Warehouse safety risk prevention and control method and system based on internet-of-things equipment

Abstract

This invention provides a warehouse security risk prevention and control method and system based on IoT devices, belonging to the field of warehouse management technology. First, it acquires multimodal sensing data collected by various types of IoT devices within the warehouse, including equipment operating parameters, environmental status parameters, cargo attribute parameters, and scene dynamic parameters. Next, it performs risk transmission medium interaction identification processing on the multimodal sensing data to obtain identification results. Based on the identification results, it generates a risk transmission blocking chain, identifies key blocking nodes and blocking strategies, and schedules prevention and control devices according to the generated results. It establishes a collaborative response process and generates collaborative response instructions for the prevention and control devices. Finally, it sends the collaborative response instructions to the control device control terminal, receives status feedback information, and adjusts the blocking chain and instructions accordingly. This invention provides comprehensive perception, accurate identification, and dynamic prevention and control, effectively ensuring warehouse security.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of warehouse management, in particular to a warehouse security risk prevention and control method and system based on Internet of Things equipment. BACKGROUND

[0002] In the field of warehouse management, warehouse security risk prevention and control has always been a crucial link. The traditional warehouse security risk prevention and control method has many limitations. On the one hand, the data collection means is relatively single, often relying on only a few devices to obtain limited information, such as monitoring the warehouse temperature only through a simple temperature sensor, or relying on manual inspection to discover potential safety hazards. The above single data collection method cannot comprehensively and accurately reflect the actual situation in the warehouse, and some key safety risk factors may be missed.

[0003] On the other hand, in terms of data processing and analysis, the traditional method lacks in-depth mining of the correlation between different types of data. The data collected by different devices usually exists in isolation, without forming an effective data fusion and interactive analysis mechanism. For example, there may be complex mutual influence relationships between the running parameters of warehouse equipment, environmental state parameters, cargo attribute parameters, and scene dynamic parameters, but the traditional method is difficult to identify these relationships and cannot predict and prevent the occurrence of safety risks in advance. Moreover, when formulating prevention and control strategies, the traditional method often lacks pertinence and flexibility, and cannot dynamically adjust the prevention and control measures according to real-time data, resulting in poor prevention and control effect and failing to effectively protect the safe operation of the warehouse. SUMMARY

[0004] In view of the above-mentioned problems, in combination with the first aspect of the present application, the present application embodiment provides a warehouse security risk prevention and control method based on Internet of Things equipment, which comprises: acquiring multi-modal perception data collected by multiple types of Internet of Things equipment in the warehouse, the multi-modal perception data including device running parameters collected by warehouse equipment sensors, environmental state parameters collected by environmental monitoring sensors, cargo attribute parameters collected by cargo state sensors, and scene dynamic parameters collected by video monitoring equipment, the multiple types of Internet of Things equipment including warehouse equipment sensors, environmental monitoring sensors, cargo state sensors, and video monitoring equipment; performing risk transmission medium interaction identification processing on the multi-modal perception data, distinguishing the risk transmission medium types corresponding to different perception data, identifying the interaction relationship between different media, and obtaining a risk transmission medium interaction identification result; generating a risk transmission blocking chain based on the risk transmission medium interaction identification result, determining the key blocking nodes in the risk transmission blocking chain and the blocking strategies corresponding to each key blocking node, and obtaining a risk transmission blocking chain generation result; Based on the results of the risk transmission blockade chain generation, the warehouse prevention and control equipment is scheduled, the signal transmission and start-up control process between equipment is established, and the collaborative response instructions of the prevention and control equipment are generated. The system sends coordinated response instructions from prevention and control equipment to the control terminal of the corresponding equipment, receives status feedback information after the equipment executes the instructions, and adjusts the risk transmission blocking chain and coordinated response instructions based on the status feedback information.

[0005] Furthermore, embodiments of the present invention also provide a warehouse security risk prevention and control system based on IoT devices, comprising: A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the aforementioned warehouse security risk prevention and control method based on IoT devices by executing the machine-executable instructions.

[0006] In another aspect, embodiments of the present invention also provide a computer program product, the computer program product including machine-executable instructions, the machine-executable instructions being stored in a computer-readable storage medium, the processor of a computer device reading the machine-executable instructions from the computer-readable storage medium, the processor executing the machine-executable instructions, causing the computer device to execute the above-described warehouse security risk prevention and control method based on IoT devices.

[0007] Based on the above, by acquiring multimodal sensing data collected from various types of IoT devices within the warehouse, covering information such as equipment operating parameters, environmental status parameters, cargo attribute parameters, and dynamic scene parameters, and then performing risk transmission medium interaction identification processing on the multimodal sensing data, it is possible to deeply explore the types of risk transmission media corresponding to different sensing data and their interactive relationships, thereby identifying potential security risk transmission paths. Based on the identification results, a risk transmission blocking chain is generated, and key blocking nodes and corresponding blocking strategies are determined. This allows for targeted severing of security risk transmission chains, effectively preventing the spread and escalation of risks. By scheduling control equipment and establishing a collaborative response mechanism based on the risk transmission blocking chain generation results, efficient information transmission and collaborative work among control equipment can be achieved, improving the response speed and collaborative effect of control equipment. By receiving status feedback information after the control equipment executes instructions and adjusting the risk transmission blocking chain and collaborative response instructions, control strategies can be adjusted in real time according to the actual situation, continuously optimizing the control effect and greatly improving the accuracy, timeliness, and effectiveness of warehouse security risk control. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of the execution flow of the warehouse security risk prevention and control method based on IoT devices provided in the embodiments of the present invention.

[0009] Figure 2This is a schematic diagram of exemplary hardware and software components of a warehouse security risk prevention and control system based on IoT devices provided in an embodiment of the present invention. Detailed Implementation

[0010] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a warehouse security risk prevention and control method based on IoT devices according to an embodiment of the present invention. The following is a detailed description of this warehouse security risk prevention and control method based on IoT devices.

[0011] Step S110: Obtain multimodal sensing data collected by various types of IoT devices in the warehouse. The multimodal sensing data includes equipment operating parameters collected by warehouse equipment sensors, environmental status parameters collected by environmental monitoring sensors, cargo attribute parameters collected by cargo status sensors, and scene dynamic parameters collected by video surveillance devices. The various types of IoT devices include warehouse equipment sensors, environmental monitoring sensors, cargo status sensors, and video surveillance devices.

[0012] In this embodiment, a large electronics warehouse is used as the application scenario. Various types of IoT devices are deployed within the warehouse to monitor various data in real time. Warehouse equipment sensors are installed on shelves, conveyors, stacker cranes, and other storage equipment to continuously collect equipment operating parameters, such as shelf load-bearing data, conveyor speed, and stacker crane lifting height and movement position. Environmental monitoring sensors are distributed throughout different areas of the warehouse, including temperature sensors, humidity sensors, and gas concentration sensors, collecting environmental status parameters such as temperature, humidity, and concentration of specific gases in the air. Cargo status sensors are attached to cargo packaging or pallets to collect cargo attribute parameters, such as weight, volume, storage time, and whether tilting or vibration has occurred. Video surveillance equipment is installed at key locations such as warehouse entrances, aisles, and shelving areas to collect dynamic scene parameters, including personnel movement trajectories, cargo handling processes, and equipment operating status images. All this multimodal sensing data is transmitted in real time to the control center via the warehouse's IoT communication network.

[0013] Step S120: Perform risk transmission medium interaction identification processing on the multimodal sensing data, distinguish the risk transmission medium types corresponding to different sensing data, identify the interaction relationship between different media, and obtain the risk transmission medium interaction identification result.

[0014] Step S121: Analyze the equipment operation parameters, environmental status parameters, cargo attribute parameters, and scene dynamic parameters in the multimodal sensing data, and extract the collection location information, collection time series, and parameter change trend of each parameter.

[0015] In this electronics warehouse, the control center analyzes the received multimodal sensing data. For equipment operating parameters, such as the operating data of a stacker crane, the collection location information for each operation of the stacker crane is extracted, i.e., its specific coordinates in the warehouse coordinate system; the collection time series is the time point corresponding to each sampling interval from the start of the stacker crane to the current moment; parameter change trends include the change of the stacker crane's operating speed over time, the increase or decrease trend of lifting height, etc. For environmental condition parameters, such as the data collected by temperature sensors, the coordinates of their installation location are extracted as the collection location information, and the temperature sampling values ​​arranged in chronological order form a collection time series. The analysis shows whether the temperature value continuously rises, falls, or remains stable over a period of time, thereby determining the parameter change trend. Regarding cargo attribute parameters, such as the weight data of a batch of electronic components, the collection location information is the shelf number and layer of the batch of goods, and the collection time series is from the time the goods entered the warehouse to the current weight detection time point. The parameter change trend may be due to the slight weight change trend caused by the compression of the goods during stacking. Scene dynamic parameters, such as personnel movement data collected by video surveillance equipment, are extracted using video analytics to obtain the location coordinates of personnel at different times as the collected location information. These coordinates are then arranged in chronological order to form a time series of collected data. The trend of parameter changes reflects the changes in the personnel's movement speed and direction.

[0016] Step S122: Perform regional classification processing on the extracted collection location information, grouping the parameter collection locations within the same warehouse partition into the same region group, and grouping the parameter collection locations within different warehouse partitions into different region groups.

[0017] The electronics warehouse is divided into multiple storage zones based on the type of goods stored and management needs. For example, Zone A stores integrated circuits, Zone B stores electronic displays, and Zone C stores batteries. After extracting the location information of each parameter, the control center categorizes the data according to the storage zone to which these locations belong. For instance, if the coordinates of a temperature sensor's location fall within the boundary of Zone A, it is assigned to Zone A; if another goods status sensor's location is in Zone B, it is assigned to Zone B. For cases where the location is exactly at the boundary between two zones, the control center can categorize the data according to preset zone priority rules, such as prioritizing the zone containing the main goods. This zone categorization process allows subsequent analysis to be conducted on a zone-by-zone basis, providing a better understanding of the risk transmission between different zones.

[0018] Step S123: Based on the historical data of risk transmission within the warehouse, the risk transmission medium is divided into air medium, cargo contact medium, equipment transmission medium, and personnel flow medium. Each risk transmission medium corresponds to a specific transmission characteristic in the historical data of risk transmission.

[0019] The control center retrieved historical risk propagation data from the electronics warehouse, which recorded various past security risk events and their propagation processes. Analysis of this historical data revealed that risk propagation primarily relied on several different media. These included: airborne media (risks propagated through airflow, such as the diffusion of harmful gases within the warehouse); cargo contact media (risks propagated through direct contact between goods, such as contaminants on goods being transferred to other goods); equipment conduction media (risks propagated through equipment and their wiring within the warehouse, such as electrical faults being transmitted to other equipment via power lines); and personnel movement media (risks propagated as people moved within the warehouse, such as people carrying contaminants from one area to another). Each risk transmission medium exhibited specific propagation characteristics in the historical risk propagation data, which will serve as the basis for subsequently identifying the media type corresponding to various parameters.

[0020] Step S124: Extract the historical propagation characteristics of air medium, cargo contact medium, equipment conduction medium, and personnel flow medium. The historical propagation characteristics of air medium correspond to continuous diffusion characteristics, the historical propagation characteristics of cargo contact medium correspond to cargo-related diffusion characteristics, the historical propagation characteristics of equipment conduction medium correspond to line conduction characteristics, and the historical propagation characteristics of personnel flow medium correspond to trajectory-related diffusion characteristics.

[0021] From the historical risk propagation data of the electronics warehouse, the control center extracted the historical propagation characteristics of the four risk transmission media mentioned above. For airborne media, the historical propagation characteristic was continuous diffusion, meaning the risk spread continuously from the source outwards without significant jumps, and the diffusion range gradually expanded over time. For cargo contact media, the historical propagation characteristic was cargo-related diffusion; the risk propagation was closely related to the storage location and handling path of the goods, typically spreading from one cargo unit to other cargo units that directly contacted it or indirectly through intermediate goods. For equipment transmission media, the historical propagation characteristic was line transmission; the risk propagated along the connecting lines of equipment within the warehouse, such as power lines and data lines, with the propagation path consistent with the direction of the lines. For personnel movement media, the historical propagation characteristic was trajectory-related diffusion; the risk propagation path was highly consistent with the movement trajectory of personnel within the warehouse, spreading from one location to another as personnel moved, exhibiting a clear following characteristic.

[0022] Step S125: Compare the changing trends of each parameter with the historical transmission characteristics of air medium, cargo contact medium, equipment transmission medium, and personnel flow medium to determine the risk transmission medium type corresponding to each parameter and form a correspondence table between parameters and media.

