Building intelligent monitoring system and method

Abstract

The application discloses a kind of building intelligent monitoring system and method, belong to building environment control system technical field, solve the energy waste and emergency response lag problem caused by the difficulty of dynamic coordination of existing system multiple regional environmental parameters.Its technical scheme points include: through distributed infrared sensor array, temperature sensor array and carbon dioxide concentration sensor array, multi-dimensional environmental parameters of building space are collected;Central controller normalizes data and constructs temperature gradient distribution, personnel density heat map and air quality index matrix spatial model, generates air conditioning linkage instruction based on preset temperature difference threshold to adjust adjacent area air supply amount proportion, generates emergency evacuation passage opening instruction based on personnel density threshold;Equipment control module executes air conditioning, lighting and security linkage operation, wherein the lighting unit is regulated according to the curvature radius of personnel trajectory brightness.The system is mainly used to realize building environment precise control and intelligent security collaborative management.

Description

Technical Field

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

[0002] In the field of building environment monitoring, multi-system collaborative control faces several technical bottlenecks. Traditional sensor deployment employs a split installation, with infrared, temperature, and gas monitoring units spatially separated, leading to inconsistent spatiotemporal data benchmarks. Building structural vibrations or equipment installation errors can easily cause sensor orientation shifts, and these shifts are 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 often use fixed empirical values, failing to fully consider the differences in the thermal characteristics of the building envelope. Especially when thermal defects exist, steady-state temperature difference calculations show significant deviations, and thermal defect identification relies on manual inspections, making it difficult to quantify surface temperature deviations. Density threshold settings are often based on static physical parameters, ignoring dynamic changes in traffic efficiency. When personnel movement speed decreases due to environmental factors, the original thresholds easily become invalid, but the high cost of speed monitoring and the difficulty in real-time quantification of the friction coefficient restrict improvements. The equipment control strategies suffer from several shortcomings: Air conditioning systems rely on temperature feedback, failing to consider differences in heat capacity between areas. High-heat-capacity and low-heat-capacity zones exhibit different temperature responses under the same airflow, and the building material parameters required for heat capacity calculations are often missing in existing buildings. Lighting systems rely on linear extrapolation for trajectory prediction, failing to respond promptly to sharp turns, resulting in delayed activation of high-brightness areas. This stems from the contradiction between the high-frequency sampling required for curvature radius calculations and the limited sensor refresh rate. Cross-device control commands lack a collaborative verification mechanism, and abnormal command detection faces a threshold setting dilemma—too lenient thresholds will miss slowly evolving faults, while too strict thresholds will lead to frequent false alarms. Furthermore, manual verification is hampered by the coupling of equipment parameters, resulting in low traceability efficiency. Cellular mesh topology deployment requires balancing coverage density with communication conflict risks. Excessive node spacing creates blind spots for monitoring small-scale functional units, while excessive density increases the probability of data packet loss. The high-precision spatial model's data synchronization requirements clash with the inherent latency of wireless transmission. These deficiencies lead to decreased energy efficiency, delayed emergency response, and increased equipment malfunction rates. Typical difficulties encountered during the improvement process include: the verification of thermal defect correction coefficients is constrained by the accessibility of building enclosure structures; the dynamic mapping of personnel movement speed and density thresholds requires the support of large-scale behavioral experiments; and the command verification threshold is difficult to standardize due to differences in equipment models. Summary of the Invention

[0003] One objective of this invention is to provide an intelligent building monitoring system and method that can effectively solve the problems of energy waste and delayed emergency response caused by the difficulty of existing systems in dynamically coordinating environmental parameters in multiple areas.

[0004] To achieve these objectives and other advantages of the present invention, according to one aspect of the present invention, an intelligent building monitoring system is provided, comprising an environmental parameter acquisition module, a central controller, an equipment control module, and a communication module;

[0005] The environmental parameter acquisition module includes an infrared sensor array, a temperature sensor array, and a carbon dioxide concentration sensor array distributed in the building's three-dimensional space; the environmental parameter acquisition module establishes a data connection with the central controller through a communication module, which adopts the LoRa wireless transmission protocol.

[0006] The central controller includes a data processing unit and a strategy generation unit. The data processing unit normalizes the received environmental parameters and generates a multi-dimensional spatial data model that includes a temperature gradient distribution map, a personnel density heat map, and an air quality index matrix. The strategy generation unit generates equipment control strategies based on the multi-dimensional 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 command is generated. When the density value in the personnel density heat map reaches a preset density threshold, an emergency evacuation passage opening command is generated.

[0007] The equipment control module includes an air conditioning control unit, a lighting control unit, and a security control unit. After receiving an air conditioning linkage command, the air conditioning control unit adjusts the air supply ratio of adjacent areas. The lighting control unit adjusts the lighting brightness in advance based on the predicted movement trajectory of the personnel density heat map for a preset time period. The security control unit receives an emergency evacuation passage opening command and opens the emergency evacuation passage.

[0008] The central controller and the equipment control module are equipped with an instruction verification mechanism. When the control instruction parameters exceed the preset threshold range for a preset number of consecutive times, the manual review process is initiated and the abnormal instruction sequence is recorded.

[0009] 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;

[0010] Each of the node devices includes an infrared sensor unit, a temperature sensor unit, and a carbon dioxide concentration sensor unit fixed within the same housing;

[0011] The node devices are distributed in a cellular grid topology in the three-dimensional space of the building, and the spacing between adjacent node devices is smaller than the geometric dimensions of the smallest functional unit of the building structure.

[0012] The optical axis of the infrared sensor unit, the orientation of the thermal surface of the temperature sensor unit, and the orientation of the gas collection port of the carbon dioxide concentration sensor unit are orthogonal in the three spatial axes.

[0013] The node device is equipped with a positioning calibration module, which uses a three-axis gyroscope to detect the orientation offset of each sensor unit in real time and triggers a mechanical correction mechanism to reset the sensor unit to the preset spatial coordinate direction.

[0014] Preferably, determining the preset temperature difference threshold includes the following steps:

[0015] During the system initialization phase, the building information model is loaded to extract the thermal conductivity coefficient, solar radiation absorptivity, and spatial volume parameters of the building envelope material.

[0016] Thermodynamic simulation calculations were performed based on typical meteorological day data to generate steady-state temperature difference matrices for adjacent areas inside the building.

[0017] Use 60% to 80% of the maximum value in the steady-state temperature difference matrix as the preset temperature difference threshold.

[0018] Preferably, the thermodynamic simulation calculation driven by typical meteorological day data includes:

[0019] Obtain historical extreme weather data for the building's location, including hourly dry-bulb temperature and solar radiation intensity for the days with the highest and lowest temperatures;

[0020] Identify thermal defects in the building envelope using infrared thermal imaging scanning.

[0021] In thermodynamic simulation, the mesh of the thermal defect region is refined, and the thermal conductivity coefficient is dynamically corrected based on the surface temperature deviation.

[0022] Perform forward heat transfer simulations for the day with the highest temperature and reverse heat transfer simulations for the day with the lowest temperature, respectively, to generate a dual-condition steady-state temperature difference matrix.

[0023] The temperature difference values ​​of the corresponding coordinates in the dual-condition steady-state temperature difference matrix are weighted and calculated to output the fused steady-state temperature difference matrix, where the fused temperature difference = 0.6 × daily temperature difference of the highest temperature + 0.4 × daily temperature difference of the lowest temperature.

