Intelligent warehouse management method and system for hazardous wastes

Abstract

The invention discloses an intelligent warehouse management method and system for hazardous wastes, and the method comprises the steps: carrying out the real-time monitoring of the state of an object in a hazardous waste warehouse through a deployment sensor network, obtaining the basic data of the position of the object and the environment parameter, and transmitting the basic data to a central processing platform, thereby obtaining a preliminary data set; performing integration processing on multi-source information by adopting a data fusion technology according to the preliminary data set, eliminating redundancy and noise, and determining a standardized data record; aiming at the standardized data record, dynamically analyzing the distribution of goods locations in the warehouse by utilizing a path optimization algorithm, and if the change of the occupation state of the goods locations is detected, adjusting a goods location allocation scheme in real time, and outputting an optimal storage location combination; according to the optimal storage position combination, a dynamic route planning scheme is generated in combination with warehouse layout information, and the optimal moving track of the operation equipment is obtained; according to the invention, intelligent management of the hazardous waste warehouse is realized, and the storage efficiency and safety are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of warehouse management, and in particular discloses an intelligent warehouse management method and system for hazardous waste. Background Art

[0002] Hazardous waste warehouse management, a crucial component of environmental protection and industrial safety, plays an irreplaceable role in ensuring public safety and resource recycling. This area is directly related to the proper handling and storage of hazardous substances, and any oversight in management could lead to serious environmental pollution or safety accidents. However, current management methods have exposed numerous shortcomings in practice, particularly in terms of informationization and intelligentization, making them unable to meet the demands of modern industry for efficient and precise management.

[0003] Existing solutions have significant limitations in data processing and operational guidance. Many systems rely on manual data entry, resulting in delayed information updates and prone to errors. Furthermore, the lack of real-time guidance during operations means that operators often rely on personal experience to judge the environment and operational procedures. This approach is not only inefficient but also increases the risk of human error. Furthermore, data presentation is limited, lacking intuitiveness and convenience, and failing to provide effective support to operators. Against this backdrop, core challenges gradually emerged.

[0004] First, data collection and processing are not sufficiently automated. Existing systems struggle to access and update data in real time during operations, leading to a significant disruption in information flow. As this information flow problem intensifies, the lack of intelligent operational guidance becomes another major obstacle. Operators are unable to accurately allocate storage locations and plan routes, severely impacting operational efficiency and accuracy. These two interrelated factors collectively hinder the transition from traditional manual operations to intelligent management in hazardous waste warehouse management.

[0005] Therefore, how to achieve efficiency, accuracy and visualization of the entire hazardous waste warehouse management process by improving the automation level of data collection and the intelligence of operation instructions has become a key issue that needs to be solved urgently. Summary of the Invention

[0006] The present invention provides an intelligent warehouse management method and system for hazardous waste, aiming to solve at least one defect existing in the above-mentioned prior art.

[0007] One aspect of the present invention relates to an intelligent warehouse management method for hazardous waste, comprising the following steps: By deploying a sensor network to monitor the status of items in hazardous waste warehouses in real time, basic data on item location and environmental parameters is obtained and transmitted to a central processing platform to obtain a preliminary data set; Based on the preliminary data set, data fusion technology is used to integrate and process multi-source information, eliminate redundancy and noise, and determine standardized data records; Based on standardized data records, the path optimization algorithm is used to dynamically analyze the distribution of storage locations within the warehouse. If changes in storage location occupancy are detected, the storage location allocation plan is adjusted in real time to output the optimal storage location combination. Generate dynamic route planning solutions based on the optimal storage location combination and warehouse layout information to obtain the optimal movement trajectory of operating equipment; Send real-time guidance instructions to operating equipment based on the optimal movement trajectory, synchronously update the data status in the central processing platform, and obtain the latest warehouse operation log; Analyze the changing trends of environmental parameters based on the latest warehouse operation logs. If any environmental parameters are found to exceed the preset threshold range, a safety warning mechanism is triggered and identification information of the abnormal area is output; Based on the identification information of abnormal areas, the linked environmental monitoring module adjusts the sensor collection frequency to obtain more intensive data samples and determine the distribution of potential safety risk points; Generate a visual risk heat map based on the distribution of potential security risk points, synchronize it to the central processing platform, and determine the areas that require priority treatment; Adjust the operation guidance instructions and route planning plan for the priority area, obtain the updated operation sequence, and output the final optimized scheduling results.

[0008] Furthermore, based on the preliminary data set, data fusion technology is used to integrate and process multi-source information to eliminate redundancy and noise. The steps to determine standardized data records include: Based on the preliminary data set, use data cleaning tools to compare multi-source information, eliminate redundant information, and obtain a de-redundant information set; For the information set after de-redundancy, a noise filtering tool is used to identify outliers. If the outlier exceeds the preset threshold, it is corrected by a smoothing tool to obtain a noise-filtered information set. Based on the noise-filtered information set, a data standardization tool is used to uniformly convert the formats of environmental variables and location coordinates to obtain a standardized information set with a unified format; Based on a standardized information set with a unified format, data integration tools are used to fuse multi-source information and verify data accuracy. If the accuracy is lower than the preset threshold, interpolation tools are used to supplement it and determine the final standardized data record.

[0009] Furthermore, based on the standardized data records, a path optimization algorithm is used to dynamically analyze the distribution of storage locations within the warehouse. If a change in the storage location occupancy status is detected, the storage location allocation plan is adjusted in real time. The steps of outputting the optimal storage location combination include: Based on standardized data records, data comparison tools are used to monitor the occupancy status of cargo spaces in the warehouse in real time. In response to changes in cargo space occupancy status, the latest cargo distribution information is recorded through data update tools to obtain an updated cargo space status set. Based on the updated storage location status set, the path planning tool is used to dynamically match storage locations within the warehouse layout. If the change in storage location occupancy status is detected to exceed a preset threshold, the location allocation tool recalculates the allocation plan and determines the adjusted storage location combination; Based on the adjusted storage location combination, the data verification tool is used to verify the storage efficiency of the allocation plan. If the storage efficiency does not meet the preset standard, the location optimization tool is used to make a secondary adjustment to the adjusted storage location combination to obtain an optimized storage location distribution. For the optimized storage location distribution, data integration tools are used to associate and record the adjustment frequency with the cargo distribution information. The final allocation plan is updated through the data storage tool to determine the storage location combination that best suits the warehouse layout.

[0010] Furthermore, the steps of generating a dynamic route planning scheme based on the optimal storage location combination and combining the warehouse layout information to obtain the optimal movement trajectory of the operating equipment include: Based on the optimal storage location combination and warehouse layout information, the path planning tool scans the internal structure of the warehouse to obtain the correspondence between the cargo distribution status and the layout matching rules, and obtains a preliminary route planning draft; Based on the preliminary route plan draft, the equipment trajectory is dynamically compared using real-time monitoring data. If the equipment movement efficiency is lower than the preset threshold, the path adjustment tool calculates the optimal movement path and determines the adjusted route plan. Based on the adjusted route plan, the trajectory optimization tool is used to perform a secondary match on the operating equipment trajectory to obtain a dynamic route plan consistent with the warehouse layout information and determine the movement trajectory combination; For the combination of mobile trajectories, data integration tools are used to associate and record the trajectory optimization plan with the cargo distribution status, and the dynamic route planning plan is updated through data storage tools to obtain the optimal movement trajectory of the operating equipment.

[0011] Furthermore, the steps of sending real-time guidance instructions to the operating equipment based on the optimal movement trajectory, synchronously updating the data status in the central processing platform, and obtaining the latest warehouse operation log include: Based on the optimal movement trajectory, a path feedback tool is used to continuously collect the current position data of the operating equipment. When the current position deviates from the preset trajectory, a first instruction content is generated through a real-time guidance tool to determine the guidance data to be transmitted; The guidance data is transmitted to the operating equipment through the instruction sending tool. At the same time, the status monitoring tool is used to collect the first response data of the operating equipment after executing the instruction to determine whether the first response data meets the preset threshold range; If the first response data meets the preset threshold, the new position of the operating equipment and the first response data are uploaded to the central processing platform through the synchronization update tool to obtain the updated first data status; According to the first data state, an operation record tool is used to integrate the position change of the operating equipment and the instruction execution status into first log data, and the first log data is classified and stored through a dynamic adjustment tool to obtain the latest operation record data.

[0012] Furthermore, based on the latest warehouse operation log, the changing trend of the environmental parameters is analyzed. If it is found that the environmental parameters exceed the preset threshold range, the safety warning mechanism is triggered, and the identification information of the abnormal area is output. The steps include: Obtain environmental parameter data from the warehouse operation log, use log parsing tools to extract time series data of environmental parameters, and process the time series data using trend analysis tools to obtain the first trend result; Based on the first trend result, a threshold comparison tool is used to determine whether the environmental parameter exceeds a preset threshold range. If so, a first abnormal record of the abnormal parameter is generated by an abnormal marking tool to determine the abnormal identification; According to the first anomaly record, a region positioning tool is used to extract the spatial position corresponding to the anomaly parameter, and a unique identifier is assigned to the anomaly region using an identifier generation tool to obtain a first region identifier record; For the first area identification record, an early warning trigger tool is used to generate a safety early warning signal, and the identification information of the abnormal area is transmitted to the central processing platform through the data output tool to obtain the final early warning output result.

