Air conditioning energy-saving control method, system and device driven by user behavior analysis

Abstract

A method, system and device for air conditioning energy-saving control driven by user behavior analysis. The method first collects user historical behavior and air conditioning control related data, then determines the key factors affecting user behavior, analyzes user behavior, generates air conditioning control strategies, and finally modifies the air conditioning control strategies based on user expectations of the air conditioning control strategies. The present invention collects and analyzes multi-dimensional data of user operation of air conditioning, mines user habits and preferences, establishes a user behavior analysis model, and comprehensively considers the user's air conditioning usage needs, thereby obtaining an energy-saving control strategy that is more in line with the user's actual needs, and has self-optimization and learning capabilities. By continuously updating and adjusting the control strategy, the user's usage experience is improved.

Description

Technical Field

[0001] The present invention relates to air conditioning control means, belongs to the field of air conditioning energy-saving control, and in particular to an air conditioning energy-saving control method, system and equipment driven by user behavior analysis. Background Art

[0002] With the rapid advancement of science and technology and the widespread application of intelligence, people's requirements for the comfort of their living environment are increasing. As an important part of modern life, the performance and energy consumption of air conditioners have received widespread attention.

[0003] However, traditional split air conditioning control methods are often limited to fixed parameters and modes of operation. For example, only fixed temperatures and wind speeds can be set, and flexible adjustments cannot be made according to actual needs. They cannot adapt to changes in different seasons and weather conditions, resulting in poor energy consumption and poor effects. Manual operation is usually required, and there is a lack of intelligent and automated functions. Automatic adjustments cannot be made according to user habits and needs. Although this method is simple and easy to operate, it fails to fully consider the differences and variability of user behaviors under different users, different environments, and different times, resulting in high energy consumption and difficulty in meeting personalized needs. The user experience is poor and it cannot truly achieve comfort and energy-saving effects.

[0004] Although more advanced control strategies are gradually being introduced into existing technologies, such as real-time data analysis based on environmental sensors, the application of these new technologies enables air-conditioning systems to respond more accurately to changes in indoor and outdoor environments and achieve dynamic adjustment. However, there is often a certain one-sidedness in the control strategies of air conditioners, which cannot fully consider user needs. Summary of the invention

[0005] The purpose of the present invention is to overcome the above-mentioned defects and problems existing in the prior art and to provide an air conditioning energy-saving control method, system and device driven by user behavior analysis with more comprehensive considerations.

[0006] To achieve the above objectives, the technical solution of the present invention is: an air conditioning energy-saving control method driven by user behavior analysis, comprising:

[0007] S1. Collecting user historical behavior and air conditioning control related data; the related data includes user operation record data of air conditioning, indoor and outdoor temperature and humidity data, air conditioning operation data, and information about the room where the air conditioning is located;

[0008] S2. Obtain key factors that affect user behavior from relevant data, analyze user behavior, and generate air conditioning control strategies;

[0009] The air conditioning control strategy specifically includes:

[0010] S21. Generate air conditioning control days per week based on air conditioning properties;

[0011] S22, generating an air conditioner on / off time for the air conditioner control day based on the air conditioner on / off time during the air conditioner control day;

[0012] S23, determining the automatic switch control mode of the air conditioner based on the user's historical behavior and combined with the indoor and outdoor temperature and humidity data;

[0013] S3. Based on the user's expectation of the air conditioning control strategy, modify the air conditioning control strategy.

[0014] The step S21 specifically includes:

[0015] S211, obtaining properties of an air conditioner; the properties of the air conditioner include a working air conditioner or a home air conditioner;

[0016] If the air conditioner is a working air conditioner, then proceed to step S212; if the air conditioner is a home air conditioner, then proceed to step S213;

[0017] S212, obtaining working day and holiday data of a designated area, and determining the air conditioning control day of each week as a working day;

[0018] S213, determining the air conditioning control day of each week as every day.

[0019] The step S22 specifically includes:

[0020] S221, collecting the user's air conditioner on / off time during each air conditioner control day in the target time period to form historical data;

[0021] S222: Calculate the average value of the on / off time of the air conditioner in the historical data, and determine the average value as the on / off time of the air conditioner on the air conditioning control day.

