Intelligent control method and system for building HVAC system

Abstract

The present invention discloses an intelligent control method and system for a building HVAC system. The system includes an information monitoring unit, a core processing unit, a scheme design unit, a real-time control unit, and an early warning management unit. By constructing an HVAC monitoring model, environmental information of the spatial area of the building is collected, so as to evaluate the building environmental quality of the spatial area in a targeted manner. In combination with the geographical coordinates of the spatial area, HVAC linkage control is performed in the local control area, and air control management schemes and equipment management signals are output to perform real-time control of the building environment, realizing air interaction from the key monitoring area to the associated area. The local linkage control improves the accuracy of HVAC control and reduces the energy consumption of air conditioning equipment. Then, the building environmental quality of the spatial area is predicted and evaluated to ensure the continuous stability of the building environmental quality, thereby improving the comfort of the working and living conditions of the people using the building.

Description

Technical Field

[0001] The present invention relates to the technical field of building environment air control, and in particular to an intelligent control method and system for a building HVAC system. Background Art

[0002] Building HVAC systems encompass the disciplines, technologies, and industries related to heating, ventilation, and air conditioning, including temperature, humidity, and air circulation control systems. Designed to provide a comfortable working and living environment, HVAC systems are widely used in architectural design and manufacturing. With the development of IoT technology, HVAC systems are gradually being integrated into building management platforms.

[0003] Since modern building designs are mainly high-rise buildings, the degree of external environmental interference experienced by the space near the building's exterior curtain wall and the space near the building's interior center varies, including differences in temperature, humidity, particulate matter, and the accumulation of harmful gases. For example, the space near the building's exterior curtain wall is more affected by temperature, humidity, and particulate matter; the space near the interior center relies on narrow atriums for gas exchange, which leads to low gas diffusion efficiency and the accumulation of harmful gases.

[0004] However, when building HVAC systems use a centralized management approach for air conditioning, they suffer from inaccurate control, making it difficult to achieve satisfactory comfort for residents within the building. On the other hand, when building HVAC systems use individual unit control for each building space, the equipment consumes a lot of energy, increasing operating and management costs. Therefore, existing building HVAC technology suffers from the difficulty in balancing air control accuracy with the energy consumption of the control equipment.

[0005] In view of the above technical defects, a solution is now proposed. Summary of the Invention

[0006] The purpose of the present invention is to solve the problem of the difficulty in balancing the air control accuracy and the energy consumption of the control equipment in the existing technology. The present invention realizes real-time control of the building environment through HVAC linkage control in the local control area, and realizes air interaction from the key monitoring area to the related areas. The local linkage control improves the HVAC control accuracy and reduces the energy consumption of the air conditioning equipment. Then, the building environment quality of the spatial area is predicted and evaluated to ensure the continuous stability of the building environment quality, thereby improving the comfort of the working and living conditions of the people using the building.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] An intelligent control method for a building HVAC system comprises the following steps:

[0009] Step 1: Construct an HVAC monitoring model to collect building environment information: Monitor the building environment through the HVAC monitoring model, construct a three-dimensional building model, divide any building BR into N0 spatial regions, mark any spatial region as a, and collect building environment information of spatial region a;

[0010] Step 2: Analyze building environment information to evaluate building environment quality: pre-process the building environment information of spatial area a and set up a parameter risk assessment model to construct a building risk parameter vector and integrate it to evaluate the building environment quality of spatial area a;

[0011] Step 3: Output the air control management plan and equipment control signals: Based on the building environment quality of spatial area a and the regional geographic coordinates, analyze and mark the key monitoring areas from N0 spatial areas, obtain the associated areas of the key monitoring areas, and output the corresponding equipment control signals through detailed analysis of the regional building environment quality. These signals are then integrated to generate the air control management plan.

[0012] Step 4: Targeted real-time control of spatial areas: By receiving air control management plans and equipment control signals, local control area HVAC linkage control is carried out in key monitoring areas and related areas to achieve real-time environmental control of the building;

[0013] Step 5: Conduct early warning management for spatial areas: Build a historical parameter matrix and set a parameter vector prediction model to predict and evaluate the building environment quality of spatial area a, generate corresponding early warning prompt signals, and thus provide early warning for the building HVAC system.

[0014] Furthermore, the specific process of building an HVAC monitoring model to monitor the building environment is as follows:

[0015] Mark any building as BR. Building BR is equipped with an air conditioning unit, which includes N0 air conditioning equipment. Then construct a three-dimensional building model, divide the building into N0 spatial areas, mark any spatial area as a, mark the air conditioning equipment in spatial area a as Qa, and collect the building environment information and regional geographic coordinates of spatial area a;

[0016] Building environment information includes temperature, humidity, wind speed, particulate matter content and harmful gas concentration; set the information collection cycle Tc to collect building environment information regularly;

[0017] The temperature, humidity, wind speed, and particulate matter content of spatial region a are labeled Wa, Sa, Fa, and HKa, respectively;

[0018] It is preset to collect n0 kinds of harmful gases, mark any harmful gas as φ, and mark the concentration of the harmful gas φ as Cφa; by combining the concentrations Cφa of n0 kinds of harmful gases, the harmful gas risk value FCa of the spatial area a is comprehensively obtained.

