Intelligent management system for goods import and export

Abstract

The invention relates to the technical field of service management, in particular to an intelligent management system for goods import and export, and the system comprises a warehouse space dynamic optimization module which dynamically adjusts goods allocation arrangement, maximizes the warehouse space utilization rate, and provides real-time space data; the intelligent goods allocation module automatically allocates goods storage positions according to a preset objective function, and ensures high efficiency and rationality of goods storage; the sorting path optimization module is used for generating an optimal sorting path by using a path optimization algorithm, so that the sorting time is shortened, and the sorting efficiency is improved; the data acquisition module is used for acquiring cargo information, position data, order information and environment data in the warehouse; and the data processing module provides data processing support for warehouse space dynamic optimization, intelligent goods allocation and sorting path optimization. According to the invention, the storage efficiency is improved, the operation cost of the warehouse is reduced, the cargo storage position is automatically allocated based on the optimization algorithm, and the efficiency and rationality of cargo storage are further improved.

Description

Technical Field

[0001] This invention relates to the field of service management technology, and in particular to an intelligent management system for the import and export of goods. Background Technology

[0002] With the acceleration of globalization and the increasing frequency of international trade, import and export management has become a crucial link in the supply chain. Traditional import and export management systems have many shortcomings in meeting the demands of modern warehousing and logistics, such as slow data processing speed, untimely information updates, excessive manual intervention, and susceptibility to errors. These problems seriously affect the efficiency of warehousing management and the smoothness of logistics operations.

[0003] While some automated and intelligent management systems are available on the market, they still face challenges in practical applications. The main problems include:

[0004] Inadequate warehouse space utilization: The existing system performs poorly in dynamically adjusting storage location and optimizing warehouse space utilization, resulting in wasted warehouse space and low storage efficiency.

[0005] Low picking efficiency: Insufficient path optimization during order picking process fails to effectively reduce picking time, and rigid path planning lacks dynamic adjustment capabilities, making it easy to encounter congestion or obstacles during operation.

[0006] Lack of real-time dynamic adjustment capability: The existing system is slow to respond to real-time environmental changes and cannot dynamically adjust warehousing and picking operations based on real-time data, affecting the overall system's flexibility and adaptability.

[0007] To overcome the above problems, the present invention provides an intelligent management system for import and export of goods. Summary of the Invention

[0008] Based on the above background, the present invention provides an intelligent management system for import and export of goods.

[0009] An intelligent management system for import and export of goods, comprising:

[0010] The warehouse space dynamic optimization module is used to monitor and analyze warehouse space usage in real time, dynamically adjust the arrangement of storage locations, maximize warehouse space utilization, and provide real-time space data.

[0011] The intelligent storage location allocation module is connected to the warehouse space dynamic optimization module. It is used to automatically allocate the storage location of goods according to the preset objective function based on the real-time space data provided by the warehouse space dynamic optimization module, so as to ensure the efficiency and rationality of goods storage.

[0012] The picking path optimization module, connected to the intelligent storage location allocation module, is used to generate the optimal picking path during the order picking process based on the goods location data and order requirements provided by the intelligent storage location allocation module, using a path optimization algorithm to reduce picking time and improve picking efficiency.

[0013] The data acquisition module, connected to the warehouse space dynamic optimization module, intelligent storage location allocation module, and picking path optimization module, is used to collect goods information, location data, order information, and environmental data in the warehouse, and transmit the collected data to the warehouse space dynamic optimization module, intelligent storage location allocation module, and picking path optimization module for processing.

[0014] The data processing module, connected to the data acquisition module, is used to clean, store, and analyze the acquired data, providing data processing support for dynamic optimization of warehouse space, intelligent location allocation, and picking path optimization.

[0015] Furthermore, the warehouse space dynamic optimization module specifically includes:

[0016] The space monitoring unit is used to monitor the storage status of goods, the usage status of storage locations, and environmental parameters in the warehouse in real time through sensors, cameras, and IoT devices installed in the import and export warehouse, and to collect data including the location of goods, storage location occupancy rate, temperature, and humidity.

[0017] The data analysis unit performs real-time analysis on the collected data, identifies warehouse space utilization issues, and discovers imbalances in warehouse space utilization and potential optimization points.

[0018] The real-time data transmission unit transmits real-time monitoring data to the intelligent location allocation module and the picking route optimization module, ensuring that these modules can make decisions and operate based on the latest warehouse space data.

