A method and device for controlling a three-defense system in a confined space based on multiple sensors

Abstract

The present invention proposes a method and device for controlling a three-defense system in a confined space based on multiple sensors. The method belongs to the field of automatic control technology. The method includes: building a sensor network based on Zigbee wireless communication technology, combining CC2530 main chip and STM32CPU, and equipping each sensor node with supporting facilities; setting attribute information for multiple sensors respectively set in each area of ​​the confined space, and obtaining historical environmental data of each area, and determining the data update frequency of each area based on the historical data. Through data collection of multiple sensor nodes and Zigbee wireless communication technology, the system can monitor the environmental parameters in the confined space in real time, such as temperature, humidity, etc., to ensure real-time update and accuracy of data.

Description

Technical Field

[0001] The invention proposes a method and a device for controlling a three-defense system in a closed space based on multiple sensors, belonging to the technical field of automatic control. Background Art

[0002] Confined spaces, such as underground mines and chemical containers, have extremely high requirements for waterproof, dustproof, and explosion-proof (referred to as "three protections") due to their special working environment. Traditional control methods often rely on a single sensor, which cannot fully monitor environmental changes, and are slow to respond, posing safety hazards. Therefore, it is particularly important to develop a confined space three-protection system control method based on multiple sensors. Summary of the invention

[0003] The present invention provides a method and device for controlling a three-defense system in a confined space based on multiple sensors, which is used to solve the problems mentioned in the above background technology:

[0004] The present invention proposes a method for controlling a confined space three-defense system based on multiple sensors, the method comprising:

[0005] S1. Based on Zigbee wireless communication technology, combined with CC2530 main chip and STM32CPU, build a sensor network and equip each sensor node with supporting facilities;

[0006] S2, setting attribute information for multiple sensors respectively set in each area of ​​the confined space, obtaining historical environmental data of each area, and determining the data update frequency of each area based on the historical data;

[0007] S3. Determine the initial data collection frequency of the sensor based on the regional attributes; determine the current data collection frequency of each sensor in the region based on the data update frequency of each region, the number of sensors and the initial data collection frequency of the sensor;

[0008] S4. Each sensor collects data within a predetermined time period according to its current data collection frequency, and sends the collected environmental data to the system control device; based on the received environmental data, an environmental data set for each area is determined;

[0009] S5. Based on the environmental data set and the set parameter value of each area, determine the dynamic threshold of the environmental parameter. When the temperature value collected by the sensor in the area exceeds the dynamic threshold, trigger an alarm or take corresponding control measures;

[0010] S6. Determine whether there is an area where environmental parameters need to be adjusted based on the dynamic threshold of the environmental parameters and the environmental data set of each area; when it is determined that there is an area where environmental parameters need to be adjusted, adjust the corresponding environmental parameters based on the data collected by sensors in the area.

[0011] The present invention proposes a multi-sensor based confined space three-defense system control device, the device comprising:

[0012] Network building module: Based on Zigbee wireless communication technology, combined with CC2530 main chip and STM32CPU, build a sensor network and equip each sensor node with supporting facilities;

[0013] Data acquisition module: setting attribute information for multiple sensors respectively set in each area of ​​the confined space, acquiring historical environmental data of each area, and determining the data update frequency of each area based on the historical data;

[0014] Frequency determination module: Determines the initial data collection frequency of the sensor based on the regional attributes; determines the current data collection frequency of each sensor in the region based on the data update frequency of each region, the number of sensors and the initial data collection frequency of the sensor;

[0015] Data transmission module: Each sensor collects data within a predetermined time period according to its current data collection frequency, and sends the collected environmental data to the system control device; based on the received environmental data, the environmental data set of each area is determined;

[0016] Threshold determination module: Determines the dynamic threshold of environmental parameters based on the environmental data set and set parameter values ​​of each area. When the temperature value collected by the sensor in the area exceeds the dynamic threshold, an alarm is triggered or corresponding control measures are taken;

[0017] Parameter adjustment module: Determine whether there is an area that needs environmental parameter adjustment based on the dynamic threshold of environmental parameters and the environmental data set of each area; when it is determined that there is an area that needs environmental parameter adjustment, adjust the corresponding environmental parameters based on the data collected by sensors in the area.

[0018] The invention has the following beneficial effects: through data collection of multiple sensor nodes and Zigbee wireless communication technology, the system can monitor environmental parameters such as temperature and humidity in a confined space in real time, ensuring real-time updating and accuracy of data; based on machine learning algorithms and dynamic threshold models, the system can automatically adjust environmental parameters, realize intelligent control, reduce manual intervention, and improve the automation level of the system; by optimizing the number and position of sensor nodes, and adjusting the data collection frequency according to actual needs, the system can reduce energy consumption and realize efficient energy utilization while ensuring monitoring effect; the system adopts modular design, which is easy to expand or adjust according to different monitoring needs, and has good flexibility and scalability; through a closed-loop feedback mechanism, the system can dynamically adjust the data collection frequency and control strategy according to the comparative analysis of real-time monitoring data and historical trends, forming a self-optimization process; the system has an anomaly detection function, can timely discover and mark abnormal values, and can trigger an alarm signal when the environmental parameter exceeds the preset dynamic threshold, and send the alarm information to relevant personnel or upper network information services through the gateway to ensure timely response and processing; through a user feedback mechanism, collect and analyze user opinions and suggestions on the monitoring system, continuously iterate and optimize deployment strategies and network configurations, and improve user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a step diagram of the method of the present invention;

[0020] Figure 2 This is a module diagram of the device described in the present invention. DETAILED DESCRIPTION

[0021] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0022] One embodiment of the present invention, as Figure 1 As shown, a method for controlling a three-defense system in a confined space based on multiple sensors, the method comprising:

[0023] S1. Based on Zigbee wireless communication technology, combined with CC2530 main chip and STM32CPU, a sensor network is constructed, and each sensor node is equipped with supporting facilities; the supporting facilities include DI / DO, AI input and output interfaces, which are used to detect relevant signals such as water leakage, door locks, gas, valve positions, and drive and control lighting switches, water valves, explosion-proof gas valves, door locks and other equipment; and the gateway, as the control host of the system, communicates with the upper network information service through the WiFi network port and the GPRS network port to ensure reliable data transmission and flexible control of the system;

[0024] S2. Setting attribute information for a plurality of sensors respectively arranged in each area of ​​the confined space, wherein the attribute information includes a sensor identifier, an area identifier, a location identifier, and an initial data collection frequency, and obtaining historical environmental data of each area, wherein the historical environmental data includes an area identifier, a date, a maximum temperature, a minimum temperature, and an average temperature, and determining a data update frequency for each area based on the historical data;

[0025] S3. Determine the initial data collection frequency of the sensor based on regional attributes, including the area of ​​the region, the preferences of users in the region, and / or the purpose of the region; determine the current data collection frequency of each sensor in the region based on the data update frequency of each region, the number of sensors, and the initial data collection frequency of the sensor;

[0026] S4. Each sensor collects data within a predetermined time period according to its current data collection frequency, and sends the collected environmental data to the system control device; the system control device determines an environmental data set for each area based on the received environmental data, wherein the environmental data set includes a sensor identifier, collection time, temperature value, etc.;

[0027] S5. Determine a dynamic threshold of the environmental parameter based on the environmental data set and the set parameter value of each area, wherein the dynamic threshold includes a dynamic upper limit value and a dynamic lower limit value; when the temperature value collected by the sensor in the area exceeds the dynamic threshold value, trigger an alarm or take corresponding control measures;

[0028] S6. Determine whether there is an area where environmental parameters need to be adjusted based on the dynamic threshold of the environmental parameters and the environmental data set of each area; when it is determined that there is an area where environmental parameters need to be adjusted, adjust the corresponding environmental parameters based on the data collected by sensors in the area.

[0029] The working principle of the above technical solution is: using Zigbee wireless communication technology as the main communication means, combining CC2530 main chip (as the core of Zigbee communication module) and STM32CPU (responsible for data processing and advanced control logic); each sensor node is equipped with DI / DO (digital input / output) and AI (analog input) interfaces to detect signals such as water leakage, door lock status, gas concentration, valve position status, etc., and can drive and control lighting switches, water valves, explosion-proof gas valves, door locks and other equipment; the gateway, as the control host of the system, has WiFi and GPRS network ports for communicating with the upper-level network information service to ensure reliable data transmission and flexible control of the system; The sensor node sends data to the gateway through the Zigbee network; the gateway transmits the data to the upper-layer network information service through WiFi or GPRS to achieve remote monitoring and control; set an identifier (used to distinguish different sensors), a regional identifier (identifying the area where the sensor is located), a location identifier (identifying the specific location of the sensor in the area) and an initial data collection frequency for each sensor node; obtain historical environmental data for each area from the database or historical records, including the area identifier, date, maximum temperature, minimum temperature and average temperature; based on historical data, analyze the environmental change characteristics of each area and determine the data update frequency of each area (i.e., the basis for adjusting the frequency of sensor data collection); consider The area of ​​the area, the preferences of users in the area (such as temperature preference, humidity preference, etc.) and / or the purpose of the area (such as office, warehouse, laboratory, etc.) together determine the initial data collection frequency of the area; combined with the data update frequency of each area, the number of sensors and the initial data collection frequency of the sensors, the current data collection frequency of each sensor in the area is calculated through an algorithm; each sensor collects data within a predetermined time length according to its current data collection frequency; the collected data includes environmental parameters such as temperature, humidity, and gas concentration; the sensor sends the collected environmental data to the gateway through the Zigbee network; the gateway receives and organizes this data to form the environmental data of each area Data set; Based on the environmental data set and set parameter values ​​of each area (such as the temperature range and humidity range set by the user), the dynamic thresholds of environmental parameters (including dynamic upper limit and dynamic lower limit) are calculated through algorithms; the dynamic thresholds can be adaptively adjusted according to the actual situation to improve the response speed and accuracy of the system; when the temperature value or other environmental parameters collected by the sensor in the area exceeds the dynamic threshold, an alarm signal is triggered or corresponding control measures are taken (such as closing the water valve, opening the explosion-proof gas valve, etc.); the alarm signal can be notified to relevant personnel through SMS, email or APP push; based on the dynamic thresholds of environmental parameters and the environmental data set of each area, determine whether there is an area where environmental parameters need to be adjusted;When it is determined that there is an area that needs to adjust environmental parameters, the environmental parameters are adjusted by controlling the corresponding equipment (such as air conditioners, humidifiers, dehumidifiers, etc.) according to the data collected by sensors in the area (such as temperature, humidity, etc.); the adjustment process can be automatic (based on preset rules and algorithms) or manual (through user intervention or remote control). ;

