Heating ventilation air conditioner automatic control system and method with intelligent regulation and control function

Abstract

The invention provides a heating ventilation air conditioner automatic control system and method with an intelligent regulation and control function. The system comprises a data acquisition module used for monitoring and collecting environment and personnel activity data in real time, including environment temperature, humidity, a COconcentration sensor and personnel distribution and activity conditions; the intelligent control system integrates a fuzzy control algorithm and a neural network model unit, processes the received data through the fuzzy control algorithm, dynamically calculates target parameters to adapt to the current environment and personnel activity requirements, predicts the future energy consumption trend through the neural network model, optimizes the operation mode, and improves the energy consumption performance. The current comfort level and the long-term energy consumption are considered to formulate an optimal control strategy; and the executing mechanism comprises a variable-frequency compressor and an air valve controller and is used for adjusting air supply parameters according to instructions of the intelligent control system so as to achieve the optimal indoor environment. The technical problems that a traditional heating ventilation air conditioning system cannot be dynamically regulated and controlled, the control algorithm is single, and monitoring data is analyzed in an isolated mode are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of HVAC control, and in particular to a HVAC automatic control system and method with intelligent regulation. Background Art

[0002] HVAC is a general term for heating, ventilation and air conditioning systems. Its main components are: Heating system: provides heat through boilers, heat pumps and other equipment; Ventilation system: introduces fresh air and exhausts dirty air to maintain air cleanliness; Air conditioning system: adjusts temperature and humidity through refrigeration / heating equipment (such as compressors, cooling towers) and humidity control devices.

[0003] Traditional HVAC systems have some technical bottlenecks in intelligent control, which are as follows: First, the system adopts a static temperature setting mechanism and cannot be adjusted dynamically according to the density of people and the intensity of activities, resulting in ineffective waste of cooling capacity. Second, the data of environmental data monitoring sensors are isolated and lack multi-data fusion analysis capabilities; third, the control strategy mainly adopts a single control algorithm (such as PID control), and its hysteresis adjustment characteristics make it difficult to take into account both transient response (response speed) and full-cycle energy consumption optimization. A typical case shows that the air-conditioning system of a CBD office building did not detect the sudden increase in the number of people in the conference room during the gathering period (10:00-12:00), resulting in local overheating, delayed operation and maintenance response, and manual intervention and adjustment. Summary of the invention

[0004] The purpose of the present invention is to provide a HVAC automatic control system and method with intelligent regulation, which solves the technical problems of traditional HVAC systems that cannot be dynamically regulated, have a single control algorithm, and isolated analysis of monitoring data.

[0005] Invention scheme:

[0006] In the first aspect, the present invention provides a heating, ventilation and air conditioning automatic control system with intelligent regulation, including: a data acquisition module for real-time monitoring and collecting environmental and personnel activity data, including a temperature sensor for real-time monitoring of indoor environmental temperature, a humidity sensor for real-time monitoring of indoor environmental humidity, CO 2 Concentration sensor for real-time monitoring of indoor CO 2The concentration level and infrared human body sensor are used to monitor the distribution and activities of indoor personnel in real time; the intelligent control system integrates fuzzy control algorithm and neural network model unit, processes the received data through the fuzzy control algorithm, and dynamically calculates the target parameters to adapt to the needs of the current environment and personnel activities, and predicts the future energy consumption trend through the neural network model, optimizes the operation mode, so as to take into account the current comfort and long-term energy consumption to formulate the best control strategy; the actuator, including the variable frequency compressor and the air valve controller, is used to adjust the air supply parameters according to the instructions of the intelligent control system to achieve the best indoor environment.

[0007] Furthermore, the system also includes a data verification module, which is used to perform correlation analysis and verification processing on the received multiple data according to preset verification rules. When there is no conflict between the related data, the multiple data are directly sent to the intelligent control system as valid data. When a conflict occurs between the data and continues to occur under preset conditions, the verification conflict result is sent to the intelligent control system.

[0008] Furthermore, it also includes a backup monitoring module and / or a related monitoring module. The intelligent control system controls the corresponding backup monitoring module and / or the related monitoring module according to the verification conflict result, obtains secondary monitoring data and / or cross-validation data, so as to ensure the collection of valid data and improve the reliability of the intelligent control system decision.

[0009] Furthermore, the intelligent control system also has a built-in weighted calculation unit, which is used to weight the multiple data collected from each area according to preset basic weights and algorithms and calculate the priority of each air supply zone. At the same time, based on the subsequent numerical changes of multiple data in each air supply zone, the weights of the corresponding data and the priority of the air supply zone are dynamically adjusted according to the preset multiple data weight dynamic adjustment rules.

