Intelligent regulation and control system for indoor environment of green building

Abstract

The invention discloses an intelligent regulation and control system for an indoor environment of a green building, and relates to the technical field of environment regulation and control, comprising the following steps: acquiring indoor and outdoor environment data of the building to obtain environment parameter data; performing environment state analysis according to the environment parameter data to obtain a state classification result; performing environment deviation calculation according to a state classification result to obtain deviation data; performing environmental influence factor identification according to the deviation data to obtain a key influence factor; performing control quantity calculation according to the key influence factors to obtain control output data; and performing environment control adjustment according to the control output data to obtain an environment adjustment result. According to the method, multi-parameter deviation calculation is achieved, the deviation result can accurately reflect the overall state deviation degree of the building environment, the environment state evaluation precision is improved, the comprehensive state of the building environment is evaluated through weighted fusion of different parameter deviations, and the evaluation comprehensiveness and sensitivity are improved.

Description

Technical Field

[0001] This invention relates to the field of environmental control technology, specifically to an intelligent indoor environmental control system for green buildings. Background Technology

[0002] With the popularization of green building concepts and the advancement of dual-carbon goals, the comfort and energy efficiency of building indoor environments have gradually become core objectives in the design of intelligent building systems. Green buildings emphasize reducing building energy consumption and achieving coordinated use of the environment and energy while ensuring the health and comfort of residents. Indoor environmental control systems typically need to consider multiple environmental parameters such as temperature, humidity, air quality, illuminance, and noise simultaneously, and their operating efficiency directly affects building energy consumption levels and the living experience.

[0003] Existing technologies for judging indoor environmental conditions have shortcomings: existing methods often judge environmental conditions based on a single indicator, ignoring the coupling relationship between parameters, making it difficult to accurately describe the overall state of the building environment. Furthermore, they only perform parameter over-limit detection, failing to reflect the overall deviation of the environment. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an intelligent control system for indoor environment in green buildings, thereby solving the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] In a first aspect, the present invention provides an intelligent control system for indoor environment in green buildings, comprising the following steps:

[0007] S1. Collect indoor and outdoor environmental data of the building to obtain environmental parameter data;

[0008] S2. Perform environmental status analysis based on environmental parameter data to obtain status classification results;

[0009] S3. Calculate the environmental deviation based on the state classification results to obtain the deviation data;

[0010] S4. Identify environmental impact factors based on deviation data to obtain key impact factors;

[0011] S5. Calculate the control quantities based on the key influencing factors to obtain the control output data;

[0012] S6. Adjust the environmental control based on the control output data to obtain the environmental adjustment results.

[0013] To further optimize this technical solution, the environmental data collection in step S1 includes:

[0014] By deploying sensors to collect indoor and outdoor environmental data of buildings, and through unified parameter definition and data processing, environmental parameter data is obtained.

[0015] To further optimize this technical solution, the environmental state analysis in step S2 includes:

[0016] Based on the obtained environmental parameter data, environmental status analysis is carried out through multi-dimensional parameter collaborative analysis to achieve the assessment of the building's indoor environment and obtain status classification results.

[0017] To further optimize this technical solution, the environmental deviation calculation in step S3 includes:

[0018] Based on the obtained state classification results, the deviation analysis model is used to calculate the degree of environmental deviation, transforming the discrete state classification results into numerical deviation data to assess the degree of deviation.

[0019] To further optimize this technical solution, the deviation analysis model includes:

[0020]

[0021] in:

[0022] Overall deviation value;

[0023] The number of environmental parameters;

[0024] : No. The deviation values ​​of each parameter;

[0025] : No. The weighting coefficients of each parameter.

[0026] To further optimize this technical solution, the deviation values ​​of the parameters include:

[0027]

[0028] in:

[0029] : No. The actual values ​​of each parameter;

[0030] : No. Ideal values ​​for each parameter;

[0031] The deviation of environmental parameters is obtained by calculating the difference between the actual and ideal values ​​of the parameters.

