A personalized intelligent air conditioning control method, system, device and storage medium

Abstract

The present application provides a personalized intelligent control method, system, device and storage medium for air conditioning, which relates to the field of air conditioning control technology. The method includes: combining the first flow of people data and historical flow of people data in the target area within the first preset time period to predict the second flow of people data within the target time period; combining the outdoor temperature and the regional characteristics of the target area to determine the reference temperature of the target area within the target time period; combining the reference temperature and the second flow of people data to determine the temperature adjustment parameters of the target area within the target time period, and formulating the initial operation plan of the air conditioner in the target area according to the temperature adjustment parameters; determining the environmental load coefficient according to the environmental monitoring parameters of the target area, and generating the target operation plan according to the environmental load coefficient. The technical effect of the present application is: predicting environmental changes in advance and adjusting the air conditioning operation parameters in time to ensure that the indoor environment can reach a comfortable state in time.

Description

Technical Field

[0001] The present application relates to the field of air conditioning control technology, and in particular to a personalized intelligent air conditioning control method, system, device and storage medium. Background Art

[0002] In large public places such as shopping malls, the air conditioning system needs to respond to these changes in a timely manner to provide a comfortable shopping environment due to the frequent changes in the flow of people and the different temperature requirements in different areas. How to accurately grasp the trend of environmental changes and achieve rapid response of air conditioning control is a technical problem that needs to be solved urgently.

[0003] The existing technology usually adopts the method of air conditioning control based on real-time monitoring data, that is, the air conditioning parameters are adjusted only after the change of human flow or temperature is detected. Although the above method can respond to environmental changes, this passive control method has obvious lag, and it is impossible to predict environmental changes in advance and adjust the air conditioning operating parameters in time, resulting in the indoor environment not being able to reach a comfortable state in time. Summary of the invention

[0004] The present application provides a personalized intelligent air conditioning control method, system, device and storage medium for predicting environmental changes in advance and adjusting air conditioning operating parameters in a timely manner to ensure that the indoor environment can reach a comfortable state in a timely manner.

[0005] In a first aspect, the present application provides a personalized intelligent air conditioning control method, the method comprising: obtaining first pedestrian flow data of a target area of ​​a shopping mall within a first preset time period before a current moment in a current date, and historical pedestrian flow data of the target area within a second preset time period after the current moment in a historical date; combining the first pedestrian flow data and the historical pedestrian flow data, predicting second pedestrian flow data within a target time period, the target time period being the second preset time period after the current moment in the current date; obtaining the outdoor temperature of the shopping mall and the regional characteristics of the target area, and determining a reference temperature of the target area within the target time period in combination with the outdoor temperature and the regional characteristics; determining a temperature adjustment parameter of the target area within the target time period in combination with the reference temperature and the second pedestrian flow data, and formulating an initial operation plan of the air conditioning in the target area within the target time period based on the temperature adjustment parameter; obtaining environmental monitoring parameters of the target area, determining an environmental load coefficient of the target area based on the environmental monitoring parameters, and adjusting the initial operation plan based on the environmental load coefficient to generate a target operation plan for the air conditioning within the target time period.

[0006] By adopting the above technical solution, the first flow of people data and historical flow of people data are obtained to predict the future second flow of people data, which realizes the early prediction of environmental change trends; the reference temperature is determined in combination with the outdoor temperature and regional characteristics, and the temperature parameters are adjusted according to the predicted second flow of people data, ensuring the accuracy and foresight of temperature control; at the same time, the environmental monitoring parameters are introduced to calculate the environmental load coefficient, and the initial operation plan is dynamically optimized and adjusted, further improving the adaptability of the control plan. This predictive control strategy can predict environmental changes in advance and adjust the air conditioning operation parameters in time to ensure that the indoor environment can reach a comfortable state in time.

[0007] Optionally, the combination of the first pedestrian flow data and the historical pedestrian flow data to predict the second pedestrian flow data within the target duration includes: determining the average pedestrian flow data within a second preset time period after the current moment in the historical date based on the historical pedestrian flow data; determining the first pedestrian flow change trend within the target time period based on the average pedestrian flow data; obtaining the second pedestrian flow data change trend within the first preset time period based on the first pedestrian flow data, and predicting the third pedestrian flow change trend within the target time period based on the second pedestrian flow data change trend; weightedly fusing the first pedestrian flow change trend and the third pedestrian flow change trend to generate a target pedestrian flow change trend within the target time period; and predicting the second pedestrian flow data within the target time period based on the first pedestrian flow data and the target pedestrian flow change trend.

[0008] By adopting the above technical solution, the first flow rate change trend is obtained by analyzing the average flow rate data of the same period in history, reflecting the periodic change law of the target area; at the same time, the fourth flow rate change trend is predicted based on the latest first flow rate data, reflecting the current real-time change characteristics; the target flow rate change trend is obtained by weighted fusion of these two trends, taking into account both historical laws and real-time dynamics, making the prediction results more accurate and reliable. This multi-dimensional prediction method effectively balances the stability of historical data and the timeliness of real-time data, improves the accuracy of flow rate prediction, and provides a reliable data basis for the subsequent formulation of air conditioning control solutions.

[0009] Optionally, the combination of the outdoor temperature and the area characteristics to determine the baseline temperature of the target area within the target time period includes: determining an initial baseline temperature of the target area within the target time period based on the outdoor temperature; obtaining the business type of the target area based on the area characteristics; and adjusting the initial baseline temperature based on the temperature demand characteristics corresponding to the business type to determine the baseline temperature of the target area within the target time period.

[0010] By adopting the above technical solution, the initial reference temperature is first determined based on the outdoor temperature, taking into account the basic impact of the indoor and outdoor temperature difference on air conditioning control; the initial reference temperature is further adjusted according to the business type of the target area and its corresponding temperature demand characteristics, reflecting the personalized temperature requirements of different business scenarios. For example, the dining area and the clothing area have different temperature requirements due to their business characteristics and user activity patterns. This temperature determination method that takes regional characteristics into account makes the setting of the reference temperature more in line with actual use needs and improves the pertinence and rationality of air conditioning control.

[0011] Optionally, the combining of the reference temperature and the second crowd flow data to determine the temperature adjustment parameters of the target area within the target time length includes: determining the crowd density of the target area within the target time length based on the second crowd flow data; determining the target temperature adjustment amount corresponding to the crowd density based on a preset correspondence between the crowd density and the temperature adjustment amount; and performing a linear superposition operation on the reference temperature and the target temperature adjustment amount to determine the temperature adjustment parameters of the target area within the target time length.