[0010] In a second aspect, the present invention also provides a HVAC automatic control method with intelligent regulation, which is used in any of the above HVAC automatic control systems with intelligent regulation, comprising the following steps: Step 1: Real-time collection of environmental and personnel data, including temperature, humidity, CO 2 concentration, personnel distribution and activities; Step 2: Process the collected data with fuzzy control algorithm and dynamically calculate the target temperature and humidity to quickly respond to the current environment and personnel distribution and activity requirements; Step 3: Use the neural network model to predict energy consumption and optimize the operating mode, taking into account both comfort and long-term energy consumption optimization; Step 4: Adjust the air supply volume and temperature through the calculated target data and optimized operating mode to achieve the best indoor environment.

[0011] Furthermore, before processing the collected data in step 2, correlation analysis and verification processing are performed on the received multiple data according to preset verification rules.

[0012] Furthermore, according to the conflict results of the correlation analysis verification, corresponding secondary monitoring data and / or cross-validation data are obtained to ensure the collection of valid data and improve the reliability of the decision-making of the intelligent control system.

[0013] Furthermore, before processing the collected data in step 2, the multiple data collected from each area are weighted according to the preset basic weights and algorithms, and the priority of each air supply zone is calculated. At the same time, based on the subsequent numerical changes of the multiple data in each air supply zone, the weights of the corresponding data and the priority of the air supply zone are dynamically adjusted according to the preset data weight dynamic adjustment rules.

[0014] The present invention provides a heating, ventilation and air conditioning automatic control system and method with intelligent regulation, which at least includes the following advantages:

[0015] (1) This system combines environmental parameters with personnel activity data, and dynamically adjusts the air conditioning operation strategy to adapt to the ever-changing indoor environment and personnel needs, ensuring the comfort of the indoor environment. This reduces the need for users to make manual adjustments, greatly improving the user experience, while also avoiding the phenomenon of over-powering in unmanned areas, greatly improving energy efficiency.

[0016] (2) This system adopts a dual algorithm collaborative working mode of fuzzy control algorithm and neural network model. The former realizes rapid response, while the latter optimizes long-term energy consumption, jointly ensuring the efficient operation of the system.

[0017] (3) This system uses the data verification module to perform correlation analysis and verification on the various data received according to preset verification rules, eliminate false positive data, and improve the reliability of collected data and system decisions.

[0018] (4) This system uses a weighted calculation unit to weight the multiple valid data collected from each area according to the preset basic weight and calculate the priority of each air supply zone, which solves the problem that the traditional system ignores the importance of key data such as personnel density and causes local environmental imbalance; this avoids the drawbacks of traditional uniform air supply and adjusts the air supply volume and temperature of each air supply zone according to the priority. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 A structural block diagram of the HVAC automatic control system provided in this embodiment;

[0021] Figure 2 A first flow chart of the HVAC automatic control method provided in this embodiment;

[0022] Figure 3 This is a second flow chart of the HVAC automatic control method provided in this embodiment. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0024] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0025] This embodiment provides a HVAC automatic control system with intelligent regulation. Figure 1 As shown in the figure, it mainly consists of three basic modules working together: Data acquisition module: deployed in each air supply area, through temperature, humidity, CO 2 The concentration sensor monitors environmental parameters in real time, and uses infrared human body sensors to detect the distribution of people and the intensity of activities, providing a multi-dimensional data basis for regulation; Intelligent control system: integrates fuzzy control algorithm and neural network model unit, the former quickly responds to real-time data to dynamically adjust target parameters (such as temperature and humidity), and the latter predicts energy consumption trends to optimize long-term operation strategies, achieving a dual balance between comfort and energy efficiency; Actuator: including variable frequency compressor and air valve controller, according to the intelligent control instructions, accurately adjust the air supply volume and temperature (such as compressor frequency ±1Hz, air valve opening ±2%), to ensure that the indoor environment quickly stabilizes to the optimal state. The system solves the problems of low energy efficiency and delayed response of traditional air-conditioning systems through multi-parameter fusion, dual algorithm collaboration, and dynamic execution control, and has the advantages of precise regulation and energy saving. In addition, communication between modules can be connected and communicated through wireless communication modules or wired methods such as wired RS-485.