[0032] To further optimize this technical solution, the identification of environmental impact factors in step S4 includes:

[0033] By analyzing deviation data, environmental parameters inside buildings are identified and screened to obtain key influencing factors, thereby ensuring the targeted nature of regulation and improving its effectiveness.

[0034] To further optimize this technical solution, the calculation of the control quantity in step S5 includes:

[0035] Based on the key influencing factors obtained in step S4, the deviation values ​​and directions of the key influencing factors are converted into control quantities to provide control signals for building environment control equipment.

[0036] To further optimize this technical solution, the environmental control adjustment in step S6 includes:

[0037] Based on the control output data, an environmental control feedback mechanism is constructed. By sensing environmental changes in real time, the control output is adjusted to bring environmental parameters closer to the set target and keep them stable, thus obtaining the environmental adjustment results.

[0038] This technical solution has been further optimized, including the following functional modules:

[0039] The module includes a data acquisition module, a status classification module, a deviation calculation module, a factor identification module, a control calculation module, and a feedback adjustment module.

[0040] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein the computer program instructions, when executed by the processor, implement the steps of a green building indoor environment intelligent control system as described in the first aspect of the present invention.

[0041] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of a green building indoor environment intelligent control system as described in the first aspect of the present invention.

[0042] Compared with existing technologies, the present invention provides an intelligent control system for indoor environment in green buildings, which has the following beneficial effects:

[0043] This intelligent indoor environment control system for green buildings uses a deviation analysis model to calculate multi-parameter deviations, enabling the deviation results to accurately reflect the degree of deviation of the overall building environment, thus improving the accuracy of environmental status assessment. Furthermore, by weightedly integrating the deviations of different parameters to assess the comprehensive status of the building environment, it enhances the comprehensiveness and sensitivity of the assessment. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic diagram of the process of an intelligent control system for indoor environment in green buildings proposed in this invention;

[0046] Figure 2 This is a flowchart illustrating the deviation analysis model of an intelligent indoor environment control system for green buildings proposed in this invention.

[0047] Figure 3 This is a schematic diagram of a module of an intelligent indoor environment control system for green buildings proposed in this invention. Detailed Implementation

[0048] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0049] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0050] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0051] Example 1:

[0052] Reference Figures 1-2 This is the first embodiment of the present invention, which provides an intelligent control system for indoor environment in green buildings, comprising the following steps:

[0053] S1. Collect indoor and outdoor environmental data of the building to obtain environmental parameter data.

[0054] In this embodiment, the environmental data collection includes:

[0055] In the indoor environmental control of green buildings, environmental data is a prerequisite for any control action. The indoor environment of a building is affected by various dynamic factors, such as heat generated by human activity, external climate conditions, equipment operating status, airflow, and changes in lighting. The combined effect of these factors causes fluctuations in temperature, humidity, light intensity, and air quality across different spatial and temporal scales. Without accurate and real-time access to this information, it is impossible to determine the current environmental state or scientifically quantify subsequent control outputs. Therefore, establishing a high-precision, multi-dimensional environmental data acquisition mechanism is fundamental to ensuring the stable and accurate operation of green building control systems.

[0056] By deploying sensors to collect data on the indoor and outdoor environment of buildings, and through unified parameter definition and data processing, dynamic, continuous, and reliable environmental parameters are obtained, thereby transforming the physical environment into a calculable digital environment, realizing the digitization of the building environment, and providing a data foundation for subsequent steps.

[0057] The process of environmental data collection includes:

[0058] Identification of monitoring targets: Determine the scope of monitoring targets inside and outside the building. Internal areas include office areas, meeting areas, public corridors, equipment rooms, etc., while external areas include sampling points for climate conditions around the building. The sensitive environmental factors vary greatly in different functional areas. Determine the environmental factors to be monitored for different areas, such as temperature, relative humidity, illuminance, carbon dioxide concentration, PM2.5 concentration, and noise level, to ensure that the collected data is complete and representative.

