An automated cold storage and distribution system

Abstract

The application relates to the technical field of cold storage warehousing automation, and discloses a cold storage automated warehousing and distribution system. An abnormal behavior monitoring module of the system collects warehousing equipment operation state data and goods storage and taking behavior data in real time, generates equipment abnormal operation indexes by analyzing the difference between goods flow frequency and equipment operation frequency, a fault prediction module positions associated warehousing nodes based on the indexes, evaluates a risk level in combination with a historical fault event mapping relationship, and predicts possible operation faults. A dynamic strategy module analyzes goods distribution characteristics according to the fault prediction result, calculates goods location migration priority and plans an optimal path, and reallocates goods to low-risk nodes to optimize warehousing configuration. A behavior analysis module compares equipment operation frequencies before and after optimization, identifies abnormal behavior modes, determines behavior deviation degrees and traces fault roots, and generates analysis results.

Description

Technical Field

[0001] This invention relates to the field of cold storage automation technology, specifically to an automated cold storage and distribution system. Background Technology

[0002] Cold storage facilities, as special storage sites operating in low-temperature environments, serve to store and handle goods sensitive to storage conditions, such as fresh food and pharmaceuticals. Their operational efficiency and stability directly affect product quality and supply chain smoothness. In traditional cold storage operations, equipment status monitoring and goods storage and retrieval management rely heavily on manual inspections and experience-based judgment, which are insufficient to meet the demands of large-scale, high-frequency goods turnover.

[0003] Low-temperature environments place stringent demands on the performance of warehousing equipment. Refrigeration units, conveyor belts, stacker cranes, and other equipment operate at low temperatures for extended periods, accelerating component aging. Even minor operational anomalies can trigger equipment malfunctions. However, current technologies lack real-time, comprehensive monitoring of equipment operating status, relying mostly on periodic checks. Data acquisition is often delayed, making it difficult to detect abnormal operational risks in a timely manner. When equipment exhibits initial anomalies such as slight unusual noises or deviations in operating parameters from standard ranges, manual monitoring often fails to detect them, leading to unnecessary downtime losses as problems are only addressed after a failure has occurred.

[0004] In the goods storage and retrieval process, the matching degree between the frequency of goods movement and the frequency of equipment operation directly affects the load balance of the equipment. Under traditional management models, goods allocation is mostly based on fixed storage location planning, ignoring the differences in real-time equipment operating status. When a certain storage node's equipment has a potential failure risk, storing goods according to the original allocation rules will exacerbate the operational pressure on that node and accelerate the occurrence of failure. At the same time, existing systems lack in-depth analysis of the correlation between goods movement and equipment operation, and cannot identify unreasonable operating patterns through data comparison, leading to both overuse and idle equipment.

[0005] In terms of fault handling, traditional methods are mostly reactive, lacking proactive predictive capabilities. Historical fault data is not effectively integrated and analyzed, making it difficult to establish a mapping relationship between storage nodes and fault events. When similar operating conditions occur, it is impossible to assess the risk level in advance. This passive response mode not only increases maintenance costs but may also cause fluctuations in the storage environment due to sudden faults, affecting product quality. Furthermore, after a fault occurs, the lack of a systematic behavioral analysis mechanism makes it difficult to trace the root cause, and similar problems may recur, affecting the long-term stable operation of the cold storage system.

[0006] As the scale of cold chain logistics expands, the level of automation in cold storage warehousing continues to improve. However, existing systems lack coordination in areas such as anomaly detection, risk prediction, dynamic allocation, and source tracing, making it difficult to meet the needs of efficient, low-consumption, and safe operation. There is an urgent need to build an integrated automated distribution system to solve these problems. Summary of the Invention

[0007] The purpose of this invention is to provide an automated cold storage and distribution system to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides an automated cold storage and distribution system, the system comprising:

[0009] The abnormal behavior monitoring module collects real-time operating status data of warehousing equipment and goods storage and retrieval behavior data, analyzes the frequency changes of goods flow, calculates the operating frequency of warehousing equipment, compares the goods flow frequency with the equipment operating frequency, judges the risk of abnormal operation of warehousing equipment, and generates abnormal operation indicators of equipment.

[0010] Based on the abnormal operation indicators of the equipment, the fault prediction module locates the storage nodes associated with abnormal operations, analyzes the mapping relationship between the storage nodes and historical fault events, assesses the fault risk level of the nodes, calculates the correlation strength between the equipment operation frequency and fault events, predicts the possible operational faults of the storage nodes, and generates equipment fault prediction results.

[0011] Based on the equipment failure prediction results, the dynamic strategy module identifies high-risk storage nodes, analyzes the distribution characteristics of goods, calculates the priority of storage location migration, plans the optimal migration path, reallocates goods to low-risk storage nodes, and generates an optimized configuration of storage nodes.

[0012] The behavior analysis module optimizes the configuration of the warehouse nodes, compares the operating frequency of the warehouse equipment before and after optimization, identifies abnormal equipment behavior patterns, determines the degree to which the equipment behavior deviates from normal characteristics, traces the root cause of system operation failures, and generates equipment behavior feature analysis results.

[0013] Preferably, the abnormal behavior monitoring module includes:

[0014] The cargo flow analysis submodule collects cargo storage and retrieval behavior data, analyzes the time series characteristics of cargo entry and exit, calculates the time interval of continuous storage and retrieval operations, counts the fluctuation of cargo flow frequency, compares the ratio of inbound to outbound flow, identifies storage areas with abnormal cargo flow, and generates cargo flow indicators.

[0015] Based on the goods flow index, the equipment operation monitoring submodule retrieves equipment operation logs in abnormal areas, analyzes the distribution of equipment operation frequency in different time periods, calculates the short-term fluctuation range of equipment operation, determines whether there is abnormal operation behavior of the equipment, and generates equipment operation fluctuation index.

[0016] The permission change statistics submodule, based on the device operation fluctuation index, calls the device permission change records, counts the number of permission changes, and calculates the abnormal probability of permission changes by combining the characteristics of goods flow and device operation data, and generates abnormal device operation indexes.

[0017] Preferably, the fault prediction module includes:

[0018] The abnormal operation identification submodule filters the operation nodes of abnormal equipment based on the abnormal operation indicators of the equipment, analyzes the correlation between operation time and intensity and equipment operation mode, calculates and classifies the distribution density of abnormal operations, identifies high-risk operation nodes, and generates a set of abnormal operation nodes.

