Energy-saving control method based on dynamic air conditioning operation data

Abstract

The present invention provides an energy-saving control method based on dynamic air conditioning operation data, which relates to the technical field of intelligent control and energy-saving optimization of air conditioning systems. The present invention generates a comfort index, an air quality index and an energy efficiency index by collecting the operation data of the air conditioning system, including temperature, humidity, air flow rate and energy consumption data, and comprehensively generates an air conditioning index, sets the control target parameters of the system, and then generates an operation standard value, and dynamically generates a control strategy, including adjusting the cooling and heating mode switching time of the fan, optimizing the operation speed and time of the fan, and adjusting the operation frequency of the compressor. Finally, if the generated air conditioning index exceeds the standard value, the control strategy adjustment is repeated until the air conditioning index returns to within the standard value. The present invention collects and analyzes the operation data of the air conditioning system, dynamically adjusts the control strategy, and realizes efficient operation of the system, improves user comfort and energy saving effects.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control and energy-saving optimization of air-conditioning systems, and in particular to an energy-saving control method based on dynamic air conditioning operation data. Background Art

[0002] With the acceleration of urbanization and the improvement of quality of life, the demand for air conditioning systems has increased significantly. However, the high energy consumption of traditional air conditioning systems has become increasingly prominent, prompting researchers and companies to seek more efficient solutions. The rapid progress of information technology, especially the application of big data, artificial intelligence, the Internet of Things and advanced sensors, has provided a technical basis for dynamic air conditioning, making real-time monitoring and autonomous optimization possible, thereby promoting the continuous innovation and practical application of energy-saving control methods.

[0003] In the prior art, publication number CN111923684A discloses a new energy passenger car air conditioning system and energy-saving control method, which includes an air conditioning device arranged in the vehicle through a main controller, and a control switch for controlling the air conditioning device, wherein the main controller is respectively connected to the control switch and the electrical signal control end of the air conditioning device, and the main controller is connected to the vehicle controller of the vehicle for obtaining the operation data of the vehicle; the main controller is used to calculate the power supply demand according to the vehicle operation data, and when the vehicle driving battery meets the power supply demand and the control switch is in the on state, the electrical signal control end of the air conditioning device is used to turn on or off the air conditioning device.

[0004] Insufficient existing technology:

[0005] Existing technologies lack real-time and accuracy. Over-reliance on vehicle operating data to calculate power demand may not reflect changes in the in-vehicle environment and passenger comfort requirements in real time. For example, the temperature, humidity and number of passengers in the vehicle may change significantly in a short period of time, and the calculation method based on operating data may not be able to capture and respond to these changes in time, resulting in the air-conditioning system not responding quickly and accurately enough.

[0006] Lacking comprehensive consideration of multiple factors, the system mainly focuses on the power supply status of the vehicle and battery, but fails to fully combine external environmental factors such as temperature, humidity, number of passengers and other variables for comprehensive judgment, which will affect energy saving effects and comfort experience.

[0007] Therefore, it is necessary to provide an energy-saving control method based on dynamic air conditioning operation data to solve the above problem.

[0008] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0009] The object of the present invention is to provide an energy-saving control method based on dynamic air conditioning operation data to solve the problems raised in the above background technology.

[0010] To achieve the above object, the present invention provides the following technical solutions:

[0011] An energy-saving control method based on dynamic air conditioning operation data, the specific steps comprising:

[0012] Step 1: Collecting operating data of the air conditioning system, the operating data including temperature, humidity, air flow rate and energy consumption data;

[0013] Step 2: The collected operation data is transmitted to a data processing unit, the data is preprocessed and analyzed, a comfort index, an air quality index and an energy efficiency index are generated, and an air conditioning index is comprehensively generated according to the generated comfort index, air quality index and energy efficiency index;

[0014] Step 3: Setting the control target parameters of the air conditioning system including a temperature setting value, a humidity setting value, a wind speed setting value and an energy consumption setting value, and generating an operation standard value using the temperature setting value, the humidity setting value, the wind speed setting value and the energy consumption setting value;

[0015] Step 4: Based on the set control target parameters, dynamically generate a control strategy, wherein the control strategy includes adjusting the switching time between the fan cooling and heating modes, optimizing the fan operating speed and operating time, and adjusting the compressor operating frequency;

[0016] Step 5: Compare the generated operation standard value with the air conditioning index. If the air conditioning index exceeds the operation standard value, repeat the operation of step 4 to adjust the system until the air conditioning index is less than the operation standard value.

