An intelligent building integrated control system and method

Abstract

The application discloses a kind of building intelligent integrated control system and method, it is related to building technical field, and its technical solution key points include the following steps: according to the scene demand parameter of each function partition of building and the historical operation feedback data of intelligent equipment, the scene control standard index and scene index type of building intelligent system are obtained;After processing and analyzing scene control standard index and scene index type, building dynamic integrated control model is constructed;Real-time scene data and the real-time state data of intelligent equipment are extracted to obtain real-time scene control index and real-time scene index type;According to real-time scene control index, the dynamic control factor of real-time control data is generated;Dynamic control factor is input to dynamic integrated control model;Effect is according to the intelligent control of respective scene demand.

Description

Technical Field

[0001] This invention relates to the field of building technology, and more specifically, to an intelligent integrated control system and method for buildings. Background Technology

[0002] Traditional control methods in the field of building intelligence often employ single-dimensional manual control or simple automatic control logic, such as controlling air conditioning or lighting equipment based solely on time or data from a single sensor. They lack a comprehensive consideration of the needs of multi-functional zoning scenarios within buildings, resulting in inaccurate fulfillment of requirements in different areas. Traditional control methods rarely incorporate historical operational feedback data from intelligent devices, leading to a lack of optimization basis for control strategies and equipment often operating in suboptimal states. This necessitates increasingly higher requirements for control accuracy, dynamism, and integration. Buildings contain a wide variety of equipment, and the lack of effective integration and linkage between systems creates information silos. Traditional control methods struggle to cope with dynamic changes in building scenarios, failing to adjust control strategies in real time, thus hindering the achievement of energy-efficient building operation goals. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide an integrated intelligent building control system and method.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A method for integrated intelligent control of buildings, comprising the following steps:

[0006] Based on the scene requirement parameters of each functional area of ​​the building and the historical operation feedback data of intelligent equipment, the scene control standard indicators and scene indicator types of the building intelligent system are obtained;

[0007] After processing and analyzing the standard indicators and types of scene control, a dynamic integrated control model for buildings is constructed.

[0008] Real-time scene control indicators and real-time scene indicator types are extracted from real-time building scene data and real-time status data of intelligent devices; dynamic control factors for real-time control data are generated based on the real-time scene control indicators.

[0009] The dynamic control factors are input into the dynamic integrated control model; the real-time scenario indicator types of the dynamic control factors are matched with the scenario indicator types to obtain the target control layer corresponding to the dynamic control factors; the dynamic control factors are analyzed with the scenario standard control factors of the target control layer to obtain the deviation control factors and redundant control factors.

[0010] The comprehensive adjustment coefficient of the real-time control data at the target control layer is obtained by processing and analyzing the deviation control factor and the redundancy control factor.

[0011] The control commands for building intelligent equipment are output based on the comprehensive adjustment coefficient.

[0012] Preferably, after processing and analyzing the scene control standard indicators and scene indicator types, a building dynamic integrated control model is constructed, specifically including the following steps:

[0013] Construct a multi-dimensional control layer based on scenario indicator types;

[0014] Based on the type of scenario indicator, the scenario control standard indicators are divided into scenario standard control factors with a multi-dimensional control layer.

[0015] A building dynamic integrated control model is constructed based on multi-dimensional control layers and scenario standard control factors.

[0016] Preferably, the deviation control factor and redundant control factor are processed and analyzed to obtain the comprehensive adjustment coefficient of the real-time control data at the target control layer, specifically including the following steps:

[0017] The first adjustment coefficient of the target control layer is obtained based on the deviation control factor analysis;

[0018] The second adjustment coefficient of the target control layer is obtained based on the redundancy control factor analysis;

[0019] The comprehensive adjustment coefficient of the real-time control data at the target control layer is obtained based on the first adjustment coefficient and the second adjustment coefficient.

[0020] Preferably, the scene control standard indicators and scene indicator types of the building intelligent system are obtained based on the scene requirement parameters of each functional area of ​​the building and the historical operation feedback data of the intelligent equipment. Specifically, this includes the following steps:

[0021] Based on the scenario requirements parameters, the basic control indicators for different building scenarios are obtained;

[0022] Based on historical operational feedback data, adaptive control indicators for optimizing equipment operation in building scenarios are obtained;

[0023] Both the basic control indicators and the adaptive control indicators are marked as the scene control standard indicators of the building intelligent system.

[0024] The types of scenario indicators include lighting adaptation indicators, temperature and humidity regulation indicators, energy consumption optimization indicators, equipment collaborative response indicators, and safety linkage indicators.

[0025] Preferably, the real-time scenario indicator type of the dynamic control factor is matched and analyzed with the scenario indicator type to obtain the target control layer corresponding to the dynamic control factor, specifically including the following steps:

[0026] Perform attribute matching between the real-time scenario indicator types of the dynamic control factors and the scenario indicator types of the multi-dimensional control layer.

