Port warehouse management system, method and equipment based on visual identification and medium

Abstract

The invention discloses a port warehouse management system, method and equipment based on visual identification and a medium, and the system comprises an image collection module which is used for collecting image information of a port warehouse area, a visual identification module which is connected with the image collection module and is used for processing and analyzing the collected image information, and a data storage module which is connected with the visual identification module and is used for storing data. The data storage module is used for storing image information collected by the image collection module and cargo information recognized by the visual recognition module, and the management control module is connected with the visual recognition module and the data storage module and used for managing and controlling port storage according to the cargo information recognized by the visual recognition module. According to the invention, rapid and accurate cargo information acquisition is realized through the high-definition camera and the visual identification technology, the data processing speed is improved through the parallel processing technology, the management accuracy is guaranteed through multiple formulas, the data safety is ensured through distributed storage, various risks are intelligently pre-warned and prevented, and the warehouse management efficiency and quality of the port are comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cross - border e - commerce security systems, and particularly to a port warehousing management system, method, device, and medium based on visual recognition. Background Art

[0002] At present, with the booming development of global trade, ports, as key nodes for cargo transportation and storage, the efficiency and accuracy of their warehousing management are of crucial importance. However, there are many drawbacks in traditional port warehousing management methods, making it difficult to meet the needs of modern logistics.

[0003] Traditional management highly relies on manual records and inspections. When goods are warehoused, it is easy to make mistakes in manually recording goods information; during inventory checks, the efficiency is low and it is difficult to ensure real - time accuracy. Manual inspections are difficult to comprehensively and timely detect abnormalities in goods, such as safety hazards caused by excessive stacking and unreasonable storage locations that affect the efficiency of goods storage and retrieval.

[0004] In terms of data processing, manually collecting and identifying goods information is slow and prone to confusion and errors. It is difficult to count the quantity when faced with a large number of goods. Traditional systems have insufficient capabilities in processing massive data, and data backlogs and processing delays are likely to occur during peak hours, making it impossible to grasp the inventory situation in real - time. The monitoring of goods status is also difficult to be real - time and continuous, and it is impossible to promptly detect the impact of environmental changes on special goods.

[0005] In terms of data storage, traditional centralized storage has the risk of single - point failures. Data is easily lost due to equipment failures, etc., and the backup mechanism is imperfect. Data analysis relies on manual experience and simple tools, lacking depth and accuracy, and it is impossible to mine potential rules and trends, making it difficult to formulate scientific warehousing strategies, such as optimizing storage layouts and arranging inventory.

[0006] With the development of port operations and technological progress, there is an urgent need for innovative solutions. It is necessary to achieve efficient and accurate goods identification and information collection, such as using visual recognition technology to automatically collect goods information and adapt to complex environments; possess fast data processing and real - time monitoring capabilities, adopt high - performance technologies and parallel algorithms to process data and real - time monitor the status of goods and the environment; build a secure and reliable data storage and backup architecture, use distributed storage and redundant backup mechanisms to ensure data security, and strengthen security management to prevent data leakage and illegal access. Summary of the Invention

[0007] The port warehousing management system, method, device, and medium based on visual recognition proposed by the present invention are used to solve the problems mentioned in the above - mentioned prior art.

[0008] To achieve the above object, the present invention adopts the following technical solutions: A port warehousing management system based on visual recognition, comprising:

[0009] An image acquisition module, which is used to acquire image information of the port storage area. The image acquisition module includes multiple high-definition cameras, which are distributed at key positions in the port storage area to ensure comprehensive coverage of the entire storage area;

[0010] A visual recognition module, which is connected to the image acquisition module and is used to process and analyze the acquired image information, identify the type, quantity, location and status information of the goods. The visual recognition module uses deep learning algorithms and is trained with a large amount of labeled data to accurately identify various different types of goods. The visual recognition module uses the following formula to evaluate the recognition accuracy: Where V is the recognition accuracy, N correct is the number of correctly recognized goods, N total is the total number of goods, S match is the total area of the accurately recognized goods, S total is the total actual area of the goods, and α and β are weight coefficients;

[0011] A data storage module, which is used to store the image information acquired by the image acquisition module and the goods information recognized by the visual recognition module. The data storage module adopts a distributed storage architecture to ensure the security and reliability of the data;