[0030] The effect of the above technical solution is: by combining the CC2530 main chip and the STM32CPU, a stable and efficient sensor network is constructed. Zigbee technology is very suitable for environmental monitoring in confined spaces due to its low power consumption, low cost and self-organizing network characteristics; the gateway, as the control host of the system, communicates with the upper network information service through WiFi and GPRS network ports to ensure reliable data transmission. This dual network guarantee improves the stability and redundancy of data transmission and reduces the risk of data loss; based on the environmental data set and set parameter values ​​of each area, the system can automatically calculate and set the dynamic thresholds of environmental parameters (including dynamic upper limit and dynamic lower limit). This dynamic adjustment method can more accurately reflect the actual environmental conditions and improve the accuracy and flexibility of monitoring; when the temperature value or other environmental parameters collected by the sensor exceeds the dynamic threshold, the system will immediately trigger an alarm or take corresponding control measures. This real-time response mechanism can quickly discover and solve problems and avoid potential safety hazards; when determining the initial data collection frequency of the sensor, the system fully considers factors such as the area of ​​the area, user preferences and regional usage. This refined management method makes data collection more targeted and can more effectively meet the needs of different regions; based on historical environmental data, the system can automatically adjust the data update frequency of each region. This adaptive adjustment method can balance data accuracy and system resource consumption and improve the overall efficiency of the system; through the communication between the gateway and the upper network information service, users can remotely monitor and control the entire system. This convenient control method allows users to understand the environmental conditions in the confined space anytime and anywhere and take necessary measures; because the system adopts a modular design, each sensor node and supporting facilities can be independently maintained and replaced. This design reduces the complexity and cost of system maintenance and improves the maintainability of the system; this technical solution is not only suitable for the three-proof system (waterproof, explosion-proof, and anti-virus) of confined spaces, but can also be widely used in various scenarios that require environmental monitoring and control, such as warehouses, laboratories, data centers, etc.; the system has good scalability, and the number and types of sensor nodes and supporting facilities can be increased according to actual needs to meet more complex monitoring and control needs.

[0031] In one embodiment of the present invention, the S1 includes:

[0032] S11. Through Zigbee wireless communication technology, the CC2530 main chip is selected as the core of the Zigbee communication module, which is responsible for network communication and data transmission, and the STM32CPU is selected as the core of data processing and control, which is responsible for receiving, processing and analyzing sensor data;

[0033] S12. Design a DI / DO interface for each sensor node to detect switch signals such as door locks and valve positions, and control related equipment such as lighting switches and door locks. Design an AI interface to detect analog signals such as water leakage and gas, and control the opening of corresponding equipment such as water valves and explosion-proof gas valves.

[0034] S13. Design and deploy a gateway device as the control host of the system, integrating WiFi and GPRS modules for remote communication of data;

[0035] S14. Determine the deployment location and number of sensor nodes, optimize according to the space size and monitoring requirements, build the Zigbee network, set the network parameters, and test the network connectivity.

[0036] The working principle of the above technical solution is as follows: the CC2530 main chip is selected as the core of the Zigbee communication module. CC2530 is a highly integrated Zigbee / RF4CE / 6LoWPAN and ZigbeePROSoC solution, which combines a powerful RF transceiver, an 8051 microcontroller and an in-system programmable flash memory. CC2530 is responsible for network communication and data transmission, and realizes wireless communication between sensor nodes through the Zigbee protocol stack; STM32CPU is selected as the core of data processing and control. The STM32 series microcontroller has the characteristics of high performance, low power consumption, easy development and rich peripheral resources. STM32CPU is responsible for receiving data from sensor nodes, processing and analyzing it, and issuing control instructions based on the analysis results; a digital input / output (DI / DO) interface is designed for each sensor node. The DI interface is used to detect switch signals such as door locks and valve positions. These signals usually represent the switch state of the device in the form of high and low levels. The DO interface is used to control related equipment such as lighting switches and door locks, and controls the switch of the equipment by outputting high and low level signals; the analog input (AI) interface is designed to detect analog signals such as water leakage and gas. These signals usually represent the numerical value of environmental parameters in the form of voltage or current. The AI ​​interface converts analog signals into digital signals through an analog-to-digital converter (ADC) for processing by the STM32CPU. At the same time, the AI ​​interface can also be used to control the opening of corresponding equipment such as water valves and explosion-proof gas valves, and adjust the operating status of the equipment by outputting analog signals; design and deploy gateway devices as the control host of the system. The gateway device integrates WiFi and GPRS modules to realize remote communication of data. The WiFi module is used to communicate with sensor nodes in the local area network, while the GPRS module is used to upload data to the upper network information service through the mobile network; the gateway device is responsible for receiving data from the sensor nodes and forwarding the data to the STM32CPU for processing. At the same time, the gateway is also responsible for forwarding the control instructions issued by the STM32CPU to the corresponding sensor nodes to realize remote control; according to the size of the space and monitoring requirements, the deployment location and number of sensor nodes are determined. The deployment of sensor nodes should cover the entire monitoring area as much as possible, and ensure that each area has enough sensor nodes to provide accurate environmental data. Build a Zigbee network, set network parameters (such as network ID, channel number, etc.), and test network connectivity. Ensure that each sensor node can establish a stable communication connection with the gateway device and can transmit data in real time.

[0037] The effect of the above technical solution is: by selecting the CC2530 main chip as the core of the Zigbee communication module and combining the STM32CPU as the core of data processing and control, a highly integrated design is achieved. This design not only reduces the complexity of the system, but also improves the stability and reliability of the system. At the same time, both CC2530 and STM32 have the characteristics of low power consumption, which helps to extend the service life of the entire system; DI / DO interface and AI interface are designed for each sensor node, so that the system can flexibly detect various environmental parameters and equipment status. The DI / DO interface can process switch signals, such as door locks, valve positions, etc., while the AI ​​interface can process analog signals, such as water leakage, gas, etc. This flexible interface design enables the system to adapt to different monitoring needs and improves the versatility and scalability of the system; the gateway device is designed and deployed as the control host of the system, integrating WiFi and GPRS modules. This design enables the system to achieve remote communication and control, and users can remotely monitor the operating status of the entire system through mobile devices or computers and issue control instructions. This remote communication and control function not only improves the convenience of the system, but also enables the system to respond quickly to various abnormal situations, improving the safety and reliability of the system; according to the size of the space and monitoring requirements, the deployment location and number of sensor nodes are determined and optimized. This optimization enables the sensor nodes to be more evenly distributed in the monitoring area, improving the monitoring accuracy and coverage of the system. At the same time, by building a Zigbee network and setting network parameters, as well as testing network connectivity, the stability and reliability of the entire system are ensured; STM32CPU, as the core of data processing and control, has powerful data processing capabilities. It can receive, process and analyze sensor data in real time, and issue control instructions based on the analysis results. This efficient data processing and analysis capability enables the system to monitor environmental parameters and equipment status more accurately, improving the monitoring accuracy and response speed of the system.

[0038] In one embodiment of the present invention, the S14 includes:

[0039] Use 3D modeling software to accurately model the monitoring area, including room layout, obstacle locations, etc., to visualize the spatial structure. According to monitoring requirements (such as security level, importance of environmental parameters, etc.), assign monitoring priorities to each area and determine key monitoring points and auxiliary monitoring points;

[0040] Combine historical data and prediction models to evaluate changes in monitoring needs in different time periods. Based on spatial analysis and demand assessment results, select sensor types for each monitoring point (such as temperature and humidity, smoke, infrared human body sensing, etc.);

[0041] Genetic algorithms are used to optimize the number and location of sensor nodes, and a rotation mechanism for sensor nodes is designed;

[0042] According to the distribution and communication requirements of sensor nodes, the Zigbee network topology (star, tree or mesh) is selected, and the potential communication paths are simulated and optimized using graph theory and network optimization algorithms;

[0043] Considering network scalability and self-healing capabilities, design node joining and exiting mechanisms, as well as automatic replacement strategies for failed nodes;

[0044] According to the Zigbee communication standard, configure network parameters, including channel selection, PANID (personal area network identifier), network key, etc., and test network performance, including signal strength, packet loss rate, delay and other indicators, and adjust network configuration according to the test results;

[0045] Test network connectivity, including direct communication between nodes and indirect communication through relay nodes. Based on actual operation data, regularly evaluate network performance and identify and resolve potential bottlenecks, such as communication blind spots and uneven energy consumption.

[0046] Based on machine learning algorithms, the sensor sampling frequency and data transmission strategy are automatically adjusted according to environmental changes and usage patterns. Through the user feedback mechanism, user opinions and suggestions on the monitoring system are collected and analyzed, and deployment strategies and network configurations are continuously iterated and optimized.

