Garden safety, environmental protection and emergency integrated management and control platform
By constructing a multi-dimensional data collection network and a dynamic weight adaptive algorithm, combined with deep learning and digital twin technology, the problems of incomplete data collection, poor information sharing, inaccurate risk assessment, and low emergency response in the park's safety and environmental protection emergency management have been solved. This has enabled efficient and accurate risk prediction and emergency response, and improved the park's safety and environmental protection management level.
Patent Information
- Application Number
- CN202511355212.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-22
- Publication Date
- 2026-02-10
AI Technical Summary
The existing emergency management of safety and environmental protection in the park suffers from problems such as incomplete data collection, poor information sharing, insufficient accuracy and timeliness of risk assessment, low emergency response efficiency, and a lack of closed-loop management and adaptive learning mechanisms.
A multi-dimensional data collection network for the park is constructed, and data fusion is carried out using dynamic weight adaptive algorithm and deep learning algorithm. A safety and environmental protection risk assessment model is established, real-time data interaction is realized based on digital twin technology, a hierarchical response mechanism is established, and information sharing and cross-departmental collaboration are realized through blockchain technology to form a closed-loop management system.
It has achieved comprehensive data collection and risk assessment, improved the accuracy and timeliness of risk identification, ensured the pertinence and efficiency of emergency response, enhanced cross-departmental collaboration efficiency, and continuously optimized management level through adaptive learning.
Smart Images
Figure CN121504696A_ABST
Abstract
Description
Technical Field
[0001] This invention provides an integrated management and control platform for park safety and environmental protection emergency response, belonging to the field of park safety and environmental protection emergency management technology. Background Technology
[0002] With the continuous development of the industrial park, the number of enterprises within the park is increasing, and production activities are becoming increasingly complex, posing significant challenges to safety, environmental protection, and emergency management. Currently, the park faces numerous problems in safety, environmental protection, and emergency management. Data collection is often limited to a single area, failing to achieve comprehensive acquisition of multi-dimensional information such as safety, environmental protection, equipment operation, and personnel movement, making it difficult for management departments to grasp the overall situation of the park. Simultaneously, severe information silos exist between various systems, hindering effective data sharing and integration, resulting in inaccurate risk assessments, delayed emergency responses, and difficulty in handling complex safety and environmental incidents.
[0003] Based on the above, the inventors discovered that:
[0004] In terms of risk assessment, traditional methods often employ fixed weights, failing to dynamically adjust based on real-time conditions and historical data. This results in insufficient accuracy and timeliness in identifying high-risk periods and areas. Emergency response mechanisms are inadequate, lacking clear standards for tiered responses and the ability to automatically generate emergency response plans. Emergency resource allocation is untimely, and cross-departmental collaboration is inefficient. Furthermore, the lack of effective closed-loop management and adaptive learning mechanisms after emergency response hinders continuous optimization of risk assessment models and emergency plans, making it difficult to adapt to the ever-changing safety and environmental protection landscape of the park.
[0005] Therefore, in view of this, we studied and improved the existing structure and proposed an integrated management and control platform for park safety, environmental protection and emergency response to solve the above-mentioned problems. Summary of the Invention
[0006] This invention provides an integrated management and control platform for park safety and environmental protection emergency response, which aims to solve the problems in existing park safety and environmental protection emergency management, such as incomplete data collection, poor information sharing, insufficient accuracy and timeliness of risk assessment, low emergency response efficiency, and lack of closed-loop management and adaptive learning mechanisms.
[0007] To address the aforementioned problems, the present invention proposes the following technical solution: an integrated management and control platform for park safety, environmental protection, and emergency response, comprising the following steps:
[0008] S1. Construct a multi-dimensional data collection network for the park to obtain real-time data on safety, environmental protection, equipment operation, and personnel movement.
[0009] S2. A dynamic weighted adaptive algorithm is used to fuse and process the collected data to establish a safety and environmental risk assessment model.
[0010] S3. Construct a virtual mapping body of the park based on digital twin technology to realize real-time data interaction between the physical park and the virtual park;
[0011] S4. Utilize deep learning algorithms to analyze fused data, predict potential safety and environmental risks, and generate early warning information;
[0012] S5. Establish a tiered response mechanism to automatically generate emergency response plans based on the warning level and push them to the relevant responsible entities;
[0013] S6. Track the entire emergency response process, form a closed-loop management system, and optimize the risk assessment model and emergency plan based on the response results.
