Intelligent archival repository management method, device and equipment

Abstract

The invention provides an intelligent archive repository management method, device and equipment, and relates to the technical field of archive management. The method comprises the following steps: acquiring multi-source archive data of an archive repository; and performing access prediction on the multi-source archive data, and generating an access popularity score of each archive in a preset time period. Detecting archive information of archives of each goods location in the archive repository; and generating a virtual warehouse corresponding to the archive warehouse based on the archive areas of the plurality of preset popularity levels and the archive information. If it is detected that the access popularity score of any file is not matched with the preset popularity level of the file area where any file is located in the virtual warehouse, a migration task instruction set is generated; and sending the migration task instruction set to an execution terminal, so that the execution terminal optimizes the archival repository and the virtual repository according to the migration task instruction set. The method is used for achieving the effect of improving the storage efficiency and the reading efficiency of the archival repository.

Description

Technical Field

[0001] This application relates to the field of archival management technology, and more specifically, to an intelligent archival storage management method, apparatus, and equipment. Background Technology

[0002] Currently, with the increasing number of archives in the archives storage, how to manage the archives more quickly and effectively has become a top priority.

[0003] In existing technologies, there are two main types of archive storage management technologies. The first type is a static storage management system, which assigns a fixed storage location number (e.g., shelf X, layer Y, number Z) to each archive or archive box and uses barcode or radio frequency identification (RFID) technology for information registration. This type of system solves the problems of "what is there" and "where is there" of the archives, but the storage location allocation logic is usually based on static attributes such as the time of entry and the type of archive, which remain unchanged once stored. This leads to the mixed storage of frequently used and infrequently used archives, and retrieving frequently used archives may require venturing deep into the storage area, resulting in long paths, long processing times, and low retrieval efficiency. The second type is a system that partially utilizes automated equipment, such as stacker cranes or AGVs (automated guided vehicles) for archive storage and retrieval. However, most of these systems only automate the "instruction execution," and the underlying storage optimization logic remains static logic issued by the user. Optimizing the archive storage based on static logic still results in the problem of frequently used and infrequently used archives being stored together, leading to low retrieval efficiency. Summary of the Invention

[0004] The purpose of this application is to provide an intelligent archive storage management method, apparatus, and equipment to solve the above-mentioned problems existing in the prior art and improve the storage efficiency and retrieval efficiency of the archive storage.

[0005] Firstly, an intelligent archive storage management method is provided, which may include: Acquire multi-source archive data from the archive repository; predict access to the multi-source archive data and generate an access popularity score for each archive within a preset time period; The system detects the archive information of each storage location in the archive warehouse; based on multiple preset popularity levels of archive areas and the archive information, it generates a virtual warehouse corresponding to the archive warehouse. If the access popularity score of any file detected in the virtual repository does not match the preset popularity level of the file area where the file is located, a migration task instruction set is generated; the migration task instruction set is sent to the execution terminal so that the execution terminal optimizes the file repository and the virtual repository according to the migration task instruction set.

[0006] Secondly, an intelligent archive storage management device is provided, which may include: The prediction module is used to acquire multi-source archive data from the archive repository; perform access prediction on the multi-source archive data, and generate an access popularity score for each archive within a preset time period; The generation module is used to detect the archive information of each storage location in the archive warehouse; based on multiple preset heat levels of archive areas and the archive information, it generates a virtual warehouse corresponding to the archive warehouse; The optimization module is used to generate a migration task instruction set if the access popularity score of any file detected in the virtual warehouse does not match the preset popularity level of the file area where the file is located; and to send the migration task instruction set to the execution terminal so that the execution terminal optimizes the file warehouse and the virtual warehouse according to the migration task instruction set.

[0007] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.

