Warehouse environment risk monitoring method and system

Abstract

The invention relates to the technical field of warehouse environment risk monitoring, in particular to a warehouse environment risk monitoring method and system, and the method comprises the following steps: obtaining a scheduling table and current operation time of forklift drivers or inspectors in a target warehouse, collecting a task load factor of a current task type and the temperature and noise level of an operation area, and obtaining a task load factor of a current task type; and calculating to obtain a scene fatigue risk value. According to the invention, by analyzing the scheduling data and real-time task load and environmental pressure indexes of the forklift driver and the inspector, the comprehensive influence of the personnel operation state and the environmental condition on the fatigue risk is quantitatively evaluated, so that the risk evaluation basis is more comprehensive and specific, and the oneness of risk judgment is avoided; according to the method, a warehouse layout map and cargo distribution information are obtained in real time, and multi-source risk factors are associated into specific warehouse grid units in combination with readings of fire smoke and toxic gas sensors in the warehouse, so that fusion of finer space granularity and more comprehensive risk information is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of warehouse environmental risk monitoring, and in particular to a warehouse environmental risk monitoring method and system. Background Art

[0002] The field of warehouse environmental risk monitoring technology mainly involves technical means for comprehensive monitoring, analysis and evaluation of various environmental risk sources within warehouse scenarios.

[0003] Existing technologies for warehouse environmental risk monitoring focus solely on a single type of environmental risk indicator, such as fire or gas leaks. This can lead to incomplete risk assessments and blind spots in monitoring results. Furthermore, existing technologies ignore the dynamically changing location and status of personnel and equipment within the warehouse, resulting in ineffective risk response decisions and difficulty aligning emergency resource scheduling with real-time conditions. This can lead to delayed responses from inspection personnel or equipment, inefficient resource utilization, and, if complex risk situations arise within the warehouse, compromised response effectiveness and even cause personal safety accidents or economic losses. Therefore, improvements are needed. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a warehouse environment risk monitoring method and system.

[0005] In order to achieve the above objectives, the present invention adopts the following technical solution, a warehouse environment risk monitoring method, comprising the following steps: Obtain the shift schedule and current working hours of forklift drivers or inspectors in the target warehouse. Then collect the task load factor of the current task type and the temperature and noise level of the working area to calculate the scenario fatigue risk value. The warehouse layout and cargo distribution information are retrieved, and the real-time readings of fire smoke sensors and toxic gas sensors are integrated. The scenario fatigue risk value and the sensor readings are associated and assigned to the corresponding grid cells of the warehouse layout to generate a set of zoning risk factors. Based on the set of zoning risk factors, high-risk areas are marked to create a fused risk situation map. Based on the integrated risk situation map, the location and working status of patrol personnel, forklifts, and firefighting robots located by the Internet of Things are obtained in real time. The location information of fixed fire hydrants, fire extinguishers, and emergency exits is retrieved to establish a resource availability status list. Based on the resource availability status list, an emergency resource response capability matrix is ​​calculated. Prioritize high-risk areas in the fused risk situation map, select resources from the emergency resource response capability matrix for matching, and generate an emergency resource scheduling instruction set.

[0006] Preferably, the step of obtaining the scenario fatigue risk value is: Analyze the forklift driver or inspector's shift schedule and the current working time period, extract the start and end times of the personnel's continuous working segments, calculate the total length of time without alternating work in the continuous period, and obtain the personnel's continuous working time value; According to the current task number, the corresponding task type is matched and the task load factor uniquely associated with the task type is extracted. The temperature sensor and noise sensor are synchronously called to obtain the temperature value and noise level value of the current working area. Each environmental value is normalized with the corresponding comfort threshold and danger threshold, and then weighted with the risk enhancement coefficient corresponding to each environmental factor to form a comprehensive environmental pressure parameter; The scenario fatigue risk value is calculated based on the personnel's continuous working time value, reference working time value, task load factor and comprehensive environmental pressure parameter.

[0007] Preferably, the steps of obtaining the partition risk factor set are: The warehouse layout and cargo distribution information are called, the layout is divided into fixed-size grid units, the cargo type, cargo storage quantity and storage location in the cargo distribution information are parsed, and each is mapped to the corresponding grid unit to generate a warehouse grid cargo distribution mapping table; Based on the warehouse grid cargo distribution mapping table, the real-time readings of the fire smoke sensor and the toxic gas sensor are called, the installation location coordinates of each sensor are analyzed and associated with the corresponding grid cells, and the smoke concentration value and the toxic gas concentration value are assigned to each grid cell one by one to generate a warehouse grid environmental sensor reading distribution table; Based on the warehouse grid environmental sensor reading distribution table and the scenario fatigue risk value, the smoke concentration value, the toxic gas concentration value and the scenario fatigue risk value are combined and assigned one by one according to the warehouse grid unit coordinates to generate a partition risk factor set.

[0008] Preferably, the steps of obtaining the fusion risk situation map are: Based on the set of zoning risk factors, safety thresholds for smoke concentration values, toxic gas concentration values, and situational fatigue risk values ​​are set. The smoke concentration values, toxic gas concentration values, and situational fatigue risk values ​​in each grid unit are compared one by one with the corresponding safety thresholds. If any value exceeds the corresponding safety threshold, the grid unit is marked as a high-risk area. Based on the risk marking results of the grid unit, regional risks are marked with color grading on the warehouse layout map to establish a fusion risk situation map.

[0009] Preferably, the steps of obtaining the resource availability status list are: Analyze the grid cell coordinate values ​​of all high-risk areas marked in the fused risk situation map, obtain the positioning coordinates and operation status identification of patrol personnel, forklifts, and fire-fighting robots, call the positioning information of fixed fire hydrants, fire extinguishers and emergency exits in the warehouse, and form a resource availability status list classified by resource type.

[0010] Preferably, the steps for obtaining the emergency resource response capability matrix are: Calculating a comprehensive responsiveness score based on the resource availability status list; Based on the comprehensive response capability score value, a comprehensive response capability score value table is constructed according to the two-dimensional combination structure of resource items and high-risk areas, with emergency resource items represented by rows and high-risk areas represented by columns, to generate an emergency resource response capability matrix.