[0012] By adopting the above technical solution, the predicted second flow data is converted into flow density, and the target temperature adjustment amount is determined according to the preset corresponding relationship, so as to establish a quantitative relationship between flow change and temperature adjustment; the reference temperature and the target temperature adjustment amount are further linearly superimposed to achieve precise adjustment of temperature parameters. This temperature adjustment method based on flow density not only takes into account the basic requirements of the reference temperature, but also quantifies the impact of changes in flow density on temperature into specific adjustment parameters, so that temperature control can adapt to regional characteristics and dynamically respond to changes in flow, thereby improving the accuracy and adaptability of temperature adjustment.

[0013] Optionally, formulating an initial operation plan for the air conditioner in the target area within the target time length based on the temperature adjustment parameters includes: determining the target supply air temperature of the air conditioner in the target area within the target time length based on the temperature adjustment parameters; determining the operating power of the air conditioner at the target supply air temperature based on the corresponding relationship between a preset supply air temperature and the operating power; determining operating parameters of the air conditioner based on the operating power, the operating parameters including at least one of a compressor speed, a fan speed and an air supply direction; and generating an initial operation plan for the air conditioner within the target time length based on the operating parameters.

[0014] By adopting the above technical solution, the temperature adjustment parameters are converted into specific target air supply temperatures, and the operating power is determined based on the preset corresponding relationship, thus establishing a correlation between the temperature demand and the air conditioning operating state; the operating power is further refined into specific operating parameters such as compressor speed, fan speed and air supply direction, thus achieving an accurate mapping from temperature demand to air conditioning control instructions. This progressive parameter conversion method systematically converts the temperature adjustment demand into an executable air conditioning operation plan, which not only ensures the accuracy of the control, but also improves the operating efficiency of the air conditioning system, making the temperature adjustment more flexible and controllable.

[0015] Optionally, the environmental monitoring parameters include measured temperature, measured humidity and measured carbon dioxide concentration, and determining the environmental load coefficient of the target area according to the environmental monitoring parameters includes: substituting the measured temperature, the measured humidity and the measured carbon dioxide concentration into a preset formula to determine the environmental load coefficient of the target area; wherein the preset formula is:

[0016] ; In the formula, L is the environmental load factor, T is the measured temperature, is the standard temperature, H is the measured humidity, is the standard humidity, C is the measured carbon dioxide concentration, is the standard carbon dioxide concentration, α is the temperature weight coefficient, β is the humidity weight coefficient, and γ is the carbon dioxide concentration weight coefficient.

[0017] By adopting the above technical solution, a quantitative index that comprehensively reflects the environmental status is constructed by weighting the deviations of the three key environmental parameters of measured temperature, measured humidity and measured carbon dioxide concentration from their standard values. The introduced temperature weight coefficient, humidity weight coefficient and carbon dioxide concentration weight coefficient can be adjusted according to the actual needs of different scenarios, making the calculation of environmental load more flexible and adaptable. This multi-parameter fusion evaluation method not only comprehensively considers multiple factors that affect the comfort of the indoor environment, but also provides an objective basis for environmental evaluation through quantitative calculation, providing a scientific decision-making basis for the dynamic adjustment of subsequent operation plans.

[0018] Optionally, adjusting the initial operation plan according to the environmental load coefficient to generate a target operation plan for the air conditioner within the target duration includes: determining the target operation power adjustment coefficient corresponding to the environmental load coefficient based on a preset correspondence between the environmental load coefficient and the operation power adjustment coefficient; arithmetically multiplying the target operation power adjustment coefficient by the operation power in the initial operation plan to obtain a target operation power; and updating the operation parameters in the initial operation plan according to the target operation power to generate a target operation plan for the air conditioner within the target duration.

[0019] By adopting the above technical solution, the environmental load coefficient is converted into an operating power adjustment coefficient, and it is multiplied by the operating power in the initial operating plan to obtain the target operating power, thus establishing a direct relationship between the environmental state and the air conditioning operation adjustment; the operating parameters are further updated based on the target operating power, thus achieving dynamic optimization of the air conditioning operation plan. This operating plan adjustment mechanism based on environmental load can accurately adjust the initial operating plan according to the real-time environmental state, so that the operation of the air conditioning system can better adapt to environmental changes, thereby improving the intelligence and environmental adaptability of the system operation.

[0020] In a second aspect, the present application provides a personalized intelligent control system for air conditioning, the system comprising: an acquisition module, a prediction module, a combination module, a formulation module and an output module; wherein the acquisition module is used to acquire first pedestrian flow data of a target area of ​​a shopping mall within a first preset time period before a current moment in a current date, and historical pedestrian flow data of the target area within a second preset time period after the current moment in a historical date; the prediction module is used to combine the first pedestrian flow data and the historical pedestrian flow data to predict the second pedestrian flow data within a target time period, wherein the target time period is the second preset time period after the current moment in the current date; the combination module is used to acquire the outdoor temperature of the shopping mall The method comprises the steps of: determining a base temperature of the target area within the target time period in combination with the outdoor temperature and the regional characteristics of the target area; the formulating module is used to determine the temperature adjustment parameters of the target area within the target time period in combination with the base temperature and the second pedestrian flow data, and formulating an initial operation plan of the air conditioner in the target area within the target time period according to the temperature adjustment parameters; and the output module is used to obtain the environmental monitoring parameters of the target area, determine the environmental load coefficient of the target area according to the environmental monitoring parameters, adjust the initial operation plan according to the environmental load coefficient, and generate a target operation plan of the air conditioner within the target time period.

[0021] In a third aspect, the present application provides an electronic device that adopts the following technical solution: it includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a computer program such as any of the above-mentioned personalized intelligent air conditioning control methods.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: a computer program that can be loaded by a processor and execute any of the above-mentioned personalized intelligent air-conditioning control methods is stored.

[0023] In summary, the present application includes at least one of the following beneficial technical effects:

[0024] By obtaining the first and historical passenger flow data to predict the future second passenger flow data, the environmental change trend can be predicted in advance; the reference temperature is determined in combination with the outdoor temperature and regional characteristics, and the temperature parameters are adjusted according to the predicted second passenger flow data to ensure the accuracy and foresight of temperature control; at the same time, the environmental monitoring parameters are introduced to calculate the environmental load coefficient, and the initial operation plan is dynamically optimized and adjusted, further improving the adaptability of the control plan. This predictive control strategy can predict environmental changes in advance and adjust the air conditioning operation parameters in time to ensure that the indoor environment can reach a comfortable state in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flowchart of a personalized intelligent air conditioner control method provided by an embodiment of the present application;

[0026] Figure 2 It is a structural schematic diagram of an air-conditioning personalized intelligent control system provided by an embodiment of the present application;

[0027] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0028] Description of the reference numerals: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0030] In the description of the embodiments of the present application, words such as "illustrative", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "illustrative", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "illustrative", "for example" or "for example" is intended to present related concepts in a concrete way.