[0027] Multi-dimensional control layers whose scenario indicator types are the same as the real-time scenario indicator types corresponding to the dynamic control factors are marked as target control layers.

[0028] Preferably, the deviation control factor and the redundancy control factor are obtained by analyzing the dynamic control factor and the scenario standard control factor of the target control layer, specifically including the following steps:

[0029] Compare the dynamic control factors with the scenario standard control factors of the target control layer;

[0030] Filter out the scene standard control factors whose dynamic control factor parameters exceed the scene adaptation threshold, and mark the dynamic control factors corresponding to the scene standard control factors as deviation control factors.

[0031] Dynamic control factors that exceed the requirements of the current scenario are marked as redundant control factors.

[0032] Preferably, the first adjustment coefficient of the target control layer is obtained based on the deviation control factor analysis, specifically including the following steps:

[0033] Calculate the parameter difference between the real-time feedback parameter of the deviation control factor and the parameter of the scenario standard control factor;

[0034] The factor adjustment coefficient of the deviation control factor is obtained based on the parameter difference and the scene importance weight;

[0035] Obtain the number of deviation control factors in the target control layer; based on the number of deviation control factors, sum the factor adjustment coefficients of all deviation control factors in the target control layer to obtain the first adjustment coefficient of the target control layer.

[0036] Preferably, the second adjustment coefficient of the target control layer is obtained based on the redundancy control factor analysis, specifically including the following steps:

[0037] Obtain the scene importance weights corresponding to the redundancy control factors, and mark the scene importance weights as the redundancy impact coefficients of the redundancy control factors;

[0038] Obtain the number of redundant control factors in the target control layer; sum the redundancy influence coefficients of all redundant control factors in the target control layer based on the number of redundant control factors to obtain the redundancy weight coefficient of the target control layer;

[0039] Obtain the total number of scene standard control factors in the target control layer;

[0040] The redundancy ratio of the target control layer is calculated based on the number of redundant control factors and the total number of scenario standard control factors.

[0041] The redundancy impact coefficient is obtained based on the redundancy ratio.

[0042] The second adjustment coefficient of the target control layer is obtained by multiplying the redundancy weight coefficient and the redundancy influence coefficient.

[0043] Preferably, the control commands for the building's intelligent equipment are output based on the comprehensive adjustment coefficient, specifically including the following steps:

[0044] Set the scene weight values ​​for the multi-dimensional control layer according to the importance of the scene indicator types corresponding to the multi-dimensional control layer;

[0045] The hierarchical adjustment coefficient of the target control layer is obtained by multiplying the scene weight value of the target control layer with the comprehensive adjustment coefficient of the real-time control data in the target control layer.

[0046] The target adjustment coefficient corresponding to the real-time control data is obtained by summing the hierarchical adjustment coefficients corresponding to all multi-dimensional control layers in the dynamic integrated control model.

[0047] Control commands for building intelligent equipment based on target adjustment coefficients.

[0048] An integrated intelligent building control system includes:

[0049] Processing module: Based on the scene requirement parameters of each functional area of ​​the building and the historical operation feedback data of intelligent equipment, the scene control standard indicators and scene indicator types of the building intelligent system are obtained;

[0050] The first analysis module: After processing and analyzing the scene control standard indicators and scene indicator types, a building dynamic integrated control model is constructed.

[0051] Extraction module: Extracts real-time building scene data and real-time status data of intelligent devices to obtain real-time scene control indicators and real-time scene indicator types; generates dynamic control factors for real-time control data based on real-time scene control indicators;

[0052] The second analysis module: inputs the dynamic control factors into the dynamic integrated control model; performs matching analysis between the real-time scenario indicator types of the dynamic control factors and the scenario indicator types to obtain the target control layer corresponding to the dynamic control factors; and analyzes the dynamic control factors and the scenario standard control factors of the target control layer to obtain the deviation control factors and redundant control factors.

[0053] The third analysis module processes and analyzes the deviation control factor and the redundant control factor to obtain the comprehensive adjustment coefficient of the real-time control data at the target control layer;

[0054] Output module: Outputs control commands for building intelligent equipment based on the comprehensive adjustment coefficient.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] This invention constructs scene control standard indicators and a dynamic integrated control model, transforming real-time scene data and equipment status data of a building into dynamic control factors. By comparing these factors with the scene standard control factors of the target control layer, deviation and redundant control factors are identified. A comprehensive adjustment coefficient is obtained based on a first and second adjustment coefficient, ultimately outputting control commands. By fully considering the scene requirement parameters of each functional zone of the building and the historical operational feedback data of intelligent equipment, the system can adapt to the diverse scenes of different functional zones within the building, enabling intelligent control according to their respective scene requirements. This method avoids ineffective equipment operation by identifying redundant control factors, thereby reducing energy consumption. Attached Figure Description

[0057] Figure 1 This is a schematic diagram illustrating the steps of an intelligent building integrated control method proposed in this invention;

[0058] Figure 2 This invention presents a schematic diagram of a building intelligent integrated control system. Detailed Implementation

[0059] 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.

