Intelligent management and control platform for enterprise safety production

Abstract

The invention discloses an intelligent management and control platform for enterprise safety production, and the platform comprises a multi-mode sensing layer which is used for deploying an industrial camera, a voiceprint sensor, a UWB positioning base station and an equipment Internet of Things sensor, and collecting the equipment state, personnel behavior and environment data in real time; the intelligent center layer comprises a data platform of a federated learning framework, a digital twin engine and a dynamic resource scheduler, and realizes multi-source data fusion and cross-system decision; the service application layer integrates an intelligent inspection system, an intelligent video analysis system, an equipment integrity management system and an emergency response system, and all the systems realize logic interlocking through a knowledge graph; and the display layer is used for providing an AR command terminal and a large visual screen and displaying the equipment health state, risk early warning and task execution progress in real time. According to the invention, through an integrated architecture of sensing, decision making, execution and verification, scattered technical modules are organically integrated to form a closed-loop management and control system, and comprehensive and intelligent enterprise safety production management and control are realized.

Description

Technical Field

[0001] This invention relates to the field of management and control platforms, specifically to an intelligent management and control platform for enterprise safety production. Background Technology

[0002] With the advancement of industrial digital transformation, enterprise safety production management is gradually evolving from traditional manual inspections and static record-keeping to intelligent and dynamic approaches. Currently, enterprises largely rely on technologies such as IoT sensors, video surveillance, and data platforms to build basic safety management systems, enabling equipment status monitoring, environmental data collection, and simple risk warnings. However, these systems generally suffer from the following limitations:

[0003] Data silos: Multi-source heterogeneous data (such as device sensing, video streams, and personnel positioning) lack deep integration, making it difficult to support cross-system collaborative decision-making;

[0004] Response lag: Risk warnings rely on threshold triggers, lack predictive analysis and dynamic resource scheduling capabilities, resulting in low emergency response efficiency;

[0005] High false alarm rate: Single sensors or visual algorithms are easily affected by environmental interference (such as high-temperature equipment being mistaken for a fire), leading to frequent false alarms;

[0006] Resource rigidity: The allocation of inspection tasks relies on fixed rules, which cannot adapt to the dynamic needs of complex scenarios. Therefore, an intelligent management and control platform for enterprise safety production is proposed. Summary of the Invention

[0007] The technical problem to be solved by this invention is: how to solve the problems of data isolation, delayed response, high false alarm rate and resource rigidity in the existing technology, and to provide an intelligent management and control platform for enterprise safety production.

[0008] The present invention solves the above-mentioned technical problems through the following technical solutions, the present invention comprising:

[0009] Multimodal perception layer: Deploy industrial cameras, voiceprint sensors, UWB positioning base stations and device IoT sensors to collect real-time data on equipment status, personnel behavior and environment;

[0010] Intelligent Central Layer: Includes a data platform, digital twin engine, and dynamic resource scheduler within a federated learning framework, enabling multi-source data fusion and cross-system decision-making;

[0011] Business application layer: integrates intelligent inspection system, intelligent video analysis system, equipment integrity management system and emergency response system, and the systems achieve logical interlocking through knowledge graph;

[0012] Presentation layer: Provides AR command terminals and large visualization screens to display equipment health status, risk warnings, and task execution progress in real time;

[0013] The platform enables adaptive switching between personnel and drone inspection modes through a dynamic resource scheduler and optimizes equipment maintenance strategies based on quantum evolution algorithms.

[0014] Furthermore, the intelligent inspection system includes a flexible mode selection module, which is used to calculate the drone mode coefficient and the personnel mode coefficient.

[0015] Calculate the drone mode coefficients:

[0016] Calculate the personnel pattern coefficient:

[0017] When λ>λ′, initiate drone inspection; when λ<λ′, initiate manual inspection.

[0018] In the formula, A is the park area (㎡), ρ is the equipment risk density (number of high-risk equipment / thousand㎡), R is the meteorological risk coefficient (0-1), Nd is the number of available drones, and v d H is the cruising speed (m / s). d Health index for drones;

[0019] S p The skill level of personnel is S (range: 0.5-1.5), calculated by weighting historical work order completion rate and training assessment score.

[0020] F p The employee fatigue index (range: 0-1);

[0021] N p The number of available inspection personnel is obtained in real time from the scheduling system.

[0022] Furthermore, the personnel fatigue index F p The calculation method is as follows:

[0023]

[0024] In the formula, F p A higher value indicates a higher level of fatigue;

[0025] HRV t For real-time heart rate variability, samples are taken every 10 seconds via a smart bracelet;

[0026] HRV base Baseline heart rate variability is calculated based on the average HRV of the individual at rest over the past 7 days.

[0027] T: This is the monitoring time window, with a default value of 10 minutes.

[0028] α is the continuous work penalty coefficient (default value: 0.2), which is determined by fitting experimental data;

[0029] Continuous working time is the cumulative working time of an employee from the start of the task to the present.

[0030] Furthermore, the process of obtaining the drone's health index is as follows:

[0031] Hd=σ(0.4·x1+0.3·x2+0.2·x3+0.1·x4);

[0032] In the formula, σ is the Sigmoid function, which is used to normalize the linear combination to the interval [0,1].

[0033] x1 is the battery cycle degradation coefficient (range: 0-1), and the calculation formula is 1 - the number of times it has been charged / the maximum number of times it has been designed to charge.

[0034] x2 is the motor vibration anomaly index (range: 0-1), which is obtained by calculating the mean square error of deviation from the normal spectrum through FFT analysis of the vibration spectrum.

[0035] x3 is the camera focus error rate (range: 0-1), calculated based on an image sharpness evaluation function (such as the Brenner gradient);

[0036] x4 represents the packet loss rate (range: 0-1), which is a real-time statistic showing the proportion of UDP packets lost in the last minute.