[0059] Sensor deployment and numbering management: Based on the distribution characteristics of the monitored objects, corresponding sensor nodes are deployed in each area. Each sensor node is assigned a unique number for data transmission and identification management. For example, temperature sensors are numbered T1-Tn, and humidity sensors are numbered H1-Hn. Each sensor is connected to the central data acquisition host through a unified communication protocol (such as RS-485 or ZigBee). The host periodically receives data and verifies the integrity of the data packets.

[0060] Signal Acquisition and Filtering: The signals acquired by the sensors directly reflect changes in the physical environment, but they are affected by transient interference, electronic noise, and external disturbances. To ensure data quality, Kalman filtering technology (which achieves dynamic error correction by weighted fusion of the current measurement value and the state prediction value at the previous moment) is used on the host side to perform real-time noise reduction and dynamic correction on all sensor signals. While maintaining the continuity and trend characteristics of the data time, high-frequency fluctuations are eliminated, ensuring the stability and reliability of the data and directly reflecting the real changes in the building environment.

[0061] Time synchronization and spatial correlation: To ensure the comparability of data under the same time base, a unified clock synchronization mechanism is set up. All sampled data are compared and calibrated according to timestamps to form a unified time series. Each sampling node has a unique spatial coordinate identifier. Parameters in different regions are paired with spatial location as an index, so that environmental data such as temperature, humidity, illuminance, and air quality can form a corresponding relationship in the same spatial dimension.

[0062] Environmental parameter set generation: After filtering and calibration of the outputs of each sensor, a unified data structure is used to define the fields and units of each parameter, such as temperature in degrees Celsius, humidity in relative humidity percentage, etc. The parameters are combined according to the parameter category. The values ​​of temperature, humidity, light, air quality, noise, etc. at the same time point are combined into an environmental parameter vector to obtain the environmental parameters at the current time. The environmental parameters obtained by continuous sampling at multiple times constitute the environmental parameter set, which is used to describe the complete trajectory of the building environment changing over time.

[0063] Outlier detection and data update: To prevent equipment failure or sudden interference from introducing erroneous data, the rate of change of data at adjacent time points is compared with the physical reasonable range. For example, if the temperature changes by more than 2°C within 10 seconds, it can be judged as an anomaly. Obvious outliers are removed and interpolation is performed to complete the data, ensuring the continuity and accuracy of the parameter set.

[0064] S2. Analyze the environmental status based on the environmental parameter data to obtain the status classification results.

[0065] In this embodiment, the environmental state analysis includes:

[0066] The indoor environment of a building is not determined by a single parameter, but by the combined effect of multiple factors. Relying solely on temperature or humidity for judgment, such as automatically deeming discomfort as an out-of-range temperature, easily overlooks the coupling relationship between parameters. For example, when the temperature is slightly higher and the humidity is lower, people may still feel comfortable, while if the temperature is moderate but the humidity is too high, comfort will decrease significantly, leading to a one-sided or distorted judgment of the environmental condition.

[0067] Based on the obtained environmental parameter data, environmental status analysis is carried out through multi-dimensional parameter collaborative analysis to achieve the assessment of the building's indoor environment and obtain status classification results. This allows for a comprehensive and accurate assessment of indoor environmental quality, making the environmental status classification more consistent with actual physical and human comfort characteristics.

[0068] Furthermore, the multidimensional parameter collaborative analysis includes:

[0069] Establish a hierarchical index system: Based on the set of environmental parameters obtained in step S1, first determine the set of main environmental parameters used for hierarchical classification, including temperature, humidity, light intensity, air quality and noise. The reference range of each parameter is set according to national standards (such as "GB / T50378-2019 Green Building Evaluation Standard"). Through normalization processing, different unit parameters are mapped to a unified standardized level space.

[0070] Multi-parameter collaborative hierarchical calculation: A multi-parameter coupled calculation method is adopted, and each parameter is assigned an influence weight coefficient to reflect its dominant role in the overall environment. For example, the weight of temperature is higher than that of noise. The standardized level values ​​corresponding to the environmental parameters are used for weighted comprehensive calculation to obtain the environmental state index. Based on the numerical range of the environmental state index, the environmental comfort level (such as comfortable, basically comfortable, slightly uncomfortable, obviously uncomfortable, and severely uncomfortable) is determined, thereby realizing the judgment of overall comfort and improving the actual representativeness of the hierarchical results.

