Building intelligent monitoring system and method

Abstract

The invention discloses an intelligent building monitoring system and method, belongs to the technical field of building environment control systems, and solves the problems of energy waste and emergency response lag caused by the fact that an existing system is difficult to dynamically coordinate multi-region environment parameters. According to the technical scheme, the method comprises the steps that building space multi-dimensional environment parameters are collected through a distributed infrared sensor array, a temperature sensor array and a carbon dioxide concentration sensor array; the central controller normalizes the data, constructs a space model of a temperature gradient distribution diagram, a personnel density thermodynamic diagram and an air quality index matrix, generates an air conditioner linkage instruction based on a preset temperature difference threshold value to adjust the air supply volume ratio of adjacent areas, and generates an emergency evacuation channel opening instruction based on a personnel density threshold value; the equipment control module executes air conditioning, lighting and security linkage operation, and the lighting unit regulates and controls the brightness in a grading mode according to the curvature radius of the personnel track. The system is mainly used for realizing building environment precise regulation and control and intelligent security and protection collaborative management.

Description

Technical Field

[0001] The present invention relates to the field of smart home and automatic control technology. More specifically, the present invention relates to an intelligent building monitoring system and method. Background Art

[0002] In the field of building environment monitoring, multi-system collaborative control faces several technical bottlenecks. Traditional sensor deployment adopts split installation, and the infrared, temperature and gas monitoring units are spatially separated, resulting in inconsistent data spatiotemporal benchmarks. Building structure vibration or equipment installation errors can easily cause sensor orientation offset, and the offset is difficult to calibrate in real time, resulting in spatial registration errors in the environmental model. Existing systems lack adaptability in dynamic threshold setting: temperature difference thresholds mostly use fixed empirical values, which do not fully consider the differences in thermal characteristics of the enclosing structure. In particular, when thermal defects exist, the calculation deviation of steady-state temperature difference is significant, and the identification of thermal defects relies on manual inspections and it is difficult to quantify surface temperature deviations; density threshold setting is often based on static physical parameters, ignoring the dynamic changes in traffic efficiency. When the speed of personnel movement is reduced due to environmental factors, the original threshold is prone to failure. However, the high cost of speed monitoring and the difficulty of real-time quantification of the friction coefficient restrict improvement. Equipment control strategies suffer from extensive limitations. Air conditioning system adjustments rely on temperature feedback, failing to account for inter-regional thermal capacity differences. High and low thermal capacity areas respond differently to the same airflow, and the building material parameters required to calculate thermal capacity are often missing in existing buildings. The lighting system's trajectory prediction relies on linear extrapolation, failing to respond promptly to sharp turns. This results in delayed activation of high-light zones. This is due to the conflict between the high-frequency sampling required to calculate the curvature radius and the limited sensor refresh rate. Cross-device control commands lack a coordinated verification mechanism, and abnormal command detection faces a dilemma in threshold setting: too loose a threshold will miss slow-moving faults, while too strict a threshold will result in frequent false alarms. Furthermore, manual verification is affected by the coupling of device parameters, resulting in inefficient traceability. Cellular grid topology deployment must balance coverage density with the risk of communication conflicts. Excessively large node spacing creates blind spots for monitoring small-scale functional units, while too dense a spacing increases the probability of packet loss. The data synchronization requirements of high-precision spatial models conflict with the inherent latency of wireless transmission. These shortcomings lead to reduced energy efficiency, delayed emergency response, and increased equipment misoperation. Typical difficulties encountered during the improvement process include: the verification of thermal defect correction coefficients is restricted by the accessibility of the building's enclosed structure; the dynamic mapping of personnel movement speed and density thresholds requires support from large-scale behavioral experiments; and the command verification threshold is difficult to standardize due to differences in equipment models. Summary of the Invention

[0003] An object of the present invention is to provide an intelligent building monitoring system and method, which can effectively solve the problems of energy waste and delayed emergency response caused by the difficulty of existing systems in dynamically coordinating multi-region environmental parameters.

[0004] To achieve these objects and other advantages of the present invention, according to one aspect of the present invention, the present invention provides an intelligent building monitoring system, comprising an environmental parameter acquisition module, a central controller, an equipment control module, and a communication module; The environmental parameter acquisition module includes an infrared sensor array, a temperature sensor array, and a carbon dioxide concentration sensor array distributed in the three-dimensional space of the building; the environmental parameter acquisition module establishes a data connection with the central controller through a communication module, and the communication module adopts the LoRa wireless transmission protocol; The central controller includes a data processing unit and a strategy generation unit. The data processing unit normalizes the received environmental parameters to generate a multidimensional spatial data model including a temperature gradient distribution map, a personnel density heat map, and an air quality index matrix. The strategy generation unit generates an equipment control strategy based on the multidimensional spatial data model. When the temperature difference between adjacent areas in the temperature gradient distribution map exceeds a preset temperature difference threshold, an air conditioning linkage instruction is generated. When the density value in the personnel density heat map reaches a preset density threshold, an emergency evacuation channel opening instruction is generated. The equipment control module includes an air conditioning control unit, a lighting control unit, and a security control unit. The air conditioning control unit adjusts the air supply ratio of adjacent areas after receiving an air conditioning linkage command. The lighting control unit adjusts the lighting brightness in advance for a preset time period based on the movement trajectory prediction results of the personnel density heat map. The security control unit receives an emergency evacuation channel opening command and opens the emergency evacuation channel. An instruction verification mechanism is provided between the central controller and the equipment control module. When the control instruction parameters exceed the preset threshold range for a preset number of consecutive times, a manual review process is started and the abnormal instruction sequence is recorded.

[0005] Preferably, the infrared sensor array, temperature sensor array and carbon dioxide concentration sensor array in the environmental parameter acquisition module are deployed in the form of an integrated node device; Each of the node devices comprises an infrared sensor unit, a temperature sensor unit, and a carbon dioxide concentration sensor unit fixed in the same housing; The node devices are distributed in a honeycomb grid topology in the three-dimensional space of the building, and the distance between adjacent node devices is smaller than the geometric size of the smallest functional unit of the building structure; The optical axis direction of the infrared sensor unit, the direction of the heat-sensitive surface of the temperature sensor unit, and the direction of the gas collection port of the carbon dioxide concentration sensor unit maintain an orthogonal relationship in the three spatial axes; The node device is internally provided with a positioning calibration module, which detects the orientation offset of each sensor unit in real time through a three-axis gyroscope and triggers a mechanical correction mechanism to reset the sensor unit to a preset spatial coordinate direction.

[0006] Preferably, determining the preset temperature difference threshold comprises the following steps: During the system initialization phase, the building information model is loaded to extract the thermal conductivity, solar radiation absorption rate, and spatial volume parameters of the building envelope materials; Driven by typical meteorological day data, thermodynamic simulation calculations are performed to generate a steady-state temperature difference matrix for adjacent areas within the building; 60% to 80% of the maximum value in the steady-state temperature difference matrix is ​​used as the preset temperature difference threshold.

[0007] Preferably, the performing of thermodynamic simulation calculations driven by typical meteorological day data includes: Obtain historical extreme weather data for the building location, including hourly dry-bulb temperature and solar radiation intensity for the highest and lowest temperature days; Identify thermal defects in building envelope structures through infrared thermal imaging scanning; In thermodynamic simulations, mesh densification is performed on thermal defect areas, and the heat transfer coefficient is dynamically corrected based on surface temperature deviations. Perform forward heat transfer simulation on the day with the highest temperature and reverse heat transfer simulation on the day with the lowest temperature to generate a dual-condition steady-state temperature difference matrix. The temperature difference values ​​of the corresponding coordinates in the dual-condition steady-state temperature difference matrix are weighted and calculated, and the fused steady-state temperature difference matrix is ​​output, where the fused temperature difference = 0.6 × the maximum temperature daily temperature difference + 0.4 × the minimum temperature daily temperature difference.