[0022] The step S23 specifically includes:

[0023] S231, obtaining indoor temperature data and outdoor temperature data, and determining a start threshold of the air conditioner;

[0024] S232: Generate a shut-down threshold for the air conditioner based on the indoor temperature data when the user turns off the air conditioner;

[0025] S233. If both the indoor temperature and the outdoor temperature reach the air conditioner's turn-on threshold, the air conditioner is automatically turned on and adjusted to a user's comfortable temperature range; if the indoor temperature reaches the air conditioner's turn-off threshold, the air conditioner is automatically turned off.

[0026] The step S3 specifically includes:

[0027] S31. The amendment to the air conditioning control day specifically includes any one or any combination of the following:

[0028] The first type: If the working air conditioner is turned on on a non-working day and there is a regularity in the target time period, then the turning-on day is added to the air conditioning control days of each week;

[0029] The second type: If the home air conditioner is not turned on on a certain day and there is a regularity in the target time period, then this day will be excluded from the air conditioning control days of the week;

[0030] S32. Modification of the air conditioner on / off time on the air conditioner control day, specifically including any one or any combination of the following:

[0031] The first method: obtaining the current indoor temperature and the time required to change the current indoor temperature to a comfortable temperature range after the air conditioner is turned on, and subtracting the air conditioner on time from the time required to change the current indoor temperature to a comfortable temperature range after the air conditioner is turned on, so as to correct the air conditioner on time;

[0032] The second method: obtaining the current indoor temperature and the time for which the current indoor temperature is maintained in the comfortable temperature range after the air conditioner is turned off, and subtracting the time for which the current indoor temperature is maintained in the comfortable temperature range from the air conditioner off time to correct the air conditioner off time;

[0033] S33, the modification of the automatic on / off control mode of the air conditioner specifically includes any one or any combination of the following:

[0034] The first type: if the user adjusts the on / off time of the air conditioner within the target time period, the on / off time adjusted by the user is used as the on / off time of the air conditioner;

[0035] The second type: if the user adjusts the comfortable temperature range after the air conditioner is turned on during the target time period, the temperature adjusted by the user is used as the comfortable temperature range;

[0036] The third type: if the user adjusts the control mode of the air conditioner within the target time period, the control mode adjusted by the user is used as the control mode of the air conditioner.

[0037] The operation record data of the air conditioner includes: operation time, operation type, and operation instruction;

[0038] The air conditioning operation data includes: air conditioning status, air conditioning operation model, wind speed, operation power, set temperature, and collection time;

[0039] The room information where the air conditioner is located includes: room volume.

[0040] In the indoor and outdoor temperature and humidity data, the calculation method of indoor temperature change is as follows:

[0041]

[0042] Q 补偿 =f(Q 制热 , Q 散热 ,ΔT,temp_out,humidity_out,temp_room,v)

[0043] Where: ΔT is the indoor temperature change, Q 制热 is the heat generated in the room per unit time, Q 散热 Q is the heat consumed in the room per unit time except for the heat absorbed by the air. 补偿 is the impact of the indoor environment per unit time and the heat that cannot be measured, ρ 空气 is the density of air, V is the volume of the room, C 空气 is the specific heat of air at normal pressure, temp_out is the outdoor temperature, humidity_out is the outdoor humidity, temp_room is the indoor temperature, ρ 空气 ×V×C 空气 The amount of heat required to raise the room by one degree;

[0044] Where: Q 制热 Including summer and winter conditions:

[0045] Q 制热(夏) =Q 辐照 +Q 传导 +Q 人 , Q 散热 =Q 空调 ;

[0046] Q 制热(冬) =Q 空调 +Q 辐照 +Q 人 , Q 散热 =Q 传导 ;

[0047] Where: Q 制热(夏) is the heat generated indoors in summer, Q 制热(冬) is the heat generated indoors in winter, Q 辐照 Q is the heat brought indoors by outdoor solar radiation. 传导 Q is the heat conducted by the temperature difference between indoor and outdoor walls. 人 is the indoor personnel data, Q 传导 The heat generated by the indoor occupants, Q 空调 The cooling / heating capacity of the air conditioner per unit time;

[0048] Q 空调 =power×eer×t;

[0049] Among them: power is the operating power of the air conditioner, eer is the energy efficiency ratio of the air conditioner, and t is the operating time of the air conditioner;

[0050] Q 辐照 =I×A×T;

[0051] Where: I is the solar radiation intensity, A is the window area, and T is the light transmittance;

[0052] Q 传导 =K×A×ΔY÷d;

[0053] Where: K is the thermal conductivity of the material, A is the heat transfer area, ΔY is the surface temperature difference, and d is the heat transfer distance;

[0054] Q 人 =n×α;

[0055] Where: n is the number of personnel, α is the personnel heat dissipation coefficient.