[0023] In this electronics warehouse scenario, the control center meticulously compared the trends of previously extracted parameters with the historical propagation characteristics of four risk transmission media. For example, the gas concentration parameter in the environmental state parameters showed a trend of spreading from a point source outwards throughout the warehouse, continuously expanding its coverage area. This trend aligns with the continuous diffusion characteristics of air, thus identifying air as the risk transmission medium. The surface contaminant detection parameter in the cargo attribute parameters showed a trend of spreading from one batch of goods to adjacent goods, consistent with the cargo-related diffusion characteristics of cargo contact media, thus corresponding to cargo contact media. The abnormal current parameter in the equipment operation parameters showed changes along the power supply line, appearing sequentially on different devices, consistent with the line conduction characteristics of equipment transmission media, thus corresponding to equipment transmission media. The personnel-carried contaminant parameter in the scenario dynamic parameters showed a path consistent with the movement trajectory of personnel, consistent with the trajectory-related diffusion characteristics of personnel flow media, thus corresponding to personnel flow media. The control center recorded each parameter and its corresponding risk transmission medium type, forming a parameter-media correspondence table.

[0024] Step S1251: Extract the historical propagation characteristics of the air medium. The historical propagation characteristics of the air medium are characterized by continuous diffusion of parameter changes, stable expansion rate of coverage area per unit time, and no obvious boundary limit of parameter changes. The historical propagation characteristics of the air medium are extracted from the historical case data of air medium propagation in warehouses.

[0025] The control center extracted historical propagation characteristics of airborne media from historical case data of airborne media propagation in the warehouse. In one historical case at this electronics warehouse, an incident occurred where a chemical leak led to an increase in the concentration of harmful gases in the air. In this incident, the gas concentration parameters exhibited a continuous diffusion pattern; that is, starting from the leak point, the gas concentration gradually diffused to the surrounding area, without suddenly appearing in a high-concentration area far from the leak point. Simultaneously, during the diffusion process, the expansion rate of the harmful gas coverage area per unit time was relatively stable. For example, the coverage area expanded by a certain percentage in the first hour after the leak, and a similar percentage was achieved in the second hour. Furthermore, the changes in these parameters did not have obvious boundary limitations; even when encountering obstacles such as shelves, the gas would gradually permeate and diffuse through gaps, and would not be completely blocked within a fixed boundary. These characteristics collectively constitute the historical propagation characteristics of the airborne media.

[0026] Step S1252: Extract the historical propagation characteristics of the medium in contact with the goods. The historical propagation characteristics of the medium in contact with the goods are characterized by parameter changes concentrated in the goods storage area, phased extension along the goods movement path, and the parameter change boundary coinciding with the goods storage boundary. The historical propagation characteristics of the medium in contact with the goods are extracted from the historical case data of the propagation of the medium in contact with the goods in the warehouse.

[0027] Based on historical case data on the propagation of static charge through contact media in warehouses, this electronics warehouse previously experienced an incident where electrostatic charge transfer occurred on the surface of electronic components due to damaged packaging. In this incident, the changes in parameters of the contact media, i.e., the distribution of static charge, were concentrated in the goods storage areas, such as the areas where electronic components were stacked on shelves, while no significant parameter changes were observed in non-goods storage areas such as warehouse aisles. Furthermore, as the damaged electronic components were moved, the propagation path of the static charge extended in stages along the goods movement path. For example, during the process of moving from shelf A to shelf B, changes in static charge parameters occurred sequentially in the shelf A area, the transport path area, and the shelf B area. Moreover, the boundaries of parameter changes coincided with the boundaries of goods storage, meaning the influence of the static charge was limited to the area where goods were stored. When it exceeded the boundaries of goods storage, the parameter changes rapidly weakened until they disappeared. These characteristics constitute the historical propagation features of the contact media.

[0028] Step S1253: Extract the historical propagation characteristics of the equipment's transmission medium. The historical propagation characteristics of the equipment's transmission medium are manifested as parameter changes being gradually transmitted along the equipment connection lines, parameter changes between adjacent equipment having a fixed time interval, and the magnitude of parameter changes gradually decreasing with the transmission distance. The historical propagation characteristics of the equipment's transmission medium are extracted from the historical case data of the transmission medium of warehouse equipment.

[0029] In the historical case data of equipment transmission media propagation in warehouses, there was an event involving abnormal current conduction caused by a stacker crane motor malfunction. In this event, the parameter changes of the equipment's transmission medium, i.e., the abnormal current, were gradually transmitted along the connection line between the stacker crane and the power supply system. First, the faulty stacker crane exhibited an abnormal current, which was then transmitted through the power supply line to an adjacent stacker crane, and then to other more distant equipment. Furthermore, there was a fixed time interval between parameter changes between adjacent devices; for example, the time interval from the faulty stacker crane to the first adjacent device exhibiting an abnormal current was a certain duration, and the time interval from the first adjacent device to the next device was roughly the same. Simultaneously, the amplitude of parameter changes gradually decreased with the transmission distance, with the largest amplitude of the abnormal current at the faulty stacker crane, and the amplitude decreasing as the distance to the device increased until no significant parameter changes occurred beyond a set distance. These characteristics constitute the historical propagation features of the equipment's transmission medium.

[0030] Step S1254: Extract the historical propagation characteristics of personnel flow media. The historical propagation characteristics of personnel flow media are manifested as the parameter changes being discretely distributed along the personnel activity trajectory, the parameter change area completely overlapping with the personnel stay area, and the parameter change duration being consistent with the personnel stay time. The historical propagation characteristics of personnel flow media are extracted from the historical case data of personnel flow media propagation in the warehouse.

[0031] Historical data on personnel movement in warehouses revealed instances of pollution transmission caused by personnel bringing dust into the warehouse. In these incidents, the changes in dust concentration, a parameter of the personnel movement medium, exhibited a discrete distribution along the personnel's movement trajectory. When personnel were in area A, dust concentration increased in area A; when personnel moved to area B, dust concentration gradually decreased in area A, while dust concentration increased in area B, demonstrating a discrete distribution. Furthermore, the areas of parameter change completely overlapped with the areas where personnel stayed; wherever personnel stayed, dust concentration parameter changes occurred, while areas not visited by personnel showed little change. Simultaneously, the duration of parameter change was consistent with the duration of personnel stay; the duration of abnormal dust concentration in a given area was roughly the same as the time personnel spent there, and the parameters in that area gradually returned to normal after personnel left. These characteristics constitute the historical transmission characteristics of personnel movement media.

[0032] Step S1255: Analyze the changing trends of equipment operating parameters, environmental status parameters, cargo attribute parameters, and scene dynamic parameters one by one, and extract the rate of change, coverage change pattern, and change boundary characteristics of each parameter.

[0033] The control center analyzes various parameters in the multimodal sensing data one by one. For equipment operating parameters, such as conveyor belt speed, the analysis examines its rate of change over a period of time—whether it changes rapidly or slowly; the pattern of coverage change refers to how the length of the conveyor belt affected by the speed change expands; and the boundary characteristics are whether the transition between the speed change area and the normal area is obvious. For environmental state parameters, such as humidity, the analysis examines whether its rate of change increases sharply or slowly; the pattern of coverage change—whether it gradually expands from a local area to the entire warehouse or remains confined to a specific area; and the boundary characteristics are whether the humidity change area has a clear boundary. For cargo attribute parameters, such as cargo temperature, the analysis examines whether its rate of change increases uniformly or rapidly; the pattern of coverage change—whether it expands from a part of the cargo to the entire cargo or remains localized; and the boundary characteristics are whether the boundary between the temperature change area and the normal area is clear. For scene dynamic parameters, such as personnel density, the analysis examines whether its rate of change is rapid gathering or slow increase; the pattern of coverage change—how the area of ​​personnel density change expands; and the boundary characteristics are whether the boundary of the area of ​​personnel density change is clearly identifiable.

[0034] Step S1256: Compare the rate of change of each parameter with the rate characteristics in the historical propagation characteristics of air medium, cargo contact medium, equipment conduction medium, and personnel flow medium; compare the change pattern of the coverage area of ​​each parameter with the range change pattern in the corresponding historical propagation characteristics; and compare the change boundary characteristics of each parameter with the boundary characteristics in the historical propagation characteristics.

[0035] After analyzing the changing trends of each parameter, the control center compared the rate of change of each parameter with the rate characteristics of the historical propagation features of the four media. For example, the rate of change of the humidity parameter was compared with the continuous diffusion rate of the air medium, the cargo-related diffusion rate of the cargo contact medium, the line conduction rate of the equipment conduction medium, and the trajectory-related diffusion rate of the personnel flow medium, to see which medium's rate characteristics were more similar to the rate of change of the humidity parameter. Similarly, the variation pattern of the coverage area of ​​the humidity parameter was compared with the variation pattern of the continuous diffusion range of the air medium, the variation pattern of the phased extension range of the cargo contact medium with the cargo movement, the variation pattern of the gradual transmission range along the route of the equipment conduction medium, and the variation pattern of the discrete distribution range of the personnel flow medium with the personnel trajectory. For the boundary characteristics of change, the boundary characteristics of the humidity parameter were compared with the boundary characteristics of the historical propagation features of the four media in a similar manner, such as the lack of obvious boundaries for the air medium, the overlap with the cargo storage boundary for the cargo contact medium, the attenuation boundary with distance for the equipment conduction medium, and the overlap with the residence area boundary for the personnel flow medium.

[0036] Step S1257: Count the number of matching items for each parameter with air medium, cargo contact medium, equipment conduction medium, and personnel flow medium. The number of matching items includes rate characteristic matching items, range variation law matching items, and boundary characteristic matching items.

[0037] After comparing each parameter with the characteristics of the four media, the control center counted the number of matching items for each parameter with each medium. Taking humidity as an example, in the comparison with air medium, if the rate of change of humidity parameter matches the rate characteristic of air medium, it is recorded as 1 rate characteristic matching item; if the pattern of change of coverage area matches the pattern of change of coverage area of ​​air medium, it is recorded as 1 coverage pattern matching item; if the boundary characteristic of change matches the boundary characteristic of air medium, it is recorded as 1 boundary characteristic matching item. Therefore, the total number of matching items for humidity parameter with air medium is 3. Similarly, the number of matching items for humidity parameter with cargo contact medium, equipment conduction medium, and personnel flow medium is counted separately, assumed to be 1, 0, and 2 items respectively. The number of matching items for all parameters is counted in the above manner.

[0038] Step S1258: Determine the media type with the most matching items as the risk transmission media type corresponding to the parameter, record each parameter and its corresponding risk transmission media type, and form a correspondence table between parameters and media.

[0039] Based on the number of matching items, the control center determines the corresponding risk transmission medium type for each parameter. For example, for the humidity parameter, there are 3 matching items with air, the most among all media; therefore, the risk transmission medium type for humidity is determined to be air. For the electrostatic charge parameter on the cargo surface, after comparison and statistics, it has the most matching items with the cargo contact medium; therefore, it corresponds to the cargo contact medium. For the abnormal current parameter, it has the most matching items with the equipment conduction medium; therefore, it corresponds to the equipment conduction medium. For the dust carried by personnel, it has the most matching items with the personnel flow medium; therefore, it corresponds to the personnel flow medium. The control center records all parameters and their corresponding risk transmission medium types, forming a parameter-medium correspondence table, which shows which risk transmission medium propagates each sensed parameter.

[0040] Step S126: Compare the acquisition time series of parameters corresponding to different media types, mark the time point when each media parameter first changes, and extract the temporal correlation of the changes in different media parameters.

[0041] The control center extracts parameters corresponding to different media types based on a parameter-media correspondence table and compares their acquisition time series. For example, parameters corresponding to air media include temperature, humidity, and gas concentration, while parameters corresponding to cargo contact media include cargo surface contaminants and cargo electrostatic charge. For each media type, the time point at which each parameter first shows an abnormal change is marked in its corresponding parameter acquisition time series. If there are multiple parameters for the same media type, each parameter has its own first change time point. Then, the control center analyzes the chronological order of these different media parameter changes and extracts the temporal correlation between them. For example, if it is found that the first change time point of the temperature parameter (air medium) in a certain area is earlier than the first change time point of the cargo surface contaminant parameter (cargo contact medium) in the same area, this may indicate that the risk propagation of the air medium precedes that of the cargo contact medium.

[0042] Step S127: Extract the region group to which the collection location of the corresponding parameters of different media belongs, and determine whether the collection locations of the parameters of different media are in the same region group or adjacent region groups.