[0008] Preferably, the method further comprises: obtaining a surface temperature distribution map of the building envelope structure by an infrared thermal imaging scanning device, calculating a theoretical surface temperature based on the ambient dry-bulb temperature, solar radiation illuminance, and envelope material properties, defining a continuous area where the difference between the actual surface temperature and the theoretical surface temperature exceeds ±1.5°C as a thermal defect area; in a thermodynamic simulation calculation, refining the mesh of the thermal defect area to 20% of the standard mesh size, and determining a thermal conductivity correction level according to a preset range within which the surface temperature deviation value belongs: When the absolute value of the deviation is between 1.6℃ and 2.5℃, the corrected thermal conductivity = the base thermal conductivity × 1.05; When the absolute value of the deviation is between 2.6℃ and 3.5℃, the corrected thermal conductivity = the base thermal conductivity × 1.10; When the absolute value of the deviation is greater than 3.6°C, the corrected thermal conductivity = the reference thermal conductivity × 1.15.

[0009] Preferably, determining the preset density threshold comprises the following steps: Obtain the physical property parameters of the evacuation passage, including the minimum clear width W, the maximum turning angle θ, and the ground friction coefficient μ; Calculate the traffic efficiency coefficient η: η = (W / W0) × cosθ × (μ / μ0), where W0 is the standard human body width, which is 0.6 m; μ0 is the standard ground friction coefficient, which is 0.5; Calculate the initial density threshold ρ according to the η value: ρ = ρ0 × (η / η0), where ρ0 is the basic density value, which is 2.0 people / m 2 ;η0 is the efficiency benchmark value, which is 0.85; Real-time monitoring of the speed v of people moving in the channel. When v is continuously lower than the theoretical speed v t When the preset ratio is reached, the density threshold ρ is updated according to the following relationship new =ρ×(v / v t ), where v t The value is 1.0 m / s.

[0010] Preferably, the air conditioning control unit performs the following when adjusting the air supply volume ratio of adjacent areas: Deploy heat flux sensor arrays at the borders of adjacent areas to monitor the heat flux density value Q, W / m in real time 2 ; Obtain the average thickness d and volume heat capacity C of the enclosure structures in the two areas respectively vol ; Calculate area heat capacity C area =C vol ×d, and calculate the area thermal tolerance ΔC between regions area ,J / (m 2 K): ΔC area =|C1-C2|, where C1 and C2 are the area heat capacities of the two regions respectively; According to Q and ΔC area The ratio of the air supply volume to the air supply volume is determined by: When Q / ΔC area When ≤0.05 K / s, K=0.7; When 0.05 K / s<Q / ΔC area When ≤0.10 K / s, K=0.6; When Q / ΔC area >0.10 K / s, K=0.5; The air supply volume is distributed according to the proportional coefficient K, that is, the air supply volume ratio of two adjacent areas is 1:K.

[0011] Preferably, when the lighting control unit adjusts the lighting brightness in advance, the following steps are performed: Based on the continuous personnel position coordinates collected by the infrared sensor array, the displacement change ΔS and the direction angle change Δθ between adjacent sampling points are calculated; The trajectory curvature radius R is calculated according to the following formula: ; When R≤3 m, immediately turn on the 90° fan-shaped strong light zone with the turning circle center as the vertex, with the lighting intensity of 150 lx; When 3 m<R≤10 m, the rectangular lighting strip of the predicted path ahead is turned on with a lighting intensity of 100 lx; When R>10 m, the 30° fan-shaped lighting area in the direction of movement is turned on 3 s in advance, with a lighting intensity of 75 lx. The present invention also provides a monitoring method based on an intelligent building monitoring system, comprising the following steps: Step 1: Synchronously collect infrared thermal radiation data, temperature data, and carbon dioxide concentration data within the building space through integrated node devices distributed in a honeycomb grid; Step 2: Normalize the data collected in step 1 to generate a decision model that includes a temperature gradient distribution map, a heat map of occupant density, and an air quality index matrix. Execute intelligent decisions based on the decision model: When the temperature difference between adjacent areas in the temperature gradient distribution map exceeds the preset temperature difference threshold, an air conditioning linkage command is generated; When the density value in the personnel density heat map reaches the preset density threshold, an emergency evacuation channel opening instruction is generated; Generate hierarchical lighting instructions based on the curvature radius R value of the personnel movement trajectory: Step 3: Perform three consecutive parameter checks on the air conditioning linkage command, emergency evacuation channel opening command, and graded lighting command generated in Step 2. If any of the checks exceeds the threshold range, a manual review is initiated; Step 4. Execute the instructions verified in step 3: adjust the air supply ratio of adjacent areas according to the air conditioning linkage instruction; control the security equipment according to the emergency evacuation channel opening instruction; operate the lighting equipment according to the graded lighting instruction.

[0012] The present invention has at least the following beneficial effects: It ensures multi-source data consistency through a distributed sensor array and spatially orthogonal layout, eliminates monitoring blind spots through a cellular grid topology, and maintains data acquisition accuracy through a real-time calibration mechanism. A temperature difference threshold setting method based on building information modeling and thermodynamic simulation integrates thermal defect identification and dynamic correction of the heat conductivity coefficient to improve threshold adaptability. The traffic efficiency coefficient is linked to real-time speed feedback to achieve dynamic optimization of the density threshold, resolving the problem of static parameter disconnection. Coupled analysis of heat flux density and thermal tolerance drives graded adjustment of the air supply ratio to avoid temperature oscillation. Graded lighting control based on trajectory curvature radius achieves instant strong light response for sharp turns (R ≤ 3 meters), path coverage for medium bends (3 < R ≤ 10 meters), and early lighting for gentle bends (R > 10 meters), eliminating turn delays. Multidimensional data modeling generates coordinated instructions, and continuous parameter verification intercepts anomalies. Ultimately, this achieves precise air supply control, timely emergency evacuation initiation, and seamless coordination of lighting guidance. Overall, this reduces spatial data error, minimizes temperature overshoot, compresses emergency response delays to less than 10 seconds, and reduces manual review workload, achieving an optimal balance between energy efficiency and safety.

[0013] Other advantages, objectives and features of the present invention will be reflected in part from the following description and will be understood by those skilled in the art through study and practice of the present invention. DETAILED DESCRIPTION

[0014] The present invention will be further described in detail below in conjunction with specific embodiments so that those skilled in the art can implement the invention with reference to the description.

[0015] It should be understood that terms such as “having”, “including” and “comprising” used herein do not preclude the existence or addition of one or more other elements or combinations thereof.

[0016] The present invention provides an intelligent building monitoring system, comprising: It includes environmental parameter acquisition module, central controller, equipment control module and communication module; The environmental parameter acquisition module includes an infrared sensor array, a temperature sensor array, and a carbon dioxide concentration sensor array distributed in the three-dimensional space of the building; the environmental parameter acquisition module establishes a data connection with the central controller through a communication module, and the communication module adopts the LoRa wireless transmission protocol; The central controller includes a data processing unit and a strategy generation unit. The data processing unit normalizes the received environmental parameters to generate a multidimensional spatial data model including a temperature gradient distribution map, a personnel density heat map, and an air quality index matrix. The strategy generation unit generates an equipment control strategy based on the multidimensional spatial data model. When the temperature difference between adjacent areas in the temperature gradient distribution map exceeds a preset temperature difference threshold, an air conditioning linkage instruction is generated. When the density value in the personnel density heat map reaches a preset density threshold, an emergency evacuation channel opening instruction is generated. The equipment control module includes an air conditioning control unit, a lighting control unit, and a security control unit. The air conditioning control unit adjusts the air supply ratio of adjacent areas after receiving an air conditioning linkage command. The lighting control unit adjusts the lighting brightness in advance for a preset time period based on the movement trajectory prediction results of the personnel density heat map. The security control unit receives an emergency evacuation channel opening command and opens the emergency evacuation channel. An instruction verification mechanism is provided between the central controller and the equipment control module. When the control instruction parameters exceed the preset threshold range for a preset number of consecutive times, a manual review process is started and the abnormal instruction sequence is recorded.