[0008] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0009] This application provides an intelligent archive storage management method, apparatus, and equipment. It acquires multi-source archive data from an archive storage facility; performs access prediction on the multi-source archive data to generate an access popularity score for each archive within a preset time period; detects archive information for each storage location in the archive storage facility; and generates a virtual storage facility corresponding to the archive storage facility based on multiple preset popularity levels of archive areas and archive information. If the access popularity score of any archive detected in the virtual storage facility does not match the preset popularity level of the archive area where that archive is located, a migration task instruction set is generated; the migration task instruction set is sent to an execution terminal so that the execution terminal optimizes the archive storage facility and the virtual storage facility according to the migration task instruction set. This solution establishes a closed-loop management mechanism that can proactively predict future access trends of archives and dynamically and intelligently adjust the physical location of archives within the storage facility based on the predicted data. This mechanism can synergistically optimize two seemingly contradictory goals: warehouse space utilization and archive retrieval efficiency. It transforms passive "archive retrieval by address" into proactive "archive preparation on demand." Through data-driven "load balancing," it maximizes the overall operational efficiency of the warehouse, reduces the ineffective operating costs of manpower and automated equipment, significantly improves retrieval efficiency, and realizes intelligent and forward-looking warehouse management. It achieves intelligent archive warehouse management based on dynamic load balancing, which can improve the storage and retrieval efficiency of the archive warehouse. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A flowchart illustrating an intelligent archive storage management method provided in this application embodiment; Figure 2 A flowchart illustrating an intelligent archive storage management method provided in this application embodiment; Figure 3 A schematic diagram of the structure of an intelligent archive storage management device provided in this application embodiment; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art. The words "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are only used to distinguish different components. The words "comprising" or "including," etc., mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but do not exclude other elements or objects. The words "connected," "coupled," or "connected," etc., are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up," "down," "left," "right," etc., are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0013] Currently, with the increasing number of archives in the archives storage, how to manage the archives more quickly and effectively has become a top priority.

[0014] In existing technologies, there are two main categories of archival storage management technologies. The first is a static storage management system, which assigns a fixed storage location number (e.g., shelf X, layer Y, number Z) to each file or file box and uses barcode or radio frequency identification (RFID) technology for information registration. This type of system solves the problems of "what is there" and "where is there" of the files, but the storage location allocation logic is usually based on static attributes such as storage time and file type, which remain unchanged once stored. This leads to frequently used files being stored together with infrequently used files, and retrieving frequently used files may require venturing deep into the storage area, resulting in long paths, long processing times, and low retrieval efficiency. The second type is a system that partially utilizes automated equipment, such as stacker cranes or AGVs (Automated Guided Vehicles) for file storage and retrieval. However, these systems mostly only automate the "instruction execution," and the underlying storage optimization logic remains static logic issued by the user. Optimizing the archival storage based on static logic still results in the problem of frequently used files being stored together with infrequently used files, leading to low retrieval efficiency. Specifically, existing technologies fail to establish a dynamic link between the physical storage location of the files and their future access needs. A file that was ignored last week might become this week's "star file" due to the launch of a new project, but it remains stored in a remote "cold palace" of the warehouse, resulting in unnecessary time and manpower consumption for each retrieval. Conversely, a once-popular file, whose access frequency drops sharply after the project ends, still occupies a prime location near the entrance, resulting in a waste of "hot zone" space.

[0015] The intelligent archive storage management method provided in this application embodiment can be applied to electronic devices, terminal devices, intelligent archive storage management devices or equipment, or other devices or equipment that can execute this embodiment, and there are no limitations on this.

[0016] Terminal equipment can be user equipment (UE) such as mobile phones, smartphones, laptops, digital broadcast receivers, personal digital assistants (PDAs), and tablet computers (PADs), handheld devices, in-vehicle devices, wearable devices, computing devices or other processing devices connected to a wireless modem, mobile stations (MS), mobile terminals, etc. This terminal has the ability to communicate with one or more core networks via a radio access network (RAN).

[0017] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0018] Figure 1 This is a flowchart illustrating an intelligent archive storage management method provided in an embodiment of this application. Figure 1 As shown, the method may include: Step S101: Obtain multi-source archive data from the archive repository; predict access to the multi-source archive data and generate an access popularity score for each archive within a preset time period.

[0019] For example, multi-source archival data from the archive repository is continuously collected from a middleware system (such as a business system, OA system, or archive borrowing system) through a pre-defined interface. This multi-source archival data includes historical borrowing records, current borrowing status, associated project information, user departments, and other diverse data. Based on this data, machine learning algorithms (such as time series analysis and collaborative filtering) are used to build a predictive model to calculate the "access popularity score" or predicted access frequency for each archival entity or collection within a pre-defined time period (such as the next month).

