Container logistics management method and system

Abstract

The invention is suitable for the technical field of container management and maintenance, and provides a container logistics management method and system. And screening out comparison maintenance samples of different comparison maintenance groups having the same use conditions as the target container group from the database. According to the method, a data-driven image analysis and parameter optimization system is established, and a whole-process method from historical sample screening, reference shape image determination to optimization of repair structure parameter calculation is provided aiming at the autonomous repair difference existing when different maintenance groups repair forklift jacks of the same kind and consistent in abrasion degree. According to the method, a preset reference model is utilized to carry out comparison and iterative optimization on a repaired forklift jack shape image, the optimal combination of key repair structure parameters such as the coverage area of a reinforcing steel plate, the repair welding thickness and the number of repair welding layers is objectively recognized, and an accurate repair suggestion scheme is generated through change value analysis.

Description

Technical Field

[0001] This invention belongs to the field of container management and maintenance technology, and in particular relates to a container logistics management method and system. Background Technology

[0002] In the existing field of container management and maintenance, container forklift bays, as crucial load-bearing components for short-distance container loading and unloading, port yard handling, and logistics transportation, are prone to structural degradation such as cracks, deformation, and metal thickness loss under long-term, high-frequency use. To ensure the long-term safe operation of containers, ports typically have multiple maintenance teams to regularly inspect and repair forklift bays. In existing technologies, maintenance personnel mainly rely on experience or general maintenance standards to adjust repair parameters such as the coverage area of ​​reinforcing steel plates, weld thickness, and number of weld layers. However, due to differences in the working habits, experience levels, and understanding of standards among different maintenance teams, even when dealing with similar containers with similar wear levels, their repair plans often differ, leading to inconsistent subsequent performance.

[0003] Currently, while the industry attempts to assess and improve the repair quality of forklift sockets through statistical experience, manual quality inspection, or simple data recording, these methods generally lack in-depth analysis based on real-world operational data and cannot accurately identify the differences in effectiveness of different repair solutions over long-term use. For example, visual inspection or simple thickness measurement alone cannot reveal the quantitative relationship between repair parameters and the subsequent durability of the container, nor can it eliminate the arbitrariness of the repair team's subjective judgment. This not only affects the predictability and consistency of repair quality but also increases the potential risks and maintenance costs of the container during subsequent transportation. Summary of the Invention

[0004] The purpose of this invention is to provide a container logistics management method and system, which aims to solve the problems mentioned in the background art.

[0005] This invention is implemented as follows: a container logistics management method, the method comprising:

[0006] Before the target maintenance team repairs the forklift sockets of the target container group, comparative maintenance samples from different comparative maintenance teams with the same usage conditions as the target container group are selected from the database.

[0007] Obtain and analyze the repaired forklift socket shape images of each comparative repair sample to determine the forklift socket deterioration index of each container group corresponding to the comparative repair sample after a preset working cycle, and determine the reference shape image with the smallest deterioration index.

[0008] Obtain a preset reference model and obtain images of the forklift socket shape after repair when the target repair group previously repaired similar container groups. Input the reference shape image and the historical repaired forklift socket shape image into the preset reference model for analysis to identify the set of optimized parameters that make the historical repaired forklift socket shape image approach the reference shape image when at least one repair structure parameter is changed.

[0009] Calculate the changes in the optimized parameter set relative to the repair structure parameters used by the target maintenance group when repairing similar container groups before, and generate a repair proposal for the forklift sockets of the target container group based on the changes.

[0010] As a further limitation of the technical solution of the present invention, the same usage conditions refer to the same container type, the same degree of wear of the forklift socket before repair, and the same usage of the forklift socket within the preset working cycle after repair.

[0011] As a further limitation of the technical solution of the present invention, the forklift socket degradation index refers to a comprehensive parameter used to characterize the degree of structural degradation of the repaired forklift socket within a preset working cycle, including a combination of at least one or more indicators such as the number of cracks in the forklift socket, the total length and width of the cracks, the amount of edge deformation and the loss of metal thickness.

[0012] As a further limitation of the technical solution of the present invention, the types of repair structure parameters include the coverage area of ​​the reinforcing steel plate, the thickness of the repair weld, and the number of repair weld layers.