[0031] Figure 1 Schematic diagram of a personalized intelligent air conditioning control method provided by the embodiment of the present application. Figure 1 As shown, the method includes S101-S105:

[0032] S101, obtaining first pedestrian flow data of a target area of ​​a shopping mall within a first preset time period before a current time on a current date, and historical pedestrian flow data of the target area within a second preset time period after the current time on a historical date.

[0033] In this embodiment, in order to realize intelligent control of the air conditioner, it is first necessary to obtain the human flow data of the target area as basic data support.

[0034] Specifically, by setting up infrared sensors, cameras and other crowd detection equipment at the entrances and exits of the target area of ​​the shopping mall, the number of people passing through the area is collected and recorded in real time. Among them, the first preset time can be set to 2 hours, that is, the real-time crowd flow data of the target area within 2 hours before the current moment is obtained, which is recorded as the first crowd flow data. At the same time, the system will also store the crowd flow data of the target area for at least one year in the past, which is recorded as historical crowd flow data. These historical crowd flow data are stored in the database at a frequency of every 5 minutes, and include changes in crowd flow under different time periods and different weather conditions.

[0035] For example, if the current time is 3:00 p.m. on January 6, 2024, the first passenger flow data is the real-time passenger flow data from 1:00 p.m. to 3:00 p.m. on that day; the historical passenger flow data includes the passenger flow records from 3:00 p.m. to 5:00 p.m. every day in the target area from January 2023 to January 2024. By obtaining these two types of data, we can not only grasp the latest passenger flow change trend in the target area, but also analyze the periodic change law of passenger flow in combination with historical data, providing a reliable data basis for subsequent passenger flow prediction.

[0036] It should be noted that the target area refers to an area with relatively independent functions in a shopping mall, such as a brand area, a dining area, a leisure and entertainment area, etc. Each target area is usually equipped with an independent air-conditioning system. By obtaining traffic data by area, personalized control of regional air conditioning can be achieved more accurately. The specific value of the first preset duration can be adjusted according to the actual application scenario, but it should not be too short or too long: too short will result in insufficient data and affect the accuracy of trend judgment; too long may reduce the reference value of historical data for the current situation.

[0037] S102, combining the first passenger flow data and the historical passenger flow data, predicting the second passenger flow data within a target time period, where the target time period is a second preset time period after the current time on the current date.

[0038] In this embodiment, in order to adjust the air-conditioning operating parameters in advance to adapt to the future changes in the flow of people, it is necessary to predict the flow of people in the target area over a period of time in the future.

[0039] Specifically, the second preset time can be set to 1 hour, that is, the pedestrian flow data within 1 hour after the current moment is predicted and recorded as the second pedestrian flow data. The prediction process first analyzes the average pedestrian flow data of the historical same period corresponding to the second preset time (for example, 3 to 4 pm every day) based on the historical pedestrian flow data. By statistically analyzing these historical data, the first pedestrian flow change trend of the period can be obtained, which reflects the regular pedestrian flow change law of the target area in the period.

[0040] At the same time, the system will analyze the real-time flow data within the first preset time period (i.e. the previous 2 hours) before the current moment to obtain the change trend of the second flow data. Based on this trend, the time series prediction algorithm is used to predict the change trend of the third flow in the next hour. This prediction fully considers the impact of the current actual situation on future flow. In order to improve the prediction accuracy, the system weightedly merges the first flow change trend and the third flow change trend, where the historical trend weight is 0.3 and the real-time prediction trend weight is 0.7, so as to obtain a more realistic target flow change trend.

[0041] For example, if the current time is 3 pm, the system first analyzes the average flow of people and its changing trend from 3 pm to 4 pm every day in the historical data, and predicts the change trend of the flow of people from 3 pm to 4 pm based on the real-time data from 1 pm to 3 pm on the same day. By weighted fusion of these two trends, a more accurate prediction result is obtained. On this basis, combined with the first flow of people data (i.e. the current flow of people base) and the change trend of the target flow of people, the specific flow of people data from 3 pm to 4 pm, i.e. the second flow of people data, can be predicted.

[0042] Based on the above embodiment, as an optional implementation, in S102, combining the first human flow data and the historical human flow data, predicting the second human flow data within the target time after the current moment specifically includes S21-S25:

[0043] S21, determining the average passenger flow data within a second preset time period after the current time in the historical date based on the historical passenger flow data.

[0044] In this embodiment, in order to accurately predict the changes in the flow of people in the future, the system adopts a method that combines historical data analysis and real-time data prediction. First, the system extracts historical data with the same date type (weekday, weekend or holiday) and the same time period as the current date from the historical flow database. For example, to predict the flow of people from 10:00 to 11:00 am on Tuesday, extract all the historical data from 10:00 to 11:00 am on Tuesday in the past few months. By calculating the arithmetic mean of these historical data, the average flow of people in the same historical period corresponding to the second preset time length (i.e., the prediction time length, which is 1 hour in this example) is obtained.

[0045] S22, determining a first passenger flow change trend within a target time period according to the average passenger flow data.

[0046] Based on the average passenger flow data obtained, the system analyzes the changing rules of passenger flow within the target time and obtains the first passenger flow change trend. The specific method is to calculate the passenger flow change rate at each time point relative to the starting time point. For example, if the average passenger flow data for a period of time shows that there are 200 people at 10:00, 300 people at 10:30, and 250 people at 11:00, two change rates can be obtained: 50% increase from 10:00 to 10:30, and 16.7% decrease from 10:30 to 11:00. These change rates constitute the first passenger flow change trend, reflecting the typical change pattern of passenger flow during this period.

[0047] S23, obtaining a change trend of second human flow data within a first preset time period according to the first human flow data, and predicting a change trend of third human flow data within a target time period according to the change trend of the second human flow data.

[0048] The system analyzes the real-time flow data within the first preset time (i.e., a period of time before the current moment, such as 30 minutes) and calculates the change trend of the second flow data. By extrapolating this trend, it predicts the possible flow changes within the future target time, i.e., the third flow change trend. For example, if the flow of people shows a trend of increasing by 20% every 15 minutes in the past 30 minutes, the system will predict the change trend in the next hour based on this growth rate.

