A Warehousing Management Method for Emergency Supplies in Smart Depots

Abstract

This invention provides a warehouse management method for emergency supplies in smart facilities. It obtains production date, shelf life, and initial environmental parameters by parsing electronic tags on emergency supplies. A dynamic suitable temperature range is generated based on a nonlinear mapping between shelf life and initial temperature value, and a dynamic suitable humidity range is generated based on a correlation model between production date and initial humidity value. The overlap between the remaining shelf life and the suitable temperature range is calculated as a first priority factor, combined with the real-time warehouse load rate to generate a second dynamic allocation factor. A priority fusion algorithm is used to integrate and generate a material allocation matrix. The target warehouse environmental parameters are monitored in real time. When parameters exceed the suitable range, a multi-level adjustment strategy is triggered. If verification fails, a parameter self-learning engine is activated, achieving intelligent control and optimization of the warehouse environment, improving the safety and management efficiency of emergency supplies storage. This application can dynamically adapt to changes in material characteristics and optimize the precision of warehouse environment control.

Description

Technical Field

[0001] This invention relates to the field of smart facility management technology, and more specifically, to a method for the storage and management of emergency supplies in smart facilities. Background Technology

[0002] In today's society, with the acceleration of urbanization and the large-scale construction of various industrial sites, the warehousing and management of emergency supplies plays a crucial role in ensuring public safety and responding to sudden disasters. Traditional emergency supply warehousing and management largely relies on manual recording and inventory. This method is not only inefficient and prone to data errors, but also struggles to quickly and accurately dispatch and allocate supplies when facing large-scale sites and complex, ever-changing disaster situations. In recent years, with the development of information technology, some sites have begun to introduce electronic management systems, recording material information through simple databases. However, these systems are mostly single-function and lack effective integration with the actual geographical environment and real-time monitoring data of the sites, failing to achieve intelligent optimization of storage node layout and dynamic scheduling decisions. During disasters, information delays and unreasonable scheduling often prevent emergency supplies from reaching the required locations in a timely and effective manner, delaying rescue efforts and causing unnecessary losses.

[0003] Existing systems employ fixed temperature and humidity threshold control, failing to dynamically adjust based on material characteristics. For example, vaccines exhibit significantly different sensitivities to environmental parameters at different shelf-life stages, but current technologies typically use uniform control standards. This leads to energy waste in the early stages of shelf life and insufficient environmental protection precision in later stages. Secondly, the material allocation logic suffers from serious flaws. Existing solutions only consider single dimensions such as warehouse space utilization, failing to establish a dynamic matching mechanism between material characteristics and warehouse environmental parameters. This results in high-value materials potentially being allocated to storage areas with poor environmental stability. Furthermore, the environmental control system has inherent defects. Conventional PID control technology is prone to oscillations when multiple warehouses operate collaboratively, prolonging environmental stabilization time and accelerating equipment wear. Finally, the system lacks adaptability. When the physical structure of the warehouse changes, existing technologies require manual parameter reconfiguration and cannot adapt to environmental changes through autonomous learning. These problems collectively increase quality risks, operational costs, and response efficiency during emergency material storage. Therefore, existing technologies urgently need improvement to address these issues. Summary of the Invention

[0004] The purpose of this application is to provide a warehouse management method for emergency supplies in smart facilities, which has the advantages of dynamically adapting to changes in the characteristics of the supplies, optimizing the precision of warehouse environment control, improving the quality of emergency supplies storage, and reducing operating costs.

[0005] This application provides a warehouse management method for emergency supplies in smart facilities. The technical solution is as follows: A warehouse management method for emergency supplies in smart facilities includes the following steps: parsing the electronic tags of emergency supplies to obtain the production date, shelf life, and initial environmental parameters; generating a dynamic suitable temperature range based on the nonlinear mapping between shelf life and initial temperature value, and generating a dynamic suitable humidity range based on the correlation model between production date and initial humidity value; calculating the overlap between the remaining shelf life and the suitable temperature range as a first priority factor, and generating a second dynamic allocation factor by combining the real-time warehouse load rate; integrating the first priority factor and the second dynamic allocation factor through a priority fusion algorithm to generate a material allocation matrix; allocating emergency supplies to target warehouses according to the material allocation matrix; monitoring the environmental parameters of the target warehouse in real time, triggering a multi-level temperature adjustment strategy when the temperature value exceeds the dynamic suitable temperature range, and triggering a multi-level humidity adjustment strategy when the humidity value exceeds the dynamic suitable humidity range; executing a stability verification protocol after each environmental adjustment, and starting a parameter self-learning engine when the verification fails.

[0006] Furthermore, this application proposes a multi-level temperature regulation strategy including: adjusting the current temperature to an initial temperature reference point higher than the target temperature value; linearly adjusting towards the target temperature value at a first cooling rate; activating a stability monitoring window when the temperature sensor first detects that the temperature value has entered the dynamic temperature suitable range; if the temperature value remains stable during the monitoring window period, adjusting in the opposite direction at a first heating rate to the center point of the dynamic temperature suitable range; when the temperature value is detected to exceed the dynamic temperature suitable range, immediately reducing the regulation rate to a second cooling rate to continue adjusting towards the target temperature value; and switching to a third compensating heating rate for reverse balancing regulation after reaching the target temperature value; wherein the initial temperature reference point is greater than the target temperature value, the first cooling rate is greater than the second cooling rate, and the second cooling rate is greater than the third compensating heating rate.

[0007] Furthermore, this application proposes a stability verification protocol including: starting a countdown window after temperature adjustment is completed; continuously acquiring multiple sets of temperature monitoring data at a fixed sampling frequency within the countdown window; dynamically comparing each set of temperature monitoring data with the boundary value of the suitable dynamic temperature range; marking abnormal data points that exceed the boundary value; calculating the standard deviation of all temperature monitoring data; statistically analyzing the occurrence frequency of abnormal data points; inputting the standard deviation and the frequency of abnormal occurrence into a composite evaluation model to generate a stability coefficient; and when the stability coefficient exceeds a critical threshold, extracting the current temperature fluctuation feature vector and triggering a parameter self-learning engine.

[0008] Furthermore, this application proposes a multi-level humidity regulation strategy including: adjusting the current humidity to an initial humidity reference point higher than the target humidity value; linearly adjusting towards the target humidity value at a first dehumidification rate; activating a stability monitoring window when the humidity sensor first detects that the humidity value has entered the dynamic humidity suitable range; if the humidity value remains stable during the monitoring window period, adjusting in the opposite direction at a first humidification rate to the center point of the dynamic humidity suitable range; when the humidity value is detected to exceed the dynamic humidity suitable range, immediately reducing the adjustment rate to a second dehumidification rate to continue adjusting towards the target humidity value; and switching to a third compensating humidification rate for reverse balancing adjustment after reaching the target humidity value; wherein the initial humidity reference point is greater than the target humidity value, the first dehumidification rate is greater than the second dehumidification rate, and the second dehumidification rate is greater than the third compensating humidification rate.

