Smart home power management system based on single-chip microcomputer

Abstract

The invention discloses a smart home power management system based on a single-chip microcomputer, and relates to the technical field of smart home, and the system comprises a first collection module, a second collection module, a first processing module, a second processing module, a third processing module, an analysis module and a central control module. The first acquisition module is used for acquiring voltage data and air conditioner power data of a user housing within a certain time period; the first processing module is electrically connected with the first acquisition module. The intelligent household power management system provided by the invention integrates an advanced single-chip microcomputer technology, realizes intelligent monitoring and management of the power condition of a user housing, and can accurately calculate the power support coefficient according to the voltage data and the air conditioner power data, thereby intelligently controlling the starting time and the operation duration of the air conditioner, avoiding power waste, and improving the power supply efficiency. The energy utilization efficiency is improved, the optimal starting time coefficient of the air conditioner can be generated, and the air conditioner is automatically started in advance for cooling without waiting.

Description

Technical Field

[0001] The present invention relates to the technical field of smart home, and in particular to a smart home power management system based on a single chip microcomputer. Background Art

[0002] Smart home power management system is an intelligent solution integrating advanced technologies, which aims to effectively monitor, control and optimize household power consumption, improve energy efficiency, reduce energy costs and achieve sustainable energy management. The system integrates technologies such as the Internet of Things, artificial intelligence, and cloud computing to connect household electrical appliances, lighting systems, heating and air conditioning systems, etc. into an intelligent network to achieve intelligent monitoring and management of power. With the continuous development of science and technology, smart home power management technology has become an indispensable part of people's lives, especially in summer. Smart home power management technology can turn on the air conditioner to cool down the room before the user gets off work, avoiding waiting for too long to cool down when returning home, providing a more comfortable life experience.

[0003] However, since summer is the peak season for electricity consumption and the charge load is high, if the electricity at the user's residence is insufficient to support the operation of the air conditioner, or the air conditioner operation time is short, but the air conditioner is still turned on to cool the user's home, it may cause a power outage, causing the room that has already been cooled to rise in temperature again because the air conditioner is no longer running. In addition, if the power load at the user's residence is limited, turning on the air conditioner in advance may cause the power load to be too large after the user arrives home, resulting in the air conditioner not being able to be used for a long time, affecting the user's use of the air conditioner after arriving home. In this regard, we propose a smart home power management system based on a single-chip microcomputer. Summary of the invention

[0004] In order to solve the above technical problems, a smart home power management system based on a single chip microcomputer is provided. This technical solution solves the above problems.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A smart home power management system based on a single chip microcomputer includes: a first acquisition module, a second acquisition module, a first processing module, a second processing module, a third processing module, an analysis module and a central control module;

[0007] The first acquisition module is used to collect voltage data and air conditioning power data at the user's residence within a certain period of time;

[0008] The first processing module is electrically connected to the first acquisition module, and the first processing module is used to process and clean the collected data to obtain the voltage instability coefficient and air conditioning power coefficient at the user's residence during the time period;

[0009] The analysis module is electrically connected to the first processing module, and the analysis module is used to establish a power support coefficient based on the voltage instability coefficient and the air conditioning power coefficient obtained within the time period;

[0010] The second processing module is electrically connected to the analysis module, and the second processing module is used to compare the power support coefficient with a preset first reference threshold and a second reference threshold of the power support coefficient to generate a supportable air conditioner operation time;

[0011] The second collection module is used to collect the air conditioner operation time data after the user arrives home during the time period;

[0012] The third processing module is electrically connected to the second acquisition module, and the third processing module is used to obtain the average operation time of the air conditioner after the user arrives home according to the air conditioner operation time data;

[0013] The central control module is electrically connected to the second processing module and the third processing module. The central control module is used to process the average air-conditioning operation time after the user arrives home and the supported air-conditioning operation time, generate the air-conditioning can be turned on in advance time, by collecting the time the user arrives home and the cooling time required for the air-conditioning, generate the air-conditioning optimal start-up time coefficient, and compare the air-conditioning optimal start-up time coefficient with the air-conditioning can be turned on in advance time. If the air-conditioning optimal time start-up coefficient is less than the advance start-up time, it means that the smart home system can turn on the air-conditioning in advance to cool down the user's home, and will not affect the subsequent use time of the air-conditioning after the user arrives home.