[0012] A management and control module, which is connected to the visual recognition module and the data storage module, and is used to manage and control the port storage according to the goods information recognized by the visual recognition module, including the inbound and outbound of goods, the allocation of storage locations, and the monitoring of inventory, etc. The management and control module uses the following formula to evaluate the rationality of storage location allocation: Where M is the rationality of storage location allocation, D optimal is the total sum of the optimal storage distances, D actual is the total actual storage distance, C access is the evaluation value of the ease of access to goods, C total is the maximum evaluation value of the ease of access, and γ and δ are weight coefficients;

[0013] An intelligent warning module, which is used to give warnings about possible abnormal situations according to the data of the visual recognition module and the management and control module, such as excessive stacking of goods, unreasonable storage locations, etc. The intelligent warning module uses the following formula to make warning judgments:

[0014] , where W is the warning value, P height is the warning weight of the goods height, H current is the current height of the goods, H threshold is the warning threshold of the goods height, μ is the warning weight of the unreasonable storage location, L current is the current degree of unreasonableness of the storage location, L thresholdis the warning threshold for unreasonable storage location, η is the warning weight for time overdue, and T current is the current storage time of the goods, and T threshold is the warning threshold for the storage time of the goods, and ∈ is the comprehensive weight adjustment coefficient;

[0015] The data analysis module deeply analyzes the data stored in the data storage module, mines potential laws and trends, and provides decision-making support for port warehousing management. The data analysis module uses the following formula to evaluate the value of data analysis: where A is the value of data analysis, λ and ω are weight coefficients, n is the number of data samples, and X i is the value of a single data sample, is the mean value of the data samples, m is the number of data features, and C ij is the correlation coefficient between data features i and j.

[0016] Furthermore, the high-definition camera in the image acquisition module has functions of automatic focusing and automatic exposure, and can automatically adjust the shooting parameters according to different lighting conditions to ensure that the captured images are clear and accurate.

[0017] Furthermore, the data storage module also includes a data backup sub-module for regularly backing up the stored data to prevent data loss. The data storage module uses the following formula to evaluate the timeliness of data backup: where B is the evaluation value of data backup timeliness, and T backup is the actual backup time interval, and T interval is the pre-set backup time interval, τ is the time weight coefficient, and S stored is the amount of stored data, and S capacity is the storage capacity, and σ is the storage amount weight coefficient.

[0018] Furthermore, the management control module includes an inbound management sub-module, an outbound management sub-module, an inventory monitoring sub-module, and a location allocation sub-module. The inbound management sub-module is used to manage the inbound process of goods, the outbound management sub-module is used to manage the outbound process of goods, the inventory monitoring sub-module is used to monitor the inventory situation in real time, and the location allocation sub-module is used to reasonably allocate storage locations according to the types and quantities of goods. The management control module uses the following formula to evaluate the inbound and outbound efficiency: where E is the evaluation value of inbound and outbound efficiency, ξ, ρ, and ζ are weight coefficients, is the total number of inbound and outbound goods per unit time, and T total is the total time, and F smoothness is the evaluation value of the smoothness of the inbound and outbound process, and F max is the maximum smoothness, and Q accuracy is the evaluation value of operation accuracy, and Q total is the maximum accuracy.

[0019] Further, the intelligent early warning module can also warn of data storage risks according to the data of the data storage module and the management control module. The intelligent early warning module uses the following formula to judge the data storage risk warning: where R is the data storage risk warning value, S usage is the used storage capacity, S capacity is the storage capacity, is the storage capacity risk weight coefficient, T access is the data access time, T threshold is the data access time threshold, is the access time risk weight coefficient, F corruption is the risk assessment value of possible data corruption, F total is the maximum risk assessment value, and ψ is the data corruption risk weight coefficient.

[0020] Further, a port warehousing management method based on visual recognition includes the following steps:

[0021] Collect image information of the port warehousing area through the image acquisition module;

[0022] Use the visual recognition module to process and analyze the collected image information, identify the type, quantity, location and status information of the goods, and evaluate the recognition accuracy according to the formula of the visual recognition module;

[0023] Store the image information and the identified goods information in the data storage module;

[0024] The management control module manages and controls the port warehousing according to the goods information identified by the visual recognition module, and evaluates the rationality of the storage location allocation and the inbound and outbound efficiency according to the formula of the management control module;

[0025] The intelligent early warning module judges whether to issue a warning according to the data of the visual recognition module and the management control module by using the formula of the intelligent early warning module, including abnormal situation warning and data storage risk warning;

[0026] The data analysis module deeply analyzes the data stored in the data storage module and evaluates the data analysis value according to the formula of the data analysis module.