[0047] The working principle of the above technical solution is as follows: First, use 3D modeling software (such as AutoCAD, SketchUp, etc.) to accurately model the monitoring area, including detailed information such as room layout, obstacle location, door and window location, etc. This helps to visualize the spatial structure and provide an intuitive reference for subsequent sensor deployment; according to monitoring requirements (such as security level, importance of environmental parameters, etc.), the monitoring area is divided into different priorities to determine key monitoring points and auxiliary monitoring points. Key monitoring points are usually areas where environmental parameters are sensitive to changes or where security risks are high, requiring more intensive sensor deployment and higher monitoring accuracy; combined with historical data and prediction models, the changes in monitoring requirements in different time periods are evaluated, and the appropriate sensor type (such as temperature and humidity sensors, smoke detectors, infrared human body sensors, etc.) is selected for each monitoring point; optimization algorithms such as genetic algorithms are used to optimize the number and location of sensor nodes to ensure coverage without dead ends and maximize communication efficiency. Genetic algorithms search for the optimal solution in the solution space by simulating natural selection and genetic mechanisms, thereby finding the best deployment plan for sensor nodes; in order to extend the service life of sensors and maintain monitoring accuracy, a rotation mechanism for sensor nodes is designed. By replacing or rotating sensor nodes regularly, performance degradation or failure caused by a single node working for a long time can be avoided; according to the distribution and communication requirements of sensor nodes, select the appropriate Zigbee network topology (such as star, tree or mesh). The star structure is suitable for scenarios with a small number of nodes and concentrated distribution; the tree structure is suitable for scenarios with a large number of nodes and a certain hierarchical structure; the mesh structure is suitable for scenarios with a large number of nodes, wide distribution and complex communication requirements; use graph theory and network optimization algorithms (such as Dijkstra algorithm, Floyd-Warshall algorithm, etc.) to simulate and optimize potential communication paths to ensure that data can be transmitted efficiently and stably; design node joining and exiting mechanisms to ensure that new nodes can join the network smoothly, while allowing faulty nodes or nodes that are no longer needed to exit the network. This helps to maintain the flexibility and scalability of the network; design an automatic replacement strategy for faulty nodes. When a node failure is detected, the replacement mechanism is automatically triggered to ensure that the network can continue to operate stably; according to the Zigbee communication standard, configure network parameters, including channel selection, PANID (personal area network identifier), network key, etc. The selection of these parameters should ensure the stability and security of the network; test the network performance, including indicators such as signal strength, packet loss rate, and latency. Adjust the network configuration based on the test results to ensure that the network performance meets the monitoring requirements; test the network connectivity, including direct communication between nodes and indirect communication through relay nodes. Ensure that each node can maintain a stable communication connection with other nodes; regularly evaluate network performance based on actual operation data, identify and solve potential bottlenecks, such as communication blind spots and uneven energy consumption.Improve the overall performance of the network by adjusting the location of sensor nodes and optimizing communication paths. Automatically adjust sensor sampling frequency and data transmission strategy based on machine learning algorithms according to environmental changes and usage patterns. This helps reduce energy consumption and improve data transmission efficiency. Collect and analyze user opinions and suggestions on the monitoring system through user feedback mechanisms. Continuously iterate and optimize deployment strategies and network configurations based on user feedback to ensure that the system can meet the actual needs of users.

[0048] The effect of the above technical solution is: the monitoring area is accurately modeled through three-dimensional modeling software, and the spatial structure is displayed in a visual way, so that the layout of the monitoring area and the location of obstacles are clear at a glance. This helps to assign reasonable monitoring priorities to each area according to monitoring needs, and determine key monitoring points and auxiliary monitoring points. This precise monitoring and efficient deployment method can ensure the coverage and monitoring accuracy of the monitoring system and improve monitoring efficiency. Combined with historical data and prediction models, the changes in monitoring needs in different time periods are evaluated, providing a scientific basis for dynamically adjusting sensor deployment strategies. Based on the results of spatial analysis and demand assessment, the appropriate sensor type is selected for each monitoring point to ensure the flexibility and adaptability of the monitoring system. At the same time, the number and location of sensor nodes are optimized using genetic algorithms to ensure coverage without dead ends and maximize communication efficiency, further improving the performance and efficiency of the monitoring system. According to the distribution and communication needs of sensor nodes, a suitable Zigbee network topology is selected, and graph theory and network optimization algorithms are used to simulate and optimize potential communication paths to ensure the stability and reliability of network communication. At the same time, the node joining and exiting mechanism and the automatic replacement strategy of faulty nodes are designed to improve the scalability and self-repairing ability of the network, so that the monitoring system can operate continuously and stably in complex environments. According to the Zigbee communication standard, the network parameters are configured, including channel selection, PANID, network key, etc., to ensure the security of network communication. The network performance is tested, including indicators such as signal strength, packet loss rate, and delay, and the network configuration is adjusted according to the test results to further optimize the network performance. This secure network configuration and performance optimization method can ensure the data security and transmission efficiency of the monitoring system. Based on the machine learning algorithm, the sensor sampling frequency and data transmission strategy are automatically adjusted according to environmental changes and usage patterns, so that the monitoring system can be intelligently adjusted according to actual conditions, improving the intelligence level and energy-saving effect of the system. At the same time, through the user feedback mechanism, the user's opinions and suggestions on the monitoring system are collected and analyzed, and the deployment strategy and network configuration are continuously iterated and optimized, so that the monitoring system can continuously adapt to new monitoring needs and environmental changes.

[0049] In one embodiment of the present invention, the S2 includes:

[0050] S21, setting a unique identifier for each sensor node, as well as a corresponding area identifier and location identifier, determining the type, range, accuracy and other related parameters of the sensor, and recording them in the system;

[0051] S22. Obtain environmental data of each area from the historical database, including date, maximum temperature, minimum temperature, and average temperature, etc., and clean and verify the historical data to remove abnormal values ​​and duplicate values;

[0052] S23. Analyze the change trend of environmental data in each area based on statistical analysis methods, such as temperature fluctuation range, change trend, etc.; determine the data update frequency of each area based on the analysis results, that is, how frequently each area needs to update environmental data;

[0053] S24. Store the determined data update frequency in the system control device.

[0054] The working principle of the above technical solution is: set a unique identifier (such as ID number) for each sensor node, as well as the corresponding area identifier (indicating the specific area where the sensor is located) and location identifier (indicating the specific location of the sensor in the area). At the same time, determine the type of sensor (such as temperature and humidity sensor, smoke sensor, etc.), range (the maximum and minimum values ​​that the sensor can measure), accuracy (the accuracy of sensor measurement) and other related parameters, and record this information in detail in the system. For example, set the ID number of the temperature and humidity sensor located in the lobby on the first floor to 001, the area identifier to "first floor lobby", the location identifier to "center of the lobby", the sensor type to "temperature and humidity", the range to "-20℃ to 50℃" and "0%RH to 100%RH", and the accuracy to "±0.5℃" and "±3%RH"; obtain the environmental data of each area from the historical database, which usually includes date, maximum temperature, minimum temperature and average temperature. Then, the historical data is cleaned and verified to remove abnormal values ​​(such as data that exceeds the sensor range or obviously does not conform to the actual situation) and duplicate values ​​(such as duplicate records at the same time point); Assume that the historical database contains environmental data of the lobby on the first floor on a certain day, including the date "2023-04-01", the highest temperature is "25℃", the lowest temperature is "18℃", and the average temperature is "22℃". After cleaning and verification, it is confirmed that these data are valid and accurate; Based on statistical analysis methods (such as time series analysis, regression analysis, etc.), the trend of environmental data changes in each area is analyzed. This includes the temperature fluctuation range (i.e., the difference between the highest and lowest temperatures), the change trend (such as rising, falling or stable), etc.; By analyzing the historical data of the lobby on the first floor, it is found that the temperature fluctuation range in this area is large in spring, with high temperature during the day and low temperature at night, and an overall upward trend. Based on this trend, it can be inferred that the area needs to update environmental data more frequently in spring to capture temperature changes; According to the analysis results, the data update frequency of each area is determined. This is usually based on a comprehensive consideration of factors such as the change trend of environmental data, monitoring needs, and the performance of sensor nodes. Then, the determined data update frequency is stored in the system control device so that the data collection of the sensor nodes can be controlled according to this frequency later; for example, for the first floor lobby, based on its temperature change trend and monitoring needs in spring, the data update frequency is determined to be once an hour. This means that the system control device will send data collection instructions to the temperature and humidity sensors in the first floor lobby every hour to obtain the latest environmental data.

[0055] The effects of the above technical solution are as follows: setting a unique identifier and a region and location identifier for each sensor node ensures the uniqueness and traceability of each sensor node in the system, which is convenient for subsequent management and maintenance. At the same time, the introduction of region and location identifiers enables the system to accurately locate the specific location of each sensor node, providing accurate spatial information for data collection and analysis; by cleaning and verifying historical data, removing abnormal values ​​and duplicate values, the quality and reliability of the data are effectively improved. This helps to reduce false alarms or omissions caused by data errors and improve the accuracy and stability of the monitoring system; the trend of environmental data changes in each area is analyzed by statistical analysis methods, and the data update frequency is determined according to the analysis results, realizing an intelligent update strategy. This strategy can dynamically adjust the data update frequency according to the changes in environmental data, which not only ensures the real-time nature of the data, but also avoids unnecessary waste of resources; the determined data update frequency is stored in the system control device, and the data collection of the sensor node is controlled according to this frequency, which helps to optimize the operating efficiency of the system. By reducing unnecessary data collection and transmission, the energy consumption and communication costs of the system are reduced, and the overall performance of the system is improved; the technical solution provides reliable data support for subsequent data analysis and decision-making through accurate data collection and intelligent update strategies. This helps managers better understand the environmental conditions of the monitored area, identify potential problems in a timely manner and take corresponding measures to ensure the safety and stable operation of the monitored area.