[0017] Furthermore, the operating data of the air conditioning system is collected according to the following method:

[0018] Collect the data of air inlet temperature and air outlet temperature, and use the temperature difference to evaluate the temperature data; measure the actual water vapor partial pressure and the saturated water vapor pressure at the same temperature, and use the ratio of the actual water vapor partial pressure to the saturated water vapor pressure at the same temperature to evaluate the humidity data; collect the air transmission distance and transmission time in the system pipeline, and calculate the air flow rate; collect the actual power of the system and the system operation time, and calculate the energy consumption data of the system, based on the formula:

[0019] ΔT=T out -T in

[0020]

[0021] E=P*t yx

[0022] Where ΔT is the temperature difference used to evaluate system performance, T out Indicates the outlet temperature, T in represents the air inlet temperature, RH represents the humidity of the air conditioning system, P va Indicates the actual water vapor partial pressure, P sat represents the saturated water vapor pressure at the same temperature, V represents the air flow rate of the air conditioning system, d represents the air transmission distance, t cs represents the air transmission time, E represents the energy consumption of the air conditioning system, P represents the actual power of the system operation, t yx Indicates the system running time.

[0023] Furthermore, the comfort index, air quality index and energy efficiency index are generated according to the following method:

[0024] Using a multi-parameter collaborative analysis algorithm, the temperature, humidity and air velocity data are comprehensively analyzed to generate the comfort index, air quality index and energy efficiency index. The formula is as follows:

[0025] CI=a*ΔT+b*RH

[0026] AQI=c*ΔT+d*RH+e*V

[0027]

[0028] Among them, CI, AQI, and EEI represent comfort index, air quality index, and energy efficiency index respectively; a represents the proportional weight of temperature difference in calculating comfort index; b represents the proportional weight of humidity in calculating comfort index; c represents the proportional weight of temperature difference in calculating air quality index; d represents the proportional weight of humidity in calculating air quality index; e represents the proportional weight of air flow rate in calculating comfort index; the weight proportions are a>b, c>d>e.

[0029] Furthermore, the air conditioning index is comprehensively generated based on the formula:

[0030] CAQP=α*CI+β*AQI+γ*EEI

[0031] Among them, α, β, and γ respectively represent the proportion weights of the comfort index, air quality index, and energy efficiency index in the comprehensive generation of the air conditioning index, and α>β>γ.

[0032] Furthermore, the control target parameters of the air conditioning system are set to include a temperature setting value, a humidity setting value, a wind speed setting value, and an energy consumption setting value, and the formula is as follows:

[0033] T set =T comfort +k1*(T outside -T comfort )

[0034] RH set =RH comfort +k2*(RH outside -RH comfort )

[0035]

[0036] Among them, T set RH set 、V set 、E set Respectively represent the temperature setting value, humidity setting value, wind speed setting value, and energy consumption setting value, T comfort Indicates the human body comfort temperature, T outside represents the outdoor temperature, k1 represents the adjustment coefficient related to temperature, which is adjusted according to the season and user preference; RH comfort Indicates human comfort humidity, RH outside represents outdoor humidity, k2 represents the adjustment coefficient related to humidity, which is adjusted based on environmental requirements; V base Indicates the basic air velocity, A room Indicates the room area, N people represents the number of people in the room, k3 represents the adjustment coefficient related to the air flow rate, which is adjusted according to the comfort level with effective ventilation; E base represents basic energy consumption, ΔU, ΔRH, ΔV represent changes in temperature, humidity, and air velocity, Δt represents time change, and k4 represents the adjustment coefficient related to energy consumption, which is adjusted based on energy-saving optimization.

[0037] Furthermore, the temperature setting value, humidity setting value, wind speed setting value and energy consumption setting value are used to generate the operation standard value, and the formula based on the formula is:

[0038]

[0039] Where S represents the generated operating standard value.

[0040] Further, the control strategy includes adjusting the switching time between the fan cooling and heating modes, optimizing the fan operating speed and operating time, and adjusting the compressor operating frequency, based on the method:

[0041] When adjusting indoor parameters, the priority should be to monitor whether the humidity deviates from the target humidity setting value. If the humidity exceeds the target range, the humidity parameters should be adjusted first, mainly by controlling the operation of the dehumidification or humidification equipment to minimize energy consumption and reach the preset humidity setting value. After the humidity is adjusted to the target range, the temperature is adjusted. According to the difference between the current temperature and the target temperature setting value, the cooling or heating mode of the fan is dynamically adjusted. By gradually adjusting the humidity and temperature, the energy consumption of the system frequently switching modes is reduced. Fine-tune the operating frequency of the compressor to minimize energy consumption while maintaining comfort;

