Intelligent energy-saving control and household-based billing method and device for multiple types of air-conditioning systems

Abstract

The present invention relates to the field of air conditioning system control and billing technology, and more specifically, to an intelligent energy-saving control and household-based billing method and device for multiple types of air conditioning systems, which is used to solve the problems that the prior art cannot combine environmental data, user behavior data and equipment status data to evaluate and analyze air conditioning energy consumption, cannot dynamically adjust the air conditioning operation mode according to the energy consumption evaluation and analysis results, and cannot optimize the operation mode according to the characteristics of different air conditioning types; by analyzing user usage through time series and identifying user patterns, it is possible to analyze the air conditioning energy consumption by integrating environmental data, user behavior data and equipment status data, can dynamically adjust the air conditioning operation mode according to the energy consumption evaluation and analysis results, and can optimize the operation mode according to the characteristics of different air conditioning types.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioning system control and billing, and more specifically, to an intelligent energy-saving control and household-based billing method and device for multiple types of air conditioning systems. Background Art

[0002] With the rapid development of the construction industry and people's increasing requirements for living comfort, air-conditioning systems have become an indispensable part of modern buildings. However, traditional air-conditioning systems often have problems such as high energy consumption, inconvenient management, and unfair cost sharing during operation. Especially when multiple users share the air-conditioning system, how to fairly and accurately calculate the air-conditioning usage fee for each user has become a technical problem that needs to be solved urgently.

[0003] The patent application with reference publication number CN118757897A discloses a control method for air conditioning energy saving, comprising the following steps: sensing the indoor space through a sensor to determine whether there are people in the room, and if there are no people, keeping the air conditioner in standby mode; if there are people, turning on the air conditioner, and taking pictures of the room through a camera installed indoors to obtain an indoor image, and calculating the indoor population density based on the indoor image; obtaining the indoor and outdoor temperature difference through a temperature difference sensor in the air conditioner, calling the building structure corresponding to the air conditioner in the database, predicting the indoor demand cooling capacity based on the indoor and outdoor temperature difference, the population density and the building structure, and obtaining the indoor demand cooling capacity; inputting the demand cooling capacity into a preset virtual twin model to obtain a cooling capacity control strategy; and performing energy-saving control on the turned-on air conditioner based on the cooling capacity control strategy; solving the technical problems of lack of comprehensive consideration of indoor and outdoor environmental changes and personnel activities, and a single and inflexible control strategy;

[0004] However, the existing technology cannot combine environmental data, user behavior data and equipment status data to evaluate and analyze the energy consumption of air conditioners, cannot dynamically adjust the operating mode of the air conditioner according to the results of energy consumption evaluation and analysis, and cannot optimize the operating mode according to the characteristics of different air conditioner types; at the same time, the existing technology cannot monitor and accurately bill the independent energy consumption of different users, and cannot take into account the different usage scenarios of the air conditioning system, and cannot achieve comprehensive energy saving and cost allocation optimization.

[0005] To this end, we propose intelligent energy-saving control and household billing methods for multiple types of air-conditioning systems to address the above problems. Summary of the invention

[0006] The purpose of the present invention is to provide an intelligent energy-saving control and household billing method and device for multiple types of air-conditioning systems, which solves the problems that the prior art cannot combine environmental data, user behavior data and equipment status data to evaluate and analyze the air-conditioning energy consumption, cannot dynamically adjust the air-conditioning operation mode according to the energy consumption evaluation and analysis results, and cannot optimize the operation mode according to the characteristics of different air-conditioning types; at the same time, the prior art cannot monitor and accurately bill the independent energy consumption of different users, and cannot take into account the different usage scenarios of the air-conditioning system, and cannot achieve comprehensive energy saving and cost allocation optimization.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] The intelligent energy-saving control and household-based billing method for multiple types of air-conditioning systems includes the following steps:

[0009] Step 1: Collect environmental data, user behavior data, and device status data in real time through IoT devices, store the collected data in the corresponding table of the MySQL database, clean the data, and store the cleaned data in a new database table;

[0010] Step 2: Analyze the air conditioning usage of users at different times of the day through time series, identify user behavior patterns, and evaluate and analyze the energy consumption of air conditioners;

[0011] Step 3: According to the evaluation and analysis results of the air conditioner's energy consumption, dynamically adjust the air conditioner's operating mode, and optimize the operating mode according to the characteristics of different air conditioner types;

[0012] Step 4: Record independent energy consumption data for each user, establish a billing system, and formulate a billing plan based on energy consumption records and usage patterns;

[0013] Step 5: Develop a responsive user interface to display current and historical energy consumption information in real time. The user interface provides air conditioning control functions. The user interface also integrates bill viewing functions and supports online payment of bills.

[0014] As a preferred embodiment of the present invention, the specific process of analyzing the usage of air conditioners through time series is as follows:

[0015] The collected environmental data, user behavior data and device status data are grouped by half an hour. The environmental data includes humidity, temperature and light intensity. The user behavior data includes switch status and set temperature. The device status data includes operation mode and power consumption.

[0016] Calculate the total energy consumption of the air conditioner in each time period, calculate the average energy consumption of the air conditioner in each time period, calculate the maximum energy consumption of the air conditioner in each time period, calculate the minimum energy consumption of the air conditioner in each time period, calculate the number of times the air conditioner is turned on and off in each time period, calculate the average value of the target temperature set by the user in each time period, calculate the ambient temperature, ambient humidity and ambient light intensity in each time period, draw line graphs and bar graphs through visualization tools to show the relationship between energy consumption and time, and find out the peak usage period and the low peak usage period.