[0019] The node risk matching submodule calls the set of abnormal operation nodes, parses the operation behavior characteristics, compares the historical failure event patterns, calculates the matching degree between the node and the failure event, evaluates the failure risk level of the operation node, and generates the node risk matching index.

[0020] The fault prediction submodule analyzes the operation frequency of abnormal nodes based on the node risk matching index, extracts operation time interval characteristics, calculates the operation fluctuation range within a short period, and combines the node risk level to predict the probability of operational failure and generate equipment fault prediction results.

[0021] Preferably, the dynamic strategy module includes:

[0022] Based on the equipment failure prediction results, the risk node identification submodule detects high-risk nodes in the warehousing network, analyzes the types and sensitivity levels of goods stored in the nodes, filters out warehousing nodes with potential operational risks, determines the range of nodes where goods need to be relocated, and generates a risk node list.

[0023] Based on the risk node list, the migration priority analysis submodule analyzes the distribution status of goods at the affected nodes, calculates the strength of the goods association and the frequency of interaction between nodes, determines the execution priority of the migration operation, and generates a location migration priority index.

[0024] Based on the location migration priority index, the storage optimization submodule parses the goods flow path between storage nodes, allocates storage resources and plans the optimal migration path, adjusts the storage permissions of low-risk nodes, and generates optimized configurations for storage nodes.

[0025] Preferably, the behavior analysis module includes:

[0026] The behavior pattern analysis submodule optimizes the configuration of the warehouse nodes, calls the operation records of the optimized equipment, compares the differences in operation characteristics before and after optimization, analyzes the magnitude and frequency of changes in operation behavior, calculates the degree of deviation of the behavior pattern from the benchmark, and generates a behavior deviation index.

[0027] The operation frequency comparison submodule compares the operation frequency of the device before and after optimization based on the behavior deviation index, analyzes the changes in operation time points, duration and frequency, determines the operation frequency fluctuation characteristics, and generates an operation frequency fluctuation index.

[0028] The anomaly root cause tracing submodule identifies equipment behaviors that deviate from the standard operating mode based on the operation frequency fluctuation index, analyzes the correspondence between abnormal behavior characteristics and system faults, locates the root cause of operational faults, and generates equipment behavior characteristic analysis results.

[0029] Preferably, the system further includes:

[0030] Based on the analysis results of the equipment behavior characteristics, the efficiency evaluation module evaluates the operational efficiency of the warehouse area, calculates the optimization weight of the equipment operation path, updates the goods allocation strategy, and generates warehouse efficiency optimization indicators.

[0031] The efficiency evaluation module includes: a path optimization evaluation submodule that analyzes the redundancy of equipment operation paths and calculates the weight of path optimization on improving warehousing efficiency; and a goods allocation strategy submodule that adjusts the goods distribution density based on the improvement weight to generate warehousing efficiency optimization indicators.

[0032] Preferably, the efficiency evaluation module includes:

[0033] Based on the analysis results of the equipment behavior characteristics, the regional load analysis submodule monitors the equipment operation load of each storage area and calculates the regional load balancing coefficient.

[0034] The strategy optimization submodule adjusts the priority weight of the goods allocation strategy based on the regional load balancing coefficient, optimizes the goods distribution density in high-frequency operation areas, and generates warehouse efficiency optimization indicators.

[0035] Preferably, the system further includes:

[0036] The strategy aggregation module receives local optimization strategies from multiple storage areas, verifies the local optimization strategies based on the global efficiency model, and aggregates the verified local optimization strategies to generate a global storage strategy.

[0037] The strategy aggregation module includes: a local strategy verification submodule that calls regional historical operation data to verify the feasibility of local optimization strategies; and a global strategy generation submodule that merges verified local strategies to update global warehouse strategies.

[0038] Preferably, the strategy aggregation module includes:

[0039] The strategy weight allocation submodule calculates the aggregate weight of the regional strategy based on the cargo throughput and equipment runtime of each warehousing area;

[0040] The global update submodule integrates local optimization strategies based on the aggregated weights, generates a global storage strategy, and updates the system strategy library.

[0041] Preferably, the system further includes:

[0042] Based on the analysis results of the device behavior characteristics, the security response module identifies high-risk operating devices, adjusts the device operation permission coefficient, allocates device operation task quotas, updates device operation verification rules, and generates device security protection configurations.

[0043] The security response module includes: an access control submodule that adjusts the operation permission level based on the frequency of abnormal device behavior; a task allocation submodule that reallocates the amount of device operation tasks based on the permission level; and a verification rule update submodule that synchronously strengthens the operation verification rules for high-risk devices.

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] This automated cold storage and distribution system provides comprehensive intelligent support for cold storage operations through the collaborative work of multiple modules. The abnormal behavior monitoring module breaks through the limitations of traditional manual monitoring. By collecting real-time dynamic data on equipment operation and goods storage and retrieval, it continuously analyzes the correlation between the frequency of goods flow and the frequency of equipment operation, enabling it to keenly detect abnormal situations where the two do not match. This real-time perception mechanism allows the system to generate corresponding risk indicators as soon as abnormal operations begin to emerge, preventing prolonged abnormal operations from causing equipment damage or errors in goods storage and retrieval.

[0046] The fault prediction module takes into account abnormal operation indicators and focuses risk analysis on specific warehouse nodes. By exploring the intrinsic relationships between nodes and historical failure events, it classifies and assesses the risk of node failures. This data-driven risk assessment method eliminates reliance on experience-based judgment and accurately identifies potential fault hazards. By calculating the correlation strength between equipment operation frequency and failure events, the system can predict possible operational failures in advance, providing proactive risk warnings for warehouse management and enabling more targeted maintenance work.

[0047] The dynamic strategy module proactively intervenes based on fault prediction results, developing dynamic allocation plans for high-risk storage nodes based on the distribution characteristics of goods. By calculating the priority of storage location migration and planning the optimal path, goods are orderly moved from high-risk nodes to low-risk nodes, achieving dynamic optimization of storage resource allocation. This flexible allocation mechanism can prevent high-risk nodes from experiencing increased fault risk due to continuous load, while balancing the operational pressure of each node and reducing overall operational interruptions caused by localized failures.

[0048] The behavior analysis module deeply analyzes the evolution of equipment behavior patterns by comparing changes in equipment operation frequency before and after optimized configuration of warehouse nodes. After identifying abnormal behavior patterns, it further determines the degree to which the behavior deviates from normal characteristics and traces the root cause of system malfunctions. This in-depth analysis capability not only clarifies the attribution of fault responsibility but also provides a basis for optimizing operating procedures and improving equipment maintenance strategies, promoting a closed loop of continuous system improvement.