[0024] Preferably, the method further includes: acquiring a surface temperature distribution map of the building envelope using an infrared thermal imaging scanning device; calculating the theoretical surface temperature based on the ambient dry-bulb temperature, solar radiation illuminance, and material properties of the building envelope; defining continuous areas where the difference between the actual surface temperature and the theoretical surface temperature exceeds ±1.5℃ as thermal defect areas; and in the thermodynamic simulation calculation, refining the mesh of the thermal defect areas to 20% of the standard mesh size, and determining the correction level of the thermal conductivity coefficient according to the preset range to which the surface temperature deviation value belongs.

[0025] When the absolute value of the deviation is between 1.6℃ and 2.5℃, the corrected thermal conductivity coefficient = reference thermal conductivity coefficient × 1.05;

[0026] When the absolute value of the deviation is between 2.6℃ and 3.5℃, the corrected thermal conductivity coefficient = reference thermal conductivity coefficient × 1.10;

[0027] When the absolute value of the deviation is greater than 3.6℃, ​​the corrected thermal conductivity coefficient is equal to the reference thermal conductivity coefficient × 1.15.

[0028] Preferably, determining the preset density threshold includes the following steps:

[0029] Obtain the physical property parameters of the evacuation route, including the minimum net width W, the maximum turning angle θ, and the ground friction coefficient μ;

[0030] 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.

[0031] The initial density threshold ρ is calculated based on the value η: ρ = ρ0 × (η / η0), where ρ0 is the base density value, which is 2.0 people / m³. 2 η0 is the efficiency benchmark value, which is 0.85.

[0032] Real-time monitoring of the movement speed v of people within the passageway; when v remains lower than the theoretical speed v0 t When the preset ratio is used, 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.

[0033] Preferably, when the air conditioning control unit adjusts the air supply volume ratio of adjacent areas, it performs the following:

[0034] An array of heat flux sensors is deployed at the boundary of adjacent regions to monitor the heat flux density value Q, W / m³, in real time. 2 ;

[0035] The average thickness d and volumetric heat capacity C of the enclosure structure in the two regions were obtained respectively. vol ; Calculate the area heat capacity C area =C vol ×d, and calculate the area thermal capacity difference ΔC between regions. area J / (m 2 ·K):

[0036] ΔC area =|C1-C2|, where C1 and C2 are the area heat capacities of the two regions, respectively;

[0037] Based on Q and ΔC area The ratio determines the air volume proportionality coefficient K:

[0038] When Q / ΔC area When K / s ≤ 0.05, K = 0.7;

[0039] When 0.05 K / s < Q / ΔC area When K / s ≤ 0.10, K = 0.6;

[0040] When Q / ΔC area When the velocity is greater than 0.10 K / s, K = 0.5;

[0041] The air supply volume is allocated according to the proportional coefficient K, that is, the air supply volume ratio between two adjacent areas is 1:K.

[0042] Preferably, the lighting control unit performs the following when adjusting the lighting brightness in advance:

[0043] 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.

[0044] Calculate the radius of curvature R of the trajectory using the following formula: ;

[0045] When R≤3 m, immediately open a 90° fan-shaped high-intensity light zone with the center of the turning circle as the vertex, with an illumination intensity of 150 lx;

[0046] When 3 m < R ≤ 10 m, turn on the rectangular lighting strip along the predicted path ahead, with an illumination intensity of 100 lx;

[0047] When R > 10 m, the 30° sector lighting area in front of the moving direction is turned on 3 seconds in advance, with a lighting intensity of 75 lx.

[0048] This invention also provides a monitoring method based on a building intelligent monitoring system, comprising the following steps:

[0049] Step 1: Simultaneously collect infrared thermal radiation data, temperature data, and carbon dioxide concentration data within the building space using integrated node devices distributed in a cellular grid.

[0050] Step 2: Normalize the data collected in Step 1 to generate a decision model that includes a temperature gradient distribution map, a population density heat map, and an air quality index matrix. Then, execute intelligent decisions based on this decision model.

[0051] 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.

[0052] When the density value in the personnel density heatmap reaches the preset density threshold, an emergency evacuation channel opening command is generated.

[0053] Generating graded lighting instructions based on the radius of curvature R of the personnel movement trajectory:

[0054] Step 3: Perform three consecutive parameter checks on the air conditioning linkage command, emergency evacuation passage opening command, and graded lighting command generated in Step 2. If any check exceeds the threshold range, a manual review will be initiated.

[0055] 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 passage opening instruction; operate the lighting equipment according to the graded lighting instruction.

[0056] This invention offers at least the following advantages: It ensures consistency of multi-source data through a distributed sensor array and spatial orthogonal layout, eliminates monitoring blind spots by combining a cellular grid topology, and maintains acquisition accuracy through a real-time calibration mechanism. A temperature difference threshold setting method based on building information modeling and thermodynamic simulation, integrating thermal defect identification and dynamic correction of the thermal conductivity coefficient, enhances threshold adaptability. Linkage between traffic efficiency coefficient and real-time speed feedback enables 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 volume ratio, avoiding temperature oscillations. Graded lighting control based on trajectory curvature radius enables immediate strong light response for sharp turns (R≤3 meters), path coverage for medium bends (3<R≤10 meters), and advance lighting for gentle bends (R>10 meters), eliminating turning delays. Multi-dimensional data modeling generates collaborative instructions, which are continuously verified to intercept anomalies, ultimately achieving precise control of air conditioning supply, timely activation of emergency evacuation, and seamless coordination of lighting guidance. Overall, this reduces spatial data error rates, decreases temperature overshoot, compresses emergency response delays to within 10 seconds, and reduces manual verification workload, achieving an optimized balance between energy efficiency and safety.

[0057] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Detailed Implementation

[0058] The present invention will now be described in further detail with reference to specific embodiments, so that those skilled in the art can implement it based on the description.

[0059] It should be understood that terms such as “having,” “comprising,” and “including” as used herein do not exclude the presence or addition of one or more other elements or combinations thereof.

[0060] This invention provides an intelligent building monitoring system, comprising:

[0061] It includes an environmental parameter acquisition module, a central controller, an equipment control module, and a communication module;

[0062] The environmental parameter acquisition module includes an infrared sensor array, a temperature sensor array, and a carbon dioxide concentration sensor array distributed in the building's three-dimensional space; the environmental parameter acquisition module establishes a data connection with the central controller through a communication module, which adopts the LoRa wireless transmission protocol.

[0063] The central controller includes a data processing unit and a strategy generation unit. The data processing unit normalizes the received environmental parameters and generates a multi-dimensional spatial data model that includes a temperature gradient distribution map, a personnel density heat map, and an air quality index matrix. The strategy generation unit generates equipment control strategies based on the multi-dimensional 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 command is generated. When the density value in the personnel density heat map reaches a preset density threshold, an emergency evacuation passage opening command is generated.

[0064] The equipment control module includes an air conditioning control unit, a lighting control unit, and a security control unit. After receiving an air conditioning linkage command, the air conditioning control unit adjusts the air supply ratio of adjacent areas. The lighting control unit adjusts the lighting brightness in advance based on the predicted movement trajectory of the personnel density heat map for a preset time period. The security control unit receives an emergency evacuation passage opening command and opens the emergency evacuation passage.

[0065] The central controller and the equipment control module are equipped with an instruction verification mechanism. When the control instruction parameters exceed the preset threshold range for a preset number of consecutive times, the manual review process is initiated and the abnormal instruction sequence is recorded.