[0043] After determining the temporal correlation of changes in different media parameters, the control center extracts the regional groups to which the collection locations of these parameters belong. For example, the temperature parameter collection location for air belongs to regional group A, the surface contaminant parameter collection location for goods in contact with the goods belongs to regional group A, and the current anomaly parameter collection location for equipment conduction belongs to regional group B. Then, it is determined whether the regional groups to which the collection locations of different media parameters belong are the same or adjacent. In the above example, the temperature parameter and the surface contaminant parameter collection locations both belong to regional group A, therefore they are in the same regional group; however, the current anomaly parameter collection location belongs to regional group B. It is necessary to determine whether regional groups A and B are adjacent regional groups. According to the warehouse zoning map, if areas A and B are physically adjacent, then they belong to adjacent regional groups; otherwise, they are non-adjacent regional groups.

[0044] Step S128: Based on the temporal correlation of parameter changes and the regional correlation of acquisition locations, calculate the interaction influence coefficient between different media, and classify direct triggering relationship, indirect influence relationship, and no correlation relationship according to the value of the interaction influence coefficient.

[0045] Step S1281: Extract the acquisition time series of parameters corresponding to different media, determine the time unit of each acquisition time series, and unify the time unit of all acquisition time series through time unit conversion.

[0046] The control center extracts the acquisition time series of parameters corresponding to different media from multimodal sensing data. For example, the acquisition time series of temperature parameters for air might be in minutes, the acquisition time series of surface contaminant parameters for cargo contact media might be in hours, the acquisition time series of current anomaly parameters for equipment conduction media might be in seconds, and the acquisition time series of personnel density parameters for personnel flow media might be in minutes. To perform time series correlation analysis, the control center needs to determine the time unit for each acquisition time series and convert them to a unified time unit, such as minutes. For time series in seconds, data from every 60 seconds is merged into a single minute data point; for time series in hours, data from each hour is split into 60 minute data points, with interpolation and other methods used to supplement intermediate data. This time unit conversion ensures that the time units of all acquisition time series are consistent, facilitating subsequent time series comparison and correlation analysis.

[0047] Step S1282: Based on a unified time unit, mark the time point when each medium parameter first changes. If there are multiple parameters for the same medium, take the time point when the earliest parameter changes as the first change time point for that medium.

[0048] After standardizing the time unit, the control center analyzed the time series of parameter acquisitions for each medium, marking the first time each parameter showed an abnormal change. For example, air medium has three parameters: temperature, humidity, and gas concentration, and their acquisition time series have been converted to minutes. In the temperature parameter's time series, a significant deviation from the normal range was found at a certain minute, and that minute was marked as the first change point for the temperature parameter; similarly, the first change points for the humidity and gas concentration parameters were found. For air medium, if the first change point for the temperature parameter was the earliest, then that point was taken as the first change point for the air medium. The same method was used to determine the first change points for cargo contact media, equipment conduction media, and personnel flow media.

[0049] Step S1283: Compare the first change time points of any two media parameters, calculate the time difference between the two first change time points, and determine the timing correlation level based on the relationship between the time difference and the preset time threshold. The smaller the time difference, the higher the timing correlation level.

[0050] The control center selects any two media, such as air and goods contact media, and compares their first change time points. Assuming the first change time point for air is T1 and for goods contact media is T2, the time difference is calculated as the absolute value of T2 minus T1. This time difference is then compared to a preset time threshold, which can be set based on the warehouse's historical data and risk propagation characteristics, for example, T0. If the time difference is less than 30% of T0, the time correlation level is considered high; if it is between 30% and 70% of T0, it is considered medium; and if it is greater than 70% of T0, it is considered low. The smaller the time difference, the closer the changes in the parameters of the two media are in time, and the higher the time correlation level, indicating a strong temporal correlation between them.

[0051] Step S1284: Retrieve the warehouse partitioning file, obtain the adjacency relationship data between different area groups from the warehouse partitioning file, determine the criteria for determining adjacent area groups, and identify adjacent area groups if the distance between the area boundaries is less than the preset distance.

[0052] The control center retrieves the warehouse zoning file, which details the boundary coordinates, area, and adjacency relationships between each zone group within the warehouse. From this file, the control center can obtain information such as which zones group A is adjacent to, and which zones group B is adjacent to. The file also sets the criteria for determining adjacent zones: if the shortest distance between the boundaries of two zones is less than a preset distance, they are considered adjacent zones. For example, if the preset distance is a certain length, and the shortest distance between the eastern boundary of zone A and the western boundary of zone B is less than this preset distance, then zone A and zone B are adjacent zones. Using this data, the control center can accurately determine the regional relationships between the zones belonging to different media parameter collection locations.

[0053] Step S1285: Extract the area groups to which the collection locations of the corresponding parameters of the two media belong. Determine whether the two area groups are adjacent based on the adjacent relationship data in the warehouse partitioning file. Determine the area association level based on the judgment result. The area association level of the same area group is the highest, the area association level of adjacent area groups is the second highest, and the area association level of non-adjacent area groups is the lowest.

[0054] For any two media, such as equipment conduction media and personnel flow media, the control center extracts the regional group to which the corresponding parameter collection locations belong. Assume the equipment conduction media parameter collection location belongs to regional group B, and the personnel flow media parameter collection location belongs to regional group C. Then, based on the adjacency relationship data in the warehouse zoning file, it is determined whether regional groups B and C are adjacent regional groups. If regional groups B and C are adjacent, their regional association level is medium; if they belong to the same regional group, the regional association level is high; if they are not adjacent regional groups, the regional association level is low. The regional association level of different media parameter collection locations is determined in this way.

[0055] Step S1286: Combining the temporal correlation level and the regional correlation level, and based on the preset correlation level combination rules, determine the interaction influence coefficient between the two media.

[0056] The control center integrates temporal and regional correlation levels, and calculates the interaction coefficient between different media according to preset correlation level combination rules. These preset rules map different combinations of temporal and regional correlation levels to different ranges of interaction coefficient values. For example, when both the temporal and regional correlation levels are high, the interaction coefficient ranges from 0.8 to 1.0; when both are high and medium, the range is 0.6 to 0.8; when both are low, the range is 0.4 to 0.6, and so on. For all possible combinations of temporal and regional correlation levels, corresponding interaction coefficient ranges are set. The control center determines the corresponding interaction coefficient range based on the temporal and regional correlation level combinations of the two media, and then fine-tunes it within this range based on factors such as specific time differences and regional distances to obtain the final interaction coefficient value.

[0057] Step S1287: Classify direct triggering relationships, indirect influence relationships, and no-association relationships based on the interaction influence coefficient values.

[0058] After calculating the interaction coefficients between different media, the control center sets two thresholds: a direct trigger threshold and an indirect influence threshold. When the interaction coefficient is greater than the direct trigger threshold, a direct triggering relationship is determined between the two media, meaning a change in one medium directly causes a change in the other. When the interaction coefficient is between the indirect influence threshold and the direct trigger threshold, an indirect influence relationship is determined, meaning the changes in the two media are related but not directly causally related. When the interaction coefficient is less than the indirect influence threshold, no correlation is determined, meaning the changes in the two media are independent and have no significant impact. For example, setting the direct trigger threshold to 0.7 and the indirect influence threshold to 0.3, if the interaction coefficient between air and cargo contact media is 0.8, greater than the direct trigger threshold, they are determined to have a direct triggering relationship. If the interaction coefficient between equipment conduction media and personnel flow media is 0.5, between the two thresholds, it is determined to have an indirect influence relationship. If the interaction coefficient between cargo contact media and personnel flow media is 0.2, less than the indirect influence threshold, it is determined to have no correlation.

[0059] Step S129: Integrate the correspondence table between parameters and media, the interaction relationship between different media and the regional group information to generate a risk transmission media interaction identification result that includes media type, corresponding parameter set, media interaction relationship description and regional association information.

[0060] The control center integrates the previously established parameter-media correspondence table, the calculated interaction relationships between different media (direct triggering relationships, indirect influence relationships, and no-association relationships), and the regional group information to which each media parameter collection location belongs. During the integration process, for each media type, its corresponding parameter set is listed, describing the interaction relationships between this media and other media, as well as the regional group information and regional associations to which the media parameter collection location belongs. Finally, a risk transmission media interaction identification result is generated. This result is presented in a structured format, showing the types of various risk transmission media within the warehouse, the parameters involved, their interaction relationships with other media, and their regional distribution.

[0061] Step S130: Generate a risk transmission blocking chain based on the risk transmission medium interaction identification results, determine the key blocking nodes in the risk transmission blocking chain and the blocking strategy corresponding to each key blocking node, and obtain the risk transmission blocking chain generation result.

[0062] Step S131: Extract the medium type, medium interaction relationship description, and regional association information from the risk transmission medium interaction identification results. Based on the medium type, medium interaction relationship description, and regional association information from the risk transmission medium interaction identification results, trace the node connection relationship of risk transmission from the initial medium to other media.

[0063] The control center extracts the medium type (e.g., air, cargo contact, equipment transmission, personnel flow) from the risk transmission medium interaction identification results; a description of the medium interaction relationship (e.g., a direct triggering relationship between air and cargo contact, or an indirect influence relationship between equipment transmission and personnel flow); and regional association information (e.g., the regional group and regional association level to which each medium parameter collection location belongs). Based on this information, the control center simulates the risk propagation process, tracing how the risk propagates from the initial medium to other media through the interactions between them. For example, assuming the initial risk occurs in the air, since the air and cargo contact are directly triggered, the risk will propagate from the air to the cargo contact; the cargo contact has an indirect influence relationship with the personnel flow, so the risk may then propagate to the personnel flow. Through this method, the node connections of risk transmission are constructed, i.e., the transmission paths and connection methods between media.

[0064] Step S132: Mark all media nodes involved in the risk transmission in the node connection relationship, and record the connection direction and connection strength between each media node and other nodes. The connection strength is determined based on the interaction influence coefficient.

[0065] In the constructed risk transmission node connection relationship, the control center marks all media nodes involved in risk transmission, namely air media nodes, cargo contact media nodes, equipment transmission media nodes, and personnel flow media nodes. For each media node, the connection direction with other nodes is recorded. For example, an air media node pointing to a cargo contact media node indicates that the risk is transmitted from the air medium to the cargo contact medium; a cargo contact media node pointing to a personnel flow media node indicates that the risk is transmitted from the cargo contact medium to the personnel flow medium, and so on. Simultaneously, the connection strength is determined based on the previously calculated interaction coefficient between different media; the larger the interaction coefficient, the stronger the connection. For example, if the interaction coefficient between air and cargo contact media is 0.8, then their connection strength is set to 0.8; if the interaction coefficient between equipment transmission media and personnel flow media is 0.5, then the connection strength is 0.5. By recording the connection direction and connection strength, the transmission characteristics of risk between different media nodes can be described more accurately.

[0066] Step S133: For each media node, simulate the change in the risk transmission path after the media node is removed, and analyze the path change to determine whether subsequent media nodes can still receive risk transmission.

[0067] Step S1331: Present the node connection relationship of risk transmission in a visual form. Each medium node is represented by a specific graphic symbol, and the transmission connection between nodes is represented by line segments. The thickness of the line segments corresponds to the connection strength.

[0068] To more intuitively analyze the node connections of risk transmission, the control center presents them in a visual format. In the visualization interface, each medium node is represented by a different specific graphic symbol; for example, air medium nodes are represented by circles, cargo contact medium nodes by squares, equipment transmission medium nodes by triangles, and personnel flow medium nodes by rhombuses. The transmission connections between nodes are represented by line segments, with the thickness of the line segment corresponding to the connection strength—the stronger the connection, the thicker the line segment. For example, the connection strength between an air medium node and a cargo contact medium node is 0.8, represented by a thicker line segment; the connection strength between an equipment transmission medium node and a personnel flow medium node is 0.5, represented by a medium-thickness line segment. Through this visualization, operators in the control center can better observe the risk transmission path and the connection strength between each node.

[0069] Step S1332: Select the first medium node from the visualized node connection relationship as the node to be simulated, and temporarily delete the node to be simulated and all connecting segments related to the node to be simulated.

[0070] In the visualization interface, the control center selects the first media node as the node to be simulated according to a preset order. For example, following the order of air media node, cargo contact media node, equipment conduction media node, and personnel flow media node, the air media node is selected first. Then, the graphic symbol of the node to be simulated, i.e., the air media node, and all connecting lines related to this node are temporarily deleted from the visualization interface, including lines pointing from the air media node to other nodes and lines pointing from other nodes to the air media node (if they exist). Through the above operation, the scenario of the media node being removed is simulated.

[0071] Step S1333: Observe the node connection relationship after deleting the node to be simulated, trace the connection path between the initial risk medium node and each subsequent medium node, check whether there is a complete connection path. If a subsequent medium node still has a complete connection path with the initial risk medium node, record the node to be simulated as a non-critical node, and at the same time record the alternative connection path between the medium node and the initial risk medium node.