[0017] In this technical solution, in the environmental parameter acquisition module, the infrared sensor array can use pyroelectric infrared sensors, the temperature sensor array can use DS18B20 digital temperature sensors, and the carbon dioxide concentration sensor array can use NDIR principle gas sensors. Each sensor array is three-dimensionally distributed with a horizontal spacing of 8 to 12 meters and a vertical spacing of 3 to 4 meters. The communication module uses LoRa wireless transmission, the operating frequency band is 433 MHz, and the transmission distance covers 200 meters. The data processing unit of the central controller uses an ARM Cortex-M7 core processor, and the policy generation unit is embedded in the rule engine. The preset temperature difference threshold is set to 3°C, 4°C or 5°C, and the preset density threshold is set to 1.8 people / m 2 2.0 people / m 2 or 2.2 people / m 2 . The air conditioning control unit of the equipment control module is connected to the variable frequency fan coil unit, the lighting control unit is connected to the LED dimming driver, and the security control unit is connected to the electromagnetic door lock. The preset number of times of the instruction verification mechanism is 3 times, and the threshold range is set as: air supply volume ratio 0.3~1.0, lighting brightness 20~1000 lx, channel opening delay 0~10 seconds. During the implementation process, the infrared sensor detects human body thermal radiation in real time, the temperature sensor collects the ambient temperature every 10 seconds, and the carbon dioxide sensor updates the concentration value every 30 seconds. The collected data is transmitted to the central controller via LoRa, and the data processing unit normalizes the raw data: the temperature value is mapped to the range of -10℃~50℃, and the personnel density is 0.1 people / m 2The resolution is gridded, and the carbon dioxide concentration is converted to a scale of 0-5000 ppm. The strategy generation unit updates the temperature gradient distribution map every 5 minutes. When the temperature difference between adjacent grids exceeds the threshold (such as 4°C) for 2 minutes, an air conditioning linkage command is generated; the personnel density heat map is refreshed every 15 seconds, and the density value exceeds the threshold (such as 2.0 people / m) for 3 consecutive times. 2 ) generates an evacuation command. During command verification, three parameters are continuously tested: the air volume ratio verification range is 0.4-0.8; any excess freezes execution and triggers manual review. During the testing phase, an office building was selected for 72-hour monitoring. Data such as the number of temperature difference exceeding threshold events, command response delay, and false trigger rate were collected. A t-test was used to analyze the significance of system improvement (α=0.05). During assembly, infrared sensors and temperature sensors can be installed in the ceiling inspection port, 2.8 meters above the ground; carbon dioxide sensors can be installed on the wall at the breathing zone height (1.2 meters). The central controller can be deployed in the low-voltage room, and the device control module can be installed in the distribution box of the controlled equipment nearby. The communication module antenna can be placed at a commanding height in the building. A distributed array of infrared sensors, temperature sensors, and carbon dioxide concentration sensors collects multi-dimensional environmental parameters from the building space and transmits them to a central controller via LoRa wireless communication, achieving comprehensive monitoring data coverage. The central controller normalizes the collected data and constructs a spatial model with temperature gradient distribution maps, occupancy density heat maps, and an air quality index matrix. Based on preset temperature difference thresholds, it generates coordinated air conditioning commands to adjust the air supply ratio between adjacent zones, preventing energy waste caused by thermal imbalances between zones. It also generates emergency evacuation channel activation commands based on occupancy density thresholds, improving responsiveness in high-density scenarios. The device control module performs air supply ratio adjustment, predictive lighting brightness adjustment, and security channel control. The lighting unit optimizes lighting zones based on occupant movement trajectories, enhancing behavioral guidance accuracy. A continuous command verification mechanism between the central controller and device control modules intercepts abnormal parameter commands, reducing the risk of device misoperation. This overall approach achieves coordinated optimization of dynamic building environmental balance and security response. This implementation enables dynamic monitoring of environmental parameters in multiple zones of a building. The temperature difference threshold mechanism reduces overshoot in air conditioning zones, the density threshold triggers improve the timeliness of emergency channel activation, and the command verification mechanism reduces the risk of device misoperation. Ultimately, the technical effects of optimizing regional temperature balance, shortening emergency response time, and improving system control reliability are achieved.

[0018] In another technical solution, the infrared sensor array, temperature sensor array, and carbon dioxide concentration sensor array in the environmental parameter acquisition module are deployed in the form of an integrated node device; Each of the node devices comprises an infrared sensor unit, a temperature sensor unit, and a carbon dioxide concentration sensor unit fixed in the same housing; The node devices are distributed in a honeycomb grid topology in the three-dimensional space of the building, and the distance between adjacent node devices is smaller than the geometric size of the smallest functional unit of the building structure; The optical axis direction of the infrared sensor unit, the direction of the heat-sensitive surface of the temperature sensor unit, and the direction of the gas collection port of the carbon dioxide concentration sensor unit maintain an orthogonal relationship in the three spatial axes; The node device is internally provided with a positioning calibration module, which detects the orientation offset of each sensor unit in real time through a three-axis gyroscope and triggers a mechanical correction mechanism to reset the sensor unit to a preset spatial coordinate direction.

[0019] In this technical solution, the integrated node device can be constructed from flame-retardant ABS engineering plastic, housing a pyroelectric infrared sensor unit, a DS18B20 temperature sensor unit, and an NDIR carbon dioxide concentration sensor unit. The nodes are distributed within the building space in a honeycomb grid topology, with horizontal spacing adjustable to 8, 10, or 12 meters, and vertical spacing adjustable to 3, 3.5, or 4 meters. The infrared sensor's optical axis is aligned along the building's X-axis, the temperature sensor's thermal sensitive surface faces the Y-axis, and the carbon dioxide sensor's gas collection port is aligned along the Z-axis, forming an orthogonal spatial layout. The positioning and calibration module utilizes an MPU6050 three-axis gyroscope to monitor the orientation offset of each sensor unit in real time. The mechanical calibration mechanism utilizes a micro-stepping motor-driven universal joint. When a single-axis offset exceeds ±1.5°, a calibration is triggered, resetting the sensor to the preset orientation. After node installation, initial calibration is performed: a laser locator is used to calibrate the preset spatial coordinates (X / Y / Z axes) and record the gyroscope's baseline values. During operation, offset measurements are collected every 5 minutes. If the infrared sensor's optical axis deflects >1.5°, the stepper motor drives the universal joint to rotate and compensate for the angular deviation. Calibration accuracy is verified by data consistency before and after the offset: in a standard temperature-controlled environment, the infrared radiation intensity readings from the same heat source are compared before and after calibration, with a requirement of ≤±5%. During the testing phase, 20 nodes were selected for an artificial offset experiment, applying 2°, 3°, and 4° step offsets in the X / Y / Z axes. Calibration completion time and data deviation were recorded. Analysis of variance was used to evaluate the change in the dispersion of temperature measurements before and after calibration (significance level α = 0.01). The node housing can be mounted on a ceiling keel bracket at a height of 2.6 to 3.0 meters above the ground; the gyroscope can be fixed to the housing's center plate, and the stepper motor can be connected to the sensor mounting base.

[0020] This implementation utilizes an orthogonal layout to ensure consistent spatial benchmarking for infrared position, temperature, and gas concentration data. The honeycomb grid layout adapts to the minimum size of a building's functional units (e.g., a 4m x 4m room). Real-time offset detection and mechanical correction mitigate data distortion caused by installation errors, maintaining the accuracy of modeling temperature gradient distribution maps and occupancy density heat maps. Ultimately, the technical effect of achieving a spatial registration error of ≤0.3 meters for multi-source sensor data is achieved. The integrated node device's housing design secures the infrared sensor, temperature sensor, and carbon dioxide concentration sensor units to a unified spatial reference, ensuring geometric registration accuracy for multi-source data acquisition. The honeycomb grid topology adapts to the building's minimum functional unit size, with horizontal spacing of 8 / 10 / 12 meters and vertical spacing of 3 / 3.5 / 4 meters ensuring seamless three-dimensional coverage. The orthogonal layout of the sensor units along all three axes ensures a strict spatial mapping of infrared positioning, temperature monitoring, and gas concentration detection data. The positioning calibration module detects triaxial offset in real time. When the offset exceeds ±1.5°, a mechanical correction mechanism is triggered, which uses a stepper motor-driven universal joint to reset the sensor's orientation, effectively suppressing data distortion caused by building vibration or installation deformation. The node device is installed on a ceiling keel bracket, balancing concealment and ease of maintenance. This achieves a multimodal sensor spatial registration error of ≤0.3 meters, improving the accuracy of modeling temperature gradient distribution maps and occupant density heat maps.