[0019] Furthermore, the specific process of setting the parameter risk assessment model is as follows:

[0020] Input parameter i and its standard interval [Qa, Qb], and obtain and output the risk value fi of parameter i by combining parameter i and its standard interval [Qa, Qb]: ;

[0021] in, It refers to the correction coefficient of parameter i, which is a constant value that keeps the risk value fi always greater than 0;

[0022] By setting the standard intervals of temperature Wa, humidity Sa, and wind speed Fa respectively and substituting them into the parameter risk assessment model for preprocessing, the risk values of temperature Wa, humidity Sa, and wind speed Fa are obtained in turn and marked as Wfa, Sfa, and Ffa respectively;

[0023] The temperature risk value Wfa, humidity risk value Sfa, wind speed risk value Ffa, particulate matter content HKa and harmful gas risk value FCa are integrated and marked as a building risk parameter vector.

[0024] Furthermore, the specific process of evaluating the building environment quality of spatial area a is as follows:

[0025] The building environment quality assessment coefficient HJa of spatial area a is obtained by combining the temperature risk value Wfa, humidity risk value Sfa, wind speed risk value Ffa, particulate matter content HKa and harmful gas risk value FCa;

[0026] The building environment quality assessment coefficient HJa is calculated according to the information collection period Tc. By measuring the building environment quality assessment coefficients of N0 spatial areas and sorting them in descending order, the top n1 spatial areas are extracted and marked as key monitoring areas.

[0027] Furthermore, the specific process of generating an air control management plan is as follows:

[0028] The regional geographic coordinates are calibrated through the three-dimensional model of the building. Any key monitoring area is marked as b, and the regional geographic coordinates of the key monitoring area b are marked as (Xb, Yb, Zb);

[0029] Taking the key monitoring area b as the reference coordinate, calculate the distance between other spatial areas and the key monitoring area b. Mark any remaining spatial area as c, and mark the regional geographic coordinates of spatial area c as (Xc, Yc, Zc). Then the distance between spatial area c and key monitoring area b is ;

[0030] Set the distance between spatial area c and key monitoring area b The threshold G0, when the distance When it is lower than the threshold G0, the spatial area c is extracted as the associated area of the key monitoring area b, and the number of associated areas is marked as m0. The m0 associated areas are integrated and marked as a linkage area group, and the linkage area group centered on the key monitoring area and its connections is integrated and marked as a local control area.

[0031] Retrieve the building environment quality assessment coefficients of m0 associated areas, mark the associated areas in descending order of the building environment quality assessment coefficients, and set the corresponding control range, so as to generate the air control local management plan and the corresponding equipment control signal, realize the HVAC linkage control of the local control area, and thus comprehensively carry out real-time environmental control of the building BR.

[0032] Furthermore, the specific process of the parameter vector prediction model is:

[0033] Input vector L to the parameter vector prediction model. Vector L includes m1 component vectors. Vector L is marked as:

[0034] ;

[0035] By averaging the m1 component vectors, we can obtain the mean coefficient σ1 of the vector L. Then, by calculating the standard deviation of the m1 component vectors, we can obtain the fluctuation coefficient σ2 of the vector L. By averaging the differences between all adjacent component vectors, we can obtain the increase rate coefficient σ3 of the vector L.

[0036] The prediction coefficient of vector L is obtained by combining the mean coefficient σ1, fluctuation coefficient σ2 and increase rate coefficient σ3 of vector L ;

[0037] The parameter vector predicts the prediction coefficient of the model output vector L .

[0038] Furthermore, the specific process of evaluating the predicted state of the building environment in spatial area a and generating an early warning signal is as follows:

[0039] The parameter risk assessment model is used to calculate the building risk parameter vector corresponding to m1 information collection cycles Tc, and the historical parameter matrix V is constructed to predict and evaluate the building environment quality of spatial area a, and the building environment quality prediction index ZLa is obtained:

[0040] ;

[0041] Among them, any row vector of the historical parameter matrix V includes the five parameter values of the building risk parameter vector, namely the temperature risk value Wfa, the humidity risk value Sfa, the wind speed risk value Ffa, the particulate matter content HKa, and the harmful gas risk value FCa; any column vector of the historical parameter matrix V represents the building risk parameter vector corresponding to m1 information collection cycles Tc;

[0042] Set up a parameter vector prediction model, integrate the column vector and substitute it into the parameter vector prediction model to obtain the prediction coefficient of the column vector, where, 、 、 、 and They are the prediction coefficients of temperature risk value Wfa, humidity risk value Sfa, wind speed risk value Ffa, particulate matter content HKa and harmful gas risk value FCa;

[0043] The environmental quality prediction index ZLa is obtained by combining the prediction coefficients of temperature risk value Wfa, humidity risk value Sfa, wind speed risk value Ffa, particulate matter content HKa and harmful gas risk value FCa;

[0044] Set the evaluation interval of the environmental quality prediction index ZLa, evaluate the building environment prediction status of spatial area a through interval comparison, and generate corresponding early warning signals.