[0056] In one embodiment of the present invention, the S23 includes:

[0057] On the basis of historical data cleaning, further data smoothing is performed and key features are extracted, including but not limited to the daily variation of temperature and humidity, periodic variation patterns (such as seasonal variation), frequency of extreme events, etc., and environmental data is decomposed into trend items, seasonal items and random items based on the time series decomposition algorithm;

[0058] Apply statistical methods (such as ARIMA model, exponential smoothing, etc.) and professional knowledge base in the field of environmental science to analyze the long-term trends and cyclical changes of environmental data in various regions;

[0059] Through machine learning algorithms (such as cluster analysis, decision trees, random forests, etc.), we can identify the change patterns of environmental data between different regions, and the association of these patterns with factors such as geographical location and climate conditions; using anomaly detection algorithms, we can identify and mark outliers in historical data;

[0060] Based on the results of trend analysis and pattern recognition, set a preliminary data update frequency for each region; set a higher update frequency for regions with drastic changes or frequent abnormal events; set a lower frequency for stable or slowly changing regions; adjust the data update frequency through a dynamic adjustment mechanism based on the comparative analysis of real-time monitoring data and historical trends; for example, when a precursor to an abnormal event is detected, automatically increase the update frequency to obtain more detailed data;

[0061] And use the prediction model to predict environmental changes in the future, triggering data collection and transmission only when the prediction results indicate that it is necessary;

[0062] After implementing the set data update frequency, regularly evaluate its impact on system performance (such as data accuracy, real-time performance, energy consumption, etc.), and adjust the data update frequency setting strategy based on the evaluation results and user feedback to form a closed-loop feedback mechanism.

[0063] The working principle of the above technical solution is: on the basis of historical data cleaning, further data smoothing is performed to eliminate noise and fluctuations in the data and improve the stability and reliability of the data; data smoothing can adopt moving average method, exponential smoothing method and other methods, and select appropriate methods for processing according to the characteristics and needs of the data; extract key features from the smoothed data, including the daily variation amplitude of temperature and humidity, periodic variation patterns (such as seasonal variation), frequency of extreme events, etc.; these features can reflect the main changes and laws of environmental data, and provide important basis for subsequent analysis and prediction; apply time series decomposition algorithm to decompose environmental data into trend items, seasonal items and random items; trend items reflect the long-term change trend of data, seasonal items reflect the periodic changes of data, and random items reflect the random fluctuations of data; apply statistical methods The long-term trends of environmental data in various regions are analyzed by using the professional knowledge base in the field of environmental science (such as ARIMA model, exponential smoothing, etc.) and environmental science. By analyzing the long-term trends, we can understand the changing patterns of environmental data and possible future development trends, and provide a basis for formulating data update frequency and prediction models. We use machine learning algorithms (such as cluster analysis, decision trees, random forests, etc.) to identify the change patterns of environmental data between different regions. Through pattern recognition, we can find the similarities and differences between environmental data in different regions, as well as the associations between these patterns and factors such as geographical location and climatic conditions. We use anomaly detection algorithms to identify and mark outliers in historical data. Outliers may be caused by sensor failures, data entry errors, etc. Anomaly detection can detect and deal with these problems in a timely manner, thereby improving the accuracy and reliability of data. Based on the results of trend analysis and pattern recognition, set a preliminary data update frequency for each area; set a higher update frequency for areas with drastic changes or frequent abnormal events; set a lower frequency for stable or slowly changing areas; dynamically adjust the data update frequency based on the comparative analysis of real-time monitoring data and historical trends; for example, when the precursor of an abnormal event is detected, automatically increase the update frequency to obtain more detailed data; when the environmental data tends to be stable, appropriately reduce the update frequency to save resources; by establishing a prediction model, predict environmental changes in the future; the prediction model can be trained and optimized based on historical data and real-time data to improve the accuracy and reliability of the prediction; after implementing the set data update frequency, regularly evaluate its impact on system performance (such as data accuracy, real-time performance, energy consumption, etc.); adjust the data update frequency setting strategy based on the evaluation results and user feedback to form a closed-loop feedback mechanism; through the closed-loop feedback mechanism, the data update frequency setting strategy can be continuously optimized to improve the overall performance of the system and user experience. For example, assuming that the temperature data of a certain area suddenly has an abnormally high value for a period of time, the abnormal value can be discovered in time through the anomaly detection algorithm and marked as abnormal data.The system can then automatically trigger the alarm mechanism to notify management personnel to conduct inspection and processing.

[0064] The effects of the above technical solutions are as follows: through data smoothing, the noise and abnormal fluctuations in the data can be effectively reduced, and the smoothness and continuity of the data can be improved; extracting key features such as the daily variation amplitude of temperature and humidity, the periodic variation pattern, etc., can help to more deeply understand the inherent laws and characteristics of environmental data; decomposing environmental data into trend items, seasonal items and random items can help to more clearly identify long-term trends, seasonal changes and random fluctuations in the data, thereby improving the accuracy of data analysis and prediction; setting a preliminary data update frequency for each area according to the environmental data change patterns and characteristics of different regions can help to reduce unnecessary data collection and transmission frequency while ensuring data accuracy, thereby saving energy consumption; through a dynamic adjustment mechanism, the data update frequency can be flexibly adjusted according to the comparative analysis of real-time monitoring data and historical trends, which can further optimize the data update strategy and improve system performance; using a prediction model to predict environmental changes in a period of time in the future, and triggering data collection and transmission only when the prediction results indicate that it is necessary, can help to further reduce unnecessary The system can collect and transmit necessary data and reduce system energy consumption; use anomaly detection algorithms to identify and mark outliers in historical data, which helps to timely discover and deal with potential environmental problems; when the precursor of an abnormal event is detected, the data update frequency is automatically increased to obtain more detailed data, which helps the system respond quickly and take corresponding measures; after implementing the set data update frequency, regularly evaluate its impact on system performance, and adjust the data update frequency setting strategy based on the evaluation results and user feedback, which helps to form a closed-loop feedback mechanism and continuously optimize system performance; this technical solution can flexibly set the data update frequency and prediction model parameters according to the change patterns and characteristics of environmental data in different regions, so as to adapt to the environmental monitoring needs of different regions; this technical solution is based on general data processing and analysis methods, such as time series decomposition, statistical methods, machine learning algorithms, etc., and is easy to integrate and expand with other technologies and methods; with the continuous advancement of technology and the continuous changes in monitoring needs, this technical solution can be easily upgraded and optimized to adapt to new monitoring needs and technical challenges.

[0065] In one embodiment of the present invention, S3 includes:

[0066] S31, determining an initial data collection frequency for each sensor node according to the attributes of the region, such as area, user preference, and purpose;

[0067] S32, combining the data update frequency, the number of sensors and the initial data collection frequency of each area, calculating the current data collection frequency of each sensor node through an optimization algorithm; the current data collection frequency is calculated by the following formula:

[0068]

[0069] in, represents the initial data collection frequency of the i-th sensor node; represents the user preference coefficient of the ith region (which can be set according to the intensity of the user's preference, for example, a value between 0.5 and 2); represents the area coefficient of the ith region (the larger the area, the smaller the coefficient may be, and vice versa); represents the usage coefficient of the ith area (different uses may lead to different collection frequency requirements); represents the number of sensors in the Ith area; represents the data update frequency requirement; α represents the adjustment coefficient in the optimization algorithm;

[0070] S33, sending the calculated current data collection frequency to each sensor node and storing it in the system control device, and the sensor node performs data collection and transmission according to the received collection frequency.

[0071] The working principle of the above technical solution is: according to the attributes of the area, such as area, user preference and purpose, an initial data collection frequency is determined for each sensor node. These attributes reflect the different requirements for monitoring accuracy in different areas. For example, in a large industrial park, due to the vast area and the presence of various industrial activities, higher monitoring accuracy may be required to timely discover potential environmental problems. Therefore, a higher initial data collection frequency can be set for the sensor nodes in this area, such as collecting data once every minute. In a residential community, due to the relatively small area and the main focus on the quality of the living environment of residents, the monitoring accuracy requirements may be relatively low. Therefore, a lower initial data collection frequency can be set for the sensor nodes in this area, such as collecting data once every hour. Combined with the data update frequency, the number of sensors and the initial data collection frequency of each area, the current data collection frequency of each sensor node is calculated through an optimization algorithm. The optimization algorithm aims to balance the accuracy and efficiency of data collection to meet the actual application needs. First, according to the data update frequency and the number of sensors in each area, the total data collection required for the area is determined. Then, the current data collection frequency of each sensor node is calculated by the optimization algorithm in combination with the initial data collection frequency and the actual application requirements (such as data transmission bandwidth, storage capacity and other constraints); the optimization algorithm may include a variety of strategies, such as energy consumption-based optimization, data quality-based optimization, etc., to ensure that the monitoring accuracy is met while achieving efficient use of data collection; the calculated current data collection frequency is sent to each sensor node and stored in the system control device. The sensor node collects and transmits data according to the received collection frequency; the system control device generates the current data collection frequency configuration information of each sensor node according to the calculation results of the optimization algorithm; the configuration information is sent to each sensor node through wireless communication or other means; after receiving the configuration information, the sensor node collects and transmits data according to the data collection frequency therein; at the same time, the system control device stores the configuration information in a local or remote database for subsequent query and management.