[0009] Furthermore, this application proposes a stability verification protocol including: starting a countdown window after humidity adjustment is completed; continuously acquiring multiple sets of humidity monitoring data at a fixed sampling frequency within the countdown window; dynamically comparing each set of humidity monitoring data with the boundary value of the suitable dynamic humidity range; marking abnormal data points that exceed the boundary value; calculating the standard deviation of all humidity monitoring data; statistically analyzing the occurrence frequency of abnormal data points; inputting the standard deviation and the frequency of abnormal occurrence into a composite evaluation model to generate a stability coefficient; and when the stability coefficient exceeds a critical threshold, extracting the current humidity fluctuation feature vector and triggering a parameter self-learning engine.

[0010] Furthermore, this application proposes a parameter self-learning engine comprising: receiving fluctuation feature vectors transmitted by a stability verification protocol; extracting environmental regulation parameter record sets for the corresponding warehouse from a historical regulation database; constructing a multi-dimensional training dataset containing regulation rate, environmental deviation value, and actual time consumption; dynamically coupling historical regulation efficiency values ​​with the current benchmark rate through a weighted fusion algorithm; introducing fluctuation feature vectors as negative feedback factors to adjust rate weight allocation; and initiating a warehouse structure compatibility diagnosis process when the same target warehouse continuously triggers self-learning requests.

[0011] Furthermore, this application proposes a warehouse structure compatibility diagnostic process that includes: controlling an infrared scanning matrix to perform three-dimensional point cloud modeling of the warehouse space; analyzing temperature field distribution data to identify regions of abrupt changes in thermal gradient; calculating the percentage of the abrupt change region in the total warehouse volume; generating a warehouse space segmentation scheme when the percentage exceeds a set threshold; dividing the warehouse into multiple independent environmental control units based on thermal map characteristics; generating customized temperature and humidity control strategies for each environmental control unit; updating the warehouse configuration database and reloading environmental control parameters.

[0012] Furthermore, this application also proposes a method that includes a material receiving agreement: opening the target warehouse access control after verifying the encrypted signature of the requester's digital certificate; scanning the RFID tag of the transport vehicle to obtain the maximum load parameters; when the difference between the load parameters and the declared weight meets the safety margin, generating three-dimensional path planning data to guide the vehicle into the predetermined unloading area; activating the unloading area environmental preprocessing system to configure initial parameters according to the environmental requirements of the target material; monitoring the material placement position in real time through a laser positioning device; activating the automatic correction robotic arm when the position deviation exceeds the allowable error; and closing all access control and updating the material topology database after the material receiving is completed.

[0013] Furthermore, this application also proposes a method that includes a risk warning protocol: continuously collecting environmental sensor data streams to generate a sliding time window average; analyzing the fluctuation intensity characteristics of historical environmental data; using a linear regression model to predict future environmental trends when the fluctuation intensity is below a set threshold; enabling a recurrent neural network model to predict future environmental evolution when the fluctuation intensity is above a set threshold; monitoring the proximity of the prediction results to the boundary of the dynamic suitable range; and generating a three-level warning response command when multiple consecutive prediction cycles show that the boundary is approaching: the first level triggers the optimization of adjustment parameters, the second level activates the backup environmental control system, and the third level executes the emergency material transfer plan.

[0014] Furthermore, this application proposes a method for generating a material allocation matrix by integrating a first priority factor and a second dynamic allocation factor through a priority fusion algorithm, comprising: constructing a three-dimensional priority vector space based on the first priority factor, wherein the three-dimensional priority vector space includes a priority intensity dimension, a time decay dimension, and an environmental compatibility dimension; projecting the second dynamic allocation factor onto the three-dimensional priority vector space to generate a dynamic weight distribution map, wherein the dynamic weight distribution map includes a warehouse load rate weight layer and a material overlap degree weight layer; applying a convolutional neural network to perform multi-scale feature extraction on the dynamic weight distribution map to generate an optimized allocation template, wherein the optimized allocation template includes a material location priority score matrix; performing spatial matching calculation between the optimized allocation template and the topology map of the target warehouse to output a material allocation matrix; wherein the material allocation matrix includes the optimal storage location coordinates and allocation confidence values ​​of each emergency material in the target warehouse.

[0015] As can be seen from the above, the storage management method and system for emergency supplies in smart stations provided in this application solves the problems of insufficient environmental control precision, simple allocation logic and poor adaptability of traditional methods by dynamically generating suitable temperature and humidity ranges, priority allocation matrices and multi-level adjustment strategies, combined with stability verification and parameter self-learning mechanisms. It has the advantages of dynamically adapting to changes in material characteristics, optimizing warehouse environmental control precision, improving the storage quality of emergency supplies and reducing operating costs. Attached Figure Description

[0016] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated in the drawings by way of example and not limitation, wherein:

[0017] Figure 1 This is a flowchart illustrating a method for warehousing and management of emergency supplies in a smart facility, provided in an embodiment of the present invention.

[0018] Figure 2 This is a flowchart illustrating a multi-level temperature regulation strategy provided in an embodiment of the present invention.

[0019] Figure 3 This is a flowchart illustrating a multi-level humidity control strategy provided in an embodiment of the present invention. Detailed Implementation

[0020] The principles and spirit of the invention will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided merely to enable those skilled in the art to better understand and implement the invention, and are not intended to limit the scope of the invention in any way. Rather, these embodiments are provided to make the invention more thorough and complete, and to fully convey the scope of the invention to those skilled in the art.

[0021] Those skilled in the art will understand that embodiments of the present invention can be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present invention can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0022] It should be noted that the number of any elements in the accompanying drawings is for illustrative purposes only and not as a limitation, and any naming is for distinction only and has no limiting meaning.

[0023] The following is for reference. Figure 1This application proposes the following steps: parsing the electronic tags of emergency supplies to obtain production date, shelf life, and initial environmental parameters; generating a dynamic suitable temperature range based on a nonlinear mapping between shelf life and initial temperature value, and generating a dynamic suitable humidity range based on a correlation model between production date and initial humidity value; calculating the overlap between the remaining shelf life and the suitable temperature range as a first priority factor, and combining this with the real-time warehouse load rate to generate a second dynamic allocation factor; integrating the first priority factor and the second dynamic allocation factor through a priority fusion algorithm to generate a material allocation matrix; allocating emergency supplies to target warehouses according to the material allocation matrix; monitoring the environmental parameters of the target warehouse in real time, triggering a multi-level temperature adjustment strategy when the temperature value exceeds the dynamic suitable temperature range, and triggering a multi-level humidity adjustment strategy when the humidity value exceeds the dynamic suitable humidity range; executing a stability verification protocol after each environmental adjustment, and starting a parameter self-learning engine when verification fails.