[0020] Step S102: Detect the archive information of each storage location in the archive warehouse; based on multiple preset heat levels of archive areas and archive information, generate a virtual warehouse corresponding to the archive warehouse.

[0021] For example, the physical space of the archive storage room is logically divided into multiple archive areas with preset popularity levels. These might include, at least, a "hot zone" near the entrance / exit and with the shortest retrieval path, a relatively remote "cold zone," and a "warm zone" in between. Simultaneously, technologies such as RFID, visual sensors, or location detectors are used to monitor the archive information of each location in the storage room in real time. This information includes the location's occupancy status and the actual information of the currently stored archives. Based on the archive areas and the archive information, a "digital twin" model, synchronized in real time with the physical archive storage room, is constructed—that is, a virtual storage room corresponding to the physical archive storage room.

[0022] Step S103: If the access popularity score of any file is detected in the virtual repository and does not match the preset popularity level of the file area where the file is located, a migration task instruction set is generated; the migration task instruction set is sent to the execution terminal so that the execution terminal optimizes the file repository and the virtual repository according to the migration task instruction set.

[0023] For example, a dynamic scheduling strategy is generated. Based on a pre-built scheduling decision engine, it runs periodically (e.g., every morning) or event-triggered (e.g., when a frequently accessed file is returned). During runtime, it compares the "access popularity score" of all files in the repository with the "preset popularity level" of the file area where the file is currently located. When a mismatch is found between the access popularity score and the preset popularity level (e.g., a file with a high popularity score is located in a cold zone, or a file with a low popularity score occupies a hot zone), the engine generates a set of "file migration task instructions," i.e., a migration task instruction set, according to preset optimization objectives (e.g., minimizing the expected total retrieval path, minimizing the workload of the scheduling task itself), and sends the migration task instruction set to the execution terminal. The execution terminal receives the migration task instruction set and optimizes the file repository and virtual repository according to the migration task instruction set.

[0024] The method provided in this application embodiment acquires multi-source archival data from an archive repository; performs access prediction on the multi-source archival data to generate an access popularity score for each archive within a preset time period. It detects the archive information of archives in each storage location within the archive repository; and generates a virtual repository corresponding to the archive repository based on multiple preset popularity levels of archive areas and the archive information. If the access popularity score of any archive detected in the virtual repository does not match the preset popularity level of the archive area where that archive is located, a migration task instruction set is generated; the migration task instruction set is sent to the execution terminal so that the execution terminal optimizes the archive repository and the virtual repository according to the migration task instruction set. This solution establishes a closed-loop management mechanism that can proactively predict future access trends of archives and dynamically and intelligently adjust the physical location of archives within the repository based on the predicted data. This mechanism can synergistically optimize two seemingly contradictory goals: warehouse space utilization and archive retrieval efficiency. It transforms passive "archive retrieval by address" into proactive "archive preparation on demand." Through data-driven "load balancing," it maximizes the overall operational efficiency of the warehouse, reduces the ineffective operating costs of manpower and automated equipment, significantly improves retrieval efficiency, and realizes intelligent and forward-looking warehouse management. It achieves intelligent archive warehouse management based on dynamic load balancing, which can improve the storage and retrieval efficiency of the archive warehouse.

[0025] Figure 2 A flowchart illustrating an intelligent archive storage management method provided in this application is shown below. Figure 2 As shown, in this embodiment... Figure 1 Based on the embodiments, the method is described in detail below, and the method includes: Step S201: Obtain multi-source archive data from the archive repository.

[0026] For example, firstly, the breadth and depth of data collection are crucial. Specifically, this requires seamless integration with multiple information platforms within enterprises and institutions through standardized API interfaces. The collected data should not be limited to the borrowing history of the records management system itself (who, when, and what records were borrowed / returned), but should extend to "leading indicator" data that can predict future needs. For instance, information on newly initiated projects and their associated records lists can be obtained from the project management system; the agenda of upcoming important meetings can be obtained from the OA system and associated with relevant background records; the functions and recent work priorities of user departments can be analyzed to infer their potential demand for certain types of records. This multi-dimensional data is then aggregated into a central data warehouse.

