File storage control method and system for file compact shelf

Abstract

The invention relates to the technical field of archive storage management, and discloses an archive compact shelf archive storage control method and system. According to the method, real-time storage parameters of each file in the file compact shelf are collected through a distributed sensor network, wherein the real-time storage parameters comprise space coordinates, physical attributes and environment contact states. Based on the real-time storage parameters, calculating the space occupancy rate of a specified partition of the compact archive shelf, and judging whether the space occupancy rate exceeds a dynamic adjustment threshold value or not. And when the space occupancy rate exceeds a dynamic adjustment threshold value, starting a multi-dimensional safety evaluation process, including file stacking stability analysis and frame body structure stress analysis. And according to an output result of the multi-dimensional security assessment process, generating a storage optimization decision signal for triggering file re-storage operation. And when the storage optimization decision signal is triggered, calculating a storage conflict index of each file, and automatically distributing an adjustment sequence of the files according to the storage conflict index.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of archive storage management, in particular to an archive dense shelf archive storage control method and system. BACKGROUND

[0002] Current archive dense shelf storage management mainly adopts manual patrol and regular arrangement methods, relying on the experience of management personnel to judge the archive storage state. The space utilization rate evaluation of the archive dense shelf is usually based on static capacity calculation, which cannot reflect the dynamic changes in the actual storage process. Existing archive arrangement work often makes passive adjustments after problems are found, lacking a real-time data-based early warning and active optimization mechanism. The solution of archive storage conflicts mainly relies on manual experience, lacking a systematic conflict detection and automatic sorting method. Archive management needs a comprehensive control method that can monitor the storage state in real time, automatically evaluate safety risks, and intelligently optimize storage plans.

[0003] Traditional archive dense shelf management has monitoring blind spots and cannot real-time perceive the changes in the storage state of the archives. The calculation of space occupancy rate is often based on simple quantity statistics, which cannot accurately reflect the actual space utilization efficiency. The safety evaluation lacks quantitative indicators, making it difficult to accurately analyze the stability of the archive stacking and the stress of the shelf structure. The archive adjustment process lacks scientific basis, and the determination of the adjustment order often relies on the personal experience of management personnel. Existing technologies need to solve the whole-process optimization problem from real-time monitoring to intelligent decision-making, especially the establishment of a dynamic evaluation model and an automatic optimization mechanism based on multi-source sensor data. The core challenge faced by archive dense shelf management is how to realize the transition from passive response to active prevention, and how to upgrade the experience-based extensive management to data-based fine-grained intelligent control. The existing methods have obvious deficiencies in data processing real-time, evaluation dimension comprehensiveness and decision automation degree, and an integrated technical solution is needed to connect the state perception, risk assessment and decision execution. SUMMARY

[0004] The purpose of the present application is to provide an archive dense shelf archive storage control method and system to solve the problems raised in the background.

[0005] To achieve the above purpose, the present application provides an archive dense shelf archive storage control method, which comprises: Collecting real-time storage parameters of each archive in the archive dense shelf through a distributed sensor network, the real-time storage parameters including the spatial coordinates, physical properties and environmental contact state of the archives; Calculating the space occupancy rate of a specified partition of the archive dense shelf based on the real-time storage parameters, and determining whether the space occupancy rate exceeds a dynamic adjustment threshold; When the space occupancy rate exceeds the dynamic adjustment threshold, a multi-dimensional safety evaluation process is started, which includes archive stack stability analysis and shelf structure stress analysis; According to the output results of the multi-dimensional safety evaluation process, a storage optimization decision signal is generated, which is used to trigger the archive relocation operation; When the storage optimization decision signal is triggered, the storage conflict index of each archive is calculated, and the adjustment order of the archive is automatically assigned according to the storage conflict index.

[0006] Preferably, the real-time storage parameters of each archive in the archive dense shelf are collected by a distributed sensor network, specifically: The radio frequency identification sensor deployed on the archive dense shelf continuously scans the archive label to obtain the identity and location coordinates of the archive; The pressure sensor array measures the weight distribution data of the archive and records the contact pressure value between the archive and the adjacent archive; The environmental monitoring sensor collects the temperature and humidity fluctuation data and vibration frequency data around the archive; The identity, location coordinates, weight distribution data, contact pressure value, temperature and humidity fluctuation data, and vibration frequency data are integrated into a real-time storage parameter data set.

[0007] Preferably, the space occupancy rate of a specified partition of the archive dense shelf is calculated based on the real-time storage parameters, specifically: The location coordinates and physical dimensions of all archives in the specified partition are extracted from the real-time storage parameter data set; The space volume model of the specified partition is reconstructed using three-dimensional modeling technology; The occupied volume of each archive in the space volume model is calculated according to the physical dimensions and location coordinates of the archive; The occupied volumes of all archives are accumulated and compared with the total volume of the space volume model to obtain the space occupancy percentage.

[0008] Preferably, the archive stack stability analysis in the multi-dimensional safety evaluation process is started, specifically: The weight distribution data and contact pressure value of the archive are obtained from the real-time storage parameter data set; A mechanical simulation model of the archive stack is constructed to simulate the center of gravity offset and pressure transmission path of the archive under different stacking configurations; The overturning risk and sliding risk of the archive stack are evaluated by iterative calculation; The archive stack stability score is output, and the lower the score, the higher the stability risk.

[0009] Preferably, the shelf structure stress analysis in the multi-dimensional safety evaluation process is started, specifically: The strain sensor data on the storage rack structure is collected to obtain real-time deformation information of the rack under the file load. A stress distribution model of the rack structure is established using a finite element analysis method to calculate the stress concentration coefficient of the key connection point. The trend of the stress distribution model under different temperature conditions is analyzed to predict the fatigue life of the rack structure. The rack structure safety index is generated based on the stress concentration coefficient and the fatigue life prediction results.