[0014] Preferably, the first processing module is used to process and clean the collected data, and obtaining the voltage instability coefficient and air conditioning power coefficient at the user's residence specifically includes:

[0015] When the first processing module performs data processing and cleaning, it first calculates the mean of the collected previous voltage data and air conditioning power data;

[0016] Among them, the voltage data mean calculation formula is:

[0017]

[0018] Where V avg Represents the mean voltage, V i represents the voltage data collected for the ith time, and n represents the total number of collections;

[0019] Among them, the mean calculation formula for air conditioning power data is:

[0020]

[0021] Where P avg represents the mean air conditioning power, Pj represents the air conditioning power data collected for the ith time, and m represents the total number of collections;

[0022] The voltage instability coefficient and the air conditioning power coefficient are calculated based on the calculated voltage mean value and air conditioning power mean value respectively;

[0023] Among them, the calculation formula of voltage instability coefficient is:

[0024]

[0025] Where U instability Indicates the voltage instability coefficient;

[0026] Among them, the calculation formula of air conditioning power coefficient is:

[0027]

[0028] Where P coefficient Indicates the air conditioning power coefficient, P max Indicates the rated maximum power of the air conditioner.

[0029] Preferably, the method for establishing the power support coefficient is:

[0030] Based on the calculated voltage instability coefficient and air conditioning power coefficient, the weight coefficients of the voltage instability coefficient and the air conditioning power coefficient are determined by an expert scoring method;

[0031] The expression of power support coefficient is:

[0032] S support =k1×U instability +k2×P coefficient

[0033] In the formula, S support represents the power support coefficient, k1 and k2 represent the weight coefficients of the voltage instability coefficient and the air conditioning power coefficient respectively.

[0034] Preferably, when the second processing module compares the power support coefficient with the first reference threshold value and the second reference threshold value of the power support coefficient set in advance, if the power support coefficient is less than the first reference threshold value S threshold1 , it means that the power supply is tight and the air conditioner can only run for a very short time. The air conditioner can only run for:

[0035] T support1 =0.1×T normal

[0036] Where, T support1 Indicates the air conditioner operation time that can be supported, T normal Indicates the reference value of air conditioner operation time under normal conditions;

[0037] If the power support coefficient is greater than the first reference threshold S threshold1 and is less than the second reference threshold S threshold2 , the air conditioner can run for:

[0038] T support2 =0.5×T normal

[0039] Where, T support2 Indicates the operating time of the air conditioner that can be supported;

[0040] If the power support coefficient is greater than the second reference threshold S threshold2 , the air conditioner can run for:

[0041] T support3 =T normal

[0042] Where, T support3 Indicates the supported operating time of the air conditioner.

[0043] Preferably, the average air conditioner operation time after the user arrives home and the air conditioner operation time that can be supported are processed to generate the air conditioner can be turned on in advance time specifically including:

[0044] Calculate the supported air conditioning operation time based on the second processing module;

[0045] The third processing module counts the total running time of the air conditioner after the user arrives home during this time period as:

[0046]

[0047] Where, T total Indicates the total time the air conditioner runs after the user arrives home during this time period, T g represents the duration of air conditioning operation after the g-th user arrives home, and p represents the number of statistics;

[0048] Calculate the average air conditioning operation time after the user arrives home:

[0049]

[0050] Where, T avg Indicates the average running time of the air conditioner after the user arrives home;

[0051] Based on the average air conditioning operation time after the user arrives home and the air conditioning operation time that can be supported, the air conditioning can be turned on in advance. The expression for the air conditioning can be turned on in advance is:

[0052] T advance =T support -T avg ×k3

[0053] Where, T advance It indicates the time that the air conditioner can be turned on in advance, and k3 indicates the adjustment coefficient.

[0054] Preferably, when the central control module generates the optimal air-conditioning start-up time coefficient, the optimal air-conditioning start-up time coefficient expression is:

[0055] T optimal =T arrival -T cooling -k4×ΔT

[0056] Where, T optimal Indicates the optimal opening time coefficient of the air conditioner, T arrival Indicates the time it takes for the user to get home, T cooling It represents the cooling time required by the air conditioner, ΔT represents the temperature difference between indoor and outdoor, and k4 represents the temperature difference influence coefficient.

