An intelligent heating ventilation air conditioning optimization control system and method

Abstract

The application discloses an intelligent heating ventilation air conditioner optimization control system and method, and relates to the technical field of intelligent control, and comprises the following steps: obtaining a first corresponding relationship through first analysis, sending a first control signal, completing control of equipment parameters in different time lengths, obtaining an optimal control time length through second analysis, sending a second control signal, and performing first compensation. Through historical data analysis and dynamic control, the application avoids overrunning or inefficient operation of equipment, can adjust equipment operation strategy according to the optimal control time length, further reduces energy consumption, adopts PID control to compensate for temperature, can quickly respond to indoor temperature changes, adjusts equipment operation strategy according to the optimal control time length, prolongs equipment life, adapts to different environments and operation conditions through historical data analysis and dynamic control, optimizes equipment operation parameters and operation time, and the system can significantly reduce energy consumption and operation cost.

Description

Technical Field

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

[0002] In recent years, the widespread adoption of IoT technology has fundamentally transformed the control and monitoring of HVAC systems. By embedding smart sensors, IoT controllers, real-time connectivity, and cloud-based analytics into HVAC systems, facility managers can monitor system status in real time, access extensive data, and leverage cloud-based analytics to enable remote management and optimization. Furthermore, the advancement of IoT technology has enabled more refined and precise control of HVAC systems, enabling integration with other building systems, such as lighting and power, to optimize overall building energy efficiency.

[0003] At present, a Chinese invention patent with publication number CN117329670A discloses a control method, device and air conditioner for optimizing the air conditioning cooling experience. The method controls the front air outlet air guide door, the opening of the side air outlet and the fan speed by comparing the remote control sensor temperature with the optimal set temperature, and controls the front air outlet air guide door, the opening of the side air outlet and the fan speed according to the comparison result of the air conditioner temperature sensor temperature with the remote control sensor temperature. However, the related technology does not adjust the temperature according to the hysteresis of the temperature adjustment, and does not evaluate various dimensions according to the different impacts caused by the different control completion times. This is not conducive to taking into account both environmental protection and the efficiency of the HVAC, as well as the life of the HVAC, and has certain limitations. Summary of the Invention

[0004] The technical problem solved by the present invention is that the related art does not adjust the temperature according to the hysteresis of the temperature adjustment, and does not evaluate various dimensions according to the different impacts caused by the different adjustment completion times. This is not conducive to taking into account both environmental protection and the efficiency of the HVAC, as well as the life of the HVAC, and has certain limitations.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, an intelligent HVAC optimization control method comprises the following steps:

[0006] Step S100, performing a first analysis on historical monitoring parameters to obtain a first correspondence between device parameters and environmental parameters, and sending a first control signal according to the first correspondence parameter;

[0007] Step S200, in response to the first control signal, completing the control of the device parameters in different time lengths, and continuously monitoring the relevant data in each time length;

[0008] Step S300: Perform a second analysis on relevant data within any time length to obtain an optimal control time length, send a second control signal according to the optimal control time length, and perform a first compensation on the temperature according to PID control.

[0009] As a preferred embodiment of the intelligent HVAC optimization control method of the present invention, the historical monitoring parameters include historical ambient temperature, historical ambient pollutant concentration, historical equipment inlet air temperature, historical equipment inlet air volume, historical equipment pressure, and historical equipment speed;

[0010] The first analysis is performed by multiple regression analysis, wherein the first corresponding relationships include a first corresponding relationship of inlet air temperature, a first corresponding relationship of inlet air volume, a first corresponding relationship of pressure, and a first corresponding relationship of rotation speed;

[0011] The first corresponding relationship of the inlet air temperature is represented by a first corresponding relationship among the historical equipment inlet air temperature, the historical ambient temperature and the historical ambient pollutant concentration;

[0012] The first corresponding relationship of the air intake volume is represented by a first corresponding relationship among the historical equipment air intake volume, the historical ambient temperature and the historical ambient pollutant concentration;

[0013] The first pressure correspondence is represented by a first correspondence between historical equipment pressure, historical ambient temperature, and historical ambient pollutant concentration;

[0014] The first corresponding relationship of the rotation speed is represented by a first corresponding relationship among historical equipment rotation speed, historical ambient temperature and historical ambient pollutant concentration;

[0015] The first analysis method of each first correspondence is the same, recording the historical equipment air inlet temperature, historical equipment air inlet volume, historical equipment pressure and historical equipment speed as equipment parameters, and recording the historical ambient temperature and historical ambient pollutant concentration as environmental parameters.

[0016] As a preferred solution of the intelligent HVAC optimization control method of the present invention, wherein: a current device parameter is obtained according to the first corresponding parameter and the current monitoring parameter, and a first control signal is sent according to the current device parameter;

[0017] The first analysis method comprises:

[0018] Obtain any type of equipment parameters, take the equipment parameters as dependent variables and the corresponding environmental parameters as independent variables, and obtain the relationship expression between the dependent variables and the independent variables through multiple regression analysis;

[0019] Traverse all types of equipment parameters and obtain the relationship expressions between each dependent variable and the corresponding independent variable;

[0020] The relationship expression between each dependent variable and the corresponding independent variable is recorded as a first corresponding relationship, and the first device parameter is obtained by inputting the environmental parameter into the first corresponding relationship.