[0072] After deleting the nodes to be simulated, the control center observes the remaining node connections on the visualization interface. Starting from the initial risk-generating medium node, it tracks whether a complete connection path still exists between it and subsequent medium nodes. For example, if the initial risk medium node is a device conduction medium node and the node to be simulated is an air medium node, after deleting the air medium node, it checks whether the device conduction medium node can still be connected to the cargo contact medium node and the personnel flow medium node through other paths. If the device conduction medium node can be connected to the cargo contact medium node through the path of device conduction medium node → cargo contact medium node, then a complete connection path exists between the cargo contact medium node and the initial risk medium node. In this case, the air medium node is recorded as a non-critical node, and the alternative connection path of device conduction medium node → cargo contact medium node is recorded.

[0073] Step S1334: If none of the subsequent media nodes have a complete connection path with the initial risk media node, then record the node to be simulated as a candidate for a critical blocking node.

[0074] If, after deleting the node to be simulated, there is no complete connection path between the initial risk medium node and all subsequent medium nodes, meaning the risk cannot be transmitted to subsequent nodes through any other path, then the control center records the node to be simulated as a candidate for a critical blocking node. For example, if the node to be simulated is a cargo contact medium node, after deleting this node, the initial risk medium node (air medium node) cannot be connected to the personnel flow medium node and the equipment conduction medium node through any other path, then the cargo contact medium node is recorded as a candidate for a critical blocking node.

[0075] Step S1335: Restore the node to be simulated and the connecting segments associated with it to the node connection relationship, and select the next medium node as the new node to be simulated.

[0076] After analyzing a node to be simulated, the control center restores the node and its previously deleted connecting segments to the node connection relationships in the risk transmission framework, returning the node connections to their original state. Then, the next medium node is selected as the new node to be simulated, and the simulation removal, path analysis, and recording operations are repeated. For example, after analyzing an air medium node, the air medium node and its connecting segments are restored, and then a cargo contact medium node is selected as the new node to be simulated.

[0077] Step S1336: Repeat the above operations of deleting the node to be simulated, observing the node connection relationship, recording the judgment result, and restoring the node to be simulated and the connecting line segment until all media nodes have completed the simulation analysis.

[0078] Following the steps outlined above, the control center sequentially simulated the removal and analysis of each media node until all nodes were processed. During this process, nodes were continuously deleted, path changes were observed, and candidate critical or non-critical nodes were recorded. Nodes were then restored, and the next node was analyzed. This cyclical process ensured that each media node received a thorough evaluation.

[0079] Step S1337: Summarize all key blocking node candidates, remove duplicate key blocking node candidates, and exclude key blocking node candidates whose coverage area is smaller than the preset range to form a preliminary list of key blocking nodes.

[0080] After completing the simulation analysis of all media nodes, the control center compiles all recorded critical blocking node candidates. Since the same media node may be recorded as a critical blocking node candidate multiple times in some cases, duplicate candidate nodes need to be removed. Then, based on a preset coverage area threshold, critical blocking node candidates with coverage areas smaller than this threshold are excluded. The coverage area refers to the size of the warehouse area affected by the media node. For example, if the media parameter collection location corresponding to a critical blocking node candidate only covers a small corner of the warehouse, its coverage area is smaller than the preset range, and therefore it is excluded. After deduplication and filtering, a preliminary list of critical blocking nodes is formed, containing all media nodes that could potentially become critical blocking nodes.

[0081] Step S134: If after removing a certain media node, all subsequent media nodes are unable to obtain risk transmission through other connection paths, then mark the media node as a key blocking node in the risk transmission blocking chain.

[0082] Based on the simulation analysis results of step S133, the control center conducts final confirmation of each candidate node in the preliminary list of critical blocking nodes. For those media nodes that, after simulation removal, all subsequent media nodes are unable to obtain risk transmission through other connection paths, they are officially marked as critical blocking nodes in the risk transmission blocking chain. For example, in the preliminary list of critical blocking nodes, both the cargo contact media node and the equipment transmission media node are candidate nodes. After confirmation, removing the cargo contact media node prevents all subsequent media nodes from obtaining risk transmission; therefore, the cargo contact media node is marked as a critical blocking node. However, after removing the equipment transmission media node, some subsequent nodes can still obtain risk transmission through other paths; therefore, it is not marked as a critical blocking node.

[0083] Step S135: Extract the medium type corresponding to each key blocking node, retrieve the historical propagation characteristics of the medium type, and extract the key dependency conditions in the medium propagation process from the historical propagation characteristics of the medium type.

[0084] The control center extracts the corresponding medium type for each critical blocking node; for example, the medium type corresponding to a critical blocking node is air. Then, it retrieves the historical propagation characteristics of that medium type in the warehouse risk propagation history data, such as the continuous diffusion characteristics of air. From these historical propagation characteristics, it analyzes and extracts the critical dependency conditions in the medium propagation process. Critical dependency conditions refer to the conditions necessary for a medium to carry out risk propagation; if these conditions are disrupted, the risk propagation of the medium will be blocked. For example, the propagation of air depends on the airflow diffusion path, so its critical dependency condition is the diffusion path; the propagation of goods contact media depends on the contact and movement between goods, so the critical dependency condition is the movement of goods; the propagation of equipment conduction media depends on the physical connection lines between equipment, so the critical dependency condition is the equipment connection; the propagation of personnel movement media depends on the activities of personnel within the warehouse, so the critical dependency condition is personnel activity.

[0085] Step S136: Formulate blocking strategies based on the key dependence conditions of media propagation. The key dependence condition for airborne media is the diffusion path, and the corresponding blocking strategy for airborne media is to adjust the ventilation system to change the diffusion direction of airborne media. The key dependence condition for cargo contact media is cargo movement, and the corresponding blocking strategy for cargo contact media is to isolate cargo and restrict the transmission of cargo contact media. The key dependence condition for equipment conduction media is equipment connection, and the corresponding blocking strategy for equipment conduction media is to cut off equipment connection to prevent the conduction of equipment conduction media. The key dependence condition for personnel flow media is personnel activity, and the corresponding blocking strategy for personnel flow media is to guide personnel activity to avoid risk areas in order to limit the propagation of personnel flow media.

[0086] For each critical blocking node, the control center formulates corresponding blocking strategies based on the media type and its critical dependencies. For airborne media, since the critical dependency is the diffusion path, the blocking strategy involves adjusting the warehouse's ventilation system to change the airflow direction, thereby altering the diffusion path and directing hazardous substances to non-sensitive areas or out of the warehouse. For cargo contact media, the critical dependency is cargo movement; the blocking strategy involves isolating potentially hazardous cargo, such as using barriers to enclose it, restricting movement and contact, and thus preventing the transmission of the cargo contact medium. For equipment-conducted media, the critical dependency is equipment connection; the blocking strategy involves severing connections between potentially hazardous equipment, such as disconnecting electrical or network lines, to prevent the transmission of the equipment-conducted medium. For personnel-related media, the critical dependency is personnel activity; the blocking strategy involves guiding personnel away from hazardous areas through broadcasts, indicator lights, and other means, altering their movement trajectories, and limiting the spread of personnel-related media.

[0087] Step S137: Based on the connection strength and coverage area of ​​the key blocking nodes, prioritize the key blocking nodes. The larger the connection strength value and the larger the coverage area value, the higher the position of the key blocking node in the ranking.

[0088] The control center acquires the connection strength and coverage area data for each critical blocking node. Connection strength is the previously recorded numerical value of the connection strength between the node and other nodes, and coverage area is a numerical representation of the size of the warehouse area affected by the critical blocking node. Then, the critical blocking nodes are prioritized. The ranking rule is that the higher the connection strength and coverage area value, the higher the priority of the critical blocking node and the higher its position in the ranking. For example, if critical blocking node A has a connection strength of 0.9 and a coverage area of ​​a relatively large value, while critical blocking node B has a connection strength of 0.7 and a coverage area of ​​a medium value, then critical blocking node A has a higher priority than critical blocking node B and will be ranked higher.

[0089] Step S1371: Extract the connection strength data of each key blocking node in the preliminary list of key blocking nodes. The connection strength data is obtained from the description of the medium interaction relationship in the risk transmission medium interaction identification results.

[0090] The control center extracts connection strength data for each critical blocking node in the preliminary list of critical blocking nodes from the media interaction relationship descriptions of the risk transmission media interaction identification results. For example, if the connection strength between an air medium node and a cargo contact medium node is recorded as 0.8 in the media interaction relationship description, then when the air medium node is listed as a candidate node in the preliminary list of critical blocking nodes, 0.8 is extracted as its connection strength data. For each candidate critical blocking node, the corresponding connection strength data is found from the interaction relationship description.

[0091] Step S1372: Measure the coverage area corresponding to each critical blocking node. The coverage area is determined based on the area of ​​the region group to which the critical blocking node belongs and the degree of correlation between adjacent region groups.

[0092] The control center measures the coverage area corresponding to each critical blocking node. First, it determines the area of ​​the region group to which the critical blocking node belongs, such as the area enclosed by the boundaries of that region group. Then, it considers the degree of correlation between this region group and adjacent region groups. A higher degree of correlation indicates a greater impact of the critical blocking node on adjacent region groups, and thus a larger coverage area. For example, if the area of ​​region group A to which the critical blocking node belongs is a certain value, and region group A has a high degree of correlation with regions groups B and C, then the coverage area of ​​the critical blocking node includes the area of ​​region group A and a portion of the areas of regions groups B and C. Specifically, this can be calculated by adding the area of ​​region group A to the area of ​​region group B multiplied by the degree of correlation coefficient, and then adding the area of ​​region group C to the area of ​​region group C multiplied by the degree of correlation coefficient. Through this comprehensive method, the coverage area of ​​each critical blocking node is determined.

[0093] Step S1373: Convert the connection strength data into a standardized connection strength value. The conversion method is to use the ratio of the connection strength of each critical blocking node to the maximum connection strength of all critical blocking nodes as the standardized connection strength value.

[0094] To eliminate the influence of different dimensions on the connection strength data of key blocking nodes and facilitate comprehensive comparison, the control center converts the connection strength data into standardized connection strength values. First, the maximum value among all the connection strength data of key blocking nodes is identified. Then, the connection strength data of each key blocking node is divided by this maximum value; the resulting ratio is the standardized connection strength value. For example, if the maximum connection strength of all key blocking nodes is 0.9, and the connection strength of a certain key blocking node is 0.6, then its standardized connection strength value is 0.6 divided by 0.9, yielding a value between 0 and 1.

[0095] Step S1374: Convert the coverage area range into a standardized coverage area value. The conversion method is to use the ratio of the coverage area range of each key blocking node to the maximum coverage area range of all key blocking nodes as the standardized coverage area value.

[0096] Similarly, the control center standardizes the coverage area. It identifies the maximum value among the coverage areas of all critical blocking nodes, then divides the coverage area of ​​each critical blocking node by this maximum value to obtain the standardized coverage area value. For example, if the maximum coverage area value is a certain area value, and the coverage area of ​​a critical blocking node is half of this maximum value, then its standardized coverage area value is 0.5.

[0097] Step S1375: Set the weights of the standardized connection strength value and the standardized coverage value. Determine the weight allocation based on historical blocking effect data. The sum of the weights of the standardized connection strength value and the standardized coverage value is 1.

[0098] The control center assigns weights to standardized connection strength and standardized coverage values ​​based on historical blocking effectiveness data from the warehouse. This historical blocking effectiveness data records the results of implementing blocking strategies at different key blocking nodes in the past. By analyzing this data, the influence of connection strength and coverage area on the blocking effectiveness is determined, and corresponding weights are assigned. For example, if the analysis finds that connection strength has a greater impact on the blocking effectiveness, then the weight of the standardized connection strength value is set to 0.6, and the weight of the standardized coverage value is set to 0.4, with a sum of 1. If historical data indicates that the coverage area has a greater impact, the weight allocation can be adjusted, such as setting the weight of the standardized connection strength value to 0.3 and the weight of the standardized coverage value to 0.7.

[0099] Step S1376: Based on the set weights, the standardized connection strength value and standardized coverage value of each critical blocking node are weighted and summed to obtain the priority score of each critical blocking node.

[0100] According to the set weights, the control center calculates a weighted sum of the standardized connectivity strength and standardized coverage values ​​for each critical blocking node. For example, if a critical blocking node has a standardized connectivity strength value of 0.8 and a weight of 0.6, and a standardized coverage value of 0.7 and a weight of 0.4, then its priority score is calculated as 0.8 multiplied by 0.6 plus 0.7 multiplied by 0.4, resulting in a comprehensive score. In this way, each critical blocking node has a corresponding priority score.

[0101] Step S1377: Sort the key blocking nodes in descending order of priority score values ​​to form a priority sequence of key blocking nodes.