[0071] Time continuity correction: Since environmental parameters are affected by dynamic disturbances, instantaneous changes may cause instability in the classification results. To improve the time reliability of the classification results, a time smoothing mechanism is introduced. The average value of the environmental state index is calculated through a continuous detection window (with a length of n time steps) as the final state output, filtering out misjudgments caused by instantaneous disturbances.

[0072] Status label generation and classification output: After multi-parameter coordination and time correction, a set of status labels is generated for each monitoring area. Each status label contains three pieces of information: area number, timestamp, and classification result (used for status description, including environmental comfort level and environmental parameter level, such as "basically comfortable", "slightly cold", "suitable humidity", etc.), so as to accurately identify local environmental deviations and achieve refined environmental perception.

[0073] S3. Calculate the environmental deviation based on the state classification results to obtain the deviation data.

[0074] In this embodiment, the environmental deviation calculation includes:

[0075] The environmental control goal of green buildings is not only to keep all parameters within the allowable range, but more importantly, to identify the degree of deviation in order to determine whether adjustments are needed. For example, the difference between a temperature 1°C and 3°C above the comfort range is significant in terms of human perception and energy consumption, and this difference cannot be quantified based on classification results alone.

[0076] Based on the obtained state classification results, the deviation analysis model is used to calculate the degree of environmental deviation, transforming the discrete state classification results into numerical deviation data, thereby assessing the degree of deviation and providing a data foundation for subsequent steps.

[0077] Furthermore, the deviation analysis model includes:

[0078]

[0079] in:

[0080] Overall deviation value: This represents the degree to which the overall environment of a region deviates from the ideal state. The larger the value, the greater the deviation and the less comfortable the environment.

[0081] The number of environmental parameters;

[0082] : No. The deviation value of each parameter, the deviation of the environmental parameter from the ideal parameter value, reflects the direction and degree of deviation of each environmental physical quantity. A deviation value greater than 0 indicates that the parameter is higher than the ideal value (such as higher temperature), and a deviation value less than 0 indicates that it is lower than the ideal value (such as insufficient humidity).

[0083] : No. The weight coefficients of each parameter represent the importance of different parameters to environmental comfort. The sum of all weight coefficients is 1. They can be set according to actual conditions, for example, temperature 0.35, humidity 0.25, air quality 0.20, illuminance 0.10, and noise 0.10.

[0084] Furthermore, the deviation values ​​of the parameters include:

[0085]

[0086] in:

[0087] : No. The actual values ​​of each parameter are obtained by averaging the environmental parameter value set corresponding to each category through the state classification results of S2 and unifying the range to [0,1]. This results in the normalized average environmental parameter value of the region in the current state, so that each state label corresponds to a unique set of actual parameters.

[0088] : No. The ideal values ​​of each parameter are obtained based on the ideal environmental parameter values ​​obtained from the green building indoor comfort standard. The benchmark values ​​are derived from the national green building standard, and the range is normalized to [0,1] and kept fixed. They are used as a reference for the deviation calculation of all areas.

[0089] The deviation of environmental parameters is obtained by calculating the difference between the actual and ideal values ​​of the parameters.

[0090] This model describes how to calculate environmental deviation based on the difference between the actual and ideal values ​​of environmental parameters.

[0091] Traditional building environment assessments often rely on static index comparisons or single-parameter over-limit alarms, monitoring only individual parameters and ignoring the coupling relationships between environmental elements. This makes it impossible to quantify the degree of deviation or reflect the directionality of the deviation. In contrast, this model transforms discrete judgment into continuous assessment through deviation calculation, improving the continuity and accuracy of the assessment. By weightedly integrating deviations of different parameters to assess overall comfort, it enhances the comprehensiveness and sensitivity of the assessment, clarifies the direction of regulation, and makes environmental regulation more targeted and directional.