[0017] As a preferred implementation of the present invention, the specific process of identifying the user behavior pattern is as follows:

[0018] The energy consumption data of each user in multiple time periods is obtained to construct a user feature data set. The content of the user feature data set includes user ID, time period, total energy consumption, average energy consumption, maximum energy consumption, minimum energy consumption, number of startups, number of shutdowns, average value of target temperature, ambient temperature, ambient humidity and ambient light intensity. The user feature data set is cleaned and standardized, and the K-means clustering algorithm is used to classify user behaviors. Users are set to be divided into three categories, i.e., K=3. The standardized user feature data set is clustered using the K-means algorithm. After clustering, each user is assigned a cluster label and grouped into class 0, class 1 and class 2 according to the cluster label. Class 0 represents energy-saving users with lower total energy consumption, average energy consumption and high set temperature. Class 1 represents ordinary users with intermediate energy consumption and startup times. Class 2 represents high-frequency users with higher total energy consumption and startup times.

[0019] As a preferred embodiment of the present invention, the specific process of evaluating and analyzing the energy consumption of the air conditioner is as follows:

[0020] The average energy consumption value of the air conditioner during operation in multiple time periods is obtained to construct a set A of average energy consumption values, the mean in set A is obtained, and the mean in set A is marked as the mean of average energy consumption values. A rectangular coordinate system is established with time as the X-axis and the mean of average energy consumption values ​​as the Y-axis. The mean curve of average energy consumption values ​​is drawn by plotting points, and at the same time, a preset mean threshold curve of average energy consumption values ​​is drawn in the coordinate system. The product value obtained by normalizing the time length corresponding to the line segment above the preset mean threshold curve of average energy consumption values ​​and the area enclosed by the upper line segment and the preset mean threshold curve of average energy consumption values ​​is obtained from the coordinate system, and it is marked as the abnormal energy consumption risk value NYF.

[0021] As a preferred embodiment of the present invention, the real-time humidity, real-time temperature and real-time light intensity of the environment in which the air conditioner is running in multiple time periods are obtained, the real-time humidity is compared with the median of the preset suitable humidity range, and the absolute value is taken to obtain the real-time humidity difference SSC, the real-time temperature is compared with the median of the preset suitable temperature range, and the absolute value is taken to obtain the real-time temperature difference SWC, and the real-time light intensity is compared with the median of the preset suitable light intensity range, and the absolute value is taken to obtain the real-time light intensity difference SGC;

[0022] By formula Obtain the environmental abnormality risk value, where b1, b2, and b3 are all preset proportional coefficients, b3>b2>b1>0;

[0023] The number of times the air conditioner is turned on, the number of times it is turned off, and the set temperature during operation of the air conditioner in multiple time periods are obtained, the number of times the air conditioner is turned on is calculated by comparing the number of times the air conditioner is turned on with the preset number of times the air conditioner is turned on, and the absolute value is taken to obtain the number of times the air conditioner is turned on, the number of times the air conditioner is turned off is calculated by comparing the number of times the air conditioner is turned off with the preset number of times the air conditioner is turned off, and the absolute value is taken to obtain the number of times the air conditioner is turned off, and the set temperature is calculated by comparing the median of the preset temperature range with the set temperature range, and the absolute value is taken to obtain the set temperature difference SWD;

[0024] By formula The abnormal behavior risk value is obtained, where c1, c2, and c3 are all preset proportional coefficients, c3>c2>c1>0.

[0025] As a preferred embodiment of the present invention, an abnormal energy consumption risk value NYF, an abnormal environment risk value HYF and an abnormal behavior risk value XYF are obtained;

[0026] By formula The energy consumption evaluation coefficient is obtained, where d1, d2, and d3 are preset proportional coefficients, and the values ​​of d1, d2, and d3 are all greater than zero. The energy consumption evaluation coefficient NPX is compared and analyzed with the preset energy consumption evaluation coefficient threshold:

[0027] If the energy consumption evaluation coefficient NPX is greater than the preset maximum value of the energy consumption evaluation coefficient threshold, it indicates that the air conditioner is in a high energy consumption mode;

[0028] If the energy consumption evaluation coefficient NPX is greater than or equal to the preset minimum energy consumption evaluation coefficient threshold value and less than or equal to the preset maximum energy consumption evaluation coefficient threshold value, it indicates that the air conditioner is in the medium energy consumption mode;

[0029] If the energy consumption evaluation coefficient NPX is less than the preset minimum energy consumption evaluation coefficient threshold value, it indicates that the air conditioner is in a low energy consumption mode.

[0030] As a preferred embodiment of the present invention, the specific process of dynamically adjusting the operation mode of the air conditioner according to the evaluation and analysis results of the energy consumption mode of the air conditioner is as follows:

[0031] If the air conditioner is determined to be in high energy consumption mode, energy-saving measures need to be taken, including lowering the air conditioner temperature setting, limiting the area where the air conditioner is operated, and adding intelligent shading or indoor ventilation methods;

[0032] If the air conditioner is determined to be in medium energy consumption mode, it is necessary to maintain the current settings and perform minor optimizations. The optimization measures include adjusting the air conditioner operating time and improving system operating efficiency.

[0033] If the air conditioner is determined to be in low-energy mode, it is necessary to relax the air conditioner settings to increase comfort. Adjustments include raising the set temperature and opening additional rooms.