[0013] As a further limitation of the technical solution of this invention, the steps of obtaining a preset reference model and obtaining the repaired forklift socket shape image when the target repair group previously repaired similar container groups, inputting the reference shape image and the historical repaired forklift socket shape image into the preset reference model for analysis, to identify the optimized parameter set that makes the historical repaired forklift socket shape image approach the reference shape image when at least one repair structure parameter is changed, include:

[0014] Obtain the preset reference model, and retrieve from the database the image of the forklift socket shape after the target repair group previously repaired a container group of the same type as the target container group;

[0015] Input the reference shape image and the historical repaired forklift socket shape image into the preset reference model, and establish an initial comparison model based on the historical repaired forklift socket shape image.

[0016] Based on the initial comparison model, the repair structure parameters such as the coverage area of ​​the reinforcing steel plate, the thickness of the weld, and the number of weld layers are adjusted sequentially, and the model results are updated in real time so that the repaired forklift socket shape image generated by the model gradually approaches the reference shape image.

[0017] When the repaired forklift socket shape image generated by the model is closest to the reference shape image, the corresponding optimized repair structure parameters are obtained and aggregated into the optimized parameter set.

[0018] As a further limitation of the technical solution of this invention, the step of calculating the change value of the optimized parameter set relative to the repair structure parameters used by the target repair group when repairing similar container groups before, and generating a repair suggestion scheme for the forklift socket of the target container group based on the change value, includes:

[0019] The database is used to retrieve various initial repair structure parameters used by the target repair group when repairing similar container groups in the past, and the changes of various optimized repair structure parameters in the optimized parameter set relative to the initial repair structure parameters are quantified. The changes include percentages or differences.

[0020] Based on the changes, the target maintenance team generates suggested information on various repair structural parameters for the forklift sockets of the target container group, and compiles the suggested information to form a repair suggestion plan.

[0021] When the target maintenance team repairs the forklift socket of the target container group, the repair proposal is sent to the target maintenance team's terminal.

[0022] A container logistics management system, the system comprising:

[0023] The sample screening module is used to screen comparative maintenance samples from the database that have the same usage conditions as the target container group before the target maintenance group repairs the forklift sockets of the target container group.

[0024] The degradation analysis module is used to acquire and analyze the repaired forklift socket shape images of each comparative repair sample in order to determine the degradation index of the forklift socket of each container group corresponding to each comparative repair sample after being put into use for a preset working cycle, and to determine the reference shape image with the smallest degradation index.

[0025] The parameter optimization module is used to obtain a preset reference model and obtain the repaired forklift socket shape image when the target repair group repaired the same type of container group before. The reference shape image and the historical repaired forklift socket shape image are input into the preset reference model for analysis to identify the set of optimized parameters that make the historical repaired forklift socket shape image approach the reference shape image when at least one repair structure parameter is changed.

[0026] The suggestion generation module is used to calculate the changes in the optimized parameter set relative to the repair structure parameters used by the target maintenance group when repairing similar container groups before, and to generate a repair suggestion scheme for the forklift sockets of the target container group based on the changes.

[0027] As a further limitation of the technical solution of the present invention, the same usage conditions refer to the same container type, the same degree of wear of the forklift socket before repair, and the same usage of the forklift socket within the preset working cycle after repair.

[0028] As a further limitation of the technical solution of the present invention, the forklift socket degradation index refers to a comprehensive parameter used to characterize the degree of structural degradation of the repaired forklift socket within a preset working cycle, including a combination of at least one or more indicators such as the number of cracks in the forklift socket, the total length and width of the cracks, the amount of edge deformation and the loss of metal thickness.

[0029] As a further limitation of the technical solution of the present invention, the types of repair structure parameters include the coverage area of ​​the reinforcing steel plate, the thickness of the repair weld, and the number of repair weld layers.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] This invention establishes a data-driven image analysis and parameter optimization system. Addressing the differences in autonomous repair methods among different repair groups when repairing similar forklift sockets with consistent wear, it proposes a comprehensive approach, from historical sample selection and reference shape image determination to the calculation of optimized repair structural parameters. This method uses a pre-defined reference model to compare and iteratively optimize the shape image of the repaired forklift socket, objectively identifying the optimal combination of key repair structural parameters such as the coverage area of ​​the reinforcing steel plate, the thickness of the weld, and the number of weld layers. Furthermore, it generates precise repair recommendations through variation value analysis.