[0060] 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.

[0061] 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.

[0062] Reference Figure 1 - Figure 2 As shown.

[0063] The embodiments further illustrate the intelligent building integrated control system and method proposed in this invention.

[0064] A method for integrated intelligent control of buildings, comprising the following steps:

[0065] Based on the scene requirement parameters of each functional area of ​​the building and the historical operation feedback data of intelligent equipment, the scene control standard indicators and scene indicator types of the building intelligent system are obtained;

[0066] After processing and analyzing the standard indicators and types of scene control, a dynamic integrated control model for buildings is constructed.

[0067] Real-time scene control indicators and real-time scene indicator types are extracted from real-time building scene data and real-time status data of intelligent devices; dynamic control factors for real-time control data are generated based on the real-time scene control indicators.

[0068] The system collects various real-time scene data within the building, such as light intensity, temperature and humidity levels, and pedestrian flow in different functional zones. It also extracts real-time status data from intelligent devices, including current lighting brightness, air conditioning operating power, and the trigger status of security sensors. This collected data is then transformed into real-time scene control indicators. These indicators are quantitative descriptions of the actual scene and device status, such as specific values ​​for light intensity in lux and temperature in degrees Celsius. Based on the category to which these indicators belong, their corresponding real-time scene indicator type is determined, which is one of the following: light adaptation indicator, temperature and humidity regulation indicator, energy consumption optimization indicator, device collaborative response indicator, or safety linkage indicator.

[0069] After obtaining the real-time scene control indicators and their types, dynamic control factors are generated based on these indicators. A dynamic control factor is a quantitative expression of the difference or state between the real-time scene control indicators and the scene control standard indicators. It reflects the deviation or redundancy between the current building scene and the standard scene. For example, if the real-time scene control indicator for the illuminance of an office area is 350 lux, while the corresponding illuminance adaptation scene control standard indicator for that area is 400 lux, a corresponding dynamic illuminance control factor is generated based on this difference. This factor reflects the deviation between the current illuminance and the standard value, thus providing a basis for subsequent control adjustments. Through this process, real-time scene and equipment status data are transformed into dynamic control factors that can be used for dynamic integrated control model analysis, enabling the regulation of intelligent building equipment.

[0070] The dynamic control factors are input into the dynamic integrated control model; the real-time scenario indicator types of the dynamic control factors are matched with the scenario indicator types to obtain the target control layer corresponding to the dynamic control factors; the dynamic control factors are analyzed with the scenario standard control factors of the target control layer to obtain the deviation control factors and redundant control factors.

[0071] The comprehensive adjustment coefficient of the real-time control data at the target control layer is obtained by processing and analyzing the deviation control factor and the redundancy control factor.

[0072] The control commands for building intelligent equipment are output based on the comprehensive adjustment coefficient.

[0073] After processing and analyzing the scene control standard indicators and scene indicator types, a building dynamic integrated control model is constructed, which includes the following steps:

[0074] Construct a multi-dimensional control layer based on scenario indicator types;

[0075] Based on the type of scenario indicator, the scenario control standard indicators are divided into scenario standard control factors with a multi-dimensional control layer.

[0076] A building dynamic integrated control model is constructed based on multi-dimensional control layers and scenario standard control factors.

[0077] First, a multi-dimensional control layer is constructed based on scene indicator types. Scene indicator types include lighting adaptation, temperature and humidity regulation, energy consumption optimization, equipment collaborative response, and security linkage. Each indicator type corresponds to an independent control layer. For example, the lighting adaptation indicator corresponds to a lighting control layer, and the temperature and humidity regulation indicator corresponds to a temperature and humidity control layer. This multi-dimensional control layer design enables targeted management of different scene requirements within the building, ensuring that the control logic for each dimension is relatively independent and precise.

[0078] Based on the type of scene indicator, scene control standards are divided into multi-dimensional control layers of scene standard control factors. Scene control standards are determined jointly by the scene requirement parameters of each functional area of ​​the building and historical operational feedback data from intelligent equipment, including basic control indicators and adaptive control indicators. Taking the lighting control layer as an example, scene control standards include the standard range of light intensity in the office area (e.g., 400-500 lux) and the standard number of lights turned on at different times. These indicators are classified into the lighting control layer as the scene standard control factors for that layer. Similarly, the temperature and humidity control layer has standard control factors such as the standard value of summer office area temperature (e.g., 24℃) and the standard range of humidity (e.g., 40%-60%). This classification provides each control layer with specific standard factors as the basis for control.