[0034] As a preferred embodiment of the present invention, the specific process of optimizing the operation mode according to the characteristics of different air conditioner types is as follows:

[0035] For multi-split air conditioners, priority scheduling analyzes the usage of each indoor unit. The system will give priority to providing cooling or heating to rooms with high usage rates. In high energy consumption mode, the system will take local adjustment measures for rooms with low usage rates. Local adjustment measures include reducing wind speed, increasing temperature settings, timed shutdown, zone control and intelligent temperature control.

[0036] For central air conditioning, through regional and time period control, the system adjusts the cooling and heating capacity of each area according to the energy consumption forecast results. During the peak hours of weekdays, the system is turned on in advance to reduce the startup load. During the off-peak hours, the set temperature is appropriately increased. The occupancy status of each room is monitored in real time using timers and sensors, and unoccupied rooms are automatically adjusted or switched to standby mode.

[0037] For split air conditioners, the self-learning algorithm can automatically optimize the operating mode according to the user's usage habits and switch to sleep mode in advance. Through mobile device integration, users can control the operating status of the air conditioner in real time from the outside, and implement remote pre-cooling or timed power on and off.

[0038] As a preferred implementation of the present invention, the specific process of establishing a billing system and formulating a billing plan is as follows:

[0039] The energy consumption data of users is collected in real time through smart meters. The smart meters independently record the energy consumption of each user in multiple time periods. The records include timestamp, user ID and energy consumption. The collected energy consumption data is stored in the MySQL database, and the data of each user is managed by user ID.

[0040] Establish a billing system, formulate a billing plan, and set billing rates. The billing rates include basic rates, peak rates, and off-peak rates. The basic rate is set at 0.52 yuan / kWh, which is applicable to time other than peak and off-peak periods. The peak rate is set at 0.74 yuan / kWh, which is applicable to 18:00 to 22:00 on weekdays. The off-peak rate is set at 0.38 yuan / kWh, which is applicable to non-peak periods from 22:00 to 18:00 the next day. In order to control peak power usage, if a user's power consumption exceeds the set threshold in a short period of time, a punitive fee will be added separately when calculating the total fee. The punitive fee rate is 1.20 yuan / kWh, and the punitive fee is the energy consumption exceeding the set threshold in a short period of time multiplied by the punitive fee rate;

[0041] When calculating the electricity bill for each user, the following formula is used:

[0042] C=E 基本 ×R 基本 +E 高峰 ×R 高峰 +E 低峰 ×R 低峰 +E 惩罚 ×R 惩罚 ;

[0043] Where C is the total cost, E 基本 Indicates the energy consumption during the basic period, E 高峰 represents the energy consumption during peak hours, E 低峰 represents the energy consumption during off-peak hours, E 惩罚 Indicates energy consumption exceeding the set threshold, R 基本 represents the basic rate, R 高峰 represents the peak rate, R 低峰 represents the off-peak rate, R 惩罚 Indicates penalty rate;

[0044] The system automatically generates a detailed bill, which includes the user ID, billing cycle, peak charge structure, off-peak charge structure, penalty charge structure and total charge.

[0045] As a preferred embodiment of the present invention, an intelligent energy-saving control and household-based billing device for multiple types of air-conditioning systems comprises:

[0046] The data collection and processing module is used to obtain environmental data, user behavior data and device status data, and store and clean the collected data;

[0047] Energy consumption evaluation and analysis module, used to analyze the user's use of air conditioners, identify user patterns, and evaluate and analyze the energy consumption of air conditioners;

[0048] The operation mode optimization module is used to dynamically adjust the operation mode according to the energy consumption evaluation and analysis results, and optimize the operation mode according to the characteristics of the air conditioner type;

[0049] The billing system module is used to record the user's independent energy consumption data, establish a billing system, formulate and implement billing plans based on energy consumption records and usage patterns, and automatically generate detailed bills;

[0050] The user interface module is used to develop a responsive user interface, display current and historical energy consumption information in real time, provide air conditioning control functions, integrate bill viewing functions in the interface and support online payment of bills.

[0051] Compared with the prior art, the advantages of the present invention are:

[0052] In the present invention, the user usage is analyzed through time series, the user mode is identified, the air conditioner energy consumption can be analyzed by integrating environmental data, user behavior data and equipment status data, the air conditioner operation mode can be dynamically adjusted according to the energy consumption evaluation analysis results, and the operation mode can be optimized according to the characteristics of different air conditioner types;

[0053] In the present invention, the independent energy consumption of each user is collected in real time through smart meters, a billing system is established, a billing plan is formulated, and peak rates and off-peak rates are set according to the peak and off-peak usage scenarios of the air-conditioning system. In order to control peak power usage, a punitive rate is set. Through the above processing, different usage scenarios of the air-conditioning system can be taken into account, thereby achieving all-round energy saving and cost allocation optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a block diagram of the intelligent energy-saving control and household-based billing method of the present invention;

[0055] Figure 2 It is a structural schematic diagram of the intelligent energy-saving control and household-based billing device for multiple types of air-conditioning systems in the present invention;

[0056] Figure 3 It is a logical flow diagram of the present invention. DETAILED DESCRIPTION

[0057] The following will combine the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all the embodiments. All other embodiments obtained by ordinary technicians in this field without creative work based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0058] Embodiment 1: Figure 1 and Figure 3As shown, the intelligent energy-saving control and household-based billing method for multiple types of air-conditioning systems proposed by the present invention includes the following steps:

[0059] Step 1: Collect environmental data, user behavior data, and device status data in real time through IoT devices, store the collected data in the corresponding table of the MySQL database, clean the data, and store the cleaned data in a new database table;

[0060] The data is cleaned and stored in a new database table. The cleaned data has significant advantages: first, it improves data quality, ensures that the information reflected is true and reliable, and reduces misleading information when making decisions; second, it enhances analysis accuracy, provides a reliable basis for data modeling and prediction, and improves system efficiency, speeds up data query and processing; in addition, reliable data better supports management decisions, makes optimization decisions more effective, and improves personalized services by analyzing user behavior data.