[0026] The system further adds a data verification module, whose core function is to verify the multi-source collected data (such as temperature and humidity, CO 2 Specifically, if there is no conflict in the data, this module directly passes it to the intelligent control system as a valid input; if a contradiction is detected between the data (for example, the infrared sensor shows that there is no one but the CO 2concentration increases abnormally), and the conflict continues to meet the preset conditions (such as abnormal data or exceeding the threshold value for a continuous period of time or for multiple consecutive times, for example, the infrared sensor has not detected the distribution density of people (such as <1 person / ㎡) or the intensity of activity (quantified value of movement frequency, such as 0%) for 3 minutes, CO 2 If the concentration exceeds the preset threshold (e.g. ≥800ppm) for 5 consecutive samplings (10 seconds apart), the conflicting results will be fed back to the intelligent control system to trigger the verification mechanism (e.g. secondary detection, multi-sensor cross-validation or manual confirmation). This design significantly reduces the risk of misjudgment through data reliability verification, ensures that the control strategy is generated based on consistent and reliable data, and further improves the robustness of the system and the accuracy of decision-making.

[0027] The system further introduces backup monitoring modules and / or related monitoring modules to deal with data verification conflict scenarios. When the data verification module detects a continuous conflict (such as the infrared sensor shows no one (or the density is below a certain threshold) but the CO 2 If the concentration is abnormal (such as exceeding a certain threshold), the intelligent control system will start the backup module (such as redundant sensor) to obtain secondary monitoring data, or link related modules (such as cameras, sound sensors, access control systems) to provide cross-verification information. Through multi-dimensional data comparison (such as people in the camera, sudden increase in sound decibels, or access control records of not leaving the scene), the system can distinguish between the presence of real people and sensor false alarms / external interference, thereby ensuring the validity of input data. This design significantly improves the reliability of intelligent control system decision-making through dynamic redundant verification and cross-system data collaboration, avoiding misregulation or energy waste caused by the failure of a single data source.

[0028] The system further introduces a weighted calculation unit, whose core function is to calculate the weights of the preset basic weights (such as temperature and humidity 20%, CO 2 Concentration weight 50%, personnel density 30%) for multiple valid data (temperature, humidity, CO 2 concentration, personnel distribution, etc.) for calculations such as direct weighted accumulation to obtain regional priority scores and generate regional priority rankings (such as high-priority areas are given priority to allocate more air supply). At the same time, the unit 2 If the concentration continues to exceed the standard or the population density increases suddenly, the weight parameters (such as CO 2For every 100ppm exceeding the threshold (800ppm), the weight is increased by 10%, and the weight is increased to 60%) and the priority is recalculated to ensure that the control strategy always meets the actual needs. Through dynamic adaptation of weights and real-time update of priorities, the system can quickly optimize the air supply, temperature or exhaust strategies in complex scenarios (such as local gatherings of people or sudden changes in air temperature and quality), taking into account the dual goals of energy efficiency and comfort, and significantly improving the accuracy of control; at the same time, it meets the personalized needs of users, avoids local environmental imbalances, and prioritizes solving major problems. In addition, a weight upper limit can be set for each data weight, and the total weight can be allowed to exceed the limit to intuitively reflect the amplification effect of the exceeded indicators.

[0029] This embodiment also provides a HVAC automatic control method with intelligent regulation, please refer to Figure 2 As shown, dynamic optimization is achieved through the following steps: Real-time acquisition of multiple parameters: synchronous acquisition of temperature, humidity, CO 2 Concentration and personnel distribution data provide comprehensive data input for control; fuzzy control fast response: use fuzzy algorithms to calculate target parameters such as temperature and humidity in real time, and quickly adapt to environmental changes and personnel activity needs (such as rapid cooling when people gather); neural network energy consumption prediction: predict future energy consumption trends based on historical and real-time data, and optimize long-term operation modes (such as reserving refrigeration capacity in advance to cope with the noon peak); precise execution control: combine target parameters with optimization strategies to dynamically adjust the frequency of variable frequency compressors and air valve openings to ensure that the indoor environment quickly stabilizes to the optimal state. This method solves the pain point of traditional systems that are difficult to balance energy efficiency and comfort through the coordination of short-term response and long-term planning, and data-driven execution control, achieving energy saving while improving user experience.

[0030] Please refer to Figure 3 As shown in the figure, the automatic control method adds a pre-data verification step on the basis of the basic steps: before the fuzzy control algorithm is processed, the system verifies the collected temperature, humidity, and CO according to preset rules (such as logic between sensor data, numerical correlation, threshold continuity, etc.). 2 The concentration and personnel distribution data are analyzed and verified. When conflicts occur between the related data, for example, if the infrared sensor shows that there is no one or the density is low but the CO 2 If the concentration rises suddenly, the relevant monitoring modules such as the camera, sound sensor or access control record will be linked to cross-verify to eliminate obviously contradictory false alarm data (such as equipment failure or instantaneous interference), or obtain secondary monitoring data through the backup monitoring module; through this step, only valid data will be input into the subsequent fuzzy control and neural network model to avoid misregulation caused by erroneous data, thereby improving the overall algorithm accuracy and system reliability, and ensuring the accurate realization of energy saving and comfort goals.