[0011] Preferably, the steps of obtaining the emergency resource scheduling instruction set are: Calling all grid cells marked as high-risk areas in the fused risk situation map, analyzing the scenario fatigue risk value, smoke concentration value, and toxic gas concentration value in each grid cell one by one, comparing the scenario fatigue risk value, smoke concentration value, and toxic gas concentration value with the corresponding safety risk thresholds one by one, calculating the number of risk values ​​exceeding the safety risk threshold in each grid cell, and arranging them in descending order according to the number of risk values ​​exceeding the limit, thereby forming a priority ranking table for high-risk areas; Based on the ranking results of each grid unit in the high-risk area priority ranking table, the comprehensive response capability score values ​​of all emergency resource items corresponding to each grid unit in the emergency resource response capability matrix are called, and the numerical values ​​of all comprehensive response capability score values ​​in each grid unit are compared one by one. The emergency resource item with the largest value is selected as the response resource of the corresponding grid unit to form a high-risk area emergency resource matching relationship table.

[0012] Preferably, the step of obtaining the emergency resource scheduling instruction set also includes: based on the high-risk area emergency resource matching relationship table, extracting the response resource type, the current position coordinates of the response resource and the grid unit coordinates of the target high-risk area corresponding to each grid unit one by one, and generating resource scheduling instructions one by one according to the information structure of the emergency resource type, departure position and target position to form an emergency resource scheduling instruction set.

[0013] The present invention also provides a warehouse environment risk monitoring system, comprising: The scenario risk assessment module obtains the shift schedule and current working hours of forklift drivers or inspectors in the target warehouse, then collects the task load factor of the current task type and the temperature and noise level of the working area to calculate the scenario fatigue risk value; The fusion risk modeling module uses the warehouse layout and cargo distribution information, and integrates the real-time readings of fire smoke sensors and toxic gas sensors. The scenario fatigue risk value and the sensor readings are associated and assigned to the corresponding grid cells of the warehouse layout to generate a set of zoning risk factors. Based on the set of zoning risk factors, high-risk areas are marked to create a fusion risk situation map. The emergency resource assessment module, based on the integrated risk situation map, obtains the real-time location and working status of patrol personnel, forklifts, and firefighting robots located by the Internet of Things, retrieves the location information of fixed fire hydrants, fire extinguishers, and emergency exits, establishes a resource availability status list, and calculates and obtains an emergency resource response capability matrix based on the resource availability status list; The resource scheduling instruction module prioritizes the high-risk areas in the fusion risk situation map, selects resources from the emergency resource response capability matrix for matching, and generates an emergency resource scheduling instruction set.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: The present invention analyzes the scheduling data, real-time task load and environmental pressure indicators of forklift drivers and inspectors to quantitatively evaluate the comprehensive impact of personnel working status and environmental conditions on fatigue risk, making the risk assessment basis more comprehensive and specific and avoiding the single nature of risk judgment; it obtains warehouse layout and cargo distribution information in real time, and combines the readings of fire smoke and toxic gas sensors in the warehouse to associate multi-source risk factors with specific warehouse grid units, achieving a finer spatial granularity and a more comprehensive integration of risk information, which facilitates the accurate definition of risk areas; it further obtains the location status information of emergency resources based on risk area division and real-time positioning, and calculates the comprehensive response capabilities of different resources for specific risk areas. Through the multi-factor fusion evaluation of emergency resource status factors, risk disposal efficiency and path response time, it improves the matching degree of resources and risks, ensures the accuracy and timeliness of emergency response resource allocation, and improves the overall efficiency and safety of warehouse environmental risk monitoring and emergency response. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Schematic diagram of the steps of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] See also Figure 1 The present invention provides a technical solution, a warehouse environment risk monitoring method, comprising the following steps: Obtain the shift schedule and current working hours of forklift drivers or inspectors in the target warehouse. Then collect the task load factor of the current task type and the temperature and noise level of the working area to calculate the scenario fatigue risk value. The warehouse layout and cargo distribution information are retrieved, and the real-time readings of fire smoke sensors and toxic gas sensors are integrated. The scenario fatigue risk value and the sensor readings are associated and assigned to the corresponding grid cells of the warehouse layout to generate a set of zoning risk factors. Based on the set of zoning risk factors, high-risk areas are marked to create a fused risk situation map. Based on the integrated risk situation map, the location and working status of patrol personnel, forklifts, and firefighting robots located by the Internet of Things are obtained in real time. The location information of fixed fire hydrants, fire extinguishers, and emergency exits is retrieved to establish a resource availability status list. Based on the resource availability status list, an emergency resource response capability matrix is ​​calculated. Prioritize high-risk areas in the fused risk situation map, select resources from the emergency resource response capability matrix for matching, and generate an emergency resource scheduling instruction set.

[0018] The steps for obtaining the scenario fatigue risk value are as follows: Analyze the forklift driver or inspector's shift schedule and the current working time period, extract the start and end times of the personnel's continuous working segments, calculate the total length of time without alternating work in the continuous period, and obtain the personnel's continuous working time value; According to the current task number, the corresponding task type is matched and the task load factor uniquely associated with the task type is extracted. The temperature sensor and noise sensor are synchronously called to obtain the temperature value and noise level value of the current working area. Each environmental value is normalized with the corresponding comfort threshold and danger threshold, and then weighted with the risk enhancement coefficient corresponding to each environmental factor to form a comprehensive environmental pressure parameter; The scenario fatigue risk value is calculated based on the personnel continuous working time value, reference working time value, task load factor and comprehensive environmental pressure parameter. The calculation formula is: ; in, Indicates personnel The scenario fatigue risk value, Indicates the continuous working time value before the personnel’s current task. Indicates the preset reference operation time value, represents the basic fatigue exacerbation index, represents the task load adjustment coefficient, Indicates the task load factor corresponding to the personnel’s current task type, Indicates the Environmental factor collection values, Indicates the The comfort threshold of each environmental factor, Indicates the The risk threshold of an environmental factor, Indicates the The risk enhancement coefficient of each environmental factor is Indicates the total number of environmental factors currently involved in the calculation (such as 2 for temperature and noise).