[0061] Step 2: Analyze the air conditioning usage of users at different times of the day through time series, identify user behavior patterns, and evaluate and analyze the energy consumption of air conditioners;

[0062] The specific process of analyzing air conditioning usage through time series is as follows:

[0063] The collected environmental data, user behavior data and device status data are grouped by half an hour. The environmental data includes humidity, temperature and light intensity. The user behavior data includes switch status and set temperature. The device status data includes operation mode and power consumption.

[0064] Calculate the total energy consumption of the air conditioner in each time period, calculate the average energy consumption of the air conditioner in each time period, calculate the maximum energy consumption of the air conditioner in each time period, calculate the minimum energy consumption of the air conditioner in each time period, calculate the number of times the air conditioner is turned on and off in each time period, calculate the average value of the target temperature set by the user in each time period, calculate the ambient temperature, ambient humidity and ambient light intensity in each time period, draw line charts and bar charts through visualization tools to show the relationship between energy consumption and time, and find out the peak usage period and the low peak usage period;

[0065] Time series analysis of air conditioning usage detects the environment, user behavior and equipment data in half-hour groups, evaluates total energy consumption, average energy consumption and switching times, and takes into account environmental factors such as temperature, humidity and light. Visualized data helps identify peak and off-peak usage periods and formulate energy-saving strategies. It also enhances users' energy-saving awareness and promotes self-regulation behavior, thereby optimizing system operation, improving energy efficiency, achieving energy conservation and emission reduction, and improving user satisfaction.

[0066] The specific process of identifying user behavior patterns is as follows:

[0067] The energy consumption data of each user in multiple time periods are collected to construct a user feature data set. The content of the user feature data set includes user ID, time period, total energy consumption, average energy consumption, maximum energy consumption, minimum energy consumption, number of startups, number of shutdowns, average value of target temperature, ambient temperature, ambient humidity and ambient light intensity. The user feature data set is cleaned and standardized, and the K-means clustering algorithm is used to classify user behaviors. Users are set to be divided into three categories, i.e., K=3. The standardized user feature data set is clustered using the K-means algorithm. After clustering, each user is assigned a cluster label and grouped into class 0, class 1 and class 2 according to the cluster label. Class 0 represents energy-saving users with low total energy consumption, average energy consumption and high set temperature. Class 1 represents ordinary users with intermediate energy consumption and startup times. Class 2 represents high-frequency users with high total energy consumption and startup times.

[0068] Identify user behavior patterns by collecting energy consumption data to build a user feature data set, and then clean and standardize it; then, use the K-means clustering algorithm to divide users into energy-saving, ordinary and high-frequency users to identify different energy consumption habits and formulate targeted energy-saving strategies; this process enhances insights into user behavior, helps companies optimize product design, improve energy management efficiency, implement effective energy-saving measures and enhance user satisfaction.

[0069] The specific process of evaluating and analyzing the energy consumption of air conditioners is as follows:

[0070] The average energy consumption values ​​of the air conditioner during operation in multiple time periods are obtained to construct a set A of average energy consumption values, the mean in set A is obtained, and the mean in set A is marked as the mean of average energy consumption values, a rectangular coordinate system is established with time as the X-axis and the mean of average energy consumption values ​​as the Y-axis, and an average energy consumption value mean curve is drawn by plotting points, and a preset average energy consumption value mean threshold curve is drawn in the coordinate system at the same time, and the product value obtained by normalizing the time length corresponding to the line segment above the preset average energy consumption value mean threshold curve and the area enclosed by the upper line segment and the preset average energy consumption value mean threshold curve is obtained from the coordinate system, and it is marked as the energy consumption abnormality risk value NYF;

[0071] The real-time humidity, real-time temperature and real-time light intensity of the environment in which the air conditioner is running in multiple time periods are obtained, and the real-time humidity is compared with the median of the preset suitable humidity range for difference calculation and the absolute value is taken to obtain the real-time humidity difference SSC, the real-time temperature is compared with the median of the preset suitable temperature range for difference calculation and the absolute value is taken to obtain the real-time temperature difference SWC, and the real-time light intensity is compared with the median of the preset suitable light intensity range for difference calculation and the absolute value is taken to obtain the real-time light intensity difference SGC. The formula Obtain an abnormal environmental risk value, where b1, b2, and b3 are all preset proportional coefficients, b3>b2>b1>0. The preset proportional coefficients are obtained by analyzing the relationship between humidity, temperature, light intensity, and energy consumption using historical data, and obtaining reasonable coefficient values ​​through machine learning methods;