[0066] In this technical solution, the environmental parameter acquisition module can use pyroelectric infrared sensors for the infrared sensor array, DS18B20 digital temperature sensors for the temperature sensor array, and NDIR gas sensors for the carbon dioxide concentration sensor array. The sensor arrays are arranged in a three-dimensional configuration with a horizontal spacing of 8-12 meters and a vertical spacing of 3-4 meters. The communication module uses LoRa wireless transmission, operating at 433 MHz, with a transmission distance of up to 200 meters. The central controller's data processing unit uses an ARM Cortex-M7 core processor, and the policy generation unit embeds a rule engine. Preset temperature difference thresholds are set to 3℃, 4℃, or 5℃, and the preset density threshold is set to 1.8 people / m³. 2 2.0 people / m 2 Or 2.2 people / m 2The equipment control module's air conditioning control unit 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 command verification attempts is 3, with threshold ranges set as follows: airflow ratio 0.3~1.0, lighting brightness 20~1000 lx, and channel opening delay 0~10 seconds. During implementation, infrared sensors detect human body heat radiation in real time, temperature sensors collect ambient temperature data every 10 seconds, and carbon dioxide sensors update concentration values ​​every 30 seconds. The collected data is transmitted to the central controller via LoRa, and the data processing unit normalizes the raw data: temperature values ​​are mapped to the -10℃~50℃ range, and personnel density is set at 0.1 people / m². 2 The resolution is rasterized, 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 grid cells exceeds a threshold (e.g., 4°C) for 2 consecutive minutes, an air conditioning linkage command is generated. The personnel density heat map is refreshed every 15 seconds. If the density value exceeds a threshold (e.g., 2.0 people / m²) three times consecutively, the trigger command is activated. 2Evacuation commands are generated upon request. Command verification involves three consecutive parameter checks: the air supply volume ratio is checked within the range of 0.4 to 0.8; if it exceeds this range, execution is frozen and manual review is triggered. During the testing phase, an office building was selected for 72-hour monitoring, collecting data such as the number of temperature difference exceeding the threshold, command response delay, and false trigger rate. A t-test was used to analyze the system improvement significance (α=0.05). During assembly, infrared and temperature sensors can be installed at the ceiling access panel, 2.8 meters above the ground; the carbon dioxide sensor can be installed at the wall's breathing zone height (1.2 meters). The central controller can be deployed in the low-voltage room, and the equipment control modules can be installed nearby in the control equipment distribution box. The communication module antenna can be placed at the highest point of the building. Multi-dimensional environmental parameters of the building space are collected through a distributed array of infrared sensors, temperature sensors, and carbon dioxide concentration sensors. These parameters are then transmitted wirelessly to a central controller via LoRa, achieving comprehensive monitoring data coverage. The central controller normalizes the collected data and constructs spatial models including a temperature gradient distribution map, a human density heatmap, and an air quality index matrix. Based on a preset temperature difference threshold, it generates air conditioning linkage commands to adjust the airflow ratio in adjacent areas, preventing energy waste caused by thermal imbalance between areas. Simultaneously, it generates emergency evacuation channel opening commands based on human density thresholds, improving the timeliness of response in high-density scenarios. The equipment control module executes air conditioning airflow ratio adjustments, predictive lighting brightness adjustments, and security channel control. The lighting unit optimizes lighting areas based on human movement trajectories, enhancing the accuracy of behavioral guidance. A continuous command verification mechanism between the central controller and the equipment control module intercepts abnormal parameter commands, reducing the risk of equipment malfunction. Overall, this achieves coordinated optimization of dynamic equilibrium control of the building environment and security response. This implementation method enables dynamic monitoring of environmental parameters in multiple building areas. The temperature difference threshold mechanism reduces over-adjustment of air conditioning zones, the density threshold trigger improves the timeliness of emergency channel activation, and the command verification mechanism reduces the risk of equipment malfunction. Ultimately, this achieves the technical effects of optimizing regional temperature uniformity, shortening emergency response time, and improving system control reliability.

[0067] 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.

[0068] Each of the node devices includes an infrared sensor unit, a temperature sensor unit, and a carbon dioxide concentration sensor unit fixed within the same housing;

[0069] The node devices are distributed in a cellular grid topology in the three-dimensional space of the building, and the spacing between adjacent node devices is smaller than the geometric dimensions of the smallest functional unit of the building structure.

[0070] The optical axis of the infrared sensor unit, the orientation of the thermal surface of the temperature sensor unit, and the orientation of the gas collection port of the carbon dioxide concentration sensor unit are orthogonal in the three spatial axes.

[0071] The node device is equipped with a positioning calibration module, which uses a three-axis gyroscope to detect the orientation offset of each sensor unit in real time and triggers a mechanical correction mechanism to reset the sensor unit to the preset spatial coordinate direction.

[0072] In this technical solution, the integrated node device can use a flame-retardant ABS engineering plastic shell, internally housing a pyroelectric infrared sensor unit, a DS18B20 temperature sensor unit, and an NDIR principle carbon dioxide concentration sensor unit. The nodes are distributed in a honeycomb grid topology within the building space, with horizontal spacing of 8 meters, 10 meters, or 12 meters, and vertical spacing of 3 meters, 3.5 meters, or 4 meters. The infrared sensor's optical axis is along the building's X-axis, the temperature sensor's thermal surface faces the Y-axis, and the carbon dioxide sensor's gas collection port is along the Z-axis, forming a spatially orthogonal layout. The positioning and calibration module can use an MPU6050 three-axis gyroscope to detect the orientation offset of each sensor unit in real time; the mechanical correction mechanism can use a micro stepper motor driving a universal joint, triggering correction when a single-axis offset exceeds ±1.5°, resetting the sensor to the preset orientation. After installation, the node device undergoes initial calibration: the preset spatial coordinate directions (X / Y / Z axes) are calibrated using a laser positioning instrument, and the gyroscope reference values ​​are recorded. The offset is collected every 5 minutes during operation. If the optical axis offset of the infrared sensor is greater than 1.5°, the stepper motor drives the universal joint to rotate and compensate for the angle difference. The calibration accuracy is verified by the consistency of data before and after offset: in a standard temperature-controlled environment, the difference in infrared radiation intensity readings of the same heat source before and after calibration is compared, and the difference is required to be ≤±5%. During the testing phase, 20 nodes are selected for artificial offset experiments, with step offsets of 2°, 3°, and 4° applied in the X / Y / Z axis directions. The calibration completion time and data deviation are recorded. Analysis of variance is used to evaluate the change in the dispersion of temperature measurement values ​​before and after calibration (significance level α=0.01). The node housing can be installed on the ceiling keel support, at a height of 2.6~3.0 meters from the ground; the gyroscope can be fixed to the center plate of the housing, and the stepper motor can be connected to the sensor mounting base.

[0073] This implementation method ensures spatial consistency of infrared position, temperature data, and gas concentration through a spatial orthogonal layout. The honeycomb grid distribution adapts to the smallest size of building functional units (such as a 4m×4m room). Real-time offset detection and mechanical correction suppress data distortion caused by installation errors, maintaining the modeling accuracy of temperature gradient distribution maps and personnel density heat maps. Ultimately, it achieves a technical effect of spatial registration error of ≤0.3 meters for multi-source sensor data. The integrated design of the node device housing fixes the infrared sensor unit, temperature sensor unit, and carbon dioxide concentration sensor unit to a unified spatial reference, ensuring the geometric registration accuracy of multi-source data acquisition. The cellular grid topology distribution adapts to the smallest functional unit size of the building, and the combination of horizontal spacing of 8 / 10 / 12 meters and vertical spacing of 3 / 3.5 / 4 meters achieves blind-spot-free coverage in three-dimensional space. The orthogonal layout of the sensor units in the three spatial axes ensures a strict spatial mapping relationship for infrared positioning, temperature monitoring, and gas concentration detection data. The positioning calibration module detects the three-axis offset in real time. When the offset exceeds ±1.5°, a mechanical correction mechanism is triggered, and the sensor orientation is reset by driving a universal joint through a stepper motor, effectively suppressing data distortion caused by building vibration or installation deformation. The deployment method of installing the node device on the ceiling keel support takes into account both concealment and ease of maintenance. The overall multi-modal sensor spatial registration error is ≤0.3 meters, improving the modeling accuracy of temperature gradient distribution maps and personnel density heat maps.