[0049] The coordinated operation of each module enables the cold storage system to move beyond the passive response mode of traditional operations, forming a complete management chain from anomaly detection, risk warning, proactive allocation to root cause improvement. Through a real-time data-driven decision-making mechanism, the system can adapt to the complexity of equipment operation and the dynamic nature of goods storage and retrieval in the low-temperature environment of cold storage, reducing losses caused by downtime due to malfunctions, ensuring the stability of goods in the storage process, and improving the utilization efficiency of storage space and equipment resources, making cold storage operations smoother and more reliable. Attached Figure Description

[0050] Figure 1 This is a timing diagram of the automated cold storage and distribution system described in this invention.

[0051] Figure 2 A flowchart illustrating the workflow for abnormal behavior monitoring;

[0052] Figure 3 This is a flowchart of the fault prediction module.

[0053] Figure 4 This is a flowchart of the efficiency evaluation module. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Please see Figure 1This invention provides an automated cold storage and distribution system, the system comprising:

[0056] The system achieves anomaly monitoring, fault prediction, and dynamic optimization of warehousing equipment through multi-module collaboration. During system operation, the anomaly monitoring module continuously collects data on the operational status of warehousing equipment and the behavior of goods storage and retrieval. By analyzing the correlation between changes in goods flow frequency and equipment operation frequency, it establishes abnormal operation indicators. The fault prediction module locates high-risk warehousing nodes based on the anomaly indicators and calculates the probability of fault occurrence by combining historical fault mode mapping relationships. The dynamic strategy module replans the location migration path based on the prediction results, transferring goods from high-risk nodes to low-risk areas. The behavior analysis module compares the equipment operation characteristics before and after optimization to trace the root cause of system malfunctions, forming a closed-loop optimization mechanism.

[0057] Example 1: See Figure 2 In an automated cold storage environment, the abnormal behavior monitoring module collects operational data in real time through a distributed sensor network. The cargo flow analysis submodule is deployed at key nodes in the storage area. It captures cargo storage and retrieval timestamps using high-frequency RFID readers and combines this with location coordinate change data recorded by laser rangefinders to construct a three-dimensional spatiotemporal matrix. This submodule uses a sliding window mechanism to process time series data, with 15-minute intervals as the basic analysis unit, dynamically calculating the standard deviation of cargo flow frequency within the window. When the standard deviation of three consecutive analysis units exceeds a preset threshold, an area anomaly marker is triggered. Simultaneously, this submodule establishes a dynamic ratio model between inbound and outbound traffic. When the ratio value of a specific area deviates from the system baseline range for more than 2 hours, a cargo flow index containing the area coordinates and deviation value is automatically generated.

[0058] The equipment operation monitoring submodule connects to the warehouse equipment controller via an industrial IoT gateway to collect real-time data on the current waveforms of the stacker crane servo motors, the start / stop signals of the conveyor line frequency converters, and the displacement sensor data of the hoist. This submodule establishes a time-based bucketing mechanism on an hourly basis, statistically analyzing histograms of the operation frequency distribution for various equipment types within each time period. Non-parametric statistical methods are used to compare the current operation distribution with historical baseline distributions. When a significant change in the distribution pattern is detected, the short-term fluctuation amplitude of equipment operation is calculated. Specifically, by analyzing the gradient changes in operation frequency between adjacent time periods, abnormal fluctuation events with sudden increases or decreases exceeding 30% are identified. For detected abnormal fluctuations, this submodule generates equipment operation fluctuation indicators that include the equipment number, fluctuation type, and duration.

[0059] The permission change statistics submodule interfaces with the enterprise permission management system, extracting device operation permission change records through a log parsing engine. This module constructs a multi-dimensional feature vector, including permission change timestamps, operator identification, target device number, and change type. Machine learning algorithms are used to analyze the spatiotemporal correlation between permission change events and goods flow indicators, calculating the probability of each permission change. When an unplanned permission change is detected on a specific device in an area of ​​abnormal goods flow, this submodule initiates a correlation probability calculation process. By analyzing the differences in device operation modes before and after the permission change, combined with the changing trends of goods flow characteristics, a quantified indicator of abnormal device operation is generated. This indicator includes core data such as anomaly probability values, impact range assessment, and a list of associated devices.

[0060] The three sub-modules form a progressive analysis chain: the flow rate analysis sub-module outputs liquidity indicators that trigger the equipment operation monitoring process; equipment operation fluctuation indicators and permission change records are integrated and analyzed in the statistics sub-module. The system uses a message queue to achieve data transfer between modules. When the flow rate analysis detects an anomaly in a region, it automatically sends a trigger command with region coordinates to the equipment operation monitoring sub-module. The equipment operation monitoring results and permission change records are matched using a time window association algorithm. The final generated equipment abnormal operation indicators contain a complete chain of abnormal events, covering a complete chain of evidence from abnormal goods flow to abnormal equipment operation and then to permission changes.

[0061] At the data processing level, the system employs a streaming computing architecture to process sensor data in real time. The cargo flow analysis submodule uses a time-series database to store access events, while the equipment operation monitoring submodule uses columnar storage to record equipment status snapshots. The permission change statistics submodule establishes a graph database to store the permission change relationship network and identifies abnormal permission propagation paths through a graph traversal algorithm. The analysis results from each submodule are uniformly encapsulated into JSON format data packets, containing metadata fields such as timestamps, spatial coordinates, and anomaly levels, and transmitted to downstream modules via the enterprise service bus.

[0062] The system employs a dynamic threshold adjustment mechanism. The anomaly thresholds in the cargo flow analysis submodule are automatically adjusted based on seasonal factors and warehouse load. The fluctuation judgment criteria of the equipment operation monitoring submodule are dynamically updated with equipment runtime, with more lenient thresholds applied initially to new equipment. The anomaly probability model of the permission change statistics submodule is automatically retrained weekly, incorporating the latest operational data to update feature weights. All threshold and model parameter changes are recorded in the audit log, forming a complete parameter adjustment trajectory.

[0063] The abnormal behavior monitoring module is deployed in a redundant design within the cold storage environment, with key sensor nodes equipped with dual power supplies. A temperature compensation algorithm is added to the data acquisition end to eliminate measurement deviations of electronic components caused by the low temperature environment of the cold storage. An internal data verification mechanism is established within the module; when the analysis results from the three sub-modules conflict, a review process is automatically initiated and a conflict report is generated. The final output of abnormal equipment operation indicators undergoes multi-layered verification, including confidence scores and verification status markers, providing highly reliable input data for downstream fault prediction.