[0072] Get the number of times the air conditioner is turned on, off and set temperatures during multiple time periods. Calculate the difference between the number of times the air conditioner is turned on and the preset number of times the air conditioner is turned on, and take the absolute value to get the difference between the number of times the air conditioner is turned on, KCC. Calculate the difference between the number of times the air conditioner is turned off and the preset number of times the air conditioner is turned off, GCC. Calculate the difference between the set temperature and the median of the preset temperature range, and take the absolute value to get the set temperature difference SWD. Obtaining the abnormal behavior risk value, where c1, c2, and c3 are all preset proportional coefficients, c3>c2>c1>0. The preset proportional coefficients are obtained by using historical usage data, analyzing the relationship between startup and shutdown behaviors, set temperature and energy consumption, and using regression analysis methods to obtain reasonable coefficient values;

[0073] Obtain the energy consumption abnormality risk value NYF, the environment abnormality risk value HYF and the behavior abnormality risk value XYF;

[0074] By formula The energy consumption evaluation coefficient is obtained, where d1, d2, and d3 are preset proportional coefficients, and the values ​​of d1, d2, and d3 are all greater than zero. The energy consumption evaluation coefficient NPX is compared and analyzed with the preset energy consumption evaluation coefficient threshold. The preset proportional coefficient is determined by the regression analysis method through the analysis of historical energy consumption data, environmental data, and user behavior data to ensure the accuracy of the calculation:

[0075] If the energy consumption evaluation coefficient NPX is greater than the preset maximum value of the energy consumption evaluation coefficient threshold, it indicates that the air conditioner is in a high energy consumption mode;

[0076] If the energy consumption evaluation coefficient NPX is greater than or equal to the preset minimum energy consumption evaluation coefficient threshold value and less than or equal to the preset maximum energy consumption evaluation coefficient threshold value, it indicates that the air conditioner is in the medium energy consumption mode;

[0077] If the energy consumption evaluation coefficient NPX is less than the preset minimum energy consumption evaluation coefficient threshold value, it indicates that the air conditioner is in low energy consumption mode;

[0078] The evaluation and analysis of air conditioning energy consumption systematically monitors energy consumption changes by constructing a set of average energy consumption values ​​and drawing trend curves, which facilitates the identification of potential energy consumption anomalies. By combining environmental factors (such as humidity, temperature, and light intensity) and user behavior (number of on / off cycles, set temperature), it is possible to deeply understand the multiple factors that affect energy consumption and calculate the abnormal risk values ​​of the environment and behavior, thereby promptly discovering risks that affect high energy consumption. Flexible preset parameters improve the adaptability of the model, allowing energy consumption modes to be clearly divided into three categories: high, medium, and low, helping users and managers to immediately understand the operating status of the air conditioner and optimize usage strategies. This process not only improves energy efficiency and promotes the realization of energy-saving goals, but also enhances user satisfaction and usage experience.

[0079] Step 3: According to the evaluation and analysis results of the air conditioner's energy consumption, dynamically adjust the air conditioner's operating mode, and optimize the operating mode according to the characteristics of different air conditioner types;

[0080] According to the evaluation and analysis results of the air conditioner's energy consumption pattern, the specific process of dynamically adjusting the air conditioner's operating mode is as follows:

[0081] If the air conditioner is determined to be in high energy consumption mode, energy-saving measures need to be taken, including lowering the air conditioner temperature setting, limiting the area where the air conditioner is operated, and adding intelligent shading or indoor ventilation methods;

[0082] If the air conditioner is determined to be in medium energy consumption mode, it is necessary to maintain the current settings and perform minor optimizations. The optimization measures include adjusting the air conditioner operating time and improving system operating efficiency.

[0083] If the air conditioner is determined to be in low energy mode, it is necessary to relax the air conditioner settings to increase comfort. Adjustment measures include raising the set temperature and opening additional rooms.

[0084] The specific process of optimizing the operation mode according to the characteristics of different air conditioner types is as follows:

[0085] For multi-split air conditioners, priority scheduling analyzes the usage of each indoor unit. The system will give priority to providing cooling or heating to rooms with high usage rates. In high energy consumption mode, the system will take local adjustment measures for rooms with low usage rates. Local adjustment measures include reducing wind speed, increasing temperature settings, timed shutdown, zone control and intelligent temperature control.

[0086] For central air conditioning, through regional and time period control, the system adjusts the cooling and heating capacity of each area according to the energy consumption forecast results. During the peak hours of weekdays, the system is turned on in advance to reduce the startup load. During the off-peak hours, the set temperature is appropriately increased. The occupancy status of each room is monitored in real time using timers and sensors, and unoccupied rooms are automatically adjusted or switched to standby mode.

[0087] For split air conditioners, the self-learning algorithm can automatically optimize the operating mode according to the user's usage habits and switch to sleep mode in advance. Through mobile device integration, users can control the operating status of the air conditioner in real time from the outside, implement remote pre-cooling or timed power on and off;

[0088] Optimizing operating modes according to air-conditioning types significantly improves energy efficiency. Multi-split air conditioners meet the needs of high-use rooms through priority scheduling. Central air conditioners use zone control and real-time monitoring to reduce startup load and extend equipment life. Split air conditioners improve comfort through self-learning algorithms and remote control. Overall, this optimization effectively reduces energy consumption and operating costs, while enhancing system flexibility and user satisfaction.

[0089] Step 4: Record independent energy consumption data for each user, establish a billing system, and formulate a billing plan based on energy consumption records and usage patterns;

[0090] The specific process of establishing a billing system and formulating a billing plan is as follows:

[0091] The energy consumption data of users is collected in real time through smart meters. The smart meters independently record the energy consumption of each user in multiple time periods. The records include timestamp, user ID and energy consumption. The collected energy consumption data is stored in the MySQL database, and the data of each user is managed by user ID.