[0042] Based on the real-time temperature data, the temperature threshold is set, the indoor temperature is compared with the temperature threshold, and the switching time of the fan cooling and heating modes is adjusted. The logic formula is as follows:

[0043]

[0044] Among them, T inside Indicates the indoor temperature, T threshold Indicates the temperature threshold, Q indicates the logic value for judging the switching time between the fan cooling and heating modes. When Q=0, it indicates that the indoor temperature is greater than the temperature threshold, and the fan cooling mode is switched; when Q=1, it indicates that the indoor temperature is less than the temperature threshold, and the fan heating mode is switched;

[0045] The fan speed is adjusted dynamically using proportional integral, and the probability distribution of relevant users is collected according to the user's work and rest time and daily activity pattern, and the fan opening and closing time is intelligently set based on the formula:

[0046]

[0047] T on =min{t|P(t)≥P threshold}

[0048] T off =max{t|P(t)≥P threshold}

[0049] Where, J represents the fan speed, K p , K i , K d They represent the proportional, integral and differential coefficients of the PID controller respectively, ΔT represents the temperature difference, t represents the fan operating time, T on 、T off They represent the fan on and off time, P(t) represents the probability distribution of user activities, and P threshold represents a preset probability threshold value, which is used to determine the operation time period of the wind turbine;

[0050] According to the difference between the actual indoor temperature and the set temperature, the compressor operating frequency is linearly adjusted according to the formula:

[0051]

[0052] Where, f represents the adjusted compressor operating frequency, f min Indicates the minimum operating frequency of the compressor, f max Indicates the maximum operating frequency of the compressor, ΔT indicates the temperature difference, ΔT max Indicates the maximum temperature difference, set by the system characteristics.

[0053] Furthermore, if the air conditioning index exceeds the operating standard value, the operation of step 4 is repeated to adjust the system until the air conditioning index is less than the operating standard value, and the logic formula is as follows:

[0054]

[0055] Wherein, S represents the operating standard value, CAQP represents the air conditioning index, and L represents the logical value for judging whether the air conditioning index exceeds the operating standard value. When L=0, it indicates that the air conditioning index does not exceed the operating standard value and does not need to be adjusted; when L=1, it indicates that the air conditioning index exceeds the operating standard value and needs to be adjusted, and the step 4 operation is repeated.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] The present invention can accurately and quickly reflect environmental changes by real-time collection and analysis of temperature, humidity, air flow rate and energy consumption data, ensure rapid response of the air conditioning system, and dynamically generate and adjust control strategies. Through real-time data feedback, the operation of fans and compressors is optimized, the operation mode, speed and frequency of fans and compressors are adjusted, the responsiveness is enhanced, and the accuracy and real-time performance of system response are improved.

[0058] By generating comfort index, air quality index and energy efficiency index, it can analyze multiple environmental and system parameters in a coordinated manner, provide multi-dimensional reference for system adjustment, and improve user comfort experience by accurately adjusting temperature, humidity and wind speed. Based on real-time data, it can consider multiple environmental and user factors and enhance the system's adaptability under various conditions.

[0059] The present invention achieves the energy-saving goal of optimizing energy use and reducing system energy consumption through the comprehensive application of real-time data collection and analysis, multi-parameter collaborative analysis, dynamic control strategies and intelligent algorithms. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION

[0061] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0062] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0063] Example:

[0064] See also Figure 1 , the present invention provides a technical solution:

[0065] An energy-saving control method based on dynamic air conditioning operation data, the specific steps comprising:

[0066] Step 1: Collecting operating data of the air conditioning system, the operating data including temperature, humidity, air flow rate and energy consumption data;

[0067] Step 2: The collected operation data is transmitted to a data processing unit, the data is preprocessed and analyzed, a comfort index, an air quality index and an energy efficiency index are generated, and an air conditioning index is comprehensively generated according to the generated comfort index, air quality index and energy efficiency index;

[0068] Step 3: Setting the control target parameters of the air conditioning system including a temperature setting value, a humidity setting value, a wind speed setting value and an energy consumption setting value, and generating an operation standard value using the temperature setting value, the humidity setting value, the wind speed setting value and the energy consumption setting value;

[0069] Step 4: Based on the set control target parameters, dynamically generate a control strategy, wherein the control strategy includes adjusting the switching time between the fan cooling and heating modes, optimizing the fan operating speed and operating time, and adjusting the compressor operating frequency;

[0070] Step 5: Compare the generated operation standard value with the air conditioning index. If the air conditioning index exceeds the operation standard value, repeat the operation of step 4 to adjust the system until the air conditioning index is less than the operation standard value.