[0064] Example 2: See Figure 3 In the operating environment of an automated cold storage system, the fault prediction module receives data streams of abnormal equipment operation indicators from the abnormal behavior monitoring module. The abnormal operation identification submodule initiates a spatial clustering analysis process, dividing the storage area into standard 1m x 1m grid cells and calculating the operation density value for each grid cell based on the abnormal equipment operation indicators. This submodule uses an adaptive threshold algorithm to identify high-density areas; when the operation density value of a grid cell exceeds twice the standard deviation of the system's dynamic benchmark value, that area is marked as a hotspot. The system automatically associates the equipment IDs within the hotspot area, generating a set of abnormal operation nodes containing equipment location coordinates, abnormal operation frequency, and density scores. For each identified abnormal node, the submodule records its operation intensity change curve over the past 24 hours, marking the peak operation period and the corresponding operation type code.

[0065] The node risk matching submodule accesses a distributed database containing historical fault event records from the past five years. Each fault case is abstracted as a feature vector, including dimensions such as faulty equipment type, time of occurrence, ambient temperature and humidity, and operational load. The submodule constructs a corresponding feature vector for the current abnormal node and calculates its similarity to historical fault cases using a vector space model. The similarity calculation employs a weighted Euclidean distance algorithm, assigning differentiated weight coefficients based on the importance of different feature dimensions. When the similarity between an abnormal node and a historical fault case exceeds a preset threshold, the system automatically labels the node's risk level and associates it with a matching historical fault case number. The node risk matching index includes a risk level score, a list of similar cases, and key matching features.

[0066] The fault prediction submodule constructs a time series analysis model. Input parameters include the time-series data of the operating frequency of abnormal nodes, equipment current waveform characteristics, and ambient temperature records. This submodule uses a sliding window mechanism to process the operating frequency data, with 10-minute intervals as the basic analysis unit, calculating the mean and standard deviation of the operating frequency within the window. By analyzing the gradient of operating frequency changes between adjacent time windows, it identifies abrupt changes in operating modes. The submodule integrates multi-dimensional data sources, including the spectral characteristics of motor vibration sensors, refrigerant pressure readings, and error logs from the equipment control system. Based on this heterogeneous data, a comprehensive evaluation model is constructed to calculate the probability of operational failure for each abnormal node within a specific future time period. The fault prediction results are output in the form of a probability distribution matrix, containing estimated fault occurrence probabilities for each node in the next 6, 12, and 24 hours.

[0067] The three sub-modules form a progressive prediction chain: the node set output by the abnormal operation identification sub-module triggers the risk matching process; the node risk matching result serves as the core input parameter for fault prediction. The system uses a publish-subscribe model for data transfer between modules. When the abnormal operation identification sub-module generates a new node set, it automatically pushes an event notification to the node risk matching sub-module. The risk matching result is fused with real-time operation data using a time alignment algorithm. The final equipment fault prediction result contains a complete risk assessment chain, covering the analysis path from abnormal operation location to historical pattern matching and future risk quantification.

[0068] At the data processing level, the system uses a time-series database to store device operation frequency data and a document database to manage historical fault case libraries. The node risk matching process uses an in-memory computing engine to accelerate vector similarity calculation, and the time-series analysis of the fault prediction submodule uses a stream processing framework to achieve real-time calculation. The intermediate results of each submodule are serialized and stored using a binary protocol, including metadata fields such as timestamps, device identifiers, and spatial coordinates, and are transmitted to downstream modules through a high-throughput message queue.

[0069] The system incorporates a predictive model update mechanism, automatically synchronizing the latest maintenance records daily with the historical fault case library of the node risk matching submodule. The evaluation model of the fault prediction submodule undergoes monthly parameter tuning, incorporating newly added fault feature data to retrain the weight coefficients. All prediction results are accompanied by a confidence score; a manual review process is automatically triggered when the confidence score falls below an acceptable level. The prediction module's output data includes a complete audit trail, recording the data source and calculation path for each prediction result.

[0070] The fault prediction module is deployed in a cold storage environment considering extreme operating conditions, and the computing nodes utilize wide-temperature-range industrial servers. Signal enhancement processing is added to the data acquisition end to eliminate the impact of electromagnetic interference from refrigeration equipment on sensor readings. An internal verification mechanism for prediction results is established within the module; when logical conflicts exist in the outputs of the three sub-modules, data traceability checks are automatically initiated and a discrepancy report is generated. The final output equipment fault prediction results undergo multiple verifications, including data quality markers and prediction reliability indices, providing highly complete input for subsequent dynamic strategy modules. The prediction period is configurable, allowing adjustment of the 6-24 hour prediction time range according to warehouse operation needs, adapting to the storage characteristics of different product types.

[0071] Example 3: In the operational architecture of the automated cold storage system, the dynamic strategy module receives equipment failure prediction results from the failure prediction module. The risk node identification submodule initiates a multi-dimensional storage node evaluation process. First, it analyzes the risk probability matrix in the failure prediction results and filters out potential failure nodes with risk values ​​exceeding 0.75. This submodule combines real-time data collected by the temperature sensor network to establish a three-dimensional temperature distribution model and identify storage areas located at the critical temperature control threshold boundary (-20℃±2℃). A spatial correlation algorithm is used to overlay and analyze high-risk equipment nodes with critical temperature areas, generating a risk node list containing node coordinates, risk level, temperature status, and characteristics of stored goods. Each entry in the list is labeled with a classification code for the sensitivity of goods to temperature fluctuations, as well as the node's topological location within the storage network.

[0072] The migration priority analysis submodule employs a multi-objective decision-making model to process the risk node list and construct a location migration evaluation system. This system considers constraints across three dimensions: path distance, time window, and product characteristics. Path distance is calculated using the shortest handling distance from the warehouse space topology map; the time window is determined based on the production scheduling plan; and product characteristics include parameters such as remaining shelf life and temperature sensitivity coefficient. The submodule calculates a comprehensive priority score for each location to be migrated. The scoring model considers the following factors: the gradient of the current node's risk value over time, the load status of adjacent nodes, and the temperature stability record of the target node. The score calculation uses a weighted summation method, where the risk change gradient weight is set as a dynamic parameter that automatically adjusts during system operation. The migration priority index output format includes the source node coordinates, the target node candidate set, the execution time window suggestion, and detailed priority scores.