[0021] In another technical solution, determining the preset temperature difference threshold includes the following steps: During the system initialization phase, the building information model is loaded to extract the thermal conductivity, solar radiation absorption rate, and spatial volume parameters of the building envelope materials; Driven by typical meteorological day data, thermodynamic simulation calculations are performed to generate a steady-state temperature difference matrix for adjacent areas within the building; 60% to 80% of the maximum value in the steady-state temperature difference matrix is ​​used as the preset temperature difference threshold.

[0022] In this technical solution, when determining the preset temperature difference threshold, the building information model can be imported into the Revit format file to extract the enclosure structure parameters: XPS insulation board (thermal conductivity coefficient 0.035 W / (m·K)) can be used for the exterior wall, and Low-E insulating glass (solar radiation absorption rate 0.25) can be used for the glass curtain wall. The spatial volume parameters are automatically calculated through the model, such as the volume of a single office room of 80~120m 3. The typical meteorological day data are derived from the records of the local meteorological station in the past ten years. The highest temperature day is July 15 (average daily temperature 32°C), and the lowest temperature day is January 20 (average daily temperature -5°C). ANSYS Fluent software is used for thermodynamic simulation. When the steady-state temperature difference matrix is ​​generated, the temperature difference between adjacent areas can be 3.2°C, 4.1°C or 5.0°C. The preset temperature difference threshold is set to 60%, 70% or 80% of the maximum steady-state temperature difference. For example, when the maximum temperature difference is 6.5°C, the threshold is 4.6°C (70%). After loading the building information model in the initialization stage, the standard grid size is divided into 0.5 m×0.5 m. Simulation settings for the highest temperature day: outdoor temperature 32°C, solar radiation illuminance 850 W / m 2 Calculate for 8 hours until thermal equilibrium state; the simulation settings for the lowest temperature day are: outdoor temperature -5℃, irradiance 150 W / m 2 . The steady-state temperature difference matrix outputs the temperature difference data of adjacent functional areas, such as the temperature difference between the corridor and the office is 4.3℃, and the temperature difference between the core tube and the open area is 5.8℃. Example of preset threshold calculation: when the maximum steady-state temperature difference is 5.8℃, take 70% to get 4.1℃. In the verification phase, the actual summer temperature difference of three buildings was selected to compare the threshold setting value with the actual imbalance value, and the correlation coefficient was analyzed by linear regression (significance level α=0.05). The building information model workstation can be deployed in the data center computer room, the meteorological database server can be connected to the meteorological bureau API interface, and the thermodynamic simulation software runs on a 32-core computing node.

[0023] This implementation method adapts the temperature difference threshold to the actual thermal environment of the building by quantifying the thermal characteristics of the envelope structure and extreme meteorological conditions; the percentage setting based on the maximum value of the steady-state temperature difference matrix avoids the misjudgment of the fixed threshold under regional thermal inertia differences. Ultimately, the accuracy of the air-conditioning linkage command is improved and the temperature adjustment lag phenomenon is reduced. By loading the building information model to accurately extract the thermal conductivity coefficient and solar radiation absorption rate parameters of the envelope structure, and combining the extreme temperature data of typical meteorological days to drive thermodynamic simulation, the temperature difference threshold setting truly reflects the thermal characteristics of the building; the percentage threshold based on the maximum value of the steady-state temperature difference matrix (60% / 70% / 80%) dynamically adapts to the thermal inertia differences in different regions, avoiding the misjudgment of fixed empirical values ​​in the glass curtain wall area and the concrete core area; the dual-condition simulation of the highest temperature day and the lowest temperature day covers the extreme thermal environment, ensuring the universality of the threshold under high heat load in summer and low heat load in winter; gridded space volume calculation (such as 80~120m2 for a single room) 3 ) improves the spatial resolution of the temperature difference matrix. Ultimately, the triggering conditions for the air conditioning linkage command match the actual thermal imbalance state, reducing the regulation lag caused by regional overcooling or overheating.

[0024] In another technical solution, performing thermodynamic simulation calculations driven by typical meteorological day data includes: Obtain historical extreme weather data for the building location, including hourly dry-bulb temperature and solar radiation intensity for the highest and lowest temperature days; Identify thermal defects in building envelope structures through infrared thermal imaging scanning; In thermodynamic simulations, mesh densification is performed on thermal defect areas, and the heat transfer coefficient is dynamically corrected based on surface temperature deviations. Perform forward heat transfer simulation on the day with the highest temperature and reverse heat transfer simulation on the day with the lowest temperature to generate a dual-condition steady-state temperature difference matrix. The temperature difference values ​​of the corresponding coordinates in the dual-condition steady-state temperature difference matrix are weighted and calculated, and the fused steady-state temperature difference matrix is ​​output, where the fused temperature difference = 0.6 × the maximum temperature daily temperature difference + 0.4 × the minimum temperature daily temperature difference.

[0025] In this technical solution, in the thermodynamic simulation of a typical meteorological day, historical extreme meteorological data can be used to call the API interface of the Meteorological Bureau. The highest temperature day is selected when the dry bulb temperature is ≥35℃ and the solar radiation illuminance is ≥900 W / m 2 The lowest temperature day is selected when the dry bulb temperature is ≤-8℃ and the irradiance is ≤180 W / m 2 Infrared thermal imaging scanning can be performed using a handheld thermal imager with a wavelength of 3 to 5 μm. The definition of a thermal defect area is: a continuous area where the actual surface temperature deviates from the theoretical calculated value by more than ±1.5°C, and the area threshold is ≥0.5 m 2 When refining the mesh for thermodynamic simulations, the mesh size in defective areas can be set to 20% of the standard size (0.5m × 0.5m), that is, 0.1m × 0.1m. Thermal conductivity correction is performed based on the deviation range: the correction factor is 1.05 for deviations between ±1.6°C and ±2.5°C, 1.10 for deviations between ±2.6°C and ±3.5°C, and 1.15 for deviations above ±3.6°C. The formula for fusion of the dual-condition steady-state temperature difference matrix is: Fusion temperature difference = 0.6 × maximum daily temperature difference + 0.4 × minimum daily temperature difference. A thermal imager performs a grid scan of the building facade (1m × 1m measurement points), and the theoretical surface temperature is calculated based on the real-time dry-bulb temperature, solar radiation, and the material properties of the building envelope. After identifying areas with deviations exceeding ±1.5°C, the mesh in the defective area is locally refined to 0.1m × 0.1m in ANSYS Fluent. Perform dual-condition simulation: set the outdoor temperature to 35°C and the irradiance to 900W / m on the day with the highest temperature 2 The lowest temperature on a day with outdoor temperature set to -8°C and irradiance set to 180 W / m 2The fusion outputs a steady-state temperature difference matrix. For example, the fused temperature difference for a defective area on a west-facing wall under dual operating conditions is 4.8°C. During the verification phase, five known areas of insulation hollowing were selected. The calculated and measured temperature differences before and after correction were compared, and significance was analyzed using a paired sample t-test (α = 0.01). The infrared thermal imaging scanner can be handheld by the inspector, and the theoretical temperature calculation module can be deployed on an edge computing terminal.

[0026] This implementation method uses a ±1.5℃ deviation threshold to accurately identify thermal defect areas, and a 20% mesh density to improve the accuracy of local heat transfer simulations. A three-level thermal conductivity correction (1.05 / 1.10 / 1.15) quantifies the impact of material degradation. A dual-condition weighted fusion (0.6 maximum value + 0.4 minimum value) allows the steady-state temperature difference matrix to take into account extreme cold and hot load scenarios. Ultimately, the accuracy of the temperature difference threshold setting for thermal defect areas of the enclosure structure is optimized. By using a ±1.5℃ deviation threshold and a ≥0.5 m 2 The area threshold accurately identifies thermal defect areas of the enclosure structure, solving the problem of omissions in manual inspections; the local grid is encrypted to 20% of the standard size (0.1m×0.1m) to improve the resolution of heat transfer simulation in the defect area; the three-level thermal conductivity coefficient correction (1.05 / 1.10 / 1.15) based on the deviation range quantifies the impact of material degradation on thermal performance; the maximum temperature day (≥35℃ / 900 W / m 2 ) and the lowest temperature day (≤-8℃ / ≤180 W / m 2 Dual-condition simulation covers extreme hot and cold load scenarios, and outputs a steady-state temperature difference matrix through weighted fusion of 0.6 and 0.4, avoiding the environmental limitations of single-condition simulation. Ultimately, this optimizes the accuracy of temperature difference threshold settings in areas with thermal defects, addressing the threshold deviation problem caused by traditional methods that ignore material aging.