[0056] The key factors affecting the user behavior include: indoor temperature, outdoor temperature, outdoor humidity, set temperature, air conditioning operation mode, operation power, and room type.

[0057] An air conditioning energy-saving control system driven by user behavior analysis, the system is applied to the above method, the system comprising:

[0058] The data collection module is used to collect the user's historical behavior and air conditioning control related data; the related data includes the user's operation record data of the air conditioner, indoor and outdoor temperature and humidity data, air conditioning operation data, and information about the room where the air conditioner is located;

[0059] The air conditioning control strategy generation module is used to obtain the key factors that affect user behavior in the relevant data, analyze the user behavior, and generate the air conditioning control strategy;

[0060] The air conditioning control strategy specifically includes:

[0061] S21. Generate air conditioning control days per week based on air conditioning properties;

[0062] S22, generating an air conditioner on / off time for the air conditioner control day based on the air conditioner on / off time during the air conditioner control day;

[0063] S23, determining the automatic switch control mode of the air conditioner based on the user's historical behavior and combined with the indoor and outdoor temperature and humidity data;

[0064] The control strategy correction module is used to correct the air conditioning control strategy based on the user's expectations for the air conditioning control strategy.

[0065] An air conditioning energy-saving control device driven by user behavior analysis, the device comprising a processor and a memory;

[0066] The memory is used to store computer program code and transmit the computer program code to the processor;

[0067] The processor is used to execute the above-mentioned air conditioning energy-saving control method driven by user behavior analysis according to the instructions in the computer program code.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] In the present invention, an air-conditioning energy-saving control method, system and device driven by user behavior analysis, the method first collects user historical behavior and air-conditioning control related data, then determines the key factors affecting the user behavior, analyzes the user's behavior, generates an air-conditioning control strategy, and finally modifies the air-conditioning control strategy based on the user's expectations for the air-conditioning control strategy; in the application, this design collects and analyzes multi-dimensional data of users operating the air-conditioning, mines user habits and preferences, establishes a user behavior analysis model, and comprehensively considers the user's air-conditioning usage needs, thereby obtaining an energy-saving control strategy that is more in line with the user's actual needs, and has self-optimization and learning capabilities, and improves the user's usage experience by continuously updating and adjusting the control strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a flow chart of the method steps of the present invention.

[0071] Figure 2 It is a schematic diagram of the system structure of the present invention.

[0072] Figure 3 It is a schematic diagram of the device structure of the present invention.

[0073] In the figure: data collection module 1, air conditioning control strategy generation module 2, control strategy correction module 3, processor 4, memory 5, computer program code 51. DETAILED DESCRIPTION

[0074] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0075] Embodiment 1:

[0076] See also Figure 1 , an air conditioning energy-saving control method based on user behavior analysis, comprising:

[0077] S1. Collecting user historical behavior and air conditioning control related data; the related data includes user operation record data of air conditioning, indoor and outdoor temperature and humidity data, air conditioning operation data, and information about the room where the air conditioning is located;

[0078] Furthermore, the operation record data of the air conditioner is mainly infrared control signals received by the air conditioner controller device when the user operates the air conditioner, specifically including: operation time, operation type, and operation instruction;

[0079] The operation types include: turning on or off the air conditioning operation, mode switching, and temperature adjustment;

[0080] The mode switching includes: cooling, heating, dehumidification, air supply or automatic mode;

[0081] The air conditioning operation data is obtained through monitoring of the air conditioning controller device, specifically including: air conditioning status, air conditioning operation model, wind speed, operating power, set temperature, and collection time;

[0082] The room information where the air conditioner is located includes: room volume.

[0083] Furthermore, the indoor and outdoor temperature and humidity data are mainly obtained through professional meteorological APIs. By obtaining hourly weather data for the next 24 hours, it helps to adjust the air conditioning operation strategy in advance based on weather changes, so as to achieve more efficient energy utilization and a more comfortable indoor environment.

[0084] S2. Obtain key factors that affect user behavior from relevant data, analyze user behavior, and generate air conditioning control strategies;

[0085] Furthermore, before analyzing user behavior, it is first necessary to clarify the key factors that affect user behavior. The key factors mainly include indoor temperature, outdoor temperature, outdoor humidity, set temperature, air conditioning operation mode, operating power, and room type. Based on the obtained key factors, a big data model of user behavior is generated.