[0014] Furthermore, the multi-dimensional data acquisition network mentioned in step S1 includes: IoT sensing devices, video surveillance systems, environmental monitoring instruments, production equipment sensors, and personnel positioning systems deployed in various areas of the park, to achieve comprehensive collection of information such as gas concentration, water quality parameters, equipment status, personnel location and behavior, and open flame smoke within the park.
[0015] Furthermore, the dynamic weight adaptive algorithm in step S2 is as follows: based on historical data and real-time feedback, the weight values of different types of data in risk assessment are automatically adjusted. For high-risk periods and areas, the weight coefficient of the corresponding monitoring data is increased to enhance the accuracy and timeliness of risk identification.
[0016] Furthermore, the implementation of digital twin technology in step S3 includes: establishing a three-dimensional model of the park, mapping the real-time collected physical parameters to the virtual model, and realizing the visualization of the park's status; at the same time, simulating the safety and environmental protection evolution process under different scenarios through the virtual model, providing support for risk prediction and emergency drills.
[0017] Furthermore, the deep learning algorithm in step S4 adopts an improved fusion model of convolutional neural network and long short-term memory network, wherein: the convolutional neural network is used to extract spatial features and identify the risk distribution in different regions; the long short-term memory network is used to analyze time series data and predict risk development trends, and the outputs of the two are fused through an attention mechanism to improve prediction accuracy.
[0018] Furthermore, the hierarchical response mechanism in step S5 includes:
[0019] Level 1 Response: The system automatically issues an alert to notify relevant inspection personnel to conduct on-site verification;
[0020] Level 2 Response: Activate enhanced regional monitoring mode, automatically allocate nearby emergency resources to stand by, and notify the regional leader;
[0021] Level 3 Response: Triggers the emergency command system, automatically generates a detailed response plan, mobilizes emergency teams, and simultaneously issues a notification to the park management and external emergency agencies.
[0022] Furthermore, it also includes cross-departmental collaborative response steps: establishing an information sharing mechanism based on blockchain technology to achieve real-time information sharing and access control among park management departments, enterprises, environmental protection agencies, and emergency rescue teams, ensuring the security and consistency of information transmission during emergency response.
[0023] Furthermore, the closed-loop management in step S6 specifically includes:
[0024] Record key moments and decision-making information during the emergency response process;
[0025] Compare the actual results of the response with the expected results of the contingency plan, and calculate the deviation value;
[0026] Based on the deviation value, a reinforcement learning algorithm is used to optimize the parameters of the risk assessment model and emergency response plan;
[0027] To create a case library and provide a reference for handling similar incidents.
[0028] Furthermore, it also includes adaptive learning steps: the system regularly analyzes historical data and handling results, automatically identifies newly emerging risk types and handling patterns, updates risk assessment models and emergency response plan libraries, and achieves continuous evolution of the system.
[0029] Furthermore, it also includes emergency command procedures based on augmented reality (AR): real-time data and response plans are overlaid on the AR devices of on-site personnel to provide visual guidance; at the same time, the command center can remotely view the on-site situation through AR technology to achieve precise command and resource allocation.
[0030] Due to the adoption of the above technical solution, the beneficial effects of the integrated management and control platform for park safety, environmental protection and emergency response of the present invention are as follows:
[0031] 1. Comprehensive data collection: By building a multi-dimensional data collection network, it is possible to obtain data on various aspects such as safety, environmental protection, equipment operation and personnel flow in the park in real time, so as to achieve a comprehensive understanding of the park's status and solve the problem of one-sided data collection in traditional methods.
[0032] 2. Precise risk assessment: The dynamic weight adaptive algorithm is used to fuse and process the data. The established safety and environmental protection risk assessment model can dynamically adjust the weights based on historical data and real-time feedback, which improves the accuracy and timeliness of identifying high-risk periods and areas.
[0033] 3. Intuitive visualization and simulation: The virtual mapping of the park, built based on digital twin technology, realizes real-time data interaction between the physical park and the virtual park. It can not only visualize the park status, but also simulate the safety and environmental protection evolution process under different scenarios, providing strong support for risk prediction and emergency drills.
[0034] 4. Efficient risk prediction and early warning: By utilizing an improved fusion model of convolutional neural networks and long short-term memory networks, spatial features can be accurately extracted and time series data can be analyzed, improving the prediction accuracy of potential safety and environmental risks and generating early warning information in a timely manner.