[0032] The entire process achieves closed-loop management from data screening and image modeling to on-site repair recommendations, significantly reducing the subjectivity and uncertainty of human experience-based judgment, improving the scientificity and consistency of container forklift socket repair, extending the overall service life of containers, reducing maintenance costs, and providing ports and logistics companies with a replicable and scalable intelligent maintenance decision-making tool. Attached Figure Description

[0033] Figure 1 A flowchart of the method provided in the embodiments of the present invention;

[0034] Figure 2 This is a flowchart illustrating the process of identifying the set of optimization parameters in the method provided in this embodiment of the invention;

[0035] Figure 3 This is a flowchart illustrating the process of generating a repair suggestion scheme in the method provided in this embodiment of the invention;

[0036] Figure 4 The application architecture diagram of the system provided in the embodiments of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0038] Figure 1 A flowchart of the method provided by an embodiment of the present invention is shown.

[0039] Specifically, a container logistics management method includes the following steps:

[0040] Step S100: Before the target maintenance group repairs the forklift sockets of the target container group, select comparison maintenance samples from the database that have the same usage conditions as the target container group.

[0041] The same usage conditions refer to the same container type, the same degree of wear on the forklift sockets before repair, and the same usage of the forklift sockets within the preset working cycle after repair, and the same number of containers in the comparison repair sample as the target container group.

[0042] In this embodiment of the invention, the forklift insertion hole refers to the structural hole reserved on the bottom of the container for the insertion and lifting of forklift forks. It is typically constructed of high-strength steel plate and is a crucial load-bearing component for short-distance loading and unloading, yard handling, and internal port scheduling. During long-term handling, loading, unloading, and stacking, the forklift insertion hole is subjected to repeated complex loads such as fork pushing, friction, and impact, making it highly susceptible to problems such as localized wear, crack propagation, and edge deformation. Its structural integrity directly affects the lifting safety and long-term turnover efficiency of the container. Therefore, the condition of the forklift insertion hole is closely related to the logistics management of containers in ports, yards, and long-distance transportation, and is one of the key factors determining container turnover cycle, maintenance costs, and safety.

[0043] The database described in this invention is a database of the cargo port to which the target maintenance group and the target container group belong. This port is usually equipped with multiple maintenance groups and stores a large amount of historical maintenance data, usage records and deterioration feedback information of other containers of the same type as the target container group.

[0044] The database includes basic container information data, forklift socket status data, maintenance record data, usage and operation data, and operational feedback data. The basic container information data records information such as container model, structural dimensions, material, production batch, manufacturing date, and cumulative service life. The forklift socket status data records the wear degree, crack distribution, deformation, metal thickness measurement, and corresponding image data of the forklift sockets before and after repair. The maintenance record data records the repair time, repair process, reinforcing steel plate coverage area, weld thickness and number of layers, and other repair structural parameters, as well as the shape image of the forklift sockets after repair. The usage and operation data records the operating frequency, load intensity, stacking height, and loading / unloading environment of the forklift sockets within a preset working cycle after repair. The operational feedback data records comprehensive deterioration indicators within a certain period after repair, including the number of cracks, total crack length and width, edge deformation, and metal thickness loss, to quantify the repair effect.

[0045] The aforementioned identical usage conditions refer to the requirement that, when screening and comparing repair samples, candidate containers should be as consistent as possible with the target container group in several key aspects. These include consistent container structural types, comparable wear levels of forklift sockets before repair, and essentially matching usage of the forklift sockets within a preset working cycle after repair, such as expected forklift operation frequency, load levels, and stacking methods. It should be noted that identical usage conditions do not require all details to be completely identical. Rather, a comprehensive comparison across multiple dimensions ensures a high degree of similarity in key factors affecting the repair effect of forklift sockets, thereby ensuring the comparability and representativeness of subsequent deterioration index comparisons. This design of identical conditions with a certain tolerance ensures data reliability and makes the invention operable and applicable in actual port operations.

[0046] Furthermore, the container logistics management method also includes the following steps:

[0047] Step S200: Obtain and parse the repaired forklift socket shape images of each comparative repair sample to determine the forklift socket deterioration index of each container group corresponding to the comparative repair sample after a preset working cycle, and determine the reference shape image with the smallest deterioration index.

[0048] The forklift socket degradation index refers to a comprehensive parameter used to characterize the degree of structural degradation of the repaired forklift socket within a preset working cycle, including a combination of at least one or more indicators such as the number of cracks in the forklift socket, the total length and width of the cracks, the amount of edge deformation, and the loss of metal thickness.