[0037] Furthermore, the drone task allocation adopts an auction algorithm, the specific process of which is as follows:

[0038] Task package division: The park is divided into a preset number of grid task packages according to the risk level of the equipment, with the grid density of high-risk areas being 3 times that of low-risk areas;

[0039] Drones calculate based on health index and distance:

[0040] P d This represents the bid price for the drone; a higher value indicates a higher priority.

[0041] R local The risk coefficient (range: 0.1-1.0) for the area where the task package is located is dynamically calculated based on equipment failure probability and historical accident data;

[0042] D current The Euclidean distance from the current location of the drone to the center of the mission package is calculated using GPS or UWB positioning data.

[0043] Dynamic allocation: The central scheduler allocates tasks in descending order of Pd to meet the total time constraint.

[0044] In the formula, N d t represents the number of available drones. d This refers to the single-player battery life.

[0045] The estimated time for the m-th task package is calculated using the following formula: Among them W d Width for drone inspection;

[0046] Conflict resolution: Conduct a second round of bidding for overlapping task packages and adjust the bid price accordingly. Reassignment;

[0047] In the formula, A overlap Let A be the area of ​​the overlapping region of the task packages. task This represents the area of ​​the original task package.

[0048] Furthermore, the intelligent video analysis system includes:

[0049] Vehicle parking detection: Based on the YOLOv8-OBB model, output the rotated bounding box of the vehicle (xc,yc,w,h,θ), with the following judgment criteria:

[0050] Line pressure detection: Hausdorff distance d H (B vehicle ,L line >15cmdH;

[0051] Steering compliance: Vehicle front steering angle deviation Δθ=∣θ vehicle -θ slot |>15°;

[0052] Smoke and fire recognition: fusing visible light and infrared data, verifying the radiant power P = ∈σ(T) using the Stefan-Boltzmann law. 4 -T0 4 If T>300℃ and P<100W / m2, it is determined to be a false alarm.

[0053] d H The Hausdorff distance measures the vehicle's bounding box B. vehicle With parking space line L line The maximum and minimum distances between them;

[0054] ∈ represents the material emissivity (range: 0-1), which is set according to the material of the smoke or flame (e.g., flame ∈ = 0.95);

[0055] σ is the Stefan-Boltzmann constant (5.67 × 10⁻⁸ W / m²K⁴);

[0056] T represents the temperature of the detection area, measured by an infrared thermal imager;

[0057] T0 is the ambient temperature;

[0058] The loss function of the YOLOv8-OBB model is: L total =0.5L cls +0.3 reg +0.2L angle ;

[0059] L angele =1-cos(θ) pred -θ gt ), avoiding angle jumps through periodic losses;

[0060] L cls For classification loss, the cross-entropy loss function is used.

[0061] L reg For bounding box regression loss, the SmoothL1 loss function is used.

[0062] L angle The angle regression loss is calculated using the formula L. angle =1-cosθ(θ) pred -θ gt This avoids 360° periodic errors.

[0063] Furthermore, the intelligent video analysis system also includes a vehicle full-dimensional anomaly detection module, used for vehicle parking compliance detection, vehicle speed detection, and vehicle abnormal behavior detection.

[0064] Furthermore, the process for verifying the proper parking of vehicles is as follows:

[0065] Parking space ownership determination: Extracting the vertices of the parking space polygon using the YOLOv8-OBB model. Determining the vehicle center (x) using the ray-mapping method c ,y c ) Whether it is within the parking space, calculate:

[0066]

[0067] If the result is odd, it is determined to be within the parking space; otherwise, an unauthorized parking alarm is triggered.

[0068] In the formula (x i ,y i ) represents the coordinates of the ii-th vertex of the parking space polygon, which are extracted using a semantic segmentation model.

[0069] (x i+1 ,y i+1 Let be the coordinates of the (i+1)th vertex of the parking space polygon, with the vertices arranged in clockwise or counterclockwise order;

[0070] II is an indicator function that takes the value 1 when the condition is true and 0 otherwise.

[0071] Denominator condition: Determine whether the horizontal position of the vehicle's center point is located to the left of the intersection of the edge from vertex i to i+1 and the horizontal ray (passing through the y-coordinate of the vehicle's center point);

[0072] Multi-view verification: A bird's-eye view is generated using fisheye cameras deployed diagonally, and the IoU (Intersection over Union) between the vehicle bounding box and the parking space lines is calculated.

[0073] If IoU < F and F = 0.8, then it is determined to be on the line;

[0074] B vehicle The area of ​​the vehicle's bounding box is calculated from the coordinates of the rotated bounding box.

[0075] B slot The area of ​​the parking space bounding box is calculated using polygons generated from the semantic segmentation results.

[0076] ∩ represents the area of ​​the intersection region, and ∪ represents the area of ​​the union region.

[0077] Furthermore, the specific process for vehicle speed detection is as follows:

[0078] Pixel-to-Actual Speed ​​Conversion: Calculate the actual speed based on camera calibration parameters.

[0079]

[0080] In the formula: v pixel The pixel displacement velocity of the vehicle in the image is calculated by the displacement of the vehicle's center point between adjacent frames;

[0081] H real The vertical height of the camera's field of view in the actual scene is measured using a calibration plate;

[0082] H image The pixel height of the corresponding region in the image is obtained from the camera resolution.

[0083] f represents the video frame rate, such as 30 frames per second;

[0084] θ is the angle between the vehicle's direction of motion and the camera's optical axis, calculated by the dot product of the vehicle's trajectory direction vector and the optical axis direction vector.

[0085] Furthermore, the specific process for detecting abnormal vehicle behavior is as follows:

[0086] Retrograde detection: Calculate the predicted position for the next K frames based on the Transformer trajectory prediction model. With respect to actual position P k Deviation:

[0087]

[0088] If D traj >2σ hist It was determined to be going against traffic;

[0089] Illegal parking detection: Statistical analysis of the time (t) a vehicle remains stationary in a no-parking zone. park If t park >t allow And the velocity variance σv 2 When the value is less than a preset value, a trailer dispatch command is triggered, such as 0.1m. 2 / s 2 ;

[0090] In the formula: The predicted future vehicle position for the kth frame is output by the Transformer trajectory prediction model.