[0079] A dynamic integrated control model for buildings is constructed based on multi-dimensional control layers and scene standard control factors. This model integrates all multi-dimensional control layers and their corresponding scene standard control factors to form a unified control center. Real-time building scene data and equipment status data are extracted and transformed into dynamic control factors. The model compares these dynamic control factors with the scene standard control factors in the corresponding multi-dimensional control layers. For example, when the real-time lighting dynamic control factor is 300 lux, while the scene standard control factor of the lighting control layer is 400 lux, the model identifies the deviation and performs subsequent calculations to obtain the adjustment coefficient. This enables multi-dimensional and dynamic integrated control of the building's intelligent system, allowing for precise adjustment of equipment operating status based on real-time conditions to meet the intelligent needs of buildings in different scenarios.

[0080] The comprehensive adjustment coefficient of the real-time control data at the target control layer is obtained by processing and analyzing the deviation control factor and the redundancy control factor, specifically including the following steps:

[0081] The first adjustment coefficient of the target control layer is obtained based on the deviation control factor analysis;

[0082] The second adjustment coefficient of the target control layer is obtained based on the redundancy control factor analysis.

[0083] Based on the scene requirement parameters of each functional area of ​​the building and the historical operation feedback data of intelligent equipment, the scene control standard indicators and scene indicator types of the building intelligent system are obtained, specifically including the following steps:

[0084] Based on the scenario requirements parameters, the basic control indicators for different building scenarios are obtained;

[0085] Based on historical operational feedback data, adaptive control indicators for optimizing equipment operation in building scenarios are obtained;

[0086] Both the basic control indicators and the adaptive control indicators are marked as the scene control standard indicators of the building intelligent system.

[0087] The types of scene indicators include lighting adaptation indicators, temperature and humidity regulation indicators, energy consumption optimization indicators, equipment collaborative response indicators, and safety linkage indicators.

[0088] First, standard control indicators for the building's intelligent system are generated. Based on scene requirement parameters, basic control indicators for different building scenes are obtained. These scene requirement parameters originate from the actual usage needs of each functional area of ​​the building. For example, in the office area of ​​a commercial building, scene requirement parameters include suitable light intensity, temperature range, and equipment operating modes for daily office work. Based on these parameters, basic control indicators for light, temperature, humidity, and equipment operation in the office area are determined. For example, the basic control indicator for light intensity in the office area is 400 lux, and the basic control indicator for temperature is 24℃. Adaptive control indicators for optimizing equipment operation in the building scenes are obtained based on historical operational feedback data. Historical operational feedback data consists of various data generated by intelligent equipment during past operation. By analyzing this data, optimization space for equipment operation can be identified. Taking the air conditioning equipment in the office area as an example, based on its historical operational feedback data, when the outdoor temperature is 28℃, adjusting the air conditioning set temperature to 25℃ can meet the comfort needs of personnel while reducing energy consumption. This optimized set temperature is the adaptive control indicator for the air conditioning equipment operation. Both the basic control indicators and the adaptive control indicators are marked as the scene control standard indicators of the building intelligent system. These indicators combine the basic requirements of the scene needs and the optimization experience of equipment operation, providing a standard basis for subsequent control model construction and equipment regulation.

[0089] The five types of scenario indicators are clearly defined, specifically including lighting adaptation indicators, temperature and humidity regulation indicators, energy consumption optimization indicators, equipment coordination response indicators, and safety linkage indicators. Lighting adaptation indicators are used to manage the lighting environment within a building, such as controlling the light intensity in different areas of office areas and corridors. Temperature and humidity regulation indicators address the temperature and humidity within a building; for example, temperature and humidity control in conference rooms and computer rooms relies on this indicator. Energy consumption optimization indicators focus on energy management of equipment operation, such as optimizing the energy consumption of air conditioning and lighting equipment. Equipment coordination response indicators focus on the coordinated operation between different devices, such as the coordinated linkage between fire-fighting equipment and ventilation equipment in the event of a fire. Safety linkage indicators are used to ensure building safety, such as the linkage between access control systems and monitoring systems. By clearly defining these five types of scenario indicators, multi-dimensional and comprehensive coverage of the building's intelligent system is achieved, ensuring that corresponding indicators can be identified and adjusted under different scenarios and needs, thereby guaranteeing the efficient and stable operation of the building's intelligent system.

[0090] The real-time scenario indicator types of the dynamic control factor are matched and analyzed with the scenario indicator types to obtain the target control layer corresponding to the dynamic control factor. This includes the following steps:

[0091] Perform attribute matching between the real-time scenario indicator types of the dynamic control factors and the scenario indicator types of the multi-dimensional control layer.

[0092] Multi-dimensional control layers whose scenario indicator types are the same as the real-time scenario indicator types corresponding to the dynamic control factors are marked as target control layers.