[0074] In another technical solution, determining the preset temperature difference threshold includes the following steps:

[0075] During the system initialization phase, the building information model is loaded to extract the thermal conductivity coefficient, solar radiation absorptivity, and spatial volume parameters of the building envelope material.

[0076] Thermodynamic simulation calculations were performed based on typical meteorological day data to generate steady-state temperature difference matrices for adjacent areas inside the building.

[0077] Use 60% to 80% of the maximum value in the steady-state temperature difference matrix as the preset temperature difference threshold.

[0078] In this technical solution, the building information model (BIM) can import Revit files to determine the preset temperature difference threshold and extract the building envelope parameters: XPS insulation boards (thermal conductivity coefficient 0.035 W / (m·K)) can be used for the exterior walls, and Low-E double-glazed windows (solar radiation absorptivity 0.25) can be used for the glass curtain wall. The spatial volume parameters are automatically calculated through the model, such as a single office room volume of 80~120 m². 3Typical meteorological day data were sourced from local meteorological station records over the past decade. The highest temperature day was selected as July 15th (daily average temperature 32℃), and the lowest temperature day as January 20th (daily average temperature -5℃). Thermodynamic simulations were performed using ANSYS Fluent software. When generating the steady-state temperature difference matrix, the temperature difference between adjacent regions could be set to 3.2℃, 4.1℃, or 5.0℃. Preset temperature difference thresholds were set to 60%, 70%, or 80% of the maximum steady-state temperature difference; for example, when the maximum temperature difference was 6.5℃, the threshold was set to 4.6℃ (70%). After loading the building information model during the initialization phase, a standard mesh size of 0.5 m × 0.5 m was created. Simulation settings for the highest temperature day: outdoor temperature 32℃, solar radiation illuminance 850 W / m². 2 The simulation was completed after 8 hours to reach thermal equilibrium. The minimum daily temperature settings were: outdoor temperature -5℃ and irradiance 150 W / m². 2 The steady-state temperature difference matrix outputs temperature difference data between adjacent functional areas, such as a 4.3℃ temperature difference between the corridor and the office, and a 5.8℃ temperature difference between the core tube and the open area. Example of preset threshold calculation: When the maximum steady-state temperature difference is 5.8℃, 70% is taken as 4.1℃. In the verification phase, measured summer temperature differences from three buildings were selected, and the threshold settings were compared with the actual imbalance values. Linear regression analysis was used to determine the correlation coefficient (significance level α=0.05). The building information modeling workstation can be deployed in a data center server room, the meteorological database server can connect to the meteorological bureau's API interface, and the thermodynamic simulation software runs on a 32-core computing node.

[0079] This implementation method quantifies the thermal characteristics of the building envelope and extreme weather conditions to adapt the temperature difference threshold to the actual building thermal environment. It avoids misjudgments due to regional differences in thermal inertia by setting a fixed threshold based on the percentage of the maximum value of the steady-state temperature difference matrix. Ultimately, this improves the accuracy of air conditioning linkage commands and reduces temperature regulation lag. By loading a building information model, it accurately extracts the thermal conductivity coefficient and solar radiation absorptivity parameters of the building envelope, and combines this with extreme temperature data from typical weather days to drive thermodynamic simulation, ensuring that the temperature difference threshold setting truly reflects the building's thermal characteristics. The percentage threshold (60% / 70% / 80%) based on the maximum value of the steady-state temperature difference matrix dynamically adapts to differences in thermal inertia in different regions, avoiding misjudgments in glass curtain wall areas and concrete core areas using fixed empirical values. Dual-condition simulation covering the highest and lowest temperature days ensures the universality of the threshold under high summer heat loads and low winter heat loads. Gridded spatial volume calculation (e.g., 80~120 m² for a single room) is used. 3 This improves the spatial resolution of the temperature difference matrix. Ultimately, it ensures that the triggering conditions for air conditioning linkage commands match the actual thermal imbalance state, reducing adjustment lag caused by regional overcooling or overheating.

[0080] In another technical solution, the thermodynamic simulation calculation driven by typical meteorological daily data includes:

[0081] Obtain historical extreme weather data for the building's location, including hourly dry-bulb temperature and solar radiation intensity for the days with the highest and lowest temperatures;

[0082] Identify thermal defects in the building envelope using infrared thermal imaging scanning.

[0083] In thermodynamic simulation, the mesh of the thermal defect region is refined, and the thermal conductivity coefficient is dynamically corrected based on the surface temperature deviation.

[0084] Perform forward heat transfer simulations for the day with the highest temperature and reverse heat transfer simulations for the day with the lowest temperature, respectively, to generate a dual-condition steady-state temperature difference matrix.

[0085] The temperature difference values ​​of the corresponding coordinates in the dual-condition steady-state temperature difference matrix are weighted and calculated to output the fused steady-state temperature difference matrix, where the fused temperature difference = 0.6 × daily temperature difference of the highest temperature + 0.4 × daily temperature difference of the lowest temperature.

[0086] In this technical solution, the historical extreme weather data in the thermodynamic simulation of a typical meteorological day can be obtained by calling the meteorological bureau's API interface. The day with the highest temperature is selected when the dry-bulb temperature is ≥35℃ and the solar radiation illuminance is ≥900 W / m². 2 The date with the lowest temperature is selected when the dry bulb temperature is ≤-8℃ and the irradiance is ≤180 W / m². 2 The date. Infrared thermal imaging scanning can be performed using a handheld thermal imager in the 3~5 μm band. The thermal defect area is defined as a continuous region where the actual surface temperature deviates from the theoretical calculation value by more than ±1.5℃, with an area threshold ≥0.5 m². 2 When refining the mesh for thermodynamic simulation, the mesh size in the defect area can be set to 20% of the standard size (0.5m × 0.5m), i.e., 0.1m × 0.1m. The thermal conductivity coefficient correction is applied based on the deviation range: 1.05 for deviations of ±1.6℃ to ±2.5℃, 1.10 for ±2.6℃ to ±3.5℃, and 1.15 for deviations above ±3.6℃. The fusion formula for the dual-condition steady-state temperature difference matrix is: Fusion temperature difference = 0.6 × daily temperature difference at the highest temperature + 0.4 × daily temperature difference at the lowest temperature. A thermal imager performs a meshed scan of the building facade (1m × 1m measuring points). The theoretical surface temperature is calculated based on real-time dry-bulb temperature, solar irradiance, and the material properties of the building envelope. After identifying areas with deviations exceeding ±1.5℃, the mesh in the defect area is locally refined to 0.1m × 0.1m in ANSYS Fluent. Perform dual-condition simulation: The highest temperature day is set with an outdoor temperature of 35℃ and a radiation illuminance of 900W / m². 2 The minimum daily temperature setting is -8℃ for outdoor temperature and 180 W / m² for irradiance. 2The system outputs a steady-state temperature difference matrix; for example, the temperature difference in a defective area on a west-facing wall under dual operating conditions is 4.8℃. During the verification phase, five known areas of insulation hollowness were selected, and the differences between the calculated and measured temperature differences before and after correction were compared. A paired-samples t-test was used to analyze the significance (α=0.01). The infrared thermal imaging scanning device can be handheld operated by the inspector, and the theoretical temperature calculation module can be deployed on an edge computing terminal.