[0049] S24, weighted fusion of the first passenger flow change trend and the third passenger flow change trend to generate a target passenger flow change trend within a target duration.

[0050] In order to improve the accuracy of prediction, the system weightedly integrates the first trend of passenger flow change (historical law) and the third trend of passenger flow change (real-time prediction). The specific method is to set weight coefficients w1 and w2 (w1+w2=1), which represent the credibility of historical data and real-time data respectively. Generally, when real-time data shows obvious persistence characteristics, the value of w2 will be increased; when real-time data fluctuates greatly, the value of w1 will be increased. The final target passenger flow change trend is obtained through weighted calculation. For example, if w1=0.4, w2=0.6, the historical trend at a certain point in time shows an increase of 30%, and the real-time trend predicts an increase of 50%, then the target trend at that point in time is an increase of 42% (0.4×30%+0.6×50%).

[0051] S25, based on the first passenger flow data, predict the second passenger flow data within the target time period according to the target passenger flow change trend.

[0052] Finally, based on the first flow data (i.e., the current actual flow), the system calculates the predicted flow at each time point within the target duration according to the target flow change trend, i.e., the second flow data. For example, if the current flow is 200 people and the target trend shows a 42% increase in the next 30 minutes, the predicted flow after 30 minutes will be 284 people. In this way, the predicted flow value within the entire target duration can be obtained.

[0053] S103, obtaining the outdoor temperature of the shopping mall and the regional characteristics of the target area, and determining a reference temperature of the target area within the target time period in combination with the outdoor temperature and the regional characteristics.

[0054] In this embodiment, in order to determine the appropriate air conditioning temperature setting value for the target area, two key factors need to be considered comprehensively: outdoor temperature and regional characteristics. First, the outdoor temperature data is collected in real time by a temperature sensor installed outside the shopping mall. According to the outdoor temperature, the initial reference temperature of the target area within the target duration is determined using a preset indoor and outdoor temperature difference calculation formula.

[0055] Specifically, when the outdoor temperature is higher than 28°C, the initial reference temperature is set to the outdoor temperature minus 6°C; when the outdoor temperature is between 20°C and 28°C, the initial reference temperature is set to 24°C; when the outdoor temperature is lower than 20°C, the initial reference temperature is set to the outdoor temperature plus 2°C. This setting method takes into account both the human body's ability to adapt to temperature differences and the need for energy saving.

[0056] At the same time, the system will obtain the regional characteristic information of the target area. Regional characteristics mainly include the location information, spatial dimensions, functional attributes, etc. of the area. By analyzing these characteristics, the system can determine the business type of the target area, such as catering area, clothing area, electronic product area, etc. Different business types have different temperature requirements due to their operating characteristics and customer activity characteristics. For example, the temperature requirement of the catering area will be lower than that of the clothing area due to the frequent activities of personnel and the heat generated by cooking utensils; the electronic product area needs to maintain a lower temperature to ensure stable operation of the equipment.

[0057] Based on the temperature demand characteristics of different business types, the system will adjust the initial benchmark temperature accordingly. The specific adjustment rules are: the dining area will be reduced by 1.5°C based on the initial benchmark temperature, the clothing area will maintain the initial benchmark temperature unchanged, the electronic product area will be reduced by 2°C, and the leisure and entertainment area will be adjusted from -1°C to +1°C according to the intensity of exercise. For example, if the current outdoor temperature is 30°C and the target area is the dining area, the initial benchmark temperature is 24°C (30°C-6°C), and the final benchmark temperature after considering the business characteristics is 22.5°C (24°C-1.5°C).

[0058] Based on the above embodiment, as an optional time-dead mode, in S103, combining the outdoor temperature and the regional characteristics, determining the reference temperature of the target area within the target time specifically includes S31-S33:

[0059] S31, determining an initial reference temperature of a target area within a target duration according to the outdoor temperature.

[0060] S32, obtaining the business type of the target area according to the regional characteristics.

[0061] S33, adjusting the initial reference temperature according to the temperature demand characteristics corresponding to the business type, and determining the reference temperature of the target area within the target time.

[0062] In this embodiment, in order to make the air-conditioning control better adapt to different environments and business requirements, the system adopts a multi-level temperature determination method. First, the system determines the initial reference temperature based on the outdoor temperature. The specific method is to determine the reasonable temperature difference between indoors and outdoors through a preset temperature difference mapping table. For example, when the outdoor temperature is 26-28°C, the recommended temperature difference is 2°C; when the outdoor temperature is 28-30°C, the recommended temperature difference is 3°C; when the outdoor temperature is 30-32°C, the recommended temperature difference is 4°C; when the outdoor temperature is higher than 32°C, the recommended temperature difference is 5°C. The initial reference temperature is obtained by subtracting the corresponding temperature difference value from the outdoor temperature. This setting takes into account the human body's ability to adapt to temperature differences and avoids discomfort caused by excessive temperature differences between indoors and outdoors.

[0063] After determining the initial reference temperature, the system needs to determine the specific business type based on regional characteristics. Regional characteristics include spatial location, functional positioning, commodity type and other information. The system has pre-established a database of correspondence between regional characteristics and business types, and determines the business type of the target area through feature matching. For example, the area on the first floor of a shopping mall that displays high-end clothing may be identified as a "boutique clothing area"; the area on the first underground floor that sells fresh food may be identified as a "fresh supermarket area"; the area in the atrium of a shopping mall with rest seats may be identified as a "customer rest area."

[0064] Different business types have their own specific temperature demand characteristics, which are mainly based on factors such as customer activity characteristics, product preservation requirements and residence time. The system has established a corresponding relationship table between business type and temperature adjustment amount. For example, for the boutique clothing area, considering that customers need to try on clothes and stay for a long time, the temperature adjustment amount is +1°C; for the fresh supermarket area, considering the food preservation needs and the fast flow of customers, the temperature adjustment amount is -1°C; for the customer rest area, considering the people sitting and staying, the temperature adjustment amount is +0.5°C. The system superimposes these adjustments with the initial reference temperature to obtain the final reference temperature.

[0065] S104, combining the reference temperature and the second passenger flow data, determining the temperature adjustment parameters of the target area within the target time, and formulating an initial operation plan for the air conditioner in the target area within the target time according to the temperature adjustment parameters.