[0035] 5. Scientific Tiered Response: The established tiered response mechanism automatically generates emergency response plans based on the warning level and pushes them to relevant responsible parties, improving the pertinence and efficiency of emergency response and ensuring that risks of different degrees can be dealt with in a timely and effective manner.
[0036] 6. Comprehensive closed-loop management: By tracking the entire emergency response process, comparing actual and expected results, and using reinforcement learning algorithms to optimize models and solutions, a case library is formed, realizing a closed loop in emergency management and continuously improving management levels.
[0037] 7. Efficient cross-departmental collaboration: The information sharing mechanism based on blockchain technology enables real-time information sharing and access control among relevant departments, ensuring the security and consistency of information transmission during emergency response and improving the efficiency of cross-departmental collaboration.
[0038] 8. Continuous self-evolution: The adaptive learning process enables the system to regularly analyze historical data and handling results, automatically update the risk assessment model and emergency plan library, and achieve continuous evolution of the system to adapt to the ever-changing park environment.
[0039] 9. Precise emergency command: AR-based emergency command procedures provide visual guidance for on-site personnel, while enabling the command center to remotely and precisely command and allocate resources, thus improving the accuracy and efficiency of emergency command. Attached Figure Description
[0040] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:
[0041] Figure 1 This is a flowchart of an integrated management and control platform for park safety, environmental protection and emergency response according to the present invention.
[0042] Figure 2 This is a data acquisition network diagram for an integrated management and control platform for park safety, environmental protection and emergency response according to the present invention.
[0043] Figure 3 This is a dynamic weighting algorithm diagram for an integrated management and control platform for park safety, environmental protection and emergency response according to the present invention.
[0044] Figure 4 This is a digital twin interactive diagram of an integrated management and control platform for park safety, environmental protection and emergency response.
[0045] Figure 5 This is a deep learning prediction graph for an integrated management and control platform for park safety, environmental protection and emergency response in this invention.
[0046] Figure 6 This invention presents a hierarchical response closed-loop diagram for an integrated management and control platform for park safety, environmental protection, and emergency response. Detailed Implementation
[0047] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0048] Example 1: Emergency Management and Control of Safety and Environmental Protection in Chemical Industrial Parks
[0049] (I) Overview of the Park
[0050] The chemical industrial park houses numerous chemical production enterprises, which mainly produce various chemical raw materials and products. There are safety risks such as leaks of toxic and harmful gases, fires, and explosions. At the same time, the production process generates environmental problems such as wastewater and exhaust gas, posing a potential threat to the surrounding environment and personnel safety.
[0051] (II) Platform Configuration
[0052] Multi-dimensional data acquisition network: IoT sensing devices are deployed in various production areas, tank areas, and wastewater treatment plants within the park, including toxic gas sensors (such as hydrogen sulfide and chlorine sensors), flame detectors, temperature sensors, and pressure sensors; environmental monitoring instruments are installed at the park's boundaries and surrounding sensitive areas to monitor atmospheric pollutant concentrations and water quality parameters; a video surveillance system is installed to cover major production areas, transportation routes, and entrances / exits; sensors are installed on production equipment to monitor equipment operating status in real time; and location wristbands are provided to staff within the park to enable real-time monitoring of personnel location and behavior.
[0053] Dynamic weighted adaptive algorithm settings: Based on the risk characteristics of the chemical industrial park, data such as toxic gas concentration, equipment operating pressure, and open flame smoke are given higher initial weights. During peak production periods, severe weather, and other high-risk periods, as well as in high-risk areas such as storage tank areas and reactor surroundings, the weight coefficients of the corresponding monitoring data are automatically increased.
[0054] Digital twin model construction: Establish a three-dimensional model covering all production equipment, storage tanks, pipelines, roads, green belts and other elements in the park, and map the real-time collected physical parameters such as toxic gas concentration, equipment temperature and personnel location into the virtual model.
[0055] Deep learning algorithm parameter adjustment: Optimize the kernel size and number of convolutional neural networks to better extract spatial features of different production areas, taking into account the spatial layout and risk distribution characteristics of chemical industrial parks; adjust the number of hidden layer nodes and training cycle of long short-term memory networks based on the time series characteristics of historical accident data.
[0056] The tiered response mechanism is further refined: Level 1 response is for situations such as slight exceedance of toxic gas concentration or minor fluctuations in equipment parameters; Level 2 response is applicable to situations such as a continuous rise in toxic gas concentration but not exceeding the critical value or equipment malfunction but not shut down; Level 3 response is for emergencies such as severe exceedance of toxic gas concentration or fire and explosion.