[0057] Preferably, the first acquisition module includes a voltage sensor and a power sensor, the voltage sensor is used to collect voltage data, and the power sensor is used to collect air conditioning power data, and the sampling frequency of the voltage sensor and the power sensor is once every 10 minutes to ensure the real-time and accuracy of the collected data, to provide sufficient sample data for subsequent precise analysis of power conditions, to avoid missing key information on power fluctuations due to too low a sampling frequency or causing data redundancy and waste of system resources due to too high a sampling frequency.

[0058] Preferably, when determining the weight coefficients of the voltage instability coefficient and the air-conditioning power coefficient, the analysis module, in addition to the expert scoring method, adopts the hierarchical analysis method for auxiliary verification and adjustment, first constructing a hierarchical structure model including the voltage stability, air-conditioning power demand and power supply capacity criterion layer and the various influencing factor scheme layers, and constructing a judgment matrix through expert comparison of the importance of each layer element, calculating the eigenvector and the maximum eigenvalue for consistency testing, and determining the weight coefficient based on a comprehensive balance of the calculation results and the expert scores.

[0059] Preferably, when generating the optimal air-conditioning opening time coefficient, the central control module considers the indoor and outdoor temperature difference factor and sets a dynamic adjustment mechanism according to different seasons.

[0060] Preferably, the central control module is connected to the user's mobile terminal to realize remote interaction. The user can view the home power parameters, air conditioning operation plan and adjustable parameters in real time through the mobile terminal, such as remotely setting the air conditioning advance start-up time range and the power support coefficient threshold fine-tuning value.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] The smart home power management system proposed in the present invention integrates advanced single-chip microcomputer technology to realize intelligent monitoring and management of the power status of the user's home. It can accurately calculate the power support coefficient according to voltage data and air-conditioning power data, thereby intelligently controlling the start-up time and operating time of the air-conditioner, avoiding power waste and improving energy utilization efficiency. It can generate the optimal start-up time coefficient of the air-conditioner according to the user's arrival time, air-conditioning cooling demand and indoor and outdoor temperature difference, and automatically turn on the air-conditioner in advance for cooling. The user can immediately enjoy a comfortable indoor temperature after arriving home without waiting for the air-conditioner to cool down, which greatly improves the convenience and comfort of life, and shows significant beneficial effects in improving power utilization efficiency, optimizing user life experience, enhancing power supply stability and reliability, and providing remote interaction and personalized setting functions. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is the connection diagram of the smart home power management system module. DETAILED DESCRIPTION

[0064] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0065] Reference Figure 1 As shown, the smart home power management system based on the single chip microcomputer includes: a first acquisition module, a second acquisition module, a first processing module, a second processing module, a third processing module, an analysis module and a central control module;

[0066] The first acquisition module is responsible for collecting voltage data and air conditioning power data at the user's residence within a specific period of time. This module is crucial because it provides basic data for subsequent analysis and processing.

[0067] The first processing module is electrically connected to the first acquisition module. The first processing module performs detailed data processing and cleaning on the collected data. Through a series of algorithms and operations, the voltage instability coefficient and air conditioning power coefficient at the user's residence during the time period are obtained from these data. These coefficients can reflect the power status at the user's residence and the operation status of the air conditioner.

[0068] The analysis module is electrically connected to the first processing module. It establishes a power support coefficient based on the voltage instability coefficient and air conditioning power coefficient obtained during the time period. This power support coefficient comprehensively considers the voltage stability and air conditioning power factors, and can provide an important basis for subsequent judgment of the air conditioning operation time.

[0069] The second processing module is electrically connected to the analysis module. It compares the power support coefficient with the first reference threshold and the second reference threshold of the power support coefficient that are preset. Through this comparison, the duration that can support the operation of the air conditioner can be generated. This duration is calculated based on the power status and the preset threshold, and can ensure that the air conditioner operates without affecting the stability of the power system.

[0070] The second acquisition module is used to collect the time data of the air conditioner running after the user arrives home during the time period. This module can record the actual use of the air conditioner by the user.

[0071] The third processing module is electrically connected to the second acquisition module. It obtains the average time of air conditioning operation after the user arrives home through a specific calculation method based on the air conditioning operation time data. This average time can reflect the user's air conditioning usage habits.