[0066] In this embodiment, in order to accurately control the operation of the air conditioner and provide a comfortable indoor environment, it is necessary to dynamically adjust the reference temperature according to the crowd density and formulate a corresponding air conditioner operation plan. First, the system calculates the crowd density based on the second crowd flow data and the area of ​​the target area. Specifically, the predicted crowd flow is divided by the effective area of ​​the target area (unit: square meters) to obtain the crowd density value per square meter. Since the human body emits heat, the change in crowd density directly affects the heat load of the area, so the reference temperature needs to be adjusted accordingly according to the crowd density.

[0067] The system has pre-established a corresponding relationship table between crowd density and temperature adjustment. For example, when the crowd density is less than 0.1 people / square meter, the temperature adjustment is 0℃; when the crowd density is 0.1-0.3 people / square meter, the temperature adjustment is -0.5℃; when the crowd density is 0.3-0.5 people / square meter, the temperature adjustment is -1.0℃; when the crowd density is 0.5-0.8 people / square meter, the temperature adjustment is -1.5℃; when the crowd density is greater than 0.8 people / square meter, the temperature adjustment is -2.0℃. The target temperature adjustment is obtained by looking up the table based on the calculated crowd density, and the adjustment is linearly superimposed with the reference temperature to obtain the temperature adjustment parameters of the target area within the target time.

[0068] For example, if the base temperature of a clothing area is 24°C, the predicted flow of people in the next hour is 300 people, and the area is 500 square meters, then the flow density is 0.6 people / square meter, the corresponding temperature adjustment amount is -1.5°C, and the final temperature adjustment parameter is 22.5°C (24°C-1.5°C). Based on the temperature adjustment parameter, the system further formulates the initial operation plan of the air conditioner. First, the target supply air temperature is determined according to the temperature adjustment parameter, and the supply air temperature is usually set to the temperature adjustment parameter minus 2-3°C. On this basis, query the corresponding relationship table between the preset supply air temperature and the operating power to determine the required operating power.

[0069] After the operating power is determined, the system will further set specific operating parameters, including compressor speed, fan speed and air supply direction. For example, when a larger cooling capacity is required, the compressor speed will be increased accordingly; according to the air supply temperature and the spatial characteristics of the target area, the fan speed will be adjusted to ensure the appropriate air supply speed; at the same time, the air supply direction will be optimized based on the regional layout characteristics to ensure uniform distribution of cold air. These operating parameters together constitute the initial operation plan of the air conditioner within the target duration.

[0070] Based on the above embodiment, as an optional implementation, in S104, combining the reference temperature and the second pedestrian flow data, determining the temperature adjustment parameters of the target area within the target time specifically includes S41-S43:

[0071] S41, determining the crowd density of the target area within the target time period according to the second crowd flow data.

[0072] In this embodiment, in order to enable the air conditioning control to accurately respond to the heat load impact caused by the change in the flow of people, the system needs to incorporate the flow of people into the temperature adjustment calculation process. First, the system calculates the flow density based on the predicted second flow data and the area of ​​the target area. The specific calculation method is to divide the predicted flow of people by the area of ​​the area to obtain the population density value per unit area. For example, if the area of ​​a certain area is 200 square meters and the predicted flow of people is 300 people, the flow density is 1.5 people / square meter. The system will continuously calculate the change in flow density within the target time length at a preset time interval (such as 15 minutes).

[0073] S42, determining a target temperature adjustment amount corresponding to the crowd density based on a preset correspondence between the crowd density and the temperature adjustment amount.

[0074] Based on a large amount of experimental data and theoretical analysis, the system has established a mapping relationship table between crowd density and temperature adjustment. This relationship mainly considers the impact of factors such as human heat dissipation and personnel activities on the heat load of the space. For example, when the crowd density is 0-0.5 people / square meter, the temperature adjustment amount is 0℃; when it is 0.5-1.0 people / square meter, the temperature adjustment amount is -0.5℃; when it is 1.0-1.5 people / square meter, the temperature adjustment amount is -1.0℃; when it is 1.5-2.0 people / square meter, the temperature adjustment amount is -1.5℃; when the crowd density is greater than 2.0 people / square meter, the temperature adjustment amount is -2.0℃. A negative temperature adjustment amount means that the target temperature needs to be lowered to offset the heat increase caused by the crowd flow.

[0075] S43, performing a linear superposition operation on the reference temperature and the target temperature adjustment amount to determine the temperature adjustment parameter of the target area within the target time period.

[0076] After determining the target temperature adjustment amount, the system will perform a linear superposition operation on it and the reference temperature determined in the previous step to obtain the final temperature adjustment parameter. The specific calculation formula is: temperature adjustment parameter = reference temperature + target temperature adjustment amount. For example, if the reference temperature at a certain moment is 28°C and the crowd density is 1.5 people / square meter, the corresponding temperature adjustment amount is -1.5°C, then the temperature adjustment parameter at that moment is 26.5°C. The system will perform similar calculations for each time point within the target duration to generate a complete sequence of temperature adjustment parameters.

[0077] It should be noted that the correspondence between crowd density and temperature adjustment is not a simple linear relationship, but is based on a large amount of experimental data and actual operating experience. The system will continuously optimize this correspondence by collecting actual operating data and analyzing the temperature adjustment effect under different crowd densities. At the same time, considering the delay in the personnel adaptation process, the change of temperature adjustment parameters needs to have a certain smoothness. Therefore, when calculating the temperature adjustment parameters at adjacent time points, the system will control the temperature change rate within a reasonable range, usually not exceeding 0.5℃ / 15 minutes.

[0078] Based on the above embodiment, as an optional implementation, in S104, formulating an initial operation plan for the air conditioner in the target area within the target duration according to the temperature adjustment parameters specifically includes S51-S54:

[0079] S51, determining a target air supply temperature of the air conditioner in the target area within a target time period according to the temperature adjustment parameter.

[0080] In this embodiment, in order to convert the temperature adjustment demand into a specific air conditioning operation instruction, the system needs to determine the air conditioning operation strategy through a series of parameter conversions. First, the system determines the target supply air temperature based on the temperature adjustment parameters. The calculation of the target supply air temperature needs to consider factors such as the difference between the target temperature and the current temperature, the relationship between the cooling capacity and the supply air temperature. For example, if the regional temperature is to be reduced from 28°C to 26.5°C, the system may set the target supply air temperature to 17°C based on the heat transfer efficiency. The specific calculation uses the heat balance equation, taking into account parameters such as the return air temperature, target temperature and cooling efficiency, and obtains the optimal supply air temperature value through iterative calculation.