[0019] Specifically, based on the forklift driver or inspector's schedule and the current working time period, the system first accesses and parses the electronic schedule data. The schedule is stored in a standard data format. Each record contains the employee number, date, planned start time, planned end time, task code and work area identification. The system obtains the current system timestamp accurate to the second through the network time protocol to locate the current working status of the personnel. In order to determine the starting point of the continuous operation, the system first searches for the job entry covering the current time point in the schedule based on the employee number and the current timestamp, and extracts its task code. For example, if the employee is found At 10:45 a.m., a forklift driver numbered W07 is currently performing a "picking in the high-bay area" operation with the task code FL-P01. The system then establishes a "no alternating operation" judgment standard, which defines whether the task type has changed and whether the rest time has interrupted the continuity. The change of task type is determined by comparing the task codes of the two previous and next operation entries to see if they belong to the same preset task category. The task category comparison table classifies specific task codes, such as FL-P01 and FL-M03, into the "forklift operation" category, and classifies IN-S01 into "manual inspection" and rest time. The interruption threshold of the duration is set at 30 minutes. This threshold refers to the provisions on work breaks and the company's internal management rules. Intervals below this threshold are considered short pauses and do not interrupt the calculation of continuous operations. The system starts from the current operation entry and searches the employee's schedule record for the day in reverse order. For the immediately previous operation entry, the system calculates the time difference between its end time and the start time of the current operation entry, and compares the task codes of the two entries to their categories. For example, the previous task is FL-M03 "flat warehouse transfer" from 08:00 to 10:00, and its task category is the same as "forklift operation", and the end time is 10:00, which is the same as the previous task. The time interval between the current task start time of 10:15 is 15 minutes, which is less than the 30-minute interruption threshold. Therefore, the system determines that the operation is continuous and traces the starting point of the continuous operation back to 08:00. The system repeats this reverse tracing process until it encounters a record with a different task category or a time interval exceeding 30 minutes. For example, the record before 08:00 is the previous day's off-duty record, and the tracing stops. Finally, the system calculates the difference between the traced continuous operation starting time (08:00) and the current system time (10:45) to obtain the personnel continuous operation time value in hours, which is 2.75 hours.

[0020] Based on the current task number, the system first performs an accurate match in the "Task Load Factor Knowledge Base". This knowledge base stores the unique task load factor corresponding to each task type. The determination of the task load factor is based on a systematic analysis of all types of work in the warehouse and is calculated by introducing a quantitative task complexity assessment model. The specific calculation method is as follows:

[0021] in, 、 、 The evaluation team, consisting of three senior safety engineers and two team leaders, gave scores for mental load, physical load and time pressure load for each task, with scores ranging from 1 to 10. 、 、 are the corresponding weight coefficients, set to 0.4, 0.4, and 0.2, respectively. These weights are based on a statistical analysis of historical warehouse safety incidents, which indicates that physical and mental workload are the primary factors leading to operational errors. For example, for the "high-bay picking" task, the scores for the three items are 7, 8, and 6, respectively. Therefore, the task load factor is:

[0022] The calculated result is normalized by dividing it by the maximum score of 10 to obtain 0.72. While extracting the task load factor, the system sends a data request to the IoT environmental sensor array deployed in the current work area based on the current work area identifier to obtain real-time temperature and noise level values. For example, if the current area temperature is 29°C and the noise level is 86 decibels, the system then normalizes these two environmental values ​​using the following formula: , in Collect values ​​for environmental factors, is the comfort threshold, The comfort threshold and the danger threshold of temperature are set at 22℃ and 35℃ according to the ISO7730 standard of the International Organization for Standardization. The comfort threshold and the danger threshold of noise are set at 70dB and 95dB according to the Occupational Safety and Health Administration (OSHA) standard. Therefore, the standardized values ​​of temperature and noise are and Finally, the standardized environmental values ​​are weighted and summed with their corresponding risk enhancement coefficients to obtain the comprehensive environmental pressure parameter. The risk enhancement coefficients ( The setting of the risk enhancement factor for noise is based on a regression analysis of nearly 100 minor accident reports in warehouses over the past two years. The analysis results show that the contribution of noise to fatigue is slightly higher than that of temperature. Therefore, the risk enhancement factor for noise is set. The risk enhancement factor of temperature is 0.4. is 0.3, and finally, the comprehensive environmental pressure parameter is calculated as: .

[0023] formula: The benefit of the formula is that the power function part on the left side of the formula describes the nonlinear growth of fatigue with working time, and its growth rate varies according to the task load ( ) is dynamically adjusted to accurately describe the differences in fatigue accumulation rates of different intensity operations. The exponential function part on the right side of the formula As an environmental stress multiplier, it quantifies the amplifying effect of harsh environments (such as high temperature and high noise) on baseline fatigue, allowing risk assessment results to reflect the specific environmental context of the operator in real time. This design, which couples individual physiological rhythms, task characteristics, and environmental factors within a unified framework, eliminates static or single-dimensional risk assessments and instead captures the complex evolution of risk under the interaction of multiple factors, providing more accurate and timely decision-making for preventive interventions and resource scheduling.

[0024] Indicates the continuous working time value before the current task of the personnel, in hours. This parameter is calculated in the previous step by analyzing the personnel schedule, determining the task continuity and interruption conditions, and reflects the time accumulation basis of fatigue. According to the calculation, the continuous working time value of the personnel is 2.75 hours, so .

[0025] Indicates the preset reference operating time value in hours. This parameter is based on the fatigue critical working time benchmark set by industry safety regulations and long-term production data statistics of enterprises. By analyzing the correlation between the operating error rate and continuous working hours of forklift drivers in the warehouse over the past year, it was found that after continuous working for more than 4 hours, the error rate showed a significant exponential increase. In combination with the national regulations on mandatory rest every 4 hours for special types of work, this reference value is set to 4 hours. .

[0026] The basic fatigue exacerbation index is a dimensionless constant that defines the basic rate at which fatigue naturally increases over time, even under the lightest load. This value was determined by taking the most relaxing "material inventory" task in the warehouse as a benchmark, recording the energy changes in the θ band of the brain waves of multiple employees during their work in a controlled environment, and performing a nonlinear regression fit with the continuous work time to obtain the baseline slope of the fatigue growth curve. The final calibration value was 1.15, so .

[0027] Represents the task load adjustment coefficient, which is a dimensionless constant used to adjust the task load factor To calibrate this coefficient, we selected two tasks: "picking in the elevated cargo area" (high load) and "transferring in the flat warehouse" (medium load). We measured the rate of decrease in the heart rate variability (HRV) of the workers while performing the tasks in a simulated environment. We compared the correlation between HRV changes under different loads and subjective fatigue scores. Through least squares optimization, we determined that the coefficient value that best matches the actual changes in physiological indicators is 0.6. .