[0071] It should be noted that temperature difference data can be used to evaluate the cooling or heating performance of the system. By recording the temperature difference between the air inlet and the air outlet, the efficiency of the system in adjusting the temperature can be determined, which can improve the comfort and response speed of the system. Humidity has an important impact on comfort, and the ratio of actual water vapor partial pressure to saturated water vapor pressure can provide accurate humidity data to ensure that the air conditioning system can effectively adjust the humidity and improve the comfort experience of passengers. Air flow rate is an important parameter for evaluating the ventilation effect of the system. The flow rate data calculated by transmission distance and transmission time can help adjust the operating mode and frequency of the fan, improve the efficiency and quality of air flow, and enhance the response speed and energy efficiency of the system. System energy consumption is a key indicator for measuring system efficiency. The energy consumption of the system can be accurately calculated through actual power and operating time data, which can optimize energy consumption management, achieve energy-saving goals, and improve the overall energy efficiency of the system.

[0072] Therefore, it is necessary to collect the operating data of the air conditioning system, based on the following method:

[0073] Collect the data of air inlet temperature and air outlet temperature, and use the temperature difference to evaluate the temperature data; measure the actual water vapor partial pressure and the saturated water vapor pressure at the same temperature, and use the ratio of the actual water vapor partial pressure to the saturated water vapor pressure at the same temperature to evaluate the humidity data; collect the air transmission distance and transmission time in the system pipeline, and calculate the air flow rate; collect the actual power of the system and the system operation time, and calculate the energy consumption data of the system, based on the formula:

[0074] ΔT=T out -T in

[0075]

[0076] E=P*t yx

[0077] Where ΔT is the temperature difference used to evaluate system performance, T out Indicates the outlet temperature, T in represents the air inlet temperature, RH represents the humidity of the air conditioning system, P va Indicates the actual water vapor partial pressure, P sat represents the saturated water vapor pressure at the same temperature, V represents the air flow rate of the air conditioning system, d represents the air transmission distance, t csrepresents the air transmission time, E represents the energy consumption of the air conditioning system, P represents the actual power of the system operation, t yx Indicates the system operation time; in the above formula, the transmission distance d usually refers to the length of the air transmission path in the air conditioning system. There are generally two situations: the first refers to the transmission distance within the pipeline system, which refers to the length of the air transmission path from the air inlet to the air outlet in the pipeline. This situation is usually applicable to central air conditioning systems or large HVAC systems. The air will have a clear transmission path when passing through the pipeline system; the second refers to the transmission distance within the equipment, which refers to the length of the air path from the air inlet to the air outlet inside the air conditioning equipment. This situation is applicable to unit air conditioning units or smaller air conditioning equipment. The transmission time t cs There are also two corresponding situations. The first refers to the transmission time of air from the air inlet to the air outlet in the pipe system, and the second refers to the transmission time of air from the air inlet to the air outlet inside the air-conditioning equipment.

[0078] It should be noted that by comprehensively analyzing temperature, humidity and air flow rate data through a multi-parameter collaborative analysis algorithm, the performance of the air conditioning system can be accurately evaluated and optimized. These indicators help the system achieve the goals of improving comfort, improving air quality and optimizing energy efficiency with higher efficiency and accuracy, thereby comprehensively improving the user experience and system operating efficiency.

[0079] Therefore, it is necessary to generate the comfort index, air quality index and energy efficiency index based on the following method:

[0080] Using a multi-parameter collaborative analysis algorithm, the temperature, humidity and air velocity data are comprehensively analyzed to generate the comfort index, air quality index and energy efficiency index. The formula is as follows:

[0081] CI=a*ΔT+b*RH

[0082] AQI=c*ΔT+d*RH+e*V

[0083]

[0084] Among them, CI, AQI, and EEI represent comfort index, air quality index, and energy efficiency index respectively; a represents the weight of the proportion of temperature difference in calculating the comfort index; b represents the weight of the proportion of humidity in calculating the comfort index; c represents the weight of the proportion of temperature difference in calculating the air quality index; d represents the weight of the proportion of humidity in calculating the air quality index; e represents the weight of the proportion of air velocity in calculating the comfort index; the weight ratios are a>b, c>d>e; in the above formula, in the formula for calculating the comfort index, the proportional weight is set to a>b because temperature has a greater direct impact on human comfort, and the human body is more sensitive to temperature changes. Too high or too low temperature will directly affect the sense of comfort, although humidity Humidity is also an important factor affecting comfort, but its impact on comfort is relatively indirect and secondary to temperature. Moderate humidity can increase comfort, but its weight is lower than temperature. In the formula for calculating the air quality index, the proportional weight is set to c>d>e. This is because temperature changes not only affect human sensations, but also affect the growth and diffusion of microorganisms and pollutants in the air. Improper temperature control may lead to a decline in air quality. Therefore, the proportional weight of temperature is set to the highest, while humidity helps to suppress the suspension of air particles such as dust and pollen, but its impact is relatively small compared to temperature. Therefore, the proportional weight of humidity is set to the second highest. Finally, air velocity affects the distribution and renewal speed of airflow, but its role in air quality is second only to temperature and humidity. Therefore, the proportional weight of air velocity is set to the lowest.