[0092] The steps for using this model include:

[0093] Data Acquisition: Based on the state classification results in step S2, data processing is performed to obtain the actual values ​​of various environmental parameters in the region. Based on national green building standards, ideal values ​​for environmental parameters were obtained. ;

[0094] Parameter calculation: Based on the obtained data, calculate the deviation values ​​of various environmental parameters. This reflects the direction and degree of deviation of each environmental physical quantity;

[0095] Deviation analysis: Based on the calculated deviation values ​​of various environmental parameters Combined with weighting coefficients Calculate the overall deviation value This is used to characterize the degree to which the overall environment of the region deviates from the ideal state, resulting in a set containing the deviation values ​​of each environmental parameter and the overall deviation value, which is used to guide the direction of adjustment.

[0096] S4. Identify environmental impact factors based on deviation data to obtain key impact factors.

[0097] In this embodiment, the identification of environmental impact factors includes:

[0098] In green building environmental control, indoor comfort is influenced by a variety of factors, but the dominant influencing factors differ depending on the building structure, climate conditions, and operating scenarios. Traditional methods often treat all parameters equally, responding to all deviations in the same way during control, resulting in energy waste and delayed response.

[0099] The purpose of this step is to identify and screen parameters or environmental factors that have a significant impact on the health and comfort of the building's indoor environment through deviation data analysis, i.e., key influencing factors, so as to ensure the targeting of regulation and improve the regulation effect.

[0100] Furthermore, the deviation data analysis includes:

[0101] Deviation data analysis: Obtain the deviation data obtained in step S3, including the deviation values ​​of environmental parameters in each region and the overall deviation value. Perform statistical analysis on the deviation data of each monitoring region and each time step, including the mean, variance and deviation trend, quantify the fluctuation characteristics of each parameter in the spatial and temporal dimensions, and provide basic data for factor screening.

[0102] Parameter contribution calculation: Each parameter has a different impact on the overall environment, and its contribution needs to be quantified. By combining the deviation value of a single parameter with its weight coefficient in S3 at each time step, the proportion of the deviation value to the overall deviation value is calculated, and the proportion at each time step is averaged to obtain the contribution of each environmental parameter in the region.

[0103] Preliminary screening of key impact factors: Based on the contribution score, environmental parameters that are higher than the preset threshold are selected as candidate key factors. The threshold can be set based on historical data statistics or building standards. For example, parameters with the top 50% contribution can be selected, so that only factors with significant environmental impact are retained, reducing redundant information.

[0104] Deviation Direction Analysis: High amplitude parameters alone cannot guarantee significant long-term impact; the stability of deviation direction is also crucial. For candidate key factors, the consistency and frequency of their deviation directions are analyzed to identify parameters with consistent directions over most of the time or region. When a parameter shows a sustained positive or negative deviation in most time steps and regions, its impact on the overall environmental state is more prominent. Parameters with high frequency and large deviation amplitude are retained as the final key influencing factors, thereby ensuring that the selected key influencing factors not only have large deviation amplitudes but also stable directions, reflecting the long-term trend impact on the environment.

[0105] Regional weight integration: Different building areas have different functional importance. The contribution of candidate key factors in different monitoring areas is weighted and summed to form a comprehensive impact score for the entire building. The weight can be determined based on the regional function (office area, corridor, public area, etc.). For example, the weight of the office area is higher than that of the corridor, so that the key impact factor identification results take into account the spatial importance and meet the actual building management needs.

[0106] Forming a set of key impact factors: The set of key impact factors is finally determined based on the comprehensive impact score. For example, the factors with the top 50% scores are selected as key impact factors. The set of key impact factors includes information such as parameter name, deviation value, contribution score, deviation direction and regional weight, so as to screen out the parameters that have the greatest impact on the built environment.

[0107] S5. Calculate the control quantities based on the key influencing factors to obtain the control output data.

[0108] In this embodiment, the calculation of the control quantity includes:

[0109] Identifying key influencing factors without corresponding control quantity calculation methods is insufficient to convert deviation data into actual control instructions. Scientific calculations are needed to generate adjustment quantities to drive building equipment to improve environmental conditions.