[0028] The task load factor corresponding to the current task type of the personnel is a dimensionless value between 0 and 1, which is calculated by the quantitative task complexity evaluation model in the previous step. For the calculation of the "high-bay picking" task, its task load factor is 0.72, so .

[0029] It is a comprehensive environmental pressure parameter, which reflects the additional physiological and psychological pressure caused by the current environment on personnel. Its calculation process and results have been described in detail. The calculation result is 0.4174, where Indicates the Environmental factor collection values, represents its comfort threshold, Indicates its danger threshold, represents its risk enhancement factor, Indicates the total number of environmental factors involved in the calculation. In this example, , environmental factors are temperature and noise. Temperature acquisition value ℃, comfort threshold ℃, danger threshold ℃, risk enhancement coefficient ;Noise collection value decibel, comfort threshold decibel, danger threshold decibel, risk enhancement factor .

[0030] Calculation process: According to the specific values ​​of the above parameters, enter the formula for calculation: Calculate the exponential portion of the composite ambient pressure parameter: ; Compute the exponential part of the time ratio term:

[0031] ; Compute the exponentiation portion of the time ratio term: ; Calculate the final scenario fatigue risk value : ; The result shows that the current scenario fatigue risk value of the forklift driver is 0.8020. This risk value is a normalized risk index. The system has three risk thresholds: low risk ( ), medium risk ( ), high risk ( The current calculated result of 0.8020 is in the low-risk range, indicating that the driver's fatigue state at the current moment is still within an acceptable range, but is approaching the medium-risk boundary.

[0032] The steps to obtain the partition risk factor set are: The warehouse layout and cargo distribution information are called, the layout is divided into fixed-size grid units, the cargo type, cargo storage quantity and storage location in the cargo distribution information are parsed, and each is mapped to the corresponding grid unit to generate a warehouse grid cargo distribution mapping table; Based on the warehouse grid cargo distribution mapping table, the real-time readings of fire smoke sensors and toxic gas sensors are called. The installation coordinates of each sensor are analyzed and associated with the corresponding grid cells. The smoke concentration value and toxic gas concentration value are assigned to each grid cell one by one to generate the warehouse grid environmental sensor reading distribution table. Based on the warehouse grid environmental sensor reading distribution table and the scenario fatigue risk value, the smoke concentration value, toxic gas concentration value and scenario fatigue risk value are merged and assigned one by one according to the warehouse grid unit coordinates to generate a partition risk factor set.

[0033] Specifically, the warehouse layout diagram in the digital computer-aided design (CAD) format of the warehouse and the cargo distribution information extracted in real time from the warehouse management system (WMS) database are called. The system first calibrates the coordinate system of the two-dimensional warehouse layout diagram, takes the southwest corner of the warehouse as the coordinate origin (0,0), and establishes a Cartesian coordinate system covering the entire warehouse area. Subsequently, the entire layout area is cut into a fixed size of 2 meters by 2 meters, and a series of grid units with unique coordinate identifiers (for example, GID-X01Y01 composed of row and column numbers) are generated. This size setting takes into account the standard pallet size (1.2 meters × 1 meter) and the minimum channel width required for forklift operation (about 3.5 meters), ensuring that each shelf unit and main channel can be accurately divided into independent grids. Then, the system starts the parsing program for the cargo distribution information, which reads the data records exported from the WMS one by one. Each record contains the material number. Based on the location code, the system queries a preconfigured "location-coordinate mapping table." This table, manually mapped during warehouse information system initialization, precisely defines the two-dimensional coordinates of the center point of each physical location (such as A-01-03-B). The system converts the parsed location coordinates (for example, x=25.4, y=66.8) into the grid cell coordinates (GID-X13Y34) where they are located. At the same time, the system associates the "material master data table" with the material code to extract detailed information about the goods, such as "acetone (flammable liquid)" or "cotton textiles (combustible solid)." Finally, the system aggregates the parsed goods type and storage quantity information into the corresponding grid cell data structure in list form. If a grid cell contains multiple locations, the cell's data structure will contain a list of all goods, ultimately generating a warehouse grid goods distribution mapping table covering the entire warehouse.

[0034] Based on the warehouse grid cargo distribution mapping table generated in the previous stage, the system subscribes to the data streams of all deployed fire smoke sensors and toxic gas sensors through the Internet of Things (IoT) Message Queue Telemetry Transport (MQTT) protocol. These sensors report real-time readings at a frequency of once every 5 seconds. The data packet content contains a unique sensor device ID, timestamp, and measurement values, such as smoke concentration (unit: PPM) and carbon monoxide concentration (unit: PPM). After receiving the data, the system first searches the "Sensor Deployment Location List" based on the sensor device ID. This list is a static configuration file generated during the installation and debugging phase of the sensor network, which records the precise three-dimensional physical coordinates (x, y, z) of each sensor. The system extracts its two-dimensional plane coordinates (x, y) and uses the same grid division logic as the previous step to associate it with a unique grid unit. Next, the system traverses all grid units and assigns them The system uses the inverse distance weighted interpolation method (IDW) to estimate the environmental readings of grid cells that directly contain one or more sensors. The system uses the inverse distance weighted interpolation method (IDW) to estimate the environmental readings of grid cells that do not directly contain any sensors. The system uses the inverse distance weighted interpolation method (IDW) to estimate the environmental readings. The specific operation is as follows: taking the center point of the grid cell to be calculated as the reference, search for all sensors of the same type within a radius of 10 meters around it, select the four closest sensors as the interpolation source, and then calculate the weighted average of these sensor readings based on the inverse of the square of the distance from each sensor to the reference point as the weight to obtain the estimated environmental reading of the grid cell. The 10-meter search radius is set based on the average speed of air convection in the warehouse and the empirical data of the effective coverage range of the sensors. By performing the above assignment or interpolation calculation on all grid cells, a real-time updated warehouse grid environmental sensor reading distribution table is finally generated.