[0045] An intelligent control system for a building HVAC system, comprising an information monitoring unit, a core processing unit, a scheme design unit, a real-time control unit, and an early warning management unit, wherein the information monitoring unit, the core processing unit, the scheme design unit, the real-time control unit, and the early warning management unit are communicatively connected to each other, and the system applies the above-mentioned intelligent control method for a building HVAC system;

[0046] The information monitoring unit is used to build an HVAC monitoring model to collect building environment information;

[0047] The core processing unit is used to analyze building environment information and evaluate building environment quality;

[0048] The scheme design unit is used to output air control management schemes and equipment control signals;

[0049] The real-time control unit is used to receive air control management plans and equipment control signals, thereby performing targeted real-time control of the space area;

[0050] The early warning management unit is used to predict and evaluate the building environment quality of the spatial area a and generate corresponding early warning prompt signals, thereby performing early warning management of the spatial area.

[0051] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0052] The present invention collects environmental information from the spatial areas of the building by constructing an HVAC monitoring model, thereby specifically evaluating the building environmental quality of the spatial areas, and combining the geographical coordinates of the spatial areas to perform HVAC linkage control within the local control area, output air control management plans and equipment management signals to perform real-time control of the building environment, and realize air interaction from the key monitoring area to the associated areas. The local linkage control improves the accuracy of HVAC control and reduces the energy consumption of air conditioning equipment. Then, by predicting and evaluating the building environmental quality of the spatial areas, the continuous stability of the building environmental quality is guaranteed, thereby improving the comfort of the working and living conditions of the people using the building. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 Shown is a schematic diagram of the process steps of the present invention;

[0054] Figure 2 Shown is a schematic diagram of module connection of the present invention. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0056] Example 1:

[0057] like Figure 1-Figure 2 As shown, an intelligent control method for a building HVAC system includes the following steps:

[0058] S1, build an HVAC monitoring model to collect building environment information: Monitor the building environment through the HVAC monitoring model, build a three-dimensional building model, divide any building BR into N0 spatial regions, mark any spatial region as a, and collect building environment information of spatial region a. The specific process is as follows:

[0059] Mark any building as BR. Building BR is equipped with an air conditioning unit, which includes N0 air conditioning equipment. Then construct a three-dimensional building model, divide the building into N0 spatial areas, mark any spatial area as a, mark the air conditioning equipment in spatial area a as Qa, and collect the building environment information and regional geographic coordinates of spatial area a;

[0060] The air conditioning equipment in space area a is marked as Qa, where air conditioning equipment includes a combination of air conditioners, ventilation fans, etc., which can be used as a single or combined device to control wind speed, temperature, and humidity;

[0061] Setting an information collection period Tc to periodically collect building environment information; building environment information includes temperature, humidity, wind speed, particulate matter content, and harmful gas concentration; temperature, humidity, and wind speed are monitored and collected by sensors; for example, temperature sensors, humidity sensors, and wind speed sensors;

[0062] Measure particulate matter content and harmful gas concentrations through optical technology;

[0063] The concentration of harmful gases in spatial region a is collected using infrared spectroscopy. Infrared spectroscopy utilizes the interaction between infrared radiation and matter. Different gas molecules absorb infrared radiation of specific wavelengths, which is then used to determine the gas composition and thus the concentration of harmful gases.

[0064] The laser particle size analyzer uses laser scattering technology to obtain a laser scattering image of spatial region a, thereby collecting the particle content in spatial region a. Laser scattering technology irradiates a laser beam onto particles, which absorb some of the energy and emit scattered light in all directions. The intensity difference of the scattered light is then used to analyze the gas in the spatial region, and the laser scattering image of spatial region a is captured and collected.