[0092] Establish a billing system, formulate a billing plan, and set billing rates. The billing rates include basic rates, peak rates, and off-peak rates. The basic rate is set at 0.52 yuan / kWh, which is applicable to time other than peak and off-peak periods. The peak rate is set at 0.74 yuan / kWh, which is applicable to 18:00 to 22:00 on weekdays. The off-peak rate is set at 0.38 yuan / kWh, which is applicable to non-peak periods from 22:00 to 18:00 the next day. In order to control peak power usage, if a user's power consumption exceeds the set threshold in a short period of time, a punitive fee will be added separately when calculating the total fee. The punitive fee rate is 1.20 yuan / kWh, and the punitive fee is the energy consumption exceeding the set threshold in a short period of time multiplied by the punitive fee rate;

[0093] When calculating the electricity bill for each user, the following formula is used:

[0094] C=E基本 ×R 基本 +E 高峰 ×R 高峰 +E 低峰 ×R 低峰 +E 惩罚 ×R 惩罚 ;

[0095] Where C is the total cost, E 基本 Indicates the energy consumption during the basic period, E 高峰 represents the energy consumption during peak hours, E 低峰 represents the energy consumption during off-peak hours, E 惩罚 Indicates energy consumption exceeding the set threshold, R 基本 represents the basic rate, R 高峰 represents the peak rate, R 低峰 represents the off-peak rate, R 惩罚 Indicates penalty rate;

[0096] The system automatically generates a detailed bill, which includes the user ID, billing cycle, peak fee structure, off-peak fee structure, penalty fee structure and total fee;

[0097] Establishing an independent energy consumption billing system has many advantages: first, users pay according to their actual energy consumption, achieving fair and transparent billing and eliminating deviations; second, by setting peak and off-peak rates, users are encouraged to use electricity during off-peak periods to achieve energy-saving effects; at the same time, the punitive charging mechanism encourages users to arrange electricity use reasonably and optimize electricity usage habits; in addition, the system provides real-time energy consumption and bill viewing functions to enhance users' sense of control; finally, the accumulated energy consumption data provides data support for the optimization of power use strategies and the development of smart grids.

[0098] Step 5: Develop a responsive user interface to display current and historical energy consumption information in real time, and support chart display of energy consumption data by day, week, and month. The user interface provides air conditioning control function, allowing users to manually adjust the air conditioning operation mode and target temperature. The user interface also integrates a bill viewing function, allowing users to view current and historical bills, and supports online bill payment;

[0099] Developing a responsive user interface has many advantages: real-time monitoring of energy consumption, viewing data charts by day, week, and month, and facilitating the analysis of electricity consumption trends; support for air conditioning control, users can adjust the operating mode and temperature; integrated bill viewing and online payment functions simplify the management process and improve transparency; responsive design ensures that the interface is well displayed on various devices and enhances accessibility. These advantages improve user experience and promote energy-saving awareness and rational electricity usage behavior.

[0100] Embodiment 2: The technical solution of the embodiment of the present invention is different from that of embodiment 1 in that:

[0101] like Figure 2 As shown, the intelligent energy-saving control and household-based billing device for multiple types of air-conditioning systems includes:

[0102] The data collection and processing module is used to obtain environmental data, user behavior data and device status data, and store and clean the collected data;

[0103] Energy consumption evaluation and analysis module, used to analyze the user's use of air conditioners, identify user patterns, and evaluate and analyze the energy consumption of air conditioners;

[0104] The operation mode optimization module is used to dynamically adjust the operation mode according to the energy consumption evaluation and analysis results, and optimize the operation mode according to the characteristics of the air conditioner type;

[0105] The billing system module is used to record the user's independent energy consumption data, establish a billing system, formulate and implement billing plans based on energy consumption records and usage patterns, and automatically generate detailed bills;

[0106] User interface module, used to develop a responsive user interface, display current and historical energy consumption information in real time, provide air conditioning control functions, integrate bill viewing functions in the interface and support online bill payment;

[0107] The core functions of the user interface module include real-time display of current energy consumption and historical energy consumption records. Users can quickly obtain energy consumption information through a simple dashboard. The design of the dashboard will use chart components, such as line charts and bar charts, to intuitively display energy consumption trends and help users understand and analyze electricity usage;

[0108] In terms of air conditioning control, users can directly operate the air conditioning equipment through the user interface module, including adjusting the temperature, turning the power on and off, and setting the timer switch. This function will be realized through the integrated control panel, and users can easily manage and optimize the use of air conditioning to achieve energy saving;

[0109] In addition, the user interface module will also integrate a bill viewing function, allowing users to quickly access personal energy bills, view monthly consumption and historical bill records. This function will use a table component to display bill details, including consumption date, total amount and electricity consumption, providing users with a transparent bill display;

[0110] In order to enhance the user experience, the user interface module supports online payment function, allowing users to pay bills conveniently. This part will integrate secure payment interfaces, such as Stripe or PayPal, to ensure the security of user information and smooth transactions during the payment process. Users only need to click the "Pay" button to complete the bill payment through the selected payment method;

[0111] The user interface module adopts responsive design to ensure good display on different devices (such as mobile phones, tablets and desktops). By using CSS frameworks (such as Bootstrap or Tailwind CSS), the adaptability of interface elements on various screen sizes is ensured.