[0027] Further, before collecting the image information, calibrate the high-definition camera in the image acquisition module to ensure that the collected image is accurate.

[0028] Further, when processing and analyzing the image information, use parallel processing technology to improve the processing speed of the visual recognition module.

[0029] Furthermore, a port warehousing management device based on visual recognition includes a processor and a memory. The memory stores a computer program, and when the processor executes the computer program, it implements a port warehousing management method based on visual recognition.

[0030] Furthermore, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements a port warehousing management method based on visual recognition.

[0031] Compared with the existing technologies, the beneficial effects of the present invention are as follows:

[0032] Through the high-definition camera and advanced visual recognition technology in the image acquisition module, the present invention can quickly and accurately identify the type, quantity, location, and status information of goods. There is no need for manual counting and recording one by one, greatly shortening the time for obtaining goods information and improving the efficiency of operations such as warehousing and outbound.

[0033] For example, when goods are warehoused, in the past, manual recording might take several minutes or even longer to process a batch of goods. Now, the system can complete identification and information entry within seconds, greatly accelerating the goods turnover speed.

[0034] By adopting parallel processing technology, the visual recognition module can process multiple tasks simultaneously, significantly improving the data processing speed. During peak hours of goods flow, it can also respond quickly to ensure the timely update of data and the real-time nature of management.

[0035] The real-time monitoring function can keep track of the dynamics of goods in the warehouse at any time, promptly detect abnormal situations and take measures quickly, avoiding the delay and expansion of problems and ensuring the smooth operation of warehousing. For example, once it is found that the goods are stacked too high, the system can immediately issue a warning, and the staff can make adjustments in time to prevent safety accidents.

[0036] The accuracy evaluation formula of the visual recognition module ensures high-precision identification of goods. By comprehensively considering multiple factors such as the correct number of identified goods and the identification area, the situations of misjudgment and missed judgment are effectively reduced.

[0037] The data analysis module deeply explores the potential laws and trends of data, providing a scientific basis for decision-making. For example, through accurate analysis of indicators such as the goods turnover rate, inventory can be reasonably arranged, avoiding inventory backlogs or shortages, and improving the accuracy of inventory management.

[0038] The rationality evaluation formula for storage location allocation and the inbound and outbound efficiency evaluation formula of the management control module make the storage location of goods more reasonable, optimize the inbound and outbound processes, reduce the number of goods handling times and time waste, and improve the operation efficiency and accuracy.

[0039] The intelligent early warning module accurately warns of various possible abnormal situations through scientific early warning formulas. For example, based on multi-parameter judgments such as the height of goods, storage time, and unreasonable degree of storage location, it can prevent risks in advance, ensuring the safety and accuracy of warehouse management.

[0040] The data storage module adopts a distributed storage architecture, avoiding the risk of data loss caused by single-point failures. Even if a certain storage node has problems, other nodes can still ensure the integrity and availability of the data.

[0041] The timeliness evaluation formula of the data backup sub-module ensures the timely data backup, and the dispersed storage of the backup data further improves the disaster resistance ability of the data. In the face of natural disasters or hardware failures, etc., it can effectively recover the data, ensuring the continuity of port warehousing operations.

[0042] The intelligent early warning module not only warns of abnormal goods storage, but also warns of data storage risks. Through the monitoring and warning of multiple aspects such as storage volume, data access time, and data corruption risk, it can timely discover and solve potential problems in data storage, ensuring the security and reliability of the data, and providing stable data support for port warehouse management. Description of the Drawings

[0043] Figure 1 It is a schematic block diagram of a port warehouse management system based on visual recognition proposed by the present invention;

[0044] Figure 2 It is a schematic block diagram of a port warehouse management method based on visual recognition proposed by the present invention. Detailed Embodiments

[0045] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0046] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. are based on the orientation or positional relationships shown in the drawings. These are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention.

[0047] In addition, the terms "first" and "second" are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined. In addition, the terms "mounted", "connected", "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below with reference to the drawings.