[0065] Evaluate the building environment quality of spatial area a through building environment information;

[0066] The temperature, humidity, wind speed, and particulate matter content of spatial region a are marked as Wa, Sa, Fa, and HKa, respectively;

[0067] It is preset to collect n0 kinds of harmful gases, mark any harmful gas as φ, and mark the concentration of harmful gas φ as Cφa;

[0068] Harmful gases include CO, CO2, H2S, CH4, NO, NO2, etc. Carbon monoxide (CO) is a colorless, odorless, and non-irritating gas. It easily combines with hemoglobin, which transports oxygen in the human body, causing oxygen to lose the opportunity to combine with hemoglobin, thereby affecting the body's ability to transport and utilize oxygen, and may cause necrosis of human tissues due to lack of oxygen; Carbon dioxide (CO2) can cause suffocation at high concentrations, especially in closed environments; Hydrogen sulfide (H2S) is a toxic gas that often exists in confined spaces, such as basements. Hydrogen sulfide poisoning can cause suffocation and poisoning accidents, especially in poorly ventilated environments. Methane (CH4) may mix with air to form explosive gases in certain circumstances, especially in closed environments; Nitrogen oxides include nitric oxide (NO) and nitrogen dioxide (NO2). These gases are irritating to the respiratory tract, and long-term inhalation may cause pulmonary edema and other respiratory diseases.

[0069] By combining n0 kinds of harmful gas concentrations Cφa, the harmful gas risk value FCa of spatial area a is comprehensively obtained: , assess the risk level of harmful gases in space area a;

[0070] Wherein, e is the conversion coefficient of the harmful gas concentration Cφa and e is greater than 0. The conversion coefficient e is preset and obtained after calculating a large amount of data. When the concentration Cφa of the harmful gas φ is higher, the harmful gas risk value FCa is higher, and the degree of harmful gas risk in the assessment space area a is higher.

[0071] S2, analyzing building environment information to evaluate building environment quality: by preprocessing the building environment information of spatial area a and setting a parameter risk assessment model, a building risk parameter vector is constructed and integrated to evaluate the building environment quality of spatial area a;

[0072] The specific process of setting up the parameter risk assessment model is as follows:

[0073] Input parameter i and its standard interval [Qa, Qb];

[0074] By combining the parameter i and its standard interval [Qa, Qb], the risk value fi of the parameter i is obtained and output:

[0075] ,in, It refers to the correction coefficient of parameter i. The correction coefficient is a constant value that keeps the risk value fi always greater than 0. When parameter i is in the standard interval [Qa, Qb], it is judged that parameter i is normal and the risk value fi of parameter i is less than ; When parameter i is not in the standard interval [Qa, Qb], it is determined that parameter i is abnormal, and the risk value fi of parameter i is greater than ;

[0076] By setting the standard intervals of temperature Wa, humidity Sa, and wind speed Fa respectively and substituting them into the parameter risk assessment model for preprocessing, the risk values of temperature Wa, humidity Sa, and wind speed Fa are obtained in turn and marked as Wfa, Sfa, and Ffa respectively;

[0077] When the temperature risk value Wfa, humidity risk value Sfa, and wind speed risk value Ffa are higher, it means that the degree of abnormality of temperature Wa, humidity Sa, and wind speed Fa deviating from the corresponding standard range is higher, indicating that the risk level of the corresponding parameters is higher;

[0078] The temperature risk value Wfa, humidity risk value Sfa, wind speed risk value Ffa, particulate matter content HKa and harmful gas risk value FCa are integrated and marked as a building risk parameter vector;

[0079] The specific process of evaluating the building environment quality of spatial area a is as follows:

[0080] By combining the temperature risk value Wfa, humidity risk value Sfa, wind speed risk value Ffa, particulate matter content HKa and harmful gas risk value FCa, the building environment quality assessment coefficient HJa of spatial area a is obtained:

[0081] ;

[0082] Among them, ε1, ε2, ε3, ε4 and ε5 are the weight factor coefficients of temperature risk value Wfa, humidity risk value Sfa, wind speed risk value Ffa, particulate matter content HKa and harmful gas risk value FCa, respectively, and ε1, ε2, ε3, ε4 and ε5 are all greater than 0. The weight factor coefficients are preset after calculation based on a large amount of experimental data, and When the temperature risk value Wfa, humidity risk value Sfa, wind speed risk value Ffa, particulate matter content HKa, and harmful gas risk value FCa are higher, the building environment quality assessment coefficient HJa is lower, and the building environment quality of the assessment space area a is worse;

[0083] The building environment quality assessment coefficient HJa is calculated in real time according to the information collection period Tc. By measuring the building environment quality assessment coefficients of N0 spatial areas and sorting them in descending order, the top n1 spatial areas are extracted and marked as key monitoring areas.