[0091] P k The actual observed vehicle position in the future k-th frame is obtained through a target tracking algorithm;

[0092] K is the prediction step size (e.g., K=5), which by default covers the trajectory within the next second;

[0093] σ hist The standard deviation of historical trajectory deviation is calculated based on statistics from 1000 past normal trajectory data.

[0094] t park The stationary time of a vehicle in a no-stopping zone is determined by the variance of the position in consecutive frames.

[0095] t allow The maximum allowed stay time is set according to the area type (e.g., fire lane limit = 0);

[0096] σ v 2 The variance of vehicle speed is calculated using the following formula:

[0097] Where v i The sampling value is the speed per second. The average velocity is σv2. If σv2 < 0.1, it is considered stationary.

[0098] Compared with the prior art, the present invention has the following advantages: the enterprise safety production intelligent management and control platform integrates multiple sensors and positioning technologies to achieve comprehensive dynamic monitoring of equipment status, personnel behavior and environmental data, thereby improving the coverage and real-time performance of safety monitoring.

[0099] Based on the federated learning framework and digital twin technology, it integrates multi-source heterogeneous data, supports cross-system collaborative decision-making, and achieves adaptive optimization of inspection mode and maintenance strategy through dynamic resource scheduling, thereby improving resource utilization efficiency.

[0100] By using an intelligent switching mechanism between drones and human inspections, and dynamically adjusting inspection methods based on health status and task requirements, the adaptability and response speed of operations in complex scenarios are enhanced. By utilizing quantum evolution algorithms and equipment integrity analysis models, early warning of equipment failures and optimization of maintenance priorities are achieved, reducing the risk of equipment downtime and extending service life.

[0101] By leveraging advanced video analytics technology, we can accurately identify illegally parked vehicles, abnormal behavior, and potential fires. Combined with a multi-dimensional verification mechanism, we can reduce misjudgments and improve the accuracy of safety management. Through knowledge graph association reasoning and historical case matching, we can generate efficient emergency response plans, shorten accident response time, and improve the scientific nature and reliability of emergency decision-making.

[0102] This system enables collaborative data computation based on a federated learning framework, ensuring privacy and security while supporting cross-platform data interoperability and improving system scalability and compatibility.

[0103] Through the integration of the above technologies, the platform significantly improves the level of intelligence in enterprise safety production, realizes closed-loop management of the entire process from risk warning and real-time monitoring to emergency response, effectively reduces the accident rate, optimizes operating costs, and provides technical support for enterprises to build an efficient and reliable safety management system. Attached Figure Description

[0104] Figure 1 This is a system structure block diagram of the present invention. Detailed Implementation

[0105] The embodiments of the present invention are described in detail below. These embodiments are implemented based on the technical solution of the present invention, and provide detailed implementation methods and specific operation processes. However, the scope of protection of the present invention is not limited to the following embodiments.

[0106] like Figure 1 As shown, this embodiment provides a technical solution: an intelligent management and control platform for enterprise safety production, comprising:

[0107] Multimodal perception layer: Deploy industrial cameras, voiceprint sensors, UWB positioning base stations and device IoT sensors to collect real-time data on equipment status, personnel behavior and environment;

[0108] Intelligent Central Layer: Includes a data platform, digital twin engine, and dynamic resource scheduler within a federated learning framework, enabling multi-source data fusion and cross-system decision-making;

[0109] Business application layer: integrates intelligent inspection system, intelligent video analysis system, equipment integrity management system and emergency response system, and the systems achieve logical interlocking through knowledge graph;

[0110] Presentation layer: Provides AR command terminals and large visualization screens to display equipment health status, risk warnings, and task execution progress in real time;

[0111] The platform enables adaptive switching between personnel and drone inspection modes through a dynamic resource scheduler and optimizes equipment maintenance strategies based on quantum evolution algorithms.

[0112] The intelligent inspection system includes a flexible mode selection module, which is used to calculate the drone mode coefficient and the personnel mode coefficient.

[0113] Calculate the drone mode coefficients:

[0114] Calculate the personnel pattern coefficient:

[0115] When λ>λ′, initiate drone inspection; when λ<λ′, initiate manual inspection.

[0116] In the formula, A is the park area (㎡), ρ is the equipment risk density (number of high-risk equipment / thousand㎡), R is the meteorological risk coefficient (0-1), Nd is the number of available drones, and v d H is the cruising speed (m / s). d Health index for drones;

[0117] S p The skill level of personnel is S (range: 0.5-1.5), calculated by weighting historical work order completion rate and training assessment score.

[0118] F p The employee fatigue index (range: 0-1);

[0119] N p The number of available inspection personnel is obtained in real time from the scheduling system;

[0120] By calculating the mode coefficients of drone and human inspection in real time, the system intelligently selects the optimal inspection method, improves task execution efficiency and reduces human decision-making errors. Combining the risk density of the park, weather conditions and equipment status, drone inspections are prioritized in high-risk or severe environments to reduce personnel safety hazards. Task allocation is dynamically adjusted based on the drone's health index, personnel skill level and fatigue level to ensure the reliability and continuity of inspection operations.

[0121] For example, if a chemical industrial park experiences a sudden thunderstorm, the meteorological risk coefficient R increases, and simultaneously, the risk density ρ of a high-risk equipment area increases significantly. The system calculates this in real time through the elastic mode selection module.

[0122] The drone model coefficient increases due to rising weather risks and the drone's health status.

[0123] The personnel mode coefficient decreased due to increased personnel fatigue and reduced inspection efficiency under severe weather conditions.

[0124] Once the system determines that the drone mode coefficient is greater than the human mode coefficient, it automatically switches to drone inspection mode.

[0125] Drones can quickly cover high-risk areas and transmit equipment status back in real time.

[0126] Personnel were evacuated from the dangerous environment, and the focus shifted to background monitoring and emergency preparedness.