[0057] Cross-departmental collaboration mechanism: Integrate park management departments, chemical enterprises, local environmental protection departments, fire and rescue teams, etc. into the blockchain information sharing network, and clarify the information access permissions of each department.
[0058] AR emergency command equipment: The park's emergency command center is equipped with an AR command platform, and on-site inspection personnel and emergency rescue personnel are equipped with AR glasses.
[0059] (III) Implementation Steps
[0060] Data Acquisition (S1): Through the above-mentioned multi-dimensional data acquisition network, data such as toxic gas concentration, equipment operating parameters, personnel location, and open flame smoke are acquired in real time.
[0061] Data fusion and risk assessment model establishment (S2): The collected data is fused using a dynamic weight adaptive algorithm, and the safety and environmental risk assessment model is continuously optimized by combining historical accident data and real-time feedback.
[0062] Digital Twin Interaction (S3): Maps real-time collected physical parameters to a digital twin model, and displays the safety and environmental status of various areas in the park in real time through the virtual model, simulating scenarios such as toxic gas leakage and diffusion, and fire spread.
[0063] Risk Prediction and Early Warning (S4): Analyze the fused data using an improved fusion model of convolutional neural networks and long short-term memory networks to predict potential risks such as toxic gas leaks, fires, and explosions, and generate early warning information based on the risk level.
[0064] Tiered Response (S5): When the system issues a Level 1 warning, it automatically pushes a warning message to the mobile APP of relevant inspection personnel, notifying them to go to the site for verification; when a Level 2 warning is issued, the area monitoring enhancement mode is activated, the video surveillance footage of the area is automatically switched to the large screen display, nearby emergency supplies (such as gas masks and fire extinguishers) are automatically allocated to stand by, and the area manager is notified to handle the situation; when a Level 3 warning is issued, the emergency command system is triggered, a detailed emergency response plan is automatically generated (such as personnel evacuation routes, rescue equipment allocation plans, etc.), emergency teams are mobilized to rush to the scene, and a notification is issued to the park management and external emergency agencies.
[0065] Emergency Response and Closed-Loop Management (S6): During the emergency response process, key milestones and decision-making information are recorded in real time. After the response is completed, the actual response effect is compared with the expected effect of the contingency plan, the deviation value is calculated, and a reinforcement learning algorithm is used to optimize the parameters of the risk assessment model and emergency plan based on the deviation value. The event is then added to the case library.
[0066] Cross-departmental collaborative response: Through the blockchain information sharing mechanism, early warning information and on-site response data are pushed to relevant departments in real time to ensure coordination among departments.
[0067] Adaptive learning: The system regularly analyzes historical data and response results to identify new risk types (such as the risk of leakage of new chemical raw materials) and response models, and updates the risk assessment model and emergency response plan library.
[0068] AR Emergency Command: On-site personnel can view the overlay display of real-time data and response plans through AR glasses, while the emergency command center can remotely view the on-site situation through the AR command platform to conduct precise command and resource allocation.
[0069] (iv) Application Effect
[0070] After its application in the chemical industrial park, the platform enabled early warnings of risks such as toxic gas leaks, fires, and explosions, improving the accuracy of warnings by over 80%. The average response time for Level 1 responses was reduced to less than 30 minutes, the efficiency of emergency resource allocation for Level 2 responses increased by 50%, and the emergency response command for Level 3 responses became more precise and efficient. The incidence of safety and environmental accidents within the park decreased by over 70%.
[0071] Example 2: Emergency Management and Control of Safety and Environmental Protection in Industrial Parks
[0072] (I) Overview of the Park
[0073] The industrial park houses various types of enterprises, including machinery manufacturing, electronics processing, and food processing. It faces environmental issues such as safety accidents caused by equipment failures and excessive emissions of production wastewater and exhaust gas. The park also experiences frequent personnel turnover, making management quite challenging.
[0074] (II) Platform Configuration
[0075] Multi-dimensional data acquisition network: Deploy equipment sensors in the production workshops of various enterprises to monitor parameters such as operating temperature and vibration frequency of machine tools, production lines and other equipment; install environmental monitoring instruments at the sewage treatment plant and exhaust gas outlets in the park to monitor water quality and pollutant concentration in exhaust gas; monitor enterprise production areas, park roads and public areas through video surveillance systems; set up vehicle recognition systems at park entrances and main roads, combined with personnel positioning cards, to collect data on personnel and vehicle movement.