[0084] S3, output air control management plan and equipment control signals: Based on the building environment quality of spatial area a and the regional geographic coordinates, analyze and mark key monitoring areas from N0 spatial areas, obtain the associated areas of the key monitoring areas, and output corresponding equipment control signals through detailed analysis of the regional building environment quality. These signals are integrated to generate an air control management plan;

[0085] S4, targeted real-time control of spatial areas: By receiving air control management plans and equipment control signals, local control area HVAC linkage control is carried out in key monitoring areas and related areas to achieve real-time environmental control of the building. The specific process is as follows:

[0086] The regional geographic coordinates are calibrated through the three-dimensional model of the building. Any key monitoring area is marked as b, and the regional geographic coordinates of the key monitoring area b are marked as (Xb, Yb, Zb);

[0087] Taking the key monitoring area b as the reference coordinate, calculate the distance between other spatial areas and the key monitoring area b. Mark any remaining spatial area as c, and mark the regional geographic coordinates of spatial area c as (Xc, Yc, Zc). Then the distance between spatial area c and key monitoring area b is :

[0088] ;

[0089] Set the distance between spatial area c and key monitoring area b The threshold G0, when the distance When it is lower than the threshold G0, the spatial area c is extracted as the associated area of the key monitoring area b, and the number of associated areas is marked as m0. The m0 associated areas are integrated and marked as a linkage area group, and the linkage area group centered on the key monitoring area and its connections is integrated and marked as a local control area.

[0090] Retrieve the building environment quality assessment coefficients of m0 associated areas, sort them in descending order, and mark the associated areas, thereby generating a local air control management plan. The local air control management plan refers to setting the control amplitudes of the air conditioning adjustment equipment in m0 associated areas from small to large by combining them and sorting them in descending order according to the building environment quality assessment coefficients. The specific value of the control amplitude is set in reference to the specific conditions of the environment and the equipment. For example, the wind speed of the ventilation fan is divided into N2 gears, and the highest wind speed gear is applied to the key monitoring area b. The lower the building environment quality assessment coefficient of the m0 associated areas, the higher the wind speed gear. The corresponding setting is made according to the actual situation.

[0091] The control range of the air conditioning equipment refers to the fan speed of the ventilation outlet, the temperature and humidity control set values, etc. By feedback analyzing the building environment information of the spatial area c, the corresponding equipment control signal is generated. The specific setting process is as follows:

[0092] By setting the thresholds of temperature risk value Wfa, humidity risk value Sfa, wind speed risk value Ffa, particulate matter content HKa and harmful gas risk value FCa respectively, and performing detailed comparison;

[0093] The thresholds for marking the temperature risk value Wfa, humidity risk value Sfa, wind speed risk value Ffa, particulate matter content HKa, and harmful gas risk value FCa are respectively the temperature threshold Gw, humidity threshold Gs, wind speed threshold Gf, particulate matter threshold Gh, and harmful gas threshold Gc;

[0094] When the temperature risk value Wfa is higher than the temperature threshold Gw, a control signal No. 1 is generated;

[0095] When the humidity risk value Sfa is higher than the humidity threshold Gs, the second control signal is generated;

[0096] When the wind speed risk value Ffa is higher than the wind speed threshold Gf, a No. 3 control signal is generated;

[0097] When the particulate matter content HKa is higher than the particulate matter threshold Gh, a fourth control signal is generated;

[0098] When the harmful gas risk value FCa is higher than the harmful gas threshold Gc, a No. 5 control signal is generated;

[0099] The first control signal and the second control signal are integrated and marked as an air conditioning equipment control signal, and the equipment parameters of the air conditioner are controlled by the air conditioning equipment control signal; wherein the temperature parameter is controlled by the first control signal, and the humidity parameter is controlled by the second control signal;

[0100] The third control signal, the fourth control signal, and the fifth control signal are integrated and marked as a fan device control signal, and the device parameters of the ventilation fan are controlled by the fan device control signal;

[0101] For example, when a No. 5 control signal is received, it indicates that the risk of harmful gases in key monitoring area b and its associated areas exceeds the threshold, and ventilation fans need to be activated for ventilation control;

[0102] Taking the key monitoring area b as the starting point, and taking the building quality of m0 associated areas in ascending order as the path, the ventilation fan is activated and the ventilation ducts of the path are connected. The air conditioning adjustment equipment of the m0 associated areas is set with a control range from large to small, so as to achieve air exchange between the key monitoring area b and the m0 associated areas, thereby realizing air replacement from the relatively confined space of the building to the window area of the building's exterior wall, ensuring the ventilation efficiency of the indoor windowless area, and different control ranges are aimed at different levels of regional environmental quality, thereby reducing the energy consumption of air conditioning equipment and realizing targeted wind speed control;

[0103] The same operation is applied to temperature and humidity. After the air temperature is cooled or dried by air conditioning equipment, air exchange between the key monitoring area and its linkage area group is achieved through ventilation ducts. The air is then transferred to the local control area with the key monitoring area as the center point and the linkage area group as the connection point, thus realizing HVAC linkage control in the local control area.

[0104] The local control area generally includes the space area near the outer curtain wall of the building and the space area near the inner center of the building. By allowing air to interact between the space area near the outer curtain wall of the building and the space area near the inner center, the temperature and humidity can be transferred from the outside to the inside, and the harmful gases can be diffused from the inside to the outside. By transferring the resources and energy of different geographical coordinate spaces under the natural state, the actual operation intensity of the air conditioning equipment can be reduced, thereby reducing the energy consumption of the equipment.