[0049] In this embodiment of the invention, the specific implementation process of step S200 may include the following: First, retrieve the repaired forklift socket shape image corresponding to each comparison and repair sample from the database, along with its usage and inspection records within a preset working cycle after repair. Then, extract features from the shape image using image recognition and digital processing technologies, such as edge detection, crack recognition, weld contour and geometric dimension analysis, to accurately obtain the shape and potential damage features of the forklift socket.

[0050] Then, the above image features are combined with the corresponding sensor monitoring data and detection data in the database. Using data fusion and comprehensive calculation methods, at least one or more indicators such as the number of forklift socket cracks, total crack length and width, edge deformation and metal thickness loss of all containers in the entire container group corresponding to the comparison and repair sample are statistically summarized, normalized and weighted to form a forklift socket deterioration index that can characterize the degree of overall structural degradation of the repaired forklift socket within a preset working cycle.

[0051] Finally, by comparing the degradation index of all the comparative repair samples, the reference shape image with the smallest degradation index was selected, providing a basis for the subsequent establishment of the reference model and the optimization of the repair structure parameters. The entire process comprehensively adopted technical means such as database management, image recognition, data fusion, statistical weighting, and pattern analysis to ensure that the obtained forklift socket degradation index can truly reflect the overall repair performance and long-term use effect of the corresponding container group.

[0052] Furthermore, the container logistics management method also includes the following steps:

[0053] Step S300: Obtain a preset reference model and acquire images of the forklift socket shape after repair when the target repair group previously repaired similar container groups. Input the reference shape image and the historical repaired forklift socket shape images into the preset reference model for analysis to identify the set of optimized parameters that make the historical repaired forklift socket shape image approach the reference shape image when at least one repair structural parameter is changed. The types of repair structural parameters include the coverage area of ​​the reinforcing steel plate, the thickness of the weld, and the number of weld layers.

[0054] Specifically, Figure 2 A flowchart for identifying the set of optimization parameters is shown.

[0055] The process of obtaining a preset reference model and acquiring images of the forklift socket shape after repair when the target repair group previously repaired similar container groups, and inputting the reference shape image and the historical repaired forklift socket shape image into the preset reference model for analysis, in order to identify the set of optimized parameters that make the historical repaired forklift socket shape image approach the reference shape image when at least one repair structural parameter is changed, specifically includes the following steps:

[0056] Step S301: Obtain the preset reference model and retrieve from the database the image of the forklift socket shape after repair when the target repair group repaired a container group of the same type as the target container group.

[0057] Step S302: Input the reference shape image and the historical repaired forklift socket shape image into the preset reference model, and establish an initial comparison model based on the historical repaired forklift socket shape image;

[0058] Step S303: Based on the initial comparison model, adjust the repair structure parameters such as the coverage area of ​​the reinforcing steel plate, the thickness of the weld, and the number of weld layers in sequence, and update the model results in real time so that the repaired forklift socket shape image generated by the model gradually approaches the reference shape image.

[0059] Step S304: When the repaired forklift socket shape image generated by the model is closest to the reference shape image, the corresponding optimized repair structure parameters are obtained and aggregated into the optimized parameter set.

[0060] In this embodiment of the invention, step S300 is proposed based on the following background: Those skilled in the art, through long-term container maintenance practice, have found that when different maintenance teams repair forklift bays of the same type and with the same degree of wear, they often adjust key repair structural parameters such as increasing or decreasing the coverage area of ​​reinforcing steel plates, or adjusting the thickness and number of weld layers, based on their own experience and habits, on top of existing standard requirements. This adjustment is not a simple matter of "the more the better" or "the less the better," but varies depending on the specific container's structural characteristics, stress conditions, and subsequent usage environment. Only under a certain combination of parameters can the repaired forklift bay maintain optimal durability and stability in long-term use. However, existing technologies typically summarize patterns through manual experience or simple statistical methods. These methods lack objective quantitative support and cannot reliably find the optimal repair solution under complex and diverse usage conditions. Therefore, this invention proposes to utilize mature and sophisticated visual recognition and image processing technologies, combined with data analysis models, to establish an objective data-driven method for screening and verifying the optimal repair solution.