[0021] As a preferred solution of the intelligent HVAC optimization control method of the present invention, wherein: the first control signal includes an air temperature control signal, an air volume control signal, a pressure control signal and a speed control signal;

[0022] Obtaining current monitoring parameters, the current monitoring parameters including current ambient temperature, current ambient pollutant concentration, current device air inlet temperature, current device air inlet volume, current device pressure, and current device speed, respectively inputting the current ambient temperature and current ambient pollutant concentration into corresponding first correspondences to obtain corresponding first device air inlet temperature, first device air inlet volume, first device pressure, and first device speed;

[0023] Calculate first, second, third, and fourth differences between the first device air inlet temperature, the first device air inlet volume, the first device pressure, and the first device speed and the current device air inlet volume, the current device pressure, and the current device speed, respectively;

[0024] Sending an air temperature control signal, an air volume control signal, a pressure control signal, and a speed control signal according to the first difference, the second difference, the third difference, and the fourth difference;

[0025] When the value of the first difference, the second difference, the third difference, or the fourth difference is negative, it indicates that the corresponding device parameter is reduced, and the reduced value is the absolute value of the value of the first difference, the second difference, the third difference, or the fourth difference;

[0026] When the value of the first difference, the second difference, the third difference, or the fourth difference is positive, it indicates that the corresponding device parameter is increased, and the increased value is the value of the first difference, the second difference, the third difference, or the fourth difference;

[0027] When the value of the first difference, the second difference, the third difference, or the fourth difference is zero, it indicates that the first regulation is not performed on the device parameter.

[0028] As a preferred embodiment of the intelligent HVAC optimization control method of the present invention, when the value of the first difference, the second difference, the third difference, or the fourth difference is not zero, in response to the first control signal, a first control is performed on the device parameter;

[0029] The first duration is set as a reference to the first duration, and the second duration is selected as a duration gradient, wherein the first duration is greater than the second duration;

[0030] The difference between the first duration and the second duration is used as the lower limit of the duration, and the sum of the first duration and the second duration is used as the upper limit of the duration. Between the lower limit and the upper limit of the duration, continuously select durations to perform a first adjustment on the device parameters, so that within the range of the selected duration, the adjustment from the current device parameters to the first device parameters is completed, and the relevant data within the selected duration is continuously monitored, and the monitoring time interval is 1 second.

[0031] As a preferred embodiment of the intelligent HVAC optimization control method described in the present invention, the relevant data includes the recovered carbon dioxide content, the concentration of eliminated harmful gases, the equipment voltage jitter rate and the total power consumed, wherein the recovered carbon dioxide content is expressed as the content of carbon dioxide emitted by the purification equipment after the equipment performs heat recovery within the selected time period, the concentration of eliminated harmful gases is expressed as the concentration of harmful gases reduced by the equipment through indoor ventilation within the selected time period, and the equipment voltage jitter rate is expressed as the average value of the equipment operating voltage change rate during the process of equipment parameter regulation within the selected time period, which is automatically calculated by a preset algorithm;

[0032] During the selected time period, the control of the device parameters is expressed as uniform control, where the uniform control is expressed as the ratio of the value of the device parameters controlled per second to the corresponding difference and the selected time period.

[0033] As a preferred solution of the intelligent HVAC optimization control method described in the present invention, the calculation method of the preset algorithm includes:

[0034] Obtain any group of adjacent device voltages, record them as the first voltage and the second voltage in chronological order, where the adjacent voltage is represented by the chronological order, calculate a first difference between the second voltage and the first voltage, calculate a first ratio between the first difference and the first voltage, and traverse the first ratios corresponding to each group of adjacent device voltages within the selected duration;

[0035] Calculating a first average value of the first ratio, and setting the first average value as the device voltage jitter rate;

[0036] As the selected duration progresses, the device voltage jitter rate is updated per second until the selected duration ends. The last value of the device voltage jitter rate is obtained, forming a numerical sequence of the device voltage jitter rate distributed according to the time series.

[0037] The control of the current equipment inlet air temperature is achieved by adjusting the water supply temperature;

[0038] The control of the current air intake volume of the equipment is achieved by adjusting the opening of the air intake valve and the air intake speed;

[0039] The control of the current equipment speed is achieved by adjusting the power of the fan;

[0040] The regulation of the current equipment pressure is achieved by adjusting the compressor speed;

[0041] Among them, the corresponding relationship between the water supply temperature, the power of the fan, the opening of the air inlet valve, the air inlet speed and the compressor speed and the corresponding equipment parameters is obtained from historical experience.

[0042] As a preferred embodiment of the intelligent HVAC optimization control method of the present invention, a method for performing a second analysis on relevant data within any time length to obtain the optimal control time length includes:

[0043] setting the first carbon dioxide content as a carbon dioxide content threshold, and setting the first power as a total power threshold;

[0044] Get any selected duration, and get the corresponding recovered carbon dioxide content and total power consumed;

[0045] Calculating a second difference between the recovered carbon dioxide content and the first carbon dioxide content, calculating a second ratio of the second difference to the first carbon dioxide content, and setting the second ratio as a first evaluation index;

[0046] Calculating a second average value of the voltage jitter rate of each device within the time period, and setting the second average value as a second evaluation indicator;

[0047] calculating a third difference between the total power consumed and the first power, calculating a third ratio of the third difference to the first power, and setting the third ratio as a third evaluation indicator;

[0048] Obtaining the original harmful gas concentration, calculating a fourth ratio of the effective elimination gas concentration to the original harmful gas concentration, selecting the reciprocal of the fourth ratio, and setting the reciprocal of the fourth ratio as a fourth evaluation indicator;

[0049] Calculate the first sum of the first evaluation index, the second evaluation index, the third evaluation index, and the fourth evaluation index, and select the optimal control time according to the first sum. The smaller the value of the first sum, the better the selected time.