[0072] The central control module is electrically connected to the second processing module and the third processing module. The central control module processes the average duration of the air conditioner operation after the user arrives home and the duration that the air conditioner can be supported to operate, and generates the duration that the air conditioner can be turned on in advance. At the same time, by collecting the time the user arrives home and the cooling time required for the air conditioner, the optimal air conditioner opening time coefficient is generated. Then, the air conditioner optimal opening time coefficient is compared with the air conditioner can be turned on in advance. If the air conditioner optimal time opening coefficient is less than the advance opening time, this means that the smart home system can turn on the air conditioner in advance to cool down the user's house, and will not affect the subsequent use time of the air conditioner after the user arrives home. Such a design can improve the user's comfort, while also making rational use of electricity resources and realizing efficient management of smart homes.

[0073] The first processing module is used to process and clean the collected data, and obtain the voltage instability coefficient and air conditioning power coefficient at the user's residence, specifically including:

[0074] The first processing module takes a series of rigorous steps in the process of data processing and cleaning. First, it calculates the mean of the collected voltage data and air conditioning power data. For voltage data, the first processing module summarizes the voltage values ​​collected at each time point and divides it by the total number of data to obtain the voltage mean. This voltage mean can reflect the average voltage level at the user's home during a specific time period.

[0075] Similarly, for the air conditioner power data, the first processing module will also perform similar operations. The air conditioner power values ​​at different time points are accumulated and then divided by the total number of data to obtain the average air conditioner power. This average air conditioner power can reflect the average power consumption of the air conditioner during the time period;

[0076] After obtaining the voltage mean and the air conditioning power mean, the first processing module will calculate the voltage instability coefficient and the air conditioning power coefficient based on the two means, respectively. For the calculation of the voltage instability coefficient, the first processing module will consider the fluctuation of the voltage data. For example, the voltage stability is measured by comparing the difference between the voltage value and the voltage mean at each time point. If the difference between the voltage value and the voltage mean is large, it means that the voltage instability is high, and accordingly, the voltage instability coefficient will also be large.

[0077] As for the calculation of the air conditioner power coefficient, the first processing module will determine it based on the air conditioner usage and power average. For example, if the power consumption of the air conditioner in certain time periods is significantly higher than the power average, it means that the air conditioner is in a high-load operation state, and the air conditioner power coefficient will also change accordingly;

[0078] The method to establish the power support coefficient is:

[0079] Based on the calculated voltage instability coefficient and air conditioning power coefficient, the expert scoring method will be used to determine the weight coefficients of the two coefficients. The expert scoring method is a commonly used decision-making method that relies on experts in professional fields to evaluate and score with their rich experience and expertise.

[0080] A group of experts with deep expertise and rich practical experience in the power and air conditioning fields will be invited. These experts include power engineers and air conditioning technology experts. They will evaluate the importance of voltage instability coefficient and air conditioning power coefficient in smart home power management systems.

[0081] Experts will independently score the voltage instability coefficient and the air conditioning power coefficient. In the scoring process, experts will consider multiple factors, such as the impact of voltage instability on electrical equipment and the contribution of air conditioning power to power consumption. Each expert will give a score based on his or her own judgment, which reflects their relative importance in the entire system.

[0082] Summarize the scores of the experts. There are many ways to summarize, such as calculating the average score or median. By summarizing the expert scores, a relatively objective weight coefficient value can be obtained.

[0083] For example, if most experts believe that voltage instability has a greater impact on the smart home system, then the voltage instability coefficient will be assigned a higher weight coefficient; conversely, if experts believe that the importance of air conditioning power in the system is relatively low, then the weight coefficient of the air conditioning power coefficient will be relatively small.

[0084] The weight coefficient determined by the expert scoring method can more scientifically and reasonably reflect the actual role of the voltage instability coefficient and the air conditioning power coefficient in the smart home power management system, providing a more accurate basis for subsequent analysis and decision-making. ;

[0085] When the second processing module compares the power support coefficient with the first reference threshold value and the second reference threshold value of the power support coefficient set in advance, if the power support coefficient is less than the first reference threshold value S threshold1 , it means that the power supply is tight and the air conditioner can only run for a very short time. The air conditioner can only run for:

[0086] T support1 =0.1×T normal

[0087] Where, T support1 Indicates the air conditioner operation time that can be supported, T normal Indicates the reference value of air conditioner operation time under normal conditions;

[0088] If the power support coefficient is greater than the first reference threshold S threshold1 and is less than the second reference threshold S threshold2 , the air conditioner can run for:

[0089] T support2 =0.5×T normal

[0090] Where, T support2 Indicates the operating time of the air conditioner that can be supported;

[0091] If the power support coefficient is greater than the second reference threshold S threshold2 , the air conditioner can run for:

[0092] T support3 =T normal

[0093] Where, T support3 Indicates the supported operating time of the air conditioner.