[0019] Furthermore, the data collected by the sensors, cameras, and IoT devices is represented as: D = {d1, d2, ..., d...} n}, where D represents the collected data set, d i This represents each collected data point, including cargo location, cargo space occupancy rate, temperature, and humidity;

[0020] The data analysis unit specifically analyzes the following:

[0021] Cargo space occupancy rate calculation: Among them, U i S represents the occupancy rate of the i-th storage location. i S represents the area occupied by the i-th storage location. total Indicates the total area of ​​the warehouse;

[0022] Cargo density calculation: Among them, D i W represents the cargo density at the i-th storage location. i V represents the weight of the goods in the i-th storage location. i This represents the volume of goods in the i-th storage location;

[0023] Temperature and humidity distribution analysis: Using temperature and humidity data collected by sensors, the temperature and humidity distribution in various areas of the warehouse is calculated.

[0024]

[0025] Among them, T avg and H avg T represents the average temperature and humidity inside the warehouse, respectively. i and H i Let represent the temperature and humidity of the i-th data point, respectively, and n represent the total number of data points.

[0026] Furthermore, the intelligent storage location allocation module generates an optimization scheme based on the results of the data analysis unit, specifically including:

[0027] Storage location reallocation: Based on storage location occupancy rate and cargo density, the following optimization objective function is adopted:

[0028] Among them, U avg and D avg These represent the average warehouse space occupancy rate and average cargo density, respectively, with λ being a weighting factor used to adjust the relative importance of the two objectives.

[0029] Stacking optimization: Based on the stacking stability and space utilization of goods, the following optimization objective function is adopted:

[0030] Among them, S i and H i Let represent the occupied area and stacking height of the i-th storage location, respectively.

[0031] Furthermore, the weighting factor λ is used to balance the relative importance of cargo space occupancy rate and cargo density in the optimization objective function. Multiple indicators are considered simultaneously using a multi-objective optimization algorithm, and the optimal λ value is automatically adjusted and determined through the optimization process.

[0032] Furthermore, the multi-objective optimization algorithm is based on the Pareto algorithm, as detailed below:

[0033] Define multiple objective functions, including maximizing storage space utilization and maximizing picking efficiency;

[0034] Using the Pareto front analysis method, a set of Pareto optimal solutions for different λ values ​​is generated;

[0035] Select the λ value that performs best in practical applications from the Pareto optimal solution set.

[0036] Furthermore, the picking path optimization module specifically includes:

[0037] The data receiving unit receives cargo location data and real-time order demand data provided by the intelligent cargo location allocation module;

[0038] The path optimization algorithm unit generates the optimal picking path based on the received goods location data and order demand data using the path optimization algorithm;

[0039] The dynamic path adjustment unit is used to monitor the warehouse environment and operational status in real time during the picking process, and dynamically adjust the picking path based on real-time data to improve picking efficiency.

[0040] The path execution unit guides warehouse operators or automated equipment to perform picking operations based on the generated optimal picking path, reducing picking time and improving picking efficiency.

[0041] Furthermore, the objective function construction of the path optimization algorithm includes:

[0042] Cargo location matrix construction:

[0043] Where D is the distance matrix of the cargo location, and the matrix element d(P) i ,P j ) indicates the location P of the goods. i and P j The distance between them;

[0044] Optimize based on cargo characteristics: Adjust the weight W of the cargo. i and volume loading capacity ratio S i / C i Incorporating these factors into the objective function, we consider a comprehensive optimization of path distance, cargo weight, and loading efficiency.

[0045] Furthermore, the objective function is expressed as:

[0046] in;

[0047] d(P i ,P i+1 ) represents the location P of the i-th cargo. i and the (i+1)th cargo location P i+1 The distance between them

[0048] W i Indicates the weight of the goods.

[0049] S i Indicates the volume of the goods.

[0050] C i Indicates the loading capacity of the goods.

[0051] α and β are weighting factors used to adjust the relative importance of each component objective.

[0052] Real-time monitoring of the warehouse environment and operational status is achieved through sensors and cameras to acquire real-time data. The dynamic path adjustment unit adjusts paths based on this data to avoid congestion and obstacles, thereby improving picking efficiency. The specific solution is as follows:

[0053] The system monitors the warehouse environment and operational status in real time using sensors, cameras, and IoT devices, including shelf occupancy, path congestion, equipment status, and obstacle locations. It receives and processes data collected by the environmental monitoring unit in real time, identifies potential path blockages, congestion, and obstacles, dynamically adjusts picking routes based on real-time data, generates optimal routes to avoid obstacles and congested areas, and sends the adjusted optimal routes to the path execution unit to guide warehouse operators or automated equipment in picking operations.