[0050] Traverse the first sum values ​​corresponding to each duration, sort the first sum values ​​in descending order, select the first sum value with the smallest value, and set the duration corresponding to the smallest sum value as the optimal control time;

[0051] A second control signal is sent according to the optimal control time length.

[0052] As a preferred embodiment of the intelligent HVAC optimization control method of the present invention, the method of performing the first temperature compensation according to PID control includes:

[0053] Initially assign values ​​to high-order proportional factors, high-order integral factors, high-order differential factors, integral orders, differential orders, proportional factors, and integral factors;

[0054] Let n ants complete their own tours according to probability. When an ant moves into a new node, it moves the node into the taboo table.

[0055] Obtain an adaptive function, perform optimal matching on the next node of the ant's node based on the adaptive function, and output the next node of the ant's node;

[0056] Obtain an ant colony algorithm expression, calculate and update the probability of the ant selecting the next node;

[0057] updating the high-order proportional factors, high-order integral factors, high-order differential factors, integral orders, differential orders, proportional factors, and integral factors based on the probabilities;

[0058] The operating device calculates the difference between the ambient temperature and the first ambient temperature;

[0059] When the difference is less than 0.5°C, outputting the high-order proportional factor, high-order integral factor, high-order differential factor, integral order, differential order, proportional factor and integral factor;

[0060] When the difference is greater than or equal to 0.5℃, clear the taboo table and circulate;

[0061] The adjusted high-order proportional factor, high-order integral factor, high-order differential factor, integral order, differential order, proportional factor and integral factor are obtained, and the hysteresis of the temperature regulation is compensated according to the adjusted high-order proportional factor, high-order integral factor, high-order differential factor, integral order, differential order, proportional factor and integral factor.

[0062] In a second aspect, an intelligent HVAC optimization control system includes a control module, a simulation module, and a compensation module;

[0063] The control module performs a first analysis on the historical monitoring parameters to obtain a first correspondence between the device parameters and the environmental parameters, and sends a first control signal according to the first corresponding parameters;

[0064] The simulation module completes the regulation of the device parameters in different time lengths in response to the first regulation signal, and continuously monitors the relevant data in each time length;

[0065] The compensation module performs a second analysis on relevant data within any time length to obtain an optimal control time length, sends a second control signal according to the optimal control time length, and performs a first compensation on the temperature according to PID control.

[0066] The beneficial effects of the present invention are as follows: through historical data analysis and dynamic regulation, the system can accurately adjust the equipment operating parameters according to actual needs, avoid excessive or inefficient operation of the equipment, and can adjust the equipment operation strategy according to the optimal regulation time length to further reduce energy consumption. It uses PID control to compensate for temperature, can quickly respond to indoor temperature changes, ensure temperature stability and comfort, adjust the equipment operation strategy according to the optimal regulation time length, reduce frequent start-stop and excessive operation of the equipment, and extend the life of the equipment. Through historical data analysis and dynamic regulation, it can continuously learn and optimize the regulation strategy to adapt to different environments and operating conditions. By optimizing equipment operating parameters and operating time, the system can significantly reduce energy consumption and reduce operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A schematic diagram of the basic flow of an intelligent HVAC optimization control method provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0068] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0069] Example, see Figure 1 , as an embodiment of the present invention, provides an intelligent HVAC optimization control method, comprising the following steps:

[0070] Step S100, performing a first analysis on historical monitoring parameters to obtain a first correspondence between device parameters and environmental parameters, and sending a first control signal according to the first correspondence parameter;

[0071] Step S200, in response to the first control signal, completing the control of the device parameters in different time lengths, and continuously monitoring the relevant data in each time length;

[0072] Step S300: Perform a second analysis on relevant data within any time length to obtain an optimal control time length, send a second control signal according to the optimal control time length, and perform a first compensation on the temperature according to PID control.

[0073] Through historical data analysis and dynamic regulation, the present invention can accurately adjust the equipment operating parameters according to actual needs, avoid excessive or inefficient operation of the equipment, adjust the equipment operation strategy according to the optimal regulation time length, further reduce energy consumption, and use PID control to compensate for temperature, which can quickly respond to indoor temperature changes and ensure temperature stability and comfort. It adjusts the equipment operation strategy according to the optimal regulation time length, reduces frequent start-up and shutdown and excessive operation of the equipment, and extends the life of the equipment. Through historical data analysis and dynamic regulation, it can continuously learn and optimize the regulation strategy to adapt to different environments and operating conditions. By optimizing equipment operating parameters and operating time, the system can significantly reduce energy consumption and reduce operating costs.