[0086] In this embodiment, the key factors affecting user behavior are determined by the Pearson correlation coefficient. By constructing a BP neural network and combining the historical data of user operation of air conditioners, the historical data of outdoor environment, the historical data of air conditioner operation, the information of the room where the air conditioner is located, etc., the user behavior analysis model and training are performed to construct a big data model of user behavior; the Pearson correlation coefficient calculation formula is as follows:

[0087]

[0088] The Pearson correlation coefficient can be used to quantify the correlation between different factors and user behavior, where x i and i denote the observed values ​​of variables x and y, respectively. and represent the means of variables x and y respectively.

[0089] Furthermore, the air conditioning control strategy specifically includes:

[0090] S21. Generate air conditioning control days per week based on air conditioning properties;

[0091] Furthermore, the step S21 specifically includes:

[0092] S211, obtaining properties of an air conditioner; the properties of the air conditioner include a working air conditioner or a home air conditioner;

[0093] If the air conditioner is a working air conditioner, then proceed to step S212; if the air conditioner is a home air conditioner, then proceed to step S213;

[0094] S212, obtaining working day and holiday data of a designated area, and determining the air conditioning control day of each week as a working day;

[0095] S213, determining the air conditioning control day of each week as every day.

[0096] S22, generating an air conditioner on / off time for the air conditioner control day based on the air conditioner on / off time during the air conditioner control day;

[0097] Furthermore, the step S22 specifically includes:

[0098] S221, collecting the user's air conditioner on / off time during each air conditioner control day in the target time period to form historical data;

[0099] S222: Calculate the average value of the on / off time of the air conditioner in the historical data, and determine the average value as the on / off time of the air conditioner on the air conditioning control day.

[0100] S23, determining the automatic switch control mode of the air conditioner based on the user's historical behavior and combined with the indoor and outdoor temperature and humidity data;

[0101] Furthermore, the step S23 specifically includes:

[0102] S231, obtaining indoor temperature data and outdoor temperature data, and determining a start threshold of the air conditioner;

[0103] S232: Generate a shut-down threshold for the air conditioner based on the indoor temperature data when the user turns off the air conditioner;

[0104] S233. If both the indoor temperature and the outdoor temperature reach the air conditioner's turn-on threshold, the air conditioner is automatically turned on and adjusted to a user's comfortable temperature range; if the indoor temperature reaches the air conditioner's turn-off threshold, the air conditioner is automatically turned off.

[0105] Furthermore, in the indoor and outdoor temperature and humidity data, the calculation method of the indoor temperature includes:

[0106]

[0107] Q 补偿 =f(Q 制热 , Q 散热 ,ΔT,temp_out,humidity_out,temp_room,v)

[0108] Where: ΔT is the indoor temperature change, Q 制热 is the heat generated in the room per unit time, Q 散热 Q is the heat consumed in the room per unit time except for the heat absorbed by the air. 补偿 is the impact of the indoor environment per unit time and the heat that cannot be measured, ρ 空气 is the density of air, V is the volume of the room, C 空气 is the specific heat of air at normal pressure, temp_out is the outdoor temperature, humidity_out is the outdoor humidity, temp_room is the indoor temperature, ρ 空气 ×V×C 空气 The amount of heat required to raise the room by one degree;

[0109] Where: Q 制热 Including summer and winter conditions:

[0110] Q 制热(夏) =Q 辐照 +Q 传导 +Q 人 , Q 散热 =Q 空调 ;

[0111] Q 制热(冬) =Q 空调 +Q 辐照 +Q 人 , Q 散热 =Q 传导 ;

[0112] Where: Q 制热(夏) is the heat generated indoors in summer, Q 制热(冬) is the heat generated indoors in winter, Q 辐照 Q is the heat brought indoors by outdoor solar radiation. 传导 Q is the heat conducted by the temperature difference between indoor and outdoor walls. 人 is the indoor personnel data, Q 传导 The heat generated by the indoor occupants, Q 空调 The cooling / heating capacity of the air conditioner per unit time;

[0113] Q 空调 =power×eer×t;

[0114] Among them: power is the operating power of the air conditioner, eer is the energy efficiency ratio of the air conditioner, and t is the operating time of the air conditioner;

[0115] Q 辐照 =I×A×T;

[0116] Where: I is the solar radiation intensity, A is the window area, and T is the light transmittance;

[0117] Q 传导 =K×A×ΔY÷d;

[0118] Where: K is the thermal conductivity of the material, A is the heat transfer area, ΔY is the surface temperature difference, and d is the heat transfer distance;

[0119] Q 人 =n×α;

[0120] Where: n is the number of personnel, α is the personnel heat dissipation coefficient.