[0094] The average air conditioner operation time after the user arrives home is processed with the air conditioner operation time that can be supported, and the air conditioner can be turned on in advance. Specifically, it includes:

[0095] Calculate the supported air conditioning operation time based on the second processing module;

[0096] The third processing module counts the total running time of the air conditioner after the user arrives home during this time period as:

[0097]

[0098] Where, T totalIndicates the total time the air conditioner runs after the user arrives home during this time period, T g represents the duration of air conditioning operation after the g-th user arrives home, and p represents the number of statistics;

[0099] Calculate the average air conditioning operation time after the user arrives home:

[0100]

[0101] Where, T avg Indicates the average running time of the air conditioner after the user arrives home;

[0102] Based on the average air conditioning operation time after the user arrives home and the air conditioning operation time that can be supported, the air conditioning can be turned on in advance. The expression for the air conditioning can be turned on in advance is:

[0103] T advance =T support -T avg ×k3

[0104] Where, T advance It indicates the time that the air conditioner can be turned on in advance, and k3 indicates the adjustment coefficient.

[0105] In the process of generating the optimal air-conditioning start-up time coefficient, the central control module will conduct comprehensive and detailed considerations, taking into account multiple important factors, including the time it takes for the user to arrive home, the cooling time required by the air conditioner, and the temperature difference between indoors and outdoors.

[0106] Regarding the time it takes for the user to arrive home, the central control module will accurately calculate the time interval from the current moment to the time the user is expected to arrive home. This calculation can be achieved in a variety of ways, such as combining the user's daily travel pattern, location information, or the time the user sets to return home. Knowing how long it takes for the user to arrive home is crucial to determining the best time to turn on the air conditioner, because if it is turned on too early, it will cause unnecessary energy waste; and if it is turned on too late, the indoor temperature will not reach a comfortable state when the user arrives home.

[0107] The cooling time required by the air conditioner is also a key factor. The central control module estimates the time required for the air conditioner to reduce the indoor temperature to a comfortable range based on the size of the indoor space, the cooling power of the air conditioner, and the current indoor temperature information. Different room sizes, different air conditioner performance, and different initial temperatures will affect the calculation of the cooling time. For example, a larger room will take longer to cool down, while a higher-powered air conditioner will be able to achieve cooling in a shorter time.

[0108] In addition, the temperature difference between indoors and outdoors cannot be ignored. The central control module monitors the indoor and outdoor temperatures in real time and calculates the difference between the two. A larger temperature difference between indoors and outdoors means that the air conditioner needs to consume more energy to lower the indoor temperature, which will also affect the cooling speed. If the temperature difference between indoors and outdoors is large, the central control module will turn on the air conditioner in advance so that the indoor temperature can reach a comfortable level when the user arrives home. If the temperature difference between indoors and outdoors is small, the cooling burden of the air conditioner is relatively light, and the start time can be adjusted.

[0109] By comprehensively considering factors such as the time it takes for the user to arrive home, the cooling time required by the air conditioner, and the temperature difference between indoor and outdoor, the central control module can more accurately generate the optimal air conditioner start-up time coefficient, thereby realizing the intelligent management of the air conditioner by the smart home system, which not only improves the user's comfort but also saves energy. The expression of the optimal air conditioner start-up time coefficient is:

[0110] T optimal =T arrival -T cooling -k4×ΔT

[0111] Where, T optimal Indicates the optimal opening time coefficient of the air conditioner, T arrival Indicates the time it takes for the user to get home, T cooling It represents the cooling time required by the air conditioner, ΔT represents the temperature difference between indoor and outdoor, and k4 represents the temperature difference influence coefficient.