[0074] Historical monitoring parameters include historical ambient temperature, historical ambient pollutant concentration, historical equipment inlet air temperature, historical equipment inlet air volume, historical equipment pressure and historical equipment speed;

[0075] The first analysis is represented by a multiple regression analysis, wherein the first corresponding relationship includes a first corresponding relationship of the inlet air temperature, a first corresponding relationship of the inlet air volume, a first corresponding relationship of the pressure, and a first corresponding relationship of the speed;

[0076] The first correspondence relationship of the inlet air temperature is expressed as the first correspondence relationship among the historical equipment inlet air temperature, the historical ambient temperature and the historical ambient pollutant concentration;

[0077] The first correspondence relationship of the air intake volume is expressed as the first correspondence relationship between the historical equipment air intake volume, the historical ambient temperature and the historical ambient pollutant concentration;

[0078] The first correspondence of pressure is expressed as the first correspondence of historical equipment pressure, historical ambient temperature and historical ambient pollutant concentration;

[0079] The first corresponding relationship of the rotation speed is expressed as the first corresponding relationship among the historical equipment rotation speed, the historical ambient temperature and the historical ambient pollutant concentration;

[0080] The first analysis method of each first correspondence is the same, recording the historical equipment air inlet temperature, historical equipment air inlet volume, historical equipment pressure and historical equipment speed as equipment parameters, and recording the historical ambient temperature and historical ambient pollutant concentration as environmental parameters.

[0081] Obtaining a current device parameter according to the first corresponding parameter and the current monitoring parameter, and sending a first control signal according to the current device parameter;

[0082] The first analysis method includes:

[0083] Obtain any type of equipment parameters, take the equipment parameters as dependent variables and the corresponding environmental parameters as independent variables, and obtain the relationship expression between the dependent variables and the independent variables through multiple regression analysis;

[0084] Traverse all types of equipment parameters and obtain the relationship expressions between each dependent variable and the corresponding independent variable;

[0085] The relationship expression between each dependent variable and the corresponding independent variable is recorded as a first corresponding relationship, and the first device parameter is obtained by inputting the environmental parameter into the first corresponding relationship.

[0086] In specific implementation, through multiple regression analysis, the system can accurately find the relationship between equipment parameters (such as inlet temperature, air volume, pressure, speed) and environmental parameters (such as ambient temperature, pollutant concentration). Data-based control is more accurate than traditional empirical control, and can better adapt to environmental changes. It adjusts equipment parameters according to actual environmental needs to avoid excessive or inefficient operation of equipment, thereby significantly reducing energy consumption. It integrates Internet of Things technology to achieve remote monitoring and centralized management, improve management efficiency, record indoor pollutant concentrations (such as carbon dioxide, volatile organic compounds, particulate matter, etc.) to analyze the impact of air quality on equipment operation, and record changes in equipment inlet temperature to analyze the relationship between inlet temperature and ambient air quality. The relationship between ambient temperature and pollutant concentration is recorded, and changes in the equipment air intake volume are used to analyze the relationship between the air intake volume, ambient temperature and pollutant concentration. Changes in the equipment operating pressure are used to analyze the relationship between pressure, ambient temperature and pollutant concentration. Changes in the equipment speed are used to analyze the relationship between speed, ambient temperature and pollutant concentration. Through multiple regression analysis, the correspondence between equipment parameters and environmental parameters is established. For example, the first correspondence of air intake temperature is expressed as: air intake temperature = a1 × ambient temperature + a2 × pollutant concentration + b, where a1, a2 and b are regression coefficients obtained by fitting historical data. Similarly, the first correspondence of air intake volume, pressure and speed is also obtained through multiple regression analysis.

[0087] The first control signal includes an air temperature control signal, an air volume control signal, a pressure control signal and a speed control signal;

[0088] Obtaining current monitoring parameters, which include current ambient temperature, current ambient pollutant concentration, current device air inlet temperature, current device air inlet volume, current device pressure, and current device speed, respectively inputting the current ambient temperature and current ambient pollutant concentration into corresponding first correspondences to obtain corresponding first device air inlet temperature, first device air inlet volume, first device pressure, and first device speed;

[0089] Calculate first, second, third, and fourth differences between the first device air inlet temperature, the first device air inlet volume, the first device pressure, and the first device speed and the current device air inlet volume, the current device pressure, and the current device speed, respectively;

[0090] Sending an air temperature control signal, an air volume control signal, a pressure control signal, and a speed control signal according to the first difference, the second difference, the third difference, and the fourth difference;

[0091] When the value of the first difference, the second difference, the third difference, or the fourth difference is negative, it indicates that the corresponding device parameter is reduced, and the reduced value is the absolute value of the value of the first difference, the second difference, the third difference, or the fourth difference;

[0092] When the value of the first difference, the second difference, the third difference, or the fourth difference is positive, it indicates that the corresponding device parameter is increased, and the increased value is the value of the first difference, the second difference, the third difference, or the fourth difference;

[0093] When the value of the first difference, the second difference, the third difference, or the fourth difference is zero, it indicates that the first regulation is not performed on the device parameter.