[0024] It should be noted that the dynamic suitable temperature range refers to a temperature control interval that changes over time based on the nonlinear relationship between the shelf life of materials and the initial temperature value. Specifically, this can be achieved by establishing a mapping relationship between the remaining shelf life and the temperature threshold using an exponential decay model or multinomial regression algorithm. Dynamically adjusting the temperature range avoids excessive energy consumption in the early stages of storage and the risk of material failure later. The dynamic suitable humidity range refers to a dynamic adjustment mechanism for the humidity control interval established based on the correlation between the production date of materials and the initial humidity value. Specifically, this can be achieved by constructing a correlation model between the production date and humidity requirements using time series analysis methods, ensuring humidity adaptability at different storage stages. The first priority factor is a quantitative indicator of the time matching degree between the remaining shelf life of materials and the current suitable temperature range. Specifically, this can be achieved by calculating the overlap between the remaining shelf life and the endpoints of the temperature range using Euclidean distance, used to identify materials nearing their expiration date that require priority processing. The second dynamic allocation factor is a dynamic weighting parameter combining the real-time storage capacity of the warehouse and the volume characteristics of the materials. Specifically, this can be achieved by using convolution operations between load rate sensor data and the three-dimensional dimensions of the materials, preventing high-priority materials from being allocated to environmentally unstable areas. The material allocation matrix refers to the material storage location optimization decision table generated by the fusion of multi-dimensional features. Specifically, it can be implemented by orthogonally projecting priority factors and allocation factors using a matrix factorization algorithm to achieve the optimal warehouse layout under multi-objective constraints.

[0025] Multi-level temperature regulation strategy refers to a control method that adjusts the rate of temperature change in stages. Specifically, it can be implemented using a piecewise linear controller combined with a feedforward compensation mechanism to suppress overshoot and oscillation caused by traditional PID control. Multi-level humidity regulation strategy refers to control logic that dynamically switches the dehumidification rate based on the degree of humidity deviation. Specifically, it can be implemented using a fuzzy control rule base combined with a rate limiter to avoid condensation problems caused by rapid humidity changes. Stability verification protocol refers to the system state confirmation process after environmental parameter adjustment. Specifically, it can be implemented using sliding window variance detection combined with anomaly detection algorithms to ensure that environmental parameters remain stable within the target range. Parameter self-learning engine refers to an adaptive optimization module based on historical adjustment data. Specifically, it can be implemented using a reinforcement learning framework combined with a Bayesian optimization algorithm to automatically correct control parameter deviations caused by changes in warehouse structure.

[0026] This application first parses the electronic tags of emergency supplies to obtain production date, shelf life, and initial environmental parameters. A dynamic suitable temperature range is generated based on a nonlinear mapping between shelf life and initial temperature value, and a dynamic suitable humidity range is generated based on a correlation model between production date and initial humidity value. The overlap between the remaining shelf life and the suitable temperature range is calculated as the first priority factor, and combined with the real-time warehouse load rate to generate a second dynamic allocation factor. A priority fusion algorithm integrates the first priority factor and the second dynamic allocation factor to generate a material allocation matrix. Emergency supplies are allocated to target warehouses according to the material allocation matrix. The environmental parameters of the target warehouse are monitored in real time. When the temperature value exceeds the dynamic suitable temperature range, a multi-level temperature adjustment strategy is triggered; when the humidity value exceeds the dynamic suitable humidity range, a multi-level humidity adjustment strategy is triggered. After each environmental adjustment, a stability verification protocol is executed; if verification fails, a parameter self-learning engine is activated.

[0027] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0028] The electronic tags for emergency supplies use RFID technology to store information such as production date, shelf life, and initial environmental parameters. RFID readers parse the tag data and transmit the information to the central control system.

[0029] The dynamic suitable temperature range is generated through a nonlinear mapping function between shelf life and initial temperature. The mapping function uses piecewise polynomial fitting, with different function parameters selected based on the material type. The dynamic suitable humidity range is generated based on a correlation model between production date and initial humidity value, constructed using time series analysis methods.

[0030] The first priority factor is derived by calculating the degree of overlap between the remaining shelf life and the suitable temperature range. The overlap is calculated using the integral area comparison method. The second dynamic allocation factor is generated by the real-time warehouse load rate, which is calculated based on warehouse space utilization and material weight distribution.

[0031] The priority fusion algorithm uses a weighted summation method to integrate the first priority factor and the second dynamic allocation factor to generate a material allocation matrix. The matrix elements represent the suitability scores of materials in each warehouse location.

[0032] Based on the material allocation matrix, the system controls automated handling equipment to allocate emergency supplies to designated locations in the target warehouse.

[0033] The environmental monitoring system employs a distributed sensor network to collect temperature and humidity data in real time. When environmental parameters are detected to exceed the dynamic suitable range, a multi-level adjustment strategy is triggered.

[0034] The multi-stage temperature control strategy comprises three phases: initial rapid adjustment, fine adjustment, and micro-adjustment, achieved by dynamically adjusting the power output of the cooling / heating equipment. The multi-stage humidity control strategy is similar, implemented by controlling the humidifier and dehumidifier.

[0035] The stability verification protocol evaluates the adjustment effect by analyzing the fluctuation characteristics of environmental parameters. When verification fails, the parameter self-learning engine optimizes the adjustment parameters based on historical data and the current state.

[0036] Through the above-described scheme, this application achieves precise environmental control throughout the entire lifecycle of emergency supplies. The generation of dynamic temperature and humidity ranges ensures optimal storage conditions for supplies at different stages. The priority-based allocation strategy improves storage space utilization while guaranteeing environmental stability for high-value supplies. The combination of multi-level adjustment strategies and stability verification effectively suppresses environmental fluctuations, reducing equipment wear and energy consumption. The parameter self-learning mechanism further enhances the system's adaptability and robustness. These technological innovations collectively improve the efficiency and reliability of emergency supply storage management, providing stronger material support for emergency response.

[0037] In some embodiments, such as Figure 2 As shown, the multi-level temperature regulation strategy includes: adjusting the current temperature to an initial temperature reference point higher than the target temperature value; linearly adjusting towards the target temperature value at a first cooling rate; activating a stability monitoring window when the temperature sensor first detects that the temperature value has entered the dynamic temperature suitable range; if the temperature value remains stable during the monitoring window period, adjusting in the opposite direction at a first heating rate to the center point of the dynamic temperature suitable range; when the temperature value is detected to exceed the dynamic temperature suitable range, immediately reducing the regulation rate to a second cooling rate to continue adjusting towards the target temperature value; and switching to a third compensating heating rate for reverse balancing regulation after reaching the target temperature value; wherein the initial temperature reference point is greater than the target temperature value, the first cooling rate is greater than the second cooling rate, and the second cooling rate is greater than the third compensating heating rate.

