluated, including: S3.1.1: Define business scenarios and key indicators, including specific functions, operational processes and key indicators of flexible warehouses, determine the scope and objectives of the evaluation, and select key performance indicators, including warehouse throughput, processing time, and turnover rate; S3.1.2: Based on the collected data, use statistical models including regression analysis and time series analysis or machine learning models including neural networks and decision trees to establish performance models of flexible warehouses and business scenarios; S3.1.3: Based on the performance model, use discrete event simulation to simulate the warehouse operation process, including work order processing, goods movement, and personnel scheduling, or use agent-based simulation to simulate the behavior and interaction of different individuals in the warehouse, such as goods, personnel, and equipment; S3.1.4: Based on the simulation results, identify potential performance bottlenecks and room for improvement based on sensitivity analysis, optimization algorithms, or decision analysis methods, and propose optimization plans for resource allocation.

9. The data carrier-based warehouse and business scenario operation method according to claim 1, characterized in that: The S3 establishes a sequential optimization model to optimize the operation process of the flexible warehouse, specifically including: S3.2.1: Considering the efficiency of the picking area and buffer area in the warehouse, the existing inventory information and the work order set information itself, the work order picking sequence optimization model is established with the goal of minimizing the total work order processing time; S3.2.2: Use the improved adaptive genetic algorithm to solve the work order picking sequence optimization model and obtain a reasonably planned work order picking sequence.

10. A system using the operation method according to any one of claims 1 to 9, characterized in that: include: Production / logistics data carrier design module: used to build and manage data carriers for work orders, shelf information, inventory status, and logistics needs inside and outside the flexible warehouse, collect real-time data inside and outside the warehouse through IoT technology, and perform data mapping and synchronization on the low-code platform; Shelf and business scenario adaptation module: Dynamically match and configure shelves and business scenarios based on data carriers; Task demand and shelf configuration optimization algorithm module: Use multi-objective optimization algorithm and dynamic path planning algorithm to analyze the real-time data in the data carrier, and dynamically adjust the storage layout and optimal logistics path of the flexible warehouse; Performance evaluation and operation performance optimization module: Build performance models of flexible warehouses and business scenarios for simulation and evaluation to optimize warehouse resource allocation, and establish sequential optimization models to optimize the operation process of flexible warehouses.

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