[0054] The objective function after real-time path optimization is expressed as:

[0055] Where, d′(P i ,P i+1 The ) represents the location P of the i-th cargo after considering real-time environmental data. i and the (i+1)th cargo location P i+1 The actual distance between them.

[0056] Furthermore, it also includes a user interaction module, which connects the data processing module and the picking path optimization module, and is used to provide a user interface, receive user input instructions, and display the generated location allocation and picking path information.

[0057] The beneficial effects of this invention are:

[0058] This invention, through a dynamic warehouse space optimization module, can monitor and analyze warehouse space usage in real time, dynamically adjust the arrangement of storage locations, and ensure maximum utilization of warehouse space. This not only improves storage efficiency but also reduces warehouse operating costs. The intelligent storage location allocation module automatically allocates goods storage locations based on optimization algorithms, further enhancing the efficiency and rationality of goods storage.

[0059] This invention utilizes an optimization algorithm to generate the optimal picking path during the order picking process, effectively reducing picking time and improving picking efficiency. Through dynamic path adjustment, the system can monitor the warehouse environment and operational status in real time, dynamically adjusting the picking path based on real-time data to avoid congestion and obstacles, improving the smoothness and accuracy of picking operations, significantly reducing picking time and error rates, and enhancing overall warehouse management efficiency.

[0060] This invention, combining real-time data and an improved shortest path algorithm, enables flexible path adjustments during the picking process, ensuring the system's adaptability to dynamically changing environments. This real-time adjustment mechanism allows the system to quickly respond to environmental changes and operational needs, ensuring the continuous and efficient operation of warehouse management. Simultaneously, the system integration modules work collaboratively, achieving seamless integration and efficient operation of each module, thus improving the overall system's stability and reliability. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 This is a schematic diagram of the system functional modules according to an embodiment of the present invention;

[0063] Figure 2 This is a schematic diagram of the warehouse space dynamic optimization module according to an embodiment of the present invention;

[0064] Figure 3 This is a schematic diagram of the picking path optimization module in an embodiment of the present invention. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0066] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0067] like Figures 1-3 As shown, an intelligent management system for import and export of goods includes:

[0068] The warehouse space dynamic optimization module is used to monitor and analyze warehouse space usage in real time, dynamically adjust the arrangement of storage locations, maximize warehouse space utilization, and provide real-time space data.

[0069] The intelligent storage location allocation module connects to the warehouse space dynamic optimization module. Based on the real-time space data provided by the warehouse space dynamic optimization module, it automatically allocates the storage location of goods according to a preset objective function to ensure the efficiency and rationality of goods storage.

[0070] The picking route optimization module, connected to the intelligent storage location allocation module, is used to generate the optimal picking route during the order picking process based on the goods location data and order requirements provided by the intelligent storage location allocation module, using a route optimization algorithm to reduce picking time and improve picking efficiency.

[0071] The data acquisition module connects to the warehouse space dynamic optimization module, the intelligent storage location allocation module, and the picking path optimization module. It is used to collect information on goods, location data, order information, and environmental data in the warehouse, and transmit the collected data to the warehouse space dynamic optimization module, the intelligent storage location allocation module, and the picking path optimization module for processing.

[0072] The data processing module, connected to the data acquisition module, is used to clean, store, and analyze the acquired data, providing data processing support for dynamic optimization of warehouse space, intelligent location allocation, and picking path optimization.

[0073] It also includes a user interaction module, which connects the data processing module and the picking path optimization module. This module provides a user interface, receives user input instructions, and displays the generated location allocation and picking path information.

[0074] The warehouse space dynamic optimization module specifically includes:

[0075] The space monitoring unit is used to monitor the storage status of goods, the usage status of storage locations, and environmental parameters in the warehouse in real time through sensors, cameras, and IoT devices installed in the import and export warehouse, and to collect data including the location of goods, storage location occupancy rate, temperature, and humidity.

[0076] The data analysis unit performs real-time analysis on the collected data, identifies warehouse space utilization issues, and discovers imbalances in warehouse space utilization and potential optimization points.