[0048] Refer to Figure 1-2 : A port warehousing management system based on visual recognition, comprising:

[0049] An image acquisition module for acquiring image information of the port warehousing area. The image acquisition module includes a plurality of high-definition cameras distributed at key positions in the port warehousing area to ensure comprehensive coverage of the entire warehousing area;

[0050] A visual recognition module connected to the image acquisition module for processing and analyzing the acquired image information to identify the type, quantity, location, and status information of the goods. The visual recognition module uses a deep learning algorithm and is trained with a large amount of labeled data to accurately identify various different types of goods. The visual recognition module uses the following formula to evaluate the recognition accuracy: Where V is the recognition accuracy, N correct is the number of correctly recognized goods, N total is the total number of goods, S match is the total area of the accurately recognized goods, S total is the total actual area of the goods, and α and β are weight coefficients;

[0051] The data storage module is used to store the image information collected by the image acquisition module and the cargo information recognized by the visual recognition module. The data storage module adopts a distributed storage architecture to ensure the security and reliability of the data;

[0052] The management and control module is connected to the visual recognition module and the data storage module, and is used to manage and control the port warehousing according to the cargo information recognized by the visual recognition module, including the inbound and outbound of goods, the allocation of storage locations, and the monitoring of inventory, etc. The management and control module uses the following formula to evaluate the rationality of storage location allocation: where M is the rationality of storage location allocation, D optimal is the total sum of the optimal storage distances, D actual is the total sum of the actual storage distances, C access is the evaluation value of cargo accessibility, C total is the maximum accessibility evaluation value, and γ and δ are weight coefficients;

[0053] The intelligent warning module is used to warn of possible abnormal situations according to the data of the visual recognition module and the management and control module, such as excessive stacking of goods and unreasonable storage locations. The intelligent warning module uses the following formula to make a warning judgment:

[0054] , where W is the warning value, P height is the warning weight of the cargo height, H current is the current cargo height, H threshold is the warning threshold of the cargo height, μ is the warning weight of the unreasonable storage location, L current is the current degree of unreasonableness of the storage location, L threshold is the warning threshold of the unreasonable storage location, η is the warning weight of the time overrun, T current is the current storage time of the cargo, T threshold is the warning threshold of the cargo storage time, and ∈ is the comprehensive weight adjustment coefficient;

[0055] The data analysis module deeply analyzes the data stored in the data storage module, mines potential laws and trends, and provides decision-making support for port warehousing management. The data analysis module uses the following formula to evaluate the value of data analysis: where A is the value of data analysis, λ and ω are weight coefficients, n is the number of data samples, X i is the value of a single data sample, is the mean value of the data samples, m is the number of data features, C ij is the correlation coefficient between data features i and j.

[0056] In the present invention, the high-definition camera in the image acquisition module has functions of automatic focusing and automatic exposure, and can automatically adjust shooting parameters according to different lighting conditions to ensure that the acquired images are clear and accurate.

[0057] In the present invention, the data storage module further includes a data backup sub-module for regularly backing up the stored data to prevent data loss. The data storage module uses the following formula to evaluate the timeliness of data backup: where B is the evaluation value of data backup timeliness, T backup is the actual backup time interval, T interval is the pre-set backup time interval, τ is the time weight coefficient, S stored is the amount of stored data, S capacity is the storage capacity, and σ is the storage amount weight coefficient.

[0058] In the present invention, the management and control module includes an inbound management sub-module, an outbound management sub-module, an inventory monitoring sub-module, and a location allocation sub-module. The inbound management sub-module is used to manage the inbound process of goods, the outbound management sub-module is used to manage the outbound process of goods, the inventory monitoring sub-module is used to monitor the inventory situation in real time, and the location allocation sub-module is used to reasonably allocate storage locations according to the types and quantities of goods. The management and control module uses the following formula to evaluate the inbound and outbound efficiency: where E is the evaluation value of inbound and outbound efficiency, ξ, ρ, and ζ are weight coefficients, is the total number of inbound and outbound goods per unit time, T total is the total time, F smoothness is the evaluation value of the smoothness of the inbound and outbound process, F max is the maximum smoothness, Q accuracy is the evaluation value of operation accuracy, Q total is the maximum accuracy.

[0059] In the present invention, the intelligent warning module can also give early warnings about data storage risks based on the data of the data storage module and the management and control module. The intelligent warning module uses the following formula to judge the data storage risk warning: where R is the data storage risk warning value, S usage is the used storage amount, S capacity is the storage capacity, is the storage amount risk weight coefficient, T access is the data access time, T threshold is the data access time threshold, is the access time risk weight coefficient, F corruption is the risk assessment value of possible data corruption, F total is the maximum risk assessment value, and ψ is the data corruption risk weight coefficient.