[0035] Based on the real-time updated warehouse grid environment sensor reading distribution table and the previously calculated situational fatigue risk value for a specific person, the system first obtains the real-time coordinates of all current workers through the personnel positioning system in the warehouse (for example, positioning tags based on ultra-wideband UWB technology) and converts these coordinates into corresponding grid unit IDs. Subsequently, the system executes a data merging process, which uses the warehouse grid environment sensor reading distribution table as the basic data structure and processes each grid unit one by one. For any grid unit, the system first checks whether there is an operator in it. If there is no operator, the situational fatigue risk value of the grid unit is assigned to 0. If there is one or more personnel in the unit, the system extracts the situational fatigue risk values ​​of all these personnel and selects the maximum value as the is the final scenario fatigue risk value of the grid unit. For example, in grid GID-X13Y34, there are two inspectors with scenario fatigue risk values ​​of 0.65 and 0.81 respectively. The scenario fatigue risk value of the grid unit is determined to be 0.81. This is based on the conservative principle of risk management and focuses on the highest risk level in the area. After completing the assignment of the scenario fatigue risk value, the system merges this value with the existing smoke concentration value and toxic gas concentration value of the grid unit to form a data tuple containing three risk dimensions. The system repeats this operation for all grid units in the warehouse, and accurately corresponds and integrates the three key risk indicators (smoke concentration, toxic gas concentration, and scenario fatigue risk) according to the unified grid coordinates, and finally generates a complete and dynamic set of partitioned risk factors.

[0036] The steps to obtain the fusion risk situation map are as follows: Based on the set of zoning risk factors, safety thresholds are set for smoke concentration values, toxic gas concentration values, and situational fatigue risk values. The smoke concentration values, toxic gas concentration values, and situational fatigue risk values ​​in each grid unit are compared one by one with the corresponding safety thresholds. If any value exceeds the corresponding safety threshold, the grid unit will be marked as a high-risk area. Based on the risk marking results of the grid unit, regional risks are marked with color grading on the warehouse layout map to establish a fusion risk situation map.

[0037] Specifically, based on the partitioned risk factor set, the system first loads the preset risk judgment safety threshold. The safety threshold of smoke concentration is set to 50PPM based on the requirements for warehouse environment early warning. This value is usually the critical point for the detector to issue an early alarm. The safety threshold of toxic gases (taking carbon monoxide as an example) is taken as its short-term exposure limit concentration (STEL) value of 25PPM, and the safety threshold of the situational fatigue risk value is based on the risk grading standard established in the previous step, and the high-risk limit value is set to 2.0. The system then starts a traversal program to read the data tuples of each grid cell in the partitioned risk factor set one by one. In each grid cell, the system compares the measured smoke concentration value, toxic gas concentration value and situational fatigue risk value with the three safety thresholds of 50PPM, 25PPM and 2.0 set above. The judgment logic is: as long as any one of the three values ​​is greater than or equal to its corresponding safety threshold, the risk status of the grid unit is marked as "high risk". If all values ​​are lower than the threshold, it is marked as "low risk". After completing the risk marking of all grid units, the system performs visual rendering on the loaded warehouse layout map based on these marking results, and uses a set of clear color grading standards to mark regional risks. Specifically, the grid cells marked as "high risk" are filled with red, and the grid cells marked as "low risk" are filled with green. At the same time, in order to provide more detailed early warning, for grid cells with values ​​between 75% and 100% of the corresponding safety threshold, the system fills them with yellow, indicating a "medium risk" or "alert" state. In this way, the abstract risk data is converted into intuitive visual information, and a fusion risk situation map is established.

[0038] The steps to obtain the resource availability status list are: Analyze the grid cell coordinate values ​​of all high-risk areas marked in the fusion risk situation map, obtain the positioning coordinates and operation status identification of patrol personnel, forklifts, and fire-fighting robots, call the positioning information of fixed fire hydrants, fire extinguishers and emergency exits in the warehouse, and form a resource availability status list classified by resource type.

[0039] Specifically, firstly, the fusion risk situation map generated in the previous stage is analyzed, and the coordinate values ​​of all grid cells marked as "high risk" are automatically screened and extracted to form a coordinate list of high-risk areas, such as [GID-X13Y34, GID-X14Y34, GID-X25Y50]. At the same time, the system sends a real-time data request to the IoT positioning network in the warehouse (a system composed of ultra-wideband UWB base stations and positioning tags) to obtain the current precise location coordinates and operation status identification of all mobile emergency resources, including patrol personnel, forklifts and fire-fighting robots. The operation status identification is a digital code, and its corresponding relationship is clearly defined in the system configuration: 01 represents "idle on standby", 02 represents "performing routine tasks", and 03 represents "charging or maintenance". ", 04 represents "faulty or offline". These statuses are automatically reported by the equipment's own control system or the personnel's handheld terminal. The system then retrieves the location information of all fixed emergency resources from a static facility information database. This database was surveyed and entered in the early stages of the warehouse's digital construction and contains the precise plane coordinates of every fire hydrant, portable fire extinguisher, and emergency exit. Finally, the system integrates the dynamically acquired mobile resource information with the static fixed resource information, and constructs a structured data list based on preset classification standards (mobile personnel, mobile equipment, fixed facilities). Each item in the list contains the resource's unique number, resource type, real-time or fixed location coordinates, and a clear availability status, thus forming a resource availability status list classified by resource type.

[0040] The steps to obtain the emergency resource response capability matrix are: Based on the resource availability status list, the comprehensive responsiveness score is calculated using the following formula: ; in, Indicates the Emergency resources items go to The comprehensive response capability score of each high-risk area, Indicates the The availability status factor of each emergency resource item is converted from the job status identifier. Indicates the Emergency resources item to respond to The treatment efficiency coefficient of the risk source in the high-risk area, Indicates the The length difference of the segment path in the horizontal direction, Indicates the The length difference of the segment path in the longitudinal direction, Indicates the The average travel speed of resource items on the segment path, Indicates the Resource items to The number of shortest path segments in high-risk areas, Indicates the Characteristic response time threshold of class resources; Based on the comprehensive response capability score value, a comprehensive response capability score value table is constructed according to the two-dimensional combination structure of resource items and high-risk areas. The rows represent emergency resource items and the columns represent high-risk areas, generating an emergency resource response capability matrix.