[0072] The effect of the above technical solution is as follows: the solution determines the initial data collection frequency for each sensor node according to the area, user preference and purpose of the area. This customized setting can ensure that the data collection needs of different areas are met, and improve the flexibility and pertinence of data collection; through the optimization algorithm, the solution can calculate and adjust the current data collection frequency of each sensor node in combination with the data update frequency and the number of sensors in each area. This dynamic adjustment mechanism can ensure that the data collection frequency matches the actual application needs and avoids the redundancy and insufficiency of data collection; a reasonable data collection frequency can reduce the energy consumption of the sensor node and extend its service life. By determining the appropriate data collection frequency for each sensor node, the solution avoids unnecessary energy consumption and achieves the goal of energy saving and consumption reduction; by optimizing the data collection frequency, the solution can reduce the redundancy of data transmission and improve the efficiency of data transmission. This helps to reduce the cost of data transmission and improve the operating efficiency of the entire system; the appropriate data collection frequency can ensure the accuracy and integrity of the data, thereby improving the accuracy of monitoring. The scheme ensures the quality of data collection by determining the appropriate data collection frequency for each sensor node, providing reliable data support for subsequent monitoring and analysis; by dynamically adjusting the data collection frequency, the scheme can ensure that data is collected in time when needed, improving the real-time nature of monitoring. This is particularly important for some application scenarios that require real-time monitoring and early warning; the scheme sends the calculated current data collection frequency to each sensor node and stores it in the system control device. This centralized control method facilitates unified management and maintenance of sensor nodes, reducing the difficulty of management and maintenance; the scheme has good scalability and can adapt to application scenarios of different scales and complexities. With the expansion and change of application scenarios, the optimization algorithm and parameters can be adjusted to adapt to new needs. By introducing the user preference coefficient, the above formula can adjust the data collection frequency according to the needs and preferences of different users, making data collection more personalized and customized. The introduction of the regional area coefficient and the use coefficient takes into account the impact of the characteristics and uses of different regions on the data collection frequency, making data collection more in line with the actual situation. The number of sensors in the formula is an important factor that affects the current data collection frequency of each sensor node. When there are a large number of sensors, the data collection frequency of a single sensor can be appropriately reduced to optimize resource allocation and avoid unnecessary energy consumption. By adjusting the adjustment coefficient in the optimization algorithm, the data collection frequency can be further fine-tuned to achieve the best balance between energy consumption and performance. The data update frequency needs to be an important parameter in the formula, which directly affects the current data collection frequency of each sensor node. When the data update frequency requirement is high, the formula will automatically adjust the data collection frequency to meet the demand, thereby ensuring the real-time and accuracy of the data.

[0073] In one embodiment of the present invention, the S4 includes:

[0074] S41, each sensor node collects data within a predetermined time length according to its current data collection frequency; the collected data includes environmental parameters such as temperature, humidity, gas concentration, and switch signals such as door locks and valve positions;

[0075] S42, the sensor node sends the collected environmental data to the gateway through the Zigbee network; the gateway receives the data from the sensor node and performs preliminary data analysis and preprocessing;

[0076] S43, the gateway forwards the processed data to the system control device, and the system control device stores the data in a database; and organizes and manages the data stored in the database according to fields such as sensor identifier, collection time, and environmental parameters;

[0077] S44. The system control device cleans and verifies the data in the database, removes abnormal values ​​and duplicate values, and performs quality assessment on the cleaned data.

[0078] The working principle of the above technical solution is as follows: each sensor node collects data within a predetermined time length according to its preset current data collection frequency. These sensor nodes can monitor a variety of environmental parameters, such as temperature, humidity, gas concentration, etc., as well as switch signals such as door locks and valve positions. Data collection is the basis of the real-time monitoring system and provides raw data for subsequent data processing and analysis; the sensor node sends the collected environmental data to the gateway through the Zigbee network. The Zigbee network is a low-power, low-data-rate wireless network technology suitable for short-range wireless connections. The gateway, as a transfer station for data transmission, receives data from the sensor node and performs preliminary data analysis and preprocessing; the gateway forwards the processed data to the system control device. After receiving the data, the system control device stores it in the database. The database organizes and manages the data according to fields such as sensor identifiers, collection time and environmental parameters to facilitate subsequent data query and analysis; the system control device cleans and verifies the data in the database. The cleaning process includes removing abnormal values ​​and duplicate values ​​to ensure the accuracy and consistency of the data. The verification process is to check the integrity, rationality and legality of the data to ensure the quality of the data. The cleaned and verified data will be quality assessed to provide reliable guarantees for subsequent data analysis and application.

[0079] The effects of the above technical solution are as follows: each sensor node collects data according to its own current data collection frequency, and this flexibility ensures the real-time and accuracy of the data; the collected data types are rich, including environmental parameters such as temperature, humidity, gas concentration, and switch signals such as door locks and valve positions, which meet various monitoring needs; the sensor node sends the collected environmental data to the gateway through the Zigbee network. The Zigbee network has the characteristics of low power consumption, low data rate, and short-range wireless connection, which is suitable for data transmission in this scenario; the gateway, as a transfer station for data transmission, receives data from the sensor node and performs preliminary data analysis and preprocessing, which effectively reduces the burden of the system control device; the gateway forwards the processed data to the system control device, and the system control device stores the data in the database. This storage method ensures the persistence and accessibility of the data; the database organizes and manages the data according to fields such as sensor identifiers, collection time and environmental parameters, which facilitates subsequent data query and analysis; through reasonable database design, rapid storage and efficient query of massive data can be achieved; the database security measures can protect the confidentiality and integrity of the data and ensure the security and reliability of the data; the system control device cleans and verifies the data in the database to remove abnormal values ​​and duplicate values. This cleaning process ensures the accuracy and consistency of the data, providing a reliable basis for subsequent data analysis; checks the integrity, rationality and legality of the data to ensure data quality; the cleaned and verified data will be quality evaluated to further improve the reliability and credibility of the data; this technical solution can monitor parameters such as temperature, humidity, and gas concentration in the environment in real time, providing important data support for environmental protection and disaster warning; by monitoring switch signals such as door locks and valve positions, it can achieve automated control and safety monitoring of smart homes.

[0080] In one embodiment of the present invention, the S43 includes:

[0081] Before forwarding the processed data to the system control device, the gateway encapsulates the data and uses an encryption algorithm (such as AES, RSA, etc.) to encrypt the encapsulated data;

[0082] The gateway and the system control device are connected through a transmission protocol (such as TCP / IP) and implement a data transmission confirmation mechanism. That is, after receiving the data, the system control device sends a confirmation message to the gateway. The gateway determines whether the data is successfully transmitted based on the confirmation information. If not, it will be retransmitted.

[0083] After receiving the data, the system control device first classifies and indexes the data according to key fields such as sensor identifier and acquisition time, and uses distributed database or cloud storage technology to store the data according to time series and regional distribution;

[0084] The stored data is compressed by a data compression algorithm. Before the data is stored, the system control device performs a preliminary quality assessment on the data.

[0085] And through the data quality monitoring system, the quality of stored data is regularly reviewed, data quality issues are discovered and handled in a timely manner, and data is backed up redundantly.

[0086] The working principle of the above technical solution is as follows: before forwarding the processed data to the system control device, the gateway will first encapsulate the data. The encapsulation process may include packaging the data into a specific format, adding necessary metadata (such as data source, timestamp, etc.), and assigning a unique identifier to the data; the encapsulated data will be encrypted using an encryption algorithm (such as AES, RSA, etc.). The purpose of encryption is to ensure the security of data during transmission and prevent the data from being stolen or tampered with by unauthorized third parties. The selection of encryption algorithms should be based on the sensitivity of the data and the security requirements of the transmission environment; the gateway and the system control device are connected through a transmission protocol (such as TCP / IP). The TCP / IP protocol is a widely used network communication protocol that can provide reliable data transmission services; in order to achieve the reliability of data transmission, the system control device will send a confirmation message to the gateway after receiving the data. The gateway determines whether the data is successfully transmitted based on the confirmation information. If the data is not successfully transmitted (for example, due to network failure or data loss), the gateway will retransmit to ensure the integrity and accuracy of the data; after receiving the data, the system control device will classify and index the data according to key fields such as sensor identifier and acquisition time. The purpose of classification and indexing is to improve the queryability and analyzability of data, so as to facilitate subsequent data processing and analysis; distributed database or cloud storage technology is used to store data according to time series and regional distribution. Distributed database and cloud storage technology can provide high availability and scalability to meet the needs of large-scale data storage; in order to save storage space and improve data transmission efficiency, the system control device will compress the data before data storage. The selection of data compression algorithm should be based on the type of data and the requirements of compression ratio; before data storage, the system control device will also conduct a preliminary quality assessment of the data. The purpose of quality assessment is to ensure the accuracy and integrity of the data and prevent low-quality data from entering the database; in order to maintain the high quality of data for a long time, the system control device will regularly review the quality of the stored data. The quality review process may include data integrity check, consistency verification and outlier detection; in order to ensure the reliability and availability of data, the system control device will perform redundant backup of the data. Redundant backup can include storing copies of data in different physical locations, and using different storage media and storage technologies. In this way, even if unexpected situations such as hardware failure or data loss occur, data can be quickly restored to ensure the normal operation of the system.

[0087] The effects of the above technical solution are as follows: the gateway encapsulates the data before forwarding it, and uses encryption algorithms (such as AES, RSA, etc.) to encrypt the encapsulated data, which effectively prevents the data from being stolen or tampered with during transmission, and improves the confidentiality and integrity of the data; the gateway and the system control device are connected through secure transmission protocols such as TCP / IP, which have certain security and reliability, and can further ensure the security of data transmission; the system control device sends confirmation information to the gateway after receiving the data, and the gateway determines whether the data is successfully transmitted based on the confirmation information. If not, retransmission is performed. This mechanism ensures reliable data transmission and avoids data loss or transmission errors; during data transmission, if network failure or data loss occurs, the gateway will retransmit until the data is successfully transmitted to the system control device. This further improves the reliability of data transmission; the system control device classifies and indexes the data according to key fields such as sensor identifiers and acquisition time, which helps to quickly retrieve and analyze data and improve the efficiency of data storage; the use of distributed databases or cloud storage technology to store data can meet the needs of large-scale data storage and has good scalability. As the amount of data increases, storage resources can be easily expanded without affecting the performance of the system; before data is stored, the system control device performs a preliminary quality assessment of the data to ensure that only high-quality data is stored. This helps reduce errors and uncertainties in subsequent data processing and analysis; through the data quality monitoring system, the quality of the stored data is regularly reviewed, and data quality problems can be discovered and handled in a timely manner. This helps to maintain the accuracy and consistency of the data and improve the quality level of the data; redundant backup of data can ensure the reliability and availability of the data. Even in the event of unexpected situations such as hardware failure or data loss, the data can be quickly restored to ensure the normal operation of the system; when necessary, it can be restored through redundant backup data to avoid serious impact of data loss on the system. This improves the fault tolerance and disaster resistance of the system.