[0121] Q 补偿 The prediction model is mainly established through the random forest regression algorithm based on the key influencing factors that affect the unmeasurable part of energy and historical data. The model can compensate for the unmeasurable part of energy and improve the accuracy of the calculation of room temperature changes.

[0122] S3. Based on the user's expectation of the air conditioning control strategy, modify the air conditioning control strategy.

[0123] The user's expectation of the air conditioning control strategy refers to whether the user repeatedly adjusts the air conditioning on / off time, set temperature, set mode, etc. after the air conditioning automatically starts control. If there are repeated adjustments, it indicates that the user has low expectations for the air conditioning control strategy. The system automatically modifies the control strategy according to the user's expectations, and the modified trigger threshold can be preset as needed.

[0124] Furthermore, the step S3 specifically includes:

[0125] S31. The amendment to the air conditioning control day specifically includes any one or any combination of the following:

[0126] The first type: If the working air conditioner is turned on on a non-working day and there is a regularity in the target time period, then the turning-on day is added to the air conditioning control days of each week;

[0127] The second type: If the home air conditioner is not turned on on a certain day and there is a regularity in the target time period, then this day will be excluded from the air conditioning control days of the week;

[0128] For example: For office air conditioners, set specific weekday modes except for statutory holidays, such as Monday to Friday or Monday to Saturday, as well as more flexible time combinations such as Monday, Wednesday, and Friday; normally it is assumed to be turned on on weekdays and not on holidays, but considering specific actual situations such as overtime and adjustment of holidays, practical considerations are made.

[0129] In specific applications, collect 24-hour air conditioning startup data on the day the air conditioner is turned on, and analyze the average startup time period;

[0130] For example: Analyze the average start and end time of the air conditioner within 2-3 days. If the average start time period is 12:00-18:00, set the air conditioner start time on the control day of each week to 12-18:00.

[0131] At the same time, you can add comprehensive considerations such as outdoor temperature and indoor temperature. Or if the temperature is 33 degrees on a certain day, it should be turned on in theory, but it has not been turned on for a long time, then you can consider whether the user's body temperature is not high, etc.

[0132] S32. Modification of the air conditioner on / off time on the air conditioner control day, specifically including any one or any combination of the following:

[0133] The first method: obtain the current indoor temperature and the time required to change the current indoor temperature to a comfortable temperature range after the air conditioner is turned on, subtract the time required to change the current indoor temperature to a comfortable temperature range after the air conditioner is turned on from the time required to change the current indoor temperature to a comfortable temperature range after the air conditioner is turned on, and correct the air conditioner turn-on time;

[0134] The second method: obtain the current indoor temperature and the time the current indoor temperature is maintained in the comfortable temperature range after the air conditioner is turned off, subtract the time the current indoor temperature is maintained in the comfortable temperature range from the air conditioner off time, and correct the air conditioner off time;

[0135] For example, if the current indoor temperature is 32 degrees, according to the previous temperature calculation formula, it takes 10 minutes to reduce the current indoor temperature to 26 degrees after the air conditioner is turned on, then the air conditioner will be turned on 10 minutes before the user's work start time of 8 o'clock, so that the user can ensure that the indoor temperature is suitable before arriving in the room, thereby improving the user's comfort experience. Similarly, the air conditioner can be turned off in advance before the user leaves the room, effectively avoiding energy waste and achieving energy saving goals.

[0136] S33, the modification of the automatic on / off control mode of the air conditioner specifically includes any one or any combination of the following:

[0137] The first type: if the user adjusts the on / off time of the air conditioner within the target time period, the on / off time adjusted by the user is used as the on / off time of the air conditioner;

[0138] For example: the current air conditioning start time is 8 o'clock, but because the user regularly arrives at the company 20 minutes earlier and turns on the air conditioner during a certain period, the system automatically adjusts the air conditioning start time to 7:40.

[0139] The second type: if the user adjusts the comfortable temperature range after the air conditioner is turned on during the target time period, the temperature adjusted by the user is used as the comfortable temperature range;

[0140] For example: the current user's comfortable temperature range is 22-26 degrees, but because the user has been unwell for a period of time and regularly raises the temperature by 2 degrees, the system will automatically adjust the air conditioner's comfort range to 24-28 degrees.