[0010] According to the output results of the multi-dimensional safety evaluation process, an optimal storage decision signal is generated, specifically: Set the file stack stability score threshold and the rack structure safety index threshold. When the file stack stability score is lower than the threshold or the rack structure safety index is lower than the threshold, a high-priority storage optimization decision signal is generated. When the file stack stability score and the rack structure safety index are both higher than the threshold but the space occupancy rate exceeds the dynamic adjustment threshold, a medium-priority storage optimization decision signal is generated. Otherwise, a no-operation signal is generated.

[0011] Preferably, the storage conflict index of each file is calculated, specifically: Extract the position coordinates and weight distribution data of each file from the real-time storage parameter data set. Calculate the Euclidean distance and weight difference ratio of each file and other files in the specified partition. For each pair of files, if the Euclidean distance is less than the safety distance threshold, mark it as a space conflict pair. For each space conflict pair, calculate the conflict intensity value based on the weight difference ratio and the contact pressure value. Cumulative conflict intensity values of all space conflict pairs for each file are obtained to obtain the storage conflict index.

[0012] Preferably, the adjustment order of the files is automatically assigned according to the storage conflict index, specifically: Get the storage conflict index and historical adjustment frequency data of each file. Weighted fusion of the storage conflict index and the historical adjustment frequency data generates a dynamic priority score. Arrange the files in descending order according to the dynamic priority score, and the files with high scores are adjusted first. Real-time update of historical adjustment frequency data to reflect the recent adjustment time of the file.

[0013] Preferably, the historical adjustment frequency data is updated through a time decay model, specifically: Record the latest adjustment timestamp and adjustment times of each file. The frequency weight is calculated by using an exponential decay function, and the closer the recent adjustment time is, the higher the weight is. The number of adjustments is multiplied by the decayed weight to obtain the normalized historical adjustment frequency data.

[0014] Preferably, the application further comprises an archival dense shelf file storage control system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor, when executing the computer program, implements the steps of the above-mentioned archival dense shelf file storage control method.

[0015] Compared with the prior art, the application has the following beneficial effects: The spatial coordinates, physical properties, and environmental contact states of each file are collected in real time by a distributed sensor network, achieving comprehensive perception of the file storage state. The spatial occupancy rate is calculated based on real-time storage parameters, which can accurately reflect the actual use of the archival dense shelf. A dynamic adjustment threshold judgment mechanism is adopted, enabling the system to automatically trigger the evaluation process according to real-time data. A multi-dimensional safety evaluation process is started, including file stacking stability analysis and shelf structure stress analysis, to comprehensively evaluate the storage safety status from different dimensions. The file stacking stability analysis considers the mutual influence between files to evaluate the stability risk of the stacking structure. The shelf structure stress analysis evaluates the bearing state of the dense shelf to prevent structural safety hazards.

[0016] A storage optimization decision signal is generated according to the safety evaluation result, achieving intelligent decision-making based on quantitative data. The storage conflict index of each file is calculated to accurately identify the conflict problems existing in the storage process. The storage conflict index considers multiple factors such as spatial overlap and physical property conflict, comprehensively reflecting the rationality of file storage. The file adjustment order is automatically assigned based on the storage conflict index, achieving optimized scheduling of the adjustment process. Files with high conflict indexes are adjusted first to improve adjustment efficiency. Files with low conflict indexes are processed later to reduce unnecessary moving operations. This intelligent sorting method based on conflict indexes can significantly improve the efficiency of file arrangement. Through real-time monitoring, intelligent evaluation, and automatic optimization of the closed-loop control, the file storage management is refined and intelligentized. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The working principle diagram of the archival dense shelf file storage control method described in the application; Figure 2 The flowchart of collecting real-time storage parameters by a distributed sensor network; Figure 3 Figure 4 The timing diagram of multi-dimensional safety evaluation of the archival dense shelf; Figure 5 ​Trend chart of file dense shelf space occupancy rate and conflict index. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor are within the scope of protection of the present application.

[0019] Please refer to Figure 1 The present application provides a file dense shelf file storage control method and system. The method comprises: collecting real-time storage parameters of each file in the file dense shelf through a distributed sensor network, wherein the real-time storage parameters include the spatial coordinates, physical properties and environmental contact state of the file; calculating the space occupancy rate of a specified partition of the file dense shelf based on the real-time storage parameters, and determining whether the space occupancy rate exceeds a dynamic adjustment threshold; when the space occupancy rate exceeds the dynamic adjustment threshold, starting a multi-dimensional safety evaluation process, wherein the multi-dimensional safety evaluation process comprises file stacking stability analysis and shelf structure stress analysis; generating a storage optimization decision signal according to the output result of the multi-dimensional safety evaluation process, wherein the storage optimization decision signal is used to trigger a file re-storage operation; when the storage optimization decision signal is triggered, calculating the storage conflict index of each file, and automatically assigning the adjustment order of the file according to the storage conflict index. The implementation of the method relies on various sensors deployed on the dense shelf and a background processing system. The system realizes dynamic management and optimization of file storage state through continuous monitoring and intelligent analysis.