[0112] The first acquisition module plays a vital role in the smart home power management system. It mainly includes voltage sensors and power sensors. Among them, the voltage sensor is responsible for collecting voltage data. It can accurately detect the voltage changes in the user's home and provide an accurate data basis for subsequent analysis. The power sensor is used to collect air-conditioning power data. By monitoring the power of the air-conditioning during operation, we can better understand the energy consumption of the air-conditioning and its impact on the power system.

[0113] It is worth noting that the sampling frequency of the voltage sensor and the power sensor is once every 10 minutes. This setting is carefully considered. On the one hand, the sampling frequency of once every 10 minutes can ensure the real-time nature of the collected data. In modern life, the power situation can change at any time, especially during peak hours or when emergencies occur. A higher sampling frequency can capture these changes in time, allowing the system to respond quickly and provide users with more stable and reliable power management services. On the other hand, this sampling frequency can also ensure the accuracy of the data. If the sampling frequency is too low, some key power fluctuation information will be missed, thus affecting the accuracy of subsequent analysis. For example, a sudden increase or decrease in voltage in a short period of time can cause damage to electrical equipment, but if the sampling frequency is too low, these changes cannot be detected in time. If the sampling frequency is too high, although the power changes can be recorded in more detail, it will also cause data redundancy and waste of system resources. The sampling frequency of once every 10 minutes finds a good balance between real-time and accuracy, providing sufficient sample data for subsequent accurate analysis of power conditions.

[0114] When determining the weight coefficients of the voltage instability coefficient and the air conditioning power coefficient, the analysis module uses a variety of methods to ensure the scientificity and reliability of the results. In addition to the expert scoring method, the hierarchical analysis method is also used for auxiliary verification and adjustment. The hierarchical analysis method is a systematic analysis method that can decompose complex problems into multiple levels and construct a judgment matrix by comparing the importance of the elements of each level. First, the analysis module will construct a hierarchical model that includes the voltage stability, air conditioning power demand and power supply capacity criterion layer and the scheme layer of each influencing factor. This model decomposes the problem in a structured manner, making the relationship between each factor clearer. Then, experts are invited to compare the importance of each layer element pairwise to construct a judgment matrix. By calculating the eigenvector and maximum eigenvalue of the judgment matrix, the consistency of the judgment matrix can be tested. If the consistency test passes, the weight coefficient of each factor can be determined based on the calculation results. Finally, the calculation results of the hierarchical analysis method are comprehensively weighed with the results of the expert scoring method to determine the final weight coefficient. This method not only gives full play to the experience and knowledge of experts, but also utilizes the scientificity and objectivity of the hierarchical analysis method, and improves the accuracy of the weight coefficient determination.

[0115] When the central control module generates the optimal air conditioning start-up time coefficient, it considers the indoor and outdoor temperature difference factors and sets a dynamic adjustment mechanism based on different seasons. Due to the different climate characteristics of different seasons, the indoor and outdoor temperature difference will also vary greatly. For example, in summer, the outdoor temperature is high and the indoor and outdoor temperature difference is large, so the air conditioner needs to consume more energy to lower the indoor temperature. In winter, the outdoor temperature is low, the indoor and outdoor temperature difference is relatively small, and the cooling burden of the air conditioner will be reduced accordingly. Therefore, the central control module will dynamically adjust the indoor and outdoor temperature difference factors according to the characteristics of different seasons. In summer, more attention will be paid to the impact of the indoor and outdoor temperature difference, and the air conditioner will be turned on in advance to ensure that the indoor temperature is comfortable when the user arrives home. In winter, the start time can be adjusted to save energy.

[0116] In addition, the central control module is also connected to the user's mobile terminal to achieve remote interaction. Users can view the power parameters, air conditioning operation plan and adjustable parameters at home in real time through the mobile terminal. For example, users can view the current voltage and power on their mobile phones to understand the power situation at home. At the same time, users can also view the operation plan of the air conditioner, including the expected start time and running time. If the user needs, the air conditioner can also be remotely set. The time range for early start-up and the power support coefficient threshold fine-tuning value. Such remote interaction functions provide great convenience for users, allowing users to monitor and adjust the smart home power management system at home anytime and anywhere, improving the user experience.