[0038] The initial temperature reference point is set with a preset offset higher than the target temperature value; for example, it is set to 1.2 times the upper limit of the dynamic temperature suitable range. The first cooling rate adopts a rapid cooling mode, and the specific value is determined according to the ratio of warehouse volume to refrigeration equipment power. When the temperature enters the dynamic temperature suitable range, the duration of the activated stability monitoring window is related to the warehouse thermal inertia parameter and can be set from 30 seconds to 2 minutes. The first heating rate is 30%-50% of the first cooling rate, the second cooling rate is 60%-80% of the first cooling rate, and the third compensating heating rate is 20%-40% of the second cooling rate.

[0039] Specifically, temperature regulation starts from an initial temperature reference point and rapidly approaches the target temperature value using a first cooling rate. When the temperature first enters the suitable dynamic temperature range, the system pauses rapid cooling and activates a stability monitoring window. If the temperature remains stable within the monitoring window, the system reverses its adjustment to the center of the temperature range to prevent the temperature from continuously dropping beyond the lower limit due to inertia. When the temperature exceeds the suitable range, the adjustment rate is immediately reduced to a second cooling rate, gradually approaching the target temperature value. After reaching the target temperature, the system switches to a third compensating heating rate to offset temperature fluctuations caused by equipment margin. For example, when the target temperature is 5°C, the initial reference point is set to 6°C, the first cooling rate decreases by 0.8°C per minute, the second cooling rate is adjusted to decrease by 0.5°C per minute, and the third compensating heating rate is set to increase by 0.2°C per minute. Through multi-level rate switching and reverse balance adjustment, the temperature oscillation amplitude is effectively suppressed, reducing the standard deviation of temperature fluctuations to below 40% of that of traditional methods.

[0040] As a preferred embodiment, the multi-stage temperature regulation strategy includes the following steps:

[0041] First, adjust the current temperature to an initial temperature reference point higher than the target temperature value. For example, the target temperature is 20°C, and the initial temperature reference point is set to 25°C.

[0042] Secondly, the temperature is linearly adjusted towards the target temperature at a first cooling rate. The first cooling rate can be set to 1℃ / minute.

[0043] When the temperature sensor first detects that the temperature value has entered the suitable dynamic temperature range, the stability monitoring window is activated. The duration of the stability monitoring window can be set to 10 minutes.

[0044] If the temperature remains stable during the monitoring window, the temperature will be adjusted in the opposite direction at the first heating rate until it reaches the center of the suitable dynamic temperature range. The first heating rate can be set to 0.5℃ / minute.

[0045] When the detected temperature value exceeds the suitable dynamic temperature range, the adjustment rate is immediately reduced to the second cooling rate to continue adjusting towards the target temperature value. The second cooling rate can be set to 0.3℃ / minute.

[0046] Once the target temperature is reached, the system switches to the third compensation heating rate for reverse balance adjustment. The third compensation heating rate can be set to 0.1℃ / minute.

[0047] In some embodiments, the stability verification protocol includes: starting a countdown window after temperature adjustment is completed; continuously acquiring multiple sets of temperature monitoring data at a fixed sampling frequency within the countdown window; dynamically comparing each set of temperature monitoring data with the boundary value of the dynamic temperature suitable range; marking abnormal data points that exceed the boundary value; calculating the standard deviation of all temperature monitoring data; statistically analyzing the occurrence frequency of abnormal data points; inputting the standard deviation and the occurrence frequency of abnormal data points into a composite evaluation model to generate a stability coefficient; and when the stability coefficient exceeds a critical threshold, extracting the current temperature fluctuation feature vector and triggering a parameter self-learning engine.

[0048] Specifically, the countdown window is set to a fixed duration interval; the fixed sampling frequency is set to collect temperature data once per minute; the dynamic threshold comparison uses a sliding window algorithm to update boundary values ​​in real time; outlier data points are marked using binary identifiers; the standard deviation calculation uses the moving average method to eliminate random noise; the anomaly frequency statistics use a time-weighted algorithm; the composite evaluation model includes a linear regression layer and an activation function layer, with an output stability coefficient ranging from 0 to 1; the critical threshold is set to an empirical value of 0.85; and the temperature fluctuation feature vector includes three dimensions: fluctuation amplitude, periodicity, and trend term.

[0049] Specifically, a countdown window is initiated immediately after temperature adjustment, collecting temperature data every minute during the window's duration. Each data set is compared in real-time with the current effective temperature range boundary values ​​using a dynamic threshold comparison module; outliers exceeding the boundaries are marked and timestamped. All collected data undergoes a standard deviation calculation module, employing a moving average method to eliminate transient interference and obtain a quantitative indicator reflecting the intensity of temperature fluctuations. The anomaly frequency statistics module calculates the percentage of anomalies per unit time based on the time distribution of the marked data.

[0050] In a preferred embodiment, a countdown window is initiated after temperature adjustment is complete. The countdown window is set to 30 minutes. Multiple sets of temperature monitoring data are continuously acquired within the countdown window at a fixed sampling frequency of once every 5 seconds. Each set of temperature monitoring data is dynamically compared to the boundary value of the suitable temperature range. The boundary value of the suitable temperature range is set to 18℃ to 22℃. Abnormal data points exceeding the boundary value are marked. The standard deviation of all temperature monitoring data is calculated. The frequency of occurrence of abnormal data points is statistically analyzed. The standard deviation and the frequency of occurrence are input into a composite evaluation model to generate a stability coefficient. The composite evaluation model uses a weighted average algorithm, where the standard deviation has a weight of 0.6 and the frequency of occurrence has a weight of 0.4. When the stability coefficient exceeds the critical threshold of 0.8, the current temperature fluctuation feature vector is extracted, and the parameter self-learning engine is triggered. The temperature fluctuation feature vector includes three dimensions: fluctuation amplitude, fluctuation period, and fluctuation trend.

[0051] In some embodiments, such as Figure 3 As shown, the multi-level humidity control strategy includes: adjusting the current humidity to an initial humidity reference point higher than the target humidity value; linearly adjusting towards the target humidity value at a first dehumidification rate; activating a stability monitoring window when the humidity sensor first detects that the humidity value has entered the dynamic humidity suitable range; if the humidity value remains stable during the monitoring window period, adjusting in the opposite direction at a first humidification rate to the center point of the dynamic humidity suitable range; when the humidity value is detected to exceed the dynamic humidity suitable range, immediately reducing the adjustment rate to a second dehumidification rate to continue adjusting towards the target humidity value; and switching to a third compensating humidification rate for reverse balancing adjustment after reaching the target humidity value; wherein the initial humidity reference point is greater than the target humidity value, the first dehumidification rate is greater than the second dehumidification rate, and the second dehumidification rate is greater than the third compensating humidification rate.