[0141] The third type: if the user adjusts the control mode of the air conditioner within the target time period, the control mode adjusted by the user is used as the control mode of the air conditioner.

[0142] For example: the current air conditioner's automatic start-up mode defaults to cooling, but as the indoor temperature changes, the indoor temperature makes the user's perceived temperature lower. During a certain period, the user repeatedly manually adjusts the control mode to air supply after running in cooling mode for 2 hours. The system then automatically learns to adjust the air conditioning mode to air supply after running in cooling mode for 2 hours.

[0143] The above is only an example of the correction mode in this article, but it is not limited to the above three. Different correction modes can be corrected individually or in any combination. By combining big data such as temperature, humidity, and user behavior analysis to make adaptive adjustments and corrections, the accuracy of the output control strategy can be continuously improved.

[0144] Embodiment 2:

[0145] See also Figure 2 , an air conditioning energy-saving control system based on user behavior analysis, the system is applied to the method described in Example 1, the system comprising:

[0146] Data collection module 1, used to collect user historical behavior and air conditioning control related data; the related data includes user operation record data of air conditioning, indoor and outdoor temperature and humidity data, air conditioning operation data, and information about the room where the air conditioning is located;

[0147] Air conditioning control strategy generation module 2, used to obtain key factors affecting user behavior from relevant data, analyze user behavior, and generate air conditioning control strategies;

[0148] The air conditioning control strategy specifically includes:

[0149] S21. Generate air conditioning control days per week based on air conditioning properties;

[0150] Further, it specifically includes:

[0151] S211, obtaining properties of an air conditioner; the properties of the air conditioner include a working air conditioner or a home air conditioner;

[0152] If the air conditioner is a working air conditioner, then proceed to step S212; if the air conditioner is a home air conditioner, then proceed to step S213;

[0153] S212, obtaining working day and holiday data of a designated area, and determining the air conditioning control day of each week as a working day;

[0154] S213, determining the air conditioning control day of each week as every day.

[0155] S22, generating an air conditioner on / off time for the air conditioner control day based on the air conditioner on / off time during the air conditioner control day;

[0156] Further, it specifically includes:

[0157] S221, collecting the user's air conditioner on / off time during each air conditioner control day in the target time period to form historical data;

[0158] S222: Calculate the average value of the on / off time of the air conditioner in the historical data, and determine the average value as the on / off time of the air conditioner on the air conditioning control day.

[0159] S23, determining the automatic switch control mode of the air conditioner based on the user's historical behavior and combined with the indoor and outdoor temperature and humidity data;

[0160] Further, it specifically includes:

[0161] S231, obtaining indoor temperature data and outdoor temperature data, and determining a start threshold of the air conditioner;

[0162] S232: Generate a shut-down threshold for the air conditioner based on the indoor temperature data when the user turns off the air conditioner;

[0163] S233. If both the indoor temperature and the outdoor temperature reach the air conditioner's turn-on threshold, the air conditioner is automatically turned on and adjusted to a user's comfortable temperature range; if the indoor temperature reaches the air conditioner's turn-off threshold, the air conditioner is automatically turned off.

[0164] A control strategy correction module 3, used to correct the air conditioning control strategy based on the user's expectations for the air conditioning control strategy;

[0165] Further, it specifically includes:

[0166] S31. The amendment to the air conditioning control day specifically includes any one or any combination of the following:

[0167] The first type: If the working air conditioner is turned on on a non-working day and there is a regularity in the target time period, then the turning-on day is added to the air conditioning control days of each week;

[0168] The second type: If the home air conditioner is not turned on on a certain day and there is a regularity in the target time period, then this day will be excluded from the air conditioning control days of the week;

[0169] S32. Modification of the air conditioner on / off time on the air conditioner control day, specifically including any one or any combination of the following:

[0170] The first method: obtain the current indoor temperature and the time required to change the current indoor temperature to a comfortable temperature range after the air conditioner is turned on, subtract the time required to change the current indoor temperature to a comfortable temperature range after the air conditioner is turned on from the time required to change the current indoor temperature to a comfortable temperature range after the air conditioner is turned on, and correct the air conditioner turn-on time;

[0171] The second method: obtain the current indoor temperature and the time the current indoor temperature is maintained in the comfortable temperature range after the air conditioner is turned off, subtract the time the current indoor temperature is maintained in the comfortable temperature range from the air conditioner off time, and correct the air conditioner off time;

[0172] S33, the modification of the automatic on / off control mode of the air conditioner specifically includes any one or any combination of the following:

[0173] The first type: if the user adjusts the on / off time of the air conditioner within the target time period, the on / off time adjusted by the user is used as the on / off time of the air conditioner;

[0174] The second type: if the user adjusts the comfortable temperature range after the air conditioner is turned on during the target time period, the temperature adjusted by the user is used as the comfortable temperature range;

[0175] The third type: if the user adjusts the control mode of the air conditioner within the target time period, the control mode adjusted by the user is used as the control mode of the air conditioner.