[0094] In the specific implementation, by obtaining the current monitoring parameters (such as the current ambient temperature, pollutant concentration, equipment inlet temperature, air volume, pressure and speed), the system can calculate the difference between the equipment parameters and the target parameters in real time. The control method based on real-time data is more accurate than the traditional timed control, and can quickly respond to environmental changes. According to the positive and negative and size of the difference, it decides whether to increase or decrease the equipment parameters to ensure that the equipment operation state is always close to the optimal value. For example, when the first difference is positive, the system will increase the equipment inlet temperature; when the first difference is negative, the system will lower the equipment inlet temperature. By accurately calculating the difference, the system can avoid excessive adjustment of equipment parameters and directly jump to the PID temperature adjustment step to reduce unnecessary energy consumption. When the difference is zero, the system will not adjust the equipment parameters, avoiding invalid operation adjustments. Assume that the current monitoring parameters are as follows: current ambient temperature: 25℃, current environmental pollutant concentration: 300ppm, current equipment inlet temperature: 20℃, current Equipment air intake volume: 1000 m³ / h, current equipment pressure: 1.2 bar, current equipment speed: 1500 rpm. The first corresponding relationship obtained through multiple regression analysis is as follows: first equipment air intake temperature: 22°C, first equipment air intake volume: 1200 m³ / h, first equipment pressure: 1.3 bar, first equipment speed: 1600 rpm. Calculated difference: first difference (inlet air temperature difference): ΔT=22−20=2°C, second difference (inlet air volume difference): Δ Q = 1200 − 1000 = 200 m³ / h, the third difference (pressure difference): ΔP = 1.3 − 1.2 = 0.1 bar, the fourth difference (speed difference): ΔN = 1600 − 1500 = 100 rpm. Control signals are sent based on the differences: air temperature control signal: increase the inlet air temperature by 2°C, air volume control signal: increase the inlet air volume by 200 m³ / h, pressure control signal: increase the equipment pressure by 0.1 bar, and speed control signal: increase the equipment speed by 100 rpm.

[0095] When the value of the first difference, the second difference, the third difference, or the fourth difference is not zero, in response to the first control signal, performing a first control on the device parameter;

[0096] The first duration is set as a reference to the first duration, and the second duration is selected as a duration gradient, wherein the first duration is greater than the second duration;

[0097] The difference between the first duration and the second duration is used as the lower limit of the duration, and the sum of the first duration and the second duration is used as the upper limit of the duration. Between the lower limit and the upper limit of the duration, continuously select durations to perform the first adjustment on the device parameters, so that within the range of the selected duration, the adjustment from the current device parameters to the first device parameters is completed, and the relevant data within the selected duration is continuously monitored, and the monitoring time interval is 1 second.

[0098] In a specific implementation, when any of the first difference (inlet air temperature), the second difference (inlet air volume), the third difference (pressure), and the fourth difference (rotation speed) is not zero, a first control signal is triggered, and a first control of the device parameter is executed. The first is used as a baseline control duration, for example, set to 300 seconds, and the second is used as a control gradient, for example, set to 60 seconds, with a lower limit of 240 seconds and an upper limit of 360 seconds. Within this range, time points (such as 240s, 270s, 300s, 330s, and 360s) are continuously selected for control experiments. Within each selected time point, the system completes the control from the current device parameter to the target parameter (the first device parameter), and monitors relevant data (such as temperature, pressure, rotation speed, pollutant concentration, etc.) during the control process in real time. The monitoring interval is 1 second to ensure data accuracy. By conducting experiments under different control time periods, the system evaluates the impact of different control speeds on device performance, environmental response, and energy consumption. Ultimately, the optimal control time period can be selected to achieve a balance between rapid response and energy saving. Real-time monitoring (1-second intervals) ensures that the system is highly sensitive to environmental changes and changes in device status.

[0099] Relevant data includes recovered carbon dioxide content, harmful gas concentration eliminated, device voltage jitter rate, and total power consumption. The recovered carbon dioxide content is expressed as the content of carbon dioxide emitted by the purification equipment after heat recovery within the selected time period. The harmful gas concentration eliminated is expressed as the harmful gas concentration reduced by the equipment during indoor ventilation within the selected time period. The device voltage jitter rate is expressed as the average value of the device operating voltage change rate during the device parameter adjustment process within the selected time period, and is automatically calculated using a pre-set algorithm.

[0100] During the selected duration, the control of the device parameters is expressed as uniform control, which is expressed as the ratio of the value of the device parameter controlled per second to the corresponding difference and the selected duration.

[0101] The calculation methods of the pre-set algorithms include:

[0102] Obtain any group of adjacent device voltages, recording them as the first voltage and the second voltage in chronological order, where adjacent is represented by chronological order. Calculate the first difference between the second voltage and the first voltage, and then calculate the first ratio of the first difference to the first voltage. Iterate through the first ratios corresponding to each group of adjacent device voltages within the selected duration.

[0103] Calculating a first average value of the first ratio, and setting the first average value as the device voltage jitter rate;

[0104] As the selected duration progresses, the device voltage jitter rate is updated per second until the selected duration ends. The last value of the device voltage jitter rate is obtained, forming a numerical sequence of the device voltage jitter rate distributed according to the time series.

[0105] The control of the current equipment inlet air temperature is achieved by adjusting the water supply temperature;

[0106] The control of the current air intake volume of the equipment is achieved by adjusting the opening of the air intake valve and the air intake speed;

[0107] The control of the current equipment speed is achieved by adjusting the power of the fan;

[0108] The regulation of the current equipment pressure is achieved by adjusting the compressor speed;

[0109] Among them, the corresponding relationship between the water supply temperature, the power of the fan, the opening of the air inlet valve, the air inlet speed and the compressor speed and the corresponding equipment parameters is obtained from historical experience.