[0052] For example, the initial humidity reference point can be set 5%-8% higher than the target humidity value. The first dehumidification rate can be set to decrease the humidity value by 3%-5% per minute, the second dehumidification rate can be adjusted to decrease the humidity value by 1%-2% per minute, and the third compensating humidification rate can be controlled to increase the humidity value by 0.5%-1% per minute. The stability monitoring window duration is set to 10-15 minutes, and the humidity fluctuation range during the window period must be less than 10% of the width of the suitable dynamic humidity range. When adjusting back to the center point, the first humidification rate adopts the same absolute value as the first dehumidification rate, but in the opposite direction.

[0053] In the initial stage, a benchmark point higher than the target value is set to ensure sufficient operational margin in the humidity adjustment process, preventing premature entry into the target range due to environmental disturbances. The first dehumidification rate rapidly approaches the target range boundary at a high speed. When the sensor detects entry into the target range, a stability monitoring window is immediately activated. If the humidity value does not exceed the range during the monitoring period, a reverse adjustment mechanism is triggered to pull the humidity value back to the center point at a symmetrical rate, eliminating the inertial deviation caused by unidirectional adjustment. If the humidity exceeds the range, the system automatically switches to a lower second dehumidification rate to continue adjustment, preventing continuous oscillations caused by adjustment inertia. After reaching the target value, the third compensating humidification rate offsets natural environmental decay through a small reverse adjustment. When the humidity in the warehouse naturally decreases due to ventilation, the compensating humidification can maintain the long-term stability of the target humidity value.

[0054] Through the above technical solutions, this application realizes a multi-level humidity regulation strategy, effectively avoiding the humidity fluctuation problem caused by traditional single regulation rates. Therefore, the system can flexibly adjust the regulation rate according to real-time humidity changes, improving the accuracy and stability of humidity control. In some of the solutions described above, the multi-level humidity regulation strategy achieves humidity control through staged regulation rates. However, after regulation, residual environmental fluctuations or equipment inertia may cause the humidity value to exceed the dynamic suitable range again. In this case, the system cannot effectively verify the continuous stability of the regulation result, posing a risk of triggering secondary regulation.

[0055] In some embodiments, a countdown window is started after humidity adjustment is completed; multiple sets of humidity monitoring data are continuously acquired at a fixed sampling frequency within the countdown window; each set of humidity monitoring data is dynamically compared with the boundary value of the suitable dynamic humidity range; abnormal data points exceeding the boundary value are marked; the standard deviation of all humidity monitoring data is calculated; the occurrence frequency of abnormal data points is statistically analyzed; the standard deviation and the occurrence frequency of abnormal data points are input into the composite evaluation model to generate a stability coefficient; when the stability coefficient exceeds the critical threshold, the current humidity fluctuation feature vector is extracted and the parameter self-learning engine is triggered.

[0056] The countdown window duration is set to a preset time period after the adjustment operation is completed; for example, it can be set to 5-10 minutes. The fixed sampling frequency is 1 humidity data acquisition per second. The dynamic threshold comparison uses a sliding window algorithm to compare real-time humidity data with the boundary values ​​of the suitable dynamic humidity range in real time. Abnormal data points are marked using a binary marking method; data exceeding the boundary value is marked as 1, otherwise marked as 0. The standard deviation is calculated using the humidity data sequence within the sliding window, obtained by taking the square root of the variance. The anomaly frequency is the ratio of the number of data points marked as 1 to the total number of sampling points. The composite evaluation model is a linear weighted model with a standard deviation weighting coefficient of 0.6 and an anomaly frequency weighting coefficient of 0.4. The critical threshold is set to 0.85; when the stability coefficient exceeds this value, the parameter self-learning engine is triggered.

[0057] After completing multi-level humidity adjustment, the system starts a countdown window and begins collecting humidity data once per second. The collected humidity data is first compared in real-time with the real-time boundary values ​​of the dynamic humidity suitable range. When data exceeds the boundary values, the system automatically marks the data point as an outlier. At the end of the countdown window, the system calculates the standard deviation of all collected data, reflecting the amplitude of humidity fluctuations, and simultaneously calculates the frequency of outlier occurrences, reflecting the frequency of humidity exceeding the limits. After inputting the standard deviation and outlier frequency into a composite evaluation model, the model outputs a stability coefficient within the range of 0-1.

[0058] In some embodiments, when the standard deviation is 0.3 and the anomaly frequency is 15%, the stability coefficient is 0.3×0.6+0.15×0.4=0.24, which is lower than the critical threshold of 0.85, and the system determines that the adjustment result is stable; if the standard deviation is 0.8 and the anomaly frequency is 30%, the stability coefficient is 0.8×0.6+0.3×0.4=0.6, which is still lower than the threshold; when the standard deviation reaches 1.2 and the anomaly frequency is 50%, the stability coefficient is 1.2×0.6+0.5×0.4=0.92, which exceeds the threshold and triggers the self-learning engine.

[0059] In some embodiments, the parameter self-learning engine includes receiving the fluctuation feature vector transmitted by the stability verification protocol, extracting the environmental regulation parameter record set of the corresponding warehouse from the historical regulation database, constructing a multi-dimensional training dataset containing regulation rate, environmental deviation value, and actual time consumption, dynamically coupling the historical regulation efficiency value with the current benchmark rate through a weighted fusion algorithm, introducing the fluctuation feature vector as a negative feedback factor to adjust the rate weight allocation, and initiating the warehouse structure compatibility diagnosis process when the same target warehouse continuously triggers self-learning requests.

[0060] Furthermore, when warehouse shelves shift, obstructing airflow channels, the 3D point cloud model generated by infrared scanning can detect regions of abrupt changes in thermal gradient. The system calculates the volume percentage of these abrupt changes; if it exceeds a preset threshold, the original warehouse is divided into multiple independent control units based on thermal map characteristics. For example, a 30-meter-long warehouse is divided into three 10-meter-long independent units, each equipped with an independent temperature and humidity controller. After the division scheme is generated, the system automatically updates the warehouse configuration database, splitting the original warehouse ID into multiple sub-warehouse IDs and reloading the environmental control parameters for each sub-unit. This process effectively eliminates environmental control inaccuracies caused by structural deformation through spatial division. For example, the original overall regulation rate of 35℃ / h can be adjusted to an independent regulation rate range of 28-42℃ / h for each sub-unit, improving the temperature field distribution uniformity to over 92%.

[0061] In some embodiments, the parameter self-learning engine receives a fluctuation feature vector transmitted by the stability verification protocol. The fluctuation feature vector contains information such as the fluctuation amplitude, frequency and duration of temperature or humidity. The fluctuation feature vector can be represented as [0.5℃, 2Hz, 30min], which represent the temperature fluctuation amplitude, frequency and duration, respectively.

[0062] Extract the environmental regulation parameter record set for the corresponding warehouse from the historical regulation database. The historical regulation database stores the regulation parameter records for each warehouse, including the regulation rate, environmental deviation value, and actual time. A record for a warehouse can be represented as [0.2℃ / min, 1.5℃, 15min], representing the regulation rate, environmental deviation value, and actual time, respectively.