[0041] Specifically, the formula: The benefit of the formula is that by introducing the availability status factor The formula can eliminate or reduce the weight of resources that are executing tasks or in a faulty state in real time to ensure the feasibility of scheduling. This achieves a precise match between resources and risk types, avoiding ineffective dispatching, such as dispatching a forklift that does not have fire extinguishing capabilities to handle a fire. The most critical factor is the exponential decay term. , which nonlinearly converts the key variable of response time into a penalty factor, accurately simulating the concept of "golden rescue time", that is, as the response time increases, the effectiveness of resources decreases exponentially.

[0042] Indicates the The availability status factor of each emergency resource item is converted from the resource's operating status identifier to quantify the resource's immediate availability. This conversion is based on a preset "status-factor" mapping table, which is set according to the difficulty of resource interruption or call. For example, the operating status identifier of patrol personnel, forklifts, and firefighting robots is the same as The conversion rule is: when the status is "idle standby" (identifier 01), When the status is "Executing regular tasks" (marker 02), it can be interrupted urgently, but there is a certain delay and cost. 0.4; when the status is "charging or maintenance" (marker 03) or "fault or offline" (marker 04), the resource is completely unavailable. For fixed facilities such as fire hydrants and fire extinguishers, their status is available by default. Always 1.0.

[0043] Indicates the Emergency resources item to respond to The efficiency coefficient of the main risk sources in each high-risk area is stored in a "resource-risk" efficiency matrix, which is jointly developed by the warehouse safety management expert team based on equipment performance specifications, personnel skill certification and emergency response plans. The matrix evaluates the effectiveness of each resource against different risks (such as fire, toxic gas, and personnel fatigue). For example, a high-risk area The main risk source is excessive smoke concentration, so for this area The setup is as follows: Firefighting robot, equipped with a high-pressure water cannon, with the highest efficiency, 0.9; portable fire extinguishers, effective but limited in range and capacity, The inspection personnel can use fire extinguishers after fire training, but their main responsibilities are to confirm the fire situation and guide. is 0.3; forklift, no fire extinguishing capability, is 0.

[0044] Represents a resource To high-risk areas The shortest travel time (in seconds) is calculated by the path planning algorithm. The system uses the A* (A-star) algorithm to calculate the shortest feasible path from the current location of the resource to the center of the high-risk area on the pre-set warehouse electronic map (including obstacles such as shelves and walls). The algorithm decomposes the path into continuous straight line segments. Segment path, and is the length of its projection on the coordinate axis, This is the length of the path (unit: meter). The average speed of the resource on the path (unit: m / s). The value is obtained from a preset speed table based on the resource type and the type of area where the path is located (such as main channel, narrow lane, personnel channel). For example, a firefighting robot in a 3.5m wide main channel 2.5 m / s in a narrow aisle between 2.5 m wide shelves 1.5 m / s.

[0045] Indicates the The characteristic response time threshold (in seconds) of a resource class defines the "golden time window" for effective response of this resource class. The setting of this threshold is based on relevant safety standards and emergency drill data analysis. For example, for fire fighting resources (firefighting robots, fire extinguishers), the critical time window for initial fire control is 60-120 seconds, so it is set as Set to 90 seconds. For inspectors who need to replace fatigued personnel, the response urgency is slightly lower. Can be set to 300 seconds.

[0046] Calculation process: Firefighting robots (resources ) for a high-risk area (area ) as an example.

[0047] Known: High-risk areas The center coordinates are (60,70), and the main risk is excessive smoke concentration.

[0048] Firefighting robot The current coordinate is (10,10) and the job status is "Idle Standby".

[0049] Get parameter values: :The robot status is "idle standby", check the table to get .

[0050] :Robots deal with fire risks, check the efficiency matrix .

[0051] : The characteristic response time threshold of the firefighting robot category is Second.

[0052] Calculate the shortest travel time: The A* algorithm plans a path, which consists of two stages: The first segment (main channel): from (10,10) to (60,10). , The distance is 50 meters. This channel is a wide main channel, and the robot speed m / s. Travel time Second.

[0053] Second segment (main channel): from (60,10) to (60,70). , The distance is 60 meters. This channel is also the main channel, the speed m / s. Travel time Second.

[0054] Shortest total travel time Second.

[0055] Substitute into the formula to calculate :

[0056] The results indicate that the firefighting robot's overall response capability for this high-risk area is rated 0.552. This score comprehensively reflects its current availability, appropriate expertise, and ability to arrive at the scene within the "golden hour" (which is significantly shorter than its characteristic response time), making it a highly preferred and efficient response option. This score will be used to construct an emergency resource response capability matrix, providing a direct quantitative basis for dispatch decisions.

[0057] Based on the series of comprehensive response capability scores calculated in the previous stage, the system starts the matrix construction program. First, the program dynamically creates a two-dimensional table data structure. The rows and columns of the table are dynamically defined according to the current emergency situation. The rows are designated to represent all emergency resource items that are in an available or semi-available state. The header of each row is the unique resource number from the resource availability status list, such as "Fire Robot-01", "Inspector-W07", and "Fire Extinguisher-C2-04". The columns are used to represent all areas marked as "high risk" in the fused risk situation map. The header of each column is the coordinate value of the corresponding high-risk grid cell, such as "GID-X13Y34" and "GID-X25Y50". Then, the system traverses all "resource-high-risk area" combination pairs through a double loop. For each combination, the system calls the unique comprehensive response capability score calculated in the previous step from the memory. , and accurately fill this value into the cell at the intersection of the corresponding row and column in the table. For example, the score of 0.552 for the high-risk area GID-X13Y34 of Fire Fighting Robot-01 will be filled into the specified cell. This filling process will cover all identified high-risk areas and all available emergency resources. For any resource that is completely unavailable (for example, in a faulty state), ) and result in a score of zero, the corresponding cell will be filled with 0. When all cells are assigned values, this two-dimensional table full of score values ​​will formally constitute the emergency resource response capability matrix at the current moment.