[0073] The storage optimization submodule performs location reallocation planning based on the migration priority index. This process requires solving a resource allocation optimization problem with constraints. The submodule establishes the following objective function:

[0074]

[0075] in: This represents the total cost of the relocation plan, where n is the number of storage locations to be relocated. This represents the migration distance of the i-th storage location. Reflecting the time consumed during the migration process, This indicates the degree of compatibility with the characteristics of the goods; , , These are the weighting coefficients for distance, time, and characteristic factors, which are dynamically configured according to the warehouse operation strategy. The submodule uses a constraint satisfaction algorithm to solve the optimization problem, generating an optimized configuration scheme that includes specific migration paths, equipment allocation, and time plans, while satisfying constraints such as refrigeration system load balancing, equipment capacity limitations, and product compatibility. Each migration task specifies a particular stacker crane or conveyor line equipment number and marks the turning points and speed control parameters in the path.

[0076] The behavior analysis module receives optimized configuration data for warehouse nodes from the dynamic strategy module and initiates the equipment behavior evaluation process. The behavior pattern analysis submodule collects operation records for each piece of equipment before and after optimization, including motor speed curves, positioning accuracy data, and energy consumption changes. This submodule constructs equipment operation feature vectors, including motion parameters such as peak acceleration, duration of constant speed segments, and braking distance, and calculates the feature differences before and after optimization using a pattern recognition algorithm. The operation frequency comparison submodule monitors changes in task allocation before and after warehouse relocation, analyzing the number of operation commands per unit time, task interval distribution, and idle time percentage. By establishing a time-series distribution model of operation frequency, it identifies structural changes in equipment operating modes.

[0077] The anomaly root cause tracing submodule integrates behavioral pattern and operation frequency analysis results to construct a causal reasoning network for abnormal equipment behavior. Network nodes represent possible causes of the anomaly, including mechanical component wear, control parameter deviations, and sensor malfunctions, while edges represent the strength of causal relationships. The submodule employs an evidence-based reasoning algorithm to perform probability propagation on the network, calculating the confidence score for each anomaly cause. For high-confidence anomaly causes, the submodule correlates them with corresponding equipment maintenance records and component replacement history to verify the rationality of the reasoning results. The final generated equipment behavior characteristic analysis results include anomaly type diagnosis, potential impact range, and a list of recommended inspection areas.

[0078] At the system implementation level, a microservice architecture is adopted to deploy each submodule. The risk node identification submodule receives fault prediction data through a message queue. The migration priority analysis submodule uses a graph database to store the warehouse network topology, accelerating the path calculation process. The solver of the storage optimization submodule runs on a dedicated computing node, supporting parallel processing of multiple migration scheme evaluations. The data acquisition end of the behavior analysis module deploys a lightweight agent program to capture status messages issued by the device controller in real time. Data exchange between modules adopts a unified interface specification, including time synchronization identifiers and spatial coordinate reference system information.

[0079] The dynamic strategy module incorporates a feedback adjustment mechanism, dynamically adjusting migration priority weights based on actual performance. When the system detects that the actual migration time for a certain type of goods exceeds the estimated value by 20%, it automatically triggers a weight recalibration process. The behavior analysis module establishes a baseline learning mechanism, continuously updating the reference range for normal operating characteristics to adapt to natural performance degradation. All configuration changes are recorded in the version control system, supporting the rewinding of strategy parameter states at any point in time.

[0080] In cold storage environments, the implementation considers the impact of extreme operating conditions, employing industrial servers with condensation-resistant designs for the computing nodes. Migration path planning takes into account changes in the friction coefficient caused by ground frost, incorporating a dynamically adjusted friction compensation factor into the path cost model. Low-temperature drift compensation circuitry is added to the signal acquisition circuitry of the behavior analysis module to ensure the accuracy of sensor readings in -25°C environments. System outputs include a complete decision-making logic chain, with each migration suggestion accompanied by data traceability information, recording the fingerprints of the original data involved in the calculations and intermediate result verification values. The dynamic strategy module is deeply integrated with the warehouse control system; the generated optimized configuration schemes can be directly converted into equipment control command sequences and distributed to the actuators via a real-time bus.

[0081] Example 4: See Figure 4 In the actual operation scenario of the frozen meat product storage area, the efficiency assessment module receives the equipment behavior characteristic analysis results output by the behavior analysis module. The path optimization assessment submodule initiates the analysis of the storage equipment's movement trajectory, collecting the operation path data of the past 72 hours for the three stacker cranes and two conveyor lines in this area. This submodule divides the 60-meter long and 40-meter wide storage space into 1200 0.5-meter × 0.5-meter grid cells, recording the dwell time and number of passes of each piece of equipment in each grid cell. By comparing the grid overlap between the actual operating trajectory of the equipment and the theoretical shortest path, the path optimization weight value of each piece of equipment is calculated. For example, when stacker crane A is moving frozen meat pallets from storage locations B7 to C12, the actual path covers 15 more grid cells than the theoretical value, and the system records the path redundancy coefficient for this task as 0.23.

[0082] The regional load analysis submodule synchronously processes equipment temperature data collected by the infrared thermal imaging system. Under an ambient temperature of -22℃, it monitors the temperature rise of equipment motors in each storage zone. This submodule establishes a mapping relationship between temperature change rate and mechanical load, calculating the real-time load coefficient for each zone by analyzing the temperature gradient of the motor casing before and after operation. The system generates a regional load heat map hourly, marking the coordinates of overloaded areas.

[0083] Table 1: Load analysis data of the warehouse area at a certain moment.

[0084]

[0085] The strategy optimization submodule integrates path optimization weights and regional load data to adjust the goods allocation strategy. For the storage characteristics of frozen meat products, this module establishes a rules for adjusting goods distribution density: high-turnover cut meat products are preferentially allocated to areas with high path optimization weights, while bulk whole meat products are allocated to areas with lower load coefficients. In specific implementation, the system detected that the load coefficient of stacker crane B in area S3 reached 0.91, and the turnover rate of rib products stored in this area was also high. The strategy optimization submodule initiated a goods redistribution, migrating 30% of the rib products to area N1, where the load coefficient was only 0.68, and simultaneously adjusting the maximum handling frequency parameter of stacker crane B.