[0127] This avoids exposing personnel to risks during thunderstorms, ensuring safety, enabling efficient inspections in high-risk equipment areas, reducing potential accidents, and optimizing resource allocation and task response speed.

[0128] Personnel fatigue index F p The calculation method is as follows:

[0129]

[0130] In the formula, F p A higher value indicates a higher level of fatigue;

[0131] HRV t For real-time heart rate variability, samples are taken every 10 seconds via a smart bracelet;

[0132] HRV base Baseline heart rate variability is calculated based on the average HRV of the individual at rest over the past 7 days.

[0133] T: This is the monitoring time window, with a default value of 10 minutes.

[0134] α is the continuous work penalty coefficient (default value: 0.2), which is determined by fitting experimental data;

[0135] Continuous working time is the cumulative working time of an employee from the start of the task to the present.

[0136] The process of obtaining the drone health index is as follows:

[0137] Hd=σ(0.4·x1+0.3·x2+0.2·x3+0.1·x4);

[0138] In the formula, σ is the Sigmoid function, which is used to normalize the linear combination to the interval [0,1].

[0139] x1 is the battery cycle degradation coefficient (range: 0-1), and the calculation formula is 1 - the number of times it has been charged / the maximum number of times it has been designed to charge.

[0140] x2 is the motor vibration anomaly index (range: 0-1), which is obtained by calculating the mean square error of deviation from the normal spectrum through FFT analysis of the vibration spectrum.

[0141] x3 is the camera focus error rate (range: 0-1), calculated based on an image sharpness evaluation function (such as the Brenner gradient);

[0142] x4 represents the packet loss rate (range: 0-1), which is a real-time statistic showing the proportion of UDP packets lost in the last minute.

[0143] By comprehensively quantifying the health level of drones by integrating multiple parameters such as battery, motor, camera and communication status, we can avoid misjudgment by a single indicator, improve the accuracy of assessment, monitor the performance degradation of key components in real time (such as battery cycling and motor vibration), trigger maintenance instructions in advance, reduce the risk of sudden failures in inspection tasks, and dynamically adjust the drone task priority based on the health index to avoid drones with insufficient performance from performing high-risk or high-load tasks, thus ensuring the success rate of operations.

[0144] For example, during routine inspections in industrial parks, the system calculates the health index of drones in real time.

[0145] Battery cycle degradation coefficient: The current number of charges is close to the design limit, x1 = 0.2 (low battery health);

[0146] Motor vibration anomaly index: Abnormal spectrum fluctuations were detected, x2 = 0.4 (requires maintenance);

[0147] Camera focus error rate: Focus blur rate x3 = 0.3 (affects image acquisition);

[0148] Packet loss rate: x4 = 0.05 (normal).

[0149] The health index calculated using the Sigmoid function is 0.28 (threshold set to 0.5). The system determines that the drone's health status is substandard and immediately performs the following actions:

[0150] Its current high-risk area inspection missions have been suspended and replaced by backup drones that meet the health index standards.

[0151] Automatically generate maintenance work orders, prompting you to replace the battery and inspect the motor;

[0152] Adjust subsequent task allocation to allow the machine to perform only low-risk, short-duration tasks until the problem is fixed.

[0153] This helps prevent drones from going out of control or crashing due to motor failure, and also prevents insufficient battery life from interrupting inspections due to battery aging.

[0154] The drone task allocation adopts an auction algorithm, and the specific process is as follows:

[0155] Task package division: The park is divided into a preset number of grid task packages according to the risk level of the equipment, with the grid density of high-risk areas being 3 times that of low-risk areas;

[0156] Drones calculate based on health index and distance:

[0157] P d This represents the bid price for the drone; a higher value indicates a higher priority.

[0158] R local The risk coefficient (range: 0.1-1.0) for the area where the task package is located is dynamically calculated based on equipment failure probability and historical accident data;

[0159] D current The Euclidean distance from the current location of the drone to the center of the mission package is calculated using GPS or UWB positioning data.

[0160] Dynamic allocation: The central scheduler allocates tasks in descending order of Pd to meet the total time constraint.

[0161] In the formula, N d t represents the number of available drones. d This refers to the single-player battery life.

[0162] The estimated time for the m-th task package is calculated using the following formula: Among them W d Width for drone inspection;

[0163] Conflict resolution: Conduct a second round of bidding for overlapping task packages and adjust the bid price accordingly. Reassignment;

[0164] In the formula, A overlap Let A be the area of ​​the overlapping region of the task packages. task The area of ​​the original task package;

[0165] By using grid division and bidding mechanisms, high-risk areas are prioritized for coverage to ensure timely investigation of safety hazards. Task allocation is optimized by combining the health status and real-time location of drones to improve resource utilization and task success rate. The secondary bidding mechanism dynamically adjusts overlapping tasks to avoid repeated inspections and shorten the overall operation time. The total time constraint formula ensures that critical tasks are completed on time and enhances emergency response efficiency.

[0166] If a high-risk alarm is triggered due to equipment malfunction in a petrochemical park, an emergency inspection of the area surrounding the leak point is required.

[0167] Task package division:

[0168] The system divides the area around the leak point into a high-risk grid (with a density three times that of the low-risk area) and generates 10 task packages.

[0169] High-risk task packages are concentrated within a 500-meter radius of the leak, while low-risk task packages are distributed in the outer perimeter.

[0170] Bid price calculation and allocation:

[0171] Drone A, health index 0.9, 200 meters from the leak point, bid price calculated as P. d =0.9×R local / D current =0.9 × 0.8 / 200 = 0.0036;

[0172] Drone B, health index 0.7, 50 meters from the leak point, bid price P d =0.7 × 0.8 / 50 = 0.0112 (higher priority)

[0173] Result: Drone B was prioritized for allocation to the high-risk core mission package due to its proximity and satisfactory health status.