[0027] Another technical solution further includes: obtaining a surface temperature distribution map of the building envelope structure using an infrared thermal imaging scanning device; calculating a theoretical surface temperature based on the ambient dry-bulb temperature, solar radiation illuminance, and the material properties of the envelope structure; defining a continuous area where the difference between the actual surface temperature and the theoretical surface temperature exceeds ±1.5°C as a thermal defect area; and refining the mesh of the thermal defect area to 20% of the standard mesh size in a thermodynamic simulation calculation. Determining a thermal conductivity correction level based on the preset range within which the surface temperature deviation value falls: When the absolute value of the deviation is between 1.6℃ and 2.5℃, the corrected thermal conductivity = the base thermal conductivity × 1.05; When the absolute value of the deviation is between 2.6℃ and 3.5℃, the corrected thermal conductivity = the base thermal conductivity × 1.10; When the absolute value of the deviation is greater than 3.6°C, the corrected thermal conductivity = the reference thermal conductivity × 1.15.

[0028] In this technical solution, the infrared thermal imaging scanning device can use a handheld thermal imager with a working band of 3~5 μm. The theoretical surface temperature is calculated based on the ambient dry-bulb temperature (obtained from the local weather station API), solar radiation illuminance (obtained from the building roof irradiance meter) and the properties of the envelope material: the benchmark thermal conductivity of concrete is 1.75 W / (m·K), and the benchmark value of the insulation material is 0.035W / (m·K). The thermal defect area is defined as a continuous area where the difference between the actual surface temperature and the theoretical value exceeds ±1.5°C, and the minimum continuous area is ≥0.5 m 2 In the thermodynamic simulation calculation, the grid of the defective area is encrypted to 20% of the standard size (0.5m×0.5m), that is, 0.1m×0.1m. The thermal conductivity correction level is implemented according to the deviation range: when the deviation is ±1.6℃~±2.5℃, the correction factor = reference value×1.05; when the deviation is ±2.6℃~±3.5℃, the correction factor = 1.10; when the deviation is ≥±3.6℃, ​​the correction factor = 1.15. The inspector scans the building facade according to the 2m×2m grid and records the actual temperature at each point. The theoretical temperature is calculated by the formula: T 理论 =T 空气 + (α×I) / h; where α is the material absorption rate (concrete 0.65, glass 0.25), I is the real-time irradiance, and h is 23 W / (m 2 K). Identify continuous out-of-tolerance areas (e.g., a concrete wall with a 3.2 m 2 The area had a deviation of +2.8°C. This area was divided into a 0.1m×0.1m grid in ANSYS Fluent. Based on the deviation of +2.8°C (which falls within the range of ±2.6°C to ±3.5°C), the concrete thermal conductivity was corrected to 1.75×1.10=1.925 W / (m·K). During the verification phase, five known hollowing areas were selected and the root mean square error (RMSE) of the simulated temperature distribution before and after correction was compared with the measured data from the thermal imager. The RMSE after correction was required to be ≤0.8°C. The thermal imager was handheld by the inspector, and the theoretical temperature calculation module ran on an edge computing terminal deployed in the equipment room.

[0029] This embodiment uses ±1.5℃ deviation and 0.5m 2 The continuous area dual condition accurately identifies thermal defects; 20% mesh size density improves local heat transfer simulation accuracy; three-level correction coefficient (1.05 / 1.10 / 1.15) quantifies the material degradation gradient to avoid over- or under-adjustment caused by homogenization correction. Finally, the accuracy of the temperature difference threshold setting in areas with hollowing and cracks is optimized. Through the ±1.5℃ temperature deviation threshold and ≥0.5 m 2The dual conditions of continuous area thresholds accurately identify thermal defects such as hollows and cracks in the envelope structure, avoiding misjudgments caused by sporadic temperature noise. Locally refining the standard grid size (0.5 m × 0.5 m) by 20% (0.1 m × 0.1 m) in defective areas significantly improves the computational resolution of local heat transfer in thermodynamic simulations. A three-level thermal conductivity correction strategy based on deviation ranges (×1.05 for ±1.6°C to ±2.5°C, ×1.10 for ±2.6°C to ±3.5°C, and ×1.15 for ≥±3.6°C) quantifies the degree of material degradation and addresses the over- or under-correction issues associated with traditional homogenization correction. Ultimately, the accuracy of temperature difference threshold settings in thermally defective areas is optimized, ensuring that air conditioning linkage commands more closely match the actual thermal state of the envelope structure.

[0030] In another technical solution, determining the preset density threshold includes the following steps: Obtain the physical property parameters of the evacuation passage, including the minimum clear width W, the maximum turning angle θ, and the ground friction coefficient μ; Calculate the traffic efficiency coefficient η: η = (W / W0) × cosθ × (μ / μ0), where W0 is the standard human body width, which is 0.6 m; μ0 is the standard ground friction coefficient, which is 0.5; Calculate the initial density threshold ρ according to the η value: ρ = ρ0 × (η / η0), where ρ0 is the basic density value, which is 2.0 people / m 2 ;η0 is the efficiency benchmark value, which is 0.85; Real-time monitoring of the speed v of people moving in the channel. When v is continuously lower than the theoretical speed v t When the preset ratio is reached, the density threshold ρ is updated according to the following relationship new =ρ×(v / v t ), where v t The value is 1.0 m / s.

[0031] In this technical solution, in the measurement of the physical property parameters of the evacuation channel, the minimum clear width W can be obtained by a laser rangefinder; the maximum turning angle θ can be measured by a digital inclinometer; and the ground friction coefficient μ can be tested by a pendulum friction coefficient tester. The traffic efficiency coefficient η is calculated using the formula: η=(W / 0.6)×cosθ×(μ / 0.5), where the standard human body width W0=0.6 meters and the standard friction coefficient μ0=0.5. The basic density value ρ0 is 2.0 people / m 2 , the efficiency benchmark value η0 is 0.85. The initial density threshold ρ = 2.0 × (η / 0.85), for example, when η = 0.7, ρ ≈ 1.65 people / m 2 The real-time personnel movement speed v can be calculated by infrared sensor array displacement detection, and the theoretical speed v tFixed at 1.0 m / s. When v is lower than 0.8v for 30 seconds t (i.e. 0.8 m / s), update the density threshold ρ new =ρ×(v / 1.0). W0 is 0.6 m, representing the standard human body width. This is calculated based on the 95th percentile male shoulder width (489 mm) in the Chinese Adult Human Body Dimension Standard (GB / T 10000-1988) plus the winter clothing value (approximately 110 mm), rounded to the nearest 0.6 m for safety design. μ0 is 0.5, representing the standard floor friction coefficient, based on the lower limit of the dynamic friction coefficient for dry stone floors (0.50-0.59) in the "Technical Code for Anti-Slip Construction Floor Engineering" (JGJ / T 331-2014). ρ0 is 2.0 people / m 2 The base density value is based on Article 5.5.21 of the Code for Fire Protection Design of Buildings (GB 50016-2014), which states that the maximum density of a passageway is 2.0 people / m². η0 is set at 0.85, the efficiency benchmark. Based on the Level of Service theory proposed by Fruin, η = 0.85 corresponds to the efficiency threshold of "Acceptable Level of Service" (LOS C). t The theoretical walking speed is 1.0 m / s, based on Article 3.2.5 of the Code for Evacuation Design of Civil Buildings (GB / T 51348-2019), which stipulates a design walking speed of 1.0 m / s for horizontal passages. Infrared sensors were used to measure the average walking speed of 100 healthy adults, which was 1.05 ± 0.15 m / s. This 1.0 m / s value covers 90% of the population.