[0086] During module operation, the path optimization and evaluation submodule continuously updates the equipment motion database. When a new handling task is added, the system pre-generates multiple candidate paths and dynamically selects the optimal route based on real-time load data. For example, when conveyor line X performs a pallet transport task from the pre-cooling room to the blast freezer, the system avoids the S4 section, which currently has a higher temperature rise, and automatically selects an alternative path through the N2 area. Simultaneously, the area load analysis submodule sets up an early warning mechanism; when it detects that the temperature change rate of a certain piece of equipment exceeds a threshold for three consecutive cycles, it automatically reduces the task allocation weight of that equipment.

[0087] The strategy optimization process employs incremental adjustments, with each goods migration not exceeding 15% of the total area storage capacity. In the frozen beef storage area case, the system identified an uneven load distribution on the eastern side: the load coefficients of the two stacker cranes differed by 0.25. The strategy optimization submodule adjusted the goods distribution in three stages: first, migrating beef brisket cuts nearing their expiration date; second, adjusting high-turnover steak products; and finally, transferring bulk beef leg inventory. After each migration, the area load coefficient was recalculated until the difference narrowed to within 0.05.

[0088] When the efficiency evaluation module outputs warehouse efficiency optimization indicators, it includes two core parameters: path optimization execution rate and load balancing improvement. The system records the changes in key indicators before and after each strategy adjustment, forming a tracking chain of optimization effects. When implemented in the frozen seafood storage area, the module detected excessive overlap of equipment paths in shelf area C. By adjusting the storage categories of adjacent shelves (separating the storage of larger whole fish boxes from smaller fish block packaging), the stacker crane turning radius was reduced by 20 centimeters, and the path overlap was reduced by 18 percentage points.

[0089] The system implementation employs a distributed computing architecture. The path optimization and evaluation submodule is deployed on edge computing nodes to process equipment location data in real time. The regional load analysis submodule directly reads the operating parameters of the equipment controlled by the PLC via the OPCUA protocol, updating the load coefficient every 5 seconds. The strategy optimization submodule's decision engine generates optimization suggestions hourly, which are automatically sent to the warehouse control system after manual confirmation. All optimization operations are recorded in the audit log, including detailed information such as operation time, executing equipment, and adjusted parameters, supporting full-process traceability analysis. Regarding low-temperature adaptability, the data processing server uses wide-temperature industrial-grade hardware, and the data transmission lines are equipped with anti-condensation protective sleeves to ensure stable operation at -25℃.

[0090] Example 5: In the operational framework of a cross-regional cold storage system, the strategy aggregation module receives local optimization strategy data streams from multiple storage zones. The local strategy verification submodule initiates a distributed verification process, first parsing the optimization strategy parameters submitted by each zone, including location adjustment plans, equipment control parameter modification suggestions, and expected efficiency improvement targets. This submodule calls the historical operation database of each zone to extract equipment operation records, goods turnover data, and energy consumption monitoring logs for the most recent three months. By constructing a virtual storage model, the strategy parameters to be verified are injected into the simulation environment to simulate the system's operating state after the strategy is executed. The simulation process considers actual environmental constraints, including refrigeration unit response delay, equipment mechanical inertia, and material performance changes under extreme temperatures. For each local strategy, the submodule outputs a verification report including a strategy feasibility score, a list of potential conflict points, and a resource requirement assessment.

[0091] The strategy weight allocation submodule processes the validated local strategy set to establish a regional contribution evaluation system. This system is based on two core dimensions: regional cargo throughput data reflects operational scale, and cumulative equipment runtime characterizes system load intensity. The submodule collects cargo inbound and outbound records for each region's most recent full quarter and calculates the daily average throughput standard deviation to assess operational stability. Simultaneously, it calculates the motor operating hours of major equipment and combines this with equipment maintenance records to calculate a health status coefficient. Through a multi-attribute decision-making algorithm, aggregated weight coefficients are assigned to each regional strategy. Regions with high and stable throughput receive a base weight bonus, while regions with equipment operating at high loads for extended periods receive a compensatory weight increase. The weight calculation results include a region identifier, weight value, analysis of key influencing factors, and information on the weight's effective period.

[0092] The global update submodule performs strategy fusion operations, employing a layered aggregation architecture to process strategies for each region. The basic operation parameter layer directly merges common settings, such as equipment acceleration curves and location allocation rules. The feature extraction layer preserves region-specific configurations, including characteristic settings such as temperature control strategies for special goods (e.g., pharmaceuticals) and dual verification mechanisms for high-value goods. The submodule constructs a global warehouse strategy tree structure, with the root node storing basic common strategies and branch nodes storing region-specific strategies. The update process uses a version control mechanism, generating a strategy snapshot and marking the effective time window for each update. The final output global warehouse strategy includes a complete set of parameters, region characteristic tags, and a strategy dependency graph.

[0093] During system operation, each storage area submits local optimization strategies via a dedicated data channel. Area A, in the frozen seafood storage area, proposes a strategy to shorten the stacker crane acceleration time, while Area B, in the vaccine storage area, suggests adding a temperature fluctuation buffering mechanism. The local strategy verification submodule processes these strategies in parallel. Simulating the execution of Area A's strategy in a virtual model, it detects potential overload risks on adjacent conveyor lines and generates a conflict warning report. The strategy weight allocation submodule calculates that Area B, due to storing highly sensitive goods, receives a higher weight coefficient, while Area A requires a lower weight due to equipment aging. During integration, the global update submodule retains Area B's temperature buffering parameters and adjusts Area A's equipment acceleration parameters to create a compatible global strategy.

[0094] The policy aggregation module has an exception handling mechanism that automatically initiates a negotiation protocol when a compatibility conflict is detected in a regional policy. The system records the historical adoption rate of policies in each region and triggers a policy optimization suggestion process for regions with consistently low adoption rates. After a global policy is published, the module continuously monitors its execution effectiveness, collecting deviations between actual operational data and expected targets for each region. When the deviation in a specific region consistently exceeds the tolerance threshold, a policy rollback instruction is automatically generated and the region administrator is notified.

[0095] At the technical implementation level, the module is deployed using a microservice architecture. The local policy verification submodule runs on server nodes with high-performance computing capabilities, while the policy weight allocation submodule uses an in-memory database to accelerate weight calculation. The global update submodule manages the policy tree using a graph database to store dependencies. The data exchange interface adopts a unified coding standard, and policy parameters use JSON Schema to define the data structure. The message middleware ensures the time-series consistency of cross-region data transmission, and each policy data packet contains a region identifier, a submission timestamp, and a policy version hash value.