[0102] The control center ranks all critical blocking nodes according to their priority scores from highest to lowest, forming a priority sequence. The critical blocking node with the highest priority score is ranked first in the sequence, the next highest score is ranked second, and so on. For example, if critical blocking node A has a priority score of 0.85, critical blocking node B has a score of 0.75, and critical blocking node C has a score of 0.6, then the priority sequence is A, B, C.

[0103] Step S1378: If there are key blocking nodes with the same priority score, compare the start response time of the key blocking nodes. The key blocking node with the smaller start response time value will be ranked higher in the sorting.

[0104] When two or more critical blocking nodes have the same priority score, the control center compares their activation response time. Activation response time refers to the time required from receiving the blocking command to the blocking strategy taking effect. Critical blocking nodes with a lower activation response time can implement blocking measures more quickly and therefore rank higher in the priority ranking. For example, if critical blocking nodes D and E both have a priority score of 0.7, but D has a shorter activation response time while E has a longer one, then D will rank higher than E in the priority ranking.

[0105] Step S1379: Record the priority sequence of key blocking nodes and the priority score of each key blocking node, and use the priority sequence of key blocking nodes and the priority score of each key blocking node as the basis for determining the execution order.

[0106] The control center records the final priority sequence of critical blocking nodes and the priority score corresponding to each critical blocking node. This information will serve as an important basis for determining the execution order of critical blocking nodes. The earlier a critical blocking node is in the priority sequence, the earlier it will be executed.

[0107] Step S138: Determine the execution order of key blocking nodes according to the priority sorting results, and set the start time of each key blocking node in combination with the medium propagation speed.

[0108] Based on the priority ranking of critical blocking nodes, the control center determines their execution order, with higher-priority critical blocking nodes executing their blocking strategies first. Simultaneously, considering the propagation speed of the medium type corresponding to each critical blocking node, the activation timing of each critical blocking node is set. For media with faster propagation speeds, the corresponding critical blocking nodes need to be activated earlier to ensure blocking is implemented before the risk spreads on a large scale. For example, airborne media spreads relatively quickly, so its corresponding critical blocking nodes are executed earlier, with activation set within a short period after the risk is identified; cargo-contact media spread relatively slowly, and the activation timing can be set slightly later, but still needs to be activated before the risk propagates to other media.

[0109] Step S139: Integrate the execution order of key blocking nodes, the start time of each key blocking node, and the blocking strategy of each key blocking node to generate a risk transmission blocking chain that includes a description of the node sequence, node medium type, start time, and blocking strategy.

[0110] The control center integrates the execution sequence of key blocking nodes, the activation timing of each node, and the corresponding blocking strategies. Key blocking nodes are arranged in execution order to form a node sequence; the medium type of each node is indicated, such as air medium or cargo contact medium; the activation timing of each node is recorded; and the blocking strategy for each node is described in detail. Finally, a risk transmission blocking chain generation result is generated, demonstrating how to implement corresponding blocking strategies at each key blocking node in sequence and at appropriate times to effectively block the transmission of risk.

[0111] Step S140: Based on the risk transmission blockade chain generation result, schedule the prevention and control equipment in the warehouse, establish the signal transmission and start-up control process between equipment, and generate collaborative response instructions for prevention and control equipment.

[0112] Step S141: Extract the key blocking nodes, the activation timing of each key blocking node, and the blocking strategy of each key blocking node from the risk transmission blocking chain generation results.

[0113] The control center extracts key information from the risk transmission chain disruption results, including a list of key disruption nodes, the planned activation time for each key disruption node, and the corresponding disruption strategy. For example, the list of key disruption nodes includes airborne nodes and cargo-contact nodes; the activation time for airborne nodes is a specific point in time, and the disruption strategy is to control the ventilation system; the activation time for cargo-contact nodes is another point in time, and the disruption strategy is to isolate the cargo.

[0114] Step S142: Based on the blocking strategies for each key blocking node, determine the type of prevention and control equipment required to implement each blocking strategy. The ventilation control strategy corresponds to ventilation control equipment, the cargo isolation strategy corresponds to cargo isolation equipment, the equipment cut-off strategy corresponds to equipment control equipment, and the personnel guidance strategy corresponds to personnel guidance equipment.

[0115] For each critical blocking node, the control center determines the type of control equipment required to implement the blocking strategy. If the blocking strategy involves adjusting the ventilation system to change the direction of airflow (a ventilation control strategy), the corresponding control equipment type is ventilation control equipment, such as fans and damper controllers. If the blocking strategy involves isolating goods and restricting the transfer of the medium they come into contact with (a goods isolation strategy), the corresponding control equipment type is goods isolation equipment, such as isolation doors and isolation belt drive devices. Equipment disconnection strategies correspond to equipment control equipment, such as circuit breakers and switch controllers, used to disconnect equipment connections. Personnel guidance strategies correspond to personnel guidance equipment, such as indicator lights and voice broadcasters, used to guide personnel movements.

[0116] Step S143: Retrieve the basic information files of all prevention and control equipment in the warehouse. The basic information files of the prevention and control equipment include equipment type, equipment identification, deployment location, current operating status, executable actions, and execution capability parameters.

[0117] The control center retrieves basic information files for all epidemic prevention and control equipment within the warehouse, which are stored in the warehouse's equipment management database. These basic information files detail the relevant information for each piece of equipment, including equipment type (e.g., ventilation control equipment, cargo isolation equipment), equipment identifier (a unique number for each device to distinguish different devices), deployment location (coordinates or area number of the equipment's specific installation location in the warehouse), current operating status (e.g., standby, normal operation, fault), executable actions (operations the equipment can perform, such as starting, stopping, and adjusting the speed of the fan), and execution capability parameters (indicators measuring the equipment's ability to perform actions, such as the maximum airflow of the fan and the closing time of the isolation door).

[0118] Step S144: Based on the area where the key blocking node is located, select prevention and control equipment from similar prevention and control equipment whose deployment location is consistent with the area where the key blocking node is located, and form a regional matching equipment list.

[0119] For each type of required prevention and control equipment, the control center selects equipment whose deployment location matches the area where the critical blocking node is located from all the prevention and control equipment of that type in the warehouse. For example, if the critical blocking node, the air medium node, is located in area group A, then the ventilation control equipment will be selected to be deployed in area group A. The information of the above equipment will be compiled into a list of area-matched equipment.

[0120] Step S145: Check the current operating status of each prevention and control device in the regional matching device list, filter the prevention and control devices whose current operating status is standby or normal operation, and form a list of devices to be evaluated.

[0121] The control center checks the current operational status of each piece of prevention and control equipment in the regional matching equipment list. It retrieves current operational status data from the basic information files of the prevention and control equipment, filtering out equipment currently in standby or normal operation, and excluding equipment in a faulty, under-maintenance, or other unavailable state. The filtered equipment is then compiled into a list of equipment to be evaluated; these are potential available prevention and control devices.

[0122] Step S146: Extract the execution capability parameters of each prevention and control device in the list of devices to be evaluated, compare the execution capability parameters of the prevention and control devices with the required capability parameters of the blocking strategy, calculate the matching degree, and select prevention and control devices with a matching degree higher than the preset threshold as usable devices.

[0123] Step S1461: Extract the execution capability parameters of each prevention and control device in the list of devices to be evaluated. The execution capability parameters of ventilation control devices include maximum ventilation intensity, vent adjustment angle range, and response delay time; the execution capability parameters of cargo isolation devices include isolation barrier extension speed, isolation range, and continuous working duration; the execution capability parameters of equipment control devices include line disconnection response time, number of lines that can be disconnected, and safety protection level; the execution capability parameters of personnel guidance devices include indicator light brightness, voice prompt coverage, and response sensitivity.

[0124] The control center extracts corresponding execution capability parameters for different types of prevention and control equipment in the equipment list to be evaluated. For ventilation control equipment, execution capability parameters include maximum ventilation intensity (the maximum airflow the equipment can provide); vent adjustment angle range (the range of angles the vent can rotate); and response delay time (the time interval from receiving an instruction to starting the action). For cargo isolation equipment, execution capability parameters include barrier extension speed (the speed at which the barrier extends from a retracted state to a fully extended state); isolation range (the length or area the barrier can cover); and continuous operating time (the maximum time the equipment can maintain an isolation state). For equipment control equipment, execution capability parameters include line disconnection response time (the time from issuing a disconnection command to the actual disconnection of the line); number of lines that can be disconnected (the number of lines the equipment can disconnect simultaneously); and safety protection level (the level of the equipment's ability to withstand external environmental influences). For personnel guidance equipment, execution capability parameters include indicator light brightness (the light intensity emitted by the indicator light); voice prompt coverage (the area where voice can be clearly transmitted); and response sensitivity (the speed at which the equipment responds to control commands).

[0125] Step S1462: Based on the blocking strategies for each key blocking node, determine the required capability parameters for each blocking strategy. The required capability parameters for the ventilation control strategy include the required ventilation intensity, the adjustment angle of the ventilation opening, and the allowable response delay; the required capability parameters for the cargo isolation strategy include the required isolation barrier extension speed, the isolation range, and the continuous working duration; the required capability parameters for the equipment disconnection strategy include the required line disconnection response time, the number of lines that can be disconnected, and the safety protection level; and the required capability parameters for the personnel guidance strategy include the required indicator light brightness, the voice prompt coverage range, and the response sensitivity.

[0126] Based on the blocking strategies at each key blocking node, the control center conducts a detailed analysis and determines the required capability parameters for each blocking strategy. For ventilation control strategies, the required capability parameters include the required ventilation intensity (airflow required to change the direction of air diffusion); the vent adjustment angle (the angle the vent needs to be adjusted to); and the allowable response delay (the maximum permissible time from the issuance of the command to the start of the ventilation system's operation). For cargo isolation strategies, the required capability parameters include the required barrier extension speed (the required extension speed for rapid isolation); the isolation range (the length or area the barrier needs to cover to ensure effective isolation); and the continuous operating duration (the minimum time the isolation state needs to be maintained). For equipment disconnection strategies, the required line disconnection response time (the maximum permissible time from the issuance of the command to line disconnection); the number of lines that can be disconnected (the number of lines that need to be disconnected simultaneously); and the security protection level (the required security protection level for the equipment to adapt to the warehouse environment). For personnel guidance strategies, the required capability parameters include the required indicator light brightness (the required brightness for personnel visibility); the voice prompt coverage area (the area the voice prompts need to cover for personnel to hear); and the response sensitivity (the required response speed for the equipment to quickly guide personnel).

[0127] Step S1463: Compare the execution capability parameters of each type of prevention and control equipment with the required capability parameters of the corresponding blocking strategy one by one, and calculate the matching ratio of each parameter. The matching ratio is the ratio of the execution capability parameters of the prevention and control equipment to the required capability parameters of the blocking strategy. If the ratio is greater than 1, it is calculated as 1.

[0128] In this embodiment, for each control device to be evaluated, the control center can iterate through all its execution capability parameters and compare them one by one with the required capability parameters of the corresponding blocking strategy. Taking ventilation control equipment as an example, its execution capability parameters include maximum ventilation intensity, vent adjustment angle range, and response delay time, while the required capability parameters of the ventilation control strategy include required ventilation intensity, required vent adjustment angle, and allowable response delay. The control center can first compare the maximum ventilation intensity of the ventilation control equipment with the required ventilation intensity and calculate the ratio between the two. If the ratio is greater than 1, it means that the maximum ventilation intensity of the equipment exceeds the requirement, and the matching ratio of this parameter is recorded as 1; if the ratio is less than 1, then the ratio is the matching ratio of this parameter. Similarly, for the vent adjustment angle range, it compares whether the upper and lower limits of the adjustable angle of the equipment can cover the required adjustment angle. If the adjustment angle range of the equipment completely includes the required angle, the matching ratio is 1; otherwise, the ratio of the overlapping part of the equipment's adjustment angle range with the required angle to the required angle is calculated as the matching ratio. The matching ratio for response latency is calculated by dividing the allowable response latency by the device's response latency. If the result is greater than 1, it is calculated as 1; otherwise, the actual ratio is used. Following the same logic, parameters for cargo isolation equipment, equipment control equipment, and personnel guidance equipment are compared, and the matching ratio for each parameter is calculated.

[0129] Step S1464: Take the average of the matching ratios of all parameters to obtain the matching degree between the prevention and control equipment and the blocking strategy. Select prevention and control equipment with a matching degree higher than the preset threshold as available equipment and include the prevention and control equipment in the available equipment list. Sort the prevention and control equipment in the available equipment list from largest to smallest matching degree value. If equipment failure occurs later, replace the faulty equipment in order of sorting.