[0087] This implementation accurately identifies thermal defect areas using a ±1.5℃ deviation threshold, and improves the accuracy of local heat transfer simulation with 20% mesh refinement. Three-level thermal conductivity correction (1.05 / 1.10 / 1.15) quantifies the impact of material degradation. Dual-condition weighted fusion (0.6 highest value + 0.4 lowest value) ensures the steady-state temperature difference matrix accommodates extreme hot and cold load scenarios. Ultimately, the accuracy of the temperature difference threshold setting for thermal defect areas in the building envelope is optimized. This is achieved by using a ±1.5℃ deviation threshold and a mesh size ≥0.5 m... 2 Area thresholding accurately identifies thermal defect areas in the building envelope, resolving omissions caused by manual inspections; local mesh refinement to 20% of the standard size (0.1m × 0.1m) improves the resolution of heat transfer simulation in defect areas; three-level thermal conductivity correction (1.05 / 1.10 / 1.15) based on deviation intervals quantifies the impact of material degradation on thermal performance; highest 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 by fusing the data with weights of 0.6 and 0.4, avoiding the environmental limitations of single-condition simulation. Finally, the accuracy of the temperature difference threshold setting in areas with thermal defects is optimized, solving the threshold deviation problem caused by neglecting material aging in traditional methods.

[0088] In another technical solution, the method further includes: acquiring a surface temperature distribution map of the building envelope using an infrared thermal imaging scanning device; calculating the theoretical surface temperature based on the ambient dry-bulb temperature, solar radiation illuminance, and the material properties of the building envelope; defining continuous areas where the difference between the actual and theoretical surface temperatures exceeds ±1.5℃ as thermal defect areas; and in the thermodynamic simulation calculation, refining the mesh of the thermal defect areas to 20% of the standard mesh size, and determining the correction level of the thermal conductivity coefficient according to the preset range to which the surface temperature deviation value belongs.

[0089] When the absolute value of the deviation is between 1.6℃ and 2.5℃, the corrected thermal conductivity coefficient = reference thermal conductivity coefficient × 1.05;

[0090] When the absolute value of the deviation is between 2.6℃ and 3.5℃, the corrected thermal conductivity coefficient = reference thermal conductivity coefficient × 1.10;

[0091] When the absolute value of the deviation is greater than 3.6℃, ​​the corrected thermal conductivity coefficient is equal to the reference thermal conductivity coefficient × 1.15.

[0092] In this technical solution, the infrared thermal imaging scanning device can be a handheld thermal imager with a working wavelength of 3~5 μm. The theoretical surface temperature is calculated based on the ambient dry-bulb temperature (taken from the local meteorological station's API), solar irradiance (taken from the building's roof radiometer), and the material properties of the building envelope: the baseline thermal conductivity of concrete is taken as 1.75 W / (m·K), and the baseline value for insulation materials is taken as 0.035 W / (m·K). The thermal defect area is defined as a continuous region where the difference between the actual surface temperature and the theoretical value continuously exceeds ±1.5℃, and the minimum continuous area is ≥0.5 m². 2 In the thermodynamic simulation calculations, the mesh in the defect area is refined to 20% of the standard size (0.5m × 0.5m), i.e., 0.1m × 0.1m. The correction level for the thermal conductivity coefficient is applied according to the deviation range: when the deviation is ±1.6℃ to ±2.5℃, the correction factor = reference value × 1.05; when ±2.6℃ to ±3.5℃, × 1.10; ≥ ±3.6℃, ​​× 1.15. Inspectors scan the building facade using a 2m × 2m mesh and record the actual temperature at each point. The theoretical temperature is calculated using the formula:

[0093] T 理论 =T 空气 + (α×I) / h; where α is the material absorptivity (0.65 for concrete, 0.25 for glass), I is the real-time irradiance, and h is taken as 23 W / (m²). 2 •K). Identify areas with continuous out-of-tolerance zones (e.g., a 3.2 m section of a concrete wall). 2 The area with a deviation of +2.8℃ was divided into 0.1m × 0.1m grids in ANSYS Fluent. Based on the deviation value of +2.8℃ (within the range of ±2.6℃ to ±3.5℃), the thermal conductivity of the concrete was corrected to 1.75 × 1.10 = 1.925 W / (m·K). In the verification phase, five known void 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 requirement was that the corrected RMSE ≤ 0.8℃. The thermal imager was handheld operated by the inspection personnel, and the theoretical temperature calculation module ran on an edge computing terminal deployed in the equipment room.

[0094] This embodiment uses a deviation of ±1.5℃ and 0.5 m 2 Dual-condition accurate identification of thermal defects in continuous regions; 20% mesh size refinement improves the accuracy of local heat transfer simulation; three-level correction coefficients (1.05 / 1.10 / 1.15) quantify the material degradation gradient, avoiding over- or under-adjustment caused by uniform correction. Finally, the accuracy of temperature difference threshold settings in areas with voids and cracks is optimized. This is achieved by using a ±1.5℃ temperature deviation threshold and a ≥0.5 m... 2The dual criteria of continuous area threshold accurately identify thermal defects such as voids and cracks in the building envelope, avoiding misjudgments caused by sporadic temperature noise. Local mesh refinement (0.1 m × 0.1 m) with 20% of the standard mesh size (0.5 m × 0.5 m) is applied to defective areas, significantly improving the computational resolution of local heat transfer in thermodynamic simulations. A three-level thermal conductivity coefficient correction strategy based on deviation intervals (×1.05 for ±1.6℃~±2.5℃, ×1.10 for ±2.6℃~±3.5℃, and ×1.15 for ≥±3.6℃) quantifies and matches the degree of material degradation, resolving the over- or under-adjustment issues of traditional uniform correction. Finally, the accuracy of the temperature difference threshold setting for areas with thermal defects is optimized, making air conditioning linkage commands more closely match the actual thermal state of the building envelope.

[0095] In another technical solution, determining the preset density threshold includes the following steps:

[0096] Obtain the physical property parameters of the evacuation route, including the minimum net width W, the maximum turning angle θ, and the ground friction coefficient μ;

[0097] 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.

[0098] The initial density threshold ρ is calculated based on the value η: ρ = ρ0 × (η / η0), where ρ0 is the base density value, which is 2.0 people / m³. 2 η0 is the efficiency benchmark value, which is 0.85.

[0099] Real-time monitoring of the movement speed v of people within the passageway; when v remains lower than the theoretical speed v0 t When the preset ratio is used, 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.

[0100] In this technical solution, the minimum net width W of the evacuation route can be obtained using a laser rangefinder; the maximum turning angle θ can be measured using a digital angle meter; and the ground friction coefficient μ can be tested using a pendulum friction coefficient measuring instrument. The passage 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 taken as 2.0 people / m². 2 The efficiency baseline value η0 is taken as 0.85. The initial density threshold ρ = 2.0 × (η / 0.85), for example, when η = 0.7, ρ ≈ 1.65 people / m². 2The real-time movement speed *v* of personnel can be calculated through displacement detection using an infrared sensor array; the theoretical speed *v* is... t Fixed at 1.0 m / s. When v remains below 0.8 v for 30 seconds... t When the speed is 0.8 m / s, update the density threshold ρ. new =ρ×(v / 1.0). Where, W0 is 0.6 m, representing the standard human body width, based on the 95th percentile shoulder width (489 mm) for males in the Chinese Adult Anthropometric Standard (GB / T 10000-1988), plus a winter clothing bonus (approximately 110 mm), rounded to 0.6 m as the safety design benchmark. μ0 is 0.5, representing the standard ground friction coefficient, referring to the lower limit of the dynamic friction coefficient of dry stone ground (0.50-0.59) in the "Technical Specification for Anti-slip of Building Ground Engineering" (JGJ / T 331-2014). ρ0 is 2.0 person / m. 2 The base density value is based on Article 5.5.21 of the fire evacuation code "Code for Fire Protection Design of Buildings" (GB 50016-2014): the limit for passageway congestion density is 2.0 people / m². η0 is set at 0.85 as the efficiency benchmark value. Based on Fruitin's Level of Service (LOS) theory, η=0.85 corresponds to the passageway efficiency threshold of "Acceptable Level of Service" (LOS C). t The theoretical walking speed was set at 1.0 m / s, based on Article 3.2.5 of the "Code for Evacuation Design of Civil Buildings" (GB / T 51348-2019): the design value for walking speed on horizontal passages is 1.0 m / s. Furthermore, the average walking speed of 100 healthy adults was measured using infrared sensors and found to be 1.05 ± 0.15 m / s. Using 1.0 m / s, the average speed covered 90% of the population.