[0174] Conflict resolution and secondary bidding:

[0175] Drones C and D simultaneously compete for adjacent overlapping mission packages. The system calculates and adjusts the bid price as follows:

[0176]

[0177] The result was that drone C won with a higher adjusted price, and drone D turned to other uncovered areas to avoid duplicate operations.

[0178] Total time constraint guarantee: the total system computation time T total =∑t m ≤N d ×t, number of drones × single drone flight time), to ensure the mission is completed within 3 hours.

[0179] The intelligent video analysis system includes:

[0180] Vehicle parking detection: Based on the YOLOv8-OBB model, output the rotated bounding box of the vehicle (xc,yc,w,h,θ), with the following judgment criteria:

[0181] Line pressure detection: Hausdorff distance d H (B vehicle ,L line >15cmdH;

[0182] Steering compliance: Vehicle front steering angle deviation Δθ=∣θ vehicle -θ slot |>15°;

[0183] Smoke and fire recognition: fusing visible light and infrared data, verifying the radiant power P = ∈σ(T) using the Stefan-Boltzmann law. 4 -T0 4 If T>300℃ and P<100W / m2, it is determined to be a false alarm;

[0184] xc and yc are the coordinates of the center point of the bounding box, representing the vehicle's position in the image.

[0185] Reference frame: Based on the image coordinate system (the top left corner is the origin (0,0), the positive x-axis is to the right, and the positive y-axis is downward).

[0186] w and h are the width and height of the bounding box, representing the actual dimensions of the vehicle.

[0187] Direction: Width along the major axis after rotation, height along the minor axis (related to the rotation angle θ).

[0188] θ is the rotation angle of the bounding box, representing the degree of tilt of the vehicle's direction relative to the reference axis.

[0189] Reference axis: Usually based on the horizontal axis (x-axis), with clockwise rotation as the positive direction (different models may have different definitions, which need to be confirmed in the context).

[0190] Range: Commonly [-90°, 90°) or [0°, 180°), for example, θ = 30° means that the vehicle's long axis is at a 30-degree angle to the horizontal axis.

[0191] d H The Hausdorff distance measures the vehicle's bounding box B. vehicle With parking space line L line The maximum and minimum distances between them;

[0192] ∈ represents the material emissivity (range: 0-1), which is set according to the material of the smoke or flame (e.g., flame ∈ = 0.95);

[0193] σ is the Stefan-Boltzmann constant (5.67 × 10⁻⁸ W / m²K⁴);

[0194] T represents the temperature of the detection area, measured by an infrared thermal imager;

[0195] T0 is the ambient temperature;

[0196] The loss function of the YOLOv8-OBB model is: L total =0.5L cls +0.3 reg +0.2L angle ;

[0197] L angele =1-cos(θ) pred -θ gt ), avoiding angle jumps through periodic losses;

[0198] By using a rotated bounding box model and multi-dimensional verification mechanisms, such as Hausdorff distance and directional angle deviation, violations such as vehicles crossing lines and unauthorized parking can be accurately identified, reducing the cost of manual verification.

[0199] By combining visible light and infrared data and using physical laws such as the Stefan-Boltzmann law, the authenticity of fire situations can be verified, effectively distinguishing high-temperature equipment from real fire sources and reducing the false alarm rate.

[0200] For example, a suspected fire was detected near a high-temperature reaction tank at a chemical plant;

[0201] Visible light detection: The camera captures a smoke-like area and initially marks it as a potential fire zone.

[0202] Infrared data fusion:

[0203] The infrared thermal imager measured the area temperature as T = 320℃, and the ambient temperature as T0 = 25℃.

[0204] Calculate the radiated power P = ∈σ(T) 4 -T 4 0)=0.95×5.67×10-8×(5934-2984)≈110W / m 2 .

[0205] Judgment criteria: T>300℃ and P<100W / m 2 It was a false alarm.

[0206] Actual P = 110 W / m 2If the temperature exceeds the threshold, the system will determine it as high-temperature equipment rather than a real fire.

[0207] This prevents accidental activation of the fire sprinkler system, thus preventing production interruptions. The system records data from high-temperature areas and prompts maintenance personnel to check the equipment's heat dissipation status.

[0208] By using trajectory prediction and speed analysis, abnormal behaviors such as driving in the wrong direction and illegal parking can be captured in real time, thereby improving the initiative and timeliness of safety supervision.

[0209] The intelligent video analysis system also includes a vehicle full-dimensional anomaly detection module, used for vehicle parking compliance detection, vehicle speed detection, and vehicle abnormal behavior detection.

[0210] The process for inspecting whether a vehicle is properly parked is as follows:

[0211] Parking space ownership determination: Extracting the vertices of the parking space polygon using the YOLOv8-OBB model. Determining the vehicle center (x) using the ray-mapping method c ,y c ) Whether it is within the parking space, calculate:

[0212]

[0213] If the result is odd, it is determined to be within the parking space; otherwise, an unauthorized parking alarm is triggered.

[0214] In the formula (x i ,y i ) represents the coordinates of the ii-th vertex of the parking space polygon, which are extracted using a semantic segmentation model.

[0215] (x i+1 ,y i+1 Let be the coordinates of the (i+1)th vertex of the parking space polygon, with the vertices arranged in clockwise or counterclockwise order;

[0216] II is an indicator function that takes the value 1 when the condition is true and 0 otherwise.

[0217] Denominator condition: Determine whether the horizontal position of the vehicle's center point is located to the left of the intersection of the edge from vertex i to i+1 and the horizontal ray (passing through the y-coordinate of the vehicle's center point);

[0218] Multi-view verification: A bird's-eye view is generated using fisheye cameras deployed diagonally, and the IoU (Intersection over Union) between the vehicle bounding box and the parking space lines is calculated.

[0219] If IoU < F and F = 0.8, then it is determined to be on the line;

[0220] B vehicle The area of ​​the vehicle's bounding box is calculated from the coordinates of the rotated bounding box.

[0221] B slot The area of ​​the parking space bounding box is calculated using polygons generated from the semantic segmentation results.