[0060] The present invention also discloses a port warehousing management method based on visual recognition, which includes the following steps:

[0061] Collect image information of the port warehousing area through an image acquisition module;

[0062] Use a visual recognition module to process and analyze the collected image information, identify the type, quantity, location, and status information of the goods, and evaluate the recognition accuracy according to the formula of the visual recognition module;

[0063] Store the image information and the identified goods information in a data storage module;

[0064] The management control module manages and controls the port warehousing according to the goods information identified by the visual recognition module, and evaluates the rationality of storage location allocation and the efficiency of inbound and outbound according to the formula of the management control module;

[0065] The intelligent warning module determines whether to issue a warning according to the data of the visual recognition module and the management control module, using the formula of the intelligent warning module, including abnormal situation warning and data storage risk warning;

[0066] The data analysis module deeply analyzes the data stored in the data storage module, and evaluates the value of data analysis according to the formula of the data analysis module.

[0067] In the present invention, before collecting the image information, the high-definition camera in the image acquisition module is calibrated. A standard object with a known size is placed at different positions and angles within the camera's field of view, and the standard object is photographed by the camera to obtain multiple groups of image data. According to the actual size of the standard object and the pixel size in the image, the pixel scale coefficient of the camera is calculated, the color balance of the camera is adjusted, by photographing standard color cards with different colors, comparing the colors in the image with the actual color of the color card, and gradually adjusting the color parameters of the camera. The focal length of the camera is finely adjusted, and by observing the clarity of objects at different distances in the image, the focal length is adjusted so that the objects can maintain clear imaging at different distances.

[0068] In the present invention, when processing and analyzing the image information, parallel processing technology is adopted to improve the processing speed of the visual recognition module. Specifically: the image data is divided into multiple small blocks, which are assigned to different processing units for simultaneous processing. Using multi-threading technology, independent processing threads are created for each image small block, and each thread independently performs feature extraction and classification recognition operations. A distributed computing framework is adopted to distribute tasks to multiple computing nodes for parallel execution. During the processing, a data synchronization mechanism is set up to optimize common image processing algorithms.

[0069] The present invention also discloses a port warehousing management device based on visual recognition, which includes a processor and a memory. The memory stores a computer program, and when the processor executes the computer program, a port warehousing management method based on visual recognition is implemented.

[0070] The present invention also discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, a port warehousing management method based on visual recognition is implemented.

[0071] The above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and all should be covered within the protection scope of the present invention.

Claims

1. A port warehouse management system based on visual recognition, characterized in that: include: An image acquisition module is used to collect image information of the port storage area. The image acquisition module includes high-definition cameras distributed at the import and export locations of the port storage area; The visual recognition module is connected to the image acquisition module and is used to process and analyze the collected image information to identify the type, quantity, location and status information of the goods. The visual recognition module uses a deep learning algorithm and uses the following formula to evaluate the recognition accuracy: Where V is the recognition accuracy, N correct is the number of correctly identified goods, N total is the total quantity of goods, S match is the total area of ​​the identified cargo, S total is the actual total cargo area, α and β are weight coefficients; A data storage module is used to store the image information collected by the image acquisition module and the cargo information identified by the visual recognition module. The data storage module adopts a distributed storage architecture to ensure the security and reliability of the data; The management control module is connected to the visual recognition module and the data storage module, and is used to manage and control the port storage according to the cargo information identified by the visual recognition module. The management control module uses the following formula to evaluate the rationality of storage location allocation: Where M is the rationality of storage location allocation, D optimal is the optimal storage distance sum, D actual is the total actual storage distance, C access is the cargo accessibility assessment value, C total is the maximum accessibility evaluation value, γ and δ are weight coefficients; The intelligent early warning module is used to warn of abnormal situations based on the data from the visual recognition module and the management control module. The early warning judgment formula of the intelligent early warning module is: Where W is the warning value, P height is the height warning weight of the cargo, H current is the current cargo height, H threshold is the cargo height warning threshold, μ is the unreasonable storage location warning weight, L current is the unreasonable degree of the current storage location, L threshold is the storage location unreasonable warning threshold, η is the time overdue warning weight, T current is the current storage time of goods, T threshold is the warning threshold of cargo storage time, ∈ is the comprehensive weight adjustment coefficient; The data analysis module conducts in-depth analysis on the data stored in the data storage module, explores potential rules and trends, and provides decision support for port storage management. The data analysis module uses the following formula to evaluate the value of data analysis: Where A is the value of data analysis, λ and ω are weight coefficients, n is the number of data samples, and X i is a single data sample value, is the data sample mean, m is the number of data features, C ij is the correlation coefficient between data features i and j.

2. The port warehouse management system based on visual recognition according to claim 1 is characterized in that: The high-definition camera in the image acquisition module has automatic focus and automatic exposure functions, and is used to automatically adjust shooting parameters according to different lighting conditions.