[0101] During the installation phase, the channel parameters are measured as follows: For example, if the width W = 1.5 meters (rangefinder reading), the turning angle θ = 45° (angle gauge reading), and the friction coefficient μ = 0.5 (measuring instrument data), then 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 / m². 2 The personnel movement speed is calculated every 5 seconds during operation: based on the time difference ΔT and distance ΔS between adjacent nodes' infrared signals, v = ΔS / ΔT. When v is detected to be ≤0.8 m / s for 6 consecutive samples (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 traffic congestion scenarios, comparing dynamic thresholds with fixed thresholds (e.g., 3.5 people / m²). 2The difference in misjudgment rate was analyzed using a chi-square test (α=0.05). The laser rangefinder can be installed at the top of the passageway entrance, the angle meter can be fixed to the corner wall, and the friction coefficient measuring instrument can be handheld. The infrared sensor array can be deployed on the side wall of the passageway, 1.2 meters above the ground.

[0102] This implementation quantifies the influence of channel physical properties through a traffic efficiency coefficient η, and the initial density threshold ρ is dynamically adjusted with the value of η; a real-time velocity feedback mechanism is used when v is consistently below 0.8v. t The system automatically lowers the density threshold to adapt to reduced traffic capacity caused by slippery surfaces or sharp turns. This ultimately improves the accuracy of emergency evacuation command triggering and reduces the risk of misjudgment or omission of congestion. The system accurately acquires the physical parameters of the passage (width W, turning angle θ, friction coefficient μ) using a laser rangefinder, digital angle meter, and friction coefficient meter. Based on the formula η=(W / 0.6)×cosθ×(μ / 0.5), the traffic efficiency coefficient is quantified, allowing the initial density threshold ρ=2.0×(η / 0.85) to dynamically adapt to different passage structural characteristics. Real-time infrared displacement detection calculates the personnel movement speed v; when v remains below 0.8 m / s for 30 seconds, the density threshold ρ is adjusted accordingly. new =ρ×(v / 1.0) automatically lowers the threshold to address the sudden drop in traffic capacity caused by slippery surfaces or sharp turns. Ultimately, this improves the accuracy of triggering emergency evacuation commands in complex passageway environments and avoids misjudgments of congestion under a fixed threshold mechanism.

[0103] In another technical solution, the air conditioning control unit performs the following when adjusting the air supply volume ratio of adjacent areas:

[0104] An array of heat flux sensors is deployed at the boundary of adjacent regions to monitor the heat flux density value Q, W / m³, in real time. 2 ;

[0105] The average thickness d and volumetric heat capacity C of the enclosure structure in the two regions were obtained respectively. vol ; Calculate the area heat capacity C area =C vol ×d, and calculate the area thermal capacity difference ΔC between regions. area J / (m 2 ·K):

[0106] ΔC area =|C1-C2|, where C1 and C2 are the area heat capacities of the two regions, respectively;

[0107] Based on Q and ΔC area The ratio determines the air volume proportionality coefficient K:

[0108] When Q / ΔC area When K / s ≤ 0.05, K = 0.7;

[0109] When 0.05 K / s < Q / ΔC area When K / s ≤ 0.10, K = 0.6;

[0110] When Q / ΔC area When the velocity is greater than 0.10 K / s, K = 0.5;

[0111] The air supply volume is allocated according to the proportional coefficient K, that is, the air supply volume ratio between two adjacent areas is 1:K.

[0112] In this technical solution, a heat flux sensor array is deployed at the boundary of adjacent areas, and a range of 0~300 W / m can be selected. 2 A thermopile sensor. The average thickness of the building envelope was obtained using an ultrasonic thickness gauge. Volumetric heat capacity C. vol Based on the materials used: 2.4 × 10⁻⁶ mm of concrete was selected. 6 J / (m 3 ·K), glass is 0.8×10 6 J / (m 3 • K). Area heat capacity calculation: C area =C vol ×d, for example, concrete zone (C vol =2.4×10 6 J / (m 3 Given K), d=0.3 m), we get C1=7.2×10 5 J / (m 2 ·K), glass zone (C) vol =0.8×10 6 J / (m 3 (·K), d=0.01m) yields C2=8×10 3 J / (m 2 ·K). Heat capacity difference ΔC area =7.12×10 5 J / (m 2 ·K). Heat flux density Q is collected every 15 seconds, based on Q / ΔC. area The proportional coefficient K is determined, and the air supply volume is allocated to the two areas at a ratio of 1:K.

[0113] A heat flow sensor is installed 2.6 meters above the ground at the boundary ceiling, and an ultrasonic thickness gauge is handheld. The area heat capacity database is stored on a local server. This embodiment introduces the average thickness parameter d of the building envelope to adjust the volumetric heat capacity C. vol Converted to area heat capacity C area (C) area =C vol ×d), making the heat capacity difference ΔC area More accurate characterization of thermal inertia per unit area; based on Q / ΔC areaA graded threshold setting for the air supply ratio coefficient K addresses the issue of inaccurate airflow distribution caused by neglecting structural thickness differences in traditional methods. This ultimately improves temperature uniformity between adjacent areas. The area heat capacity C is calculated based on the measured thickness d of the building envelope. area (C) area =C vol ×d), making the heat capacity difference ΔC area Accurately reflects the difference in thermal inertia per unit area; combined with the boundary heat flux density Q, according to Q / ΔC area The three-level threshold dynamic setting of the air supply ratio coefficient K; the 1:K air supply allocation mechanism gives higher air supply weight to high heat capacity areas (K<1), solving the problem of temperature regulation lag caused by differences in structural thickness. Ultimately, it optimizes the thermal balance control accuracy of adjacent areas.

[0114] Real-world testing in a typical office setting:

[0115] Normal operating conditions (Q / ΔC) area =0.03 K / s): Using K=0.7, the temperature difference is stabilized at 1.2±0.3℃, and energy consumption is reduced by 18% compared with traditional temperature control;

[0116] Sudden load conditions (computer room equipment startup, Q / ΔC) area =0.08 K / s): Using K=0.6, the temperature difference balance is restored within 120 seconds to prevent temperature overshoot of 2.1℃;

[0117] Extreme operating conditions (cold radiation from curtain walls in winter, Q / ΔC) area =0.15 K / s): With K=0.5, the temperature difference drops from 4.5℃ to 1.8℃ in just 90 seconds, saving 23% energy and without oscillation.

[0118] Ultimately, the accuracy of thermal balance control in adjacent areas was optimized, reducing the temperature difference fluctuation range by 62% and increasing the response speed by 3 times.

[0119] In another technical solution, the lighting control unit performs the following when adjusting the lighting brightness in advance:

[0120] 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.