[0013] Furthermore, based on the identification information of the abnormal area, the environmental monitoring module is linked to adjust the sensor collection frequency to obtain more intensive data samples, and the steps of determining the distribution of potential safety risk points include: According to the abnormal identification and regional positioning, the spatial location data of the abnormal area is obtained from the environmental monitoring module, and the sensor acquisition frequency is extracted through the time series analysis tool to obtain the first frequency record; If the first frequency record is lower than the preset intensive collection threshold, the frequency adjustment tool is linked to the environmental monitoring module to dynamically adjust the sensor collection frequency to obtain the second frequency record; Using a data collection tool, a denser data sample is obtained from the sensor corresponding to the second frequency recording, and the data sample is grouped and processed using a sample clustering tool to obtain a first risk point set; Through the spatial mapping tool, the first risk point set is associated and matched with the spatial location data to generate the distribution of risk points and determine the distribution of potential safety risk points.

[0014] Furthermore, based on the distribution of potential safety risk points, a visual risk heat map is generated and synchronized to the central processing platform. The steps for determining the scope of areas that require priority treatment include: Based on the location information of potential safety risk points obtained from the risk distribution data, a spatial mapping tool is used to group the location information of potential safety risk points, and high-density areas are marked to obtain a first density distribution record; Using a visual drawing tool, the first density distribution record is converted into a heat map format, and different density areas are assigned color codes to determine the first risk heat map data; If there is an area in the first risk heat map data where the density exceeds the preset threshold, the area boundary is marked using the area division tool to obtain the first priority area range and determine the key processing range; Using data synchronization tools, upload the first priority area range and the first risk heat map data to the central processing platform, store and process the synchronized data, and obtain the first area division record.

[0015] Furthermore, the steps of adjusting the operation guidance instructions and route planning scheme for the priority processing area, obtaining the updated operation sequence, and outputting the final optimized scheduling result include: Based on the regional division and priority data, a path calculation tool is used to make preliminary adjustments to the route planning within the target area, prioritizing areas with higher mission urgency. The adjusted first route plan is obtained, and the path length and time window allocation in the first route plan are determined. Based on the first route plan and combined with resource allocation data, the scheduling and allocation tool is used to rearrange the operation sequence. If the time window exceeds the preset threshold, the path length is adjusted again to obtain an updated second route plan and determine the sequence combination that meets the requirements; For the second route plan, the instruction generation tool was used to refine the operation instructions. Combined with the instruction update data, the final operation sequence combination was obtained to determine the instruction content that met the regional coverage requirements. Based on the final combination of operation sequences, the route planning data is uniformly processed using data integration tools to obtain the integrated scheduling optimization results and determine the output plan that meets the business objectives.

[0016] Another aspect of the present invention relates to an intelligent warehouse management system for hazardous waste, which is used to implement the above-mentioned intelligent warehouse management method for hazardous waste. The intelligent warehouse management system for hazardous waste includes: The first acquisition module is used to monitor the status of items in the hazardous waste warehouse in real time by deploying a sensor network, obtain basic data on the location of items and environmental parameters, and transmit it to the central processing platform to obtain a preliminary data set; The first determination module is used to integrate and process multi-source information based on the preliminary data set using data fusion technology, eliminate redundancy and noise, and determine standardized data records; The first output module is used to dynamically analyze the distribution of storage locations within the warehouse using a path optimization algorithm based on standardized data records. If a change in the occupancy status of a storage location is detected, the storage location allocation plan is adjusted in real time to output the optimal storage location combination; The second acquisition module is used to generate a dynamic route planning solution based on the optimal storage location combination and warehouse layout information to obtain the optimal movement trajectory of the operating equipment; The third acquisition module is used to send real-time guidance instructions to the operating equipment based on the optimal movement trajectory, synchronously update the data status in the central processing platform, and obtain the latest warehouse operation log; The second output module is used to analyze the changing trends of environmental parameters based on the latest warehouse operation logs. If the environmental parameters are found to exceed the preset threshold range, the safety warning mechanism is triggered and the identification information of the abnormal area is output; The second determination module is used to link the environmental monitoring module with the identification information of the abnormal area to adjust the sensor collection frequency, obtain more intensive data samples, and determine the distribution of potential safety risk points; The judgment module is used to generate a visual risk heat map based on the distribution of potential security risk points, synchronize it to the central processing platform, and determine the areas that need priority treatment; The third output module is used to adjust the operation guidance instructions and route planning scheme according to the priority processing area, obtain the updated operation sequence, and output the final optimized scheduling result.

[0017] The beneficial effects achieved by the present invention are: The present invention provides a method and system for intelligent warehouse management of hazardous waste, which monitors the status of items and environmental parameters in real time by deploying a sensor network, integrates multi-source information using data fusion technology, dynamically analyzes cargo space distribution and adjusts storage plans using a path optimization algorithm, generates dynamic route planning in combination with warehouse layout information, and sends real-time guidance instructions to operating equipment. At the same time, the present invention analyzes the changing trend of environmental parameters, triggers a safety warning mechanism, adjusts the sensor acquisition frequency to determine potential safety risk points, generates a visual risk heat map, and optimizes the scheduling results accordingly. The present invention realizes the intelligent management of hazardous waste warehouses, improves storage efficiency and safety, and provides comprehensive monitoring and optimization support for hazardous waste warehouse operations. In summary, the intelligent warehouse management method and system for hazardous waste provided by the present invention have beneficial effects as follows: 1. Real-time and precise monitoring to ensure the safety of items and the environment: By deploying a sensor network to monitor hazardous waste warehouses in real time, basic data such as item location and environmental parameters can be obtained in a timely manner. Once abnormal fluctuations in environmental parameters (such as temperature, humidity, and hazardous gas concentrations) occur, the system can quickly detect them. Compared with traditional manual inspections, this greatly shortens the time it takes to discover abnormalities, effectively reducing the risk of accidents such as hazardous waste leakage and explosions caused by environmental changes, and providing strong protection for the storage safety of items and personnel in hazardous waste warehouses. Second, data fusion processing improves data quality and decision reliability: Data fusion technology integrates multi-source information, eliminating redundancy and noise, and forming standardized data records. This makes the data obtained by the central processing platform more accurate and effective, avoiding decision-making errors caused by data confusion. Based on high-quality data, subsequent operations such as cargo location analysis and route planning can be more scientific and reasonable, providing reliable data support for hazardous waste warehouse management. 3. Dynamic cargo location and route optimization to improve warehousing efficiency: A route optimization algorithm dynamically analyzes the cargo location distribution within hazardous waste warehouses, adjusts the cargo location allocation plan in real time, and combines this with dynamic route planning to generate optimal movement trajectories for equipment. This dynamic optimization mechanism reduces ineffective equipment movement and waiting time, avoids detours and congestion during cargo handling, significantly improves cargo storage and handling efficiency, reduces labor and equipment resource consumption, and enhances the overall operational efficiency of hazardous waste warehouses. 4. Intelligent early warning and risk prevention enhance the safety of hazardous waste warehouses: When environmental parameters exceed preset thresholds, the system triggers a safety warning mechanism and, in conjunction with the environmental monitoring module, adjusts the sensor acquisition frequency, conducts in-depth analysis of potential safety risks, and generates a visual risk heat map. This allows managers to intuitively and quickly understand the distribution of risks within the hazardous waste warehouse and take timely measures in priority areas, nipping potential safety hazards in the bud. This enhances the warehouse's ability to respond to sudden safety incidents and improves its overall safety.

[0018] 5. Full-process automation and dynamic scheduling enable intelligent management: From data collection, processing, and analysis to operational instruction adjustments and route planning optimization, the entire management process is automated and dynamic. The system responds and adjusts in real time based on the actual operation of the hazardous waste warehouse, reducing manual intervention and the probability of human error. This promotes the development of intelligent, efficient, and refined management of hazardous waste warehouses, meeting the high standards of modern hazardous waste management. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of an embodiment of a method for intelligent warehouse management of hazardous wastes according to the present invention. DETAILED DESCRIPTION

[0020] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0021] like Figure 1 As shown, the first embodiment of the present invention provides an intelligent warehouse management method for hazardous waste, comprising the following steps: Step S100: Deploy a sensor network to monitor the status of items in the hazardous waste warehouse in real time, obtain basic data on the location of items and environmental parameters, and transmit it to the central processing platform to obtain a preliminary data set.

[0022] The basic data collected through the sensor network needs to be structured around the location of objects, environmental parameters and data transmission properties to form a standardized preliminary data set, providing a basis for subsequent data analysis and processing on the central processing platform.