[0058] The steps to obtain the emergency resource scheduling instruction set are: Call all grid cells marked as high-risk areas in the fusion risk situation map, analyze the scenario fatigue risk value, smoke concentration value, and toxic gas concentration value in each grid cell one by one, compare the scenario fatigue risk value, smoke concentration value, and toxic gas concentration value with the corresponding safety risk threshold value one by one, calculate the number of risk values ​​exceeding the safety risk threshold in each grid cell, and sort them in descending order according to the number of risk values ​​exceeding the limit, forming a priority ranking table for high-risk areas; Based on the ranking results of each grid unit in the high-risk area priority ranking table, call the comprehensive response capability score value of all emergency resource items corresponding to each grid unit in the emergency resource response capability matrix, compare the values ​​of all comprehensive response capability scores in each grid unit one by one, select the emergency resource item with the largest value as the response resource for the corresponding grid unit, and form a high-risk area emergency resource matching relationship table; Based on the emergency resource matching relationship table of high-risk areas, the response resource type, current location coordinates of the response resource, and grid unit coordinates of the target high-risk area corresponding to each grid unit are extracted one by one. Resource scheduling instructions are generated one by one according to the information structure of emergency resource type, departure location, and target location to form an emergency resource scheduling instruction set.

[0059] Specifically, the system calls a list of all grid cells marked as high-risk areas in the fusion risk situation map. For each grid cell in the list, the system analyzes the three specific indicators of scenario fatigue risk value, smoke concentration value and toxic gas concentration value from its associated partition risk factor set one by one. Then, the system compares these three values ​​with the safety risk thresholds set in the previous step. Specifically, the scenario fatigue risk value is compared with 2.0, the smoke concentration value is compared with 50PPM, and the toxic gas concentration value is compared with 25PPM. The system initializes a counter for each grid cell. Whenever a risk value is greater than or equal to its corresponding safety risk threshold, the counter of the grid cell is increased by one. For example, for grid cell GID-X13Y34, its risk data is {scenario fatigue risk value: 2.2; smoke concentration: 65PPM; toxic gas concentration: 10PPM}, then the scenario fatigue risk value (2.0) is greater than or equal to the corresponding safety risk threshold. 2≥2.0) and smoke concentration (65PPM≥50PPM) exceed the limit, the counter value is 2. For grid cell GID-X25Y50, its risk data is {scenario fatigue risk value: 1.8; smoke concentration: 55PPM; toxic gas concentration: 15PPM}. Only the smoke concentration exceeds the limit, and the counter value is 1. After completing the statistics of the exceeded risk items of all high-risk area grid cells, the system sorts all high-risk area grid cells in descending order according to the values ​​of their counters. If there are cases where the counter values ​​are the same, the grid cells are further sorted according to the sum of the exceedance percentages of the exceeded risk items in each grid cell. The exceedance percentage calculation formula is: ((actual value - threshold) / threshold). The percentages of all exceeded items are added together, and the one with the larger sum ranks higher. In this way, a high-risk area priority ranking table is generated that clearly indicates the degree of danger and complexity of each high-risk area.

[0060] Based on the high-risk area priority ranking table generated in the previous stage, the system processes each high-risk grid unit one by one in order of priority from high to low. For the currently processed grid unit, the system calls the emergency resource response capability matrix generated in the previous step and extracts the entire column of data corresponding to the grid unit. This column of data contains the comprehensive response capability score of all available emergency resource items for this specific high-risk grid unit. The system then compares the numerical values ​​of the score values ​​in this column and identifies and selects the resource item with the highest comprehensive response capability score from all candidate emergency resource items by performing a maximum value search operation. This resource item is determined to be the optimal choice for processing the current high-risk grid unit. For example, for the grid unit GID-X13Y34 with the highest priority, the system finds the corresponding column in the emergency resource response capability matrix and finds that the score of fire robot-01 is 0 .552, Inspector-W07's score is 0.213, and Fire Extinguisher-C2-04's score is 0.450. After comparing these values, the system determines that Fire Robot-01 (0.552) is the best response resource and immediately establishes a unique matching relationship between "Fire Robot-01" and "GID-X13Y34". At the same time, to avoid repeated resource scheduling, once a resource (such as Fire Robot-01) is allocated, it will be temporarily marked as "assigned" in the subsequent matching process for other low-priority high-risk areas and will no longer participate in the calculation unless a higher-priority task requires it. The system will reallocate it according to the preset preemption rules. The system completes the matching of the optimal resources for each high-risk area in the sorting table in turn, and records each matching result (including the high-risk area coordinates and the selected response resource number), eventually forming a complete high-risk area emergency resource matching relationship table.

[0061] Based on the high-risk area emergency resource matching relationship table, the system starts the instruction generation program, which reads the matching records in the table one by one. For each record, the system first extracts three key information: the grid unit coordinates of the target high-risk area, such as GID-X13Y34, the unique number of the matched response resource, such as fire robot-01, and the type of the response resource, namely "fire robot". The system then queries the resource availability status list to obtain the current real-time location coordinates of the response resource "fire robot-01", such as (10,10). At the same time, the system converts the grid unit coordinates GID-X13Y34 of the target high-risk area into the physical coordinates of its center point, such as (60,70). At this point, the core elements required for a dispatch instruction ("who to send", "where to go", and "where to go") are all ready, and the system then follows a predefined instruction template. This information is formatted with a template structure of "{Command ID: XXXX, Resource Type: [Resource Type], Resource Number: [Resource Number], Departure Location: [Departure Coordinates], Target Location: [Target Coordinates], Command Status: Pending}". The extracted information is entered into the template to generate a specific dispatch instruction, for example, "{Command ID: CMD001, Resource Type: Firefighting Robot, Resource Number: Firefighting Robot-01, Departure Location: (10,10), Target Location: (60,70), Command Status: Pending}". For personnel-type resources, the instruction content will also include a concise task description, such as "Go to the target area to confirm the fire situation and use fire-fighting equipment". The system repeats this process for each record in the matching relationship table, generating structured resource dispatch instructions one by one, and then compiling all generated instructions to form the final emergency resource dispatch instruction set.

[0062] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A warehouse environmental risk monitoring method, characterized in that: The following steps are involved: Obtain the shift schedule and current working hours of forklift drivers or inspectors in the target warehouse. Then collect the task load factor of the current task type and the temperature and noise level of the working area to calculate the scenario fatigue risk value. The warehouse layout and cargo distribution information are retrieved, and the real-time readings of fire smoke sensors and toxic gas sensors are integrated. The scenario fatigue risk value and the sensor readings are associated and assigned to the corresponding grid cells of the warehouse layout to generate a set of zoning risk factors. Based on the set of zoning risk factors, high-risk areas are marked to create a fused risk situation map. Based on the integrated risk situation map, the location and working status of patrol personnel, forklifts, and firefighting robots located by the Internet of Things are obtained in real time. The location information of fixed fire hydrants, fire extinguishers, and emergency exits is retrieved to establish a resource availability status list. Based on the resource availability status list, an emergency resource response capability matrix is ​​calculated. Prioritize high-risk areas in the fused risk situation map, select resources from the emergency resource response capability matrix for matching, and generate an emergency resource scheduling instruction set.