[0031] Please refer to Figure 3As shown in the figure, this automatic control method adds a step of weighted calculation of air supply priority on the basis of the basic steps: before the fuzzy control algorithm is processed, the effective data collected from multiple areas are weightedly calculated by preset basic weights to generate the initial air supply area priority; at the same time, a data association response mechanism is established to dynamically adjust the calculation weight of the corresponding data according to preset rules by real-time monitoring of the change trend of subsequent data in each area (for example, when the population density is ≥ 2 people / ㎡, the weight is increased to 10%), thereby realizing the adaptive optimization of the air supply area priority (for example, high priority air valve opening + 30% compressor frequency + 10Hz). This dual weight mechanism (basic + dynamic) not only ensures the rationality of the initial control of the system, but also improves the control accuracy according to real-time operation data.

[0032] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A heating, ventilation and air conditioning automatic control system with intelligent regulation, characterized in that: include: Data acquisition module, used for real-time monitoring and collection of environmental and personnel activity data, including temperature sensors for real-time monitoring of indoor environmental temperature, humidity sensors for real-time monitoring of indoor environmental humidity, CO2 concentration sensors for real-time monitoring of indoor CO2 concentration levels, and infrared human body sensors for real-time monitoring of indoor personnel distribution and activities; The intelligent control system integrates fuzzy control algorithm and neural network model unit. It processes the received data through fuzzy control algorithm and dynamically calculates the target parameters to adapt to the needs of the current environment and personnel activities. It also predicts the future energy consumption trend through the neural network model and optimizes the operation mode, so as to take into account the current comfort and long-term energy consumption to formulate the best control strategy; The actuator, including the variable frequency compressor and the air valve controller, is used to adjust the air supply parameters according to the instructions of the intelligent control system to achieve the best indoor environment.

2. The HVAC automatic control system with intelligent regulation according to claim 1, characterized in that: It also includes a data verification module, which is used to perform correlation analysis and verification processing on the received multiple data according to preset verification rules. When there is no conflict between the related data, the multiple data are directly sent to the intelligent control system as valid data. When a conflict occurs between the data and continues to occur under preset conditions, the verification conflict result is sent to the intelligent control system.

3. The HVAC automatic control system with intelligent regulation according to claim 2, characterized in that: It also includes a backup monitoring module and / or a related monitoring module. The intelligent control system controls the corresponding backup monitoring module and / or the related monitoring module according to the verification conflict result, obtains secondary monitoring data and / or cross-validation data, so as to ensure the collection of valid data and improve the reliability of the intelligent control system decision.

4. The HVAC automatic control system with intelligent regulation according to claim 1, characterized in that: The intelligent control system also has a built-in weighted calculation unit, which is used to weight the multiple data collected from each area according to the preset basic weight and algorithm and calculate the priority of each air supply zone. At the same time, based on the subsequent numerical changes of the multiple data of each air supply zone, the weight of the corresponding data and the priority of the air supply zone are dynamically adjusted according to the preset multiple data weight dynamic adjustment rules.

5. A method for automatic control of a heating, ventilation and air conditioning system with intelligent regulation, used in the automatic control system of a heating, ventilation and air conditioning system with intelligent regulation according to any one of claims 1 to 4, characterized in that: The following steps are involved: Step 1: Collect environmental and personnel data in real time, including temperature, humidity, CO2 concentration, personnel distribution and activities; Step 2: Process the collected data using a fuzzy control algorithm to dynamically calculate the target temperature and humidity to quickly respond to the needs of the current environment, personnel distribution, and activities; Step 3: Use the neural network model to predict energy consumption and optimize the operation mode, taking into account both comfort and long-term energy consumption optimization; Step 4: Adjust the air volume and temperature based on the calculated target data and optimized operation mode to achieve the best indoor environment.

6. The HVAC automatic control method with intelligent regulation according to claim 5, characterized in that: Before processing the collected data in step 2, correlation analysis and verification processing are performed on the received multiple data according to preset verification rules.

7. The HVAC automatic control method with intelligent regulation according to claim 6, characterized in that: Verify conflict results based on correlation analysis, and obtain corresponding secondary monitoring data and / or cross-validation data to ensure the collection of valid data and improve the reliability of intelligent control system decision-making.

8. The HVAC automatic control method with intelligent regulation according to claim 5, characterized in that: Before processing the collected data in step 2, the multiple data collected from each area are weighted according to the preset basic weights and algorithms, and the priority of each air supply zone is calculated. At the same time, based on the subsequent numerical changes of the multiple data in each air supply zone, the weights of the corresponding data and the priority of the air supply zone are dynamically adjusted according to the preset data weight dynamic adjustment rules.

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