[0222] ∩ represents the area of ​​the intersection region, and ∪ represents the area of ​​the union region;

[0223] By combining ray casting with a semantic segmentation model, the system accurately determines whether a vehicle is parked in an authorized parking space, avoiding misjudgments caused by visual obstruction or complex environments. It uses a fisheye camera to generate a bird's-eye view and verifies line crossing behavior through intersection-over-union (IoU) to reduce blind spots in single-view detection, improve the reliability of results, automatically detect the parking position and direction of vehicles, trigger alarms in real time and guide corrections, reduce the cost of manual inspections, and improve parking management efficiency.

[0224] For example, in a parking lot within a business park, a private car attempted to cross the lines and occupy two parking spaces.

[0225] System processing flow:

[0226] Parking space ownership determination:

[0227] The semantic segmentation model extracts the coordinates of the polygon vertices of the target parking space and generates the parking space boundary (as shown in the blue polygon in the figure).

[0228] Calculate the parity of the intersection point between the vehicle's center point (red dot) and the parking space boundary using the ray casting method:

[0229] If the horizontal ray extends from the center of the vehicle to the right and intersects the boundary of the parking space an even number of times, it is determined that the vehicle is not fully parked in the parking space.

[0230] Multi-view line pressure verification:

[0231] Fisheye cameras deployed diagonally generate a bird's-eye view of the parking lot, and the IoU between the vehicle bounding boxes (green boxes) and the parking space lines (yellow lines) is calculated:

[0232] IoU = 0.4 (far below the threshold of 0.8), confirming that the vehicle is seriously crossing the line.

[0233] The system links with the electronic display screen and broadcast to display a message: "License plate number XXXX illegally parked across the lane."

[0234] At the same time, an alarm is pushed to the administrator terminal, and it is suggested to "dispatch patrol personnel to handle the situation on site".

[0235] Furthermore, the specific process for vehicle speed detection is as follows:

[0236] Pixel-to-Actual Speed ​​Conversion: Calculate the actual speed based on camera calibration parameters.

[0237]

[0238] In the formula: v pixel The pixel displacement velocity of the vehicle in the image is calculated by the displacement of the vehicle's center point between adjacent frames;

[0239] H real The vertical height of the camera's field of view in the actual scene is measured using a calibration plate;

[0240] H image The pixel height of the corresponding region in the image is obtained from the camera resolution.

[0241] f represents the video frame rate, such as 30 frames per second;

[0242] θ is the angle between the vehicle's direction of motion and the camera's optical axis, calculated by the dot product of the vehicle's trajectory direction vector and the optical axis direction vector;

[0243] By fusing camera calibration parameters with motion direction angles, a high-precision conversion from pixel speed to actual physical speed is achieved, avoiding speed measurement errors caused by viewing angle tilt or distance differences. It dynamically adapts to different camera installation heights, angles, and resolutions, ensuring the reliability of the speed measurement algorithm in complex scenarios (such as slopes and curves). Based on frame rate and displacement analysis, it quickly captures vehicle speeding or abnormally low-speed behavior, improving proactive traffic safety control capabilities.

[0244] Inside the industrial park, a truck was speeding on a curve due to a blind spot, threatening pedestrian safety.

[0245] First, perform pixel displacement calculation. The displacement of the vehicle's center point between adjacent frames is 50 pixels. The video frame rate is f = 30Hz. Calculate the pixel velocity v. pixel = 50 pixels / frame × 30 frames / second = 1500 pixels / second.

[0246] Parameter calibration and angle correction: Perform camera calibration parameters, including the actual vertical height H. real =5m, the corresponding pixel height H in the image image = 800 pixels;

[0247] The angle between the vehicle's direction of motion and the camera's optical axis is θ = 45°, so sinθ≈0.707 is calculated.

[0248] Actual speed conversion, substituting into the formula: v real =1500×5 / 800×30×0.707≈19.9m / s (≈71.6km / h), the speed limit in the enterprise park is 30km / h, the system judges it to be speeding.

[0249] At this time, the audible and visual alarm device is triggered to remind the driver to slow down, and the speeding record is simultaneously pushed to the management platform to generate a violation report.

[0250] The specific process for detecting abnormal vehicle behavior is as follows:

[0251] Retrograde detection: Calculate the predicted position for the next K frames based on the Transformer trajectory prediction model. With respect to actual position P k Deviation:

[0252]

[0253] If D traj >2σ hist It was determined to be going against traffic;

[0254] Illegal parking detection: Statistical analysis of the time (t) a vehicle remains stationary in a no-parking zone. park If t park >t allow And the velocity variance σv 2 When the value is less than a preset value, a trailer dispatch command is triggered, such as 0.1m. 2 / s 2 ;

[0255] In the formula: The predicted future vehicle position for the kth frame is output by the Transformer trajectory prediction model.

[0256] P k The actual observed vehicle position in the future k-th frame is obtained through a target tracking algorithm;

[0257] K is the prediction step size (e.g., K=5), which by default covers the trajectory within the next second;

[0258] σ hist The standard deviation of historical trajectory deviation is calculated based on statistics from 1000 past normal trajectory data.

[0259] t park The stationary time of a vehicle in a no-stopping zone is determined by the variance of the position in consecutive frames.

[0260] t allow The maximum allowed stay time is set according to the area type (e.g., fire lane limit = 0);

[0261] σ v 2 The variance of vehicle speed is calculated using the following formula:

[0262] Where v i The sampling value is the speed per second. The average velocity is given by σv2. If σv2 < 0.1, the system is considered stationary.

[0263] By using trajectory prediction models to predict abnormal vehicle driving directions in advance, the risk of traffic accidents caused by driving in the wrong direction can be reduced. By combining stationary time and speed variance analysis, temporary parking and illegal parking can be accurately distinguished, avoiding misjudgment and optimizing law enforcement efficiency. Real-time detection and automatic alarm mechanisms shorten the processing delay and improve the initiative and timeliness of traffic management.