[0027] Secondly, data preprocessing and feature engineering are crucial for the success of the model. The raw data needs to be cleaned (removing outliers and noise), normalized, and structured.

[0028] Step S202: Perform access prediction on multi-source archive data and generate access popularity score for each archive within a preset time period.

[0029] In one example, S202 includes: constructing feature variables for prediction; extracting features from multi-source archive data to obtain multiple feature variables; using a prediction model to predict access to the feature variables and outputting the access popularity score of each archive within a preset time period.

[0030] For example, feature engineering is first performed to construct feature variables that effectively reflect the probability of access to archives, such as: "number of days since last access", "total number of historical borrowings", "number of borrowings in the last 30 days", "current stage of the project (start / in progress / ending)", "borrower's job level", and "years since archive creation". Feature extraction is performed on multi-source archive data to obtain multiple feature variables. Based on these feature variables, a prediction model is constructed and trained. This embodiment can use various machine learning models as prediction models. For example, for archives with obvious time trends, a Long Short-Term Memory (LSTM) network can be used, which can effectively capture long-term dependencies and periodic patterns in archive borrowing behavior. For new archives or archives with sparse data, content-based recommendation or collaborative filtering algorithms can be used to predict access probabilities by analyzing user groups that have borrowed similar archives or analyzing borrowing patterns of archives with similar attributes (such as keywords or categories). Model training is not completed all at once, but rather uses an incremental learning mechanism. The electronic device uses newly generated data daily for model retraining and fine-tuning to ensure the accuracy and timeliness of predictions. Ultimately, the predictive model outputs a standardized "access popularity score" (e.g., 0-100) for each archive entity in the database, which intuitively reflects the likelihood of it being accessed in the next cycle.

[0031] Step S203: Detect the file information of each storage location in the file warehouse.

[0032] In one example, the file information includes the occupancy status of the storage location and the actual information of the files currently stored there.

[0033] For example, to comprehensively perceive the status of archive information and ensure the accuracy of the digital twin model, a solution is adopted that primarily uses Radio Frequency Identification (RFID) technology, supplemented by visual recognition. Each archive box is affixed with a passive ultra-high frequency (UHF) RFID tag, and each storage location is also equipped with an RFID tag as an address identifier. Fixed or mobile RFID readers are installed at the archive storage entrances and exits, main passageways, and on automated robots. When an archive is accessed or moved, the tag information of the archive and the tag information of the target storage location are read in real time and uploaded to electronic devices, achieving second-level updates of location information. In addition, cameras are installed in key areas, utilizing AI visual recognition technology. This helps identify archives that have not been correctly read by RFID (such as damaged tags) and monitors whether storage locations are occupied by foreign objects, serving as redundancy and supplement to the RFID solution and ensuring 100% accuracy in perceiving the status of the archive storage. This real-time updated digital twin model provides the upper-level scheduling engine with all the physical world status information needed for decision-making.

[0034] Step S204: Based on the overall retrieval cost, divide the archive storage room into multiple pre-set popularity levels of archive areas; wherein, the overall retrieval cost includes any one or more of the following: physical distance from the entrance / exit of the archive storage room to the storage location, shelf height, and aisle width.

[0035] In one example, the archive areas with preset heat levels include hot, warm, and cold zones.

[0036] For example, this step of dynamic warehouse zoning establishes the physical foundation and digital mirror for intelligent scheduling. The logical definition of warehouse zoning is central. Zoning is not a simple geometric division, but rather an isopleth division based on "comprehensive retrieval cost," resulting in multiple pre-defined archive areas with varying levels of popularity. Comprehensive cost considers not only the physical distance from the entrance / exit to the storage location, but also factors such as shelf height (retrieval of archives from lower shelves is faster than higher shelves) and aisle width (wide aisles allow robots to pass faster or for people and vehicles to travel side-by-side). System administrators can flexibly define the range of "hot zones," "warm zones," and "cold zones" on a 3D visualization interface through drag-and-drop or parameter settings. For example, all storage locations can be sorted from low to high comprehensive retrieval cost, with the first 20% defined as hot zones, the last 50% as cold zones, and the remainder as warm zones. These zoning definitions are dynamic and can be adjusted at any time according to changes in the warehouse layout.