[0130] In this embodiment, after calculating the matching ratios of various parameters of the control equipment, the control center can add up these matching ratios and then divide by the total number of parameters to obtain the average matching ratio of the control equipment, i.e., the matching degree. For example, if the matching ratio of the isolation barrier extension speed of a certain cargo isolation equipment is 0.9, the matching ratio of the isolation range is 1, and the matching ratio of the continuous working time is 0.85, then the matching degree of the equipment is the sum of these three matching ratios divided by 3. The control center can preset a matching degree threshold, which is set according to the risk level and control requirements of the warehouse. When the matching degree of the control equipment is higher than the matching degree threshold, it is identified as a usable equipment and included in the usable equipment list. For the control equipment in the usable equipment list, they are sorted in descending order of matching degree, and the equipment ranked higher is selected first to implement the blocking strategy. When the equipment performing the task fails, the control center can automatically select the next equipment with the highest matching degree as a replacement according to the ranking of the usable equipment list to ensure the continuous execution of the blocking strategy.

[0131] Step S147: Based on the activation timing of the critical blocking node and the activation preparation time of the available devices, set the activation time of the available devices to the same time point as the activation timing of the critical blocking node.

[0132] In this embodiment, the activation timing of the critical blocking node is determined based on the risk transmission speed and priority, and available devices need to start executing the blocking strategy precisely at this activation timing. The control center can obtain the activation preparation time of each device from the basic information file of the available devices, that is, the time required for the device to start executing actions from receiving the activation command. To ensure that the devices start precisely at the activation timing of the critical blocking node, the control center can set the device's activation command sending time to the activation timing of the critical blocking node minus the device's activation preparation time. For example, if the activation timing of the critical blocking node is T, and the activation preparation time of an available device is t, then the control center can send the activation command to the device at time Tt, thereby ensuring that the device starts precisely at time T, completely consistent with the activation timing of the critical blocking node. For multiple devices that need to work collaboratively, the control center can calculate the activation command sending time of each device separately to ensure that all devices can start simultaneously at the activation timing of the critical blocking node.

[0133] Step S148: Assign a unique digital code to each available device as a cooperative signal identifier. The digital codes are arranged sequentially according to the execution order of the available devices.

[0134] In this embodiment, to ensure accurate identification and transmission of coordination signals between control devices, the control center assigns a unique digital code as a coordination signal identifier to each available device. These digital codes are arranged sequentially according to the execution order of the available devices; for example, the device executing first is coded as 1001, the device executing second is coded as 1002, and so on. The length of the digital code is determined based on the total number of control devices in the warehouse, ensuring the uniqueness and scalability of the code. The coordination signal identifier is stored in the control center's device management database and associated with the device's identification information, enabling rapid identification of the device during signal transmission.

[0135] Step S149: Determine the signal transmission order between available devices. After the previous available device completes the blocking strategy, it sends an execution completion signal to the control center. After receiving the execution completion signal, the control center sends a start signal to the subsequent available devices.

[0136] In this embodiment, the signal transmission order among available devices is determined based on the execution order of key blocking nodes. The control center can establish a signal transmission chain, stipulating that after the previous available device completes its blocking strategy, it immediately sends an execution completion signal to the control center. The execution completion signal includes information such as the device's coordination signal identifier and the completion status of the execution action. After receiving the execution completion signal, the control center can verify the signal to confirm that the blocking strategy of the device has been successfully executed. After successful verification, the control center can send a start signal to the next available device. The start signal includes the device's coordination signal identifier, start time, execution action, and other instructions. Through the above relay-style signal transmission method, it is ensured that available devices start sequentially according to the predetermined execution order to achieve coordinated response. If a device malfunctions during execution and cannot send an execution completion signal, the control center can skip the device after a preset timeout period and directly send a start signal to the next available device, recording the information of the malfunctioning device for subsequent maintenance and troubleshooting.

[0137] Step S1410: Determine the action to be performed for each available device. The action must match the corresponding blocking strategy. The action to be performed for ventilation control device is to adjust the direction and intensity of the ventilation opening. The action to be performed for cargo isolation device is to extend the isolation barrier. The action to be performed for equipment control device is to disconnect the connection line. The action to be performed for personnel guidance device is to turn on the indicator light and play voice prompts.

[0138] In this embodiment, the action of each available device is determined according to its corresponding blocking strategy to ensure effective blocking of risk transmission. For ventilation control equipment, the corresponding blocking strategy is to regulate the ventilation system to change the direction of air medium diffusion. Therefore, the action is to adjust the direction and ventilation intensity of the vents, guiding the hazardous material to non-sensitive areas by changing the direction and speed of airflow. The blocking strategy of cargo isolation equipment is to isolate cargo and restrict the transmission of the medium that the cargo comes into contact with. The action is to extend the isolation barrier to form a physical barrier around the hazardous cargo, preventing contact between the cargo and the medium. The blocking strategy of equipment control equipment is to cut off the equipment connection to prevent the transmission of the medium that the equipment can conduct. The action is to cut off the connection lines between the equipment, such as power lines and data lines, interrupting the transmission path of the risk. The blocking strategy of personnel guidance equipment is to guide personnel to avoid the risk area to limit the spread of the medium that the personnel can move through. The action is to turn on the indicator lights and play voice prompts. The indicator lights are used to indicate the boundaries of safe passages and risk areas, and the voice prompts are used to inform personnel of the risk situation and evacuation routes, guiding personnel away from the risk area.

[0139] Step S1411: Integrate the device identifier, start time, execution action, coordination signal identifier and signal transmission sequence of available devices to generate a prevention and control device coordination response instruction containing device identifier, start time, execution action and coordination signal rules.

[0140] In this embodiment, after determining the device identifier, start-up time, execution action, coordination signal identifier, and signal transmission sequence of available devices, the control center can integrate the above information to generate a coordinated response instruction for prevention and control devices. The coordinated response instruction is a detailed execution plan, containing a unique identifier for each available device so that the control center can accurately identify and control the device; the device's start-up time to ensure timely activation at critical blocking nodes; specific execution actions to guide how the device implements the blocking strategy; and coordination signal rules to specify how devices transmit signals and coordinate their work. For example, the instruction might specify that device A performs the action of adjusting the direction and intensity of the ventilation opening at start-up time T1, and after completion, sends a completion signal containing coordination signal identifier 1001 to the control center. Upon receiving this signal, the control center sends a start signal containing coordination signal identifier 1002 to device B. Device B then performs the action of extending the isolation barrier at start-up time T2, and so on. After the coordinated response instruction is generated, it can be sent to the control terminals of each available device through the warehouse's device communication network. The control terminals then control the devices to perform corresponding operations according to the instructions.

[0141] Step S150: Send the coordinated response command of the prevention and control equipment to the control terminal of the corresponding prevention and control equipment, receive the status feedback information after the prevention and control equipment executes the command, and adjust the risk transmission blocking chain and the coordinated response command of the prevention and control equipment based on the status feedback information.

[0142] For example, in step S151: send the coordinated response instructions of the prevention and control equipment to the control terminal of the corresponding prevention and control equipment, and record the sending time and receiving terminal identifier of each coordinated response instruction of the prevention and control equipment.

[0143] In this embodiment, when sending coordinated response commands to the prevention and control equipment, the control center can assign a unique transmission sequence number to each command and record the transmission time and the identifier of the receiving terminal. The transmission time is accurate to the millisecond level to facilitate subsequent tracking of command transmission delays. The receiving terminal identifier is a unique identifier for the control terminal of the prevention and control equipment, ensuring that commands are accurately sent to the target device. The control center can store the transmission time, receiving terminal identifier, and transmission sequence number in a log file for querying and troubleshooting in case of command transmission problems. For example, when a control terminal fails to receive a command, the control center can check the command transmission status through the log file to determine whether the transmission failed or the receiving terminal malfunctioned.

[0144] Step S152: Set the instruction reception confirmation time. If the control terminal of the prevention and control equipment receives the collaborative response instruction of the prevention and control equipment within the instruction reception confirmation time, it sends an instruction reception confirmation signal to the control center, and the control center records the confirmation reception time. If the control terminal of the prevention and control equipment does not send an instruction reception confirmation signal within the instruction reception confirmation time, the control center resends the collaborative response instruction of the prevention and control equipment, and the number of resends does not exceed the preset limit.

[0145] In this embodiment, the control center can set a command reception confirmation timeout, which is determined based on the latency characteristics of the communication network of the equipment within the warehouse. When the control terminal of the control equipment receives a coordinated response command, it needs to send a command reception confirmation signal to the control center within the confirmation timeout. The confirmation signal contains the command's transmission sequence number and the receiving terminal's identifier. After receiving the confirmation signal, the control center can record the confirmation reception time, completing the command transmission confirmation process. If the control center does not receive a confirmation signal from the control terminal within the confirmation timeout, it can consider the command transmission failed, and the control center can resend the command. There is a preset upper limit to the number of resends. When the upper limit is reached, the control center can stop sending, mark the control terminal as faulty, and simultaneously select the next highest-ranked backup device from the available device list, generate a new coordinated response command, and send it to the backup device's control terminal.

[0146] Step S153: After receiving the coordinated response instruction from the prevention and control equipment, the control terminal of the prevention and control equipment controls the prevention and control equipment to perform operations according to the start time and execution action in the instruction. During the execution process, the prevention and control equipment collects its own operating status data in real time. The operating status data includes the progress of the execution action, the working parameters of key components, and energy consumption data.

[0147] In this embodiment, after receiving the coordinated response command, the control terminal of the prevention and control equipment can parse the command and extract information such as the start time and execution actions. The control terminal synchronizes the start time with its own real-time clock. When the start time is reached, the control terminal controls the prevention and control equipment to start performing the corresponding operation. During the operation, the control terminal collects operational status data in real time through the internal sensors of the equipment. The execution action progress data reflects the completion status of the equipment's actions, such as the proportion of the extended length of the isolation barrier to the total length; key component operating parameters include the current, voltage, and temperature of the equipment motor, used to monitor whether the working status of the equipment components is normal; energy consumption data reflects the energy consumption of the equipment during the execution process. The above operational status data is transmitted to the control terminal's cache in real time and packaged and sent to the control center at set time intervals.

[0148] Step S154: After the prevention and control equipment completes its operation, the control terminal integrates the equipment's operating status data and execution results into status feedback information and sends it to the control center through the equipment communication network.

[0149] In this embodiment, after the control equipment completes all operations in the coordinated response command, the control terminal integrates the operational status data and execution results collected during the execution process to form status feedback information. The execution results include information such as whether the operation was successfully completed and whether the expected blocking effect was achieved. For example, the execution result of the ventilation control equipment might be "the ventilation outlet direction has been adjusted to the set angle, the ventilation intensity has reached the required value, and the air medium diffusion direction has changed." The control terminal compresses and encrypts the operational status data to reduce data transmission volume and ensure data security, and then sends the status feedback information to the control center through the warehouse's equipment communication network. The status feedback information includes the equipment identifier, command transmission sequence number, execution completion time, operational status data summary, and execution results, so that the control center can fully understand the equipment's execution status.

[0150] Step S155: The control center receives status feedback information, extracts the execution results of the prevention and control equipment from the status feedback information, compares the execution results of the prevention and control equipment with the expected effect data of the blocking strategy, and determines whether the execution results of the prevention and control equipment have achieved the expected effect of the blocking strategy.

[0151] In this embodiment, after receiving status feedback information, the control center can decrypt and parse the information to extract the execution results of the prevention and control equipment. Simultaneously, the control center can obtain the expected effect data of the blocking strategy from the risk transmission blocking chain generation results. This expected effect data is set based on historical risk prevention and control experience and simulation results, and is used to measure the execution goals of the blocking strategy. For example, the expected effect data for a ventilation control strategy might be "the concentration of harmful gases in the risk area is reduced to below the safe threshold within a specified time." The control center compares the execution results of the prevention and control equipment with the expected effect data and analyzes the differences between the two. If all indicators in the execution results meet or exceed the requirements of the expected effect data, it is determined that the execution results of the prevention and control equipment have achieved the expected effect of the blocking strategy; if any indicator in the execution results does not meet the requirements of the expected effect data, it is determined that the expected effect has not been achieved.

[0152] Step S156: If the execution result of the prevention and control equipment achieves the expected effect of the blocking strategy, then keep the current risk transmission blocking chain and the coordinated response instructions of the prevention and control equipment unchanged, and record the execution data as the basis for subsequent optimization.

[0153] In this embodiment, when the control center determines that the execution result of the prevention and control equipment has achieved the expected effect of the blocking strategy, it can maintain the current risk transmission blocking chain and the coordinated response instructions of the prevention and control equipment unchanged, and continue to monitor the risk status within the warehouse. Simultaneously, the control center can record relevant data during this execution process, including the identification of the prevention and control equipment, start time, execution parameters, operating status data, execution results, and changes in environmental parameters. This execution data will be stored in the warehouse's risk prevention and control database, serving as a basis for subsequent optimization of the risk transmission blocking chain generation algorithm, adjustment of the coordinated strategy of the prevention and control equipment, and improvement of the blocking strategy's effectiveness. For example, by analyzing multiple sets of execution data, the control center can discover that certain prevention and control equipment performs better under specific environmental conditions, thus prioritizing these devices in the generation of subsequent coordinated response instructions.