[0020] Embodiment 1: Please refer to Figure 2In specific implementation, the radio frequency identification sensor deployed on the file dense shelf works in a periodic scanning mode, the reading antenna of the radio frequency identification sensor is arranged in layers according to the structure of the dense shelf, a directional antenna array is installed above and below each layer of the partition, and the working frequency of the radio frequency identification sensor adopts the ultra-high frequency band to ensure sufficient reading range and anti-interference capability. A passive radio frequency identification tag is attached to the surface of each file box or dossier, the tag internally stores a globally unique identifier as the identity of the file, when the file is placed in the dense shelf, the radio frequency identification sensor obtains the identity of the file and the accurate position coordinates of the file by scanning the radio frequency identification tag, the determination of the position coordinates relies on the physical position signal strength indication value of the reading antenna and the phase difference positioning algorithm, so as to locate the file to a specific partition, column, layer and order position. In specific implementation, the pressure sensor array is integrated in the form of a flexible film on the surface of the file storage partition, the pressure sensor array is composed of a plurality of pressure sensitive units arranged in a matrix, each pressure sensitive unit can independently measure the pressure value borne on its surface, when the file is placed on the partition, the weight of the file is distributed on one or more pressure sensitive units, the pressure sensor array calculates the weight distribution data of the file and the contact pressure distribution diagram between the bottom surface of the file and the partition by measuring these pressure values and combining the known distribution coordinates of the pressure sensitive units. At the same time, the pressure sensor array can also monitor the contact between the file and the side wall of the adjacent file, when the files are closely arranged, the lateral contact pressure will be detected by the adjacent pressure sensor units and recorded as the contact pressure value between the file and the adjacent file. In specific implementation, the environmental monitoring sensor includes a high-precision temperature and humidity sensor and a three-axis vibration sensor, the temperature and humidity sensor is installed at a plurality of key points in the dense shelf in a uniform distribution manner to collect the temperature and humidity fluctuation data of the air around the file, the temperature and humidity sensor has a data recording function and can continuously record and output the time sequence of the temperature value and the relative humidity value. The vibration sensor is fixedly installed on the key structural nodes such as the load-bearing column and the track connection of the dense shelf, the vibration sensor adopts the micro-electro-mechanical system technology and can sensitively capture the slight vibration of the dense shelf caused by external vibration, personnel operation or file movement and output vibration frequency data and vibration amplitude data. In specific implementation, the radio frequency identification sensor, the pressure sensor array and the environmental monitoring sensor are connected to a data concentrator through a field bus network, the data concentrator is responsible for preliminary checking, filtering and time stamp marking of the original sensor data, and then sends the integrated data to the central processing unit.The central processing unit runs a data fusion algorithm to align and correlate the identification, location coordinates, weight distribution data, contact pressure values, temperature and humidity fluctuation data, and vibration frequency data from different sensors according to a unified time base and spatial reference system. Finally, a structured real-time storage parameter dataset is generated. The real-time storage parameter dataset is stored in the system's non-volatile memory in the form of a database table. Each record corresponds to the complete status information of a file at a specific moment.

[0021] In practice, the calculation of space occupancy is performed by the space analysis module of the central processing unit. The module first queries the latest records of all archives within the specified partition from the real-time stored parameter dataset, extracting the location coordinate field from each record. Simultaneously, based on the archive's identification, it queries the corresponding archive's physical dimensions from a pre-set archive information database. These physical dimensions include the standard length, width, and height of the archive box or file. In the implementation, a 3D modeling technology is used to reconstruct the spatial volume model of the specified partition. The 3D modeling process is based on computer-aided design drawings of the archive mobile shelving, which contain the precise geometric dimensions and internal structural divisions of the specified partition. The space analysis module converts the drawing information into a 3D volumetric mesh model. This 3D volumetric mesh model accurately represents the boundaries of the specified partition and the space occupied by fixed structures such as beams and columns. The total volume of the model is the net space volume available for storing archives. In practice, the volume occupied by each file in the spatial volume model is calculated based on its physical dimensions and location coordinates. For most standard file boxes with regular shapes, the volume is directly calculated by multiplying the length, width, and height from the physical dimensions. The calculated volume is a cuboid with its axis aligned with the coordinate axis of the mobile shelving unit. For a few irregularly shaped special files, the system uses the dimensions of its smallest circumscribed cuboid to approximate the volume, ensuring the conservatism and security of the calculation results. The volume model of each file is placed in the corresponding position of the 3D volumetric mesh model according to its location coordinates for visualization and quantitative analysis of space occupancy. In practice, the accumulated volume of all files is obtained by traversing all file records within a specified partition, arithmetically summing their individual volume calculations, and obtaining the total file volume. The spatial analysis module then divides the total file volume by the total volume of the spatial volume model, multiplies the result by 100%, and finally obtains the space occupancy percentage of the specified partition. The space occupancy percentage is a dynamically changing value that reflects the real-time utilization tension of space resources within the specified partition and is one of the core decision-making bases for determining whether file storage optimization adjustments are needed. In some embodiments, the spatial analysis module can simultaneously calculate the space occupancy rate of multiple partitions and perform horizontal comparisons, providing data support for a global archive scheduling strategy. It is understood that the accuracy of the space occupancy rate calculation directly depends on the accuracy of sensor positioning and the completeness of the archive physical size data.

[0022] In practical implementation, the continuous scanning process of RFID sensors involves configuring the scanning cycle and data update frequency. The scanning cycle can be set according to management needs; a shorter scanning cycle can be set during periods of frequent file access to improve data real-time performance, while a longer scanning cycle can be set during quiet periods to reduce system power consumption. RFID sensors may encounter multi-tag collisions during scanning. In practical implementation, a time-slot-based ALOHA anti-collision algorithm is used to coordinate the responses of multiple RFID tags, ensuring that each tag is correctly identified and that identification is not confused. The calculation of location coordinates requires combining the received signal strength indications from multiple reading antennas. The two-dimensional or three-dimensional coordinates of the file are calculated using triangulation or fingerprint positioning methods. The origin of the coordinate system is usually defined at a fixed corner of the mobile shelving unit. In practical implementation, the pressure-sensitive cell density of the pressure sensor array directly affects the measurement accuracy of weight distribution data and contact pressure values. Higher cell density can generate a more detailed pressure distribution cloud map, thus more accurately determining the placement posture and center of gravity of the file, but it also increases the data volume and processing burden. The pressure sensor array requires periodic calibration to eliminate the impact of temperature drift and zero-point drift caused by long-term use on measurement accuracy. Optionally, weight distribution data can be further used to estimate the center of gravity of the archives, a crucial input parameter for subsequent archive stacking stability analysis. In practice, temperature and humidity fluctuation data collected by environmental monitoring sensors are not only used for environmental monitoring, but their trends can also affect the readings of pressure and strain sensors; therefore, temperature compensation is sometimes necessary during data fusion. Vibration frequency data is primarily used to determine whether the mobile shelving unit has been subjected to abnormal external force impact; continuous abnormal vibration may be an early sign of structural deformation or loosening. It is understood that all sensor data transmitted via the bus network employs a communication protocol with error checking to ensure the integrity and reliability of the real-time parameter dataset. Optionally, the central processing unit can set data quality check rules to mark or interpolate sensor data with abnormal signals or missing data, preventing low-quality data from affecting the accuracy of subsequent analyses.