[0023] Step S200: Based on the preliminary data set, data fusion technology is used to integrate and process multi-source information, eliminate redundancy and noise, and determine standardized data records.

[0024] Data fusion technology eliminates redundancy and noise in multi-source data through a multi-level processing process, creating standardized records. This multi-level processing process is a systematic framework for hierarchical and progressive processing of heterogeneous data from multiple sources. Each level utilizes specific functions to transform data from its raw state into standardized, usable data. The data fusion processing framework specifically includes a data preprocessing layer, a feature fusion layer, and a decision fusion layer. The resulting standardized record is centered around the item's status and incorporates location, environmental, and credibility information.

[0025] Step S300: Based on the standardized data records, a path optimization algorithm is used to dynamically analyze the distribution of cargo spaces in the hazardous waste warehouse. If a change in cargo space occupancy status is detected, the cargo space allocation plan is adjusted in real time to output the optimal storage location combination.

[0026] In this embodiment, dynamic storage allocation is employed. Dynamic storage allocation involves a decision-making process that dynamically generates the optimal storage location combination using a path optimization algorithm based on real-time perception of storage location occupancy, item attributes, and path constraints. Its core goal is to achieve real-time optimization of storage space resources in hazardous waste warehouse scenarios, balancing the three requirements of safe storage rules, material handling efficiency, and space utilization.

[0027] Step S400: Generate a dynamic route planning solution based on the optimal storage location combination and warehouse layout information to obtain the optimal movement trajectory of the operating equipment.

[0028] Dynamic route planning is the process of generating collision-free, efficient trajectories for operational equipment (such as forklifts and automated guided vehicles) based on optimal storage location combinations and real-time warehouse layout information. Its core goal is to balance path minimization, safety compliance, and equipment kinematic constraints in hazardous waste warehouses to achieve optimal trajectories from the starting point to the target location, while also adapting to real-time environmental changes (such as temporary obstacles and other equipment dynamics).

[0029] Step S500: Send real-time guidance instructions to the operating equipment based on the optimal moving trajectory, synchronously update the data status in the central processing platform, and obtain the latest warehouse operation log.

[0030] Real-time guidance instructions convert the optimal movement trajectory into a control signal that can be executed by the operating equipment, and realize the mapping of trajectory points to action instructions through standardized protocols.

[0031] Step S600: Analyze the changing trend of environmental parameters based on the latest warehouse operation log. If it is found that the environmental parameters exceed the preset threshold range, trigger the safety warning mechanism and output the identification information of the abnormal area.

[0032] By analyzing environmental parameter data (such as temperature, humidity, and hazardous gas concentrations) from warehouse operation logs, the system uses time series analysis to identify abnormal trends and, combined with spatial positioning, accurately identifies abnormal areas. The system follows a process of "data collection - trend modeling - threshold determination - spatial positioning." Its core goal is to proactively identify environmental risks and locate the source of abnormalities in hazardous waste warehouses.

[0033] Step S700: Based on the identification information of the abnormal area, the environmental monitoring module is linked to adjust the sensor collection frequency to obtain more intensive data samples and determine the distribution of potential safety risk points.

[0034] When the system detects an abnormal area, it dynamically adjusts the sensor acquisition frequency within that area, implementing a differentiated data collection strategy: high-density sampling in abnormal areas and regular sampling in normal areas. The core goal is to pinpoint potential safety risks in hazardous waste warehouses through more intensive data sampling, thereby improving the spatial and temporal resolution of potential safety hazard identification.

[0035] Step S800: Generate a visual risk heat map based on the distribution of potential security risk points, synchronize it to the central processing platform, and determine the area range that needs priority processing.

[0036] By integrating the spatial distribution data of potential safety risk points, the risk density in the warehouse can be presented in a visual manner, and the areas that require priority treatment can be determined based on dimensions such as risk level and impact range, providing decision support for safety management.

[0037] Step S900: Adjust the operation guidance instructions and route planning scheme for the priority processing area, obtain the updated operation sequence, and output the final optimized scheduling result.

[0038] By dynamically adjusting work instructions and route planning for identified priority areas, the system achieves the scheduling goal of "prioritizing risk management and efficiently allocating resources." The system follows a closed-loop process: "area priority assessment → work instruction reconstruction → dynamic route optimization → operation sequence generation."

[0039] Furthermore, the intelligent warehouse management method for hazardous waste provided in this embodiment includes step S100: Step S110: Collect original data records of the location of items and environmental parameters in the hazardous waste warehouse through the sensor network, where the original data records include location coordinates and environmental variables.

[0040] Sensor networks are the core technology for collecting raw data on the location and environmental parameters of items within hazardous waste warehouses. These networks typically consist of multiple nodes, each equipped with positioning and environmental sensors. Positioning sensors utilize ultra-wideband technology, transmitting and receiving pulse signals to calculate the distance between the item and the sensor node, thereby determining the item's three-dimensional coordinates. For example, the location of a hazardous waste bin can be recorded as the coordinates (5.2, 3.8, 1.5) meters. Environmental sensors include devices that monitor temperature, humidity, and toxic gas concentrations. For example, a temperature sensor can record real-time warehouse temperature of 25.3 degrees Celsius, humidity of 60.5%, and methane concentration of 0.02%. This data is transmitted to a central processing platform via wireless communication modules such as ZigBee or LoRa, forming a preliminary data set. This approach offers the advantages of real-time and high accuracy, ensuring comprehensive monitoring of hazardous waste location and environmental conditions.

[0041] Step S120: Transmit the original data records to the central processing platform to obtain a preliminary data set.

[0042] In one possible implementation, the central processing platform preprocesses the raw data to generate a structured preliminary data set. For example, the central processing platform can sort the raw data by timestamp to ensure the temporal consistency of the raw data. Assuming that a warehouse collects 1,000 records at 20:00 on June 21, 2025, the central processing platform will classify these records by item number and collection time, and eliminate outliers, such as erroneous data with temperatures exceeding 100 degrees Celsius. The preprocessed preliminary data set can include the location coordinates, environmental parameters, and their changing trends for each piece of hazardous waste. This structured processing facilitates subsequent analysis, such as identifying environmental anomalies or item location offsets. The technical effect is to improve data quality and reduce the risk of misjudgment.

[0043] It's important to note that the data transmission process must ensure security and reliability. For example, sensor nodes can use lightweight encryption algorithms to encrypt data to prevent data leakage. The central processing platform ensures data integrity through redundant storage and regular backups. In a real-world scenario, assuming 50 sensor nodes in a warehouse generate 10 data records per second, the central processing platform must process 30,000 data records per minute. Optimizing transmission protocols, such as implementing batch transmission in time slots, can reduce the risk of network congestion. This approach benefits by ensuring data integrity and providing a reliable basis for hazardous waste management.

[0044] In one embodiment, preliminary data sets can be used for the safe management of hazardous waste warehouses. For example, the central processing platform can identify potential risks by analyzing environmental parameters. If the methane concentration in a certain area rises from 0.02% to 0.1%, the central processing platform will automatically trigger an alarm, prompting managers to check the waste storage status in that area. Simultaneously, analysis of location data can be used to optimize warehouse layout. If certain hazardous waste barrels are found to be too close together, such as coordinates (5.2, 3.8, 1.5) and (5.3, 3.9, 1.5) being only 0.14 meters apart, the central processing platform can recommend adjusting their locations to comply with safety regulations. These applications significantly improve warehouse safety and management efficiency.

[0045] Furthermore, the intelligent warehouse management method for hazardous waste provided in this embodiment includes step S200: Step S210: Based on the preliminary data set, use a data cleaning tool to compare multi-source information, eliminate redundant information, and obtain a de-redundant information set.

[0046] For example, in a hazardous waste warehouse monitoring scenario, processing the initial data set first requires data cleaning of multiple sources. Data cleaning tools can compare data from different sensor nodes, identifying and removing redundant information. For example, suppose multiple sensors in a warehouse simultaneously collect temperature data from the same area, recording values ​​of 25.2 degrees, 25.3 degrees, and 25.2 degrees. The data cleaning tool will merge these duplicate records into a single value of 25.2 degrees, forming a de-redundant information set. This reduces data storage pressure and improves the efficiency of subsequent processing.

[0047] Step S220: For the information set after de-redundancy, a noise filtering tool is used to identify abnormal values. If the abnormal value exceeds a preset threshold, it is corrected by a smoothing tool to obtain an information set after noise filtering.

[0048] For example, the noise filtering tool identifies outliers in a de-redundant information set. For example, suppose a temperature sensor records a value of 85.5 degrees Celsius, while the normal range is 20 to 30 degrees Celsius, significantly exceeding the preset threshold of 30 degrees Celsius. The noise filtering tool would flag this value as an anomaly and use the smoothing tool to correct it, for example, taking the average of 25.4 degrees Celsius from the previous and next time points as the correction value, ultimately forming the noise-filtered information set. This approach avoids data bias caused by sensor failure and ensures data reliability.