[0037] Step S205: Based on multiple preset popularity levels of archive areas and archive information, generate a virtual archive corresponding to the archive archive.

[0038] In one example, the virtual warehouse includes a mapping table between storage locations and file areas, as well as a real-time warehouse status diagram; the real-time warehouse status diagram includes the location of each file and the occupancy status of each storage location.

[0039] For example, based on the archive areas and archive information, a "digital twin" model synchronized in real time with the physical archive storage room is constructed; that is, a virtual storage room corresponding to the physical archive storage room. The virtual storage room includes a mapping table between storage locations and archive areas, as well as a real-time storage room status diagram. Each archive area may include multiple storage locations; the real-time storage room status diagram includes the location of each archive, the occupancy status of each storage location, etc.

[0040] Step S206: If the access popularity score of any file is detected in the virtual warehouse and does not match the preset popularity level of the file area where the file is located, then a migration task instruction set is generated.

[0041] In one example, S206 includes: if the access popularity score of any file is detected in the virtual warehouse and does not match the preset popularity level of the file area where the file is located, then the total retrieval cost and the total transportation cost are determined according to the preset optimization scheduling model, the access popularity score and the file information; and a migration task instruction set is generated according to the total retrieval cost and the total transportation cost.

[0042] In one example, “generating a migration task instruction set based on total retrieval cost and total transportation cost” includes: if there are multiple unidirectional migration tasks with position swapping relationships, then based on the total retrieval cost and total transportation cost, the unidirectional migration tasks with position swapping relationships are merged into one exchange task, and a migration task instruction set is generated.

[0043] For example, this step is mainly used to generate a dynamic scheduling strategy. The core of the scheduling decision engine is a multi-objective optimization algorithm, whose inputs are: 1) the latest "access popularity score" of all files; 2) the current location of all files and the preset popularity level of the file area where the files are located; 3) the location list of all available storage locations in the file warehouse; and 4) preset scheduling rules and constraints.

[0044] The first step is for the engine to traverse all files. When it finds a file with a popularity score greater than 80 (high popularity) currently in the "cold zone", or a file with a popularity score less than 10 (low popularity) in the "hot zone", it triggers a potential migration request.

[0045] The core of the algorithm is optimizing the scheduling process. Simply migrating all mismatched files could result in a huge and unnecessary workload. Therefore, the engine employs a more intelligent algorithm, such as simulated annealing or a genetic algorithm. The objective function of the algorithm is designed as: Minimize(W1 * F_access_cost + W2 * F_move_cost). Here, F_access_cost is the total future access cost calculated based on the predicted popularity score and file location (i.e., the cost of all predicted borrowing times multiplied by their respective access paths), and F_move_cost is the total transportation cost required to execute the current scheduling plan. W1 and W2 are weighting factors that can be adjusted by the administrator to determine whether to prioritize future access efficiency or reduce current processing workload. The algorithm iteratively calculates to find an optimal file migration plan (i.e., a set containing a series of "from A to B" instructions) that minimizes the above objective function value, at which point the migration task instruction set is generated.

[0046] Furthermore, the engine possesses load balancing and opportunity window scheduling capabilities. It avoids executing large-scale processing tasks during peak daytime work periods to prevent disruption to normal inbound and outbound operations. Optimized scheduling tasks are cached and prioritized for execution during "opportunity windows" at night or during off-peak hours. Simultaneously, it can perform "task chain optimization." For example, when file A needs to be moved from cold zone S1 to hot zone S2, if S2 happens to contain file B that needs to be moved to the cold zone, the engine will merge the unidirectional migration tasks with a position swap relationship into a single swap task, generating a composite task of "A and B swapping positions," rather than two independent unidirectional movements, thus significantly improving scheduling efficiency.

[0047] Step S207: Send the migration task instruction set to the execution terminal so that the execution terminal can optimize the archive repository and virtual repository according to the migration task instruction set.

[0048] In one example, S207 includes: if the execution terminal is a robot scheduling system, the migration task instruction set is sent to the robot scheduling system through a preset warehouse control system interface, so that the robot scheduling system controls the automated robot to execute the migration task instruction set and complete the optimization of the archive warehouse and virtual warehouse; if the execution terminal is a human, the migration task instruction set and navigation information are pushed to the administrator's terminal, so that the administrator executes the migration task instruction set and completes the optimization of the archive warehouse and virtual warehouse.