[0081] S52: Determine the operating power of the air conditioner at the target air supply temperature based on the corresponding relationship between the preset air supply temperature and the operating power.

[0082] After determining the target air supply temperature, the system needs to determine the required operating power based on the correspondence between the preset air supply temperature and the operating power. This correspondence is usually determined by the performance curve of the air conditioner, taking into account technical parameters such as cooling capacity and energy efficiency ratio. For example, when the target air supply temperature is 17°C, if the rated power of the air conditioner is 5kW, it may need to run at 80% of the rated power, that is, 4kW of operating power, according to the performance curve. The system will calculate the power demand curve for the corresponding period based on the changes in the temperature adjustment parameters within the target duration.

[0083] S53, determining operating parameters of the air conditioner according to the operating power, the operating parameters including at least one of a compressor speed, a fan speed and an air supply direction.

[0084] After determining the operating power, the system converts it into specific operating parameters. This includes key control parameters such as compressor speed, fan speed and air supply direction. The compressor speed mainly affects the cooling capacity and is directly related to the operating power. For example, an operating power of 4kW may correspond to an operating frequency of 45Hz for the compressor. The fan speed needs to take into account noise control and comfort requirements while ensuring that the air supply volume meets the demand. Usually, variable frequency speed regulation is adopted, such as setting it to medium speed operation of 800rpm. The setting of the air supply direction needs to consider the regional layout and temperature distribution uniformity, and horizontal air supply or 45-degree angle air supply may be adopted.

[0085] S54: Generate an initial operation plan for the air conditioner within the target duration according to the operation parameters.

[0086] Based on the above operating parameters, the system generates a complete initial operating plan, including a specific sequence of control instructions at each time point. This plan uses time as the axis and specifies in detail the change process of various parameters of the air conditioner within the target duration. For example, in the cooling stage, high wind speed may be used for rapid adjustment first, and then switched to medium wind speed for maintenance when approaching the target temperature; in the temperature maintenance stage, low wind speed operation may be used to save energy. The entire plan needs to ensure the smoothness of parameter changes to avoid increased energy consumption and decreased comfort caused by frequent switching.

[0087] It should be noted that the setting of operating parameters requires comprehensive consideration of multiple factors. The first is the cooling effect, ensuring that the expected temperature regulation target can be achieved; the second is energy efficiency, minimizing energy consumption while meeting the cooling demand; the third is comfort requirements, including air flow, noise control, etc.; and finally, the operating reliability of the equipment must be considered to avoid frequent start-stop and excessive load.

[0088] In addition, the system has established a parameter self-optimization mechanism. By collecting actual operation data, analyzing temperature change response characteristics, energy consumption and user feedback, the system continuously optimizes various parameter settings. For example, if a certain parameter combination is found to significantly reduce energy consumption while ensuring comfort, the system will automatically adjust the parameter configuration plan. At the same time, considering that the characteristics of the equipment may change over time, the system will also regularly update the performance curve and parameter correspondence to ensure continuous optimization of the control effect.

[0089] S105, obtaining environmental monitoring parameters of the target area, determining the environmental load coefficient of the target area according to the environmental monitoring parameters, adjusting the initial operation plan according to the environmental load coefficient, and generating a target operation plan for the air conditioner within a target duration.

[0090] In this embodiment, in order to make the air conditioning operation plan better adapt to the actual environmental conditions, the system needs to monitor and dynamically adjust the environmental parameters of the target area in real time. Specifically, multiple environmental monitoring sensors are set in the target area to collect environmental monitoring parameters such as temperature, humidity and carbon dioxide concentration in real time. These sensors are usually evenly distributed in different locations in the target area to ensure the representativeness of the data. Among them, the measurement accuracy of the temperature sensor is ±0.1℃, the measurement accuracy of the humidity sensor is ±2%RH, and the measurement accuracy of the carbon dioxide concentration sensor is ±50ppm.

[0091] In order to comprehensively evaluate the environmental conditions, the system uses a preset formula to calculate the environmental load factor L. The formula is: ; In the formula, L is the environmental load factor, T is the measured temperature, is the standard temperature, H is the measured humidity, is the standard humidity, C is the measured carbon dioxide concentration, is the standard carbon dioxide concentration, α is the temperature weight coefficient, β is the humidity weight coefficient, and γ is the carbon dioxide concentration weight coefficient.

[0092] The preset formula is divided into three parts from a mathematical structure, which respectively reflect the impact of temperature, humidity and carbon dioxide concentration on environmental load. The formula consists of three parts, the first part is: , where the absolute difference between the measured temperature and the standard temperature is used to measure the degree of temperature deviation. The absolute value is used because the environmental load will increase whether the temperature is higher or lower than the standard value. Multiplying by the weight coefficient α is to reflect the importance of the temperature factor in the overall environmental assessment. Temperature deviation directly affects the thermal balance and comfort of the human body and is the most critical parameter in environmental control. Therefore, its weight coefficient is usually set to the maximum value of 0.5.

[0093] The second part is: , reflecting the impact of humidity on environmental load. The absolute difference between the measured humidity and the standard humidity is also used to calculate the deviation. Humidity will affect the heat dissipation process and body temperature of the human body, but its impact is second only to temperature. Therefore, the weight coefficient β is set to 0.3.

[0094] The third part is: , represents the contribution of air quality to environmental load. Carbon dioxide concentration is an important indicator for measuring air quality, and its deviation is also calculated using absolute difference. Since carbon dioxide concentration mainly affects air freshness and has a relatively small direct impact on human comfort, it is given the smallest weight coefficient γ=0.2. The three parts are linearly superimposed to obtain the final environmental load coefficient. This linear weighted model not only ensures the simplicity of calculation, but also can better reflect the comprehensive impact of various environmental factors, which is convenient for real-time calculation and engineering implementation.

[0095] For example, if the actual temperature of the target area is 26°C, the relative humidity is 65%, and the carbon dioxide concentration is 1000ppm at a certain moment, the environmental load coefficient L = 0.5×|26-24|+0.3×|65-55|+0.2×|1000-800| / 100=1+3+0.4=4.4. The larger the environmental load coefficient, the more the current environmental conditions deviate from the ideal state, and the air conditioning operation plan needs to be adjusted to a greater extent.

[0096] The system pre-establishes the corresponding relationship between the environmental load coefficient and the operating power adjustment coefficient. For example, when the environmental load coefficient is between 0-2, the operating power adjustment coefficient is 1.0; when it is between 2-4, it is 1.1; when it is between 4-6, it is 1.2; when it is between 6-8, it is 1.3; when it is greater than 8, it is 1.4. According to the calculated environmental load coefficient, the target operating power adjustment coefficient is obtained by looking up the table, and the coefficient is multiplied by the operating power in the initial operation plan to obtain the adjusted target operating power.