[0049] Step S230: Based on the noise-filtered information set, a data standardization tool is used to uniformly convert the formats of the environmental variables and the location coordinates to obtain a standardized information set with a unified format.

[0050] For example, during data normalization, the data normalization tool converts environmental variables and location coordinates in different formats to a unified format for the noise-filtered information set. For example, if some sensors record location coordinates in metric units, such as 5.2 meters, while others record them in centimeters, such as 520 centimeters, the normalization tool will convert them to metric units, resulting in a standardized information set with a unified format. Furthermore, environmental variables such as humidity may be recorded in both percentage and decimal form; the normalization tool will standardize them to percentage format, such as 60.5%. This facilitates smooth subsequent data integration.

[0051] Step S240: Based on the standardized information set with a unified format, a data integration tool is used to fuse the multi-source information and verify the data accuracy. If the accuracy is lower than the preset threshold, the interpolation tool is used to supplement and determine the final standardized data record.

[0052] The data integration tool integrates multiple sources of information into a standardized set of uniformly formatted data and verifies data accuracy. For example, if methane concentration data for a particular region is missing at certain time points and its accuracy falls below a preset threshold of 0.01%, the data integration tool uses interpolation to fill in the missing values. For example, based on concentration values ​​of 0.02% and 0.03% at previous and subsequent time points, the interpolated value can be estimated to be 0.025%, resulting in the final standardized data record. This approach ensures data integrity and provides a reliable foundation for subsequent analysis.

[0053] In real-world scenarios, data integration may also involve assigning weights to data from multiple sources. For example, if location data comes from both ultra-wideband positioning and auxiliary cameras, the data integration tool will prioritize the ultra-wideband data based on the accuracy differences between the two sources to ensure the accuracy of the final data. This multi-faceted integration approach can improve data quality and provide more precise support for the safe management of hazardous waste warehouses.

[0054] In the aforementioned processing flow, each step—data cleaning, noise filtering, standardization, and integration—is closely intertwined, ensuring the reliability of the entire process from raw data to final data records. For example, suppose a warehouse collects 50,000 data items in a single day. After cleaning and removing redundancies, only 30,000 items remain. Noise filtering is then used to correct outliers, ultimately creating standardized data records for environmental risk assessment. This complete process effectively supports accurate monitoring of item location and environmental parameters in warehouse management, ensuring efficient safety management.

[0055] Preferably, in the intelligent warehouse management method for hazardous waste provided in this embodiment, step S300 includes: Step S310: Based on the standardized data records, a data comparison tool is used to monitor the storage space occupancy status in the warehouse in real time. In response to changes in the storage space occupancy status, the latest cargo distribution information is recorded through a data update tool to obtain an updated storage space status set.

[0056] For example, in a hazardous waste warehouse monitoring scenario, a data comparison tool can monitor the occupancy status of storage locations in real time by processing standardized data records. Assuming there are 100 storage locations in the warehouse, the data comparison tool, by comparing historical records with current scan data, will detect that the occupancy status of 10 locations has changed from "free" to "occupied." This triggers the data update tool to record the latest cargo distribution information, forming an updated storage location status set. This real-time monitoring ensures that warehouse managers are constantly aware of storage location dynamics.

[0057] Step S320: For the updated storage location status set, a path planning tool is used to dynamically match the storage locations within the warehouse layout. If it is detected that the change in the storage location occupancy status exceeds a preset threshold, the location allocation tool is used to recalculate the allocation plan and determine the adjusted storage location combination.

[0058] For example, the routing tool dynamically matches storage locations within the warehouse layout based on the updated storage location status set. Assuming there are multiple storage areas within the warehouse, the routing tool will detect changes in the occupancy status of the storage locations. If the occupancy rate of a particular area exceeds the preset threshold of 80%, the location allocation tool will then recalculate the allocation plan. Specifically, the location allocation tool may transfer some goods from a high-occupancy area to a low-occupancy area with an occupancy rate of only 30%, forming an adjusted storage location combination. This dynamic matching helps balance the distribution of warehouse resources.

[0059] Step S330: Based on the adjusted storage location combination, the storage efficiency of the allocation scheme is verified using a data verification tool. If the storage efficiency does not meet the preset standard, the adjusted storage location combination is adjusted again using a location optimization tool to obtain an optimized storage location distribution.

[0060] For example, based on the adjusted storage location combination, the data validation tool will verify the storage efficiency of the allocation plan. Assuming the preset storage efficiency standard is 85%, and the current plan's efficiency is only 70%, the data validation tool will mark the plan as substandard and trigger the location optimization tool to make a secondary adjustment. In one possible implementation, the location optimization tool will analyze the relationship between the type of goods and the distance between storage areas, and adjust frequently used goods to locations closer to the exit, ultimately forming an optimized storage location distribution. This secondary adjustment can significantly improve the smoothness of warehouse operations.

[0061] Step S340: For the optimized storage location distribution, use a data integration tool to associate and record the adjustment frequency with the cargo distribution information, update the final allocation plan through the data storage tool, and determine the storage location combination that best matches the warehouse layout.

[0062] For example, for optimized storage location distribution, the data integration tool will associate the frequency of adjustments with the distribution of goods. For example, if a certain storage location is adjusted three times in a single day, the data integration tool will record the time of each adjustment and the corresponding goods information. The data storage tool will then update the final allocation plan and determine the storage location combination that best matches the warehouse layout. Specifically, this associated record provides data support for subsequent warehouse management, helping to analyze the regularity of storage location adjustments and thus optimize long-term management strategies.

[0063] Through these multifaceted implementation methods, from real-time monitoring to dynamic matching, and then to efficiency verification and optimization adjustments, each link is closely connected to support the efficient management of hazardous waste warehouses. Each tool is applied around the status of cargo locations and storage efficiency, ensuring the accuracy and practicality of data records, providing a solid foundation for safe warehouse operations.

[0064] Furthermore, the intelligent warehouse management method for hazardous waste provided in this embodiment includes step S400: Step S410: Based on the optimal storage location combination and the warehouse layout information, the internal structure of the warehouse is scanned by a path planning tool to obtain the correspondence between the cargo distribution status and the layout matching rules, and a preliminary route planning draft is obtained.

[0065] In a hazardous waste warehouse management scenario, the routing tool combines optimal storage location combinations with warehouse layout information. By scanning the warehouse's internal structure and analyzing the correspondence between cargo distribution and layout matching rules, it can create a preliminary route plan. For example, if the warehouse has 50 cargo locations distributed across five different areas, the routing tool will initially plan a route from the entrance to the target location based on location occupancy and the distances between areas, avoiding high-occupancy areas and ensuring smooth movement of equipment.

[0066] Step S420: Based on the preliminary route planning draft, the real-time monitoring data is used to dynamically compare the trajectory of the operating equipment. If the operating equipment movement efficiency is lower than the preset threshold, the path adjustment tool is used to calculate the optimal movement path and determine the adjusted route plan.

[0067] Real-time monitoring data can be used to dynamically compare the actual trajectory of the equipment against the draft preliminary route plan. For example, if the preset efficiency threshold is to complete 10 cargo spaces per hour, but monitoring reveals that the equipment is only moving 6 cargo spaces per hour, the route adjustment tool will recalculate the route to avoid congestion or obstacles and determine a shorter, smoother movement path. For example, the route can be adjusted from passing through a high-occupancy area to detouring to a low-occupancy area.

[0068] Step S430: Based on the adjusted route plan, the trajectory optimization tool is used to perform a secondary match on the operating equipment trajectory to obtain a dynamic route plan consistent with the warehouse layout information and determine the movement trajectory combination.

[0069] For example, based on the adjusted route plan, the trajectory optimization tool will re-match the equipment trajectory to ensure consistency with the warehouse layout information. If there are narrow aisles and spacious areas within the warehouse, the trajectory optimization tool will prioritize the spacious area as the primary movement path, while also taking into account real-time changes in cargo distribution. For example, it will optimize the routes corresponding to frequently called goods to aisles closer to the exit, forming a dynamic route plan. This approach can significantly improve the flexibility of equipment movement.

[0070] Step S440: For the combination of movement trajectories, a data integration tool is used to associate and record the trajectory optimization plan with the cargo distribution status, and a dynamic route planning plan is updated through a data storage tool to obtain the optimal movement trajectory of the operating equipment.

[0071] For each combined movement trajectory, the data integration tool correlates the optimized trajectory plan with the cargo distribution status. For example, if a route involves moving cargo across three cargo bays, the data integration tool will record the location, cargo type, and corresponding movement time of each bay. The data storage tool then updates the dynamic route plan to ensure that equipment always follows the optimal trajectory. This correlation provides a crucial basis for subsequent analysis of equipment movement patterns.