[0105] Obtain the corresponding local control area and its air control local management plan through n1 key monitoring areas, and integrate all the air control local management plans as the air control management plan, so as to comprehensively carry out real-time environmental control of the building BR;

[0106] S5, early warning management of spatial areas: construct a historical parameter matrix and set a parameter vector prediction model to predict and evaluate the building environment quality of spatial area a, generate corresponding early warning prompt signals, and thus provide early warning for the building HVAC system

[0107] The parameter risk assessment model is used to calculate the building risk parameter vector corresponding to m1 information collection cycles Tc, and the historical parameter matrix V is constructed to predict and evaluate the building environment quality of spatial area a, and the building environment quality prediction index ZLa is obtained:

[0108] ;

[0109] Among them, any row vector of the historical parameter matrix V includes the five parameter values of the building risk parameter vector, namely the temperature risk value Wfa, the humidity risk value Sfa, the wind speed risk value Ffa, the particulate matter content HKa, and the harmful gas risk value FCa; any column vector of the historical parameter matrix V represents the building risk parameter vector corresponding to m1 information collection cycles Tc; for example It refers to the temperature risk value Wfa corresponding to the first information collection period Tc; It refers to the humidity risk value Sfa corresponding to the first information collection period Tc; It refers to the temperature risk value Wfa corresponding to the second information collection period Tc;

[0110] The specific process of the parameter vector prediction model is:

[0111] Input vector L to the parameter vector prediction model. Vector L includes m1 component vectors. Vector L is marked as:

[0112] ;

[0113] By averaging the m1 component vectors, we can obtain the mean coefficient σ1 of the vector L. Then, by calculating the standard deviation of the m1 component vectors, we can obtain the fluctuation coefficient σ2 of the vector L. By averaging the differences between all adjacent component vectors, we can obtain the increase rate coefficient σ3 of the vector L.

[0114] but, ; ; ;

[0115] The prediction coefficient of vector L is obtained by combining the mean coefficient σ1, fluctuation coefficient σ2 and increase rate coefficient σ3 of vector L : ;

[0116] Where ω is the logarithmic base and is preset to be greater than 1; when the mean coefficient σ1, fluctuation coefficient σ2 and increase coefficient σ3 of vector L are higher, the prediction coefficient of vector L is The higher it is, the higher the overall level of vector L is, the greater the volatility is, and the more obvious the growth trend is, indicating that the risk level of vector L is higher;

[0117] The parameter vector predicts the prediction coefficient of the model output vector L ;

[0118] Set up a parameter vector prediction model, integrate the column vector and substitute it into the parameter vector prediction model to obtain the prediction coefficient of the column vector. Combine the prediction coefficients of the temperature risk value Wfa, humidity risk value Sfa, wind speed risk value Ffa, particulate matter content HKa, and harmful gas risk value FCa to obtain the environmental quality prediction index ZLa.

[0119] The five column vectors of the historical parameter matrix V are sequentially input into the parameter vector prediction model, thereby outputting the corresponding prediction coefficients in sequence and marking them as temperature prediction coefficient Rw, humidity prediction coefficient Rs, wind speed prediction coefficient Rf, particle prediction coefficient Rh, and harmful gas prediction coefficient Rc respectively;

[0120] Then, by combining the temperature prediction coefficient Rw, humidity prediction coefficient Rs, wind speed prediction coefficient Rf, particle prediction coefficient Rh and harmful gas prediction coefficient Rc, we can obtain the environmental quality prediction index ZLa:

[0121] ;

[0122] Among them, η1, η2, η3, η4 and η5 are weight factor coefficients of temperature prediction coefficient Rw, humidity prediction coefficient Rs, wind speed prediction coefficient Rf, particle prediction coefficient Rh and harmful gas prediction coefficient Rc, and η1, η2, η3, η4 and η5 are all preset to be greater than 0; the weight coefficients are preset and obtained after a large amount of experimental data is measured, and ; When the temperature prediction coefficient Rw, humidity prediction coefficient Rs, wind speed prediction coefficient Rf, particle prediction coefficient Rh and harmful gas prediction coefficient Rc are higher, the environmental quality prediction index ZL is lower, and the predicted state of the building environment quality of the evaluation space area a is worse;

[0123] Set the evaluation interval of the environmental quality prediction index ZLa, evaluate the building environment prediction status of spatial area a through interval comparison, and generate corresponding early warning signals;

[0124] There are U evaluation intervals for the preset environmental quality prediction index ZLa. Any evaluation interval is marked as Qu. When the environmental quality prediction index ZLa is in the evaluation interval Qu, a u-level early warning prompt signal is generated, and corresponding early warning prompts are issued, thereby giving corresponding reminders to the management personnel of the background center and taking corresponding management plans to ensure the continuous stability of the building environment quality.