[0097] Based on the target operating power, the system adjusts the operating parameters accordingly. For example, when the environmental load factor is 4.4, the corresponding operating power adjustment factor is 1.2. If the compressor operating power of the initial operating plan is 2000W, the adjusted target operating power is 2400W. The system then updates the compressor speed, fan speed and other operating parameters to generate the final target operating plan.

[0098] Based on the above embodiment, as an optional implementation, in S105, adjusting the initial operation plan according to the environmental load factor to generate a target operation plan for the air conditioner within the target duration specifically includes S61-S63:

[0099] S61, determining a target operating power adjustment coefficient corresponding to the environmental load coefficient based on a preset corresponding relationship between the environmental load coefficient and the operating power adjustment coefficient.

[0100] In this embodiment, in order to make the air-conditioning operation better adapt to the influence of comprehensive environmental factors, the system needs to optimize and adjust the initial operation plan according to the environmental load coefficient. First, the system determines the adjustment parameters based on the pre-established mapping relationship between the environmental load coefficient and the operating power adjustment coefficient. This mapping relationship reflects the power adjustment range required under different environmental load levels, and is usually expressed in the form of a piecewise function. For example, when the environmental load coefficient is in the range of 0.8-1.2, the operating power adjustment coefficient is linearly related to it, which can be expressed as: adjustment coefficient = 0.5 × environmental load coefficient + 0.5; when the environmental load coefficient is less than 0.8, the adjustment coefficient is fixed at 0.9; when the environmental load coefficient is greater than 1.2, the adjustment coefficient is fixed at 1.1. This setting ensures a sensitive response to environmental changes and avoids system instability caused by over-adjustment.

[0101] S62, arithmetically multiplying the target operating power adjustment coefficient by the operating power in the initial operating plan to obtain the target operating power.

[0102] After determining the target operating power adjustment coefficient, the system performs arithmetic multiplication of it with the operating power in the initial operating plan to obtain the optimized target operating power. For example, if the power of the initial operating plan at a certain moment is 4kW, and the environmental load factor is 1.1, the corresponding adjustment coefficient is 1.05, then the optimized target operating power is 4.2kW. The system will perform similar calculations for each time point within the target duration to generate a complete power adjustment sequence. This adjustment method can adjust the air conditioner output power in a timely manner according to changes in the environmental load, and achieve rapid response to environmental changes.

[0103] S63, updating the operating parameters in the initial operating plan according to the target operating power, and generating a target operating plan for the air conditioner within the target duration.

[0104] Based on the calculated target operating power, the system needs to update the various operating parameters in the initial operating plan accordingly. This includes recalculating parameters such as compressor speed, fan speed, and air supply direction. For example, when the operating power increases from 4kW to 4.2kW, the compressor frequency may need to be increased from 45Hz to 47Hz, and the fan speed may need to be increased from 800rpm to 840rpm. During the parameter update process, the system will ensure the matching between various parameters to maintain the overall operating efficiency of the air-conditioning system. At the same time, considering the needs of equipment protection and energy efficiency optimization, the system will control the rate and range of parameter adjustment within a reasonable range.

[0105] It should be noted that the adjustment of operating parameters needs to follow a specific priority principle. Usually, the compressor speed is adjusted first to change the cooling capacity, followed by adjusting the fan speed to optimize the air supply effect, and finally considering adjusting the air supply direction to improve the air flow distribution. This adjustment strategy can ensure the control effect and minimize energy consumption. For example, when a small increase in cooling capacity is required, it is first achieved by increasing the compressor speed to avoid the increase in energy consumption caused by adjusting multiple parameters at the same time.

[0106] In addition, the system has established a smooth transition mechanism for parameter adjustment. When the environmental load factor changes, the relevant parameters will not change suddenly, but will be gradually adjusted to the target value within the preset time window. For example, power adjustment may be completed smoothly within 3-5 minutes, avoiding the decrease in comfort and equipment stress caused by drastic changes. At the same time, the system will monitor the adjustment effect in real time, and if it is found that some parameter combinations are not effective, it will be fine-tuned and optimized in time.

[0107] Based on the above method, the present application also discloses a personalized intelligent control system for air conditioners, such as Figure 2As shown, Figure 2 is a structural diagram of an air-conditioning personalized intelligent control system provided by an embodiment of the present application, the system includes: an acquisition module, a prediction module, a combination module, a formulation module and an output module; wherein,

[0108] An acquisition module is used to acquire first human flow data of a target area of ​​a shopping mall within a first preset time period before a current moment in a current date, and historical human flow data of a target area within a second preset time period after a current moment in a historical date; a prediction module is used to combine the first human flow data and the historical human flow data to predict second human flow data within a target time period, where the target time period is the second preset time period after the current moment in the current date; a combination module is used to acquire the outdoor temperature of the shopping mall and the regional characteristics of the target area, and determine a reference temperature of the target area within a target time period by combining the outdoor temperature and the regional characteristics; a formulation module is used to determine temperature adjustment parameters of the target area within a target time period by combining the reference temperature and the second human flow data, and formulate an initial operation plan of the air conditioner in the target area within the target time period according to the temperature adjustment parameters; an output module is used to acquire environmental monitoring parameters of the target area, determine the environmental load coefficient of the target area according to the environmental monitoring parameters, adjust the initial operation plan according to the environmental load coefficient, and generate a target operation plan of the air conditioner within the target time period.

[0109] It should be noted that: when the system provided in the above embodiment realizes its functions, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0110] See also Figure 3 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0111] The communication bus 1002 is used to realize the connection and communication between these components.

[0112] The user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0113] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0114] Among them, the processor 1001 may include one or more processing cores. The processor 1001 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1001 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001, and it can be implemented separately through a chip.

[0115] Among them, the memory 1005 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may optionally also be at least one storage device located away from the aforementioned processor 1001. As Figure 3 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of a personalized intelligent air conditioner control method.

[0116] exist Figure 3 In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 1001 can be used to call an application program storing a personalized intelligent control method for air conditioning in the memory 1005. When executed by one or more processors, the electronic device executes one or more methods described in the above embodiments.

[0117] An electronic device readable storage medium stores instructions, which, when executed by one or more processors, enable the electronic device to execute one or more of the methods described in the above embodiments.