[0110] Based on the key influencing factors obtained in step S4, this step transforms the deviation values ​​and directions of the key influencing factors into executable control quantities, providing direct and quantitative control signals for building environment control equipment. This ensures that the control quantities both improve environmental deviations and avoid over-adjustment that could lead to control instability.

[0111] Implementation methods include:

[0112] Obtain key impact factor deviation data: Obtain deviation data from the key impact factor set output by S4, including parameter name, deviation value, contribution score, deviation direction and regional weight, etc., to ensure that the data is processed synchronously according to the monitoring area and time so that the subsequent control quantity calculation corresponds to the building functional area;

[0113] Determine the control target and amplitude mapping: Based on the amplitude and direction of each key influencing factor in the deviation data, determine the control target (increase or decrease). If the deviation is positive, the control quantity decreases the environmental value (e.g., cooling or ventilation); if the deviation is negative, the control quantity increases the environmental value (e.g., heating or humidification). The magnitude of the deviation directly determines the amplitude of the control output. The larger the amplitude, the stronger the output. Linear (simple, easy to implement, and continuous, but may require frequent adjustments under small fluctuations) or nonlinear functions (more in line with actual control needs, less sensitive to small deviations, and faster response to large deviations) are usually used to map the control quantity so that the output value is continuous and adjustable, avoiding over- or under-control. For example, if the temperature deviation is 0.25 and higher than the set value, the control quantity is 25% of the cooling power.

[0114] Introducing contribution weighting: Key impact factors have different overall environmental impacts. Parameters with higher contributions should generate larger control outputs. The control quantities are adjusted according to contribution weights. After weighting, the control output value of each key impact factor is calculated. This allows for focused regulation of high-contribution factors and reduced regulation of low-contribution factors, improving the targeting of regulation effects, avoiding over-regulation of low-impact parameters, and saving energy and equipment resources.

[0115] Boundary constraints and stability checks: Apply upper and lower limit constraints to each control output to avoid exceeding the actual operating range of the equipment and to prevent frequent start-ups and shutdowns or environmental oscillations caused by excessively rapid changes in control quantities. At the same time, check the output change rate and set limits on the change rate to prevent excessive oscillations or rapid switching that could lead to environmental instability. For example, the adjustment range per minute should not exceed 10% of the maximum value. The constraints are set according to the building equipment capacity and safety regulations, such as the maximum cooling capacity of the HVAC system and the maximum air volume of the air purifier, thereby ensuring the safe and reliable operation of the building equipment and enhancing the smoothness and comfort of environmental control.

[0116] Generate control output dataset: Summarize the weighted and constrained control outputs of each key influencing factor to form a control output dataset, including the name of the key influencing factor, control direction, control magnitude, regional information and time step identifier.

[0117] S6. Adjust the environmental control based on the control output data to obtain the environmental adjustment results.

[0118] In this embodiment, the environmental control adjustment includes:

[0119] Due to the complexity of building spaces and the coupling characteristics of multiple factors, the indoor environment of buildings has inertia and delay characteristics (such as lag in temperature changes, slow recovery of humidity, and uneven air flow). A single control output often cannot stabilize environmental parameters quickly. If only static control output is relied upon, the system will oscillate or over-adjust in a short period of time, making it difficult to achieve a true balance between comfort and energy efficiency.

[0120] Based on the control output data, an environmental control feedback mechanism is constructed. By sensing environmental changes in real time, the control output is adjusted so that the environmental parameters gradually approach the set target and remain stable, thus obtaining the environmental adjustment result. This transforms the environmental parameter control process from a one-time static execution to a continuous adjustment process, reducing adjustment errors and improving steady-state accuracy.

[0121] Furthermore, the environmental control feedback mechanism includes:

[0122] Environmental feedback acquisition: After the device implements control output, indoor and outdoor environmental parameters are collected again through high-precision sensors to obtain real-time environmental status parameters;

[0123] Steady-state deviation calculation: Compare the real-time environmental state parameters with the ideal environmental parameter values ​​to obtain a new deviation. If the absolute value of the deviation is less than the set threshold (the allowable error threshold for each parameter, such as temperature ±0.5℃, humidity ±3%, etc.), the factor is considered to have stabilized. Otherwise, continue to execute the feedback correction process.