[0264] Reverse traffic detection within the enterprise park

[0265] A section of road in the industrial park was temporarily converted into a one-way street due to construction, and a vehicle mistakenly entered the oncoming lane.

[0266] First, trajectory prediction and deviation calculation: The Transformer model predicts the vehicle position for the next 5 frames (about 1 second) and generates the predicted trajectory (blue dashed line).

[0267] The actual trajectory (solid red line) shows the vehicle continuously moving in the opposite direction, with a deviation of D. traj =3.5m (threshold 2σ) hist =2.0m).

[0268] If the deviation exceeds a threshold, the system determines it as reverse behavior.

[0269] Real-time intervention:

[0270] The electronic road signs within the park flash a "driving in the wrong direction warning" to remind drivers to turn around immediately.

[0271] The management platform within the park was simultaneously notified, and patrol vehicles were dispatched to intercept the vehicle.

[0272] For example, vehicles may be parked in fire lanes within a business park, waiting for people to retrieve documents, and staying there for longer than permitted.

[0273] First, perform a stationary time and velocity variance analysis to detect the vehicle's dwell time t in the no-stopping zone. park = 6 minutes (allowed time t) allow =0 minutes);

[0274] velocity variance σ 2 v = 0.05 m² / s (determined to be stationary).

[0275] Illegal parking determination:

[0276] Satisfying t park >t allow And σ 2 v =<0.1, triggering an alarm and sending a text message to the vehicle owner via license plate recognition: "Please leave the fire lane immediately, otherwise the vehicle will be towed." If the vehicle is not moved within 5 minutes, a towing dispatch order will be automatically generated.

[0277] The equipment integrity management system includes: a quantum evolution prediction model: decomposing equipment vibration signals into frequency band energies E1-E5, and inputting them into a quantum neural network. Where θ is optimized using a variable quantum algorithm;

[0278] Maintenance queue optimization, solving mixed integer programming modulus:

[0279]

[0280] Constraint ∑x i ≤M,x i ∈{0.1};

[0281] In the formula, This is a predicted value for the remaining useful life of the equipment.

[0282] E1-E5 represent the frequency band energy percentages after wavelet packet decomposition;

[0283] θ is an adjustable parameter of the quantum neural network (QNN), which is optimized by the variable quantum algorithm (VQE).

[0284] To reduce the cost of a single repair for the backup II;

[0285] Failure probability i is the failure probability of device i (range: 0-1), output by the prediction model;

[0286] Criticality i is the criticality level of equipment ii (range: 1-5), set according to the degree of impact on production;

[0287] x i For a binary decision variable, x i =1 indicates immediate repair, x i =0 indicates that repairs will not be carried out at this time;

[0288] The digital twin engine includes: real-time CFD simulation: solving the Navier-Stokes equations for the leakage scenario, with mesh generation satisfying the CFL condition.

[0289] Virtual-Real Linkage Verification: Injecting Virtual Fault Event Set V test Required response accuracy A ccuracyvirtual ≥95%;

[0290] In the formula, Δx is the spatial grid step size, and Δt is the time step size.

[0291] u, v, w are the fluid velocity components, which are obtained by solving the Navier-Stokes equations.

[0292] Emergency response systems are implemented using knowledge graphs:

[0293] Entity association reasoning: When event E is detected, retrieve the associated entity set R(E) = {e|path(E,e)≤2} and generate a disposal plan;

[0294] Multi-strategy evaluation: Calculating the utility value of each strategy Select U i The scheme is executed with a value greater than 0.8;

[0295] In the formula, U i The utility value of emergency response plan i (range: 0-1);

[0296] ω j For historical case H j The weight (range: 0-1) is dynamically adjusted based on the timeliness and effectiveness of the case.

[0297] Sim(S i H j ): The similarity between scheme i and historical case j (range: 0-1), calculated by cosine similarity.

[0298] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0299] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0300] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. An intelligent management and control platform for enterprise safety production, characterized in that, include: Multimodal perception layer: Deploy industrial cameras, voiceprint sensors, UWB positioning base stations and device IoT sensors to collect real-time data on equipment status, personnel behavior and environment; Intelligent Central Layer: Includes a data platform, digital twin engine, and dynamic resource scheduler within a federated learning framework, enabling multi-source data fusion and cross-system decision-making; Business application layer: integrates intelligent inspection system, intelligent video analysis system, equipment integrity management system and emergency response system, and the systems achieve logical interlocking through knowledge graph; Display layer: Provides AR command terminal and visualization screen to display equipment health status, risk warnings and task execution progress in real time.

2. The intelligent management and control platform for enterprise safety production according to claim 1, characterized in that: The intelligent inspection system includes a flexible mode selection module, which is used to calculate the drone mode coefficient and the personnel mode coefficient. Calculate the drone mode coefficients: Calculate the personnel pattern coefficient: When λ>λ′, initiate drone inspection; when λ<λ′, initiate manual inspection. In the formula, A is the park area, ρ is the equipment risk density, R is the meteorological risk coefficient, Nd is the number of available drones, and v d For cruising speed, H d Health index for drones; S p The skill level of personnel is calculated based on a weighted average of historical work order completion rate and training assessment score. F p The employee fatigue index; N p The number of available inspection personnel is obtained in real time from the scheduling system.

3. The intelligent management and control platform for enterprise safety production according to claim 2, characterized in that... The personnel fatigue index F p The calculation method is as follows: In the formula, F p A higher value indicates a higher level of fatigue; HRV t For real-time heart rate variability; HRV base Baseline heart rate variability; T: The monitoring time window; α is the penalty coefficient for continuous work; Continuous working time is the cumulative working time of an employee from the start of the task to the present.