[0077] The real-time data transmission unit transmits real-time monitoring data to the intelligent location allocation module and the picking route optimization module, ensuring that these modules can make decisions and operate based on the latest warehouse space data.

[0078] Data collected by sensors, cameras, and IoT devices is represented as: D = {d1, d2, ..., d} n}, where D represents the collected data set, d i This represents each collected data point, including cargo location, cargo space occupancy rate, temperature, and humidity;

[0079] The data analysis unit specifically analyzes the following:

[0080] Cargo space occupancy rate calculation: Among them, U i S represents the occupancy rate of the i-th storage location. i S represents the area occupied by the i-th storage location. total Indicates the total area of ​​the warehouse;

[0081] Cargo density calculation: Among them, D i W represents the cargo density at the i-th storage location. i V represents the weight of the goods in the i-th storage location. i This represents the volume of goods in the i-th storage location;

[0082] Temperature and humidity distribution analysis: Using temperature and humidity data collected by sensors, the temperature and humidity distribution in various areas of the warehouse is calculated.

[0083]

[0084] Among them, T avg and H avg T represents the average temperature and humidity inside the warehouse, respectively. i and H i Let represent the temperature and humidity of the i-th data point, respectively, and n represent the total number of data points.

[0085] The intelligent storage location allocation module generates optimization solutions based on the results of the data analysis unit, specifically including:

[0086] Storage location reallocation: Based on storage location occupancy rate and cargo density, the following optimization objective function is adopted:

[0087] Among them, U avg and D avg These represent the average warehouse space occupancy rate and average cargo density, respectively, with λ being a weighting factor used to adjust the relative importance of the two objectives.

[0088] Stacking optimization: Based on the stacking stability and space utilization of goods, the following optimization objective function is adopted:

[0089] Among them, S i and H i Let represent the occupied area and stacking height of the i-th storage location, respectively.

[0090] The weighting factor λ is used to balance the relative importance of cargo space occupancy rate and cargo density in the optimization objective function. The multi-objective optimization algorithm considers multiple indicators at the same time, and the optimal value of λ is automatically adjusted and determined through the optimization process.

[0091] The multi-objective optimization algorithm is based on the Pareto algorithm, as follows:

[0092] Define multiple objective functions, including maximizing storage space utilization and maximizing picking efficiency;

[0093] Using the Pareto front analysis method, a set of Pareto optimal solutions for different λ values ​​is generated;

[0094] Select the λ value that performs best in practical applications from the Pareto optimal solution set.

[0095] Define two objective functions:

[0096] Objective function for minimizing warehouse occupancy rate:

[0097] The objective function for minimizing cargo density is:

[0098] Using the Pareto front analysis method, the set of Pareto optimal solutions for different λ values ​​is generated. The steps are as follows:

[0099] 1. Generate initial solution set: Within a given range, generate multiple initial solutions (i.e., different values ​​of λ): λ1, λ2, ..., λ m ;

[0100] 2. Calculate the objective value for each solution: For each λ value, calculate the corresponding objective function values ​​f1 and f2:

[0101]

[0102] 3. Construct the Pareto front: Compare the objective values ​​of all solutions and find the non-dominated solutions (i.e., no other solution is better than this solution in all objectives). These non-dominated solutions constitute the Pareto front.

[0103] For every pair of solutions (A, B): solution A is preferred over solution B if f1(A)≤f1(B) and f2(A)≤f2(B), and at least one of them is strictly less than the other;

[0104] If solutions A and B are not mutually dominant, then they both belong to the Pareto front.

[0105] 4. Select the optimal solution: Select the solution with the best performance from the Pareto front solution set.

[0106] The picking path optimization module specifically includes:

[0107] The data receiving unit receives cargo location data and real-time order demand data provided by the intelligent cargo location allocation module;

[0108] The path optimization algorithm unit generates the optimal picking path based on the received goods location data and order demand data using the path optimization algorithm;

[0109] The dynamic path adjustment unit is used to monitor the warehouse environment and operational status in real time during the picking process, and dynamically adjust the picking path based on real-time data to improve picking efficiency.

[0110] The path execution unit guides warehouse operators or automated equipment to perform picking operations based on the generated optimal picking path, reducing picking time and improving picking efficiency.