[0176] Embodiment 3:

[0177] See also Figure 3 , an air conditioning energy-saving control device driven by user behavior analysis, the device comprising a processor 4 and a memory 5;

[0178] The memory 5 is used to store computer program code 51 and transmit the computer program code 51 to the processor 4;

[0179] The processor 4 is used to execute the air conditioning energy saving control method based on user behavior analysis and driven according to the instructions in the computer program code 51 described in Example 1.

[0180] Generally speaking, the computer instructions for implementing the method of the present invention may be carried in any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media may include any computer-readable media, except for the signal itself that is temporarily propagating.

[0181] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EKROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0182] Computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, SMalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages, in particular, Python suitable for neural network computing and platform frameworks based on TensorFlow, PyTorch, etc. can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer or to an external computer (for example, using an Internet service provider to connect via the Internet) through any type of network, including a local area network (LAN) or a wide area network (WAN).

[0183] The above-mentioned device and non-temporary computer-readable storage medium can be found in the specific description of an air conditioning energy-saving control method driven by user behavior analysis and its beneficial effects, which will not be repeated here.

[0184] Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. An air conditioning energy-saving control method based on user behavior analysis, characterized in that: include: S1. Collecting user historical behavior and air conditioning control related data; the related data includes user operation record data of air conditioning, indoor and outdoor temperature and humidity data, air conditioning operation data, and information about the room where the air conditioning is located; S2. Obtain key factors that affect user behavior from relevant data, analyze user behavior, and generate air conditioning control strategies; The air conditioning control strategy specifically includes: S21. Generate air conditioning control days per week based on air conditioning properties; S22, generating an air conditioner on / off time for the air conditioner control day based on the air conditioner on / off time during the air conditioner control day; S23, determining the automatic switch control mode of the air conditioner based on the user's historical behavior and combined with the indoor and outdoor temperature and humidity data; S3, based on the user's expectations of the air conditioning control strategy, modify the air conditioning control strategy; The step S3 specifically includes: S31. The amendment to the air conditioning control day specifically includes any one or any combination of the following: The first type: If the working air conditioner is turned on on a non-working day and there is a regularity in the target time period, then the turning-on day is added to the air conditioning control days of each week; The second type: If the home air conditioner is not turned on on a certain day and there is a regularity in the target time period, then this day will be excluded from the air conditioning control days of the week; S32. Modification of the air conditioner on / off time on the air conditioner control day, specifically including any one or any combination of the following: The first method: obtaining the current indoor temperature and the time required to change the current indoor temperature to a comfortable temperature range after the air conditioner is turned on, and subtracting the air conditioner on time from the time required to change the current indoor temperature to a comfortable temperature range after the air conditioner is turned on, so as to correct the air conditioner on time; The second method: obtaining the current indoor temperature and the time for which the current indoor temperature is maintained in the comfortable temperature range after the air conditioner is turned off, and subtracting the time for which the current indoor temperature is maintained in the comfortable temperature range from the air conditioner off time to correct the air conditioner off time; S33, the modification of the automatic on / off control mode of the air conditioner specifically includes any one or any combination of the following: The first type: if the user adjusts the on / off time of the air conditioner within the target time period, the on / off time adjusted by the user is used as the on / off time of the air conditioner; The second type: If the user adjusts the comfortable temperature range after the air conditioner is turned on during the target time period, the temperature adjusted by the user is used as the comfortable temperature range; The third type: if the user adjusts the control mode of the air conditioner within the target time period, the control mode adjusted by the user is used as the control mode of the air conditioner.

2. The air conditioning energy saving control method based on user behavior analysis drive according to claim 1 is characterized in that: The step S21 specifically includes: S211, obtaining properties of an air conditioner; the properties of the air conditioner include a working air conditioner or a home air conditioner; If the air conditioner is a working air conditioner, proceed to step S212; if the air conditioner is a home air conditioner, proceed to step S213; S212, obtaining working day and holiday data of a designated area, and determining the air conditioning control day of each week as a working day; S213, determining the air conditioning control day of each week as every day.