[0093] First, attribute matching is performed between the real-time scene indicator types of the dynamic control factor and the scene indicator types of the multi-dimensional control layer. The dynamic control factor is generated from real-time building scene data and real-time status data of intelligent equipment. Its corresponding real-time scene indicator type belongs to one of the following: lighting adaptation, temperature and humidity regulation, energy consumption optimization, equipment collaborative response, and safety linkage. The multi-dimensional control layer is constructed based on these five scene indicator types, with each control layer having its corresponding scene indicator type. For example, if the real-time scene indicator type of the dynamic control factor is lighting adaptation, then it needs to be attribute-matched with the scene indicator type of the lighting control layer in the multi-dimensional control layer to determine whether they belong to the same type.

[0094] Multi-dimensional control layers whose scene indicator types match the real-time scene indicator types corresponding to dynamic control factors are marked as target control layers. When the real-time scene indicator type of a dynamic control factor is a temperature and humidity regulation indicator, and attribute matching reveals that the scene indicator type of the temperature and humidity control layer within the multi-dimensional control layers matches, then the temperature and humidity control layer is marked as the target control layer. This is to determine which control layer the dynamic control factor should be analyzed and processed in subsequent steps, ensuring targeted control. For example, in a shopping mall scenario within a commercial building, if real-time scene data shows a temperature of 30℃ in a certain area, and the real-time scene indicator type corresponding to the generated dynamic control factor is a temperature and humidity regulation indicator, the temperature and humidity control layer is marked as the target control layer through attribute matching. Subsequently, temperature-related equipment can be controlled within this control layer.

[0095] The deviation control factor and the redundancy control factor are obtained by analyzing the dynamic control factor and the scenario standard control factor of the target control layer. The specific steps include:

[0096] Compare the dynamic control factors with the scenario standard control factors of the target control layer;

[0097] Filter out the scene standard control factors whose dynamic control factor parameters exceed the scene adaptation threshold, and mark the dynamic control factors corresponding to the scene standard control factors as deviation control factors.

[0098] Dynamic control factors that exceed the requirements of the current scenario are marked as redundant control factors.

[0099] The first step is to compare the parameters of the dynamic control factor with the standard control factor of the target control layer. The dynamic control factor is a quantitative indicator generated based on the real-time building scene and equipment status, while the standard control factor of the target control layer is a standard parameter determined by scene requirements and historical data under that control layer. Taking the lighting control layer of the office area as an example, the dynamic control factor is a real-time illuminance of 300 lux, while the standard control factor of this layer is an illuminance of 400 lux, with a scene adaptation threshold of 380-420 lux. By directly comparing these two parameters, the numerical differences between them are clarified.

[0100] When the dynamic control factor parameter exceeds the scene adaptation threshold, the dynamic control factor associated with the corresponding scene standard control factor will be marked as a deviation control factor. If the real-time illumination of 300 lux is lower than the scene adaptation threshold of 380 lux, then the dynamic control factor is a deviation control factor.

[0101] Dynamic control factors that exceed the needs of the current scenario are marked as redundant control factors. For example, in an empty office area at night, the lighting equipment may still be on. In this case, the dynamic control factor for illumination exceeds the needs of the current scenario and is considered redundant. This distinction clarifies which factors deviate from the standard and which are redundant due to exceeding actual needs, providing a precise basis for subsequent adjustment coefficient calculations and equipment control command outputs.

[0102] The first adjustment coefficient of the target control layer is obtained based on the deviation control factor analysis, specifically including the following steps:

[0103] Calculate the parameter difference between the real-time feedback parameter of the deviation control factor and the parameter of the scenario standard control factor;

[0104] The factor adjustment coefficient of the deviation control factor is obtained based on the parameter difference and the scene importance weight;

[0105] Obtain the number of deviation control factors in the target control layer; based on the number of deviation control factors, sum the factor adjustment coefficients of all deviation control factors in the target control layer to obtain the first adjustment coefficient of the target control layer.

[0106] First, calculate the parameter difference between the real-time feedback parameter of the deviation control factor and the control parameter of the scene standard control factor. The real-time feedback parameter of the deviation control factor is a quantified value reflecting the current actual state in the dynamic control factor, while the control parameter of the scene standard control factor is a standard value set in the target control layer. For example, in the lighting control layer of the office area, if the real-time feedback parameter of a certain deviation control factor is 300 lux, and the control parameter of the scene standard control factor in this layer is 400 lux, then the parameter difference is -100 lux.

[0107] Next, the factor adjustment coefficient of the deviation control factor is obtained based on the parameter difference and the scene importance weight. The scene importance weight is set according to the importance of the scene indicator type in the overall operation of the building. Assuming that the scene importance weight of the lighting adaptation indicator is 0.6, the factor adjustment coefficient can be calculated by multiplying the parameter difference by the scene importance weight, that is, -100×0.6=-60.