[0110] In specific implementation, the voltage jitter rate is updated once per second to form a time series (such as jitter rate values ​​at 0s, 1s, 2s,..., n seconds). Voltage fluctuations usually reflect changes in equipment load, grid interference or unstable equipment operation. Monitoring the voltage jitter rate helps to detect system anomalies in a timely manner to prevent equipment damage or control failure. By updating the jitter rate every second, the system can quickly respond to voltage changes, adjust control strategies, and improve stability. Based on the mapping relationship of historical experience, the system can accurately adjust each parameter according to the current state to avoid over-adjustment or invalid operation. The joint control of multiple parameters (temperature, air volume, pressure, speed) can achieve the optimal overall energy efficiency of the system, rather than local optimization.

[0111] The method of performing a second analysis on the relevant data within any time length to obtain the optimal control time length includes:

[0112] setting the first carbon dioxide content as a carbon dioxide content threshold, and setting the first power as a total power threshold;

[0113] Get any selected duration, and get the corresponding recovered carbon dioxide content and total power consumed;

[0114] calculating a second difference between the recovered carbon dioxide content and the first carbon dioxide content, calculating a second ratio of the second difference to the first carbon dioxide content, and setting the second ratio as a first evaluation index;

[0115] Calculate a second average value of the voltage jitter rate of each device within the time period, and set the second average value as a second evaluation indicator;

[0116] calculating a third difference between the total power consumed and the first power, calculating a third ratio of the third difference to the first power, and setting the third ratio as a third evaluation indicator;

[0117] Obtaining the original harmful gas concentration, calculating a fourth ratio of the effective elimination gas concentration to the original harmful gas concentration, selecting the reciprocal of the fourth ratio, and setting the reciprocal of the fourth ratio as a fourth evaluation indicator;

[0118] Calculate the first sum of the first evaluation index, the second evaluation index, the third evaluation index, and the fourth evaluation index, and select the optimal control time according to the first sum. The smaller the value of the first sum, the better the selected time.

[0119] Traverse the first sum values ​​corresponding to each duration, sort the first sum values ​​in descending order, select the first sum value with the smallest value, and set the duration corresponding to the smallest sum value as the optimal control time;

[0120] A second control signal is sent according to the optimal control time length.

[0121] In the specific implementation, four dimensions, namely environmental protection (CO2 recovery), energy consumption (power control), stability (voltage jitter) and air quality (harmful gas removal), are taken into consideration simultaneously, avoiding the deviation caused by the optimization of a single indicator. Through quantitative evaluation, the system automatically selects the most balanced regulation time to avoid system instability caused by too fast regulation or energy efficiency reduction caused by too slow regulation. It can be embedded in an intelligent control system to achieve self-learning and self-optimization, adapt to different environments, loads and operating conditions, and focus not only on power consumption, but also on CO2 recovery and harmful gas removal, in line with the requirements of green buildings and sustainable development.

[0122] The method for performing a first temperature compensation according to PID control includes:

[0123] Initially assign values ​​to high-order proportional factors, high-order integral factors, high-order differential factors, integral orders, differential orders, proportional factors, and integral factors;

[0124] Let n ants complete their own tours according to probability. When an ant moves into a new node, it moves the node into the taboo table.

[0125] Obtain an adaptive function, perform optimal matching on the next node of the ant's node based on the adaptive function, and output the next node of the ant's node;

[0126] Get the ant colony algorithm expression, calculate and update the probability of the ant selecting the next node;

[0127] Updates high-order proportional factors, high-order integral factors, high-order differential factors, integral orders, differential orders, proportional factors, and integral factors based on probabilities;

[0128] The operating device calculates the difference between the ambient temperature and the first ambient temperature;

[0129] When the difference is less than 0.5℃, the high-order proportional factor, high-order integral factor, high-order differential factor, integral order, differential order, proportional factor and integral factor are output;

[0130] When the difference is greater than or equal to 0.5℃, clear the taboo table and circulate;

[0131] The adjusted high-order proportional factor, high-order integral factor, high-order differential factor, integral order, differential order, proportional factor and integral factor are obtained, and the hysteresis of the temperature regulation is compensated according to the adjusted high-order proportional factor, high-order integral factor, high-order differential factor, integral order, differential order, proportional factor and integral factor.

[0132] In the specific implementation, each ant selects the next node based on probability. The node represents a set of PID parameter combinations. After each move, the node is added to the taboo table to avoid repeated searches. The objective function usually uses the integral of the absolute value of the temperature error (ITAE), overshoot, and adjustment time. The smaller the objective function, the better the parameter set. The probability of the ant selecting the next node is updated using the ant colony algorithm expression (such as the state transition probability formula). After each iteration, the PID parameters are updated based on the ant's selection result. The equipment is run and the difference between the current ambient temperature and the target temperature (the first ambient temperature) is calculated in real time. When the temperature difference is <0.5°C, the control effect is considered to meet the requirements and the currently tuned PID parameters are output. If the difference is ≥0.5°C, the taboo table is cleared and the ant roaming search is repeated. The final tuning parameters are used in the high-order PID controller to compensate for the hysteresis of temperature regulation and improve the system response speed and stability. The combination of the ant colony algorithm and high-order PID control forms an intelligent, adaptive, and high-precision temperature control strategy.