[0063] Construct a multidimensional training dataset containing adjustment rate, environmental deviation value, and actual time consumption. Organize the data in the historical data set into a multidimensional matrix, with each row representing the parameter combination of one adjustment process.

[0064] A weighted fusion algorithm dynamically couples historical adjustment efficiency values ​​with the current benchmark rate. The weighted fusion algorithm can employ an exponential moving average method, assigning higher weights to recent data. The adjustment rate can be expressed as: New Rate = α × Current Benchmark Rate + (1-α) × Historical Average Rate, where α is the weighting coefficient. The value of α can be dynamically adjusted based on the value of the fluctuation characteristic vector. When the fluctuation amplitude is large, the value of α is reduced to decrease the impact of the current benchmark rate.

[0065] When the same target warehouse triggers self-learning requests consecutively, the warehouse structure compatibility diagnostic process is initiated. The threshold for consecutive triggers can be set to 3 times. The diagnostic process will re-evaluate and adjust the warehouse space structure.

[0066] In some embodiments, the warehouse structure compatibility diagnosis process includes: controlling an infrared scanning matrix to perform three-dimensional point cloud modeling of the warehouse space; analyzing temperature field distribution data to identify regions of abrupt changes in thermal gradient; calculating the percentage of the abrupt change region in the total warehouse volume; generating a warehouse space segmentation scheme when the percentage exceeds a set threshold; dividing the warehouse into multiple independent environmental control units based on thermal map characteristics; generating customized temperature and humidity control strategies for each environmental control unit; updating the warehouse configuration database and reloading environmental control parameters.

[0067] The infrared scanning matrix consists of multiple arrays of infrared sensors, covering the three-dimensional space of the warehouse along a preset scanning path. 3D point cloud modeling accuracy is controlled by point cloud density parameters, with the point spacing adjustable from 5 to 10 centimeters. Regions exhibiting abrupt changes in thermal gradients are defined as those with a temperature change rate exceeding 0.5℃ / m². 2 The continuous spatial region is defined. A threshold value of 5% of the total warehouse volume is set; spatial segmentation is triggered when the proportion of abrupt change areas reaches this threshold. The division of independent environmental control units is based on the continuity characteristics of temperature distribution in the thermal map, and the segmentation boundaries can be automatically generated using the Voronoi diagram algorithm.

[0068] More specifically, when warehouse shelf displacement causes abnormal thermal distribution, the infrared scanning matrix acquires spatial temperature data at a scanning frequency of 20 profiles per second. The point cloud modeling module converts the discrete temperature data into a 3D mesh model with temperature attributes, with the mesh cell size set to 10cm×10cm×10cm. The temperature field analysis algorithm detects the temperature gradient between adjacent meshes and marks the boundaries of regions where the gradient value exceeds a threshold. The volume calculation module accumulates the number of cubic meshes in all abrupt regions; when the total exceeds 5% of the total number of meshes in the warehouse, the spatial segmentation engine is activated. During the segmentation scheme generation process, a region growing algorithm based on thermal gradients is used to merge temperature-continuous regions into independent control units. Each unit is configured with an independent environmental control strategy, including differentiated temperature setpoints and adjustment rate parameters. The updated warehouse configuration database is synchronized to the execution terminals of each environmental control unit through distributed storage nodes.

[0069] In some embodiments, the material receiving protocol includes: opening the target warehouse access control after verifying the encrypted signature of the requester's digital certificate; scanning the RFID tag of the transport vehicle to obtain the maximum load parameter; generating three-dimensional path planning data to guide the vehicle into the predetermined unloading area when the difference between the load parameter and the declared weight meets the safety margin; activating the unloading area environmental preprocessing system to configure initial parameters according to the environmental requirements of the target material; monitoring the material placement position in real time through a laser positioning device; activating the automatic correction robotic arm when the position deviation exceeds the allowable error; and closing all access control systems and updating the material topology database after the material receiving is completed.

[0070] The encrypted signature verification uses an asymmetric encryption algorithm to decrypt and verify the digital certificate. The three-dimensional path planning data is generated into a warehouse three-dimensional model through LiDAR scanning, and the minimum turning radius is calculated by combining the vehicle wheelbase parameters.

[0071] Specifically, when a transport vehicle arrives at the warehouse, the access control system first intercepts the encrypted signature of the digital certificate and decrypts and verifies it using the public key pre-installed in the security chip. After successful verification, the radio frequency reader scans the electronic tag embedded in the vehicle chassis, extracts the maximum load parameter, and compares it with the data declared in the logistics system. When the load difference is within the safety margin, the path planning engine uses point cloud data to construct a 3D navigation map and generates a guiding path that includes turning warning points and speed restriction zones.

[0072] In a preferred embodiment, during the material receiving process, when a transport vehicle arrives at the station entrance, the system first verifies the 2048-bit RSA encrypted signature in the X.509 standard digital certificate provided by the requester. Upon successful verification, the electronic access control system of the target warehouse is activated, and simultaneously, a millimeter-wave radar array deployed at the entrance scans the vehicle's chassis structure. The acquired RFID data is decoded to extract the vehicle's maximum load parameters, and the difference between this and the declared weight is calculated. When the difference falls within a preset ±5% safety margin range, a three-dimensional path planning data package containing height restrictions and turning radius is generated. This data package guides the vehicle along a preset trajectory into the unloading area via an onboard terminal, during which a laser rangefinder monitors the vehicle's deviation in real time. When the unloading area environmental preprocessing system is activated, it automatically matches initial temperature and humidity parameters based on the category code of the materials to be received and establishes an environmental baseline through a distributed sensor array. During material unloading, a laser positioning device deployed on the warehouse roof collects the coordinates of the QR code on the outer packaging of the materials at a frequency of 10Hz. When the deviation between the pallet positioning point and the preset coordinates exceeds 50mm, a six-degree-of-freedom hydraulic robotic arm is triggered to perform a lateral correction operation. When the warehousing process is completed, the system automatically generates timestamp data containing material location codes and environmental parameter records, and synchronously updates it to the distributed material topology database.

[0073] In some embodiments, the access control to the target warehouse is opened after verifying the encrypted signature of the requester's digital certificate to ensure the legality of the operation; the maximum load parameter is obtained by scanning the radio frequency identification of the transport vehicle; when the difference between the load parameter and the declared weight meets the safety margin, three-dimensional path planning data is generated; the safety margin can be set to within 5% to allow the vehicle to enter; the environmental pretreatment system of the unloading area is activated to configure the initial parameters according to the environmental requirements of the target materials, specifically including adjusting the temperature to the center value of the dynamic suitable range 2 hours in advance; the placement position of the materials is monitored in real time by a laser positioning device; when the position deviation exceeds the allowable error, the automatic correction robotic arm is activated, and the error threshold is set to ±5 cm; after the materials are put into storage, all access control is closed and the material topology database is updated.