[0108] Then, the number of deviation control factors in the target control layer is obtained. Based on the number of deviation control factors, the factor adjustment coefficients of all deviation control factors in the target control layer are summed to obtain the first adjustment coefficient of the target control layer. Assuming there is another deviation control factor in this illumination control layer with a factor adjustment coefficient of -40, and there are a total of 2 deviation control factors in this layer, the first adjustment coefficient is -60 + (-40) = -100. This calculation process can quantify the comprehensive influence of all deviation control factors in the target control layer, ensuring that the adjustment of building intelligent equipment is both accurate and meets the importance requirements of the scenario.

[0109] The second adjustment coefficient of the target control layer is obtained based on the redundancy control factor analysis, specifically including the following steps:

[0110] Obtain the scene importance weights corresponding to the redundancy control factors, and mark the scene importance weights as the redundancy impact coefficients of the redundancy control factors;

[0111] Obtain the number of redundant control factors in the target control layer; sum the redundancy influence coefficients of all redundant control factors in the target control layer based on the number of redundant control factors to obtain the redundancy weight coefficient of the target control layer;

[0112] Obtain the total number of scene standard control factors in the target control layer;

[0113] The redundancy ratio of the target control layer is calculated based on the number of redundant control factors and the total number of scenario standard control factors.

[0114] The redundancy impact coefficient is obtained based on the redundancy ratio.

[0115] The second adjustment coefficient of the target control layer is obtained by multiplying the redundancy weight coefficient and the redundancy influence coefficient.

[0116] The comprehensive adjustment coefficient of the real-time control data at the target control layer is obtained based on the first adjustment coefficient and the second adjustment coefficient.

[0117] First, the scene importance weights corresponding to the redundant control factors are obtained and marked as the redundancy influence coefficients of the redundant control factors. The scene importance weights are set according to the importance of the scene index type to which the factor belongs in the building operation. For example, if the scene importance weight of the lighting redundancy control factor for a certain office area to which the illumination adaptation index belongs is 0.5, then the redundancy influence coefficient of the redundant control factor is 0.5.

[0118] The number of redundant control factors in the target control layer is obtained. Based on this number, the redundancy influence coefficients of all redundant control factors in the target control layer are summed to obtain the redundancy weight coefficient of the target control layer. Assuming that the target control layer has 3 redundant control factors with redundancy influence coefficients of 0.5, 0.4, and 0.3 respectively, the redundancy weight coefficient is 0.5 + 0.4 + 0.3 = 1.2.

[0119] Obtain the total number of scene standard control factors in the target control layer. Calculate the redundancy ratio of the target control layer based on the number of redundant control factors and the total number of scene standard control factors. If the total number of scene standard control factors in this control layer is 10 and the number of redundant control factors is 3, then the redundancy ratio is 3 ÷ 10 = 0.3. Obtain the redundancy impact coefficient based on the redundancy ratio. Here, it is assumed that the redundancy impact coefficient has a linear relationship with the redundancy ratio, i.e., the redundancy impact coefficient is 0.3.

[0120] The second adjustment coefficient of the target control layer is obtained by multiplying the redundancy weight coefficient and the redundancy influence coefficient. Taking the above values ​​as an example, the second adjustment coefficient is 1.2 × 0.3 = 0.36.

[0121] The comprehensive adjustment coefficient of the real-time control data at the target control layer is obtained based on the first and second adjustment coefficients. Assuming the first adjustment coefficient is -0.5, the comprehensive adjustment coefficient is -0.5 + 0.36 = -0.14. This comprehensively considers the impact of deviation and redundancy on the target control layer, providing accurate quantitative basis for subsequent output of control commands for building intelligent equipment, ensuring that equipment regulation not only conforms to scenario standards but also avoids redundant resource consumption.

[0122] The control commands for building intelligent equipment are output based on the comprehensive adjustment coefficient, specifically including the following steps:

[0123] Set the scene weight values ​​for the multi-dimensional control layer according to the importance of the scene indicator types corresponding to the multi-dimensional control layer;

[0124] The hierarchical adjustment coefficient of the target control layer is obtained by multiplying the scene weight value of the target control layer with the comprehensive adjustment coefficient of the real-time control data in the target control layer.

[0125] The target adjustment coefficient corresponding to the real-time control data is obtained by summing the hierarchical adjustment coefficients corresponding to all multi-dimensional control layers in the dynamic integrated control model.

[0126] Control commands for building intelligent equipment based on target adjustment coefficients.

[0127] First, the scene weight values ​​for the multi-dimensional control layer are set according to the importance of the corresponding scene indicator types. Different scene indicator types have different importance in building intelligent control. For example, the safety linkage indicator is related to building safety, so its corresponding control layer scene weight value will be set higher, say 0.8; while the scene weight value for the lighting adaptation indicator is set to 0.5.

[0128] The hierarchical adjustment coefficient of the target control layer is obtained by multiplying the scene weight value of the target control layer with the comprehensive adjustment coefficient of the real-time control data in the target control layer. For example, if the scene weight value of a target control layer is 0.8 and the comprehensive adjustment coefficient of the layer is 0.6, then the hierarchical adjustment coefficient is 0.8 × 0.6 = 0.48.