[0088] In one embodiment of the present invention, S5 includes:

[0089] S51, based on the environmental data set and set parameter values ​​(such as safety standards, comfort requirements, etc.) of each area, a dynamic threshold model of environmental parameters is obtained by training the historical data and real-time data through a machine learning algorithm;

[0090] S52, using the trained model, calculate the dynamic threshold of the environmental parameter of each area according to the current environmental data set, wherein the dynamic threshold includes a dynamic upper limit value and a dynamic lower limit value; and adjust the dynamic threshold according to environmental changes and time changes; wherein the dynamic threshold of the environmental parameter is obtained by:

[0091]

[0092] in, Indicates the dynamic upper limit value of the environmental parameter of the ith area; represents the lower limit of the environmental dynamics of the ith region; Indicates the current environmental parameter value (such as temperature) of the i-th area; represents the average value of the historical environmental parameters of the ith region; represents the standard deviation of the historical environmental parameters of the ith region; represents the safety standard factor for the ith area (for example, for temperature, it may be 1.05 to allow a 5% deviation); represents the comfort requirement coefficient of the ith area (similarly, it can be 1.02 to indicate a 2% deviation); β represents the dynamic adjustment coefficient predicted by the model, which can be a weight based on factors such as time, season, and weather; Represents a small positive number used to avoid the threshold calculation result being zero;

[0093] S53, the system control device monitors the temperature value and other environmental parameters collected by the sensor in each area in real time; when the temperature value exceeds the dynamic threshold, an alarm signal is triggered, and the alarm information is sent to the upper network information service or relevant personnel through the gateway;

[0094] S54. According to a preset control strategy or algorithm (such as a PID control algorithm), corresponding control measures are automatically taken, such as adjusting the air-conditioning temperature, turning on ventilation equipment, etc., and the execution results of the control measures are fed back to the system control device.

[0095] The working principle of the above technical solution is: based on the historical environmental data set (such as temperature, humidity, air quality, etc.) and set parameter values ​​(such as safety standards, comfort requirements, etc.) of each area, the dynamic threshold model of environmental parameters is obtained through machine learning algorithm training. For example, assuming that in a certain office area, historical data shows that employees are most comfortable when the temperature is 22-26℃, and considering safety standards and energy-saving requirements, a machine learning task can be set to use historical temperature data, employee comfort feedback, energy consumption data, etc. as input to train a model to predict the optimal temperature range (i.e., dynamic threshold) of the area under different times and weather conditions. Using the trained model, the dynamic threshold of environmental parameters in each area is calculated based on the current environmental data set (such as real-time temperature, humidity, outdoor weather, etc.). The dynamic threshold includes a dynamic upper limit value and a dynamic lower limit value, which will be adjusted according to environmental changes and time changes. For example, in the above office area, if the current outdoor temperature suddenly rises, the model may predict that in order to maintain employee comfort, the upper limit value of the indoor temperature needs to be appropriately lowered to avoid overheating. The system control device monitors environmental parameters such as temperature values ​​collected by sensors in each area in real time. When the temperature value exceeds the dynamic threshold (whether it exceeds the upper or lower limit), an alarm signal is triggered, and the alarm information is sent to the upper network information service or relevant personnel through the gateway. For example, if the real-time temperature of the office area exceeds the dynamic upper limit predicted by the model, the system may automatically trigger an alarm to notify the management personnel or automatically adjust the air conditioning system to lower the temperature. According to the preset control strategy or algorithm (such as PID control algorithm), the corresponding control measures are automatically taken, such as adjusting the air conditioning temperature, turning on the ventilation equipment, etc. The execution results of the control measures are fed back to the system control device for further monitoring and adjustment. For example, after the above alarm is triggered, the system may automatically adjust the set temperature of the air conditioning system to lower the temperature of the office area. At the same time, the system will also monitor the adjusted temperature value and continue to adjust or maintain the current state as needed.

[0096] The effects of the above technical solution are as follows: the dynamic threshold model of environmental parameters obtained through machine learning algorithm training can automatically adjust the threshold according to historical data and real-time data, making the monitoring and control of environmental parameters more intelligent and accurate; the system can automatically take corresponding control measures according to the preset control strategy or algorithm, such as adjusting the air-conditioning temperature, turning on the ventilation equipment, etc., without human intervention, thereby improving the automation level of the system; the dynamic threshold can be adjusted according to environmental changes and time changes, so that the system can better adapt to the needs of different environments and different time periods, thereby improving the environmental adaptability of the system; through intelligent monitoring and control, the system can automatically adjust the equipment operation status according to the actual environmental parameters and needs, avoiding unnecessary energy consumption and significantly improving the energy saving effect; the system takes safety standards into consideration when training the dynamic threshold model to ensure that the environmental parameters are within the range of the actual environment. The number fluctuates within a safe range, which improves the safety of the system; by dynamically adjusting environmental parameters, the system can maintain the comfort of the indoor environment, meet people's comfort requirements, and improve the quality of work and life; the system can monitor the environmental parameters collected by sensors in each area in real time, and trigger an alarm signal when the temperature value exceeds the dynamic threshold, and promptly notify relevant personnel to handle it, which improves the efficiency of operation and maintenance; the alarm information is sent to the upper network information service or relevant personnel through the gateway, which can quickly locate the fault point and take corresponding measures, speeding up the fault response speed; the system will continuously accumulate environmental data and the execution results of control measures during operation, which can provide strong support for subsequent optimization and decision-making; based on the accumulated data, the system can further train and optimize the model, improve the intelligent level of decision-making, and provide the possibility for more efficient environmental management. By introducing the concept of dynamic threshold, the above formula can adjust the monitoring threshold in real time according to the changes in current environmental parameters and historical data, making monitoring more flexible and accurate; the safety standard coefficient and comfort requirement coefficient in the formula can be set according to the characteristics and needs of different areas, so as to meet the monitoring requirements in different scenarios. The dynamic adjustment coefficient of the model prediction in the formula takes into account the impact of factors such as time, season, and weather on environmental parameters, so that the dynamic threshold can be automatically adjusted as environmental conditions change, enhancing the adaptability of the monitoring system; by updating the current environmental parameter value in real time and combining historical data, the threshold can be dynamically adjusted to adapt to environmental changes in different time periods and weather conditions. By setting a reasonable dynamic threshold, the allocation of monitoring resources can be optimized to avoid unnecessary waste of resources. The adjustment of the dynamic threshold can reduce false alarms and missed alarms caused by improper setting of fixed thresholds, and improve the accuracy and reliability of the monitoring system. In scenarios with high comfort requirements (such as smart homes, smart offices, etc.), by adjusting the comfort requirement coefficient, the monitoring system can be more in line with user needs and preferences, and improve user experience and comfort. The adjustment of the dynamic threshold can make the monitoring system closer to the actual needs of users while maintaining security, thereby improving user satisfaction.

[0097] In one embodiment of the present invention, S6 includes:

[0098] S61, the system control device determines whether there is an area where environmental parameters need to be adjusted by comparing the difference between the current environmental parameters and the dynamic threshold value according to the environmental parameter dynamic threshold value and the environmental data set of each area;

[0099] S62. When it is determined that there is an area that needs to adjust the environmental parameters, the system control device calculates the environmental parameter values ​​that need to be adjusted (such as temperature setting value, humidity setting value, etc.) according to the data collected by the sensors in the area and the preset control strategy or algorithm; wherein the environmental parameter values ​​are obtained by the following formula:

[0100]

[0101] in, represents the environmental parameter value that needs to be adjusted at time t; e(t) represents the environmental parameter deviation at time t, which is calculated as ; represents the ideal parameter setting value of the ith region at time t; represents the deviation integral from the initial time to time t; represents the deviation differential at time t; , , They represent the dynamic PID control coefficients at time t respectively; α, β, γ represent the nonlinear adjustment coefficients; Indicates external disturbance factors, such as weather changes, personnel movement, etc.;

[0102] S63, the system control device sends the calculated environmental parameter value that needs to be adjusted to the corresponding execution device (such as air conditioner, humidifier, etc.) through the gateway; the execution device performs adjustment operations according to the received instructions, such as adjusting temperature, humidity, etc.;

[0103] S64, the execution device feeds back the adjusted state information to the system control device; the system control device collects and analyzes data again according to the fed-back information to form a closed-loop control process.

[0104] The working principle of the above technical solution is: the system control device first obtains the environmental data set of each area, which includes environmental parameters such as temperature, humidity, and light; at the same time, the system control device also obtains the dynamic thresholds of the environmental parameters, which are obtained through training of machine learning algorithms based on historical data and real-time data; the system control device compares the current environmental parameters with the dynamic thresholds to determine whether there are areas where environmental parameters need to be adjusted. If the current environmental parameter exceeds the range of the dynamic threshold, the system considers that adjustment is required; when it is determined that there is an area where environmental parameter adjustment is required, the system control device will calculate the environmental parameter value that needs to be adjusted based on the data collected by the sensors in the area and the preset control strategy or algorithm; these control strategies or algorithms may include PID control algorithm, fuzzy control algorithm, etc., which can calculate the amount that needs to be adjusted based on the difference between the current environmental parameter and the target value; the system control device sends the calculated environmental parameter value that needs to be adjusted to the corresponding execution device through the gateway; after receiving the instruction, the execution device (such as air conditioner, humidifier, etc.) will adjust the operation according to the requirements of the instruction, such as adjusting the temperature, humidity, etc.; during the adjustment process, the execution device may feedback the adjustment progress or status information to the system control device in real time so that the system control device can understand the adjustment situation in real time; the execution device feeds back the adjusted status information to the system control device; the system control device collects and analyzes data again based on the feedback information to evaluate whether the adjustment effect meets the expectations; if the adjustment effect does not meet the expectations, the system control device may calculate the environmental parameter value that needs to be adjusted again and send it to the execution device for a new round of adjustment; this process will be repeated until the environmental parameter reaches the dynamic threshold range, forming a closed-loop control process.