[0111] The objective function construction for the path optimization algorithm includes:

[0112] Cargo location matrix construction:

[0113] Where D is the distance matrix of the cargo location, and the matrix element d(P) i ,P j ) indicates the location P of the goods. i and P j The distance between them;

[0114] Optimize based on cargo characteristics: Adjust the weight W of the cargo. i and volume loading capacity ratio S i / C iIncorporating these factors into the objective function, we consider a comprehensive optimization of path distance, cargo weight, and loading efficiency.

[0115] The objective function is expressed as:

[0116] in;

[0117] d(P i ,P i+1 ) represents the location P of the i-th cargo. i and the (i+1)th cargo location P i+1 The distance between them

[0118] W i Indicates the weight of the goods.

[0119] S i Indicates the volume of the goods.

[0120] C i Indicates the loading capacity of the goods.

[0121] α and β are weighting factors used to adjust the relative importance of each component objective.

[0122] Real-time monitoring of the warehouse environment and operational status is achieved through sensors and cameras to acquire real-time data. The dynamic path adjustment unit adjusts paths based on this data to avoid congestion and obstacles, thereby improving picking efficiency. The specific solution is as follows:

[0123] The system monitors the warehouse environment and operational status in real time using sensors, cameras, and IoT devices, including shelf occupancy, path congestion, equipment status, and obstacle locations. It receives and processes data collected by the environmental monitoring unit in real time, identifies potential path blockages, congestion, and obstacles, dynamically adjusts picking routes based on real-time data, generates optimal routes to avoid obstacles and congested areas, and sends the adjusted optimal routes to the path execution unit to guide warehouse operators or automated equipment in picking operations.

[0124] The objective function after real-time path optimization is expressed as:

[0125] Where, d′(P i ,P i+1 The ) represents the location P of the i-th cargo after considering real-time environmental data. i and the (i+1)th cargo location P i+1 The actual distance between them.

[0126] Algorithm steps:

[0127] 1. Real-time data acquisition: E = {e1, e2, ..., e} m}, where E represents the set of real-time environmental data collected, e i This represents each collected environmental data point, such as shelf occupancy, path congestion, and obstacle location.

[0128] 2. Data Processing and Analysis: Process and analyze real-time data to identify potential path blockages, congestion, and obstacles, forming an environmental state matrix.

[0129]

[0130] Where S is the environment state matrix, and the matrix element s ij Represents path P i To P j Passability (0 indicates impassable, 1 indicates passable).

[0131] 3. Path Adjustment Algorithm: Based on the environment state matrix S and the initial path matrix D, an improved shortest path algorithm is used to calculate the optimal path to avoid obstacles and congested areas.

[0132]

[0133] Generate the optimal path to avoid obstacles and congested areas, and adjust the picking path in real time to ensure maximum picking efficiency.

[0134] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention is limited to these examples; within the framework of the invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.

[0135] This invention is intended to cover all such substitutions, modifications, and variations falling within the broad scope of the claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. An intelligent management system for import and export of goods, characterized in that, include: The warehouse space dynamic optimization module is used to monitor and analyze warehouse space usage in real time, dynamically adjust the arrangement of storage locations, maximize warehouse space utilization, and provide real-time space data. The intelligent storage location allocation module is connected to the warehouse space dynamic optimization module. It is used to automatically allocate the storage location of goods according to the preset objective function based on the real-time space data provided by the warehouse space dynamic optimization module, so as to ensure the efficiency and rationality of goods storage. The picking path optimization module, connected to the intelligent storage location allocation module, is used to generate the optimal picking path during the order picking process based on the goods location data and order requirements provided by the intelligent storage location allocation module using a path optimization algorithm. The data acquisition module, connected to the warehouse space dynamic optimization module, intelligent storage location allocation module, and picking path optimization module, is used to collect goods information, location data, order information, and environmental data in the warehouse, and transmit the collected data to the warehouse space dynamic optimization module, intelligent storage location allocation module, and picking path optimization module for processing. The data processing module, connected to the data acquisition module, is used to clean, store, and analyze the acquired data, providing data processing support for dynamic optimization of warehouse space, intelligent location allocation, and picking path optimization.

2. The intelligent management system for import and export of goods according to claim 1, characterized in that, The warehouse space dynamic optimization module specifically includes: The space monitoring unit is used to monitor the storage status of goods, the usage status of storage locations, and environmental parameters in the warehouse in real time through sensors, cameras, and IoT devices installed in the import and export warehouse, and to collect data including the location of goods, storage location occupancy rate, temperature, and humidity. The data analysis unit performs real-time analysis on the collected data, identifies warehouse space utilization issues, and discovers imbalances in warehouse space utilization and potential optimization points. The real-time data transmission unit transmits real-time monitoring data to the intelligent location allocation module and the picking path optimization module.