[0023] Example 2: See Figure 3In practice, the input data for the stability analysis of the stacked archives comes from the real-time storage parameter dataset. The analysis process is executed by the stability assessment module running within the central processing unit. The stability assessment module first filters the current records of all archives within a specified partition from the real-time storage parameter dataset and extracts the weight distribution data and contact pressure values ​​from each record. The weight distribution data is a multi-dimensional vector describing the spatial distribution of the archive mass. It is typically obtained by processing a pressure distribution map measured by a pressure sensor array using a centroid calculation algorithm. The contact pressure value is a scalar or a set of scalars, recording the pressure values ​​between the archive and the sides of adjacent archives, and between the archive and the shelf partitions. In practice, constructing a mechanical simulation model of the stacked archives is the core step in the stability analysis. The mechanical simulation model abstracts the entire stacked archive system into a multibody dynamics system. Each archive in the system is modeled as a rigid body with mass, volume, center of mass position, and moment of inertia. The external dimensions of the rigid body are defined by the physical dimensions of the archive. The contact interfaces between archives and between archives and the partitions of the mobile shelving unit are assigned coefficients of friction and elastic recovery. These coefficients are set based on the physical properties of the archive packaging materials; for example, kraft paper archive boxes, plastic archive boxes, or cloth archive boxes correspond to different contact mechanics parameters. The boundary conditions of the mechanical simulation model include the fixed constraints of the mobile shelving unit partitions and possible side baffle constraints. In practice, the simulation of the center of gravity shift and pressure transmission path of archives under different stacking configurations is completed through numerical iteration. The initial state of the simulation is the current actual stacking state detected by the sensors. The mechanical simulation model calculates the force balance and moment balance of each archive in the entire stacking system under the action of gravity. The center of gravity shift refers to the positional deviation of the overall center of mass of the stack relative to its theoretical geometric center. The pressure transmission path describes how the weight of the archives is transmitted to the lower archives and partitions through the contact points. The mechanical simulation model displays the distribution of the pressure chain in a visual manner. In practical implementation, the overturning and sliding risks of the stacked archives are assessed through iterative calculations. The overturning risk is assessed by checking whether the projection of the overall center of gravity of the stack onto the horizontal plane falls within the stable support polygon formed by the bottom layer of archives. If the projection point is close to or exceeds the polygon boundary, the overturning risk is high. The sliding risk is assessed based on Coulomb's law of friction. For each contact interface, the mechanical simulation model calculates the ratio of its tangential force to its normal force and compares this ratio with the static friction coefficient of the interface. If the ratio is close to or exceeds the static friction coefficient, a sliding risk is determined to exist at that point. Iterative calculations consider various potential disturbance factors, such as simulating the application of a standard value of horizontal acceleration (simulating minor collisions or seismic disturbances), and observing whether the stacked system can still remain stable. In some embodiments, the mechanical simulation model can also perform parameter sensitivity analysis, observing the robustness of the stability conclusions by fine-tuning the friction coefficient or archive position.

[0024] In practice, outputting the file stack stability score is a process of quantifying physical risk into a score. The stability score is a value between 0 and 100, with a lower score indicating a higher stability risk. The scoring algorithm first calculates the overturning risk coefficient and the sliding risk coefficient separately. The overturning risk coefficient is inversely proportional to the shortest distance from the center of gravity projection point to the boundary of the supporting polygon; the closer the distance, the higher the coefficient and the greater the risk. The sliding risk coefficient is directly proportional to the ratio of the maximum tangential force to the maximum static friction force on the contact interface; the higher the ratio, the higher the coefficient and the greater the risk. Then, the overturning risk coefficient and the sliding risk coefficient are weighted and combined according to their importance to obtain a comprehensive risk value. Finally, a preset mapping function converts the comprehensive risk value into a stability score of 0-100. The mapping function is usually designed as a non-linear function, so that in high-risk areas, small changes in risk will cause large changes in the score, thus serving as an early warning. The file stack stability score, along with a detailed simulation analysis report (such as the center of gravity position, risk coefficient, and key risk point identification), is output as a key basis for subsequent generation and storage of optimization decision signals. It is understandable that the calculation frequency of the file stack stability score can be synchronized with the space occupancy assessment cycle, or a one-time emergency assessment can be triggered when a significant change in the contact pressure value is detected. Optionally, the mechanical simulation model can learn the parameter patterns under historical stable states, thereby assisting in the judgment of the degree of anomaly in the current state.

[0025] In practical implementation, the construction of the mechanical simulation model relies on accurate physical parameters, some derived from real-time sensor measurements and others from a pre-built database of archival material properties. Weight distribution data must accurately reflect the true mass distribution of the archive. For archives with non-uniform internal materials, such as those bound with metal on one side, the center of gravity may significantly deviate from the geometric center; the pressure sensor array needs sufficient resolution to detect this asymmetry. The accuracy of contact pressure values ​​directly affects the results of the slip risk assessment; pressure sensors need to be calibrated regularly to ensure reliable measurements. In practice, the numerical solution of the mechanical simulation model typically employs explicit or implicit time integration algorithms. For large-scale stacked systems, the computational load can be substantial, thus requiring a balance between computational accuracy and real-time performance. It is understood that the mechanical simulation model is a simplified, idealized model that may not fully reproduce all complex physical phenomena in the real world, but its purpose is to provide a rapid, repeatable, and quantitative assessment of stability risks. In some embodiments, to verify the effectiveness of the mechanical simulation model, the model's predictions can be compared with a small number of physical experimental observations, and model parameters (such as the coefficient of friction) can be fine-tuned accordingly. Optionally, for archives with unique shapes or extremely high value, more refined finite element models can be created for local analysis, but this falls outside the scope of regular batch processing. The archive stacking stability analysis process is fully automated, requiring no manual intervention, and the analysis results are fed back to the system decision-making level in real time.