[0032] During the installation phase, measure channel parameters: For example, if the width W = 1.5 meters (rangefinder reading), the rotation angle θ = 45° (angle meter reading), and the friction coefficient μ = 0.5 (measurement instrument data), calculate η = (1.5 / 0.6) × cos45° × (0.5 / 0.5) = 2.5 × 0.707 × 1 ≈ 1.77. The initial density threshold ρ = 2.0 × (1.77 / 0.85) ≈ 4.16 people / m 2 During operation, the speed of movement of personnel is calculated every 5 seconds: based on the time difference ΔT of the infrared signals of adjacent nodes and the distance ΔS, v = ΔS / ΔT. When it is detected that the v sampling value is ≤ 0.8 m / s for 6 consecutive times (i.e. for 30 seconds), the threshold ρ is updated. new =4.16×(0.8 / 1.0)=3.33 people / m 2 The verification phase simulates sudden congestion scenarios and compares dynamic thresholds with fixed thresholds (e.g. 3.5 people / m 2) were analyzed for significance using a chi-square test (α = 0.05). The laser rangefinder can be installed at the top of the tunnel entrance, the inclinometer can be fixed to the corner wall, and the friction coefficient meter can be handheld. The infrared sensor array can be deployed on the side wall of the tunnel, 1.2 meters above the ground.

[0033] This implementation method quantifies the influence of channel physical properties by the traffic efficiency coefficient η, and the initial density threshold ρ is dynamically adjusted with the value of η; the real-time speed feedback mechanism is continuously lower than 0.8v t The density threshold is automatically lowered when the vehicle is moving, adapting to the reduced traffic capacity caused by slippery surfaces or large turns. Ultimately, the accuracy of the triggering time of emergency evacuation instructions is improved, reducing the risk of misjudgment or omission of congestion. The physical parameters of the channel (width W, angle θ, friction coefficient μ) are accurately obtained through laser rangefinders, digital angle meters and friction coefficient meters. The traffic efficiency coefficient is quantified based on the formula η=(W / 0.6)×cosθ×(μ / 0.5), so that the initial density threshold ρ=2.0×(η / 0.85) dynamically adapts to different channel structural characteristics; real-time infrared displacement detection calculates the personnel movement speed v, and when v is lower than 0.8 m / s for 30 seconds, press ρ new =ρ×(v / 1.0) automatically lowers the threshold to address the sudden drop in traffic capacity caused by slippery surfaces or large turns. This ultimately improves the accuracy of triggering emergency evacuation commands in complex channel environments and avoids misjudgments of congestion under fixed threshold mechanisms.

[0034] In another technical solution, the air conditioning control unit adjusts the air supply volume ratio of adjacent areas by performing: Deploy heat flux sensor arrays at the borders of adjacent areas to monitor the heat flux density value Q, W / m in real time 2 ; Obtain the average thickness d and volume heat capacity C of the enclosure structures in the two areas respectively vol ; Calculate area heat capacity C area =C vol ×d, and calculate the area thermal tolerance ΔC between regions area ,J / (m 2 K): ΔC area =|C1-C2|, where C1 and C2 are the area heat capacities of the two regions respectively; According to Q and ΔC area The ratio of the air supply volume to the air supply volume is determined by: When Q / ΔC area When ≤0.05 K / s, K=0.7; When 0.05 K / s<Q / ΔC area When ≤0.10 K / s, K=0.6; When Q / ΔC area>0.10 K / s, K=0.5; The air supply volume is distributed according to the proportional coefficient K, that is, the air supply volume ratio of two adjacent areas is 1:K.

[0035] In this technical solution, a heat flux sensor array is deployed at the boundary of adjacent areas, and the range can be selected from 0 to 300 W / m 2 Thermopile sensor. The average thickness of the enclosure structure is obtained by ultrasonic thickness gauge. Volume heat capacity C vol Determined by material: Concrete takes 2.4×10 6 J / (m 3 ·K), for glass, 0.8×10 6 J / (m 3 ·K). Calculation of area heat capacity: C area =C vol ×d, such as concrete area (C vol =2.4×10 6 J / (m 3 ·K), d=0.3 m) and C1=7.2×10 5 J / (m 2 ·K), glass area (C vol =0.8×10 6 J / (m 3 K), d = 0.01m) and C2 = 8 × 10 3 J / (m 2 ·K). Thermal tolerance ΔC area =7.12×10 5 J / (m 2 ·K). The heat flux Q is collected every 15 seconds and is calculated based on Q / ΔC area The proportional coefficient K is determined by the value, and the air supply volume is distributed to the two areas at a ratio of 1:K.

[0036] The heat flux sensor is installed at the boundary ceiling 2.6 meters above the ground, and the ultrasonic thickness gauge can be handheld. The area heat capacity database is stored in the local server. This embodiment introduces the average thickness parameter d of the enclosure structure to convert the volume heat capacity C vol Convert to area heat capacity C area (C area =C vol × d), so that the thermal tolerance ΔC area More accurate characterization of thermal inertia per unit area; based on Q / ΔC area The air supply ratio coefficient K is set by the graded threshold to solve the problem of inaccurate air supply distribution caused by the traditional method ignoring the difference in structure thickness. Ultimately, the temperature balance of adjacent areas is improved. The area heat capacity C is calculated by measuring the thickness d of the enclosure structure. area (C area =C vol× d), so that the thermal tolerance ΔC area Accurately reflect the difference in thermal inertia per unit area; combined with the boundary heat flux density Q, according to Q / ΔC area A three-level threshold dynamically sets the air supply ratio coefficient K. A 1:K air supply distribution mechanism gives higher air supply weight to high-heat capacity areas (K<1), resolving temperature regulation lags caused by varying structural thicknesses. Ultimately, this optimizes thermal balance control accuracy across adjacent areas.

[0037] Measured in a typical office scenario: Normal working conditions (Q / ΔC area =0.03 K / s): Using K=0.7, the temperature difference is stable at 1.2±0.3℃, and the energy consumption is reduced by 18% compared with traditional temperature control; Sudden load conditions (machine room equipment startup, Q / ΔC area =0.08 K / s): Using K=0.6, the temperature difference balance is restored within 120 seconds, preventing the temperature from overshooting by 2.1°C; Extreme working conditions (curtain wall cold radiation in winter, Q / ΔC area =0.15 K / s): Using K=0.5, the temperature difference drops from 4.5°C to 1.8°C in just 90 seconds, saving 23% energy and without oscillation.

[0038] Ultimately, the thermal balance control accuracy of adjacent areas was optimized, the temperature difference fluctuation range was reduced by 62%, and the response speed was increased by 3 times.

[0039] In another technical solution, the lighting control unit performs the following when adjusting the lighting brightness in advance: Based on the continuous personnel position coordinates collected by the infrared sensor array, the displacement change ΔS and the direction angle change Δθ between adjacent sampling points are calculated; The trajectory curvature radius R is calculated according to the following formula: ; When R≤3 m, immediately turn on the 90° fan-shaped strong light zone with the turning circle center as the vertex, with the lighting intensity of 150 lx; When 3 m<R≤10 m, the rectangular lighting strip of the predicted path ahead is turned on with a lighting intensity of 100 lx; When R>10 m, the 30° fan-shaped lighting area in the direction of movement is turned on 3 s in advance, with a lighting intensity of 75 lx. In this technical solution, the lighting control unit uses an infrared sensor array to collect the coordinates of a person's location, with a sampling interval of 0.5 seconds. The displacement change ΔS is used to calculate the straight-line distance between adjacent sampling points, and the angular change Δθ is used to calculate the heading angle difference between three consecutive points. The trajectory curvature radius R is calculated using the formula R = |ΔS| / |Δθ| (angle units are radians). The lighting response strategy is implemented in a hierarchical manner: when R ≤ 3 meters, a 90° sector-shaped bright light zone with an intensity of 150 lx, centered at the turning circle, is immediately activated. When 3 meters < R ≤ 10 meters, a rectangular lighting strip with a width of 1.2 meters and an intensity of 100 lx is activated along the predicted path ahead. When R > 10 meters, a 30° sector-shaped lighting zone with an intensity of 75 lx is activated three seconds in advance in the direction of movement. A 0-10V dimming LED driver is recommended. The infrared sensor outputs a real-time coordinate sequence, such as three consecutive points P1 (x1, y1), P2 (x2, y2), and P3 (x3, y3). Calculate ΔS = √((x² - x1)² + (y² - y1)²), and Δθ = |arctan((y³ - y²) / (x³ - x2)) - arctan((y² - y1) / (x² - x1))|. If ΔS = 1.2 meters and Δθ = 0.4 radians, then R = 1.2 / 0.4 = 3 meters (triggering the 90° bright light zone). During the testing phase, three turning behaviors were simulated: sharp turns (R = 2.5 meters), medium turns (R = 6 meters), and gentle turns (R = 15 meters). The delay from turn detection to full illumination was measured. The response requirements for sharp turns were ≤ 0.3 seconds, for medium turns ≤ 1 second, and for gentle turns, a lead time of 3 seconds was met. Infrared sensors were installed on the corridor sidewalls 2.2 meters above the floor. Luminaires in the 90° bright light zone were deployed at the ceiling turning nodes. Rectangular lighting strips consisted of linear LEDs, and the 30° fan-shaped zones were tilted 22 degrees.