[0129] The hierarchical adjustment coefficients corresponding to all multi-dimensional control layers in the dynamic integrated control model are accumulated to obtain the target adjustment coefficient corresponding to the real-time control data. Assuming that the hierarchical adjustment coefficient of the illumination control layer is 0.3 and the hierarchical adjustment coefficient of the temperature and humidity control layer is 0.25, the target adjustment coefficient is 0.48 + 0.3 + 0.25 = 1.03.

[0130] Control commands for building intelligent devices are output based on the target adjustment coefficient. The target adjustment coefficient reflects the comprehensive adjustment needs of real-time control data across all multi-dimensional control layers, and specific device control commands are generated based on the target adjustment coefficient. For example, if the target adjustment coefficient indicates that the safety linkage control layer needs to strengthen monitoring, a command to increase the monitoring frequency is sent to the security equipment; if the target adjustment coefficient indicates that the lighting control layer needs adjustment, a command to adjust the brightness is sent to the lighting equipment. This is how the building intelligent devices are controlled.

[0131] An integrated intelligent building control system includes:

[0132] Processing module: Based on the scene requirement parameters of each functional area of ​​the building and the historical operation feedback data of intelligent equipment, the scene control standard indicators and scene indicator types of the building intelligent system are obtained;

[0133] The first analysis module: After processing and analyzing the scene control standard indicators and scene indicator types, a building dynamic integrated control model is constructed.

[0134] Extraction module: Extracts real-time building scene data and real-time status data of intelligent devices to obtain real-time scene control indicators and real-time scene indicator types; generates dynamic control factors for real-time control data based on real-time scene control indicators;

[0135] The second analysis module: inputs the dynamic control factors into the dynamic integrated control model; performs matching analysis between the real-time scenario indicator types of the dynamic control factors and the scenario indicator types to obtain the target control layer corresponding to the dynamic control factors; and analyzes the dynamic control factors and the scenario standard control factors of the target control layer to obtain the deviation control factors and redundant control factors.

[0136] The third analysis module processes and analyzes the deviation control factor and the redundant control factor to obtain the comprehensive adjustment coefficient of the real-time control data at the target control layer;

[0137] Output module: Outputs control commands for building intelligent equipment based on the comprehensive adjustment coefficient.

[0138] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0139] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0140] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A building intelligent integrated control method, characterized in that, The method includes the following steps: Based on the scene requirement parameters of each functional area of ​​the building and the historical operation feedback data of intelligent equipment, the scene control standard indicators and scene indicator types of the building intelligent system are obtained; After processing and analyzing the standard indicators and types of scene control, a dynamic integrated control model for buildings is constructed. Real-time scene control indicators and real-time scene indicator types are extracted from real-time building scene data and real-time status data of intelligent devices; dynamic control factors for real-time control data are generated based on the real-time scene control indicators. The dynamic control factors are input into the dynamic integrated control model; the real-time scenario indicator types of the dynamic control factors are matched with the scenario indicator types to obtain the target control layer corresponding to the dynamic control factors; the dynamic control factors are analyzed with the scenario standard control factors of the target control layer to obtain the deviation control factors and redundant control factors. Specifically, this includes the following steps: Compare the dynamic control factors with the scenario standard control factors of the target control layer; Filter out the scene standard control factors whose dynamic control factor parameters exceed the scene adaptation threshold, and mark the dynamic control factors corresponding to the scene standard control factors as deviation control factors. Dynamic control factors that exceed the requirements of the current scenario are marked as redundant control factors; The comprehensive adjustment coefficient of the real-time control data at the target control layer is obtained by processing and analyzing the deviation control factor and the redundancy control factor. The control commands for building intelligent equipment are output based on the comprehensive adjustment coefficient.

2. The building intelligent integrated control method according to claim 1, characterized in that, After processing and analyzing the scene control standard indicators and scene indicator types, a building dynamic integrated control model is constructed, which includes the following steps: Construct a multi-dimensional control layer based on scenario indicator types; Based on the type of scenario indicator, the scenario control standard indicators are divided into scenario standard control factors with a multi-dimensional control layer. A building dynamic integrated control model is constructed based on multi-dimensional control layers and scenario standard control factors.

3. The building intelligent integrated control method according to claim 2, characterized in that, The comprehensive adjustment coefficient of the real-time control data at the target control layer is obtained by processing and analyzing the deviation control factor and the redundancy control factor, specifically including the following steps: The first adjustment coefficient of the target control layer is obtained based on the deviation control factor analysis; The second adjustment coefficient of the target control layer is obtained based on the redundancy control factor analysis; The comprehensive adjustment coefficient of the real-time control data at the target control layer is obtained based on the first adjustment coefficient and the second adjustment coefficient.