[0096] The system maintains a global policy version repository, supporting historical policy status rollback. Administrators can query policy configurations at any point in time and compare parameter differences between different versions. The policy release mechanism adopts a canary release mode; new policies are first tested in select areas, and a decision is made on whether to update to the entire system after collecting actual operational data. All policy change operations are recorded in a blockchain-based notarization system to ensure the immutability of these records.

[0097] Regarding low-temperature adaptability, the computing nodes of the strategy aggregation module are deployed in a temperature-controlled server room to avoid direct exposure to the cold storage environment. Data transmission uses fiber optic media to prevent condensation effects, and interface devices are equipped with heating and defrosting devices. The module has an internal data verification process that performs format verification and logical integrity checks on received regional policies, discarding non-compliant data packets and generating error reports. Global policy output includes detailed metadata descriptions, including policy applicability conditions, parameter adjustment ranges, and expected impact analysis, for reference by each region.

[0098] Example 6: In the operating environment of a low-temperature pharmaceutical storage area, the safety response module receives the equipment behavior characteristic analysis results output by the behavior analysis module. The access control submodule initiates a real-time access control evaluation process, parsing the behavior deviation index value in the input data. When the behavior deviation index of a stacker crane continuously exceeds a set threshold, this submodule automatically retrieves the equipment operation access control database and extracts the role access control configuration of the current equipment operator. The system calculates the access control downgrade coefficient based on the deviation index exceeding the limit, dynamically adjusting the execution range of equipment control commands. For example, if a vacuum insulation panel handling equipment experiences abnormal vibration causing its deviation index to rise to 1.8, the system downgrades its operation access control from level three to level one, restricting it to only executing basic handling commands and disabling the high-speed operation mode. Access control changes are transmitted to the equipment controller in real time via the industrial bus, and the change record is written to the safety audit log, including the timestamp, equipment number, original access control level, new access control level, and change trigger value.

[0099] The task allocation submodule synchronously processes equipment failure risk level data and establishes a flexible task allocation mechanism. This module maintains the task queue capacity parameters for each piece of equipment and dynamically adjusts the maximum task load based on the real-time risk level. For high-risk equipment, the system adopts a gradual task reduction strategy: when the risk level is first detected to increase, the task load is reduced to 80% of the baseline value; if the risk persists, it is further reduced to 60%. In practical applications in the biopharmaceutical storage area, when a shuttle system's risk level rises to an orange alert due to guide wheel wear, the system automatically reduces its parallel task count from 5 to 3 and extends the task interval. The task redistribution process considers equipment load balancing; the reduced tasks are automatically transferred to lower-risk equipment in the same area for execution, and the transfer record indicates the original equipment risk status.

[0100] The verification rule update submodule implements a multi-layered security verification mechanism. When equipment is marked as high-risk, this module adds a vibration characteristic authentication step above the standard control command verification layer. Before performing critical operations, the equipment must simultaneously meet the dual conditions of passing PLC command verification and matching real-time vibration spectrum. During refrigeration unit maintenance, when a hoisting device was lifting a compressor, the system detected a 0.3-second time delay deviation between its horizontal movement command and the acceleration sensor mode, immediately interrupting the operation and initiating a self-diagnostic program. The verification rule base adopts a modular design, including a basic verification rule set and dynamically loadable extended rule packages. High-risk equipment automatically activates the enhanced verification logic in the extended rule package.

[0101] A closed-loop safety management system is established during system operation. In the vaccine storage area example, an automated guided vehicle (AGV) experienced an abnormally high deviation index due to frost buildup on its navigation sensors. The access control submodule reduced its maximum operating speed from 2 m / s to 1 m / s, and the task allocation submodule reduced its handling workload by 40%. When the vehicle performed a pallet handover operation, the verification rule update submodule activated secondary verification using laser ranging. Upon detecting an excessive positioning deviation, the handover instruction was canceled. Simultaneously, the system automatically generated a maintenance work order and pushed it to the equipment management department.

[0102] The technical implementation employs a layered architecture. The access control engine is deployed on a security server and interacts with the device controller via the OPCUA protocol. The task allocation service runs on a load-balanced cluster, monitoring the task queue status of each device in real time. The verification rule manager adopts a microservice design, supporting hot-update verification logic. All security operations are processed asynchronously through message queues to ensure system response timeliness. Data storage uses an encrypted database, and security audit logs include operator digital signatures and device hardware fingerprints.

[0103] The system employs version control for security policies, generating a policy snapshot with each permission change or rule update. Administrators can revert to the security configuration status at any point in time and compare the differences in control parameters before and after policy changes. Regarding low-temperature adaptability, the security verification terminal is equipped with a freeze-proof touchscreen, and control signal transmission uses anti-interference coaxial cables. Vibration sensors are fitted with temperature-controlled protective covers to eliminate measurement deviations caused by cold contraction.

[0104] The fault injection testing mechanism periodically verifies the effectiveness of security responses. The system automatically simulates abnormal equipment scenarios, including sensor failure, mechanical jamming, and communication interruption, recording the response time and handling accuracy of the security modules. Test results are used to optimize privilege downgrade thresholds and task reduction gradient parameters. All security events generate handling reports, including raw data snapshots, response action sequences, and final handling results.

[0105] The equipment recovery mechanism employs a progressive permission restoration process. Once the malfunctioning equipment completes maintenance and passes its safety self-check, the system restores its functionality in three phases: first, basic operating permissions are restored; second, the workload is gradually increased; and finally, the enhanced verification rules are lifted. Each phase includes a 24-hour observation period, during which the equipment's behavior is continuously monitored. Only after normal operation is confirmed does the system proceed to the next recovery phase. In the case of handling refrigerated organ transport boxes, a hydraulic lifting platform, after being repaired, underwent a 72-hour phased recovery process before finally returning to normal operation.

[0106] 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.