[0040] This implementation uses a real-time, graded response based on the curvature radius R: When R ≤ 3 meters, strong light is applied to address blind spots in sharp turns; when R < 10 meters, a rectangular strip covers the predicted path; and when R > 10 meters, preemptive lighting is applied to accommodate gentle curves. This ultimately reduces lighting response delays during turns and improves behavioral guidance safety. By calculating the curvature radius R of the person's trajectory in real time (R = |ΔS| / |Δθ|), a graded lighting response is triggered: when R ≤ 3 meters, a 90° sector of strong light (150 lx) is immediately activated to address blind spots in sharp turns; when R < 10 meters, a rectangular strip of light (100 lx) is activated to cover the predicted path; and when R > 10 meters, a 30° sector of light (75 lx) is activated three seconds in advance to accommodate gentle curves. This curvature radius grading mechanism adapts to different turning behavior characteristics, addressing the lighting delay issue associated with traditional linear prediction and improving the timeliness of visual guidance on complex paths.

[0041] The present invention also provides a monitoring method based on an intelligent building monitoring system, comprising the following steps: Step 1: Synchronously collect infrared thermal radiation data, temperature data, and carbon dioxide concentration data within the building space through integrated node devices distributed in a honeycomb grid; Step 2: Normalize the data collected in step 1 to generate a decision model that includes a temperature gradient distribution map, a heat map of occupant density, and an air quality index matrix. Execute intelligent decisions based on the decision model: When the temperature difference between adjacent areas in the temperature gradient distribution map exceeds the preset temperature difference threshold, an air conditioning linkage command is generated; When the density value in the personnel density heat map reaches the preset density threshold, an emergency evacuation channel opening instruction is generated; Generate hierarchical lighting instructions based on the curvature radius R value of the personnel movement trajectory: Step 3: Perform three consecutive parameter checks on the air conditioning linkage command, emergency evacuation channel opening command, and graded lighting command generated in Step 2. If any of the checks exceeds the threshold range, a manual review is initiated; Step 4. Execute the instructions verified in step 3: adjust the air supply ratio of adjacent areas according to the air conditioning linkage instruction; control the security equipment according to the emergency evacuation channel opening instruction; operate the lighting equipment according to the graded lighting instruction.

[0042] In this technical solution, the integrated node devices are deployed in a honeycomb grid topology with a horizontal spacing of 10 meters and a vertical spacing of 3.5 meters. Each node synchronously collects infrared thermal radiation, temperature and CO2 concentration data with a sampling frequency of 1 Hz. The data processing unit of the central controller normalizes the raw data: the temperature value is mapped to the range of -10℃~50℃ (resolution 0.1℃), the population density is rasterized (0.1 people / m 2 The CO2 concentration is linearly converted to a scale of 0 to 5000 ppm. The decision model updates the temperature gradient distribution map, occupant density heat map, and air quality index matrix every 30 seconds. The preset temperature difference threshold is 4.0°C. When the temperature difference between adjacent grids is ≥4.0°C for 2 minutes, an air conditioning linkage command is generated. The preset density threshold is 2.0 people / m 2 , when the grid value in the density heat map is ≥ 2.0 people / m3 for three consecutive times 2 An evacuation command is generated when the trajectory curvature radius R is calculated as |ΔS| / |Δθ|. If R ≤ 3 meters, a 90° strong light zone (150 lx) is triggered. If 3 < R ≤ 10 meters, a rectangular lighting strip (100 lx) is triggered. If R > 10 meters, a 30° fan-shaped zone (75 lx) is activated 3 seconds in advance.

[0043] The specific implementation process is as follows: Step 1: The node device uploads sensor data packets every 1 second, which are aggregated to the central controller via the LoRa gateway.

[0044] Step 2: The data processing unit performs normalization and spatial interpolation to build a decision model. Example of a temperature difference exceeding a threshold: A sustained temperature difference of 4.2°C (>4.0°C) between office area A (25.3°C) and corridor B (29.5°C) generates an air conditioning linkage command.

[0045] Step 3: Verify command parameters three times (at 5-second intervals): Air supply ratio verification range 0.4-0.8, lighting intensity verification range 50-200 lx, and channel delay verification range 0-10 seconds. If the air supply ratio command is 0.35 (out of range), execution is frozen and manual review is triggered.

[0046] Step 4: Execute the verified commands: the air conditioning control unit adjusts the fan coil unit's air volume ratio, the security unit unlocks the electromagnetic door lock, and the lighting unit dims according to the R value. During the testing phase, scenarios such as temperature difference exceeding the threshold, density exceeding the limit, and sudden temperature change are simulated 20 times each. The end-to-end latency from command generation to execution is recorded, with an average requirement of ≤2.5 seconds.

[0047] The node device is installed at the ceiling inspection port, the central controller is deployed in the weak current room, and the LoRa gateway is placed at the commanding height of the building. This implementation method ensures the temporal and spatial consistency of data through synchronous collection of cellular grids; the decision model integrates multi-dimensional parameters to generate collaborative instructions; triple instruction verification intercepts parameter anomalies; and the hierarchical execution mechanism realizes closed-loop control of air conditioning supply ratio adjustment, precise opening and closing of emergency passages, and dynamic guidance of lighting. Ultimately, a collaborative execution system for building environment monitoring and emergency response is constructed. The integrated node device of the cellular grid topology realizes the synchronous collection of multi-source environmental data to ensure the unified temporal and spatial benchmarks of temperature, population density and air quality parameters; the decision model integrates the temperature gradient distribution map, the population density heat map and the air quality index matrix to generate collaborative instructions, so that air conditioning linkage (temperature difference ≥ 4.0℃ for 2 minutes), emergency evacuation (density ≥ 2.0 people / m 2 The system triggers the system three times in a row (for example, three times in a row) and lighting grading (based on the curvature radius R value) to precisely match actual operating conditions. Three consecutive command parameter checks prevent risks such as excessive air supply ratios (<0.4 or >0.8) and abnormal lighting intensity (<50 lx or >200 lx). The execution phase dynamically adjusts the air supply ratio, opens and closes evacuation routes, and operates lighting equipment based on the verification results, achieving a closed-loop multi-system control system. This ultimately forms a complete chain of building environment monitoring, decision-making, verification, and execution, improving system coordination and reliability under complex operating conditions.

[0048] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the description and implementation methods. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to specific details.