[0076] Dynamic weight adaptive algorithm settings: Data such as equipment operating vibration frequency and wastewater and exhaust gas pollutant concentrations are given higher initial weights. During peak production seasons and equipment maintenance periods, the weight of equipment operating data is increased; during environmentally sensitive periods such as rainy seasons, the weight of monitoring data from wastewater treatment plants and exhaust gas emission outlets is increased.
[0077] Digital twin model construction: Construct a 3D model that includes elements such as factory buildings, production equipment, sewage treatment facilities, roads, and parking lots of various enterprises, and map data such as equipment operating status, pollutant emission concentration, and personnel and vehicle flow.
[0078] Deep learning algorithm parameter adjustment: Adjust the structure of the convolutional neural network according to the production layout and equipment distribution of different enterprises; optimize the parameters of the long short-term memory network based on the time series characteristics of equipment failure and environmental protection exceeding standards data.
[0079] The tiered response mechanism is refined as follows: Level 1 response is for minor equipment malfunctions and minor exceedances of pollutant emission concentrations; Level 2 response is applicable to situations where equipment malfunctions may affect production and pollutant emission concentrations continue to rise; and Level 3 response is for situations where serious equipment malfunctions lead to shutdowns and pollutant emission concentrations seriously exceed standards.
[0080] Cross-departmental collaboration mechanism: Integrating the park management committee, various enterprises, environmental protection departments, equipment maintenance units, etc. into the blockchain information sharing system.
[0081] AR emergency command equipment allocation: Provide the park management center and enterprise equipment maintenance personnel with corresponding AR equipment.
[0082] (III) Implementation Steps
[0083] Data Acquisition (S1): Real-time acquisition of equipment operating parameters, pollutant emission data, and personnel and vehicle movement information through a multi-dimensional data acquisition network.
[0084] Data fusion and risk assessment model establishment (S2): Data is fused using a dynamic weight adaptive algorithm to establish a safety and environmental risk assessment model suitable for industrial parks.
[0085] Digital Twin Interaction (S3): Real-time display of each enterprise's production status, environmental indicators, etc. in the digital twin model, simulating the impact of equipment failure spread and excessive pollutant emissions on the surrounding environment.
[0086] Risk Prediction and Early Warning (S4): Utilize deep learning algorithms to analyze and fuse data, predict equipment failure risks and environmental compliance risks, and generate corresponding early warning information.
[0087] Tiered Response (S5): In the event of a Level 1 warning, the company's equipment maintenance personnel and environmental protection specialists will be notified to conduct on-site verification; in the event of a Level 2 warning, regional monitoring of the company will be strengthened, maintenance personnel and environmental treatment equipment will be deployed to stand by, and the company's responsible person will be notified; in the event of a Level 3 warning, the emergency command system will be activated, an emergency response plan will be generated, professional maintenance teams and environmental emergency treatment equipment will be mobilized, and the park management committee and relevant external departments will be notified.
[0088] Emergency Response and Closed-Loop Management (S6): Record the emergency response process, compare the effects, optimize models and solutions, and form a case library.
[0089] Cross-departmental collaborative handling: Blockchain enables information sharing among departments to collaboratively address equipment malfunctions and environmental issues.
[0090] Adaptive learning: The system regularly analyzes data and updates risk assessment models and contingency plans to adapt to production changes in different enterprises.
[0091] AR Emergency Command: Maintenance personnel obtain equipment maintenance guidance through AR glasses, and the command center remotely guides maintenance and environmental treatment work through the AR platform.
[0092] (iv) Application Effect
[0093] After the platform was implemented, the accuracy of equipment failure early warning in the industrial park increased by 70%, and equipment repair time was shortened by 40%; the problem of excessive emissions of production wastewater and exhaust gas was dealt with in a timely manner, and the number of environmental complaints decreased by 60%; personnel and vehicle management became more orderly, and the overall operational efficiency of the park increased by 30%.
[0094] Example 3: Emergency Management and Control of Safety and Environmental Protection in Logistics Parks
[0095] (I) Overview of the Park
[0096] The logistics park contains a large number of warehouses and transport vehicles, mainly engaged in cargo storage and transportation. There are problems such as fire risk caused by cargo stacking, excessive exhaust emissions from transport vehicles, and safety management in densely populated areas.