[0118] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0119] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0120] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0121] 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, that is, they 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.

[0122] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0123] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0124] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A personalized intelligent air conditioning control method, characterized in that: The method comprises: Obtaining first pedestrian flow data of a target area of ​​a shopping mall within a first preset time period before a current moment on a current date, and historical pedestrian flow data of the target area within a second preset time period after the current moment on a historical date; Combining the first human flow data and the historical human flow data, predicting the second human flow data within a target duration, the target duration being a second preset duration after the current moment in the current date; Acquire the outdoor temperature of the shopping mall and the regional characteristics of the target area, and determine the reference temperature of the target area within the target duration by combining the outdoor temperature and the regional characteristics; Determine the temperature adjustment parameters of the target area within the target duration in combination with the reference temperature and the second passenger flow data, and formulate an initial operation plan of the air conditioner in the target area within the target duration according to the temperature adjustment parameters; Acquire environmental monitoring parameters of the target area, determine the environmental load coefficient of the target area according to the environmental monitoring parameters, adjust the initial operation plan according to the environmental load coefficient, and generate a target operation plan for the air conditioner within the target duration; the environmental monitoring parameters include measured temperature, measured humidity and measured carbon dioxide concentration, Determining the environmental load coefficient of the target area according to the environmental monitoring parameters includes: substituting the measured temperature, the measured humidity and the measured carbon dioxide concentration into a preset formula to determine the environmental load coefficient of the target area; wherein the preset formula is: ; In the formula, L is the environmental load factor, T is the measured temperature, is the standard temperature, H is the measured humidity, is the standard humidity, C is the measured carbon dioxide concentration, is the standard carbon dioxide concentration, α is the temperature weight coefficient, β is the humidity weight coefficient, and γ is the carbon dioxide concentration weight coefficient.

2. The personalized intelligent control method for air conditioning according to claim 1, characterized in that: The method of combining the first human flow data and the historical human flow data to predict the second human flow data within the target duration includes: determining the average human flow data within the second preset time period after the current moment in the historical date based on the historical human flow data; determining the first human flow change trend within the target time period based on the average human flow data; obtaining the second human flow data change trend within the first preset time period based on the first human flow data, and predicting the third human flow change trend within the target time period based on the second human flow data change trend; weightedly fusing the first human flow change trend and the third human flow change trend to generate the target human flow change trend within the target time period; and predicting the second human flow data within the target time period based on the first human flow data and the target human flow change trend.

3. The personalized intelligent air conditioning control method according to claim 1, characterized in that: The method of combining the outdoor temperature and the regional characteristics to determine the base temperature of the target area within the target duration includes: determining an initial base temperature of the target area within the target duration based on the outdoor temperature; obtaining the business type of the target area based on the regional characteristics; and adjusting the initial base temperature based on the temperature demand characteristics corresponding to the business type to determine the base temperature of the target area within the target duration.

4. The personalized intelligent control method for air conditioning according to claim 1, characterized in that: The method of combining the reference temperature and the second human flow data to determine the temperature adjustment parameters of the target area within the target time period includes: determining the human flow density of the target area within the target time period based on the second human flow data; determining the target temperature adjustment amount corresponding to the human flow density based on a preset correspondence between the human flow density and the temperature adjustment amount; and performing a linear superposition operation on the reference temperature and the target temperature adjustment amount to determine the temperature adjustment parameters of the target area within the target time period.

5. The personalized intelligent control method for air conditioning according to claim 1, characterized in that: The formulating, based on the temperature adjustment parameters, an initial operation plan for the air conditioner in the target area within the target time period includes: determining, based on the temperature adjustment parameters, a target air supply temperature for the air conditioner in the target area within the target time period; determining, based on a correspondence between a preset air supply temperature and an operating power, an operating power of the air conditioner at the target air supply temperature; determining, based on the operating power, operating parameters of the air conditioner, the operating parameters including at least one of a compressor speed, a fan speed, and an air supply direction; and generating, based on the operating parameters, an initial operation plan for the air conditioner within the target time period.

6. The personalized intelligent control method for air conditioning according to claim 1, characterized in that: The step of adjusting the initial operation plan according to the environmental load coefficient to generate a target operation plan for the air conditioner within the target duration includes: determining the target operation power adjustment coefficient corresponding to the environmental load coefficient based on a correspondence between a preset environmental load coefficient and an operation power adjustment coefficient; arithmetically multiplying the target operation power adjustment coefficient by the operation power in the initial operation plan to obtain a target operation power; and updating the operation parameters in the initial operation plan according to the target operation power to generate a target operation plan for the air conditioner within the target duration.

7. An air-conditioning personalized intelligent control system, characterized in that: The system comprises: an acquisition module, a prediction module, a combination module, a formulation module and an output module; wherein the acquisition module is used to acquire first pedestrian flow data of a target area of ​​a shopping mall within a first preset time period before a current moment in a current date, and historical pedestrian flow data of the target area within a second preset time period after the current moment in a historical date; the prediction module is used to combine the first pedestrian flow data with the historical pedestrian flow data to predict second pedestrian flow data within a target time period, wherein the target time period is the second preset time period after the current moment in the current date; the combination module is used to acquire the outdoor temperature of the shopping mall and the regional characteristics of the target area, and determine a reference temperature of the target area within the target time period by combining the outdoor temperature and the regional characteristics; the formulation module is used to combine the reference temperature and the historical pedestrian flow data to predict the second pedestrian flow data within a target time period. The second flow of people data is used to determine the temperature adjustment parameters of the target area within the target time, and formulate an initial operation plan of the air conditioner in the target area within the target time according to the temperature adjustment parameters; the output module is used to obtain the environmental monitoring parameters of the target area, determine the environmental load coefficient of the target area according to the environmental monitoring parameters, and adjust the initial operation plan according to the environmental load coefficient to generate a target operation plan for the air conditioner within the target time; the environmental monitoring parameters include measured temperature, measured humidity and measured carbon dioxide concentration, and determining the environmental load coefficient of the target area according to the environmental monitoring parameters includes: substituting the measured temperature, the measured humidity and the measured carbon dioxide concentration into a preset formula to determine the environmental load coefficient of the target area; wherein the preset formula is: ; In the formula, L is the environmental load factor, T is the measured temperature, is the standard temperature, H is the measured humidity, is the standard humidity, C is the measured carbon dioxide concentration, is the standard carbon dioxide concentration, α is the temperature weight coefficient, β is the humidity weight coefficient, and γ is the carbon dioxide concentration weight coefficient.

8. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 6.

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