[0061] The preset reference model can be a mature image recognition or deep learning model already in use, such as a convolutional neural network (CNN) or a 3D reconstruction model, or it can be a pattern recognition algorithm specifically designed for industrial defect detection. This model possesses the ability to perform multi-dimensional extraction of image features, similarity calculation, and iterative optimization, providing a reliable analytical tool for optimizing forklift socket repair parameters.

[0062] In the specific implementation process, step S302 involves inputting the reference shape image and the historically repaired forklift socket shape image into a preset reference model to establish an initial comparison model based on the historically repaired forklift socket shape image. This initial comparison model can quantify the morphological differences between the historically repaired image and the reference image, providing a benchmark for subsequent optimization. Step S303, based on the initial comparison model, sequentially adjusts the repair structure parameters such as the coverage area of ​​the reinforcing steel plate, the thickness of the weld, and the number of weld layers in different regions, and updates the model output in real time, so that the repaired forklift socket shape image generated by the model gradually approaches the reference shape image. This process can employ mature parameter optimization techniques such as iterative optimization, gradient descent, or genetic algorithms. Step S304, when the repaired image generated by the model is closest to the reference image, automatically records the values ​​of various repair structure parameters at this time and summarizes them into an optimized parameter set to guide subsequent actual repair operations.

[0063] The core significance of step S300 lies in its ability to objectively identify and extract the combination of repair structural parameters that ensures optimal durability of forklift bays during subsequent use, through data-driven and visually intelligent methods. This solves the technical problem of existing technologies relying on manual experience and lacking precise and objective judgment. Furthermore, this solution is specifically designed for forklift bays—a crucial part of containers that is subject to long-term handling impacts and friction—making it uniquely applicable and valuable for widespread application in container repair and logistics management.

[0064] Furthermore, the container logistics management method also includes the following steps:

[0065] Step S400: Calculate the change value of the optimized parameter set relative to the repair structure parameters used by the target maintenance group when repairing similar container groups before, and generate a repair suggestion plan for the forklift socket of the target container group based on the change value.

[0066] Specifically, Figure 3 A flowchart for generating a patch suggestion scheme is shown.

[0067] The specific steps involved in calculating the changes in the optimized parameter set relative to the repair structural parameters used by the target maintenance team when repairing similar container groups, and generating a repair proposal for the forklift sockets of the target container group based on these changes, include the following:

[0068] Step S401: Obtain from the database the various initial repair structure parameters used by the target repair group when repairing similar container groups before, and quantify the change values ​​of various optimized repair structure parameters in the optimized parameter set relative to the initial repair structure parameters. The change values ​​include percentages or differences.

[0069] Step S402: Based on the change value, generate suggested information on various repair structure parameters of the forklift sockets of the target container group for the target maintenance group, and compile the suggested information to form a repair suggestion plan;

[0070] Step S403: When the target maintenance group repairs the forklift socket of the target container group, the repair suggestion is sent to the terminal of the target maintenance group.

[0071] In this embodiment of the invention, the specific implementation process of step S401 is as follows: retrieve various initial repair structure parameters from the database when the target maintenance group previously repaired a similar container group with the same type and wear conditions as the target container group, including the coverage area of ​​the reinforcing steel plate, the thickness of the weld, and the number of weld layers; then call the data analysis and calculation module to compare the aforementioned initial repair structure parameters with the corresponding optimized repair structure parameters in the optimized parameter set one by one, and automatically calculate the change value of each repair structure parameter through the algorithm. The change value can be expressed intuitively as a percentage or difference, providing a data basis for generating accurate repair suggestions.

[0072] The specific implementation process of step S402 is as follows: input the various change values ​​obtained in step S401 into the repair suggestion generation module, and automatically generate suggestion information for the target maintenance group through data modeling and logical reasoning methods, such as the recommended coverage range of the reinforcing steel plate, the optimal adjustment range of the welding thickness and number of layers, etc., and integrate and optimize all suggestion information to finally output a complete forklift socket repair suggestion plan, so that the plan can directly guide the actual operation of the maintenance group.

[0073] The specific implementation process of step S403 is as follows: When the target maintenance group actually performs the forklift socket repair operation on the target container group, the system will directly push the repair suggestion plan to the terminal equipment of the maintenance group, such as mobile workstation, portable console or smart wearable device; at the same time, it can monitor the operation progress and key parameters at the maintenance site in real time, and through dynamic comparison with the repair suggestion plan, it will prompt or optimize the deviation that occurs in the construction process in real time, thereby ensuring the strict implementation of the repair plan and the stability of the repair quality.