[0107] 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 cold storage automated warehousing system, characterized in that, The system comprises: The abnormal behavior monitoring module collects the operation state data and the goods access behavior data of the warehouse equipment in real time, analyzes the frequency change of the goods flow, calculates the operation frequency of the warehouse equipment, compares the goods flow frequency with the equipment operation frequency, judges the abnormal operation risk of the warehouse equipment, and generates the equipment abnormal operation index; The fault prediction module is based on the equipment abnormal operation index, locates the warehouse node associated with the abnormal operation, analyzes the mapping relationship between the warehouse node and the historical fault event, evaluates the fault risk level of the node, calculates the correlation strength between the equipment operation frequency and the fault event, predicts the possible operation failure of the warehouse node, and generates the equipment fault prediction result; The dynamic strategy module is based on the equipment fault prediction result, identifies the high-risk warehouse node, analyzes the goods distribution characteristics, calculates the goods location migration priority, plans the optimal migration path, and reallocates the goods to the low-risk warehouse node, and generates the warehouse node optimization configuration; The behavior analysis module is based on the warehouse node optimization configuration, compares the operation frequency of the warehouse equipment before and after optimization, identifies the abnormal equipment behavior mode, determines the degree of deviation of the equipment behavior from the normal characteristics, traces the root cause of the system operation failure, and generates the equipment behavior characteristic analysis result; The abnormal behavior monitoring module comprises: The goods flow analysis submodule collects the goods access behavior data, analyzes the goods in-out time sequence characteristics, calculates the time interval of continuous access operation, counts the goods flow frequency fluctuation, compares the warehouse-in and warehouse-out flow ratio, identifies the abnormal goods flow area of the warehouse, and generates the goods flow index; The equipment operation monitoring submodule is based on the goods flow index, retrieves the equipment operation log of the abnormal area, analyzes the equipment operation frequency distribution in different time periods, calculates the short-term fluctuation range of the equipment operation, determines whether the equipment has abnormal operation behavior, and generates the equipment operation fluctuation index; The permission change statistical submodule is based on the equipment operation fluctuation index, calls the equipment permission change record, counts the permission change times, combines the goods flow characteristics and the equipment operation data, calculates the abnormal probability of the permission change, and generates the equipment abnormal operation index.

2. The cold storage automated warehousing system of claim 1, wherein, The fault prediction module comprises: The abnormal operation identification submodule is based on the equipment abnormal operation index, filters the operation nodes of the abnormal equipment, analyzes the correlation between the operation time and intensity and the equipment operation mode, calculates and classifies the abnormal operation distribution density, identifies the high-risk operation node, and generates the abnormal operation node set; The node risk matching submodule calls the abnormal operation node set, analyzes the operation behavior characteristics, compares the historical fault event mode, calculates the matching degree between the node and the fault event, evaluates the fault risk level of the operation node, and generates the node risk matching index; The fault prediction submodule is based on the node risk matching index, analyzes the operation frequency of the abnormal node, extracts the operation time interval characteristics, calculates the operation fluctuation range in the short period, combines the node risk level to predict the operation failure probability, and generates the equipment fault prediction result.

3. The cold storage automated warehousing and distribution system of claim 1, wherein, The dynamic strategy module comprises: The risk node identification submodule detects high-risk nodes in the warehouse network based on the equipment failure prediction result, analyzes the type and sensitivity level of goods stored in the nodes, screens warehouse nodes with operation risks, determines the range of nodes that need to migrate goods, and generates a risk node list; The migration priority analysis submodule analyzes the distribution state of goods in the affected nodes based on the risk node list, calculates the correlation strength and interaction frequency between nodes, determines the execution priority of the migration operation, and generates a warehouse location migration priority index; The storage optimization submodule analyzes the goods flow path between warehouse nodes based on the warehouse location migration priority index, allocates storage resources and plans an optimal migration path, adjusts the storage permissions of low-risk nodes, and generates an optimized warehouse node configuration.

4. The cold storage automated warehousing and distribution system of claim 1, wherein, The behavior analysis module includes: The behavior pattern analysis submodule calls the operation records of the optimized equipment based on the optimized warehouse node configuration, compares the differences in operation characteristics before and after optimization, analyzes the amplitude and frequency of changes in operation behavior, calculates the degree of deviation of the behavior pattern from the benchmark, and generates a behavior deviation index; The operation frequency comparison submodule compares the operation frequencies of the equipment before and after optimization based on the behavior deviation index, analyzes the changes in operation time points, duration, and frequency, determines the operation frequency fluctuation characteristics, and generates an operation frequency fluctuation index; The abnormal root cause tracing submodule identifies the equipment behavior deviating from the standard operation pattern based on the operation frequency fluctuation index, analyzes the correspondence between abnormal behavior characteristics and system failures, locates the root cause of the operation failure, and generates a device behavior characteristic analysis result.

5. The cold storage automated warehousing and distribution system of claim 1, wherein, The system further includes: The efficiency evaluation module evaluates the operation efficiency of the warehouse area based on the device behavior characteristic analysis result, calculates the device operation path optimization weight, updates the goods distribution strategy, and generates a warehouse efficiency optimization index; The efficiency evaluation module includes: a path optimization evaluation submodule that analyzes the redundancy of the device operation path and calculates the improvement weight of path optimization on warehouse efficiency; and a goods distribution strategy submodule that adjusts the goods distribution density based on the improvement weight and generates a warehouse efficiency optimization index.

6. The cold storage automated warehousing system of claim 5, wherein, The efficiency evaluation module includes: The regional load analysis submodule monitors the device operation load of each warehouse area based on the device behavior characteristic analysis result and calculates a regional load balancing coefficient; The strategy optimization submodule adjusts the priority weight of the goods distribution strategy based on the regional load balancing coefficient, optimizes the goods distribution density in high-frequency operation areas, and generates a warehouse efficiency optimization index.

7. The cold storage automated warehousing system of claim 6, wherein, The system further includes: The strategy aggregation module receives local optimization strategies of multiple warehouse areas, verifies the local optimization strategies based on a global efficiency model, and aggregates the verified local optimization strategies to generate a global warehouse strategy; The strategy aggregation module includes: a local strategy verification submodule that verifies the feasibility of the local optimization strategy by calling regional historical operation data; and a global strategy generation submodule that updates the global warehouse strategy by fusing the verified local strategies.

8. The cold storage automated warehousing system of claim 7, wherein, The strategy aggregation module includes: A strategy weight allocation submodule calculates the aggregation weight of the regional strategy based on the goods throughput and device operation duration of each warehouse area; The global updating submodule integrates the local optimization strategies based on the aggregated weights, generates a global warehouse strategy, and updates a system strategy library.

9. The cold storage automated warehousing and distribution system of claim 1, wherein, The system further comprises: The security response module identifies high-risk operating equipment based on the equipment behavior feature analysis result, adjusts an equipment operation permission coefficient, allocates an equipment operation task quota, updates an equipment operation verification rule, and generates an equipment security protection configuration. The security response module comprises: a permission control submodule that adjusts an operation permission level according to an equipment abnormal behavior frequency; a task allocation submodule that reallocates an equipment operation task amount based on the permission level; and a verification rule updating submodule that synchronously strengthens the operation verification rule of high-risk equipment.

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