4. The intelligent management and control platform for enterprise safety production according to claim 2, characterized in that: The process of obtaining the drone health index is as follows: Hd=σ(0.4·x1+0.3·x2+0.2·x3+0.1·x4); In the formula, σ is the Sigmoid function, which is used to normalize the linear combination to the interval [0,1]. x1 is the battery cycle degradation coefficient, which is calculated as 1 - the number of times already charged / the maximum number of designed charges. x2 is the motor vibration anomaly index, which is obtained by calculating the mean square error of the deviation from the normal spectrum through FFT analysis of the vibration spectrum. x3 is the camera focus error rate, calculated based on the image sharpness evaluation function; x4 represents the packet loss rate, which is a real-time statistic showing the proportion of UDP packets lost in the most recent minute.

5. The intelligent management and control platform for enterprise safety production according to claim 2, characterized in that: The drone task allocation adopts an auction algorithm, and the specific process is as follows: Task package division: The park is divided into a preset number of grid task packages according to the risk level of the equipment, with the grid density of high-risk areas being 3 times that of low-risk areas; Drones calculate based on health index and distance: P d This represents the bid price for the drone; a higher value indicates a higher priority. R local Risk coefficient of the area where the task package is located; D current The distance from the drone's current position to the center of the mission package is expressed in Euclidean form. Dynamic allocation: The central scheduler allocates tasks in descending order of Pd to meet the total time constraint. In the formula, N d t represents the number of available drones. d This refers to the single-player battery life. The estimated time for the m-th task package is calculated using the following formula: Among them W d Width for drone inspection; Conflict resolution: Conduct a second round of bidding for overlapping task packages and adjust the bid price accordingly. Reassignment; In the formula, A overlap Let A be the area of ​​the overlapping region of the task packages. task This represents the area of ​​the original task package.

6. The intelligent management and control platform for enterprise safety production according to claim 1, characterized in that: The intelligent video analysis system includes: Vehicle parking detection: Based on the YOLOv8-OBB model, output the rotated bounding box of the vehicle (xc,yc,w,h,θ), with the following judgment criteria: Line pressure detection: Hausdorff distance d H (B vehicle ,L line >15cmdH; Steering compliance: Vehicle front steering angle deviation Δθ=∣θ vehicle -θ slot |>15°; Smoke and fire recognition: fusing visible light and infrared data, verifying the radiant power P = ∈σ(T) using the Stefan-Boltzmann law. 4 -T0 4 If T>300℃ and P<100W / m2, it is determined to be a false alarm; d H The Hausdorff distance measures the vehicle's bounding box B. vehicle With parking space line L line The maximum and minimum distances between them; ∈ represents the material emissivity, which is set according to the material of the smoke or flame. σ is the Stefan-Boltzmann constant; T represents the temperature of the detection area, measured by an infrared thermal imager; T0 is the ambient temperature; The loss function of the YOLOv8-OBB model is: L total =0.5L cls +0.3 reg +0.2L angle ; L angele =1-cos(θ) pred -θ gt ), and avoid angle jumps through periodic loss.

7. The intelligent management and control platform for enterprise safety production according to claim 6, characterized in that: The intelligent video analysis system also includes a vehicle full-dimensional anomaly detection module, used for vehicle parking compliance detection, vehicle speed detection, and vehicle abnormal behavior detection.

8. The intelligent management and control platform for enterprise safety production according to claim 7, characterized in that: The process for inspecting whether a vehicle is properly parked is as follows: Parking space ownership determination: Extracting the vertices of the parking space polygon using a preset model. Determining the vehicle center (x) using the ray-mapping method c ,y c ) Whether it is within the parking space, calculate: If the result is odd, it is determined to be within the parking space; otherwise, an unauthorized parking alarm is triggered. In the formula (x i ,y i ) represents the coordinates of the i-th vertex of the parking space polygon, which are extracted using a semantic segmentation model; (x i+1 ,y i+1 Let be the coordinates of the (i+1)th vertex of the parking space polygon, with the vertices arranged in clockwise or counterclockwise order; II is an indicator function that takes the value 1 when the condition is true and 0 otherwise; Denominator condition: Determine whether the horizontal position of the vehicle's center point is located to the left of the intersection of the edge from vertex i to i+1 and the horizontal ray; Multi-view verification: Generate a bird's-eye view using fisheye cameras deployed diagonally, and calculate the IoU between the vehicle bounding box and the parking space lines. If IoU < F and F = 0.8, then it is determined to be on the line; B vehicle The area of ​​the vehicle's bounding box; B slot This represents the area of ​​the parking space's boundary frame. ∩ represents the area of ​​the intersection region, and ∪ represents the area of ​​the union region.

9. The intelligent management and control platform for enterprise safety production according to claim 7, characterized in that: The specific process for vehicle speed detection is as follows: Pixel-to-Actual Speed ​​Conversion: Calculate the actual speed based on camera calibration parameters. In the formula: v pixel The pixel displacement velocity of the vehicle in the image; H real This represents the vertical height of the camera's field of view in the actual scene. H image The pixel height of the corresponding region in the image; f is the video frame rate; θ is the angle between the vehicle's direction of motion and the camera's optical axis.

10. The intelligent management and control platform for enterprise safety production according to claim 7, characterized in that: The specific process for detecting abnormal vehicle behavior is as follows: Retrograde detection: Calculate the predicted position for the next K frames based on the Transformer trajectory prediction model. With respect to actual position P k Deviation: If D traj >2σ hist It was determined to be going against traffic; Illegal parking detection: Statistical analysis of the time (t) a vehicle remains stationary in a no-parking zone. park If t park >t allow And the velocity variance σv 2 When the value is less than the preset value, a trailer dispatch command is triggered; In the formula: The predicted future vehicle position in the k-th frame; P k The actual observed vehicle position in the future k-th frame is obtained through a target tracking algorithm; K is the prediction step size; σ hist The standard deviation of the historical trajectory deviation; t park The time a vehicle remains stationary in a no-parking zone; t allow The maximum permitted stay time; σ v 2 Variance of vehicle speed; Where v i The sampling value is the speed per second. The average velocity is σv2. If σv2 < 0.1, it is considered stationary.

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