[0112] Working principle of the present invention: When in use, the present invention analyzes user usage through time series analysis and identifies user patterns. It can analyze the air-conditioning energy consumption by integrating environmental data, user behavior data and equipment status data. It can dynamically adjust the air-conditioning operation mode according to the energy consumption evaluation analysis results, and can optimize the operation mode according to the characteristics of different air-conditioning types. It can also collect the independent energy consumption of each user in real time through smart meters, establish a billing system, formulate a billing plan, and set peak rates and low-peak rates according to the peak and low-peak usage scenarios of the air-conditioning system. In order to control peak electricity usage, a punitive rate is set. Through the above processing, different usage scenarios of the air-conditioning system can be taken into account, thereby achieving all-round energy saving and cost allocation optimization.

[0113] The above are only preferred specific implementation modes of the present invention; however, the protection scope of the present invention is not limited thereto; any technician familiar with the technical field within the technical scope disclosed by the present invention; any equivalent replacement or change based on the technical solution and improved concept of the present invention shall be covered within the protection scope of the present invention.

Claims

1. Intelligent energy-saving control and household-based billing method for multi-type air-conditioning systems, characterized in that: The following steps are involved: Step 1: Collect environmental data, user behavior data, and device status data in real time through IoT devices, store the collected data in the corresponding table of the MySQL database, clean the data, and store the cleaned data in a new database table; Step 2: Analyze the air conditioning usage of users at different times of the day through time series, identify user behavior patterns, and evaluate and analyze the energy consumption of air conditioners; The specific process of analyzing air conditioning usage through time series is as follows: The collected environmental data, user behavior data and device status data are grouped by half an hour. The environmental data includes humidity, temperature and light intensity. The user behavior data includes switch status and set temperature. The device status data includes operation mode and power consumption. Calculate the total energy consumption of the air conditioner in each time period, calculate the average energy consumption of the air conditioner in each time period, calculate the maximum energy consumption of the air conditioner in each time period, calculate the minimum energy consumption of the air conditioner in each time period, calculate the number of times the air conditioner is turned on and off in each time period, calculate the average value of the target temperature set by the user in each time period, calculate the ambient temperature, ambient humidity and ambient light intensity in each time period, draw line charts and bar charts through visualization tools to show the relationship between energy consumption and time, and find out the peak usage period and the low peak usage period; The specific process of identifying user behavior patterns is as follows: The energy consumption data of each user in multiple time periods is obtained to construct a user feature data set. The content of the user feature data set includes user ID, time period, total energy consumption, average energy consumption, maximum energy consumption, minimum energy consumption, number of startups, number of shutdowns, average value of target temperature, ambient temperature, ambient humidity, and ambient light intensity. The user feature data set is cleaned and standardized, and the K-means clustering algorithm is used to classify user behaviors. Users are set to be divided into three categories, i.e., K=3. The standardized user feature data set is clustered using the K-means algorithm. After clustering, each user is assigned a cluster label and grouped into class 0, class 1, and class 2 according to the cluster label. Class 0 represents energy-saving users with low total energy consumption, average energy consumption, and high set temperature. Class 1 represents ordinary users with intermediate energy consumption and startup times. Class 2 represents high-frequency users with high total energy consumption and startup times. Step 3: According to the evaluation and analysis results of the air conditioner's energy consumption, dynamically adjust the air conditioner's operating mode, and optimize the operating mode according to the characteristics of different air conditioner types; The specific process of evaluating and analyzing the energy consumption of air conditioners is as follows: The average energy consumption values ​​of the air conditioner during operation in multiple time periods are obtained to construct a set A of average energy consumption values, the mean in set A is obtained, and the mean in set A is marked as the mean of average energy consumption values, a rectangular coordinate system is established with time as the X-axis and the mean of average energy consumption values ​​as the Y-axis, and an average energy consumption value mean curve is drawn by plotting points, and a preset average energy consumption value mean threshold curve is drawn in the coordinate system at the same time, and the product value obtained by normalizing the time length corresponding to the line segment above the preset average energy consumption value mean threshold curve and the area enclosed by the upper line segment and the preset average energy consumption value mean threshold curve is obtained from the coordinate system, and it is marked as the energy consumption abnormality risk value NYF; The real-time humidity, real-time temperature and real-time light intensity of the environment in which the air conditioner is running in multiple time periods are obtained, and the real-time humidity is compared with the median of the preset suitable humidity range to perform difference calculation and take the absolute value to obtain the real-time humidity difference SSC, the real-time temperature is compared with the median of the preset suitable temperature range to perform difference calculation and take the absolute value to obtain the real-time temperature difference SWC, and the real-time light intensity is compared with the median of the preset suitable light intensity range to perform difference calculation and take the absolute value to obtain the real-time light intensity difference SGC; By formula Obtain an abnormal environmental risk value, where b1, b2, and b3 are all preset proportional coefficients, b3>b2>b1>0; The number of times the air conditioner is turned on, the number of times it is turned off, and the set temperature during operation of the air conditioner in multiple time periods are obtained, the number of times the air conditioner is turned on is calculated by comparing the number of times the air conditioner is turned on with the preset number of times the air conditioner is turned on, and the absolute value is taken to obtain the number of times the air conditioner is turned on, the number of times the air conditioner is turned off is calculated by comparing the number of times the air conditioner is turned off with the preset number of times the air conditioner is turned off, and the absolute value is taken to obtain the number of times the air conditioner is turned off, and the set temperature is calculated by comparing the median of the preset temperature range with the set temperature range, and the absolute value is taken to obtain the set temperature difference SWD; By formula Obtain the risk value of abnormal behavior, where c1, c2, and c3 are all preset proportional coefficients, c3>c2>c1>0; Step 4: Record independent energy consumption data for each user, establish a billing system, and formulate a billing plan based on energy consumption records and usage patterns; Step 5: Develop a responsive user interface to display current and historical energy consumption information in real time. The user interface provides air conditioning control functions. The user interface also integrates bill viewing functions and supports online payment of bills.