[0026] In practice, the file stacking stability analysis process and the frame structure stress analysis process are executed in parallel, together forming a multi-dimensional safety assessment process. The stability assessment module and the stress analysis module exchange and synchronize data through the system's internal message bus. After the file stacking stability score is calculated, the score value is compared with the preset file stacking stability score threshold, and the comparison result is directly input into the decision signal generation logic. File stacking stability analysis not only focuses on static stability but also on quasi-static processes. For example, when automated equipment accesses adjacent files, it may disturb the current stacking system. The mechanical simulation model can simulate the impact of such slow loading or unloading processes on stability. The iterative calculation process may involve multiple adjustments to the small positional deviations of the files to simulate the placement inaccuracies that may exist in the actual environment, thereby assessing the stacking system's tolerance to errors. It can be understood that file stacking stability analysis is an important technical means to ensure the safety of the file entity. Its core lies in prevention, anticipating potential risks through calculation and simulation, and taking measures in advance. Optionally, the system can record historical data for each stability analysis to form a stability change trend chart, providing information support for long-term management and maintenance. The implementation of archive stacking stability analysis effectively elevates traditional experience-based archive management to an intelligent management level based on data-driven and physical simulation.

[0027] Example 3: In specific implementation, the stress analysis process of the shelving structure is executed by the stress analysis module of the central processing unit. The stress analysis module continuously receives strain sensor data deployed on the surface of key load-bearing components of the mobile shelving unit through a dedicated data acquisition interface. The strain sensors are constructed in the form of Wheatstone bridges, with the bridge arms composed of precision resistance strain gauges. The resistance strain gauges are firmly attached to the surface of the shelving unit's columns, beams, and main load-bearing connecting plates. When the shelving structure undergoes slight deformation under the load of archives, the resistance strain gauges will generate a resistance change proportional to the strain. The Wheatstone bridge converts the resistance change into a weak voltage signal output. These voltage signals are amplified and filtered by the signal conditioning circuit, and then converted into digital strain sensor data by a high-precision analog-to-digital converter. The strain sensor data directly reflects the real-time deformation information at the measuring point location, i.e., the micro-strain value. The stress analysis module collects strain sensor data from all measuring points at a preset sampling frequency (e.g., several times per second) and stores it together with the corresponding measuring point location identifiers to form a time-series dataset of shelving structure deformation. In some embodiments, in order to eliminate the influence of temperature on strain sensor readings, a temperature compensation sensor is also installed near each measuring point. The stress analysis module uses the readings of the temperature compensation sensor to correct the original strain sensor data when calculating the actual deformation.

[0028] In practical implementation, establishing a stress distribution model of the shelving structure using the finite element method (FEM) is the core step in stress analysis. The FEM model is based on precise 3D computer-aided design drawings of the mobile shelving unit, which include the geometry, dimensions, material properties, and connection relationships of all load-bearing components. The stress analysis module discretizes the 3D geometric model into numerous small, simple-shaped elements, which are interconnected through nodes to form a finite element mesh. The weight load of the archives and their distribution information are applied to the finite element model as load conditions. The weight load is determined by weight distribution data measured by a pressure sensor array, and its application location is determined by position coordinates obtained from RFID sensors. The weight of the shelving unit itself is automatically included as a volume load. The model's constraints are set according to the actual fixing method of the mobile shelving unit to the ground or rails, such as setting fixed constraints at the supports. The stress analysis module calls the finite element solver to solve for the displacement and stress fields of the entire finite element mesh under given loads and constraints. Based on the calculated nodal stresses, the stress concentration factor at key connection points can be further calculated. The stress concentration factor is the ratio of the local maximum stress to the nominal stress, used to identify areas of abnormally high stress. In some embodiments, the finite element analysis can employ linear static analysis, assuming the material is in a linear elastic state with minimal deformation; for cases requiring consideration of material nonlinearity or large deformation, a more complex nonlinear finite element analysis can be used.

[0029] In practical implementation, analyzing the changing trends of the stress distribution model under different temperature conditions is a crucial part of assessing the long-term safety of the structure. Temperature and humidity fluctuation data, especially temperature data, collected by environmental monitoring sensors are used by the stress analysis module for thermodynamic coupling analysis. The elastic modulus and coefficient of thermal expansion of materials change with temperature, and these temperature changes induce thermal stress in the frame structure. The stress analysis module studies the thermally induced changes in the stress distribution model by altering the material parameters in the finite element model and applying a simulated temperature field. By repeatedly performing finite element analysis under a series of temperature conditions, curves showing the stress values ​​at key locations as a function of temperature can be obtained, thereby understanding the extent to which temperature affects the stress state of the frame structure. In some embodiments, this analysis helps identify weak points that may become critical under extreme temperature conditions.