[0049] For example, the generated migration task instruction set can be pushed to the warehouse administrator's work terminal (such as a PDA) in the form of a task list, or sent directly to the robot scheduling system in the warehouse. The robot scheduling system can control automated robots (such as AGVs or robotic arms) to execute the migration task instruction set. The execution terminal (human or automated robot) completes the physical location migration of the files according to the migration task instruction set.

[0050] Optionally, this step serves as a bridge between virtual decision-making and the physical world, characterized by dual-mode compatibility in task assignment. For highly automated archive warehouses, electronic devices directly send migration task instruction sets to the robot scheduling system via a standard WCS (Warehouse Control System) interface. The instruction format is clear and unambiguous, for example: {TaskID: T001,RobotID: AGV03, Action: MOVE, Source: {Shelf: A01, Level: 03, Slot: 05},Target: {Shelf: C10, Level: 02, Slot: 01}, ArchiveID: ARC9527}. The robot scheduling system receives the migration task instruction set and sends it to automated robots in idle or busy states. After receiving the instructions, the automated robots autonomously plan their paths, determine retrieval and placement times, and perform the retrieval and placement operations.

[0051] Optionally, for archives primarily managed manually, tasks can be pushed to the administrator's handheld PDA or tablet in a visual manner. The terminal interface will display an optimized task list and provide clear navigation instructions for each task, such as "Please go to shelf A01, floor 3, slot 5, retrieve the 'XX Project Contract File', and move the retrieved file to shelf C10, floor 2, slot 1".

[0052] Therefore, this embodiment does not rely on specific hardware devices and can guide manual operation as well as drive robot execution, exhibiting good compatibility and universality of implementation. The modular design of each step allows core components such as the prediction model and optimization algorithm to be continuously iterated and upgraded according to business development, ensuring the long-term advanced nature of the system and guaranteeing high flexibility and scalability in accessing files.

[0053] Step S208: Receive execution feedback returned by the execution terminal; wherein, the execution feedback includes the first RFID scan information of the file after the position is adjusted, and the second RFID scan information of the file at the new position after the position is adjusted.

[0054] For example, during or after the migration, execution feedback is sent to an electronic device by scanning the identification of the file and the new storage location (such as RFID or QR code). Upon receiving the execution feedback, the electronic device updates the file location information in the "digital twin" model, completing a closed loop of dynamic scheduling. The execution feedback includes the first RFID scan information of the file after its relocation and the second RFID scan information of the file at its new location.

[0055] Step S209: If it is determined that the first RFID scanning information and the second RFID scanning information match, then it is determined that the execution terminal has optimized the archive warehouse and the virtual warehouse based on the execution feedback.

[0056] For example, closed-loop feedback and exception handling ensure the system's robustness. Whether performed by a human or an automated robot, a confirmation scan is required as the final step in completing the operation. The operator scans the RFID / QR code of the file and then the RFID / QR code of the new storage location. The electronic system performs a matching verification in the background; only when the "correct file" is placed in the "correct new location" is the task marked as "complete," and the file location information in the database is atomically updated. If the scan information does not match, or the robot reports an execution failure (e.g., the storage location is occupied, or the grab failed), the electronic system immediately records the exception and sends an alert to the administrator. For failed tasks, the electronic system can either return the failed task to the scheduling pool or attempt to allocate an alternative available storage location, thus forming a complete closed loop of problem detection, problem resolution, and result confirmation. This rigorous closed-loop mechanism eliminates the problem of data inconsistency with the actual items caused by execution errors.