2. The warehouse environment risk monitoring method according to claim 1, characterized in that: The steps for obtaining the scenario fatigue risk value are as follows: Analyze the forklift driver or inspector's shift schedule and the current working time period, extract the start and end times of the personnel's continuous working segments, calculate the total length of time without alternating work in the continuous period, and obtain the personnel's continuous working time value; According to the current task number, the corresponding task type is matched and the task load factor uniquely associated with the task type is extracted. The temperature sensor and noise sensor are synchronously called to obtain the temperature value and noise level value of the current working area. Each environmental value is normalized with the corresponding comfort threshold and danger threshold, and then weighted with the risk enhancement coefficient corresponding to each environmental factor to form a comprehensive environmental pressure parameter; The scenario fatigue risk value is calculated based on the personnel's continuous working time value, reference working time value, task load factor and comprehensive environmental pressure parameter.

3. The warehouse environment risk monitoring method according to claim 1, characterized in that: The steps for obtaining the partition risk factor set are: The warehouse layout and cargo distribution information are called, the layout is divided into fixed-size grid units, the cargo type, cargo storage quantity and storage location in the cargo distribution information are parsed, and each is mapped to the corresponding grid unit to generate a warehouse grid cargo distribution mapping table; Based on the warehouse grid cargo distribution mapping table, the real-time readings of the fire smoke sensor and the toxic gas sensor are called, the installation location coordinates of each sensor are analyzed and associated with the corresponding grid cells, and the smoke concentration value and the toxic gas concentration value are assigned to each grid cell one by one to generate a warehouse grid environmental sensor reading distribution table; Based on the warehouse grid environmental sensor reading distribution table and the scenario fatigue risk value, the smoke concentration value, the toxic gas concentration value and the scenario fatigue risk value are combined and assigned one by one according to the warehouse grid unit coordinates to generate a partition risk factor set.

4. The warehouse environment risk monitoring method according to claim 1, characterized in that: The steps for obtaining the fusion risk situation map are: Based on the set of zoning risk factors, safety thresholds for smoke concentration values, toxic gas concentration values, and situational fatigue risk values ​​are set. The smoke concentration values, toxic gas concentration values, and situational fatigue risk values ​​in each grid unit are compared one by one with the corresponding safety thresholds. If any value exceeds the corresponding safety threshold, the grid unit is marked as a high-risk area. Based on the risk marking results of the grid unit, regional risks are marked with color grading on the warehouse layout map to establish a fusion risk situation map.

5. The warehouse environment risk monitoring method according to claim 1, characterized in that: The steps for obtaining the resource availability status list are: Analyze the grid cell coordinate values ​​of all high-risk areas marked in the fused risk situation map, obtain the positioning coordinates and operation status identification of patrol personnel, forklifts, and fire-fighting robots, call the positioning information of fixed fire hydrants, fire extinguishers and emergency exits in the warehouse, and form a resource availability status list classified by resource type.

6. The warehouse environment risk monitoring method according to claim 1, characterized in that: The steps for obtaining the emergency resource response capability matrix are: Calculating a comprehensive responsiveness score based on the resource availability status list; Based on the comprehensive response capability score value, a comprehensive response capability score value table is constructed according to the two-dimensional combination structure of resource items and high-risk areas, with emergency resource items represented by rows and high-risk areas represented by columns, to generate an emergency resource response capability matrix.

7. The warehouse environment risk monitoring method according to claim 1, characterized in that: The steps for obtaining the emergency resource scheduling instruction set are: Calling all grid cells marked as high-risk areas in the fused risk situation map, analyzing the scenario fatigue risk value, smoke concentration value, and toxic gas concentration value in each grid cell one by one, comparing the scenario fatigue risk value, smoke concentration value, and toxic gas concentration value with the corresponding safety risk thresholds one by one, calculating the number of risk values ​​exceeding the safety risk threshold in each grid cell, and arranging them in descending order according to the number of risk values ​​exceeding the limit, thereby forming a priority ranking table for high-risk areas; Based on the ranking results of each grid unit in the high-risk area priority ranking table, the comprehensive response capability score values ​​of all emergency resource items corresponding to each grid unit in the emergency resource response capability matrix are called, and the numerical values ​​of all comprehensive response capability score values ​​in each grid unit are compared one by one. The emergency resource item with the largest value is selected as the response resource of the corresponding grid unit to form a high-risk area emergency resource matching relationship table.

8. The warehouse environment risk monitoring method according to claim 1, characterized in that: The step of obtaining the emergency resource scheduling instruction set also includes: based on the high-risk area emergency resource matching relationship table, extracting the response resource type, the current location coordinates of the response resource and the grid unit coordinates of the target high-risk area corresponding to each grid unit one by one, and generating resource scheduling instructions one by one according to the information structure of the emergency resource type, departure location and target location to form an emergency resource scheduling instruction set.

9. A warehouse environment risk monitoring system according to any one of claims 1 to 8, characterized in that: include: The scenario risk assessment module obtains the shift schedule and current working hours of forklift drivers or inspectors in the target warehouse, then collects the task load factor of the current task type and the temperature and noise level of the working area to calculate the scenario fatigue risk value; The fusion risk modeling module uses the warehouse layout and cargo distribution information, and integrates the real-time readings of fire smoke sensors and toxic gas sensors. The scenario fatigue risk value and the sensor readings are associated and assigned to the corresponding grid cells of the warehouse layout to generate a set of zoning risk factors. Based on the set of zoning risk factors, high-risk areas are marked to create a fusion risk situation map. The emergency resource assessment module, based on the integrated risk situation map, obtains the real-time location and working status of patrol personnel, forklifts, and firefighting robots located by the Internet of Things, retrieves the location information of fixed fire hydrants, fire extinguishers, and emergency exits, establishes a resource availability status list, and calculates and obtains an emergency resource response capability matrix based on the resource availability status list; The resource scheduling instruction module prioritizes the high-risk areas in the fusion risk situation map, selects resources from the emergency resource response capability matrix for matching, and generates an emergency resource scheduling instruction set.

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