[0121] Calculate the radius of curvature R of the trajectory using the following formula: ;

[0122] When R≤3 m, immediately open a 90° fan-shaped high-intensity light zone with the center of the turning circle as the vertex, with an illumination intensity of 150 lx;

[0123] When 3 m < R ≤ 10 m, turn on the rectangular lighting strip along the predicted path ahead, with an illumination intensity of 100 lx;

[0124] When R > 10 m, the 30° sector lighting area in front of the moving direction is turned on 3 seconds in advance, with a lighting intensity of 75 lx.

[0125] In this technical solution, the lighting control unit collects the position coordinates of personnel based on an infrared sensor array, with a sampling interval of 0.5 seconds. The displacement change ΔS calculates the straight-line distance between adjacent sampling points, and the directional angle change Δθ calculates the difference in heading angles among three consecutive points. The trajectory curvature radius R is calculated using the formula R=|ΔS| / |Δθ| (the angle unit is radians). The lighting response strategy is executed in stages: when R≤3 meters, a 90° fan-shaped high-intensity light zone with the turning center as its vertex is immediately activated, with an illumination intensity of 150 lx; ​​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° fan-shaped lighting zone in front of the direction of movement is activated 3 seconds in advance, with an intensity of 75 lx. The LED dimming driver can be a 0~10V dimming model. The infrared sensor outputs a coordinate sequence in real time, such as three consecutive points P1(x1,y1), P2(x2,y2), and P3(x3,y3). Calculate ΔS = √((x2-x1)² + (y2-y1)²), 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° high-intensity light zone). During the testing phase, simulate three turning behaviors: sharp turn (R = 2.5 meters), medium curve (R = 6 meters), and gentle curve (R = 15 meters). Measure the delay time from detection of the turn to full illumination. The requirements are: sharp turn response ≤ 0.3 seconds, medium curve ≤ 1 second, and gentle curve meeting a 3-second lead time. Infrared sensors are installed on the corridor side wall 2.2 meters above the ground. Lights in the 90° high-intensity light zone are deployed at the ceiling turning nodes. Rectangular lighting strips consist of linear LEDs, and lights in the 30° fan-shaped zone are projected at a 22° angle.

[0126] This implementation uses a real-time tiered response based on the radius of curvature R: R ≤ 3 meters provides strong light to address blind spots during sharp turns; 3 < R ≤ 10 meters provides rectangular lighting to cover the predicted path; and R > 10 meters provides advance lighting to adapt to gentle curves. This ultimately reduces lighting response delay during personnel turns, improving the safety of behavioral guidance. By calculating the radius of curvature R of the personnel's movement trajectory in real time (R = |ΔS| / |Δθ|), a tiered lighting response is triggered: when R ≤ 3 meters, a 90° fan-shaped strong light zone (150 lx) is immediately activated to address the risk of blind spots during sharp turns; when 3 < R ≤ 10 meters, a rectangular lighting zone (100 lx) is activated to cover the predicted path; and when R > 10 meters, a 30° fan-shaped zone (75 lx) is activated 3 seconds in advance to adapt to gentle curves. This tiered radius of curvature mechanism matches different turning behavior characteristics, solving the lighting delay problem of traditional linear prediction and improving the timeliness of visual guidance on complex paths.

[0127] This invention also provides a monitoring method based on a building intelligent monitoring system, comprising the following steps:

[0128] Step 1: Simultaneously collect infrared thermal radiation data, temperature data, and carbon dioxide concentration data within the building space using integrated node devices distributed in a cellular grid.

[0129] Step 2: Normalize the data collected in Step 1 to generate a decision model that includes a temperature gradient distribution map, a population density heat map, and an air quality index matrix. Then, execute intelligent decisions based on this decision model.

[0130] 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.

[0131] When the density value in the personnel density heatmap reaches the preset density threshold, an emergency evacuation channel opening command is generated.

[0132] Generating graded lighting instructions based on the radius of curvature R of the personnel movement trajectory:

[0133] Step 3: Perform three consecutive parameter checks on the air conditioning linkage command, emergency evacuation passage opening command, and graded lighting command generated in Step 2. If any check exceeds the threshold range, a manual review will be initiated.

[0134] 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 passage opening instruction; operate the lighting equipment according to the graded lighting instruction.

[0135] In this technical solution, the integrated node devices are deployed in a cellular 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 at a sampling frequency of 1 Hz. The central controller's data processing unit normalizes the raw data: temperature values ​​are mapped to the -10℃ to 50℃ range (resolution 0.1℃), and personnel density is rasterized (0.1 people / m²). 2 (Accuracy), CO2 concentration is linearly converted to a scale of 0~5000 ppm. The decision model updates the temperature gradient distribution map, personnel density heat map, and air quality index matrix every 30 seconds. The preset temperature difference threshold is 4.0℃; when the temperature difference between adjacent grids is ≥4.0℃ for 2 minutes, an air conditioning linkage command is generated; the preset density threshold is 2.0 people / m². 2 When the grid value in the density heatmap is ≥2.0 h / m³ for three consecutive times... 2 Evacuation commands are generated in real time; the radius of curvature R of the trajectory is calculated according to |ΔS| / |Δθ|. R≤3 meters triggers a 90° strong light zone (150 lx), 3<R≤10 meters triggers a rectangular lighting strip (100 lx), and R>10 meters activates a 30° sector zone (75 lx) 3 seconds in advance.

[0136] The specific implementation process is as follows:

[0137] Step 1: The node device uploads sensor data packets every 1 second, which are then aggregated to the central controller via the LoRa gateway.

[0138] Step 2: The data processing unit performs normalization and spatial interpolation to build a decision model. Example of temperature difference exceeding threshold judgment: The continuous temperature difference between office area A (25.3℃) and corridor B (29.5℃) is 4.2℃ (>4.0℃), generating an air conditioning linkage command.

[0139] Step 3: Command parameter verification is performed three times consecutively (with a 5-second interval): air conditioning air supply ratio verification range 0.4~0.8, lighting intensity verification 50~200 lx, channel delay verification 0~10 seconds. If the air supply ratio command is 0.35 (out of range), execution is frozen and manual review is triggered.

[0140] Step 4: Execute verified commands: The air conditioning control unit adjusts the fan coil unit's airflow ratio, the security unit unlocks the electromagnetic door lock, and the lighting unit dims according to the R-value. During the testing phase, simulate temperature difference exceeding the threshold, density exceeding the limit, and sudden rotation scenarios 20 times each, and record the end-to-end delay from command generation to execution, requiring an average of ≤2.5 seconds.

[0141] The node devices are installed in the ceiling access panels, the central controller is deployed in the low-voltage room, and the LoRa gateway is placed at the highest point of the building. This implementation method ensures spatiotemporal consistency of data through synchronous acquisition via a cellular mesh; the decision model integrates multi-dimensional parameters to generate collaborative instructions; triple instruction verification intercepts parameter anomalies; and a hierarchical execution mechanism achieves closed-loop control of air conditioning supply ratio adjustment, precise opening and closing of emergency passages, and dynamic lighting guidance. Ultimately, a collaborative execution system for building environment monitoring and emergency response is constructed. The integrated node devices in the cellular mesh topology achieve synchronous acquisition of multi-source environmental data, ensuring the spatiotemporal consistency of temperature, personnel density, and air quality parameters; the decision model integrates temperature gradient distribution maps, personnel density heat maps, and air quality index matrices to generate collaborative instructions, enabling air conditioning linkage (temperature difference ≥ 4.0℃ for 2 minutes) and emergency evacuation (density ≥ 2.0 people / m²). 2 The system uses a three-times consecutive verification of the air supply ratio and lighting classification (based on the radius of curvature R value) to precisely match actual operating conditions. Three consecutive verifications of command parameters intercept risks such as excessive air supply ratios (<0.4 or >0.8) and abnormal lighting intensity (<50 lx or >200 lx). In the execution phase, the system dynamically adjusts the air supply ratio, opens and closes evacuation routes, and operates lighting equipment based on the verification results, achieving a closed-loop control of multiple systems. Ultimately, this forms a complete link of building environment monitoring, decision-making, verification, and execution, improving the system's collaborative reliability under complex operating conditions.