[0154] Step S157: If the execution result of the prevention and control equipment does not achieve the expected effect of the blocking strategy, extract the operation status data from the status feedback information and analyze the reasons for the failure to meet expectations.

[0155] In this embodiment, when the control equipment fails to achieve the expected results, the control center can extract detailed operational status data from the status feedback information to conduct an in-depth analysis of the reasons for the failure. First, the execution progress data is checked to determine whether the equipment has fully executed the actions in the command. If the actions are not fully executed, it may be due to mechanical failure or a problem with the power system. Second, the operating parameters of key components are analyzed to see if there are any abnormalities in component operation, such as excessive motor current or excessive temperature, which may lead to a decrease in equipment performance and failure to achieve the expected results. Then, energy consumption data is checked. If energy consumption is abnormally high or low, it may be due to abnormal equipment load or leakage. In addition, the influence of environmental factors is considered, such as the effect of ventilation control equipment potentially being affected by airflow disturbances within the warehouse, causing the direction of hazardous substance diffusion to not change as expected. By comprehensively analyzing these operational status data and environmental factors, the control center determines the specific reasons why the control equipment's performance fails to meet expectations.

[0156] Step S158: If the reason for not achieving the expected result is equipment failure, select the backup equipment ranked first from the list of available equipment, update the equipment identifier in the prevention and control equipment collaborative response instruction, and adjust the signal transmission order between the equipment.

[0157] In this embodiment, if the analysis determines that the failure to meet expectations is due to a malfunction in the control equipment, the control center can immediately select a high-ranking backup device from the available equipment list. The selection of backup devices is based on previous matching ranking to ensure high matching accuracy and reliability. The control center can update the device identifier in the coordinated response command for the control equipment, replacing the faulty device with the backup device, and adjust its startup time according to the backup device's startup preparation time to ensure consistency with the startup timing of critical blocking nodes. Simultaneously, since the equipment has been replaced, the signal transmission sequence between devices also needs adjustment. The control center can re-plan the signal transmission chain to ensure that the backup device can successfully receive the completion signal from the preceding device and start up, as well as send completion signals to subsequent devices. The updated coordinated response command will be sent to the control terminal of the backup device and the control terminals of other associated devices, ensuring that the entire coordinated response process is not affected by the equipment failure.

[0158] Step S159: If the reason for not achieving the expected result is a deviation in the execution action, adjust the execution action parameters in the coordinated response command of the prevention and control equipment according to the execution action progress data in the status feedback information.

[0159] In this embodiment, when the reason for not achieving the expected result is a deviation in the execution action, such as the ventilation outlet angle of the ventilation control equipment not being adjusted correctly, or the isolation barrier of the cargo isolation equipment not extending sufficiently, the control center can determine the specific degree of deviation based on the execution action progress data in the status feedback information. Then, the control center can adjust the execution action parameters in the coordinated response command to correct the deviation. For example, if the ventilation outlet needs to be adjusted to a certain angle but is actually adjusted to another angle, the control center can calculate the angle deviation value and set the target angle in the new execution action parameters as the deviation value plus the current actual angle to compensate for the previous deviation. The adjusted execution action parameters are sent to the control terminal of the prevention and control equipment via a new coordinated response command. The control terminal controls the equipment to continue executing actions according to the new parameters until the expected execution effect is achieved.

[0160] Step S1510: If the reason for not achieving the expected result is insufficient adaptability of the blocking strategy, re-analyze the media propagation characteristics corresponding to the key blocking nodes, adjust the blocking strategy content, and update the blocking strategy description in the risk transmission blocking chain.

[0161] In this embodiment, if the reason for failure to achieve the expected results is insufficient adaptability of the blocking strategy itself—that is, the current blocking strategy cannot effectively cope with the actual risk transmission situation—the control center can re-analyze the media propagation characteristics corresponding to the key blocking nodes. By reviewing the risk transmission media interaction identification results, the historical propagation characteristics and current parameter change trends of this media type are extracted to identify areas where the blocking strategy does not match the actual propagation characteristics. For example, the actual diffusion speed of the air medium is faster than the speed in the historical propagation characteristics, causing the original ventilation control strategy to be unable to disperse the risky substances in a timely manner. Based on the results of the re-analysis, the control center can adjust the content of the blocking strategy, such as increasing ventilation intensity or changing the layout of ventilation openings. The adjusted blocking strategy will be updated in the risk transmission blocking chain, and the blocking strategy description will also be modified accordingly to reflect the new blocking measures. At the same time, the control center can reschedule the prevention and control equipment according to the updated blocking strategy and generate new collaborative response instructions for the prevention and control equipment.

[0162] Step S1511: The adjusted risk transmission blocking chain and the coordinated response instructions of the prevention and control equipment are sent to the control terminal of the relevant prevention and control equipment through the equipment communication network. The prevention and control equipment executes the operation according to the adjusted instructions, and at the same time records the adjustment content and the reason for the adjustment.

[0163] In this embodiment, after adjusting the risk transmission blocking chain and the coordinated response instructions of the prevention and control equipment, the control center can send the adjusted instructions to the control terminals of the relevant prevention and control equipment through the equipment communication network. The relevant prevention and control equipment includes all equipment affected by the adjustment, such as backup equipment and equipment requiring changes to signal transmission order. Upon receiving the adjusted instructions, the control terminal can immediately control the equipment to execute operations according to the new instructions. Simultaneously, the control center can record the details of this adjustment, including the description of the blocking strategy before and after the adjustment, changes in the coordinated response instructions, the identification of the involved equipment, and the reasons for the adjustment, such as equipment failure, deviation in execution actions, or insufficient adaptability of the blocking strategy. These adjustment records will be stored in the system log for subsequent auditing and analysis of the risk prevention and control process.

[0164] Based on the same inventive concept, please refer to Figure 2 The diagram shows a schematic block diagram of a warehouse security risk prevention and control system 100 based on IoT devices provided in this application embodiment. It can be the control center or server of the above embodiment. The warehouse security risk prevention and control system 100 based on IoT devices can include a communication unit 110, a machine-readable storage medium 120 and a processor 130.

[0165] In this embodiment, both the machine-readable storage medium 120 and the processor 130 are located in the IoT-based warehouse security risk prevention and control system 100 and are separately configured. Alternatively, the machine-readable storage medium 120 can also be integrated into the processor 130 and can communicate and interact with external systems through the communication unit 110. The machine-readable storage medium 120 is used to store machine-executable instructions for executing the scheme of this application, and the processor 130 is used to execute the machine-executable instructions stored in the machine-readable storage medium 120 to implement the IoT-based warehouse security risk prevention and control method provided in the aforementioned method embodiment.

[0166] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A warehouse security risk prevention and control method based on IoT devices, characterized in that, The method includes: The system acquires multimodal sensing data collected by various types of IoT devices within the warehouse. This multimodal sensing data includes equipment operating parameters collected by warehouse equipment sensors, environmental status parameters collected by environmental monitoring sensors, cargo attribute parameters collected by cargo status sensors, and scene dynamic parameters collected by video surveillance devices. These various types of IoT devices include warehouse equipment sensors, environmental monitoring sensors, cargo status sensors, and video surveillance devices. Multimodal sensing data is processed for risk transmission medium interaction identification, distinguishing the types of risk transmission media corresponding to different sensing data, identifying the interaction relationships between different media, and obtaining the risk transmission medium interaction identification results; Based on the risk transmission medium interaction identification results, a risk transmission blocking chain is generated, the key blocking nodes in the risk transmission blocking chain and the blocking strategy corresponding to each key blocking node are determined, and the risk transmission blocking chain generation result is obtained. Based on the results of the risk transmission blockade chain generation, the warehouse prevention and control equipment is scheduled, the signal transmission and start-up control process between equipment is established, and the collaborative response instructions of the prevention and control equipment are generated. The system sends coordinated response instructions from prevention and control equipment to the control terminal of the corresponding equipment, receives status feedback information after the equipment executes the instructions, and adjusts the risk transmission blocking chain and coordinated response instructions based on the status feedback information.

2. The warehouse security risk prevention and control method based on IoT devices according to claim 1, characterized in that, The process of performing risk transmission medium interaction identification processing on multimodal sensing data, distinguishing the risk transmission medium types corresponding to different sensing data, identifying the interaction relationships between different media, and obtaining the risk transmission medium interaction identification results include: Analyze the equipment operating parameters, environmental status parameters, cargo attribute parameters, and scene dynamic parameters in multimodal sensing data, and extract the collection location information, collection time series, and parameter change trends of each parameter; The extracted location information is categorized by region, grouping parameter collection locations within the same warehouse partition into the same region group, and grouping parameter collection locations within different warehouse partitions into different region groups. Based on historical data on risk transmission within the warehouse, the risk transmission medium is divided into air medium, cargo contact medium, equipment transmission medium, and personnel flow medium. Each risk transmission medium corresponds to specific transmission characteristics in the historical data on risk transmission. Historical propagation characteristics of air medium, cargo contact medium, equipment conduction medium, and personnel flow medium are extracted. The historical propagation characteristics of air medium correspond to continuous diffusion characteristics, the historical propagation characteristics of cargo contact medium correspond to cargo-related diffusion characteristics, the historical propagation characteristics of equipment conduction medium correspond to line conduction characteristics, and the historical propagation characteristics of personnel flow medium correspond to trajectory-related diffusion characteristics. By comparing the changing trends of each parameter with the historical transmission characteristics of air medium, cargo contact medium, equipment transmission medium, and personnel flow medium, the risk transmission medium type corresponding to each parameter is determined, and a correspondence table between parameters and media is formed. By comparing the acquisition time series of parameters corresponding to different media types, the time point when the first change of each media parameter is marked, and the temporal correlation of the changes of different media parameters is extracted; Extract the region group to which the collection location of the corresponding parameters of different media belongs, and determine whether the collection locations of the parameters of different media are in the same region group or adjacent region groups; Based on the temporal correlation of parameter changes and the regional correlation of acquisition locations, the interaction influence coefficient between different media is calculated, and the direct triggering relationship, indirect influence relationship, and no correlation relationship are divided according to the value of the interaction influence coefficient. By integrating the correspondence table between parameters and media, the interaction relationship between different media, and regional group information, a risk transmission media interaction identification result is generated, which includes media type, corresponding parameter set, media interaction relationship description, and regional association information.

3. The warehouse security risk prevention and control method based on IoT devices according to claim 2, characterized in that, The process involves comparing the changing trends of each parameter with the historical transmission characteristics of air medium, cargo contact medium, equipment transmission medium, and personnel flow medium to determine the risk transmission medium type corresponding to each parameter, forming a parameter-medium correspondence table, including: Historical propagation characteristics of airborne media were extracted. These characteristics were characterized by continuous diffusion of parameter changes, a stable rate of expansion of coverage area per unit time, and no obvious boundary limitations in parameter changes. These historical propagation characteristics of airborne media were extracted from historical case data of airborne media propagation in warehouses. The historical propagation characteristics of the medium in contact with goods were extracted. These characteristics were characterized by parameter changes concentrated in the goods storage area, phased extension along the goods movement path, and the overlap of parameter change boundaries with the goods storage boundaries. The historical propagation characteristics of the medium in contact with goods were extracted from historical case data of goods contact with the medium in warehouses. The historical propagation characteristics of the equipment's transmission medium were extracted. These characteristics are manifested in the gradual transmission of parameter changes along the equipment connection lines, the fixed time interval between parameter changes between adjacent equipment, and the gradual attenuation of the parameter change amplitude with the transmission distance. The historical propagation characteristics of the equipment's transmission medium were extracted from historical case data of the transmission medium propagation of warehouse equipment. Historical propagation characteristics of personnel flow media were extracted. These characteristics were manifested in the following ways: parameter changes were discretely distributed along the personnel activity trajectory; the areas of parameter changes completely overlapped with the areas where personnel stayed; and the duration of parameter changes was consistent with the duration of personnel stay. These historical propagation characteristics of personnel flow media were extracted from historical case data of personnel flow media in warehouses. Analyze the changing trends of equipment operating parameters, environmental status parameters, cargo attribute parameters, and scene dynamic parameters one by one, and extract the rate of change, coverage change pattern, and change boundary characteristics of each parameter. The rate of change of each parameter is compared with the rate characteristics in the historical propagation characteristics of air medium, cargo contact medium, equipment conduction medium, and personnel flow medium; the change pattern of the coverage area of ​​each parameter is compared with the range change pattern in the corresponding historical propagation characteristics; and the change boundary characteristics of each parameter are compared with the boundary characteristics in the historical propagation characteristics. The number of matching items for each parameter with air medium, cargo contact medium, equipment conduction medium, and personnel flow medium is counted. The number of matching items includes rate characteristic matching items, range variation law matching items, and boundary characteristic matching items. The media type with the most matching items is determined as the risk transmission media type corresponding to the parameter. Each parameter and its corresponding risk transmission media type are recorded to form a table of correspondence between parameters and media.