[0074] Encrypted signature verification is completed through a blockchain certificate verification module to prevent unauthorized access. Load parameter scanning uses UHF RFID technology to read the vehicle's electronic tag in real time; an early warning mechanism is triggered when the declared weight deviates from the measured load by more than 3%. 3D path planning generates the optimal driving path based on the BIM model, avoiding obstacles within the warehouse and matching the vehicle's turning radius.

[0075] The entire process can be linked through IoT gateways, and upon completion of warehousing, timestamp data containing material coordinates and environmental parameters is automatically generated and written to the blockchain database. This achieves closed-loop control of the warehousing process, eliminates human error, and ensures that materials are under control from the very beginning of the warehousing process.

[0076] In one preferred embodiment, the risk warning protocol execution process includes continuously collecting temperature and humidity sensor data streams within the warehouse and generating a sliding time window average at five-minute intervals. Historical environmental data is input into a feature analysis module, which determines the intensity of fluctuations by calculating the variance of data within adjacent time windows. When the variance is detected to be below 0.5 for three consecutive hours, a linear regression model is activated to predict the environmental trend curve for the next six hours; when the variance exceeds 0.5, a pre-trained recurrent neural network model is switched to predict environmental evolution. The prediction results are compared in real time with the boundary values ​​of the dynamic temperature and humidity suitable range. When the temperature value approaches the upper limit of the dynamic temperature suitable range for three consecutive prediction cycles, a three-level response is triggered sequentially: the first-level response optimizes the temperature regulation parameters by adjusting the fan speed setpoint of the air conditioning unit; the second-level response activates the backup condensation device deployed on the top of the warehouse; and the third-level response generates an emergency material transfer plan including transfer route planning and a target warehouse matching list, while simultaneously activating an automated handling robot to perform the transfer task.

[0077] In some embodiments, the scheme of integrating a first priority factor and a second dynamic allocation factor to generate a material allocation matrix using a priority fusion algorithm includes: constructing a three-dimensional priority vector space based on the first priority factor, the three-dimensional priority vector space including a priority intensity dimension, a time decay dimension, and an environmental compatibility dimension; projecting the second dynamic allocation factor onto the three-dimensional priority vector space to generate a dynamic weight distribution map, the dynamic weight distribution map including a warehouse load rate weight layer and a material overlap weight layer; applying a convolutional neural network to perform multi-scale feature extraction on the dynamic weight distribution map to generate an optimized allocation template, the optimized allocation template including a material location priority score matrix; performing spatial matching calculation between the optimized allocation template and the topology map of the target warehouse to output a material allocation matrix; wherein the material allocation matrix includes the optimal storage location coordinates and allocation confidence values ​​of each emergency material in the target warehouse.

[0078] A three-dimensional priority vector space quantifies material storage demand through three orthogonal dimensions. The priority intensity dimension reflects the degree of matching between the remaining shelf life of materials and current environmental conditions; the time decay dimension characterizes the sensitivity of environmental demand to changes over time; and the environmental compatibility dimension assesses the suitability of materials for existing warehouse storage conditions. The dynamic weight distribution map uses a dual-channel data overlay method. The warehouse load rate weight layer maps the real-time capacity status of each warehouse, while the material overlap weight layer indicates the degree of matching between material environmental demand and warehouse conditions. The convolutional neural network uses a three-layer convolutional kernel structure: the first layer extracts warehouse area features, the second layer identifies material distribution patterns, and the third layer generates a spatial priority distribution heatmap. Spatial matching calculation uses the Euclidean distance algorithm to compare the similarity between the two-dimensional coordinates of the optimized allocation template and the three-dimensional coordinates of the warehouse topology map.

[0079] Furthermore, when constructing the three-dimensional priority vector space, the storage requirements of each material are converted into vector coordinates. The priority intensity dimension value is calculated from the overlap between the remaining shelf life and the suitable temperature range, the time decay dimension value is exponentially decayed based on the time difference between the production date and the current time, and the environmental compatibility dimension value is determined by the difference between the suitable humidity range and the current humidity in the warehouse.

[0080] When generating the dynamic weight distribution map, the warehouse load rate weight layer uses a color gradient to represent the remaining capacity ratio of each warehouse, while the material overlap weight layer reflects the degree of environmental matching through a transparency parameter. When applying convolutional neural network processing, the first layer of 5×5 convolutional kernels identifies the boundary features of warehouse areas, the second layer of 3×3 convolutional kernels detects material clustering patterns, and the third layer of 1×1 convolutional kernels generates a priority scoring matrix for each storage location.

[0081] When performing spatial matching calculations, the priority score matrix is ​​compared point by point with the shelf coordinates on the warehouse topology map. The coordinate point with the smallest Euclidean distance is selected as the optimal storage location. At the same time, the difference in environmental compatibility between this location and adjacent materials is calculated as the sub-configuration confidence value.

[0082] Preferably, when constructing the three-dimensional priority vector space, the priority intensity dimension maps the overlap between the remaining shelf life of the material and the suitable temperature range, the time decay dimension corresponds to the duration coefficient from the material's production date to the current time, and the environmental compatibility dimension reflects the matching degree between the material's initial humidity parameters and the warehouse's current humidity conditions. A matrix multiplication operation is performed between the warehouse's real-time load rate and the material type weight factor to generate a dynamic weight distribution map containing a warehouse load rate weight layer and a material overlap weight layer. The warehouse load rate weight layer is composed of the current storage capacity ratio of each warehouse, and the material overlap weight layer is generated based on the matching degree matrix between the material type and the warehouse's environmental control capabilities. A pre-trained convolutional neural network model is used to perform feature extraction on the dynamic weight distribution map. The first convolutional layer uses a 3×3 kernel to extract local load features, and the second convolutional layer captures cross-warehouse association patterns through dilated convolution. Finally, an optimized allocation template containing a material location priority score matrix is ​​output. When performing spatial matching calculations between the optimized allocation template and the 3D topological map of the target warehouse, the Hungarian algorithm can also be used to solve the optimal correspondence between material coordinates and warehouse storage units, outputting a material allocation matrix containing the optimal storage location coordinates and sub-configuration confidence values. The sub-configuration confidence values ​​are calculated inversely proportional to the Euclidean distance between the coordinate points and the stable zone of the warehouse environment.