[0074] The above steps have yielded several beneficial effects. First, data-driven quantitative analysis enables precise comparison between the optimized parameter set and historical parameters, ensuring that the repair recommendations are based on sufficient data. Second, the automatic generation and delivery of repair plans significantly reduces the uncertainty and delays caused by human judgment, improving the efficiency and consistency of maintenance operations. Third, the real-time monitoring and dynamic prompts ensure that the repair plans are implemented to a high standard on-site, avoiding reduced repair effectiveness due to execution deviations.

[0075] Overall, this invention, through data-driven and intelligent image analysis, achieves a complete closed-loop management of container forklift socket repair solutions, from historical data screening and optimal parameter identification to final repair suggestion generation and implementation monitoring. Its beneficial effects include: significantly improving the scientific rigor and accuracy of forklift socket repair, extending the service life of containers during multiple turnovers, reducing maintenance and downtime costs, and enhancing the overall operational safety and efficiency of ports and logistics. This technology is not only applicable to large international freight ports but can also be extended to various logistics scenarios such as container yards, railways, and inland ports, providing an effective solution for intelligent operation and maintenance of containers throughout their entire lifecycle, and possesses broad application prospects and industrial promotion value.

[0076] Furthermore, Figure 4 An application architecture diagram of the system provided in an embodiment of the present invention is shown.

[0077] In another preferred embodiment of the present invention, a container logistics management system includes:

[0078] The sample screening module 100 is used to screen comparative maintenance samples from the database that have the same usage conditions as the target container group before the target maintenance group repairs the forklift sockets of the target container group.

[0079] The same usage conditions refer to the same container type, the same degree of wear on the forklift socket before repair, and the same usage of the forklift socket within the preset working cycle after repair.

[0080] Furthermore, the container logistics management system also includes:

[0081] The degradation analysis module 200 is used to acquire and analyze the repaired forklift socket shape images of each comparative repair sample in order to determine the degradation index of the forklift socket of each container group corresponding to each comparative repair sample after being put into use for a preset working cycle, and to determine the reference shape image with the smallest degradation index.

[0082] The forklift socket degradation index refers to a comprehensive parameter used to characterize the degree of structural degradation of the repaired forklift socket within a preset working cycle, including a combination of at least one or more indicators such as the number of cracks in the forklift socket, the total length and width of the cracks, the amount of edge deformation, and the loss of metal thickness.

[0083] Furthermore, the container logistics management system also includes:

[0084] The parameter optimization module 300 is used to acquire a preset reference model and obtain images of the repaired forklift socket shapes from previous repairs of similar container groups by the target repair group. The reference shape image and historical repaired forklift socket shape images are input into the preset reference model for analysis to identify a set of optimized parameters that, by changing at least one repair structural parameter, make the historical repaired forklift socket shape image approximate the reference shape image. The types of repair structural parameters include the area covered by the reinforcing steel plate, the thickness of the weld, and the number of weld layers.

[0085] Furthermore, the container logistics management system also includes:

[0086] It is recommended that module 400 be used to calculate the changes in the optimized parameter set relative to the repair structure parameters used by the target maintenance group when repairing similar container groups before, and to generate a repair suggestion scheme for the forklift sockets of the target container group based on the changes.

[0087] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0088] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0089] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0090] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0091] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A container logistics management method, characterized in that, The method includes: Before the target maintenance team repairs the forklift sockets of the target container group, comparative maintenance samples from different comparative maintenance teams with the same usage conditions as the target container group are selected from the database. Obtain and analyze the repaired forklift socket shape images of each comparative repair sample to determine the forklift socket deterioration index of each container group corresponding to the comparative repair sample after a preset working cycle, and determine the reference shape image with the smallest deterioration index. Obtain a preset reference model and obtain images of the forklift socket shape after repair when the target repair group previously repaired similar container groups. Input the reference shape image and the historical repaired forklift socket shape image into the preset reference model for analysis to identify the set of optimized parameters that make the historical repaired forklift socket shape image approach the reference shape image when at least one repair structure parameter is changed. Calculate the changes in the optimized parameter set relative to the repair structure parameters used by the target maintenance group when repairing similar container groups before, and generate a repair proposal for the forklift sockets of the target container group based on the changes.