[0097] (II) Platform Configuration
[0098] Multi-dimensional data collection network: Smoke sensors, temperature sensors, and humidity sensors are installed in the warehouses; video surveillance systems and vehicle exhaust monitoring equipment are installed on roads and parking lots within the park; positioning devices are provided for warehouse managers and freight drivers; weight sensors and video surveillance are installed in the cargo loading and unloading areas to monitor the stacking of goods.
[0099] Dynamic weight adaptive algorithm settings: Data such as smoke concentration, temperature, and vehicle exhaust emission concentration are given higher initial weights. During peak cargo loading and unloading periods, holidays, and in areas such as flammable goods storage areas and densely populated vehicle areas, the weight coefficients of the corresponding monitoring data are increased.
[0100] Digital twin model construction: Establish a three-dimensional model that includes elements such as warehouses, roads, parking lots, and loading and unloading platforms, and map physical parameters such as smoke concentration, temperature, vehicle location, and cargo stacking status.
[0101] Deep learning algorithm parameter adjustment: Optimize the convolutional neural network according to the spatial layout of the logistics park (such as the arrangement of warehouses and the direction of roads) to better identify the risk distribution in different areas; adjust the parameters of the long short-term memory network according to the time pattern of goods entering and leaving the warehouse and vehicle flow.
[0102] The tiered response mechanism is refined as follows: Level 1 response is for situations such as a slight increase in warehouse temperature or a slight exceedance of vehicle exhaust emissions standards; Level 2 response is applicable to situations such as smoke sensor alarms but no fire confirmation, or traffic congestion that may cause an accident; Level 3 response is for emergencies such as a warehouse fire, a large number of vehicles stranded and exhaust emissions severely exceeding standards.
[0103] Cross-departmental collaboration mechanism: Integrating park management departments, logistics companies, traffic management departments, fire departments, and other relevant entities into the blockchain information sharing network.
[0104] AR emergency command equipment deployment: AR equipment will be provided to the park's dispatch center and fire and rescue personnel.
[0105] (III) Implementation Steps
[0106] Data Acquisition (S1): Real-time acquisition of data such as environmental parameters, vehicle information, personnel location, and cargo stacking status of the warehouse through a multi-dimensional data acquisition network.
[0107] Data fusion and risk assessment model establishment (S2): Data fusion is carried out using a dynamic weight adaptive algorithm to establish a safety and environmental risk assessment model for logistics parks.
[0108] Digital Twin Interaction (S3): Maps real-time data to a digital twin model to display the safety and environmental status of the park and simulate scenarios such as fire spread and traffic congestion.
[0109] Risk Prediction and Early Warning (S4): Utilize deep learning algorithms to predict risks such as fire and excessive vehicle emissions, and generate early warning information.
[0110] Tiered Response (S5): In the event of a Level 1 warning, warehouse management personnel and environmental inspection personnel will be notified to conduct on-site verification; in the event of a Level 2 warning, regional monitoring will be strengthened, fire-fighting equipment and traffic control personnel will be deployed to stand by, and the regional leader will be notified; in the event of a Level 3 warning, the emergency command system will be activated, an emergency response plan will be generated (such as personnel evacuation, fire rescue route planning, etc.), the fire brigade and traffic management department will be mobilized, and the park management and external agencies will be notified.
[0111] Emergency Response and Closed-Loop Management (S6): Record the response process, optimize models and solutions, and create a case library.
[0112] Cross-departmental collaborative response: Blockchain enables information sharing among departments to coordinate fire fighting, traffic control, and other tasks.
[0113] Adaptive learning: The system regularly analyzes data, updates risk assessment models and contingency plans, and adapts to changes in logistics operations.
[0114] AR Emergency Command: On-site personnel obtain handling guidance through AR devices, and the command center remotely directs emergency work.
[0115] (iv) Application Effect
[0116] After the platform was implemented, the accuracy rate of fire accident early warning in the logistics park reached over 90%, and the fire accident rate decreased by 80%; the problem of excessive vehicle exhaust emissions was addressed in a timely manner, and the air quality in the park improved significantly; vehicle dispatching efficiency increased by 50%, and cargo turnaround time was shortened by 30%.
[0117] Example 4: Emergency Management and Control of Safety and Environmental Protection in Science and Technology Parks
[0118] (I) Overview of the Park
[0119] The science and technology park houses numerous high-tech enterprises, R&D centers, and laboratories, posing safety and environmental risks such as laboratory chemical leaks, precision equipment malfunctions, and substandard treatment of research wastewater. Therefore, the park has high requirements for safe operation and environmental quality.