[0083] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for warehousing and management of emergency supplies for smart stations, characterized in that, Includes the following steps: Analyze the electronic tags of emergency supplies to obtain production date, shelf life, and initial environmental parameters; A dynamic suitable temperature range is generated based on a nonlinear mapping between shelf life and initial temperature value, and a dynamic suitable humidity range is generated based on a correlation model between production date and initial humidity value. The degree of overlap between the remaining shelf life and the suitable temperature range is calculated as the first priority factor, and combined with the real-time warehouse load rate to generate the second dynamic allocation factor. A material allocation matrix is ​​generated by integrating the first priority factor and the second dynamic allocation factor through a priority fusion algorithm. Emergency supplies are allocated to target warehouses based on the material allocation matrix; Real-time monitoring of target warehouse environmental parameters; triggering multi-level temperature adjustment strategy when temperature exceeds dynamic temperature suitable range; triggering multi-level humidity adjustment strategy when humidity exceeds dynamic humidity suitable range. After each environmental adjustment, a stability verification protocol is executed, and a parameter self-learning engine is started when the verification fails. The stability verification protocol includes: Start the countdown window after temperature adjustment is complete; Multiple sets of temperature monitoring data are continuously acquired at a fixed sampling frequency within the countdown window; Each set of temperature monitoring data is dynamically compared with the boundary value of the suitable dynamic temperature range using a dynamic threshold. Mark outlier data points that exceed the boundary values; Calculate the standard deviation of all temperature monitoring data; Analyze the frequency of occurrence of outlier data points; The standard deviation and the frequency of anomalies are input into the composite evaluation model to generate the stability coefficient. When the stability coefficient exceeds the critical threshold, extract the current temperature fluctuation feature vector and trigger the parameter self-learning engine. The parameter self-learning engine includes: Receive the fluctuation feature vector transmitted by the stability verification protocol; Extract the environmental regulation parameter record set for the corresponding warehouse from the historical regulation database; Construct a multidimensional training dataset that includes adjustment rate, environmental deviation value, and actual time consumption; The historical adjustment efficiency value is dynamically coupled with the current benchmark rate through a weighted fusion algorithm; A fluctuation eigenvector is introduced as a negative feedback factor to adjust the rate weight allocation; When the same target warehouse triggers self-learning requests consecutively, the warehouse structure compatibility diagnosis process is initiated.

2. The method according to claim 1, characterized in that, The multi-level temperature regulation strategy includes: Adjust the current temperature to an initial temperature reference point that is higher than the target temperature value; The temperature is linearly adjusted towards the target temperature value at the first cooling rate. The stability monitoring window is activated when the temperature sensor first detects that the temperature value has entered the suitable dynamic temperature range. If the temperature remains stable during the monitoring window period, it will be adjusted in reverse at the first heating rate to the center of the suitable dynamic temperature range. When the detected temperature value exceeds the suitable dynamic temperature range, the adjustment rate is immediately reduced to the second cooling rate to continue adjusting towards the target temperature value. Once the target temperature is reached, switch to the third compensation heating rate for reverse balance adjustment. The initial temperature reference point is greater than the target temperature value, the first cooling rate is greater than the second cooling rate, and the second cooling rate is greater than the third compensating heating rate.

3. The method according to claim 1, characterized in that, The multi-level humidity control strategy includes: Adjust the current humidity to an initial humidity reference point that is higher than the target humidity value; The humidity is linearly adjusted towards the target value at the first dehumidification rate. The stability monitoring window is activated when the humidity sensor first detects that the humidity value has entered the suitable dynamic humidity range. If the humidity value remains stable during the monitoring window period, it will be adjusted in reverse at the first humidification rate to the center of the suitable dynamic humidity range. When the detected humidity value exceeds the suitable dynamic humidity range, the adjustment rate is immediately reduced to the second dehumidification rate to continue adjusting towards the target humidity value. Once the target humidity value is reached, switch to the third compensation humidification rate for reverse balance adjustment. The initial humidity reference point is greater than the target humidity value, the first dehumidification rate is greater than the second dehumidification rate, and the second dehumidification rate is greater than the third compensating humidification rate.

4. The method according to claim 3, characterized in that, The stability verification protocol includes: Start the countdown window after humidity adjustment is complete; Multiple sets of humidity monitoring data are continuously acquired at a fixed sampling frequency within the countdown window; Each set of humidity monitoring data is dynamically compared with the boundary value of the suitable humidity range using dynamic thresholds. Mark outlier data points that exceed the boundary values; Calculate the standard deviation of all humidity monitoring data; Analyze the frequency of occurrence of outlier data points; The standard deviation and the frequency of anomalies are input into the composite evaluation model to generate the stability coefficient. When the stability coefficient exceeds the critical threshold, the current humidity fluctuation feature vector is extracted and the parameter self-learning engine is triggered.

5. The method according to claim 4, characterized in that, The warehouse structure compatibility diagnosis process includes: Control the infrared scanning matrix to create a 3D point cloud model of the warehouse space; Analyze temperature field distribution data to identify regions of abrupt changes in thermal gradient; Calculate the percentage of the abrupt change area relative to the total warehouse volume; When the percentage exceeds the set threshold, a warehouse space partitioning scheme is generated; Based on the characteristics of the heat map, the warehouse is divided into multiple independent environmental control units; Generate customized temperature and humidity control strategies for each environmental control unit; Update the repository configuration database and reload the environment control parameters.

6. The method according to claim 1, characterized in that, The method also includes a material receiving agreement: After verifying the cryptographic signature of the requester's digital certificate, open the access control to the target repository; Scan the radio frequency identification of the transport vehicle to obtain the maximum load parameters; when the difference between the load parameters and the declared weight meets the safety margin, generate three-dimensional path planning data to guide the vehicle into the designated unloading area; Activate the environmental pretreatment system in the unloading area and configure initial parameters according to the environmental requirements of the target materials; The placement of materials is monitored in real time using a laser positioning device; The automatic correction robotic arm is activated when the position deviation exceeds the allowable error. After the materials are put into storage, close all access control systems and update the materials topology database.

7. The method according to claim 1, characterized in that, The method also includes a risk warning protocol: Continuously collect environmental sensor data streams to generate a sliding time window average value; Analyze the fluctuation intensity characteristics of historical environmental data; When the volatility intensity is below a set threshold, a linear regression model is used to predict future environmental trends. When the intensity of fluctuations exceeds a set threshold, a recurrent neural network model is activated to predict future environmental evolution. The degree of closeness between the monitoring and prediction results and the dynamic suitable range boundary; When multiple consecutive forecast periods show the boundary approaching, a Level 3 early warning response instruction is generated: The first stage triggers parameter optimization, the second stage activates the backup environmental control system, and the third stage executes the emergency material transfer plan.

8. The method according to claim 1, characterized in that, The step of integrating the first priority factor and the second dynamic allocation factor through a priority fusion algorithm to generate a material allocation matrix includes: A three-dimensional priority vector space is constructed based on the first priority factor, which includes a priority intensity dimension, a time decay dimension, and an environment compatibility dimension. The second dynamic allocation factor is projected onto the three-dimensional priority vector space to generate a dynamic weight distribution map, which includes a warehouse load rate weight layer and a material overlap weight layer. A convolutional neural network is used to extract multi-scale features from the dynamic weight distribution map to generate an optimized allocation template, which includes a material location priority score matrix. The optimized allocation template is spatially matched with the topology map of the target warehouse to calculate the resource allocation matrix. The resource allocation matrix contains the optimal storage location coordinates and allocation confidence values ​​of each emergency resource in the target warehouse.

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