[0105] The effects of the above technical solutions are as follows: by real-time monitoring of environmental parameters and comparing them with dynamic thresholds, the system can accurately identify areas that need to be adjusted, avoiding unnecessary energy waste and over-adjustment; the system can calculate accurate environmental parameter adjustment values ​​based on real-time data collected by sensors in the area, combined with preset control strategies or algorithms, to ensure that environmental parameters reach the optimal state; the realization of precise regulation enables the system to adjust environmental parameters according to actual needs, avoiding ineffective consumption of energy and achieving the effect of energy saving optimization; the closed-loop control process enables the system to continuously optimize and adjust according to feedback information, further improving energy utilization efficiency; the system can maintain the comfort of the indoor environment and improve people's quality of life and work by accurately regulating environmental parameters such as temperature and humidity; the setting of dynamic thresholds enables the system to automatically adjust environmental parameters according to environmental changes and time changes, ensuring that the environment is always in the best state; when regulating environmental parameters, the system will consider safety standards to ensure that environmental parameters fluctuate within a safe range, avoiding safety accidents caused by abnormal environmental parameters; the closed-loop control process enables the system to detect and handle abnormal situations in a timely manner, improving the safety and stability of the system The system can automatically adjust the threshold value according to historical data and real-time data through the dynamic threshold model of environmental parameters obtained by machine learning algorithm training, thus realizing intelligent control. The system can automatically calculate the adjustment value according to the preset control strategy or algorithm, and send it to the execution device for adjustment without manual intervention, thus improving the automation level of the system. The system can monitor environmental parameters in real time, and trigger the alarm signal in time when an abnormality is found, and send the alarm information to relevant personnel or upper network information service through the gateway. After adjustment, the execution device will feedback the status information to the system control device to form a closed-loop control process, thus improving the automatic monitoring and feedback capability of the system. Through the closed-loop control process, the system can continuously optimize and adjust according to the feedback information, thus reducing the frequency and difficulty of manual intervention and improving the operation and maintenance efficiency. The system can collect and analyze environmental data in real time, providing accurate data support for operation and maintenance personnel, thus facilitating their troubleshooting and repair. When an abnormality is found, the system can trigger the alarm signal in time, and send the alarm information to relevant personnel or upper network information service through the gateway, thus speeding up the fault response speed. The closed-loop control process enables the system to detect and handle abnormal situations in time, thus avoiding the expansion and deterioration of faults. The above formula uses dynamic PID (proportional-integral-differential) control coefficients, which change over time and can adjust the control strategy according to the real-time situation, making the system more adaptable to dynamic changes in the environment. The introduction of nonlinear adjustment coefficients and nonlinear functions enables the control system to more accurately handle nonlinear environmental parameter changes and improve the accuracy and stability of control.The formula takes into account the deviation, integral and differential of environmental parameters at the same time, and comprehensively processes these three errors through the PID control strategy to achieve more comprehensive error correction and improve the response speed and accuracy of the control system. The δ(t) term in the formula represents external disturbance factors, such as weather changes, personnel flow, etc., which enables the control system to consider and respond to sudden changes in the external environment and improve the robustness and reliability of the system. The various parameters in the formula can be adjusted and optimized according to the specific application scenario, so that the control system can be applied to different environments and needs. By comprehensively considering multiple factors and introducing nonlinear control characteristics, the formula can achieve more accurate environmental parameter control and meet the user's dual needs for environmental comfort and energy saving.

[0106] One embodiment of the present invention, as Figure 2 As shown, a closed space three-defense system control device based on multiple sensors, the device includes:

[0107] Network construction module: Based on Zigbee wireless communication technology, combined with CC2530 main chip and STM32CPU, a sensor network is constructed, and each sensor node is equipped with supporting facilities; the supporting facilities include DI / DO, AI input and output interfaces, which are used to detect relevant signals such as water leakage, door locks, gas, valve positions, and drive and control lighting switches, water valves, explosion-proof gas valves, door locks and other equipment; and the gateway, as the control host of the system, communicates with the upper network information service through the WiFi network port and the GPRS network port to ensure reliable data transmission and flexible control of the system;

[0108] Data acquisition module: setting attribute information for multiple sensors respectively set in each area of ​​the confined space, the attribute information including sensor identifier, area identifier, location identifier and initial data collection frequency, and acquiring historical environmental data of each area, the historical environmental data including area identifier, date, maximum temperature, minimum temperature and average temperature, and determining the data update frequency of each area based on the historical data;

[0109] Frequency determination module: determining the initial data collection frequency of the sensor based on the regional attributes, including the area of ​​the region, the preferences of users in the region and / or the purpose of the region; determining the current data collection frequency of each sensor in the region based on the data update frequency of each region, the number of sensors and the initial data collection frequency of the sensor;

[0110] Data transmission module: Each sensor collects data within a predetermined time length according to its current data collection frequency, and sends the collected environmental data to the system control device; the system control device determines the environmental data set of each area based on the received environmental data, and the environmental data set includes a sensor identifier, collection time, temperature value, etc.;

[0111] Threshold determination module: based on the environmental data set and the set parameter value of each area, determine the dynamic threshold of the environmental parameter, the dynamic threshold includes a dynamic upper limit value and a dynamic lower limit value; when the temperature value collected by the sensor in the area exceeds the dynamic threshold, trigger an alarm or take corresponding control measures;

[0112] Parameter adjustment module: Determine whether there is an area that needs environmental parameter adjustment based on the dynamic threshold of environmental parameters and the environmental data set of each area; when it is determined that there is an area that needs environmental parameter adjustment, adjust the corresponding environmental parameters based on the data collected by sensors in the area.

[0113] The working principle of the above technical solution is: using Zigbee wireless communication technology as the main communication means, combining CC2530 main chip (as the core of Zigbee communication module) and STM32CPU (responsible for data processing and advanced control logic); each sensor node is equipped with DI / DO (digital input / output) and AI (analog input) interfaces to detect signals such as water leakage, door lock status, gas concentration, valve position status, etc., and can drive and control lighting switches, water valves, explosion-proof gas valves, door locks and other equipment; the gateway, as the control host of the system, has WiFi and GPRS network ports for communicating with the upper-level network information service to ensure reliable data transmission and flexible control of the system; The sensor node sends data to the gateway through the Zigbee network; the gateway transmits the data to the upper-layer network information service through WiFi or GPRS to achieve remote monitoring and control; set an identifier (used to distinguish different sensors), a regional identifier (identifying the area where the sensor is located), a location identifier (identifying the specific location of the sensor in the area) and an initial data collection frequency for each sensor node; obtain historical environmental data for each area from the database or historical records, including the area identifier, date, maximum temperature, minimum temperature and average temperature; based on historical data, analyze the environmental change characteristics of each area and determine the data update frequency of each area (i.e., the basis for adjusting the frequency of sensor data collection); consider The area of ​​the area, the preferences of users in the area (such as temperature preference, humidity preference, etc.) and / or the purpose of the area (such as office, warehouse, laboratory, etc.) together determine the initial data collection frequency of the area; combined with the data update frequency of each area, the number of sensors and the initial data collection frequency of the sensors, the current data collection frequency of each sensor in the area is calculated through an algorithm; each sensor collects data within a predetermined time length according to its current data collection frequency; the collected data includes environmental parameters such as temperature, humidity, and gas concentration; the sensor sends the collected environmental data to the gateway through the Zigbee network; the gateway receives and organizes this data to form the environmental data of each area Data set; Based on the environmental data set and set parameter values ​​of each area (such as the temperature range and humidity range set by the user), the dynamic thresholds of environmental parameters (including dynamic upper limit and dynamic lower limit) are calculated through algorithms; the dynamic thresholds can be adaptively adjusted according to the actual situation to improve the response speed and accuracy of the system; when the temperature value or other environmental parameters collected by the sensor in the area exceeds the dynamic threshold, an alarm signal is triggered or corresponding control measures are taken (such as closing the water valve, opening the explosion-proof gas valve, etc.); the alarm signal can be notified to relevant personnel through SMS, email or APP push; based on the dynamic thresholds of environmental parameters and the environmental data set of each area, determine whether there is an area where environmental parameters need to be adjusted;When it is determined that there is an area that needs to adjust environmental parameters, the environmental parameters are adjusted by controlling the corresponding equipment (such as air conditioners, humidifiers, dehumidifiers, etc.) according to the data collected by sensors in the area (such as temperature, humidity, etc.); the adjustment process can be automatic (based on preset rules and algorithms) or manual (through user intervention or remote control). ;

[0114] The effect of the above technical solution is: by combining the CC2530 main chip and the STM32CPU, a stable and efficient sensor network is constructed. Zigbee technology is very suitable for environmental monitoring in confined spaces due to its low power consumption, low cost and self-organizing network characteristics; the gateway, as the control host of the system, communicates with the upper network information service through WiFi and GPRS network ports to ensure reliable data transmission. This dual network guarantee improves the stability and redundancy of data transmission and reduces the risk of data loss; based on the environmental data set and set parameter values ​​of each area, the system can automatically calculate and set the dynamic thresholds of environmental parameters (including dynamic upper limit and dynamic lower limit). This dynamic adjustment method can more accurately reflect the actual environmental conditions and improve the accuracy and flexibility of monitoring; when the temperature value or other environmental parameters collected by the sensor exceeds the dynamic threshold, the system will immediately trigger an alarm or take corresponding control measures. This real-time response mechanism can quickly discover and solve problems and avoid potential safety hazards; when determining the initial data collection frequency of the sensor, the system fully considers factors such as the area of ​​the area, user preferences and regional usage. This refined management method makes data collection more targeted and can more effectively meet the needs of different regions; based on historical environmental data, the system can automatically adjust the data update frequency of each region. This adaptive adjustment method can balance data accuracy and system resource consumption and improve the overall efficiency of the system; through the communication between the gateway and the upper network information service, users can remotely monitor and control the entire system. This convenient control method allows users to understand the environmental conditions in the confined space anytime and anywhere and take necessary measures; because the system adopts a modular design, each sensor node and supporting facilities can be independently maintained and replaced. This design reduces the complexity and cost of system maintenance and improves the maintainability of the system; this technical solution is not only suitable for the three-proof system (waterproof, explosion-proof, and anti-virus) of confined spaces, but can also be widely used in various scenarios that require environmental monitoring and control, such as warehouses, laboratories, data centers, etc.; the system has good scalability, and the number and types of sensor nodes and supporting facilities can be increased according to actual needs to meet more complex monitoring and control needs.