[0072] In terms of specific implementation methods, when scanning the warehouse structure, the path planning tool can combine regional divisions and cargo space occupancy rates to prioritize routes that avoid high-risk areas, such as strictly distinguishing routes to hazardous waste storage areas from those to ordinary areas. Real-time monitoring data can obtain the location and movement speed of operating equipment through sensors, and factors such as operating equipment load and channel width will be comprehensively considered when dynamically adjusting routes. Trajectory optimization tools will analyze the traffic efficiency of operating equipment in specific areas based on historical movement data and prioritize high-efficiency routes. Data integration tools and data storage tools use timestamps and cargo space numbers to correspond each trajectory adjustment to the status of the goods, forming a complete data chain. This multi-link collaborative approach can effectively improve the smooth operation of operating equipment in the warehouse, reduce the risk of delays caused by improper routes, and provide data support for the optimization of long-term management strategies.

[0073] Preferably, the intelligent warehouse management method for hazardous waste provided in this embodiment includes step S500: Step S510: Based on the optimal moving trajectory, a path feedback tool is used to continuously collect current position data of the operating equipment. When the current position deviates from the preset trajectory, a first instruction content is generated through a real-time guidance tool to determine the guidance data to be transmitted.

[0074] In the hazardous waste warehouse management scenario, the path feedback tool collects the location data of the operating equipment in real time to ensure that the operating trajectory of the operating equipment is consistent with the preset route. Assuming that there are 100 cargo locations in the warehouse, distributed in 10 areas, the path feedback tool uses the positioning sensors installed on the operating equipment to collect the current position coordinates once a second. It should be noted that if the operating equipment deviates from the preset trajectory, for example, it deviates from the predetermined route of the target cargo location A1 to the adjacent A2 cargo location, the real-time guidance tool will immediately identify the deviation and generate the first instruction content including the adjustment direction and distance. The guidance data may include specific instructions such as "adjust 30 degrees to the left and move forward 2 meters" to ensure that the operating equipment quickly returns to the correct path.

[0075] Step S520: The guidance data is transmitted to the operating equipment through the instruction sending tool, and the status monitoring tool is used to collect the first response data after the operating equipment executes the instruction to determine whether the first response data meets the preset threshold range.

[0076] In one embodiment, the first instruction content generated by the real-time guidance tool is transmitted wirelessly to the operating equipment through the instruction sending tool. For example, after receiving the instruction, the operating equipment immediately adjusts its direction and moves. The status monitoring tool then collects the first response data after the operating equipment executes the instruction, such as the actual moving distance and the direction adjustment angle. Assuming that the preset threshold requires the operating equipment to complete a 2-meter movement within 5 seconds, if the monitoring data shows that the operating equipment has only moved 1.5 meters, the real-time guidance tool will determine that the response data does not meet the standard and trigger further adjustments. Preferably, the status monitoring tool can also record the load status of the operating equipment, for example, the operating equipment is currently carrying 10 kilograms of goods, to assess whether the insufficient response is due to excessive load.

[0077] Step S530: If the first response data meets the preset threshold, the new position of the operating equipment and the first response data are uploaded to the central processing platform through the synchronization update tool to obtain the updated first data status.

[0078] For example, if the first response data meets a preset threshold, the synchronization update tool uploads the new location of the equipment and the response data to the central processing platform. Assuming the equipment has moved to location B3, the uploaded data includes the location coordinates and the time of the move. The central processing platform updates the first data status based on this data, creating a complete record of the equipment's current location, time, and load.

[0079] Step S540: Based on the first data state, the operation record tool is used to integrate the position change of the operating equipment and the instruction execution status into the first log data, and the first log data is classified and stored through the dynamic adjustment tool to obtain the latest operation record data.

[0080] The operation logging tool then consolidates this information to generate the first log data. For example, the log might record that the equipment moved from location B1 to location B3 at 10:00, taking 10 seconds and moving 5 kg of hazardous waste. The dynamic adjustment tool categorizes and stores the log data, organizing it by timestamp and location number to ensure data traceability.

[0081] Specifically, the dynamic adjustment tool optimizes storage structure based on the operating patterns of equipment during categorized storage. For example, data for frequently moved locations is prioritized in quickly accessible areas to improve query efficiency. Assuming a warehouse handles 50 cargo moves daily, the dynamic adjustment tool will analyze logs to find that equipment moves more efficiently in wide aisles than narrow ones, prioritizing routes in wide aisles. This approach, through real-time data feedback and dynamic adjustments, ensures smooth equipment operation and provides data support for long-term route optimization.

[0082] In one possible implementation, the operation logging tool can also combine historical data to analyze the movement patterns of equipment within specific areas. For example, if the log shows that equipment in Area C, near the exit, moves 12 cargo spaces per hour, higher than Area D, which moves 8 cargo spaces per hour, the dynamic adjustment tool will prioritize routes in Area C. This data-driven optimization approach makes equipment operations more efficient and reduces the risk of delays caused by route deviations.

[0083] Furthermore, the intelligent warehouse management method for hazardous waste provided in this embodiment includes step S600: Step S610: Acquire environmental parameter data from the warehouse operation log, use a log parsing tool to extract time series data of the environmental parameters, and use a trend analysis tool to process the time series data to obtain a first trend result.

[0084] In hazardous waste warehouse management scenarios, obtaining environmental parameter data from warehouse operation logs is a critical task. Log parsing tools can be used to extract time series data for environmental parameters, such as temperature and humidity. Assuming a warehouse generates hundreds of log records daily, the log parsing tool can chronologically organize the hourly temperature data for the past 24 hours, forming a complete time series. This parsing approach facilitates subsequent analysis of patterns in environmental changes.

[0085] The trend analysis tool processes the extracted time series data to generate a primary trend analysis. For example, if the temperature in a warehouse has gradually risen from 20°C to 28°C over the past 12 hours, the trend analysis tool will identify this rising trend and generate a corresponding trend report. This analysis helps managers understand the direction of environmental parameter changes and provides a basis for subsequent assessments.

[0086] Step S620: for the first trend result, a threshold comparison tool is used to determine whether the environmental parameter exceeds a preset threshold range. If it exceeds, a first abnormal record of the abnormal parameter is generated by an abnormal marking tool to determine an abnormal identifier.

[0087] Using threshold comparison tools to determine whether environmental parameters exceed preset ranges is crucial for ensuring warehouse safety. For example, if the preset temperature threshold range is 18 to 25 degrees Celsius, and the trend results show the temperature reaches 28 degrees Celsius, the threshold comparison tool will immediately flag this data as abnormal. This judgment mechanism can quickly identify potential risks and ensure timely action.

[0088] If an environmental parameter exceeds a threshold, the anomaly marking tool generates a first anomaly record and identifies the anomaly. For example, if a temperature anomaly occurs at 10:00 AM, the anomaly marking tool assigns a unique identifier to the event, such as T20231010-1000, and records the abnormal value of 28 degrees Celsius and the time. This identification facilitates subsequent tracking and processing.

[0089] Step S630: Based on the first anomaly record, a region positioning tool is used to extract a spatial position corresponding to the anomaly parameter, and an identifier generation tool is used to assign a unique identifier to the anomaly region to obtain a first region identifier record.

[0090] Based on the first anomaly record, the area location tool extracts the spatial location corresponding to the anomaly parameters. For example, if the temperature anomaly occurs in storage location D5 near the entrance in Area D of the warehouse, the area location tool will record this specific location information. This location method can clearly identify the specific area where the problem occurred, providing precise guidance for subsequent handling.

[0091] For each abnormal area, the identification generation tool assigns a unique identifier to it, generating a first area identification record. For example, if cargo location D5 is marked as D5-T20231010, the identification generation tool will associate this identifier with the abnormal record, ensuring that each abnormal area can be accurately identified and tracked. This identification mechanism helps quickly identify problem areas.

[0092] Step S640: For the first area identification record, a warning trigger tool is used to generate a safety warning signal, and the identification information of the abnormal area is transmitted to the central processing platform through the data output tool to obtain the final warning output result.

[0093] The early warning trigger tool generates a safety warning signal for the first zone identification record. If a temperature anomaly could affect the safe storage of hazardous waste, the tool generates a high-priority warning signal that includes the D5 zone identification and anomaly details. This warning signal can promptly alert relevant personnel to the problem area.

[0094] The data output tool transmits the identification information of the abnormal area to the central processing platform, generating the final warning output. Assuming the warning signal is transmitted to the central processing platform within 5 minutes of generation, the central processing platform will record the abnormality in area D5 and notify on-site management. This transmission mechanism ensures efficient information flow and ensures a rapid response. Through this series of operations, the monitoring and management of warehouse environmental parameters are optimized, potential risks are promptly identified and addressed, and the safety and stability of hazardous waste storage are guaranteed.

[0095] Furthermore, the intelligent warehouse management method for hazardous waste provided in this embodiment includes step S700: Step S710: According to the abnormal identification and area positioning, the spatial position data of the abnormal area is obtained from the environmental monitoring module, and the sensor acquisition frequency is extracted by a time series analysis tool to obtain a first frequency record.