[0142] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details.

Claims

1. A building intelligent monitoring system, characterized in that, It includes 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 building's three-dimensional space; the environmental parameter acquisition module establishes a data connection with the central controller through a communication module, which 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 and generates a multi-dimensional spatial data model that includes a temperature gradient distribution map, a personnel density heat map, and an air quality index matrix. The strategy generation unit generates equipment control strategies based on the multi-dimensional 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 command is generated. When the density value in the personnel density heat map reaches a preset density threshold, an emergency evacuation passage opening command is generated. The equipment control module includes an air conditioning control unit, a lighting control unit, and a security control unit. After receiving an air conditioning linkage command, the air conditioning control unit adjusts the air supply ratio of adjacent areas. The lighting control unit adjusts the lighting brightness in advance based on the predicted movement trajectory of the personnel density heat map for a preset time period. The security control unit receives an emergency evacuation passage opening command and opens the emergency evacuation passage. The central controller and the equipment control module are equipped with an instruction verification mechanism. When the control instruction parameters exceed the preset threshold range for a preset number of consecutive preset number of times, the manual review process is initiated and the abnormal instruction sequence is recorded. The determination of 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 coefficient, solar radiation absorptivity, and spatial volume parameters of the building envelope material. Thermodynamic simulation calculations were performed based on typical meteorological day data to generate steady-state temperature difference matrices for adjacent areas inside the building. Use 60% to 80% of the maximum value in the steady-state temperature difference matrix as the preset temperature difference threshold.

2. The building intelligent monitoring system as described in 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 includes an infrared sensor unit, a temperature sensor unit, and a carbon dioxide concentration sensor unit fixed within the same housing; The node devices are distributed in a cellular grid topology in the three-dimensional space of the building, and the spacing between adjacent node devices is smaller than the geometric dimensions of the smallest functional unit of the building structure. The optical axis of the infrared sensor unit, the orientation of the thermal surface of the temperature sensor unit, and the orientation of the gas collection port of the carbon dioxide concentration sensor unit are orthogonal in the three spatial axes. The node device is equipped with a positioning calibration module, which uses a three-axis gyroscope to detect the orientation offset of each sensor unit in real time and triggers a mechanical correction mechanism to reset the sensor unit to the preset spatial coordinate direction.

3. The building intelligent monitoring system as described in claim 1, characterized in that, The thermodynamic simulation calculations performed under typical meteorological day data include: Obtain historical extreme weather data for the building's location, including hourly dry-bulb temperature and solar radiation intensity for the days with the highest and lowest temperatures; Identify thermal defects in the building envelope using infrared thermal imaging scanning. In thermodynamic simulation, the mesh of the thermal defect region is refined, and the thermal conductivity coefficient is dynamically corrected based on the surface temperature deviation. Perform forward heat transfer simulations for the day with the highest temperature and reverse heat transfer simulations for the day with the lowest temperature, respectively, 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 to output the fused steady-state temperature difference matrix, where the fused temperature difference = 0.6 × daily temperature difference of the highest temperature + 0.4 × daily temperature difference of the lowest temperature.

4. The building intelligent monitoring system as described in claim 3, characterized in that, Further includes: The surface temperature distribution map of the building envelope is obtained by infrared thermal imaging scanning device. The theoretical surface temperature is calculated based on the ambient dry-bulb temperature, solar radiation irradiance and the material properties of the building envelope. The continuous area where the difference between the actual surface temperature and the theoretical surface temperature exceeds ±1.5℃ is defined as the thermal defect area. In thermodynamic simulation calculations, the mesh for thermal defect regions is refined to 20% of the standard mesh size, and the correction level for thermal conductivity is determined based on a preset range to which the surface temperature deviation value belongs. When the deviation is within ±1.6℃ to ±2.5℃, the corrected thermal conductivity coefficient = reference thermal conductivity coefficient × 1.05; When the deviation is within ±2.6℃ to ±3.5℃, the corrected thermal conductivity coefficient = reference thermal conductivity coefficient × 1.10; When the deviation is above ±3.6℃, ​​the corrected thermal conductivity coefficient = reference thermal conductivity coefficient × 1.

15.

5. The building intelligent monitoring system as described in claim 1, characterized in that, Determining the preset density threshold includes the following steps: Obtain the physical property parameters of the evacuation route, including the minimum net 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. The initial density threshold ρ is calculated based on the value η: ρ = ρ0 × (η / η0), where ρ0 is the base density value, which is 2.0 people / m³. 2 η0 is the efficiency benchmark value, which is 0.

85. Real-time monitoring of the movement speed v of people within the passageway; when v remains lower than the theoretical speed v0 t When the preset ratio is used, 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.

6. The building intelligent monitoring system as described in claim 1, characterized in that, When the air conditioning control unit adjusts the air supply volume ratio of adjacent areas, it performs the following: An array of heat flux sensors is deployed at the boundary of adjacent regions to monitor the heat flux density value Q, W / m³, in real time. 2 ; Obtain the average thicknesses d1 and d2 and the volumetric heat capacity C of the enclosure structures in the two regions. vol ; Calculate the area heat capacity C area =C vol ×d, and calculate the area thermal capacity difference ΔC between regions. area , kJ / (m 2 ·K): ΔC area =|C1-C2|, where C1 and C2 are the area heat capacities of the two regions, respectively; Based on Q and ΔC area The product value determines the air supply volume proportionality coefficient K: When Q×ΔC area ≤50 W·K / m 2 At that time, K=0.7; When 50 W·K / m 2 <Q×ΔC area ≤100 W·K / m 2 At that time, K=0.6; When Q×ΔC area >100 W·K / m 2 At that time, K=0.5; The air supply volume is allocated according to the proportional coefficient K, that is, the air supply volume ratio between two adjacent areas is 1:K.

7. The building intelligent monitoring system as described in claim 1, characterized in that, 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 orientation angle change Δθ between adjacent sampling points are calculated. Calculate the radius of curvature R of the trajectory using the following formula: ; When R≤3 m, immediately open a 90° fan-shaped high-intensity light zone with the center of the turning circle as the vertex, with an illumination intensity of 150 lx; When 3 m < R ≤ 10 m, turn on the rectangular lighting strip along the predicted path ahead, with an illumination intensity of 100 lx; When R > 10 m, the 30° sector lighting area in front of the moving direction is turned on 3 seconds in advance, with a lighting intensity of 75 lx.

8. A monitoring method based on the building intelligent monitoring system according to any one of claims 1 to 7, characterized in that, Includes the following steps: Step 1: Simultaneously collect infrared thermal radiation data, temperature data, and carbon dioxide concentration data within the building space using integrated node devices distributed in a cellular grid. Step 2: Normalize the data collected in Step 1 to generate a decision model that includes a temperature gradient distribution map, a population density heat map, and an air quality index matrix. Then, execute intelligent decisions based on this 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 heatmap reaches the preset density threshold, an emergency evacuation channel opening command is generated. Generating graded lighting instructions based on the radius of curvature R of the personnel movement trajectory: Step 3: Perform three consecutive parameter checks on the air conditioning linkage command, emergency evacuation passage opening command, and graded lighting command generated in Step 2. If any check exceeds the threshold range, a manual review will be 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 passage opening instruction; operate the lighting equipment according to the graded lighting instruction.