[0115] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for controlling a three-defense system in a confined space based on multiple sensors, characterized in that: The method comprises: S1. Based on Zigbee wireless communication technology, combined with CC2530 main chip and STM32CPU, build a sensor network and equip each sensor node with supporting facilities; S2, setting attribute information for multiple sensors respectively set in each area of ​​the confined space, wherein the attribute information includes a node identifier, an area identifier, a location identifier, and an initial data sampling frequency, and obtaining historical environmental data of each area, and determining a data update frequency of each area based on the historical data; S3. Determine the initial data collection frequency of the sensor based on the regional attributes, including the regional identifier, date, maximum temperature, minimum temperature and average temperature; determine the current data collection frequency of each sensor in the region based on the data update frequency of each region, the number of sensors and the initial data collection frequency of the sensor; S4. Each sensor collects data within a predetermined time period according to its current data collection frequency, and sends the collected environmental data to the system control device; based on the received environmental data, an environmental data set for each area is determined; S5. Based on the environmental data set and the set parameter value of each area, determine the dynamic threshold of the environmental parameter. When the temperature value collected by the sensor in the area exceeds the dynamic threshold, trigger an alarm or take corresponding control measures; S6. Determine whether there is an area where environmental parameters need to be adjusted based on the dynamic threshold of the environmental parameters and the environmental data set of each area; when it is determined that there is an area where environmental parameters need to be adjusted, adjust the corresponding environmental parameters based on the data collected by the sensors in the area; The S5 comprises: S51, based on the environmental data set and the set parameter values ​​of each area, a dynamic threshold model of environmental parameters is obtained by training the historical data and real-time data through a machine learning algorithm; S52, using the trained model, calculate the dynamic threshold of the environmental parameter of each area according to the current environmental data set, and adjust the dynamic threshold according to environmental changes and time changes; S53, the system control device monitors the environmental parameters collected by sensors in each area in real time; when the temperature value exceeds the dynamic threshold, an alarm signal is triggered, and the alarm information is sent to the upper network information service or relevant personnel through the gateway; S54. According to the preset control strategy or algorithm, corresponding control measures are automatically taken, and the execution results of the control measures are fed back to the system control device.

2. According to claim 1, a method for controlling a three-defense system in a confined space based on multiple sensors is characterized in that: Said S1 comprises: S11. Through Zigbee wireless communication technology, the CC2530 main chip is selected as the core of the Zigbee communication module, and the STM32CPU is selected as the core of data processing and control; S12. Design a DI / DO interface for each sensor node to detect switch signals and control related devices, and design an AI interface to detect analog signals and control the opening of corresponding devices; S13. Design and deploy a gateway device as the control host of the system, integrating WiFi and GPRS modules for remote communication of data; S14. Determine the deployment location and number of sensor nodes, optimize according to the space size and monitoring requirements, build the Zigbee network, set the network parameters, and test the network connectivity.

3. According to the multi-sensor based confined space three-defense system control method of claim 2, it is characterized in that: The S14 comprises: Use 3D modeling software to accurately model the monitoring area, assign monitoring priorities to each area based on monitoring needs, and determine key monitoring points and auxiliary monitoring points; Combine historical data and prediction models to evaluate changes in monitoring needs in different time periods, and select sensor types for each monitoring point based on spatial analysis and demand assessment results; Genetic algorithms are used to optimize the number and location of sensor nodes, and a rotation mechanism for sensor nodes is designed; According to the distribution and communication requirements of sensor nodes, the Zigbee network topology is selected, and the potential communication paths are simulated and optimized using graph theory and network optimization algorithms; According to the Zigbee communication standard, configure network parameters, test network performance, and adjust network configuration based on test results; Test network connectivity and regularly evaluate network performance based on actual operation data to identify and resolve potential bottlenecks; Based on machine learning algorithms, the sensor sampling frequency and data transmission strategy are automatically adjusted according to environmental changes and usage patterns. Through the user feedback mechanism, user opinions and suggestions on the monitoring system are collected and analyzed, and deployment strategies and network configurations are continuously iterated and optimized.

4. According to the multi-sensor based confined space three-defense system control method of claim 1, it is characterized in that: The S2 comprises: S21, setting a unique identifier for each sensor node, as well as a corresponding area identifier and location identifier, determining relevant parameters of the sensor, and recording them in the system; S22. Obtain environmental data of each region from the historical database, clean and verify the historical data, and remove abnormal values ​​and duplicate values; S23. Analyze the change trend of environmental data in each region based on statistical analysis methods, and determine the data update frequency of each region according to the analysis results, that is, how often each region needs to update environmental data; S24. Store the determined data update frequency in the system control device.

5. According to claim 4, a method for controlling a three-defense system in a confined space based on multiple sensors is characterized in that: The S23 comprises: On the basis of historical data cleaning, data smoothing is further performed, key features are extracted, and environmental data is decomposed into trend items, seasonal items, and random items based on the time series decomposition algorithm; Apply statistical methods and knowledge base to analyze long-term trends and cyclical changes in environmental data in various regions; Use machine learning algorithms to identify patterns of environmental data changes between different regions, and use anomaly detection algorithms to identify and mark outliers in historical data; Based on the results of trend analysis and pattern recognition, set a preliminary data update frequency for each region; through a dynamic adjustment mechanism, adjust the data update frequency based on the comparative analysis of real-time monitoring data and historical trends; After implementing the set data update frequency, regularly evaluate its impact on system performance, and adjust the data update frequency setting strategy based on the evaluation results and user feedback to form a closed-loop feedback mechanism.

6. According to the multi-sensor based confined space three-defense system control method of claim 1, it is characterized in that: The S3 includes: S31, determining an initial data collection frequency for each sensor node according to the attributes of the area; S32, combining the data update frequency, the number of sensors and the initial data collection frequency of each area, calculating the current data collection frequency of each sensor node through an optimization algorithm; S33, sending the calculated current data collection frequency to each sensor node and storing it in the system control device, and the sensor node performs data collection and transmission according to the received collection frequency.

7. According to claim 1, a method for controlling a three-defense system in a confined space based on multiple sensors is characterized in that: The S4 comprises: S41, each sensor node collects data within a predetermined time length according to its current data collection frequency; S42, the sensor node sends the collected environmental data to the gateway through the Zigbee network; the gateway receives the data from the sensor node and performs preliminary data analysis and preprocessing; S43, the gateway forwards the processed data to the system control device, and the system control device stores the data in a database; and organizes and manages the data stored in the database according to fields; S44. The system control device cleans and verifies the data in the database, removes abnormal values ​​and duplicate values, and performs quality assessment on the cleaned data.

8. According to claim 1, a method for controlling a three-defense system in a confined space based on multiple sensors is characterized in that: The S6 comprises: S61, the system control device determines whether there is an area where environmental parameters need to be adjusted by comparing the difference between the current environmental parameters and the dynamic threshold value according to the environmental parameter dynamic threshold value and the environmental data set of each area; S62: When it is determined that there is an area where environmental parameters need to be adjusted, the system control device calculates the environmental parameter value that needs to be adjusted based on the data collected by the sensors in the area and the preset control strategy or algorithm; S63, the system control device sends the calculated environmental parameter value that needs to be adjusted to the corresponding execution device through the gateway; the execution device performs adjustment operations according to the received instructions; S64, the execution device feeds back the adjusted state information to the system control device; the system control device collects and analyzes data again according to the fed-back information to form a closed-loop control process.

9. A multi-sensor based confined space three-defense system control device, characterized in that: The device comprises: Network building module: Based on Zigbee wireless communication technology, combined with CC2530 main chip and STM32CPU, build a sensor network and equip each sensor node with supporting facilities; Data acquisition module: setting attribute information for multiple sensors respectively set in each area of ​​the confined space, wherein the attribute information includes a node identifier, an area identifier, a location identifier, and an initial data sampling frequency, and acquiring historical environmental data of each area, and determining the data update frequency of each area based on the historical data; A frequency determination module: determining an initial data collection frequency of a sensor based on regional attributes, including a region identifier, date, maximum temperature, minimum temperature, and average temperature; determining a current data collection frequency of each sensor in the region based on the data update frequency of each region, the number of sensors, and the initial data collection frequency of the sensors; Data transmission module: Each sensor collects data within a predetermined time period according to its current data collection frequency, and sends the collected environmental data to the system control device; based on the received environmental data, the environmental data set of each area is determined; Threshold determination module: Determines the dynamic threshold of environmental parameters based on the environmental data set and set parameter values ​​of each area. When the temperature value collected by the sensor in the area exceeds the dynamic threshold, an alarm is triggered or corresponding control measures are taken; Parameter adjustment module: determines whether there is an area that needs environmental parameter adjustment based on the dynamic threshold of environmental parameters and the environmental data set of each area; when it is determined that there is an area that needs environmental parameter adjustment, adjusts the corresponding environmental parameters based on the data collected by sensors in the area; The threshold determination method of the threshold determination module includes: Based on the environmental data set and set parameter values ​​of each area, a dynamic threshold model of environmental parameters is obtained by training historical data and real-time data through machine learning algorithms; Using the trained model, the dynamic thresholds of environmental parameters in each area are calculated based on the current environmental data set, and the dynamic thresholds are adjusted according to environmental changes and time changes; The system control equipment monitors the environmental parameters collected by sensors in each area in real time; when the temperature value exceeds the dynamic threshold, an alarm signal is triggered and the alarm information is sent to the upper network information service or relevant personnel through the gateway; According to the preset control strategy or algorithm, the corresponding control measures are automatically taken, and the execution results of the control measures are fed back to the system control device.

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