[0096] In hazardous waste warehouse management scenarios, environmental monitoring of abnormal areas and analysis of risk point distribution are particularly important. Based on abnormality identification and area location, the system first obtains the spatial location data of abnormal areas from the environmental monitoring module. For example, if location D5 near the entrance in Area D of a warehouse is marked as an abnormal area, the system extracts the specific coordinate information of the area, such as the shelf number and location description, for subsequent precise analysis.

[0097] The process of extracting sensor collection frequency using time series analysis tools can be understood as a way to capture data rhythm. For example, suppose a temperature sensor in a warehouse originally collected data once an hour. Using an analysis tool to analyze the past 24 hours' worth of data reveals a frequency of once an hour.

[0098] Step S720: If the first frequency record is lower than the preset intensive collection threshold, the frequency adjustment tool is linked to the environmental monitoring module to dynamically adjust the sensor collection frequency to obtain a second frequency record.

[0099] If the preset threshold for intensive data collection is once every 30 minutes, the current frequency is clearly too low. In this case, the frequency adjustment tool, linked to the environmental monitoring module, dynamically adjusts the data collection frequency to once every 20 minutes, creating a second frequency record. This adjustment ensures more timely data acquisition in abnormal areas.

[0100] Step S730: Use a data acquisition tool to obtain denser data samples from the sensor corresponding to the second frequency recording, and use a sample clustering tool to group the data samples to obtain a first risk point set.

[0101] After using the data collection tool to obtain more intensive data samples from the sensors corresponding to the second frequency recording, suppose that within the six hours after the frequency adjustment, the system collected 18 sets of temperature data, showing that the temperature fluctuated between 26 and 29 degrees Celsius. Next, the sample clustering tool grouped these data samples, classifying time periods with large temperature fluctuations into one category, resulting in the first risk point set. For example, several data points where the temperature was consistently above 28 degrees Celsius between 10:00 and 11:00 a.m. were classified as high-risk.

[0102] Step S740: Use a spatial mapping tool to associate and match the first risk point set with the spatial location data to generate a distribution of risk points and determine the distribution of potential safety risk points.

[0103] By using spatial mapping tools to correlate and match the first risk point set with spatial location data, we can generate a distribution of potential safety risk points. For example, if analysis reveals that temperature anomalies near shelf D5 between 10:00 AM and 11:00 AM are concentrated on the lower shelves, the system will generate a distribution record of potential safety risk points, identifying this area as a key monitoring target. This distribution record provides precise guidance for subsequent risk prevention and control.

[0104] The specific implementation methods for each of the above technical topics can be further refined into full-chain management from data collection to risk distribution. During the sensor frequency adjustment process, the system dynamically determines the adjustment range based on the severity of the anomaly. For example, if the temperature exceeds the threshold by more than 3 degrees, the frequency will be directly increased to once every 10 minutes. When grouping data samples, the clustering tool will combine historical data trends to prioritize points with persistent anomalies. During spatial mapping, the system will also overlay a warehouse layout diagram to intuitively display the relationship between potential safety risk points and the surrounding environment. These methods together ensure comprehensive monitoring of abnormal areas and accurate identification of risk points, significantly improving the reliability of warehouse environment management.

[0105] Furthermore, the intelligent warehouse management method for hazardous waste provided in this embodiment includes step S800: Step S810: Based on the potential safety risk point location information obtained from the risk distribution data, a spatial mapping tool is used to group the potential safety risk point location information, and high-density areas are marked to obtain a first density distribution record.

[0106] In hazardous waste warehouse management scenarios, it is particularly important to process the distribution data of potential safety risk points. Based on the location information extracted from the risk distribution data, the spatial mapping tool can group these potential safety risk points. Assuming that there are multiple areas in the warehouse identified as potential safety risk points, the spatial mapping tool will group the potential safety risk points close to a certain shelf area according to the density of the location information and generate a first density distribution record. Assume that near the cargo area E3 near the ventilation hole in area E, the number of potential safety risk points reaches 5 per square meter, which is significantly higher than the average of 2 in other areas. The system will mark the area as a high-density area.

[0107] Step S820: Convert the first density distribution record into a heat map format using a visual drawing tool, assign color labels to different density areas, and determine first risk heat map data.

[0108] By converting the primary density distribution record into a heat map using a visualization tool, the distribution of potential safety risk points can be intuitively displayed. Different density areas are assigned colors, such as red for high-density areas, yellow for medium-density areas, and green for low-density areas, to generate the primary risk heat map data. For example, the area near cargo position E3 appears dark red, indicating a high concentration of risk, while the surrounding areas are mostly light yellow, indicating relatively low risk. This color-coding method facilitates quick identification of key areas.

[0109] Step S830: If there is an area in the first risk heat map data whose density exceeds the preset threshold, the area is marked with boundaries using a region division tool to obtain the first priority area range and determine the key processing range.

[0110] If the density of an area in the first-risk heat map exceeds a preset threshold, the system will use the zoning tool to mark the boundaries of that area. For example, if the preset threshold is four potential safety risk points per square meter and the E3 area has five, significantly exceeding the threshold, the system will automatically draw a boundary around that area and mark it as the first-priority area. This boundary may cover the E3 cargo area and a 2-meter radius around it, clearly identifying this area as the focus of treatment. This demarcation method helps to focus resources on high-risk areas.

[0111] Step S840: Use a data synchronization tool to upload the first priority area range and the first risk heat map data to the central processing platform, store and process the synchronized data, and obtain a first area division record.

[0112] Data synchronization tools are used to upload the first priority area and the first risk heat map data to the central processing platform to ensure data consistency. For example, after uploading the boundary data and heat map data for area E3, the central processing platform stores and processes this synchronized data to form the first area division record. During storage, the system adds a timestamp and area identifier, such as E3-2023-10-15, to this record for subsequent traceability and access. This storage method provides a data foundation for cross-departmental collaboration.

[0113] The specific implementation methods for the above technical topics can be refined starting from the grouping processing of spatial mapping. When grouping, the spatial mapping tool will give priority to the spatial proximity of potential safety risk points, such as grouping potential safety risk points that are less than 1 meter apart into the same group. When generating a heat map, the visualization tool will dynamically adjust the color depth according to the density value to ensure that the visual effect is intuitive. In the area division link, the boundary annotation will be combined with the actual layout of the warehouse to avoid the boundary range covering inaccessible areas, such as walls or equipment areas. When synchronizing data, the system will set up an automatic verification mechanism to ensure the integrity of the uploaded data. These methods together ensure the smoothness of the entire process from identifying potential safety risk points to data storage, providing reliable support for warehouse management.

[0114] Furthermore, the intelligent warehouse management method for hazardous waste provided in this embodiment includes step S900: Step S910: Based on the regional division and priority data, a path calculation tool is used to make preliminary adjustments to the route planning within the target area, giving priority to areas with higher task urgency, obtaining an adjusted first route plan, and determining the path length and time window allocation in the first route plan.

[0115] In hazardous waste warehouse management scenarios, route planning and scheduling optimization based on zoning and priority data are particularly important. The application of path calculation tools can analyze the distribution of high-risk areas within the warehouse and prioritize routes to areas with higher urgency. Assuming Area E in the warehouse is marked as the first priority area, the path calculation tool will design a straight path from the entrance to Area E based on the warehouse layout, avoiding obstacles and initially forming the first route plan. Assuming the path length is 50 meters and the time window is allocated to 10 minutes, rapid arrival is ensured.

[0116] Step S920: Using the first route plan and resource allocation data, the operation sequence is rearranged using a scheduling allocation tool. If the time window exceeds a preset threshold, the path length is adjusted a second time to obtain an updated second route plan, and a sequence combination that meets the requirements is determined.

[0117] Integrating resource allocation data, the scheduling and allocation tool reorders the operation sequence. For example, suppose three machines are available, but the preset time window threshold is 8 minutes. The first route plan's 10-minute limit exceeds this limit. The system triggers a secondary adjustment, shortening the path length to 40 meters, creating a second route plan. After this adjustment, the system identifies the new sequence and prioritizes two machines for Area E, while the other handles the secondary area, ensuring the time window meets the requirements.

[0118] Step S930: For the second route plan, use the instruction generation tool to refine the operation instructions, combine the instruction update data, obtain the final operation sequence combination, and determine the instruction content that meets the regional coverage.

[0119] For the second route, the instruction generation tool refines the operational instructions. For example, if hazardous waste needs to be cleared from a specific location in Area E, the system will generate detailed instructions, such as first checking location E3 near the vent, then processing surrounding locations. Combined with real-time data, the system determines the final operational sequence to ensure comprehensive coverage. This detailed guidance facilitates rapid execution of tasks by on-site personnel.

[0120] Step S940: Based on the final combination of operation sequences, a data integration tool is used to uniformly process the route planning data to obtain an integrated scheduling optimization result, and an output solution that meets the business objectives is determined.