3. The air conditioning energy saving control method based on user behavior analysis drive according to claim 2 is characterized in that: The step S22 specifically includes: S221, collecting the user's air conditioner on / off time during each air conditioner control day in the target time period to form historical data; S222: Calculate the average value of the on / off time of the air conditioner in the historical data, and determine the average value as the on / off time of the air conditioner on the air conditioning control day.

4. The air conditioning energy saving control method based on user behavior analysis drive according to claim 3 is characterized in that: The step S23 specifically includes: S231, obtaining indoor temperature data and outdoor temperature data, and determining a start threshold of the air conditioner; S232: Generate a shut-down threshold for the air conditioner based on the indoor temperature data when the user turns off the air conditioner; S233. If both the indoor temperature and the outdoor temperature reach the air conditioner's turn-on threshold, the air conditioner is automatically turned on and adjusted to a user's comfortable temperature range; if the indoor temperature reaches the air conditioner's turn-off threshold, the air conditioner is automatically turned off.

5. The air conditioning energy saving control method based on user behavior analysis drive according to claim 1 is characterized in that: The operation record data of the air conditioner includes: operation time, operation type, and operation instruction; The air conditioning operation data includes: air conditioning status, air conditioning operation model, wind speed, operation power, set temperature, and collection time; The room information where the air conditioner is located includes: room volume.

6. The air conditioning energy saving control method based on user behavior analysis drive according to claim 1, characterized in that: In the indoor and outdoor temperature and humidity data, the calculation method of indoor temperature change is as follows: ； ； in: is the indoor temperature change value, is the heat generated indoors per unit time, It is the heat consumed in the room per unit time except the heat absorbed by the air. The impact of the indoor environment per unit time and the heat that cannot be measured, is the density of air, is the room volume, is the specific heat of air at normal pressure, is the outdoor temperature, is the outdoor humidity, is the indoor temperature, The amount of heat required to raise the room by one degree; in: Including summer and winter conditions: ， ； ， ； in: The heat generated indoors in summer, The heat generated indoors in winter. The heat brought indoors by outdoor solar radiation. The heat conducted by the temperature difference between indoor and outdoor walls. The heat generated for the people inside the room, The cooling / heating capacity of the air conditioner per unit time; ； in: is the air conditioner operating power, is the air conditioning energy efficiency ratio, The operating time of the air conditioner; ； in: is the solar radiation intensity, is the window area, is the light transmittance; ； in: is the thermal conductivity of the material, is the heat transfer area, is the surface temperature difference, is the heat transfer distance; ； in: is the number of personnel, is the personnel heat dissipation coefficient.

7. The air conditioning energy saving control method based on user behavior analysis drive according to claim 1, characterized in that: The key factors affecting the user behavior include: indoor temperature, outdoor temperature, outdoor humidity, set temperature, air conditioning operation mode, operation power, and room type.

8. An air conditioning energy-saving control system driven by user behavior analysis, characterized in that: The system is applied to the method described in any one of claims 1 to 7, and the system comprises: A data collection module (1) is used to collect user historical behavior and air conditioning control related data; the related data includes user operation record data of the air conditioner, indoor and outdoor temperature and humidity data, air conditioning operation data, and information about the room where the air conditioner is located; The air conditioning control strategy generation module (2) is used to obtain key factors affecting user behavior from relevant data, analyze user behavior, and generate an air conditioning control strategy; The air conditioning control strategy specifically includes: S21. Generate air conditioning control days per week based on air conditioning properties; S22, generating an air conditioner on / off time for the air conditioner control day based on the air conditioner on / off time during the air conditioner control day; S23, determining the automatic switch control mode of the air conditioner based on the user's historical behavior and combined with the indoor and outdoor temperature and humidity data; The control strategy correction module (3) is used to correct the air conditioning control strategy based on the user's expectations for the air conditioning control strategy.

9. An air conditioning energy-saving control device driven by user behavior analysis, characterized in that: The device comprises a processor (4) and a memory (5); The memory (5) is used to store computer program code (51) and transmit the computer program code (51) to the processor (4); The processor (4) is used to execute the air conditioning energy saving control method based on user behavior analysis according to any one of claims 1 to 7 according to the instructions in the computer program code (51).

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