[0057] The method provided in this application embodiment acquires multi-source archive data from an archive storage facility. Access prediction is performed on the multi-source archive data to generate an access popularity score for each archive within a preset time period. Archive information for each storage location in the archive storage facility is detected. Based on the overall retrieval cost, the archive storage facility is partitioned to obtain multiple archive areas with preset popularity levels; wherein, the overall retrieval cost includes any one or more of the following: the physical distance from the archive storage facility's entrance / exit to the storage location, shelf height, and aisle width. Based on the multiple preset popularity levels of the archive areas and the archive information, a virtual storage facility corresponding to the archive storage facility is generated. If the access popularity score of any archive detected in the virtual storage facility does not match the preset popularity level of the archive area where that archive is located, a migration task instruction set is generated; the migration task instruction set is sent to the execution terminal so that the execution terminal optimizes the archive storage facility and the virtual storage facility according to the migration task instruction set. Execution feedback returned by the execution terminal is received; wherein, the execution feedback includes the first RFID scan information of the archive after the adjustment of its position and the second RFID scan information of the archive at its new position after the adjustment of its position. If the first RFID scan information and the second RFID scan information are determined to match, it is determined that the execution terminal has optimized the archive storage room and the virtual storage room based on the execution feedback. This solution establishes a closed-loop management mechanism that can proactively predict future access trends of archives and dynamically and intelligently adjust the physical location of archives within the storage room based on the predicted data. This mechanism can synergistically optimize the seemingly contradictory goals of storage room space utilization and archive retrieval efficiency, transforming passive "address-based file retrieval" into proactive "on-demand file preparation." Through data-driven "load balancing," it maximizes the overall operational efficiency of the storage room, reduces the ineffective operating costs of manpower and automated equipment, significantly improves retrieval efficiency, and achieves intelligent and forward-looking storage room management. It realizes intelligent archive storage room management based on dynamic load balancing, solving two core pain points in existing archive storage room management methods: low access efficiency and passive management strategies, thereby improving the storage efficiency, retrieval efficiency, and management efficiency of the archive storage room. Significantly improving retrieval efficiency means that by placing frequently accessed files in "hot zones," the average addressing and delivery paths for archivists or robots are greatly shortened, reducing the average retrieval time by 30%-60%. For urgent retrieval requests, the response speed has seen a qualitative leap, improving service satisfaction. Achieving intelligent and forward-looking warehouse management means transforming a passive and lagging management model into a proactive and predictive one, capable of "foresight," optimizing the file layout before access needs occur, and evolving the warehouse from a static "warehouse" into a dynamic, self-optimizing "organism."Reducing operating costs means that the improvement in retrieval efficiency directly translates into savings in labor costs. For automated warehouses, the total mileage and operating time of robots are reduced, which reduces energy consumption and equipment wear and tear, and extends the service life of equipment. Dynamic sorting can also effectively integrate fragmented idle storage locations, indirectly improving space utilization.

[0058] Corresponding to the above method, embodiments of this application also provide an intelligent archive storage management device, such as... Figure 3 As shown, the device includes: Prediction module 41 is used to acquire multi-source archive data from the archive repository; perform access prediction on the multi-source archive data, and generate an access popularity score for each archive within a preset time period; The generation module 42 is used to detect the archive information of each storage location in the archive warehouse; and generate a virtual warehouse corresponding to the archive warehouse based on multiple preset heat levels of archive areas and the archive information. The optimization module 43 is used to generate a migration task instruction set if the access popularity score of any file detected in the virtual warehouse does not match the preset popularity level of the file area where the file is located; and to send the migration task instruction set to the execution terminal so that the execution terminal optimizes the file warehouse and the virtual warehouse according to the migration task instruction set.

[0059] The functions of each functional unit of the intelligent archive storage management device provided in the above embodiments of this application can be implemented through the above methods and steps. Therefore, the specific working process and beneficial effects of each unit in the intelligent archive storage management device provided in the embodiments of this application will not be repeated here.

[0060] This application also provides an electronic device, such as... Figure 4 As shown, it includes a processor 510, a communication interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540.

[0061] Memory 530 is used to store computer programs; The processor 510 performs the above steps when executing the program stored in the memory 530.

[0062] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0063] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0064] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0065] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0066] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 1 The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0067] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform any of the intelligent archive management methods described in the above embodiments.

[0068] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the intelligent archive management methods described in the above embodiments.

[0069] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0070] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0071] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0072] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0073] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0074] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims in this application and their equivalents, then this application also intends to include these modifications and variations.