[0085] It should be noted that by weighted integration of the comfort index, air quality index and energy efficiency index, the overall performance of the air conditioning system can be comprehensively evaluated. Such a comprehensive index can more accurately reflect the system's overall performance in providing a comfortable environment, maintaining air quality and optimizing energy use, thereby providing a more scientific and reliable basis for system optimization and decision-making.

[0086] Therefore, it is necessary to comprehensively generate the air conditioning index, based on the formula:

[0087] CAQP=α*CI+β*AQI+γ*EEI

[0088] Among them, α, β, and γ respectively represent the proportional weights of the comfort index, air quality index, and energy efficiency index in the comprehensive generation of air conditioning index, and α>β>γ; in the above formula, the proportional weights are set to α>β>γ because comfort is directly related to user experience, and the comprehensive adjustment of temperature and humidity has the most direct impact on human comfort and is the most obvious factor perceived by users. Uncomfortable temperature and humidity will immediately affect people's feelings, so it should be given the highest weight in the comprehensive index; although air quality directly affects human health, its impact is usually more gradual and indirect. Maintaining good air quality is an important condition for ensuring long-term health, so it is given the second highest weight in the comprehensive index; and energy efficiency is crucial to reducing operating costs and environmental impacts. Although the direct perception of users is weak, optimizing energy efficiency is the key to achieving sustainable development, so it still needs to be considered in the comprehensive index, but with the lowest weight.

[0089] It should be noted that by precisely adjusting key environmental factors, the optimal balance of comfort, air quality and energy efficiency in the indoor environment can be ensured. Through the dynamic adjustment of these parameters, the system can respond to changes in the external environment and user needs, provide a personalized comfort experience, while optimizing energy use, reducing operating costs and promoting sustainable development.

[0090] Therefore, it is necessary to set the control target parameters of the air conditioning system, including the temperature setting value, humidity setting value, wind speed setting value and energy consumption setting value, based on the formula:

[0091] T set =T comfort +k1*(T outside -T comfort )

[0092] RH set =RH comfort +k2*(RH outside -RH comfort )

[0093]

[0094] Among them, T set RH set 、V set 、E set Respectively represent the temperature setting value, humidity setting value, wind speed setting value, and energy consumption setting value, T comfort Indicates the human body comfort temperature, T outside represents the outdoor temperature, k1 represents the adjustment coefficient related to temperature, which is adjusted according to the season and user preference; RH comfort Indicates human comfort humidity, RH outsiderepresents outdoor humidity, k2 represents the adjustment coefficient related to humidity, which is adjusted based on environmental requirements; V base Indicates the basic air velocity, A room Indicates the room area, N people represents the number of people in the room, k3 represents the adjustment coefficient related to the air flow rate, which is adjusted according to the comfort level with effective ventilation; E base represents basic energy consumption, ΔU, ΔRH, ΔV represent the changes in temperature, humidity and air flow rate, specifically the difference between the temperature, humidity and air flow rate data collected at the current moment and the temperature, humidity and air flow rate data recorded by the last system parameter, Δt represents the time change, specifically the difference between the current data collection moment and the last data collection moment, k4 represents the adjustment coefficient related to energy consumption, which is adjusted based on energy-saving optimization; in the above formula, the temperature set value is calculated because the indoor temperature is dynamically adjusted according to the changes in the outdoor temperature to minimize the difference with the comfortable temperature and improve the comfort; the humidity set value is calculated because the indoor humidity is adjusted according to the changes in the outdoor humidity to ensure that it is close to the comfortable humidity and enhance comfort; the wind speed set value is calculated because the air flow rate is adjusted according to the room area and the number of people to ensure effective ventilation and comfort; the energy consumption set value is calculated because the energy consumption is optimized by reducing the temperature, humidity and wind speed change rate, improving the energy efficiency of the system and reducing operating costs.