[0133] Through historical data analysis and dynamic regulation, the present invention can accurately adjust the equipment operating parameters according to actual needs, avoid excessive or inefficient operation of the equipment, adjust the equipment operation strategy according to the optimal regulation time length, further reduce energy consumption, and use PID control to compensate for temperature, which can quickly respond to indoor temperature changes and ensure temperature stability and comfort. It adjusts the equipment operation strategy according to the optimal regulation time length, reduces frequent start-up and shutdown and excessive operation of the equipment, and extends the life of the equipment. Through historical data analysis and dynamic regulation, it can continuously learn and optimize the regulation strategy to adapt to different environments and operating conditions. By optimizing equipment operating parameters and operating time, the system can significantly reduce energy consumption and reduce operating costs.

[0134] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium may be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0135] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that modifications or equivalent replacements of the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention should be included in the scope of the claims of the present invention.

Claims

1. An intelligent HVAC optimization control method, characterized in that: The following steps are involved: Step S100: Perform a first analysis on historical monitoring parameters to obtain a first correspondence between device parameters and environmental parameters, and send a first control signal based on the first correspondence parameter, wherein the first control signal includes an air temperature control signal, an air volume control signal, a pressure control signal, and a speed control signal; Step S200, in response to the first control signal, completing the control of the device parameters in different time lengths, and continuously monitoring the relevant data in each time length; Step S300, performing a second analysis on relevant data within any time length to obtain an optimal control time length, sending a second control signal according to the optimal control time length, and performing a first compensation on the temperature according to PID control; The method for performing a first temperature compensation according to PID control includes: Initially assign values ​​to high-order proportional factors, high-order integral factors, high-order differential factors, integral orders, differential orders, proportional factors, and integral factors; Let n ants complete their own tours according to probability. When an ant moves into a new node, it moves the node into the taboo table. Obtain an adaptive function, perform optimal matching on the next node of the ant's node based on the adaptive function, and output the next node of the ant's node; Obtain an ant colony algorithm expression, calculate and update the probability of the ant selecting the next node; updating the high-order proportional factors, high-order integral factors, high-order differential factors, integral orders, differential orders, proportional factors, and integral factors based on the probabilities; The operating device calculates the difference between the ambient temperature and the first ambient temperature; When the difference is less than 0.5°C, outputting the high-order proportional factor, high-order integral factor, high-order differential factor, integral order, differential order, proportional factor and integral factor; When the difference is greater than or equal to 0.5℃, clear the taboo table and circulate; The adjusted high-order proportional factor, high-order integral factor, high-order differential factor, integral order, differential order, proportional factor and integral factor are obtained, and the hysteresis of the temperature regulation is compensated according to the adjusted high-order proportional factor, high-order integral factor, high-order differential factor, integral order, differential order, proportional factor and integral factor.

2. The intelligent HVAC optimization control method according to claim 1, characterized in that: The historical monitoring parameters include historical ambient temperature, historical ambient pollutant concentration, historical equipment inlet air temperature, historical equipment inlet air volume, historical equipment pressure and historical equipment speed; The first analysis is performed by multiple regression analysis, wherein the first corresponding relationships include a first corresponding relationship of inlet air temperature, a first corresponding relationship of inlet air volume, a first corresponding relationship of pressure, and a first corresponding relationship of rotation speed; The first corresponding relationship of the inlet air temperature is represented by a first corresponding relationship among the historical equipment inlet air temperature, the historical ambient temperature and the historical ambient pollutant concentration; The first corresponding relationship of the air intake volume is represented by a first corresponding relationship among the historical equipment air intake volume, the historical ambient temperature and the historical ambient pollutant concentration; The first pressure correspondence is represented by a first correspondence between historical equipment pressure, historical ambient temperature, and historical ambient pollutant concentration; The first corresponding relationship of the rotation speed is represented by a first corresponding relationship among historical equipment rotation speed, historical ambient temperature and historical ambient pollutant concentration; The first analysis method of each first correspondence is the same, recording the historical equipment air inlet temperature, historical equipment air inlet volume, historical equipment pressure and historical equipment speed as equipment parameters, and recording the historical ambient temperature and historical ambient pollutant concentration as environmental parameters.

3. The intelligent HVAC optimization control method according to claim 2, characterized in that: Obtaining a current device parameter according to the first corresponding parameter and the current monitoring parameter, and sending a first control signal according to the current device parameter; The first analysis method comprises: Obtain any type of equipment parameters, take the equipment parameters as dependent variables and the corresponding environmental parameters as independent variables, and obtain the relationship expression between the dependent variables and the independent variables through multiple regression analysis; Traverse all types of equipment parameters and obtain the relationship expressions between each dependent variable and the corresponding independent variable; The relationship expression between each dependent variable and the corresponding independent variable is recorded as a first corresponding relationship, and the first device parameter is obtained by inputting the environmental parameter into the first corresponding relationship.

4. The intelligent HVAC optimization control method according to claim 1, wherein: Obtaining current monitoring parameters, the current monitoring parameters including current ambient temperature, current ambient pollutant concentration, current device air inlet temperature, current device air inlet volume, current device pressure, and current device speed, respectively inputting the current ambient temperature and current ambient pollutant concentration into corresponding first correspondences to obtain corresponding first device air inlet temperature, first device air inlet volume, first device pressure, and first device speed; Calculate first, second, third, and fourth differences between the first device air inlet temperature, the first device air inlet volume, the first device pressure, and the first device speed and the current device air inlet volume, the current device pressure, and the current device speed, respectively; Sending an air temperature control signal, an air volume control signal, a pressure control signal, and a speed control signal according to the first difference, the second difference, the third difference, and the fourth difference; When the value of the first difference, the second difference, the third difference, or the fourth difference is negative, it indicates that the corresponding device parameter is reduced, and the reduced value is the absolute value of the value of the first difference, the second difference, the third difference, or the fourth difference; When the value of the first difference, the second difference, the third difference, or the fourth difference is positive, it indicates that the corresponding device parameter is increased, and the increased value is the value of the first difference, the second difference, the third difference, or the fourth difference; When the value of the first difference, the second difference, the third difference, or the fourth difference is zero, it indicates that the first regulation is not performed on the device parameter.