Claims

1. A method for managing an intelligent archive storage facility, characterized in that, The method includes: Acquire multi-source archive data from the archive repository; predict access to the multi-source archive data and generate an access popularity score for each archive within a preset time period; The system detects the archive information of each storage location in the archive warehouse; based on multiple preset popularity levels of archive areas and the archive information, it generates a virtual warehouse corresponding to the archive warehouse. If the access popularity score of any file detected in the virtual repository does not match the preset popularity level of the file area where the file is located, a migration task instruction set is generated; the migration task instruction set is sent to the execution terminal so that the execution terminal optimizes the file repository and the virtual repository according to the migration task instruction set.

2. The method as described in claim 1, characterized in that, Access prediction is performed on the multi-source archive data to generate an access popularity score for each archive within a preset time period, including: Construct feature variables for prediction; perform feature extraction on the multi-source archival data to obtain multiple feature variables; The prediction model is used to predict the access popularity of the feature variables and outputs the access popularity score of each file within a preset time period.

3. The method as described in claim 1, characterized in that, The archive information includes the occupancy status of the storage space and the actual information of the archives currently stored therein; The archive areas with preset temperature levels include hot zones, warm zones, and cold zones; The virtual warehouse includes a mapping table between storage locations and file areas, as well as a real-time warehouse status diagram; the real-time warehouse status diagram includes the location of each file and the occupancy status of each storage location.

4. The method as described in claim 1, characterized in that, If the access popularity score of any file detected in the virtual repository does not match the preset popularity level of the file area where the file is located, a migration task instruction set is generated, including: If the access popularity score of any file is detected in the virtual warehouse and does not match the preset popularity level of the file area where the file is located, the total retrieval cost and the total handling cost are determined according to the preset optimization scheduling model, the access popularity score and the file information. Based on the total retrieval cost and total transportation cost, a migration task instruction set is generated.

5. The method as described in claim 4, characterized in that, Based on the total retrieval cost and total transportation cost, a migration task instruction set is generated, including: If there are multiple unidirectional migration tasks with position swapping relationships, then based on the total retrieval cost and total transportation cost, the unidirectional migration tasks with position swapping relationships are merged into one swapping task, and a migration task instruction set is generated.

6. The method as described in claim 1, characterized in that, Sending the migration task instruction set to the execution terminal, so that the execution terminal optimizes the archive repository and the virtual repository according to the migration task instruction set, including: If the execution terminal is a robot scheduling system, the migration task instruction set is sent to the robot scheduling system through a preset warehouse control system interface, so that the robot scheduling system controls the automated robot to execute the migration task instruction set and complete the optimization of the archive warehouse and the virtual warehouse; If the execution terminal is manually operated, the migration task instruction set and navigation information are pushed to the administrator's terminal so that the administrator can execute the migration task instruction set and complete the optimization of the archive warehouse and the virtual warehouse.

7. The method according to any one of claims 1-6, characterized in that, The method further includes: Based on the overall access cost, the archive storage room is divided into multiple archive areas with preset popularity levels; wherein, the overall access cost includes any one or more of the following: The physical distance from the entrance / exit of the archive storage room to the storage location, shelf height, and aisle width.

8. The method according to any one of claims 1-6, characterized in that, The method further includes: The execution feedback returned by the execution terminal is received; wherein the execution feedback includes the first radio frequency identification (RFID) scan information of the file after the position is adjusted, and the second RFID scan information of the file at the new position after the position is adjusted. If it is determined that the first RFID scan information and the second RFID scan information match, then it is determined that the execution terminal has optimized the archive warehouse and the virtual warehouse based on the execution feedback.

9. An intelligent archive storage management device, characterized in that, The device includes: The prediction module is used to acquire multi-source archive data from the archive repository; perform access prediction on the multi-source archive data, and generate an access popularity score for each archive within a preset time period; The generation module is used to detect the archive information of each storage location in the archive warehouse; based on multiple preset heat levels of archive areas and the archive information, it generates a virtual warehouse corresponding to the archive warehouse; The optimization module is used to generate a migration task instruction set if the access popularity score of any file detected in the virtual warehouse does not match the preset popularity level of the file area where the file is located; and to send the migration task instruction set to the execution terminal so that the execution terminal optimizes the file warehouse and the virtual warehouse according to the migration task instruction set.

10. An electronic device, characterized in that, The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of any one of claims 1-8.

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