3. The intelligent management system for import and export of goods according to claim 2, characterized in that, The data collected by the sensors, cameras, and IoT devices are represented as: D = {d1, d2, ..., d...} n }, where D represents the collected data set, d i This represents each collected data point, including cargo location, cargo space occupancy rate, temperature, and humidity; The data analysis unit specifically analyzes the following: Cargo space occupancy rate calculation: Among them, U i S represents the occupancy rate of the i-th storage location. i S represents the area occupied by the i-th storage location. total Indicates the total area of ​​the warehouse; Cargo density calculation: Among them, D i W represents the cargo density at the i-th storage location. i V represents the weight of the goods in the i-th storage location. i This represents the volume of goods in the i-th storage location; Temperature and humidity distribution analysis: Using temperature and humidity data collected by sensors, the temperature and humidity distribution in various areas of the warehouse is calculated. Among them, T avg and H avg T represents the average temperature and humidity inside the warehouse, respectively. i and H i Let represent the temperature and humidity of the i-th data point, respectively, and n represent the total number of data points.

4. The intelligent management system for import and export of goods according to claim 2, characterized in that, The intelligent storage location allocation module generates an optimization plan based on the results of the data analysis unit, specifically including: Storage location reallocation: Based on storage location occupancy rate and cargo density, the following optimization objective function is adopted: Among them, U avg and D avg These represent the average warehouse space occupancy rate and average cargo density, respectively, with λ being a weighting factor used to adjust the relative importance of the two objectives. Stacking optimization: Based on the stacking stability and space utilization of goods, the following optimization objective function is adopted: Among them, S i and H i Let represent the occupied area and stacking height of the i-th storage location, respectively.

5. The intelligent management system for import and export of goods according to claim 4, characterized in that, The weighting factor λ is used to balance the relative importance of cargo space occupancy rate and cargo density in the optimization objective function. The multi-objective optimization algorithm considers multiple indicators at the same time, and the optimal λ value is automatically adjusted and determined through the optimization process.

6. The intelligent management system for import and export of goods according to claim 5, characterized in that, The multi-objective optimization algorithm is based on the Pareto algorithm, as detailed below: Define multiple objective functions, including maximizing storage space utilization and maximizing picking efficiency; Using the Pareto front analysis method, a set of Pareto optimal solutions for different λ values ​​is generated; Select the λ value that performs best in practical applications from the Pareto optimal solution set.

7. The intelligent management system for import and export of goods according to claim 1, characterized in that, The picking path optimization module specifically includes: The data receiving unit receives cargo location data and real-time order demand data provided by the intelligent cargo location allocation module; The path optimization algorithm unit generates the optimal picking path based on the received goods location data and order demand data using the path optimization algorithm; The dynamic path adjustment unit is used to monitor the warehouse environment and operational status in real time during the picking process, and dynamically adjust the picking path based on real-time data to improve picking efficiency. The path execution unit guides warehouse operators or automated equipment to perform picking operations based on the generated optimal picking path, reducing picking time and improving picking efficiency.

8. The intelligent management system for import and export of goods according to claim 7, characterized in that, The objective function construction of the path optimization algorithm includes: Cargo location matrix construction: Where D is the distance matrix of the cargo location, and the matrix element d(P) i ,P j ) indicates the location P of the goods. i and P j The distance between them; Optimize based on cargo characteristics: Adjust the weight W of the cargo. i and volume loading capacity ratio S i / C i Incorporating these factors into the objective function, we consider a comprehensive optimization of path distance, cargo weight, and loading efficiency.

9. The intelligent management system for import and export of goods according to claim 8, characterized in that, The objective function is expressed as: in; d(P i ,P i+1 ) represents the location P of the i-th cargo. i and the (i+1)th cargo location P i+1 The distance between them W i Indicates the weight of the goods. S i Indicates the volume of the goods. C i Indicates the loading capacity of the goods. α and β are weighting factors used to adjust the relative importance of each component objective.

10. The intelligent management system for import and export of goods according to claim 1, characterized in that, It also includes a user interaction module, which connects the data processing module and the picking path optimization module, and is used to provide a user interface, receive user input instructions and display the generated location allocation and picking path information.

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