[0030] In practical implementation, the fatigue life prediction of the frame structure is based on the cumulative damage theory. The loads borne by the frame structure during daily use vary over time. This variable-amplitude stress cycling leads to gradual material damage and may eventually cause fatigue failure. The stress analysis module first extracts the stress spectrum from long-term strain sensor data using algorithms such as rainflow counting, i.e., the number of cycles corresponding to different stress amplitudes and average stresses. Then, combined with the fatigue characteristic curves (SN curves) of the frame structure material, the Miner linear cumulative damage rule is used to predict the fatigue life. A commonly used simplified cumulative damage model can be expressed as: in: Represents the cumulative damage level, when At this time, fatigue failure may occur. (Symbol) This represents the total number of different stress level grades identified from the stress spectrum. (Symbol) The corresponding one is in the first The actual number of stress cycles recorded at each stress level. (Symbol) This indicates that, based on the SN curve of the material, at the th The critical number of cycles required to cause fatigue failure of the specimen at each stress level. This is determined by monitoring... The growth rate of fatigue life can be used to estimate the remaining fatigue life of the frame structure under current usage conditions. It is understood that this is a predictive model based on specific assumptions, and the actual fatigue life is affected by many other factors, but the prediction results can provide valuable reference for maintenance decisions.

[0031] In practice, generating the structural safety index by combining stress concentration factor and fatigue life prediction results is a multi-indicator fusion process. The structural safety index is a comprehensive, dimensionless value used to intuitively reflect the immediate safety status of the structural frame. Generating the structural safety index typically involves the following steps: First, the maximum equivalent stress value obtained from finite element analysis is compared with the yield strength of the structural frame material to calculate a stress utilization factor. Second, the stress concentration factor at key connection points is considered; a higher stress concentration factor has a greater negative impact on the safety index. Third, fatigue life prediction results are introduced, typically using the ratio of remaining fatigue life to design life as an influencing factor. Finally, the consistency between real-time deformation information from strain sensor data and the finite element analysis results may also be considered as a correction to the model confidence level. The stress analysis module uses a weighted average or fuzzy logic rule base to synthesize the above factors into a structural safety index between 0 and 1 or between 0 and 100; a higher index indicates better safety. It is understandable that the threshold for the structural safety index needs to be carefully set based on safety regulations and engineering experience. Optionally, the stress analysis module can periodically generate structural health assessment reports, detailing stress distribution, stress concentration factor trends, and the progression of cumulative damage, providing a basis for preventative maintenance. The frame structure stress analysis process and the archive stacking stability analysis process together constitute a multi-dimensional and in-depth monitoring and assurance system for the safety of archive storage.

[0032] Example 4: In practical implementation, setting the file stacking stability scoring threshold and the shelving structure safety index threshold are prerequisites for generating storage optimization decision signals. These thresholds are determined comprehensively based on the design specifications of the mobile shelving, the safe working stress of the materials, the rarity of the files, and historical safe operation data. These thresholds are stored as configuration parameters in the system's non-volatile memory, allowing system administrators to adjust them according to specific application scenarios and security level requirements. For example, for mobile shelving storing high-value ancient books, the file stacking stability scoring threshold and shelving structure safety index threshold can be set more strictly, while for mobile shelving storing ordinary documents, the thresholds can be appropriately relaxed. The decision signal generation module receives the file stacking stability score from the stability assessment module and the shelving structure safety index from the stress analysis module in real time and compares these two values ​​with the preset thresholds. The decision-making logic is designed as a priority judgment mechanism. When the file stack stability score or the shelf structure safety index is below its threshold, it indicates a clear and urgent safety risk. The system will immediately generate a high-priority storage optimization decision signal, which requires the system to initiate a file re-storage operation at the highest response level. When both the file stack stability score and the shelf structure safety index are above their respective thresholds, but the space occupancy rate exceeds the dynamic adjustment threshold, it indicates that although the current state is structurally safe, space utilization is approaching saturation, which may affect access efficiency or increase the probability of future risks. The system will generate a medium-priority storage optimization decision signal, which instructs the system to perform optimization adjustments if resources permit. If none of the above conditions are met, i.e., all evaluation indicators are within the safety threshold range and the space occupancy rate is normal, the decision signal generation module generates a no-operation signal, and the system maintains its current operating state. The storage optimization decision signal, as an instruction packet containing a priority identifier, is sent to the file adjustment scheduler, triggering subsequent processes.

[0033] In practice, calculating the storage conflict index for each file is a crucial step executed in response to storage optimization decision signals, and this calculation is performed by the conflict analysis module. The conflict analysis module first extracts the current location coordinates and weight distribution data of each file within a specified partition from the real-time storage parameter dataset. The location coordinates are used for spatial relationship analysis, while the weight distribution data is used to assess the differences in physical attributes between files. For any two different files within a specified partition, the conflict analysis module calculates their three-dimensional Euclidean distance, which directly reflects the spatial proximity of the files. Simultaneously, the conflict analysis module calculates the weight difference ratio between these two files. The weight difference ratio is defined as the ratio of the absolute value of the weight difference between the two files to their average weight; it is a dimensionless number used to quantify weight dissimilarity. The system presets a safe distance threshold, which is typically slightly larger than the average physical size of the files to provide a safety margin for file operations. For each pair of files, if the calculated Euclidean distance is less than the safe distance threshold, the conflict analysis module marks this pair of files as a spatial conflict pair, meaning that the two files are too close in space and may pose a risk of mutual interference. For each identified spatial conflict pair, the conflict analysis module further calculates its conflict intensity value, which is a function that combines the weight difference ratio and the contact pressure value. A larger weight difference ratio indicates a greater difference in physical inertia between the two files, potentially leading to inconsistent motion responses to external disturbances and exacerbating their interaction. The contact pressure value directly quantifies the magnitude of the existing mechanical interaction force between the files. The conflict analysis module uses a predefined conflict intensity calculation function to map the weight difference ratio and contact pressure value (using the measured contact pressure value if the two files are in lateral contact; otherwise, using a default minimum value) to a conflict intensity value representing the severity of the current conflict pair. Finally, for each file within a specified partition, the conflict analysis module iterates through all spatial conflict pairs containing that file and sums the corresponding conflict intensity values. The resulting sum is the storage conflict index for that file. A higher storage conflict index indicates poorer compatibility between the file's current storage location and its surrounding environment, making it a primary source of the poor overall condition of the current partition and thus requiring priority adjustment. Refer to Table 1 for parameters for calculating the conflict index of archive storage.