2. The container logistics management method according to claim 1, characterized in that, The same usage conditions refer to the same container type, the same degree of wear on the forklift socket before repair, and the same usage of the forklift socket within the preset working cycle after repair.

3. The container logistics management method according to claim 1, characterized in that, The forklift socket degradation index refers to a comprehensive parameter used to characterize the degree of structural degradation of the repaired forklift socket within a preset working cycle, including a combination of at least one or more indicators such as the number of cracks in the forklift socket, the total length and width of the cracks, the amount of edge deformation, and the loss of metal thickness.

4. The container logistics management method according to claim 1, characterized in that, The types of repair structural parameters include the area covered by the reinforcing steel plate, the thickness of the weld, and the number of weld layers.

5. The container logistics management method according to claim 4, characterized in that, The steps of obtaining a preset reference model and acquiring images of the forklift socket shape after repair when the target repair group previously repaired similar container groups, inputting the reference shape image and the historical repaired forklift socket shape image into the preset reference model for analysis, to identify the optimized parameter set that makes the historical repaired forklift socket shape image approach the reference shape image when at least one repair structural parameter is changed, include: Obtain the preset reference model, and retrieve from the database the image of the forklift socket shape after the target repair group previously repaired a container group of the same type as the target container group; Input the reference shape image and the historical repaired forklift socket shape image into the preset reference model, and establish an initial comparison model based on the historical repaired forklift socket shape image. Based on the initial comparison model, the repair structure parameters such as the coverage area of ​​the reinforcing steel plate, the thickness of the weld, and the number of weld layers are adjusted sequentially, and the model results are updated in real time so that the repaired forklift socket shape image generated by the model gradually approaches the reference shape image. When the repaired forklift socket shape image generated by the model is closest to the reference shape image, the corresponding optimized repair structure parameters are obtained and aggregated into the optimized parameter set.

6. The container logistics management method according to claim 5, characterized in that, The steps for calculating the changes in the optimized parameter set relative to the repair structural parameters used by the target maintenance team when repairing similar container groups, and generating a repair proposal for the forklift sockets of the target container group based on these changes, include: The database is used to retrieve various initial repair structure parameters used by the target repair group when repairing similar container groups in the past, and the changes of various optimized repair structure parameters in the optimized parameter set relative to the initial repair structure parameters are quantified. The changes include percentages or differences. Based on the changes, the target maintenance team generates suggested information on various repair structural parameters for the forklift sockets of the target container group, and compiles the suggested information to form a repair suggestion plan. When the target maintenance team repairs the forklift socket of the target container group, the repair proposal is sent to the target maintenance team's terminal.

7. A container logistics management system, characterized in that, The system includes: The sample screening module is used to screen comparative maintenance samples from the database that have the same usage conditions as the target container group before the target maintenance group repairs the forklift sockets of the target container group. The degradation analysis module is used to acquire and analyze the repaired forklift socket shape images of each comparative repair sample in order to determine the degradation index of the forklift socket of each container group corresponding to each comparative repair sample after being put into use for a preset working cycle, and to determine the reference shape image with the smallest degradation index. The parameter optimization module is used to obtain a preset reference model and obtain the repaired forklift socket shape image when the target repair group repaired the same type of container group before. The reference shape image and the historical repaired forklift socket shape image are input into the preset reference model for analysis to identify the set of optimized parameters that make the historical repaired forklift socket shape image approach the reference shape image when at least one repair structure parameter is changed. The suggestion generation module is used to calculate the changes in the optimized parameter set relative to the repair structure parameters used by the target maintenance group when repairing similar container groups before, and to generate a repair suggestion scheme for the forklift sockets of the target container group based on the changes.

8. The container logistics management system according to claim 7, characterized in that, The same usage conditions refer to the same container type, the same degree of wear on the forklift socket before repair, and the same usage of the forklift socket within the preset working cycle after repair.

9. The container logistics management system according to claim 8, characterized in that, The forklift socket degradation index refers to a comprehensive parameter used to characterize the degree of structural degradation of the repaired forklift socket within a preset working cycle, including a combination of at least one or more indicators such as the number of cracks in the forklift socket, the total length and width of the cracks, the amount of edge deformation, and the loss of metal thickness.

10. The container logistics management system according to claim 9, characterized in that, The types of repair structural parameters include the area covered by the reinforcing steel plate, the thickness of the weld, and the number of weld layers.

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