2. The intelligent energy-saving control and household-based billing method for multi-type air-conditioning systems according to claim 1 is characterized in that: Obtain the energy consumption abnormality risk value NYF, the environment abnormality risk value HYF and the behavior abnormality risk value XYF; By formula The energy consumption evaluation coefficient is obtained, where d1, d2, and d3 are preset proportional coefficients, and the values ​​of d1, d2, and d3 are all greater than zero. The energy consumption evaluation coefficient NPX is compared and analyzed with the preset energy consumption evaluation coefficient threshold: If the energy consumption evaluation coefficient NPX is greater than the preset maximum value of the energy consumption evaluation coefficient threshold, it indicates that the air conditioner is in a high energy consumption mode; If the energy consumption evaluation coefficient NPX is greater than or equal to the preset minimum energy consumption evaluation coefficient threshold value and less than or equal to the preset maximum energy consumption evaluation coefficient threshold value, it indicates that the air conditioner is in the medium energy consumption mode; If the energy consumption evaluation coefficient NPX is less than the preset minimum energy consumption evaluation coefficient threshold value, it indicates that the air conditioner is in a low energy consumption mode.

3. The intelligent energy-saving control and household-based billing method for multi-type air-conditioning systems according to claim 2 is characterized in that: According to the evaluation and analysis results of the air conditioner's energy consumption pattern, the specific process of dynamically adjusting the air conditioner's operating mode is as follows: If the air conditioner is determined to be in high energy consumption mode, energy-saving measures need to be taken, including lowering the air conditioner temperature setting, limiting the area where the air conditioner is operated, and adding intelligent shading or indoor ventilation methods; If the air conditioner is determined to be in medium energy consumption mode, it is necessary to maintain the current settings and perform minor optimizations. The optimization measures include adjusting the air conditioner operating time and improving system operating efficiency. If the air conditioner is determined to be in low-energy mode, it is necessary to relax the air conditioner settings to increase comfort. Adjustments include raising the set temperature and opening additional rooms.

4. The intelligent energy-saving control and household-based billing method for multi-type air-conditioning systems according to claim 1 is characterized in that: The specific process of optimizing the operation mode according to the characteristics of different air conditioner types is as follows: For multi-split air conditioners, priority scheduling analyzes the usage of each indoor unit. The system will give priority to providing cooling or heating to rooms with high usage rates. In high energy consumption mode, the system will take local adjustment measures for rooms with low usage rates. Local adjustment measures include reducing wind speed, increasing temperature settings, timed shutdown, zone control and intelligent temperature control. For central air conditioning, through regional and time period control, the system adjusts the cooling and heating capacity of each area according to the energy consumption forecast results. During the peak hours of weekdays, the system is turned on in advance to reduce the startup load. During the off-peak hours, the set temperature is appropriately increased. The occupancy status of each room is monitored in real time using timers and sensors, and unoccupied rooms are automatically adjusted or switched to standby mode. For split air conditioners, the self-learning algorithm can automatically optimize the operating mode according to the user's usage habits and switch to sleep mode in advance. Through mobile device integration, users can control the operating status of the air conditioner in real time from the outside, and implement remote pre-cooling or timed power on and off.

5. The intelligent energy-saving control and household-based billing method for multi-type air-conditioning systems according to claim 1 is characterized in that: The specific process of establishing a billing system and formulating a billing plan is as follows: The energy consumption data of users is collected in real time through smart meters. The smart meters independently record the energy consumption of each user in multiple time periods. The records include timestamp, user ID and energy consumption. The collected energy consumption data is stored in the MySQL database, and the data of each user is managed by user ID. Establish a billing system, formulate a billing plan, and set billing rates. The billing rates include basic rates, peak rates, and off-peak rates. The basic rate is set at 0.52 yuan / kWh, which is applicable to time other than peak and off-peak periods. The peak rate is set at 0.74 yuan / kWh, which is applicable to 18:00 to 22:00 on weekdays. The off-peak rate is set at 0.38 yuan / kWh, which is applicable to non-peak periods from 22:00 to 18:00 the next day. In order to control peak power usage, if a user's power consumption exceeds the set threshold in a short period of time, a punitive fee will be added separately when calculating the total fee. The punitive fee rate is 1.20 yuan / kWh, and the punitive fee is the energy consumption exceeding the set threshold in a short period of time multiplied by the punitive fee rate; When calculating the electricity bill for each user, the following formula is used: C=E 基本 ×R 基本 +E 高峰 ×R 高峰 +E 低峰 ×R 低峰 +E 惩罚 ×R 惩罚 ; Where C is the total cost, E 基本 Indicates the energy consumption during the basic period, E 高峰 represents the energy consumption during peak hours, E 低峰 represents the energy consumption during off-peak hours, E 惩罚 Indicates energy consumption exceeding the set threshold, R 基本 represents the basic rate, R 高峰 represents the peak rate, R 低峰 represents the off-peak rate, R 惩罚 Indicates penalty rate; The system automatically generates a detailed bill, which includes the user ID, billing cycle, peak charge structure, off-peak charge structure, penalty charge structure and total charge.

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