[0095] It should be noted that integrating multiple key environmental parameters into a standard value can help to comprehensively evaluate the comfort and air quality of the indoor environment, and can provide a unified indicator to help the system automatically adjust the operating status to achieve optimal environmental control and energy utilization efficiency. It can simplify management and monitoring, and help staff and monitoring systems to more intuitively understand and manage the performance of the air conditioning system, facilitate debugging and optimization, and through a standard value, ensure the consistency of system operation at different times and conditions, and maintain a high level of user experience and energy efficiency.

[0096] Therefore, it is necessary to use the temperature setting value, humidity setting value, wind speed setting value and energy consumption setting value to generate the operation standard value, and the formula based on it is:

[0097]

[0098] Wherein, S represents the generated operating standard value; in the above formula, the operating standard value is generated by the geometric mean of the four set values. Unlike the arithmetic mean, the geometric mean is not sensitive to extreme values. The geometric mean is used to characterize the operating standard value in order to avoid excessive influence of a single outlier on the comprehensive index and to improve the stability and reliability of the index.

[0099] It should be noted that the dynamic optimization control of fans and compressors is achieved through the comprehensive application of real-time temperature data, user behavior patterns and intelligent control algorithms. This not only improves the comfort and stability of the indoor environment, but also significantly improves the energy efficiency of the system, reduces unnecessary energy consumption and operating costs. For example, if the humidity is too high, the operating frequency of the cooling system can be appropriately increased, and the dehumidification process can be accelerated with appropriate wind speed; if the humidity is too low, the operating frequency of the cooling system can be reduced, and the air flow rate can be appropriately increased, reducing the use of additional humidity control equipment. Through intelligent and automated adjustments, the system can respond more flexibly to changes in the environment and demand, providing advanced solutions for modern intelligent buildings and energy-saving equipment management.

[0100] Therefore, the control strategy includes adjusting the switching time of the fan cooling and heating modes, optimizing the fan operation speed and operation time, and adjusting the compressor operation frequency, based on the method:

[0101] When adjusting indoor parameters, the priority should be to monitor whether the humidity deviates from the target humidity setting value. If the humidity exceeds the target range, the humidity parameters should be adjusted first, mainly by controlling the operation of the dehumidification or humidification equipment to minimize energy consumption and reach the preset humidity setting value. After the humidity is adjusted to the target range, the temperature is adjusted. According to the difference between the current temperature and the target temperature setting value, the cooling or heating mode of the fan is dynamically adjusted. By gradually adjusting the humidity and temperature, the energy consumption of the system frequently switching modes is reduced. Fine-tune the operating frequency of the compressor to minimize energy consumption while maintaining comfort;

[0102] Based on the real-time temperature data, the temperature threshold is set, the indoor temperature is compared with the temperature threshold, and the switching time of the fan cooling and heating modes is adjusted. The logic formula is as follows:

[0103]

[0104] Among them, T inside Indicates the indoor temperature, T threshold Indicates the temperature threshold, Q indicates the logic value for judging the switching time between the fan cooling and heating modes. When Q=0, it indicates that the indoor temperature is greater than the temperature threshold, and the fan cooling mode is switched; when Q=1, it indicates that the indoor temperature is less than the temperature threshold, and the fan heating mode is switched;

[0105] The fan speed is adjusted dynamically using proportional integral, and the probability distribution of relevant users is collected according to the user's work and rest time and daily activity pattern, and the fan opening and closing time is intelligently set based on the formula:

[0106]

[0107] T on=min{t|P(t)≥P threshold}

[0108] T off =max{t|P(t)≥P threshold}

[0109] Where, J represents the fan speed, K p , K i , K d They represent the proportional, integral and differential coefficients of the PID controller respectively, ΔT represents the temperature difference, t represents the fan operating time, T on 、T off They represent the fan on and off time, P(t) represents the probability distribution of user activities, and P threshold represents the preset probability threshold, which is used to determine the operation time period of the fan. In the above formula, the fan speed is dynamically adjusted using the PID proportional integral, K p *ΔT means proportional control to adjust the fan speed according to the instantaneous temperature difference. Indicates integral control, taking into account the influence of temperature difference accumulation to achieve more stable control. represents differential control, predicting temperature changes and reducing temperature fluctuations; in the above formula, the calculation of the intelligent setting of the fan on and off time is based on the probability distribution P(t) of the user activity and the preset probability threshold P threshold , and calculate T on The formula represents finding the solution that satisfies P(t)≥P threshold The earliest time point t of the condition, that is, the time point when the user activity probability first reaches or exceeds the preset threshold, is used to set the fan start time. When the user activity probability reaches or exceeds the threshold, the system believes that the user is likely to need air conditioning during this time period, so the fan is turned on. off The formula represents finding the solution that satisfies (t) ≥ P threshold The latest time point t of the condition, that is, the time point when the user activity probability reaches or exceeds the preset threshold for the last time, is used to set the fan shutdown time. When the user activity probability reaches or exceeds the threshold for the last time, the system believes that the user is unlikely to need air conditioning after this time period, so the fan is turned off.