[0124] Control increment generation: Based on the new deviation data, the control output is corrected using a proportional correction method (P control). The output increment is adjusted according to the direction and magnitude of the deviation. If the deviation is positive (parameter is higher than the set value), the control output decreases; if it is negative (parameter is lower than the set value), the control output increases.

[0125] Iterative update and steady-state judgment: The new control output is used for the next round of equipment regulation, while monitoring the new environmental response. If the deviation of all parameters from the ideal environmental parameter value is less than the set threshold within several consecutive cycles (usually 3 to 5), the environment is considered to have entered a steady state, and the environmental adjustment result is obtained. If the condition is not met, the entire process from deviation calculation to control correction is repeated.

[0126] Example 2:

[0127] Reference Figure 3 This is the second embodiment of the present invention, which provides a smart control system for indoor environment in green buildings, including the following functional modules:

[0128] Data acquisition module: Used to collect multi-dimensional environmental data of the building's interior and exterior in real time, including parameters such as temperature, humidity, air quality index, carbon dioxide concentration, illuminance, and noise level, forming an environmental parameter set to provide raw input data for the system;

[0129] State classification module: used to comprehensively analyze and classify the collected environmental parameters, and transform complex environmental states into structured classification results;

[0130] Deviation calculation module: used to calculate the actual degree of deviation based on the environmental state classification results, quantitatively reflecting the gap between the current environment and the target environment;

[0131] Factor identification module: used to identify key environmental impact factors that cause deviations and to determine the priority and intensity of regulation.

[0132] Control Calculation Module: Calculates the corresponding control output data based on the set of key influencing factors, converts the deviation into control commands for the equipment, and realizes active adjustment of the building environment;

[0133] Feedback Adjustment Module: By collecting and analyzing the execution results in real time, the control output is dynamically corrected, so that the environmental parameters gradually converge to a steady state.

[0134] Example 3:

[0135] In practical applications, this system can be used for intelligent indoor environmental control in green and energy-saving office buildings. It is used to achieve comprehensive and stable control of multiple environmental factors such as indoor temperature, humidity, air quality, illuminance and noise under complex external climate change conditions. The energy-saving operation scenario of a high-rise commercial office building is used as a typical example for illustration.

[0136] In this office building, the system is first deployed in the building's environmental monitoring network. Distributed sensor nodes collect environmental parameters from different floors, orientations, and functional areas (such as meeting rooms, offices, and rest areas), including indoor and outdoor temperature and relative humidity. Each node samples every 10 seconds, and the data is uploaded to the control center via the local area network, forming a real-time environmental parameter set. Data correction and synchronization mechanisms ensure the consistency and timeliness of data from different sampling points.

[0137] The system comprehensively classifies the current environmental status of the building based on the collected parameter data. For example, when the office area temperature is between 22°C and 26°C, humidity is between 45% and 60%, CO2 concentration is below 800 ppm, and illuminance is maintained at 500 lx, the system determines the environmental status as "comfortable." If the temperature or humidity deviates from the standard range, the status is classified as "slight deviation" or "severe deviation." This classification reflects the overall comfort level of the indoor environment and provides a basis for subsequent deviation analysis.

[0138] By calculating the difference between the actual monitored parameters and the target comfort parameters, the deviation data of each environmental element is obtained. For example, when the indoor temperature is 28℃ and the target temperature is 24℃, the system automatically calculates the temperature deviation as +4℃, which is normalized to 0.4. Combined with the weighting coefficients of each parameter, the overall environmental deviation index is obtained.

[0139] Based on the deviation data, the system further identifies the key environmental factors affecting the deviation. By calculating the contribution of each factor to the deviation value, it is determined that the main influencing factors for the building in the current period are temperature and CO2 concentration, indicating that heat load and air renewal efficiency are the main reasons for the decline in comfort, and their contribution is determined.