[0034] Table 1: Calculation Parameters for Archive Storage Conflict Index In practice, the conflict intensity calculation function can be a linear weighted sum function, for example: conflict intensity value = The standardized contact pressure value is obtained by normalizing the actual measured contact pressure value by dividing it by a reference pressure value. It can be understood that calculating the storage conflict index provides a quantitative and objective basis for subsequent adjustment order allocation, avoiding blind adjustments. Optionally, when calculating Euclidean distance, the conflict analysis module can consider the actual shape of the archives rather than just point masses for more accurate collision detection, but this increases computational complexity. The storage conflict index, along with space occupancy rate, archive stacking stability score, and shelving structure safety index, constitutes a multi-index evaluation system, enabling the system to comprehensively perceive the operational status of the mobile shelving unit from multiple dimensions such as space, stability, and structural strength. In some embodiments, the system can periodically output a conflict analysis report, listing the archives with the highest storage conflict index and their main sources of conflict, providing managers with intuitive insights. Optionally, for high-priority storage optimization decision signals, the system may adopt a more aggressive conflict judgment strategy, such as lowering the safety distance threshold, to identify more potential risk points. The processes of generating and storing optimization decision signals and calculating and storing conflict indices are the core components of automated, closed-loop control, which closely link monitoring, evaluation, and execution actions.

[0035] See Figure 4 This figure presents the dynamic changes of the file stacking stability score and the shelving structure safety index over time, while also indicating the stability threshold and safety index threshold. In the analysis, the file stacking stability score assesses the risk of tipping over and sliding of the file stack using a mechanical simulation model; the lower the score, the higher the risk. The shelving structure safety index, based on strain sensor data and finite element analysis, reflects the stress distribution and fatigue life of the shelving under file load. The time-series changes show that both fluctuate. When the file stacking stability score is below the stability threshold or the shelving structure safety index is below the safety index threshold, it indicates a safety risk, requiring the triggering of a storage optimization decision signal. If both are above the threshold but the space occupancy rate exceeds the limit, a medium-priority optimization signal should also be generated to adjust the storage status. This figure provides an intuitive, time-series assessment basis for the safety status monitoring and storage optimization of the file mobile shelving, helping to achieve dynamic safety management of file storage.

[0036] Example 5: In specific implementation, the process of automatically allocating the adjustment order of archives based on the storage conflict index is executed by the scheduling management module. Upon receiving the storage optimization decision signal, the scheduling management module immediately initiates the workflow. First, the scheduling management module retrieves the current storage conflict index of each archive within the specified partition from the system's central database. The storage conflict index is calculated and provided by the conflict analysis module. Simultaneously, the scheduling management module queries the historical adjustment frequency data of each archive from the archive adjustment history database. The historical adjustment frequency data is a structured record containing fields such as archive identification, most recent adjustment timestamp, and total number of adjustments. In specific implementation, the key step is to weight and fuse the storage conflict index with the historical adjustment frequency data to generate a dynamic priority score. This weighted fusion process requires assigning a weight coefficient to the storage conflict index. Assign a weighting coefficient to the historical frequency adjustment data. Weighting coefficient and weighting coefficients The value of is between 0 and 1, and satisfies . + The relationship is equal to 1. Weighting coefficient. Typically greater than the weighting factor This prioritizes resolving current storage conflicts over the management goal of balancing file adjustment frequency, reflecting the urgency of such resolution. The dynamic priority score is calculated using a linear weighted model: Dynamic Priority Score = *Standardized storage conflict index+ *Standardized historical adjustment frequency data. The standardized storage conflict index is a dimensionless value obtained by dividing the original storage conflict index by the maximum value of the storage conflict indices of all files within a specified partition. The standardized historical adjustment frequency data is obtained by processing the original historical adjustment frequency data using a time decay model. The scheduling management module sorts all files within the specified partition in descending order according to the calculated dynamic priority score, generating a file adjustment sequence. The file with the highest dynamic priority score is placed at the beginning of this sequence, meaning that this file will be prioritized for scheduling and relocation. This sorting mechanism ensures that system resources are prioritized to alleviate the most severe spatial conflicts and security risks. Simultaneously, by introducing historical adjustment frequency data as a regulating factor, it avoids the repeated and frequent movement of a few specific files due to their sensitive locations, thus achieving protective management of the file entities.

[0037] In some embodiments, the archive adjustment history database is organized using an efficient data structure, such as storing records independently for each archive and accelerating queries through indexes to handle a potentially large number of archives and frequent query and update operations. After the archive adjustment sequence is generated, the scheduling management module sends the sequence to the archive access execution agency, which then performs the retrieval and re-storage operations on the archives in sequence. In specific implementations, a batch size may be set, for example, adjusting only the top five archives with the highest dynamic priority scores at a time, then reassessing the partition status, and deciding whether to continue adjusting. This iterative approach can avoid over-adjustment. Optional, weighting coefficients... and weighting coefficients The specific values ​​can be set by the administrator through the system configuration interface to adapt to the management strategies of different archives. In some scenarios, where real-time security is more important, extremely high values ​​may be set. In some scenarios, the focus may be more on archival preservation and therefore appropriate improvements may be made. The value of . Optionally, for newly added archives that have not yet had any adjustment records, their standardized historical adjustment frequency data can be set to 0, or a default initial value can be given, so that their dynamic priority score depends primarily on the storage conflict index.

[0038] See Figure 5 The graph visually presents the dynamic relationship between the space occupancy rate (blue line) and the file storage conflict index (red line) of a designated partition. The space occupancy rate is calculated by extracting the location coordinates and physical dimensions of files within the designated partition, reconstructing the spatial volume model using 3D modeling technology, summing the file occupancy volume, and comparing it with the total volume. The conflict index is obtained by calculating the Euclidean distance and weight difference ratio between each file and other files, summing the conflict intensity values ​​of spatial conflict pairs. The graph shows that a higher space occupancy rate is often accompanied by an increase in the conflict index. This aligns with the project's workflow logic of "initiating a multi-dimensional security assessment when the space occupancy rate exceeds the dynamic adjustment threshold, and then calculating the storage conflict index to allocate the adjustment order." This provides data support for the scheduling and management module to generate file adjustment sequences, facilitating intelligent storage optimization and secure management of the file mobile shelving.