4. The warehouse security risk prevention and control method based on IoT devices according to claim 2, characterized in that, The time-series correlation based on parameter changes and the regional correlation of acquisition locations are used to calculate the interaction influence coefficient between different media. Based on the interaction influence coefficient values, direct triggering relationships, indirect influence relationships, and no-correlation relationships are categorized, including: Extract the acquisition time series of parameters corresponding to different media, determine the time unit of each acquisition time series, and unify the time unit of all acquisition time series through time unit conversion; Based on a unified time unit, the time point when each medium parameter first changes is marked. If there are multiple parameters for the same medium, the time point when the earliest parameter changes is taken as the first change time point for that medium. Compare the first change time points of any two media parameters, calculate the time difference between the two first change time points, and determine the time series correlation level based on the relationship between the time difference and the preset time threshold. The smaller the time difference, the higher the time series correlation level. Retrieve the warehouse partitioning file, obtain the adjacency data between different area groups from the warehouse partitioning file, determine the criteria for determining adjacent area groups, and identify areas whose boundary distance is less than a preset distance as adjacent area groups. Extract the area group to which the collection location of the corresponding parameters of the two media belongs. Determine whether the two area groups are adjacent based on the adjacent relationship data in the warehouse partitioning file. Determine the area association level based on the judgment result. The area association level of the same area group is the highest, the area association level of adjacent area groups is the second highest, and the area association level of non-adjacent area groups is the lowest. By combining temporal correlation level and regional correlation level, and based on preset correlation level combination rules, the degree of interaction between the two media is determined. The interaction influence level is compared with the preset influence level threshold to determine the interaction relationship between different media, and the determination results of the interaction relationship between all different media are recorded to form a list of media interaction relationships.

5. The warehouse security risk prevention and control method based on IoT devices according to claim 1, characterized in that, The process of generating a risk transmission blocking chain based on the risk transmission medium interaction identification results, determining the key blocking nodes in the risk transmission blocking chain and the blocking strategy corresponding to each key blocking node, and obtaining the risk transmission blocking chain generation result includes: Extract the medium type, medium interaction relationship description, and regional association information from the risk transmission medium interaction identification results. Based on the medium type, medium interaction relationship description, and regional association information from the risk transmission medium interaction identification results, trace the node connection relationship of risk transmission from the initial medium to other media. In the node connection relationship of risk transmission, mark all media nodes involved in the transmission, and record the connection direction and connection strength between each media node and other nodes. The connection strength is determined based on the interaction influence coefficient. For each media node, simulate the change in the risk transmission path after the media node is removed, and analyze the path change to determine whether subsequent media nodes can still receive risk transmission. If removing a certain media node prevents all subsequent media nodes from obtaining risk transmission through other connection paths, then that media node is marked as a critical blocking node in the risk transmission blocking chain. Extract the medium type corresponding to each key blocking node, retrieve the historical propagation characteristics of that medium type, and extract the key dependency conditions in the medium propagation process from the historical propagation characteristics of that medium type. Based on the key conditions for medium transmission, blocking strategies are formulated. For airborne media, the key condition is the diffusion path, and the corresponding blocking strategy is to adjust the ventilation system to change the diffusion direction of the airborne media. For cargo-contact media, the key condition is cargo movement, and the corresponding blocking strategy is to isolate the cargo and restrict the transmission of the cargo-contact media. For equipment-conducted media, the key condition is equipment connection, and the corresponding blocking strategy is to disconnect the equipment connection to prevent the transmission of the equipment-conducted media. For personnel-flow media, the key condition is personnel activity, and the corresponding blocking strategy is to guide personnel activity to avoid risk areas to limit the transmission of personnel-flow media. Based on the connection strength and coverage area of ​​key blocking nodes, the key blocking nodes are prioritized and ranked. The larger the connection strength value and the larger the coverage area value, the higher the position of the key blocking node in the ranking. The execution order of critical blocking nodes is determined according to the priority ranking results, and the start time of each critical blocking node is set according to the media propagation speed. By integrating the execution order of key blocking nodes, the activation timing of each key blocking node, and the blocking strategy of each key blocking node, a risk transmission blocking chain generation result is generated, which includes the node sequence, node medium type, activation timing, and blocking strategy description.

6. The warehouse security risk prevention and control method based on IoT devices according to claim 5, characterized in that, For each media node, the process simulates the change in risk transmission path after the removal of that media node. The analysis of this path change determines whether subsequent media nodes can still receive risk transmission data. This includes: The node connections of risk transmission are presented in a visual form. Each medium node is represented by a specific graphic symbol, and the transmission connection between nodes is represented by line segments. The thickness of the line segments corresponds to the connection strength. Select the first medium node from the visualized node connection relationship as the node to be simulated, and temporarily delete the node to be simulated and all connecting segments related to the node to be simulated. Monitor the node connection relationship after deleting the node to be simulated, trace the connection path between the initial risk medium node and each subsequent medium node, check whether there is a complete connection path, if a subsequent medium node still has a complete connection path with the initial risk medium node, record the node to be simulated as a non-critical node, and record the alternative connection path between the medium node and the initial risk medium node. If no complete connection path exists between any subsequent media node and the initial risk media node, then the node to be simulated is recorded as a candidate for a critical blocking node. Restore the node to be simulated and the connecting line segments associated with it to the node connection relationship, and select the next medium node as the new node to be simulated; Repeat the above steps of deleting the node to be simulated, monitoring the node connection relationship, recording the judgment result, and restoring the node to be simulated and the connecting line segment until all media nodes have completed the simulation analysis. All key blocking node candidates are summarized, duplicate key blocking node candidates are removed, and key blocking node candidates with coverage areas smaller than the preset range are excluded to form a preliminary list of key blocking nodes.

7. The warehouse security risk prevention and control method based on IoT devices according to claim 5, characterized in that, The prioritization of key blocking nodes based on their connection strength and coverage area includes: The connection strength data of each key blocking node in the preliminary list of key blocking nodes is extracted. The connection strength data is obtained from the description of the medium interaction relationship in the risk transmission medium interaction identification results. The coverage area corresponding to each key blocking node is measured. The coverage area is determined based on the area of ​​the region group to which the key blocking node belongs and the degree of correlation between adjacent region groups. The connection strength data is converted into a standardized connection strength value. The conversion method is to use the ratio of the connection strength of each critical blocking node to the maximum connection strength of all critical blocking nodes as the standardized connection strength value. The coverage area is converted into a standardized coverage area value. The conversion method is to use the ratio of the coverage area of ​​each key blocking node to the maximum coverage area of ​​all key blocking nodes as the standardized coverage area value. Set weights for standardized connection strength values ​​and standardized coverage values, and determine the weight allocation based on historical blocking effect data. The sum of the weights of the standardized connection strength values ​​and the standardized coverage values ​​is 1. The standardized connection strength value and standardized coverage value of each critical blocking node are weighted and summed according to the set weights to obtain the priority score of each critical blocking node. The key blocking nodes are sorted in descending order of priority score to form a priority sequence of key blocking nodes; If there are key blocking nodes with the same priority score, then the start response time of the key blocking nodes is compared. The key blocking node with the smaller start response time value is ranked higher in the sorting. Record the priority sequence of key blocking nodes and the priority score of each key blocking node, and use the priority sequence of key blocking nodes and the priority score of each key blocking node as the basis for determining the execution order.

8. The warehouse security risk prevention and control method based on IoT devices according to claim 1, characterized in that, The process of scheduling warehouse control equipment based on the risk transmission disruption chain generation result, establishing signal transmission and activation control procedures between equipment, and generating coordinated response instructions for control equipment includes: Extract the key blocking nodes, the activation timing of each key blocking node, and the blocking strategies of each key blocking node from the risk transmission blocking chain generation results; Based on the blocking strategies at each key blocking node, determine the type of prevention and control equipment required to implement each blocking strategy: ventilation control strategy corresponds to ventilation control equipment, cargo isolation strategy corresponds to cargo isolation equipment, equipment cut-off strategy corresponds to equipment control equipment, and personnel guidance strategy corresponds to personnel guidance equipment. Retrieve the basic information files of all prevention and control equipment in the warehouse. The basic information files of the prevention and control equipment include equipment type, equipment identification, deployment location, current operating status, executable actions, and execution capability parameters. Based on the location of key blocking nodes, select control equipment from similar control equipment whose deployment locations are consistent with the locations of key blocking nodes to form a regional matching equipment list; Check the current operating status of each prevention and control device in the regional matching device list, filter the prevention and control devices that are currently in standby or normal operation, and form a list of devices to be evaluated; Extract the execution capability parameters of each prevention and control device in the list of devices to be evaluated, compare the execution capability parameters of the prevention and control devices with the required capability parameters of the blocking strategy, calculate the matching degree, and select prevention and control devices with a matching degree higher than the preset threshold of the matching degree as usable devices; Based on the activation timing of the critical blocking nodes and the activation preparation time of available devices, the activation time of available devices is set to the same time point as the activation timing of the critical blocking nodes. Each available device is assigned a unique digital code as a cooperative signal identifier, and the digital codes are arranged sequentially according to the execution order of the available devices. The signal transmission order among available devices is determined. After the previous available device completes the blocking strategy, it sends an execution completion signal to the control center. After receiving the execution completion signal, the control center sends a start signal to the subsequent available devices. Determine the action to be performed for each available device. The action must match the corresponding blocking strategy. The action of the ventilation control device is to adjust the direction and intensity of the ventilation opening. The action of the cargo isolation device is to extend the isolation barrier. The action of the equipment control device is to disconnect the connection line. The action of the personnel guidance device is to turn on the indicator light and play the voice prompt. Integrate the device identifiers, start times, actions, coordination signal identifiers, and signal transmission sequences of available devices to generate a coordinated response command for prevention and control devices that includes device identifiers, start times, actions, and coordination signal rules.

9. The warehouse security risk prevention and control method based on IoT devices according to claim 8, characterized in that, The process of extracting the execution capability parameters of each prevention and control device in the list of devices to be evaluated, comparing the execution capability parameters of the prevention and control devices with the required capability parameters of the blocking strategy, calculating the matching degree, and selecting prevention and control devices with a matching degree higher than a preset threshold as usable devices includes: Extract the execution capability parameters of each prevention and control device in the list of devices to be evaluated. The execution capability parameters of ventilation control equipment include maximum ventilation intensity, vent adjustment angle range, and response delay time; the execution capability parameters of cargo isolation equipment include isolation barrier extension speed, isolation range, and continuous working duration; the execution capability parameters of equipment control equipment include line disconnection response time, number of lines that can be disconnected, and safety protection level; and the execution capability parameters of personnel guidance equipment include indicator light brightness, voice prompt coverage, and response sensitivity. Based on the blocking strategies for each key blocking node, the required capability parameters for each blocking strategy are determined. The required capability parameters for the ventilation control strategy include the required ventilation intensity, the adjustment angle of the ventilation opening, and the allowable response delay; the required capability parameters for the cargo isolation strategy include the required isolation barrier extension speed, the isolation range, and the continuous working duration; the required capability parameters for the equipment disconnection strategy include the required line disconnection response time, the number of lines that can be disconnected, and the safety protection level; and the required capability parameters for the personnel guidance strategy include the required indicator light brightness, the voice prompt coverage, and the response sensitivity. The execution capability parameters of each type of prevention and control equipment are compared one by one with the required capability parameters of the corresponding blocking strategy. The matching ratio of each parameter is calculated. The matching ratio is the ratio of the execution capability parameter of the prevention and control equipment to the required capability parameter of the blocking strategy. If the ratio is greater than 1, it is calculated as 1. The matching ratio of all parameters is averaged to obtain the matching degree between the prevention and control equipment and the blocking strategy. Prevention and control equipment with a matching degree higher than the preset threshold is selected and included in the list of available equipment. The available equipment list is sorted from highest to lowest matching score. If equipment malfunctions, the malfunctioning equipment is replaced in order of matching score.

10. A warehouse security risk prevention and control system based on IoT devices, characterized in that, include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; The processor is configured to execute the warehouse security risk prevention and control method based on any one of claims 1 to 9 by executing the machine-executable instructions.

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