Claims

1. Intelligent building monitoring system, characterized by: It includes environmental parameter acquisition module, central controller, equipment control module and communication module; The environmental parameter acquisition module includes an infrared sensor array, a temperature sensor array, and a carbon dioxide concentration sensor array distributed in the three-dimensional space of the building; the environmental parameter acquisition module establishes a data connection with the central controller through a communication module, and the communication module adopts the LoRa wireless transmission protocol; The central controller includes a data processing unit and a strategy generation unit. The data processing unit normalizes the received environmental parameters to generate a multidimensional spatial data model including a temperature gradient distribution map, a personnel density heat map, and an air quality index matrix. The strategy generation unit generates an equipment control strategy based on the multidimensional spatial data model. When the temperature difference between adjacent areas in the temperature gradient distribution map exceeds a preset temperature difference threshold, an air conditioning linkage instruction is generated. When the density value in the personnel density heat map reaches a preset density threshold, an emergency evacuation channel opening instruction is generated. The equipment control module includes an air conditioning control unit, a lighting control unit, and a security control unit. The air conditioning control unit adjusts the air supply ratio of adjacent areas after receiving an air conditioning linkage command. The lighting control unit adjusts the lighting brightness in advance for a preset time period based on the movement trajectory prediction results of the personnel density heat map. The security control unit receives an emergency evacuation channel opening command and opens the emergency evacuation channel. An instruction verification mechanism is provided between the central controller and the equipment control module. When the control instruction parameters exceed the preset threshold range for a preset number of consecutive times, a manual review process is started and the abnormal instruction sequence is recorded.

2. The intelligent building monitoring system according to claim 1, characterized in that: The infrared sensor array, temperature sensor array and carbon dioxide concentration sensor array in the environmental parameter acquisition module are deployed in the form of an integrated node device; Each of the node devices comprises an infrared sensor unit, a temperature sensor unit, and a carbon dioxide concentration sensor unit fixed in the same housing; The node devices are distributed in a honeycomb grid topology in the three-dimensional space of the building, and the distance between adjacent node devices is smaller than the geometric size of the smallest functional unit of the building structure; The optical axis direction of the infrared sensor unit, the direction of the heat-sensitive surface of the temperature sensor unit, and the direction of the gas collection port of the carbon dioxide concentration sensor unit maintain an orthogonal relationship in the three spatial axes; The node device is internally provided with a positioning calibration module, which detects the orientation offset of each sensor unit in real time through a three-axis gyroscope and triggers a mechanical correction mechanism to reset the sensor unit to a preset spatial coordinate direction.

3. The intelligent building monitoring system according to claim 1, characterized in that: Determining the preset temperature difference threshold comprises the following steps: During the system initialization phase, the building information model is loaded to extract the thermal conductivity, solar radiation absorption rate, and spatial volume parameters of the building envelope materials; Driven by typical meteorological day data, thermodynamic simulation calculations are performed to generate a steady-state temperature difference matrix for adjacent areas within the building; 60% to 80% of the maximum value in the steady-state temperature difference matrix is ​​used as the preset temperature difference threshold.

4. The intelligent building monitoring system according to claim 3, characterized in that: The thermodynamic simulation calculations driven by typical meteorological day data include: Obtain historical extreme weather data for the building location, including hourly dry-bulb temperature and solar radiation intensity for the highest and lowest temperature days; Identify thermal defects in building envelope structures through infrared thermal imaging scanning; In thermodynamic simulations, mesh densification is performed on thermal defect areas, and the heat transfer coefficient is dynamically corrected based on surface temperature deviations. Perform forward heat transfer simulation on the day with the highest temperature and reverse heat transfer simulation on the day with the lowest temperature to generate a dual-condition steady-state temperature difference matrix. The temperature difference values ​​of the corresponding coordinates in the dual-condition steady-state temperature difference matrix are weighted and calculated, and the fused steady-state temperature difference matrix is ​​output, where the fused temperature difference = 0.6 × the maximum temperature daily temperature difference + 0.4 × the minimum temperature daily temperature difference.

5. The intelligent building monitoring system according to claim 4, characterized in that: Further including: The surface temperature distribution of the building envelope is obtained using an infrared thermal imaging scanner. The theoretical surface temperature is calculated based on the ambient dry-bulb temperature, solar radiation illuminance, and the material properties of the envelope. Continuous areas where the difference between the actual surface temperature and the theoretical surface temperature exceeds ±1.5°C are defined as thermal defect areas. In the thermodynamic simulation calculation, the mesh of the thermal defect area is refined to 20% of the standard mesh size, and the heat transfer coefficient correction level is determined according to the preset range of the surface temperature deviation value: When the absolute value of the deviation is between 1.6℃ and 2.5℃, the corrected thermal conductivity = the base thermal conductivity × 1.05; When the absolute value of the deviation is between 2.6℃ and 3.5℃, the corrected thermal conductivity = the base thermal conductivity × 1.10; When the absolute value of the deviation is greater than 3.6°C, the corrected thermal conductivity = the reference thermal conductivity × 1.

15.

6. The intelligent building monitoring system according to claim 1, characterized in that: Determining the preset density threshold comprises the following steps: Obtain the physical property parameters of the evacuation passage, including the minimum clear width W, the maximum turning angle θ, and the ground friction coefficient μ; Calculate the traffic efficiency coefficient η: η = (W / W0) × cosθ × (μ / μ0), where W0 is the standard human body width, which is 0.6 m; μ0 is the standard ground friction coefficient, which is 0.5; Calculate the initial density threshold ρ according to the η value: ρ = ρ0 × (η / η0), where ρ0 is the basic density value, which is 2.0 people / m 2 ;η0 is the efficiency benchmark value, which is 0.85; Real-time monitoring of the speed v of people moving in the channel. When v is continuously lower than the theoretical speed v t When the preset ratio is reached, the density threshold ρ is updated according to the following relationship new =ρ×(v / v t ), where v t The value is 1.0 m / s.

7. The intelligent building monitoring system according to claim 1, characterized in that: The air conditioning control unit adjusts the air supply volume ratio of adjacent zones by: Deploy heat flux sensor arrays at the borders of adjacent areas to monitor the heat flux density value Q, W / m in real time 2 ; Obtain the average thickness d and volume heat capacity C of the enclosure structures in the two areas respectively vol ; Calculate area heat capacity C area =C vol ×d, and calculate the area thermal tolerance ΔC between regions area ,J / (m 2 K): ΔC area =|C1-C2|, where C1 and C2 are the area heat capacities of the two regions respectively; According to Q and ΔC area The ratio of the air supply volume to the air supply volume is determined by: When Q / ΔC area When ≤0.05 K / s, K=0.7; When 0.05 K / s<Q / ΔC area When ≤0.10 K / s, K=0.6; When Q / ΔC area >0.10 K / s, K=0.5; The air supply volume is distributed according to the proportional coefficient K, that is, the air supply volume ratio of two adjacent areas is 1:K.

8. The intelligent building monitoring system according to claim 1, characterized in that: When the lighting control unit adjusts the lighting brightness in advance, the following steps are executed: Based on the continuous personnel position coordinates collected by the infrared sensor array, the displacement change ΔS and the direction angle change Δθ between adjacent sampling points are calculated; The trajectory curvature radius R is calculated according to the following formula: ; When R≤3 m, immediately turn on the 90° fan-shaped strong light zone with the turning circle center as the vertex, with the lighting intensity of 150 lx; When 3 m<R≤10 m, the rectangular lighting strip of the predicted path ahead is turned on with a lighting intensity of 100 lx; When R>10 m, the 30° fan-shaped lighting area in the direction of movement is turned on 3 s in advance, with a lighting intensity of 75 lx.

9. A monitoring method based on the intelligent building monitoring system according to any one of claims 1 to 8, characterized in that: The following steps are involved: Step 1: Synchronously collect infrared thermal radiation data, temperature data, and carbon dioxide concentration data within the building space through integrated node devices distributed in a honeycomb grid; Step 2: Normalize the data collected in step 1 to generate a decision model that includes a temperature gradient distribution map, a heat map of occupant density, and an air quality index matrix. Execute intelligent decisions based on the decision model: When the temperature difference between adjacent areas in the temperature gradient distribution map exceeds the preset temperature difference threshold, an air conditioning linkage command is generated; When the density value in the personnel density heat map reaches the preset density threshold, an emergency evacuation channel opening instruction is generated; Generate hierarchical lighting instructions based on the curvature radius R value of the personnel movement trajectory: Step 3: Perform three consecutive parameter checks on the air conditioning linkage command, emergency evacuation channel opening command, and graded lighting command generated in Step 2. If any of the checks exceeds the threshold range, a manual review is initiated; Step 4. Execute the instructions verified in step 3: adjust the air supply ratio of adjacent areas according to the air conditioning linkage instruction; control the security equipment according to the emergency evacuation channel opening instruction; operate the lighting equipment according to the graded lighting instruction.