[0039] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0040] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for controlling the storage of archives in a mobile shelving unit, characterized in that, Includes the following steps: The real-time storage parameters of each file in the archive mobile shelving are collected through a distributed sensor network. The real-time storage parameters include the spatial coordinates, physical attributes and environmental contact status of the file. The space occupancy rate of a specified partition of the file mobile shelving is calculated based on real-time storage parameters, and it is determined whether the space occupancy rate exceeds the dynamic adjustment threshold. When the space occupancy rate exceeds the dynamic adjustment threshold, a multi-dimensional security assessment process is initiated, which includes file stacking stability analysis and frame structure stress analysis. Based on the output of the multi-dimensional security assessment process, a storage optimization decision signal is generated, which is used to trigger the file restorage operation. When the storage optimization decision signal is triggered, the storage conflict index of each file is calculated, and the adjustment order of the files is automatically assigned according to the storage conflict index.

2. The method for controlling the storage of archives in a mobile shelving system according to claim 1, characterized in that, The real-time storage parameters of each file in the mobile shelving unit are collected through a distributed sensor network, specifically: Radio frequency identification (RFID) sensors deployed on the filing cabinets continuously scan the file tags to obtain the file's identification and location coordinates; The pressure sensor array measures the weight distribution data of the file and records the contact pressure value between the file and adjacent files; Environmental monitoring sensors collect data on temperature and humidity fluctuations and vibration frequencies around the archives; The data on identification, location coordinates, weight distribution, contact pressure, temperature and humidity fluctuations, and vibration frequency are integrated into a real-time parameter dataset.

3. The method for controlling the storage of archives in a mobile shelving system according to claim 2, characterized in that, The space occupancy rate of a specified partition in the mobile shelving unit is calculated based on real-time storage parameters, specifically: Extract the location coordinates and physical dimensions of all files within a specified partition from the real-time stored parameter dataset; Reconstruct the spatial volume model of a specified partition using 3D modeling technology; Calculate the volume occupied by each file in the spatial volume model based on the file's physical dimensions and location coordinates; The total volume occupied by all files is summed and compared with the total volume of the space volume model to obtain the space occupancy percentage.

4. The method for controlling the storage of archives in a mobile shelving system according to claim 3, characterized in that, Initiate file stacking stability analysis in the multi-dimensional security assessment process, specifically as follows: Obtain the weight distribution data and contact pressure values ​​of the archives from the real-time stored parameter dataset; A mechanical simulation model of file stacking was constructed to simulate the center of gravity shift and pressure transmission path of files under different stacking configurations; The risk of tipping over and sliding of the file stack is assessed through iterative calculations; Output a stability score for the stacked archives; the lower the score, the higher the stability risk.

5. The method for controlling the storage of archives in a mobile shelving system according to claim 4, characterized in that, Initiate the structural stress analysis of the frame structure in the multi-dimensional safety assessment process, specifically as follows: Collect strain sensor data on the frame structure to obtain real-time deformation information of the frame under archive load; A stress distribution model of the frame structure was established using the finite element analysis method, and the stress concentration factor at key connection points was calculated. The stress distribution model was analyzed to predict the fatigue life of the frame structure under different temperature conditions. By combining the stress concentration factor and fatigue life prediction results, a structural safety index is generated.

6. The method for controlling the storage of archives in a mobile shelving system according to claim 5, characterized in that, Based on the output of the multi-dimensional security assessment process, optimization decision signals are generated and stored, specifically: Set thresholds for file stacking stability scoring and frame structure safety index; When the file stacking stability score is lower than the threshold or the shelf structure safety index is lower than the threshold, a high-priority storage optimization decision signal is generated. When the file stacking stability score and the shelf structure safety index are both higher than the threshold, but the space occupancy rate exceeds the dynamic adjustment threshold, a medium-priority storage optimization decision signal is generated. Otherwise, generate a no-operation signal.

7. The method for controlling the storage of archives in a mobile shelving system according to claim 6, characterized in that, Calculate the storage conflict index for each file, specifically as follows: Extract the location coordinates and weight distribution data of each file from the real-time stored parameter dataset; Calculate the Euclidean distance and weight difference ratio between each file and other files in the specified partition; For each pair of files, if the Euclidean distance is less than the safe distance threshold, it is marked as a spatial conflict pair; For each spatial conflict pair, the conflict intensity value is calculated based on the weight difference ratio and the contact pressure value. The storage conflict index is obtained by summing the conflict intensity values ​​of all space conflict pairs for each file.

8. The method for controlling the storage of archives in a mobile shelving system according to claim 7, characterized in that, The file adjustment order is automatically allocated based on the storage conflict index, specifically as follows: Obtain the storage conflict index and historical adjustment frequency data for each file; The conflict index is weighted and merged with historical adjustment frequency data to generate a dynamic priority score; Files are sorted in descending order based on dynamic priority scores, with files of higher scores being adjusted first. Historical adjustment frequency data is updated in real time to reflect the most recent adjustment time of the archives.

9. The method for controlling the storage of archives in a mobile shelving system according to claim 8, characterized in that, Historical adjustment frequency data is updated using a time decay model, specifically: Record the most recent adjustment timestamp and the number of adjustments for each file; The adjustment frequency weights are calculated using an exponential decay function, with higher weights awarded to the most recent adjustments. Multiply the number of adjustments by the decayed weights to obtain standardized historical adjustment frequency data.

10. A file storage control system for a mobile shelving unit, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the file storage control method for the file mobile shelving as described in any one of claims 1 to 9.

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