[0125] An intelligent control system for a building HVAC system, comprising an information monitoring unit, a core processing unit, a scheme design unit, a real-time control unit, and an early warning management unit, wherein the information monitoring unit, the core processing unit, the scheme design unit, the real-time control unit, and the early warning management unit are communicatively connected to each other, and the system applies the above-mentioned intelligent control method for a building HVAC system;

[0126] The information monitoring unit is used to build an HVAC monitoring model to collect building environment information;

[0127] The core processing unit is used to analyze building environment information and evaluate building environment quality;

[0128] The scheme design unit is used to output air control management schemes and equipment control signals;

[0129] The real-time control unit is used to receive air control management plans and equipment control signals, thereby performing targeted real-time control of the space area;

[0130] The early warning management unit is used to predict and evaluate the building environment quality of the spatial area a and generate corresponding early warning prompt signals, thereby performing early warning management of the spatial area.

[0131] In summary, the present invention collects environmental information from the spatial areas of the building by constructing an HVAC monitoring model, thereby specifically evaluating the building environment quality of the spatial area, and combines the geographical coordinates of the spatial area to perform HVAC linkage control in the local control area, outputs corresponding air control management plans and equipment management signals, so as to perform real-time control of the building environment, realize air interaction from the key monitoring area to the associated area, and local linkage control improves the accuracy of HVAC control and reduces the energy consumption of air conditioning equipment. Then, by predicting and evaluating the building environment quality of the spatial area, the continuous stability of the building environment quality is guaranteed, thereby improving the comfort of the working and living conditions of the people using the building.

[0132] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technical personnel in this field for each set of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0133] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by those skilled in the art according to actual conditions.

[0134] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. An intelligent control method for a building HVAC system, characterized by: The following steps are involved: Step 1: Construct an HVAC monitoring model to collect building environment information: Monitor the building environment through the HVAC monitoring model, construct a three-dimensional building model, divide any building BR into N0 spatial regions, mark any spatial region as a, and collect building environment information of spatial region a; Building environment information includes temperature, humidity, wind speed, particulate matter content and harmful gas concentration; the temperature, humidity, wind speed and particulate matter content of space area a are marked as Wa, Sa, Fa and HKa respectively; the information collection period Tc is set to collect building environment information regularly; Step 2: Analyze building environment information to evaluate building environment quality: pre-process the building environment information of spatial area a and set up a parameter risk assessment model to construct a building risk parameter vector and integrate it to evaluate the building environment quality of spatial area a; The specific process of setting up the parameter risk assessment model is as follows: input parameter i and its standard interval [Qa, Qb], and obtain and output the risk value fi of parameter i by combining parameter i and its standard interval [Qa, Qb]: ;in, It refers to the correction coefficient of parameter i, which is a constant value that keeps the risk value fi always greater than 0; By setting the standard intervals of temperature Wa, humidity Sa, and wind speed Fa respectively and substituting them into the parameter risk assessment model for preprocessing, the risk values of temperature Wa, humidity Sa, and wind speed Fa are obtained in turn and marked as Wfa, Sfa, and Ffa respectively; It is preset to collect n0 kinds of harmful gases, mark any harmful gas as φ, and mark the concentration of harmful gas φ as Cφa; by combining the concentrations Cφa of n0 kinds of harmful gases, the harmful gas risk value FCa of spatial area a is comprehensively obtained; The temperature risk value Wfa, humidity risk value Sfa, wind speed risk value Ffa, particulate matter content HKa and harmful gas risk value FCa are integrated and marked as a building risk parameter vector; Step 3: Output the air control management plan and equipment control signals: Based on the building environment quality of spatial area a and the regional geographic coordinates, analyze and mark the key monitoring areas from N0 spatial areas, obtain the associated areas of the key monitoring areas, and output the corresponding equipment control signals through detailed analysis of the regional building environment quality. These signals are then integrated to generate the air control management plan. Step 4: Targeted real-time control of spatial areas: By receiving air control management plans and equipment control signals, local control area HVAC linkage control is carried out in key monitoring areas and related areas to achieve real-time environmental control of the building; Step 5: Conduct early warning management for spatial areas: Build a historical parameter matrix and set a parameter vector prediction model to predict and evaluate the building environment quality of spatial area a, generate corresponding early warning prompt signals, and thus provide early warning for the building HVAC system.

2. The intelligent control method for a building HVAC system according to claim 1, characterized in that: The specific process of building an HVAC monitoring model to monitor the building environment is as follows: Mark any building as BR. Building BR is equipped with an air conditioning unit, which includes N0 air conditioning equipment. Then construct a three-dimensional building model, divide the building into N0 spatial areas, mark any spatial area as a, mark the air conditioning equipment in spatial area a as Qa, and collect the building environment information and regional geographic coordinates of spatial area a.