4. The building intelligent integrated control method according to claim 3, characterized in that, Based on the scene requirement parameters of each functional area of ​​the building and the historical operation feedback data of intelligent equipment, the scene control standard indicators and scene indicator types of the building intelligent system are obtained, specifically including the following steps: Based on the scenario requirements parameters, the basic control indicators for different building scenarios are obtained; Based on historical operational feedback data, adaptive control indicators for optimizing equipment operation in building scenarios are obtained; Both the basic control indicators and the adaptive control indicators are marked as the scene control standard indicators of the building intelligent system. The types of scenario indicators include lighting adaptation indicators, temperature and humidity regulation indicators, energy consumption optimization indicators, equipment collaborative response indicators, and safety linkage indicators.

5. The building intelligent integrated control method according to claim 4, characterized in that, The real-time scenario indicator types of the dynamic control factor are matched and analyzed with the scenario indicator types to obtain the target control layer corresponding to the dynamic control factor. This includes the following steps: Perform attribute matching between the real-time scenario indicator types of the dynamic control factors and the scenario indicator types of the multi-dimensional control layer. Multi-dimensional control layers whose scenario indicator types are the same as the real-time scenario indicator types corresponding to the dynamic control factors are marked as target control layers.

6. The building intelligent integrated control method according to claim 5, characterized in that, The first adjustment coefficient of the target control layer is obtained based on the deviation control factor analysis, specifically including the following steps: Calculate the parameter difference between the real-time feedback parameter of the deviation control factor and the parameter of the scenario standard control factor; The factor adjustment coefficient of the deviation control factor is obtained based on the parameter difference and the scene importance weight; Obtain the number of deviation control factors in the target control layer; based on the number of deviation control factors, sum the factor adjustment coefficients of all deviation control factors in the target control layer to obtain the first adjustment coefficient of the target control layer.

7. The building intelligent integrated control method according to claim 6, characterized in that, The second adjustment coefficient of the target control layer is obtained based on the redundancy control factor analysis, specifically including the following steps: Obtain the scene importance weights corresponding to the redundancy control factors, and mark the scene importance weights as the redundancy impact coefficients of the redundancy control factors; Obtain the number of redundant control factors in the target control layer; sum the redundancy influence coefficients of all redundant control factors in the target control layer based on the number of redundant control factors to obtain the redundancy weight coefficient of the target control layer; Obtain the total number of scene standard control factors in the target control layer; The redundancy ratio of the target control layer is calculated based on the number of redundant control factors and the total number of scenario standard control factors. The redundancy impact coefficient is obtained based on the redundancy ratio. The second adjustment coefficient of the target control layer is obtained by multiplying the redundancy weight coefficient and the redundancy influence coefficient.

8. The building intelligent integrated control method according to claim 7, characterized in that, The control commands for building intelligent equipment are output based on the comprehensive adjustment coefficient, specifically including the following steps: Set the scene weight values ​​for the multi-dimensional control layer according to the importance of the scene indicator types corresponding to the multi-dimensional control layer; The hierarchical adjustment coefficient of the target control layer is obtained by multiplying the scene weight value of the target control layer with the comprehensive adjustment coefficient of the real-time control data in the target control layer. The target adjustment coefficient corresponding to the real-time control data is obtained by summing the hierarchical adjustment coefficients corresponding to all multi-dimensional control layers in the dynamic integrated control model. Control commands for building intelligent equipment based on target adjustment coefficients.

9. A building intelligent integrated control system, applied to the building intelligent integrated control method according to any one of claims 1 to 8, characterized in that, include: Processing module: Based on the scene requirement parameters of each functional area of ​​the building and the historical operation feedback data of intelligent equipment, the scene control standard indicators and scene indicator types of the building intelligent system are obtained; The first analysis module: After processing and analyzing the scene control standard indicators and scene indicator types, a building dynamic integrated control model is constructed. Extraction module: Extracts real-time building scene data and real-time status data of intelligent devices to obtain real-time scene control indicators and real-time scene indicator types; Dynamic control factors are generated based on real-time scenario control indicators to produce real-time control data. The second analysis module: inputs the dynamic control factors into the dynamic integrated control model; performs matching analysis between the real-time scenario indicator types of the dynamic control factors and the scenario indicator types to obtain the target control layer corresponding to the dynamic control factors; analyzes the dynamic control factors and the scenario standard control factors of the target control layer to obtain the deviation control factors and redundant control factors, specifically including the following steps: Compare the dynamic control factors with the scenario standard control factors of the target control layer; Filter out the scene standard control factors whose dynamic control factor parameters exceed the scene adaptation threshold, and mark the dynamic control factors corresponding to the scene standard control factors as deviation control factors. Dynamic control factors that exceed the requirements of the current scenario are marked as redundant control factors; The third analysis module processes and analyzes the deviation control factor and the redundant control factor to obtain the comprehensive adjustment coefficient of the real-time control data at the target control layer; Output module: Outputs control commands for building intelligent equipment based on the comprehensive adjustment coefficient.

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