[0110] According to the difference between the actual indoor temperature and the set temperature, the compressor operating frequency is linearly adjusted according to the formula:

[0111]

[0112] Where, f represents the adjusted compressor operating frequency, f min Indicates the minimum operating frequency of the compressor, f maxIndicates the maximum operating frequency of the compressor, ΔT indicates the temperature difference, ΔT max Indicates the maximum temperature difference, set by the system characteristics.

[0113] It should be noted that the logic formula ensures that the air conditioning system can continuously monitor and adjust the air conditioning index in a timely manner to maintain the best operating state through a simple automated judgment mechanism. It improves system efficiency and reliability, simplifies operation and maintenance management, improves user comfort and satisfaction, and optimizes energy use to achieve energy saving and consumption reduction.

[0114] Therefore, if the air conditioning index exceeds the operating standard value, it is necessary to repeat the step 4 operation to adjust the system until the air conditioning index is less than the operating standard value, based on the logical formula:

[0115]

[0116] Wherein, S represents the operating standard value, CAQP represents the air conditioning index, and L represents the logical value for judging whether the air conditioning index exceeds the operating standard value. When L=0, it indicates that the air conditioning index does not exceed the operating standard value and does not need to be adjusted; when L=1, it indicates that the air conditioning index exceeds the operating standard value and needs to be adjusted, and the step 4 operation is repeated.

[0117] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0118] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0119] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0120] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. An energy-saving control method based on dynamic air conditioning operation data, characterized in that: The specific steps include: Step 1: Collecting operating data of the air conditioning system, the operating data including temperature, humidity, air flow rate and energy consumption data; Step 2: The collected operation data is transmitted to the data processing unit, the data is preprocessed and analyzed, and a comfort index, an air quality index and an energy efficiency index are generated, and an air conditioning index is comprehensively generated according to the generated comfort index, air quality index and energy efficiency index; Step 3: Setting the control target parameters of the air conditioning system including a temperature setting value, a humidity setting value, a wind speed setting value and an energy consumption setting value, and generating an operation standard value using the temperature setting value, the humidity setting value, the wind speed setting value and the energy consumption setting value; Step 4: Based on the set control target parameters, dynamically generate a control strategy, which includes adjusting the switching time between the fan cooling and heating modes, optimizing the fan operating speed and operating time, and adjusting the compressor operating frequency, based on the following method: When adjusting indoor parameters, the priority should be to monitor whether the humidity deviates from the target humidity setting value. If the humidity exceeds the target range, the humidity parameters should be adjusted first, mainly by controlling the operation of the dehumidification or humidification equipment to minimize energy consumption and reach the preset humidity setting value. After the humidity is adjusted to the target range, the temperature is adjusted. According to the difference between the current temperature and the target temperature setting value, the cooling or heating mode of the fan is dynamically adjusted. By gradually adjusting the humidity and temperature, the energy consumption of the system frequently switching modes is reduced, and the operating frequency of the compressor is fine-tuned to minimize energy consumption while maintaining comfort. Based on the real-time temperature data, the temperature threshold is set, the indoor temperature is compared with the temperature threshold, and the switching time of the fan cooling and heating modes is adjusted. The logic formula is as follows: ； in, Indicates the indoor temperature. Indicates the temperature threshold, Q indicates the logic value for judging the switching time between the fan cooling and heating modes. When Q=0, it indicates that the indoor temperature is greater than the temperature threshold, and the fan cooling mode is switched; when Q=1, it indicates that the indoor temperature is less than the temperature threshold, and the fan heating mode is switched; The fan speed is adjusted dynamically using proportional integral, and the probability distribution of relevant users is collected according to the user's work and rest time and daily activity pattern, and the fan opening and closing time is intelligently set based on the formula: ； in, Indicates the fan speed, Represent the proportional, integral and differential coefficients of the PID controller respectively, represents the temperature difference, t represents the fan operation time, Respectively represent the fan on and off time, represents the probability distribution of user activities, represents a preset probability threshold value, which is used to determine the operation time period of the wind turbine; According to the difference between the actual indoor temperature and the set temperature, the compressor operating frequency is linearly adjusted according to the formula: ； in, Indicates the adjusted compressor operating frequency, Indicates the minimum operating frequency of the compressor. Indicates the maximum operating frequency of the compressor. Represents the temperature difference, Indicates the maximum temperature difference, which is set by the system characteristics; Step 5: Compare the generated operating standard value with the air conditioning index. If the air conditioning index exceeds the operating standard value, repeat the step 4 operation to adjust the system until the air conditioning index is less than the operating standard value.