5. The intelligent HVAC optimization control method according to claim 4, characterized in that: When the value of the first difference, the second difference, the third difference, or the fourth difference is not zero, in response to the first control signal, performing a first control on the device parameter; The first duration is set as a reference to the first duration, and the second duration is selected as a duration gradient, wherein the first duration is greater than the second duration; The difference between the first duration and the second duration is used as the lower limit of the duration, and the sum of the first duration and the second duration is used as the upper limit of the duration. Between the lower limit and the upper limit of the duration, continuously select durations to perform a first adjustment on the device parameters, so that within the range of the selected duration, the adjustment from the current device parameters to the first device parameters is completed, and the relevant data within the selected duration is continuously monitored, and the monitoring time interval is 1 second.

6. The intelligent HVAC optimization control method according to claim 5, characterized in that: The relevant data include the recovered carbon dioxide content, the concentration of harmful gases eliminated, the equipment voltage jitter rate and the total power consumed, wherein the recovered carbon dioxide content is expressed as the content of carbon dioxide emitted by the purification equipment after the equipment performs heat recovery within the selected time period, the concentration of harmful gases eliminated is expressed as the concentration of harmful gases reduced by the equipment during indoor ventilation within the selected time period, and the equipment voltage jitter rate is expressed as the average value of the equipment operating voltage change rate during the process of equipment parameter regulation within the selected time period, which is automatically calculated by a preset algorithm; During the selected time period, the control of the device parameters is expressed as uniform control, where the uniform control is expressed as the ratio of the value of the device parameters controlled per second to the corresponding difference and the selected time period.

7. The intelligent HVAC optimization control method according to claim 6, characterized in that: The calculation method of the preset algorithm includes: Obtain any group of adjacent device voltages, record them as the first voltage and the second voltage in chronological order, where the adjacent voltage is represented by the chronological order, calculate a first difference between the second voltage and the first voltage, calculate a first ratio between the first difference and the first voltage, and traverse the first ratios corresponding to each group of adjacent device voltages within the selected duration; Calculating a first average value of the first ratio, and setting the first average value as the device voltage jitter rate; As the selected duration progresses, the device voltage jitter rate is updated per second until the selected duration ends. The last value of the device voltage jitter rate is obtained, forming a numerical sequence of the device voltage jitter rate distributed according to the time series. The control of the current equipment inlet air temperature is achieved by adjusting the water supply temperature; The control of the current air intake volume of the equipment is achieved by adjusting the opening of the air intake valve and the air intake speed; The control of the current equipment speed is achieved by adjusting the power of the fan; The regulation of the current equipment pressure is achieved by adjusting the compressor speed; Among them, the corresponding relationship between the water supply temperature, the power of the fan, the opening of the air inlet valve, the air inlet speed and the compressor speed and the corresponding equipment parameters is obtained from historical experience.

8. The intelligent HVAC optimization control method according to claim 1, wherein: The method of performing a second analysis on the relevant data within any time length to obtain the optimal control time length includes: setting the first carbon dioxide content as a carbon dioxide content threshold, and setting the first power as a total power threshold; Get any selected duration, and get the corresponding recovered carbon dioxide content and total power consumed; Calculating a second difference between the recovered carbon dioxide content and the first carbon dioxide content, calculating a second ratio of the second difference to the first carbon dioxide content, and setting the second ratio as a first evaluation index; Calculating a second average value of the voltage jitter rate of each device within the time period, and setting the second average value as a second evaluation indicator; calculating a third difference between the total power consumed and the first power, calculating a third ratio of the third difference to the first power, and setting the third ratio as a third evaluation indicator; Obtaining the original harmful gas concentration, calculating a fourth ratio of the effective elimination gas concentration to the original harmful gas concentration, selecting the reciprocal of the fourth ratio, and setting the reciprocal of the fourth ratio as a fourth evaluation indicator; Calculate the first sum of the first evaluation index, the second evaluation index, the third evaluation index, and the fourth evaluation index, and select the optimal control time according to the first sum. The smaller the value of the first sum, the better the selected time. Traverse the first sum values ​​corresponding to each duration, sort the first sum values ​​in descending order, select the first sum value with the smallest value, and set the duration corresponding to the smallest sum value as the optimal control time; A second control signal is sent according to the optimal control time length.

9. An intelligent HVAC optimization control system, the system being used to execute the intelligent HVAC optimization control method according to claim 1, characterized in that: Including control module, simulation module and compensation module; The control module performs a first analysis on the historical monitoring parameters to obtain a first correspondence between the device parameters and the environmental parameters, and sends a first control signal according to the first corresponding parameters; The simulation module completes the regulation of the device parameters in different time lengths in response to the first regulation signal, and continuously monitors the relevant data in each time length; The compensation module performs a second analysis on relevant data within any time length to obtain an optimal control time length, sends a second control signal according to the optimal control time length, and performs a first compensation on the temperature according to PID control.

Images