[0117] The use process of the present invention is as follows: collecting voltage and power coefficient to establish a power support coefficient; comparing the power support coefficient to generate the time duration that can support air conditioning; collecting the time duration of air conditioning operation to generate the time duration that can be turned on in advance; collecting data to generate the optimal time coefficient for turning on the air conditioner; comparing the coefficient with the time duration to decide whether to turn on the air conditioner in advance.

[0118] To sum up, the advantages of the present invention are: it can intelligently monitor and manage the power status of the user's home, calculate the power support coefficient according to the voltage and air-conditioning power data, control the air-conditioning start time and running time, avoid power waste, and improve energy utilization efficiency. It can generate the best air-conditioning start time coefficient according to the user's arrival time, air-conditioning cooling demand and indoor and outdoor temperature difference, turn on the air-conditioning for cooling in advance, improve life convenience and comfort, and have significant effects in improving power utilization efficiency, optimizing user life experience, enhancing power supply stability and reliability, and providing remote interaction and personalized setting functions.

[0119] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. The smart home power management system based on single chip microcomputer is characterized by: include: A first acquisition module, a second acquisition module, a first processing module, a second processing module, a third processing module, an analysis module and a central control module; The first acquisition module is used to collect voltage data and air conditioning power data at the user's residence within a certain period of time; The first processing module is electrically connected to the first acquisition module, and the first processing module is used to process and clean the collected data to obtain the voltage instability coefficient and air conditioning power coefficient at the user's residence during the time period; The analysis module is electrically connected to the first processing module, and the analysis module is used to establish a power support coefficient based on the voltage instability coefficient and the air conditioning power coefficient obtained within the time period; The second processing module is electrically connected to the analysis module, and the second processing module is used to compare the power support coefficient with a preset first reference threshold and a second reference threshold of the power support coefficient to generate a supportable air conditioner operation time; The second collection module is used to collect the air conditioner operation time data after the user arrives home during the time period; The third processing module is electrically connected to the second acquisition module, and the third processing module is used to obtain the average operation time of the air conditioner after the user arrives home according to the air conditioner operation time data; The central control module is electrically connected to the second processing module and the third processing module. The central control module is used to process the average air-conditioning operation time after the user arrives home and the supported air-conditioning operation time, generate the air-conditioning can be turned on in advance time, by collecting the time the user arrives home and the cooling time required for the air-conditioning, generate the air-conditioning optimal start-up time coefficient, and compare the air-conditioning optimal start-up time coefficient with the air-conditioning can be turned on in advance time. If the air-conditioning optimal time start-up coefficient is less than the advance start-up time, it means that the smart home system can turn on the air-conditioning in advance to cool down the user's home, and will not affect the subsequent use time of the air-conditioning after the user arrives home.

2. The single chip microcomputer-based smart home power management system according to claim 1 is characterized in that: The first processing module is used to process and clean the collected data, and obtain the voltage instability coefficient and air conditioning power coefficient at the user's residence, specifically including: When the first processing module performs data processing and cleaning, it first calculates the mean of the collected previous voltage data and air conditioning power data; Among them, the voltage data mean calculation formula is: Where V avg represents the voltage mean, Vi represents the voltage data collected for the ith time, and n represents the total number of collections; Among them, the mean calculation formula for air conditioning power data is: Where P avg represents the mean air conditioning power, P j represents the air conditioning power data collected for the ith time, and m represents the total number of collections; The voltage instability coefficient and the air conditioning power coefficient are calculated based on the calculated voltage mean value and air conditioning power mean value respectively; Among them, the calculation formula of voltage instability coefficient is: Where U instability Indicates the voltage instability coefficient; Among them, the calculation formula of air conditioning power coefficient is: Where P coefficient Indicates the air conditioning power coefficient, P max Indicates the rated maximum power of the air conditioner.

3. The single chip microcomputer-based smart home power management system according to claim 1, characterized in that: The method to establish the power support coefficient is: Based on the calculated voltage instability coefficient and air conditioning power coefficient, the weight coefficients of the voltage instability coefficient and the air conditioning power coefficient are determined by an expert scoring method; The expression of power support coefficient is: S support =k1×U instability +k2×P coefficient In the formula, S support represents the power support coefficient, k1 and k2 represent the weight coefficients of the voltage instability coefficient and the air conditioning power coefficient, respectively.