3. The port warehouse management system based on visual recognition according to claim 1 is characterized in that: The data storage module also includes a data backup submodule, which is used to regularly back up the stored data. The data storage module uses the following formula to evaluate the timeliness of data backup: Where B is the evaluation value of data backup timeliness, T backup is the actual backup time interval, T interval is the preset backup time interval, τ is the time weight coefficient, S stored is the amount of stored data, S capacity is the storage capacity, and σ is the storage weight coefficient.

4. The port warehouse management system based on visual recognition according to claim 1 is characterized in that: The management control module includes an inbound management submodule, an outbound management submodule, an inventory monitoring submodule and a location allocation submodule. The inbound management submodule is used to manage the inbound process of goods, the outbound management submodule is used to manage the outbound process of goods, the inventory monitoring submodule is used to monitor the inventory situation in real time, and the location allocation submodule is used to reasonably allocate storage locations according to the type and quantity of goods. The management control module uses the following formula to evaluate the efficiency of inbound and outbound storage: Where E is the evaluation value of the efficiency of inbound and outbound storage, ξ, ρ and ζ are weight coefficients, T is the total number of goods entering and leaving the warehouse in a unit time. total is the total time, F smoothness is the evaluation value of the smoothness of the inbound and outbound processes, F max For maximum smoothness, Q accuracy is the operation accuracy evaluation value, Q total For maximum accuracy.

5. The port warehouse management system based on visual recognition according to claim 1 is characterized in that: The intelligent early warning module can also issue early warnings for data storage risks based on the data of the data storage module and the management control module. The intelligent early warning module uses the following formula to make early warning judgments for data storage risks: Where R is the data storage risk warning value, S usage is the used storage, S capacity is the storage capacity, κ is the storage risk weight coefficient, T access is the data access time, T threshold is the data access time threshold, is the access time risk weight coefficient, F corruption is the risk assessment value of possible data corruption, F total is the maximum risk assessment value, and ψ is the data damage risk weight coefficient.

6. A method for applying the port warehouse management system based on visual recognition according to claims 1-5, characterized in that: The following steps are involved: S1, collecting image information of the port storage area through the image acquisition module; S2. Use the visual recognition module to process and analyze the collected image information, identify the type, quantity, location and status information of the goods, and evaluate the recognition accuracy according to the formula of the visual recognition module; S3, storing the image information and the identified cargo information in a data storage module; S4. The management and control module manages and controls the port warehousing according to the cargo information identified by the visual recognition module, and evaluates the rationality of storage location allocation and the efficiency of warehousing and outbound storage according to the formula of the management and control module; S5, the intelligent warning module uses the formula of the intelligent warning module to determine whether to issue a warning based on the data of the visual recognition module and the management control module, including abnormal situation warning and data storage risk warning; S6. The data analysis module performs in-depth analysis on the data stored in the data storage module and evaluates the data analysis value according to the formula of the data analysis module.

7. The port warehouse management method based on visual recognition according to claim 6 is characterized in that: Before collecting image information, the high-definition camera in the image acquisition module is calibrated. Standard objects of known size are placed at different positions and angles within the camera's field of view. The standard objects are photographed by the camera to obtain multiple sets of image data. The pixel ratio coefficient of the camera is calculated based on the actual size of the standard object and the pixel size in the image. The color balance of the camera is adjusted. By shooting standard color cards with different colors and comparing the difference between the color in the image and the actual color card color, the color parameters of the camera are gradually adjusted, and the focal length of the camera is fine-tuned. By observing the clarity of objects in the image at different distances, the focal length is adjusted so that the objects can maintain clear imaging at different distances.

8. The port warehouse management method based on visual recognition according to claim 6 is characterized by: When processing and analyzing image information, parallel processing technology is used to improve the processing speed of the visual recognition module. Specifically, the image data is divided into multiple small blocks and assigned to different processing units for simultaneous processing. Multi-threading technology is used to create an independent processing thread for each image block. Each thread independently performs feature extraction and classification and recognition operations. A distributed computing framework is used to assign tasks to multiple computing nodes for parallel execution. During the processing, a data synchronization mechanism is set up to optimize commonly used image processing algorithms.

9. A port storage management device based on visual recognition, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and when the processor executes the computer program, the port warehouse management method based on visual recognition as described in any one of claims 6 to 8 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the port warehouse management method based on visual recognition described in any one of claims 6 to 8 is implemented.

Images