[0120] (II) Platform Configuration
[0121] Multi-dimensional data acquisition network: Chemical concentration sensors, temperature and humidity sensors, and ventilation system sensors are installed in each laboratory; equipment operation status sensors are installed in the precision equipment room; water quality monitoring instruments are installed in the park's wastewater treatment plant; laboratories, R&D centers, and public areas are monitored through a video surveillance system; and researchers are equipped with positioning badges to monitor their location and access to restricted areas.
[0122] Dynamic weighted adaptive algorithm settings: Data such as chemical concentrations, precision equipment operating parameters, and research wastewater quality indicators are given higher initial weights. During peak experimental periods, when precision equipment is running at full capacity, and in areas such as laboratories and wastewater treatment plants, the weight coefficients of the corresponding monitoring data are increased.
[0123] Digital twin model construction: Establish a three-dimensional model that includes elements such as laboratory layout, equipment location, pipeline routes, sewage treatment facilities, and green belts, and map physical parameters such as chemical concentration, equipment operating status, and water quality parameters.
[0124] Deep learning algorithm parameter adjustment: Optimize the structure of the convolutional neural network according to the spatial distribution and equipment layout of the laboratory; adjust the parameters of the long short-term memory network according to the time pattern of experimental operation and equipment operation.
[0125] The tiered response mechanism is refined as follows: Level 1 response is for situations such as slight exceedance of chemical concentration or minor abnormality of equipment parameters; Level 2 response is applicable to situations such as continuous rise in chemical concentration or potential equipment malfunctions; and Level 3 response is for emergencies such as large-scale chemical leaks, serious malfunctions of precision equipment, or paralysis of wastewater treatment systems.
[0126] Cross-departmental collaboration mechanism: Integrating park management departments, various technology companies, R&D centers, environmental protection departments, professional rescue organizations, etc. into the blockchain information sharing system.
[0127] AR emergency command equipment deployment: AR equipment will be provided to emergency personnel in the park's emergency management center and laboratories.
[0128] (III) Implementation Steps
[0129] Data Acquisition (S1): Real-time acquisition of data such as laboratory chemical concentration, equipment operating parameters, wastewater quality, and personnel location through a multi-dimensional data acquisition network.
[0130] Data fusion and risk assessment model establishment (S2): Data fusion is carried out using a dynamic weight adaptive algorithm to establish a safety and environmental protection risk assessment model for the science and technology park.
[0131] Digital Twin Interaction (S3): Maps real-time data to a digital twin model to display the safety and environmental status of the park and simulate scenarios such as chemical spills and equipment failures.
[0132] Risk Prediction and Early Warning (S4): Utilize deep learning algorithms to predict risks such as chemical leaks and equipment failures, and generate early warning information.
[0133] Tiered Response (S5): In the event of a Level 1 warning, laboratory management personnel and equipment maintenance personnel will be notified to conduct on-site verification; in the event of a Level 2 warning, regional monitoring will be strengthened, emergency response materials will be allocated and put on standby, and the company's responsible person will be notified; in the event of a Level 3 warning, the emergency command system will be activated, an emergency response plan will be generated, professional rescue teams will be mobilized, and the park management and external departments will be notified.
[0134] Emergency Response and Closed-Loop Management (S6): Record the response process, optimize models and solutions, and create a case library.
[0135] Cross-departmental collaborative handling: Blockchain enables information sharing among departments to collaboratively address safety and environmental issues.
[0136] Adaptive learning: The system regularly analyzes data, updates risk assessment models and contingency plans, and adapts to changes in scientific and technological research and development activities.
[0137] AR Emergency Command: On-site personnel obtain handling guidance through AR devices, and the command center remotely directs emergency work.
[0138] (iv) Application Effect
[0139] After the platform was implemented, the accuracy rate of early warning of chemical leaks in the science park's laboratories increased by 85%, and the time for handling precision equipment failures was shortened by 50%; the compliance rate of scientific research wastewater treatment increased to 98%, and the level of safety and environmental protection management in the park was significantly improved.
[0140] The present invention and its embodiments have been described above. This description is not restrictive. In short, if a person skilled in the art is inspired by this description and designs a similar structure and embodiment without departing from the spirit of the present invention, such design should fall within the protection scope of the present invention.