3. The intelligent control method for a building HVAC system according to claim 2, characterized in that: The specific process of evaluating the building environment quality of spatial area a is as follows: The building environment quality assessment coefficient HJa of spatial area a is obtained by combining the temperature risk value Wfa, humidity risk value Sfa, wind speed risk value Ffa, particulate matter content HKa and harmful gas risk value FCa; The building environment quality assessment coefficient HJa is calculated according to the information collection period Tc. By measuring the building environment quality assessment coefficients of N0 spatial areas and sorting them in descending order, the top n1 spatial areas are extracted and marked as key monitoring areas.

4. The intelligent control method for a building HVAC system according to claim 3, characterized in that: The specific process of generating an air control management plan is as follows: The regional geographic coordinates are calibrated through the three-dimensional model of the building. Any key monitoring area is marked as b, and the regional geographic coordinates of the key monitoring area b are marked as (Xb, Yb, Zb); Taking the key monitoring area b as the reference coordinate, calculate the distance between other spatial areas and the key monitoring area b. Mark any remaining spatial area as c, and mark the regional geographic coordinates of spatial area c as (Xc, Yc, Zc). Then the distance between spatial area c and key monitoring area b is ; Set the distance between spatial area c and key monitoring area b The threshold G0, when the distance When it is lower than the threshold G0, the spatial area c is extracted as the associated area of the key monitoring area b, and the number of associated areas is marked as m0. The m0 associated areas are integrated and marked as a linkage area group, and the linkage area group centered on the key monitoring area and its connections is integrated and marked as a local control area. Retrieve the building environment quality assessment coefficients of m0 associated areas, mark the associated areas in descending order of the building environment quality assessment coefficients, and set the corresponding control range, so as to generate the air control local management plan and the corresponding equipment control signal, realize the HVAC linkage control of the local control area, and thus comprehensively carry out real-time environmental control of the building BR.

5. The intelligent control method for a building HVAC system according to claim 4, characterized in that: The specific process of the parameter vector prediction model is: Input vector L to the parameter vector prediction model. Vector L includes m1 component vectors. Vector L is marked as: ； By averaging the m1 component vectors, we can obtain the mean coefficient σ1 of the vector L. Then, by calculating the standard deviation of the m1 component vectors, we can obtain the fluctuation coefficient σ2 of the vector L. By averaging the differences between all adjacent component vectors, we can obtain the increase rate coefficient σ3 of the vector L. The prediction coefficient of vector L is obtained by combining the mean coefficient σ1, fluctuation coefficient σ2 and increase rate coefficient σ3 of vector L ; The parameter vector predicts the prediction coefficient of the model output vector L .

6. The intelligent control method for a building HVAC system according to claim 5, characterized in that: The specific process of evaluating the predicted state of the building environment in spatial area a and generating early warning signals is as follows: The parameter risk assessment model is used to calculate the building risk parameter vector corresponding to m1 information collection cycles Tc, and the historical parameter matrix V is constructed to predict and evaluate the building environment quality of spatial area a, and the building environment quality prediction index ZLa is obtained: ； Among them, any row vector of the historical parameter matrix V includes the five parameter values of the building risk parameter vector, namely the temperature risk value Wfa, the humidity risk value Sfa, the wind speed risk value Ffa, the particulate matter content HKa, and the harmful gas risk value FCa; any column vector of the historical parameter matrix V represents the building risk parameter vector corresponding to m1 information collection cycles Tc; Set up a parameter vector prediction model, integrate the column vector and substitute it into the parameter vector prediction model to obtain the prediction coefficient of the column vector, where: 、 、 、 and They are the prediction coefficients of temperature risk value Wfa, humidity risk value Sfa, wind speed risk value Ffa, particulate matter content HKa and harmful gas risk value FCa; The environmental quality prediction index ZLa is obtained by combining the prediction coefficients of temperature risk value Wfa, humidity risk value Sfa, wind speed risk value Ffa, particulate matter content HKa and harmful gas risk value FCa; Set the evaluation interval of the environmental quality prediction index ZLa, evaluate the building environment prediction status of spatial area a through interval comparison, and generate corresponding early warning signals.

7. An intelligent control system for a building HVAC system, characterized by: The system comprises an information monitoring unit, a core processing unit, a scheme design unit, a real-time control unit, and an early warning management unit, wherein the information monitoring unit, the core processing unit, the scheme design unit, the real-time control unit, and the early warning management unit are communicatively connected to each other, and the system applies an intelligent control method for a building HVAC system according to any one of claims 1 to 6 above; The information monitoring unit is used to build an HVAC monitoring model to collect building environment information; The core processing unit is used to analyze building environment information and evaluate building environment quality; The scheme design unit is used to output air control management schemes and equipment control signals; The real-time control unit is used to receive air control management plans and equipment control signals, thereby performing targeted real-time control of the space area; The early warning management unit is used to predict and evaluate the building environment quality of the spatial area a and generate corresponding early warning prompt signals, thereby performing early warning management of the spatial area.

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