2. The energy-saving control method based on dynamic air conditioning operation data according to claim 1, characterized in that: Collecting the operating data of the air conditioning system is based on the following methods: Collect the data of air inlet temperature and air outlet temperature, and use the temperature difference to evaluate the temperature data; measure the actual water vapor partial pressure and the saturated water vapor pressure at the same temperature, and use the ratio of the actual water vapor partial pressure to the saturated water vapor pressure at the same temperature to evaluate the humidity data; collect the air transmission distance and transmission time in the system pipeline, and calculate the air flow rate; collect the actual power of the system and the system operation time, and calculate the energy consumption data of the system, based on the formula: ； in represents the temperature difference used to evaluate system performance, Indicates the outlet temperature. represents the air inlet temperature, RH represents the humidity of the air conditioning system, represents the actual water vapor partial pressure, represents the saturated water vapor pressure at the same temperature, represents the saturated water vapor pressure at the same temperature, V represents the air flow rate of the air conditioning system, d represents the air transmission distance, represents the air transmission time, E represents the energy consumption of the air conditioning system, and P represents the actual power of the system operation. Indicates the system running time.

3. The energy-saving control method based on dynamic air conditioning operation data according to claim 2, characterized in that: The comfort index, air quality index and energy efficiency index are generated based on the following method: Using a multi-parameter collaborative analysis algorithm, the temperature, humidity and air velocity data are comprehensively analyzed to generate the comfort index, air quality index and energy efficiency index. The formula is as follows: ； in, They represent comfort index, air quality index, and energy efficiency index respectively; a represents the weight of the temperature difference in the calculation of the comfort index; b represents the weight of the humidity in the calculation of the comfort index; c represents the weight of the temperature difference in the calculation of the air quality index; d represents the weight of the humidity in the calculation of the air quality index; e represents the weight of the air velocity in the calculation of the air quality index; the weight ratio is as follows: .

4. The energy-saving control method based on dynamic air conditioning operation data according to claim 3, characterized in that: The air conditioning index is comprehensively generated based on the formula: ； in, They respectively represent the weights of the comfort index, air quality index, and energy efficiency index in the comprehensive generation of air conditioning index, and .

5. The energy-saving control method based on dynamic air conditioning operation data according to claim 1, characterized in that: The control target parameters of the air conditioning system include the temperature setting value, humidity setting value, wind speed setting value and energy consumption setting value, and the formula is as follows: ； in, Respectively represent the temperature setting value, humidity setting value, wind speed setting value, and energy consumption setting value. Indicates the human body's comfortable temperature. Indicates the outdoor temperature, represents the temperature-related adjustment factor, adjusted according to season and user preference; Indicates the comfortable humidity for human body. Indicates outdoor humidity. Represents the adjustment factor related to humidity, adjusted based on environmental requirements; represents the basic air velocity, Indicates the room area. Indicates the number of people in the room. Indicates the adjustment factor related to air flow rate, which is adjusted according to the comfort level with effective ventilation; represents the basic energy consumption, Indicates the changes in temperature, humidity, and air velocity. represents the time variation, Represents the adjustment coefficient related to energy consumption, which is adjusted based on energy-saving optimization.

6. The energy-saving control method based on dynamic air conditioning operation data according to claim 5, characterized in that: The operating standard value is generated by using the temperature setting value, humidity setting value, wind speed setting value and energy consumption setting value, and the formula based on it is: ； Where S represents the generated operating standard value.

7. The energy-saving control method based on dynamic air conditioning operation data according to claim 1, characterized in that: If the air conditioning index exceeds the operating standard value, repeat step 4 to adjust the system until the air conditioning index is less than the operating standard value. The logic formula is: ； Wherein, S represents the operating standard value, CAQP represents the air conditioning index, and L represents the logical value for judging whether the air conditioning index exceeds the operating standard value. When L=0, it indicates that the air conditioning index does not exceed the operating standard value and does not need to be adjusted; when L=1, it indicates that the air conditioning index exceeds the operating standard value and needs to be adjusted, and step 4 is repeated.

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