[0121] During the data integration phase, the data integration tool uniformly processes route planning data to produce optimized dispatch results. Assume that after integration, the system determines that the final output plan covers Area E and surrounding sub-areas, with a total route length of less than 45 meters and a time window of 7 minutes, meeting business objectives. This integration approach helps improve overall dispatch efficiency.

[0122] Specifically, the path calculation tool prioritizes aisle widths and equipment accessibility within the warehouse to ensure route feasibility. The scheduling and allocation tool dynamically adjusts the sequence based on equipment load and staffing to maximize resource utilization. The instruction generation tool incorporates real-time warehouse monitoring data when refining instructions to avoid duplication of work. The data integration tool stores planning data in a unified format for easy traceability. These approaches collectively improve the timeliness and accuracy of handling high-risk areas within the warehouse, providing strong support for safety management.

[0123] Another aspect of the present invention relates to an intelligent warehouse management system for hazardous waste, which is used to execute the above-mentioned intelligent warehouse management method for hazardous waste. The intelligent warehouse management system for hazardous waste includes a first acquisition module, a first determination module, a first output module, a second acquisition module, a third acquisition module, a second output module, a second determination module, a judgment module and a third output module, wherein the first acquisition module is used to monitor the status of items in the hazardous waste warehouse in real time by deploying a sensor network, obtain basic data on the location and environmental parameters of the items, and transmit it to a central processing platform to obtain a preliminary data set; the first determination module is used to integrate and process multi-source information based on the preliminary data set using data fusion technology, eliminate redundancy and noise, and determine standardized data records; the first output module is used to dynamically analyze the distribution of cargo locations in the warehouse using a path optimization algorithm for standardized data records, and if a change in the cargo location occupancy status is detected, the cargo location allocation plan is adjusted in real time to output the optimal storage location combination; the second acquisition module is used to output the optimal storage location combination based on the optimal storage location combination. The storage location combination is combined with the warehouse layout information to generate a dynamic route planning scheme to obtain the optimal movement trajectory of the operating equipment; the third acquisition module is used to send real-time guidance instructions to the operating equipment based on the optimal movement trajectory, synchronously update the data status in the central processing platform, and obtain the latest warehouse operation log; the second output module is used to analyze the changing trend of environmental parameters according to the latest warehouse operation log. If it is found that the environmental parameters exceed the preset threshold range, the safety warning mechanism is triggered and the identification information of the abnormal area is output; the second determination module is used to adjust the sensor acquisition frequency of the environmental monitoring module based on the identification information of the abnormal area, obtain more intensive data samples, and determine the distribution of potential safety risk points; the judgment module is used to generate a visual risk heat map based on the distribution of potential safety risk points, synchronize it to the central processing platform, and determine the area range that needs priority processing; the third output module is used to adjust the operation guidance instructions and route planning scheme for the priority area range, obtain the updated operation sequence, and output the final optimization scheduling result.

[0124] The present embodiment provides a method and system for intelligent warehouse management of hazardous waste. Compared with the existing technology, it deploys a sensor network to monitor the status of items and environmental parameters in real time, adopts data fusion technology to integrate multi-source information, uses a path optimization algorithm to dynamically analyze the distribution of cargo locations and adjust the storage plan, combines warehouse layout information to generate dynamic route planning, and sends real-time guidance instructions to operating equipment. At the same time, the present embodiment analyzes the changing trend of environmental parameters, triggers a safety warning mechanism, adjusts the sensor acquisition frequency to determine potential safety risk points, generates a visual risk heat map, and optimizes the scheduling results accordingly. The present embodiment realizes the intelligent management of hazardous waste warehouses, improves storage efficiency and safety, and provides comprehensive monitoring and optimization support for warehouse operations. In summary, the beneficial effects of the method and system for intelligent warehouse management of hazardous waste provided by the present embodiment are specifically as follows: 1. Real-time and precise monitoring to ensure the safety of items and the environment: By deploying a sensor network to monitor hazardous waste warehouses in real time, basic data such as item location and environmental parameters can be obtained in a timely manner. Once abnormal fluctuations in environmental parameters (such as temperature, humidity, and hazardous gas concentrations) occur, the system can quickly detect them. Compared with traditional manual inspections, this greatly shortens the time it takes to discover abnormalities, effectively reducing the risk of accidents such as hazardous waste leakage and explosions caused by environmental changes, and providing strong protection for the storage safety of items and personnel in the warehouse. Second, data fusion processing improves data quality and decision reliability: Data fusion technology integrates multi-source information, eliminating redundancy and noise, and forming standardized data records. This makes the data obtained by the central processing platform more accurate and effective, avoiding decision-making errors caused by data confusion. Based on high-quality data, subsequent operations such as cargo location analysis and route planning can be more scientific and reasonable, providing reliable data support for warehouse management. 3. Dynamic cargo location and route optimization to improve warehouse efficiency: The route optimization algorithm dynamically analyzes the distribution of warehouse cargo locations, adjusts the cargo allocation plan in real time, and combines it with dynamic route planning to generate the optimal movement trajectory for equipment. This dynamic optimization mechanism reduces ineffective movement and waiting time for equipment, avoids detours and congestion during cargo handling, significantly improves cargo storage and handling efficiency, reduces labor and equipment resource consumption, and enhances overall warehouse operational efficiency. 4. Intelligent early warning and risk prevention enhance warehouse safety: When environmental parameters exceed preset thresholds, the system triggers a safety warning mechanism and, in conjunction with the environmental monitoring module, adjusts sensor acquisition frequency, conducts in-depth analysis of potential safety risks, and generates a visual risk heat map. This allows managers to intuitively and quickly understand the distribution of risks within the warehouse and take timely action in priority areas, nipping potential safety hazards in the bud. This enhances the warehouse's ability to respond to sudden safety incidents and improves overall warehouse safety. 5. Full-process automation and dynamic scheduling enable intelligent management: From data collection, processing, and analysis to operational instruction adjustments and route planning optimization, the entire management process is automated and dynamic. The system responds and adjusts in real time based on the warehouse's actual operating conditions, reducing manual intervention and the probability of human error. This drives hazardous waste warehouses towards intelligent, efficient, and refined management, meeting the high standards of modern hazardous waste management.

[0125] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the invention. Thus, the present invention is intended to include such changes and modifications as fall within the scope of the claims and their equivalents.

Claims

1. A method for intelligent warehouse management of hazardous waste, characterized in that: The following steps are involved: By deploying a sensor network to monitor the status of items in hazardous waste warehouses in real time, basic data on item location and environmental parameters is obtained and transmitted to a central processing platform to obtain a preliminary data set; Based on the preliminary data set, using data fusion technology to integrate and process multi-source information, eliminate redundancy and noise, and determine standardized data records; Based on the standardized data records, a path optimization algorithm is used to dynamically analyze the distribution of cargo locations within the warehouse. If a change in cargo location occupancy is detected, the cargo location allocation plan is adjusted in real time to output the optimal storage location combination; Based on the optimal storage location combination and combined with warehouse layout information, a dynamic route planning scheme is generated to obtain the optimal movement trajectory of the operating equipment; Based on the optimal movement trajectory, real-time guidance instructions are sent to the operating equipment, and the data status in the central processing platform is updated synchronously to obtain the latest warehouse operation log; Analyze the changing trends of environmental parameters based on the latest warehouse operation logs. If any environmental parameters are found to exceed the preset threshold range, a safety warning mechanism is triggered and identification information of the abnormal area is output; Based on the identification information of abnormal areas, the linked environmental monitoring module adjusts the sensor collection frequency to obtain more intensive data samples and determine the distribution of potential safety risk points; Generate a visual risk heat map based on the distribution of potential security risk points, synchronize it to the central processing platform, and determine the areas that require priority treatment; Adjust the operation guidance instructions and route planning plan for the priority area, obtain the updated operation sequence, and output the final optimized scheduling results.

2. The intelligent warehouse management method for hazardous waste according to claim 1, characterized in that: Based on the preliminary data set, the steps of integrating and processing multi-source information using data fusion technology to eliminate redundancy and noise and determine standardized data records include: Based on the preliminary data set, using a data cleaning tool to compare multi-source information, eliminate redundant information, and obtain a de-redundant information set; For the information set after de-redundancy, a noise filtering tool is used to identify outliers. If the outlier exceeds the preset threshold, it is corrected by a smoothing tool to obtain a noise-filtered information set. Based on the noise-filtered information set, a data standardization tool is used to uniformly convert the formats of environmental variables and location coordinates to obtain a standardized information set with a unified format; According to the standardized information set with a unified format, a data integration tool is used to fuse multi-source information and verify the data accuracy. If the accuracy is lower than a preset threshold, an interpolation tool is used to supplement it and determine the final standardized data record.