[0140] Based on the identified key influencing factors, the corresponding control output data is calculated. Taking the office building air conditioning system as an example, when the temperature deviation is 0.4, the cooling power is calculated to increase by 40% based on the amplitude mapping relationship.

[0141] After the control action is executed, the environmental response results after the control are collected in real time are adjusted through dynamic feedback. If the temperature drops too quickly or the air humidity is too low after the air conditioner is adjusted, the control output is corrected so that the environmental parameters tend to stabilize in a short period of time, forming a long-term sustainable comfortable environment.

[0142] Example 4:

[0143] This embodiment also provides a computer device applicable to a green building indoor environment intelligent control system, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize a green building indoor environment intelligent control system as proposed in the above embodiment.

[0144] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements a green building indoor environment intelligent control system as proposed in the above embodiments.

[0145] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0146] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0147] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0148] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0149] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0150] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A green building indoor environment intelligent regulation system, characterized in that, The method comprises the following steps: S1, collecting building indoor and outdoor environment data to obtain environment parameter data; S2, analyzing the environment state according to the environment parameter data to obtain state classification results; S3, calculating the environment deviation according to the state classification results to obtain deviation data; S4, identifying the environment influence factor according to the deviation data to obtain key influence factors; S5, calculating the control quantity according to the key influence factors to obtain control output data; S6, adjusting the environment control according to the control output data to obtain environment adjustment results.

2. The green building indoor environment intelligent regulation and control system according to claim 1, characterized in that, The environment data collection in step S1 comprises: The building indoor and outdoor environment data is collected by arranging sensors, and the environment parameter data is obtained through unified parameter definition and data processing.

3. The green building indoor environment intelligent regulation system according to claim 1, characterized in that, The environment state analysis in step S2 comprises: According to the obtained environment parameter data, the environment state is analyzed through multi-dimensional parameter collaborative analysis to evaluate the building indoor environment and obtain the state classification results.

4. The green building indoor environment intelligent regulation and control system according to claim 1, characterized in that, The environment deviation calculation in step S3 comprises: According to the obtained state classification results, the deviation analysis model is used to calculate the environment deviation degree, and the discrete state classification results are converted into numerical deviation data to evaluate the deviation degree.

5. The green building indoor environment intelligent regulation and control system according to claim 4, characterized in that, The deviation analysis model comprises: The parameter deviation value comprises: : overall bias value; : number of environmental parameters; : the deviation value of the first parameter the deviation value of the second parameter : the weight coefficient of the first parameter the weight coefficient of the second parameter 6. The green building indoor environment intelligent regulation and control system according to claim 5, characterized in that, The deviation of the environment parameter is obtained by calculating the difference between the actual value and the ideal value of the parameter. The environment influence factor identification in step S4 comprises: : the actual value of the first parameter; and the actual value of the second parameter. : the ideal value of the first parameter; and the ideal value of the second parameter. The environment parameters in the building are identified and screened through deviation data analysis to obtain key influence factors, thereby ensuring the pertinence of regulation and control and improving the regulation and control effect.

7. The green building indoor environment intelligent regulation system according to claim 1, characterized in that, The control quantity calculation in step S5 comprises: According to the key influence factors obtained in step S4, the deviation value and deviation direction of the key influence factors are converted into control quantity to provide regulation and control signals for building environment regulation and control equipment.

8. The green building indoor environment intelligent regulation system according to claim 1, characterized in that, The environment control adjustment in step S6 comprises: According to the control output data, the environment control feedback mechanism is constructed, the control output is adjusted by real-time sensing of environment changes, the environment parameters are close to the set target and remain stable, and the environment adjustment results are obtained.

9. The green building indoor environment intelligent regulation system according to claim 1, characterized in that, The method comprises the following functional modules: Data collection module, state classification module, deviation calculation module, factor identification module, control calculation module, feedback adjustment module.

10. The green building indoor environment intelligent regulation system according to claim 1, characterized in that, ​ ​

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