Claims
1. An integrated management and control platform for park safety, environmental protection, and emergency response, comprising the following steps: S1. Construct a multi-dimensional data collection network for the park to obtain real-time data on safety, environmental protection, equipment operation, and personnel movement. S2. A dynamic weighted adaptive algorithm is used to fuse the collected data and establish a safety and environmental risk assessment model. S3. Construct a virtual mapping body of the park based on digital twin technology to realize real-time data interaction between the physical park and the virtual park; S4. Utilize deep learning algorithms to analyze fused data, predict potential safety and environmental risks, and generate early warning information; S5. Establish a tiered response mechanism to automatically generate emergency response plans based on the warning level and push them to the relevant responsible entities; S6. Track the entire emergency response process, form a closed-loop management system, and optimize the risk assessment model and emergency plan based on the response results.
2. The integrated management and control platform for park safety, environmental protection and emergency response as described in claim 1, characterized in that: The multi-dimensional data acquisition network mentioned in step S1 includes: IoT sensing devices, video surveillance systems, environmental monitoring instruments, production equipment sensors, and personnel positioning systems deployed in various areas of the park, to achieve comprehensive collection of information such as gas concentration, water quality parameters, equipment status, personnel location and behavior, and open flame smoke within the park.
3. The integrated management and control platform for park safety, environmental protection and emergency response as described in claim 1, characterized in that: The dynamic weight adaptive algorithm in step S2 is as follows: based on historical data and real-time feedback, the weight values of different types of data in risk assessment are automatically adjusted. For high-risk periods and areas, the weight coefficient of the corresponding monitoring data is increased to enhance the accuracy and timeliness of risk identification.
4. The integrated management and control platform for park safety, environmental protection and emergency response as described in claim 1, characterized in that: The implementation of digital twin technology in step S3 includes: establishing a three-dimensional model of the park, mapping the real-time collected physical parameters to the virtual model, and realizing the visualization of the park's status; at the same time, simulating the safety and environmental protection evolution process under different scenarios through the virtual model to provide support for risk prediction and emergency drills.
5. The integrated management and control platform for park safety, environmental protection and emergency response according to claim 1, characterized in that: The deep learning algorithm in step S4 adopts an improved fusion model of convolutional neural network and long short-term memory network, wherein: the convolutional neural network is used to extract spatial features and identify the risk distribution in different regions; the long short-term memory network is used to analyze time series data and predict risk development trends. The outputs of the two are fused through an attention mechanism to improve prediction accuracy.
6. The integrated management and control platform for park safety, environmental protection and emergency response according to claim 1, characterized in that: The graded response mechanism in step S5 includes: Level 1 Response: The system automatically issues an alert to notify relevant inspection personnel to conduct on-site verification; Level 2 Response: Activate enhanced regional monitoring mode, automatically allocate nearby emergency resources to stand by, and notify the regional leader; Level 3 Response: Triggers the emergency command system, automatically generates a detailed response plan, mobilizes emergency teams, and simultaneously issues a notification to the park management and external emergency agencies.
7. The integrated management and control platform for park safety, environmental protection and emergency response according to claim 1, characterized in that: It also includes cross-departmental collaborative response steps: establishing an information sharing mechanism based on blockchain technology to achieve real-time information sharing and access control among park management departments, enterprises, environmental protection agencies, and emergency rescue teams, ensuring the security and consistency of information transmission during emergency response.
8. The integrated management and control platform for park safety, environmental protection and emergency response as described in claim 1, characterized in that: The closed-loop management in step S6 specifically includes: Record key moments and decision-making information during the emergency response process; Compare the actual results of the response with the expected results of the contingency plan, and calculate the deviation value; Based on the deviation value, a reinforcement learning algorithm is used to optimize the parameters of the risk assessment model and emergency response plan; To create a case library and provide a reference for handling similar incidents.
9. The integrated management and control platform for park safety, environmental protection and emergency response according to claim 1, characterized in that: It also includes adaptive learning steps: the system regularly analyzes historical data and handling results, automatically identifies newly emerging risk types and handling modes, updates risk assessment models and emergency response plan libraries, and achieves continuous evolution of the system.
10. The integrated management and control platform for park safety, environmental protection and emergency response according to claim 1, characterized in that: It also includes emergency command procedures based on augmented reality (AR): real-time data and response plans are overlaid on the AR devices of on-site personnel to provide visual guidance; at the same time, the command center can remotely view the on-site situation through AR technology to achieve precise command and resource allocation.