4. The smart home power management system based on a single chip microcomputer according to claim 1 is characterized in that: When the second processing module compares the power support coefficient with the first reference threshold value and the second reference threshold value of the power support coefficient set in advance, if the power support coefficient is less than the first reference threshold value S threshold1 , it means that the power supply is tight and the air conditioner can only run for a very short time. The air conditioner can only run for: T support1 =0.1×T normal Where, T support1 Indicates the air conditioner operation time that can be supported, T normal Indicates the reference value of air conditioner operation time under normal conditions; If the power support coefficient is greater than the first reference threshold S threshold1 and is less than the second reference threshold S threshold2 , the air conditioner can run for: T support2 =0.5×T normal Where, T support2 Indicates the operating time of the air conditioner that can be supported; If the power support coefficient is greater than the second reference threshold S threshold2 , the air conditioner can run for: T support3 =T normal Where, T support3 Indicates the supported operating time of the air conditioner.

5. The smart home power management system based on single chip microcomputer according to claim 1 is characterized in that: The average air conditioner operation time after the user arrives home is processed with the air conditioner operation time that can be supported, and the air conditioner can be turned on in advance. Specifically, it includes: Calculate the supported air conditioning operation time based on the second processing module; The third processing module counts the total running time of the air conditioner after the user arrives home during this time period as: Where, T total Indicates the total time the air conditioner runs after the user arrives home during this time period, T g represents the duration of air conditioning operation after the g-th user arrives home, and p represents the number of statistics; Calculate the average air conditioning operation time after the user arrives home: Where, T avg Indicates the average running time of the air conditioner after the user arrives home; Based on the average air conditioning operation time after the user arrives home and the air conditioning operation time that can be supported, the air conditioning can be turned on in advance. The expression for the air conditioning can be turned on in advance is: T advance =T support -T avg ×k3 Where, T advance It indicates the time that the air conditioner can be turned on in advance, and k3 indicates the adjustment coefficient.

6. The single chip microcomputer-based smart home power management system according to claim 1, characterized in that: When the central control module generates the optimal air conditioning start-up time coefficient, it comprehensively considers the time it takes for the user to arrive home, the cooling time required for the air conditioner, and the indoor and outdoor temperature difference factors. The optimal air conditioning start-up time coefficient expression is: T optimal =T arrival -T cooling -k4×ΔT Where, T optimal Indicates the optimal opening time coefficient of the air conditioner, T arrival Indicates the time it takes for the user to get home, T cooling It represents the cooling time required by the air conditioner, ΔT represents the temperature difference between indoor and outdoor, and k4 represents the temperature difference influence coefficient.

7. The single chip microcomputer-based smart home power management system according to claim 1, characterized in that: The first acquisition module includes a voltage sensor and a power sensor. The voltage sensor is used to collect voltage data, and the power sensor is used to collect air-conditioning power data. The sampling frequency of the voltage sensor and the power sensor is once every 10 minutes to ensure the real-time and accuracy of the collected data, provide sufficient sample data for the subsequent precise analysis of the power status, and avoid missing key information on power fluctuations due to too low a sampling frequency or causing data redundancy and waste of system resources due to too high a sampling frequency.

8. The single chip microcomputer-based smart home power management system according to claim 1, characterized in that: When determining the weight coefficients of the voltage instability coefficient and the air conditioning power coefficient, the analysis module uses the hierarchical analysis method for auxiliary verification and adjustment in addition to the expert scoring method. First, a hierarchical structure model including the voltage stability, air conditioning power demand and power supply capacity criterion layer and the various influencing factor solution layers is constructed. After experts compare the importance of the elements of each layer, a judgment matrix is ​​constructed, and the eigenvector is calculated to test the consistency with the maximum eigenvalue. The weight coefficient is determined based on a comprehensive balance between the calculation results and the expert scores.

9. The single chip microcomputer-based smart home power management system according to claim 1, characterized in that: When generating the optimal air-conditioning start-up time coefficient, the central control module considers the indoor and outdoor temperature difference factors and sets a dynamic adjustment mechanism according to different seasons.

10. The single chip microcomputer-based smart home power management system according to claim 1, characterized in that: The central control module is connected to the user's mobile terminal to realize remote interaction. The user can view the home power parameters, air conditioning operation plan and adjustable parameters in real time through the mobile terminal, such as remotely setting the air conditioning to be turned on in advance for a long time range and the power support coefficient threshold fine-tuning value.

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