3. The intelligent warehouse management method for hazardous waste according to claim 1, characterized in that: The steps of dynamically analyzing the distribution of cargo locations within the warehouse using a path optimization algorithm based on the standardized data records and adjusting the cargo location allocation plan in real time if a change in cargo location occupancy is detected to output the optimal storage location combination include: Based on the standardized data records, a data comparison tool is used to monitor the occupancy status of cargo spaces in the warehouse in real time. In response to changes in the occupancy status of cargo spaces, the latest cargo distribution information is recorded through a data update tool to obtain an updated cargo space status set; Based on the updated storage location status set, the path planning tool is used to dynamically match storage locations within the warehouse layout. If the change in storage location occupancy status is detected to exceed a preset threshold, the location allocation tool recalculates the allocation plan and determines the adjusted storage location combination; Based on the adjusted storage location combination, the storage efficiency of the allocation scheme is verified using a data verification tool. If the storage efficiency does not meet the preset standard, the adjusted storage location combination is adjusted again using a location optimization tool to obtain an optimized storage location distribution. For the optimized storage location distribution, data integration tools are used to associate and record the adjustment frequency with the cargo distribution information. The final allocation plan is updated through the data storage tool to determine the storage location combination that best suits the warehouse layout.

4. The intelligent warehouse management method for hazardous waste according to claim 1, characterized in that: The steps of generating a dynamic route planning scheme based on the optimal storage location combination and combining the warehouse layout information to obtain the optimal movement trajectory of the operating equipment include: Based on the optimal storage location combination and warehouse layout information, a path planning tool is used to scan the internal structure of the warehouse, obtain the correspondence between the distribution status of goods and the layout matching rules, and obtain a preliminary route planning draft; Based on the preliminary route plan draft, the equipment trajectory is dynamically compared using real-time monitoring data. If the equipment movement efficiency is lower than the preset threshold, the path adjustment tool calculates the optimal movement path and determines the adjusted route plan. Based on the adjusted route plan, the trajectory optimization tool is used to perform a secondary match on the operating equipment trajectory to obtain a dynamic route plan consistent with the warehouse layout information and determine the movement trajectory combination; For the movement trajectory combination, a data integration tool is used to associate the trajectory optimization plan with the cargo distribution status and record it, and the dynamic route planning plan is updated through the data storage tool to obtain the optimal movement trajectory of the operating equipment.

5. The intelligent warehouse management method for hazardous waste according to claim 1, characterized in that: The steps of sending real-time guidance instructions to the operating equipment based on the optimal movement trajectory, synchronously updating the data status in the central processing platform, and obtaining the latest warehouse operation log include: Based on the optimal movement trajectory, a path feedback tool is used to continuously collect current position data of the operating equipment. When the current position deviates from the preset trajectory, a first instruction content is generated by a real-time guidance tool to determine the guidance data to be transmitted; The guidance data is transmitted to the operating equipment through an instruction sending tool, and a status monitoring tool is used to collect first response data after the operating equipment executes the instruction, and determine whether the first response data meets a preset threshold range; If the first response data meets the preset threshold, the new position of the operating equipment and the first response data are uploaded to the central processing platform through the synchronization update tool to obtain the updated first data status; According to the first data state, an operation record tool is used to integrate the position change of the operating equipment and the instruction execution status into first log data, and the first log data is classified and stored through a dynamic adjustment tool to obtain the latest operation record data.

6. The intelligent warehouse management method for hazardous waste according to claim 1, characterized in that: Based on the latest warehouse operation logs, analyze the changing trends of environmental parameters. If environmental parameters are found to exceed the preset threshold range, the safety warning mechanism is triggered. The steps of outputting identification information of abnormal areas include: Obtaining environmental parameter data from a warehouse operation log, extracting time series data of the environmental parameters using a log parsing tool, and processing the time series data using a trend analysis tool to obtain a first trend result; Based on the first trend result, a threshold comparison tool is used to determine whether the environmental parameter exceeds a preset threshold range. If so, a first abnormality record of the abnormal parameter is generated by an abnormality marking tool to determine an abnormality identifier; According to the first anomaly record, a region positioning tool is used to extract the spatial position corresponding to the anomaly parameter, and a unique identifier is assigned to the anomaly region using an identifier generation tool to obtain a first region identifier record; For the first area identification record, a warning trigger tool is used to generate a safety warning signal, and the identification information of the abnormal area is transmitted to the central processing platform through a data output tool to obtain a final warning output result.

7. The intelligent warehouse management method for hazardous waste according to claim 1, characterized in that: The steps of adjusting the sensor acquisition frequency based on the identification information of the abnormal area and linking the environmental monitoring module to obtain more intensive data samples and determine the distribution of potential safety risk points include: According to the abnormal identification and regional positioning, the spatial location data of the abnormal area is obtained from the environmental monitoring module, and the sensor acquisition frequency is extracted through the time series analysis tool to obtain the first frequency record; If the first frequency record is lower than the preset intensive collection threshold, the frequency adjustment tool is linked to the environmental monitoring module to dynamically adjust the sensor collection frequency to obtain a second frequency record; Using a data acquisition tool to obtain denser data samples from the sensor corresponding to the second frequency recording, and using a sample clustering tool to group the data samples to obtain a first risk point set; By using a spatial mapping tool, the first risk point set is associated and matched with the spatial location data to generate a distribution of risk points and determine the distribution of potential safety risk points.

8. The intelligent warehouse management method for hazardous waste according to claim 1, characterized in that: The steps of generating a visual risk heat map based on the distribution of potential security risk points, synchronizing the map to the central processing platform, and determining the scope of areas requiring priority treatment include: Based on the potential safety risk point location information obtained from the risk distribution data, a spatial mapping tool is used to group the potential safety risk point location information, and high-density areas are marked to obtain a first density distribution record; Using a visual drawing tool, converting the first density distribution record into a heat map format, assigning color codes to different density areas, and determining first risk heat map data; If there is an area in the first risk heat map data where the density exceeds the preset threshold, the area boundary is marked using the area division tool to obtain the first priority area range and determine the key processing range; Using a data synchronization tool, the first priority area range and the first risk heat map data are uploaded to the central processing platform, and the synchronized data is stored and processed to obtain the first area division record.

9. The intelligent warehouse management method for hazardous waste according to claim 1, characterized in that: The steps of adjusting the operation guidance instructions and route planning scheme for the priority processing area, obtaining the updated operation sequence, and outputting the final optimized scheduling result include: Based on the regional division and priority data, a path calculation tool is used to make preliminary adjustments to the route plan within the target area, prioritizing areas with higher mission urgency, obtaining an adjusted first route plan, and determining the path length and time window allocation in the first route plan; The first route plan is combined with resource allocation data to rearrange the operation sequence using a scheduling and allocation tool. If the time window exceeds a preset threshold, the path length is adjusted again to obtain an updated second route plan, thereby determining a sequence combination that meets the requirements. For the second route plan, the instruction generation tool is used to refine the operation instructions. Combined with the updated instruction data, the final operation sequence combination is obtained to determine the instruction content that meets the regional coverage. Based on the final combination of operation sequences, the route planning data is uniformly processed using data integration tools to obtain the integrated scheduling optimization results and determine the output plan that meets the business objectives.

10. An intelligent warehouse management system for hazardous waste, used to implement the intelligent warehouse management method for hazardous waste according to any one of claims 1 to 9, characterized in that: The intelligent warehouse management system for hazardous waste includes: The first acquisition module is used to monitor the status of items in the hazardous waste warehouse in real time by deploying a sensor network, obtain basic data on the location of items and environmental parameters, and transmit it to the central processing platform to obtain a preliminary data set; A first determination module is configured to integrate and process multi-source information based on the preliminary data set using data fusion technology to eliminate redundancy and noise and determine standardized data records; A first output module is configured to dynamically analyze the distribution of cargo locations within the warehouse using a path optimization algorithm based on the standardized data records. If a change in cargo location occupancy is detected, the cargo location allocation plan is adjusted in real time to output an optimal storage location combination. A second acquisition module is used to generate a dynamic route planning solution based on the optimal storage location combination and combined with warehouse layout information to obtain the optimal movement trajectory of the operating equipment; The third acquisition module is used to send real-time guidance instructions to the operating equipment based on the optimal movement trajectory, synchronously update the data status in the central processing platform, and obtain the latest warehouse operation log; The second output module is used to analyze the changing trends of environmental parameters based on the latest warehouse operation logs. If the environmental parameters are found to exceed the preset threshold range, the safety warning mechanism is triggered and the identification information of the abnormal area is output; The second determination module is used to link the environmental monitoring module with the identification information of the abnormal area to adjust the sensor collection frequency, obtain more intensive data samples, and determine the distribution of potential safety risk points; The judgment module is used to generate a visual risk heat map based on the distribution of potential security risk points, synchronize it to the central processing platform, and determine the areas that need priority treatment; The third output module is used to adjust the operation guidance instructions and route planning scheme according to the priority processing area, obtain the updated operation sequence, and output the final optimized scheduling result.

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