Remote electric heating energy-saving control method and system based on Internet of Things

Abstract

The invention discloses a remote electric heating energy-saving control method and system based on the Internet of Things, and relates to an electric heating control method and system and relates to the technical field of electric heating control. Heating areas of a heating place are divided, and each independent area is identified as a heating area; video shooting is conducted on each heating area, human bodies in the areas are recognized through the visual analysis technology, and behavior characteristics and stay characteristics of the human bodies are analyzed; similarity classification is conducted on the multiple application data sets of each heating area, heating area application data sets are formed, statistical analysis is conducted on time characteristics of the data sets, and basic heating demand coefficients of different time sections are determined according to analysis results; based on the basic heating demand coefficients, driving control is conducted on an electric heating driving module; according to the method, through intelligent identification and analysis of human body behaviors and staying characteristics, accurate prediction and response to heating demands are realized, and improvement of energy saving performance and comfort of a heating system is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of electric heating control, and in particular to a remote electric heating energy-saving control method and system based on the Internet of Things. Background Art

[0002] With the improvement of people's living standards and the enhancement of environmental awareness, the comfort, convenience and economy of winter heating have become the focus of users. Traditional heating methods often have problems such as high energy consumption, inflexible regulation and poor user experience. Especially in northern my country, where winter is extremely cold and the demand for heating is high, traditional coal-fired heating methods are not only highly polluting, but also difficult to achieve precise control and energy-saving goals.

[0003] In recent years, with the rapid development of Internet of Things technology, the application of Internet of Things in the field of heating has gradually received attention. Internet of Things technology realizes remote monitoring and intelligent control of heating systems through various sensors, controllers and wireless communication modules, greatly improving the automation level and energy efficiency of heating systems. However, most of the electric heating systems on the market are still at the stage of local control or simple remote monitoring, and cannot achieve true intelligent and energy-saving control. Summary of the invention

[0004] The purpose of the present invention is to provide an electric heating energy-saving control method and system that can perform intelligent control on electric heating and reduce energy consumption.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: The electric heating energy-saving control method based on the remote Internet of Things includes: Dividing the heating areas of the heating places, the division method includes determining the electric heating drive modules distributed in the heating places, and the place areas where each electric heating drive module can independently control heating, and identifying the place areas as heating areas; Take video of each heating area, and use visual analysis technology to identify the human body in the heating area, analyze the human body's behavioral characteristics and the characteristics of staying in the heating area, and record the human body's behavioral characteristics and staying characteristics in the heating area, which are defined as the heating area application data group; Perform similarity classification on several heating area application data groups corresponding to each heating area to obtain several heating area application data sets, perform statistical analysis on the time characteristics of the several heating area application data sets, and determine the basic heating demand coefficients of different time periods based on the analysis results; The electric heating drive module is driven based on the basic heating demand coefficient in different time periods.

[0006] In some embodiments disclosed in the present invention, the electric heating energy-saving control method based on the remote Internet of Things also includes: A specific network address is set for each heating area. The user terminal determines the heating area for remote control by selecting the network address. The method of selecting the network address includes scanning the QR code corresponding to the network address, or selecting a virtual button module that establishes a mapping relationship with the network address.

[0007] In some embodiments disclosed in the present invention, the standard driving power of each heating area is determined. The method for determining the standard driving power of each heating area includes: Obtain temperature values ​​collected by an outdoor temperature sensor and an indoor temperature sensor, record them as outdoor temperature values ​​and indoor temperature values, and construct an outdoor temperature value curve and an indoor temperature value curve based on the outdoor temperature values ​​and indoor temperature values ​​at different time nodes; Calculate the negative temperature difference between the outdoor temperature value curve and the indoor temperature value curve at different time nodes, and construct a negative temperature difference curve; A standard temperature line is set for the indoor temperature value curve, the standard temperature difference between the indoor temperature value curve and the standard temperature line at different time nodes is calculated, and a standard temperature difference curve is constructed; Based on the standard temperature difference curve, the initial driving power for heating the heating area at different time nodes is determined, and based on the negative temperature difference curve, the first power correction coefficient for the initial driving power at different time nodes is determined, and the product of the first power correction coefficient and the initial driving power is calculated to obtain the standard driving power.

[0008] In some embodiments disclosed in the present invention, the method for determining the initial driving power for heating a heating area at different time nodes includes: A number of standard temperature difference intervals are set, and each standard temperature difference interval corresponds to a specific initial driving power; Comparing the standard temperature difference curve with the standard temperature difference interval, and configuring corresponding initial driving powers for different curve sections based on the standard temperature difference intervals that different curve sections in the standard temperature difference curve respectively conform to; The method for determining the first power correction coefficient for the initial driving power at different time nodes includes: A number of negative temperature difference intervals are set, and each negative temperature difference interval is correspondingly set with a specific first power correction coefficient; The negative temperature difference curve is compared with the negative temperature difference interval, and the first power correction coefficients of the different curve sections are determined based on the negative temperature difference intervals that the different curve sections in the negative temperature difference curve respectively conform to.

[0009] In some embodiments disclosed in the present invention, the method for analyzing the behavioral characteristics of a human body and the characteristics of staying in a heating area includes: Divide the heating area into blocks, including different types of work blocks, activity blocks, rest blocks, transition blocks, service blocks and special-purpose blocks. Based on the main heating purposes of the heating area, determine the blocks that need to be monitored, and identify the blocks that need to be monitored as monitoring blocks; Based on the type of the monitoring block, determine the locking mode of the human behavior characteristics, and record the start time of the human behavior characteristics entering the locking mode as the locking mode time segment; The residence time of the human behavior characteristics of non-locked mode in different monitoring blocks is analyzed. If the residence time is greater than or equal to the preset value, and the human behavior characteristics meet the preset repetitive behavior judgment rules, the human behavior characteristics at this time will be recorded and recorded as the behavior attention time segment; The lock mode time period and the attention time period are identified as stay characteristics in the heating area.

[0010] In some embodiments disclosed in the present invention, the method for determining a human behavior characteristic to enter a lock mode includes: Use the on-site camera in the heating area to shoot the on-site video, pre-process the on-site video, and use the human body detection algorithm to analyze the pre-processed on-site video, delineate the human body blocks, and identify and mark different parts of the human body; A human body block coordinate system is set for the human body block, and the coordinates of each marked part are determined in real time, and the distance parameters between the coordinates of different parts relative to the coordinates of the central part of the human body are calculated to construct a distance parameter group between the parts; According to different types of human body movements, a reference part distance parameter interval group is set, and the reference part distance parameter interval group includes several reference part distance parameter intervals. The reference part distance parameter interval group to which the part distance parameter group belongs is judged in real time. If the time length is greater than or equal to the preset value, the human body behavior characteristics are determined to enter the locking mode.

[0011] In some embodiments disclosed in the present invention, the method for determining whether a human behavior characteristic meets a preset repetitive behavior determination rule includes: The reference inter-part distance parameter interval group to which the inter-part distance parameter group belongs is judged in real time, and it is analyzed that the reference inter-part distance parameter interval group transformed within the first preset time length is less than or equal to the preset value, and the duration of the reference inter-part distance parameter interval group to which the repeated mapping belongs is greater than or equal to the second preset time length, then it is determined that the human behavior characteristics meet the preset repetitive behavior judgment rules.

[0012] In some embodiments disclosed in the present invention, a method for similarity classification of a plurality of heating area application data sets corresponding to each heating area includes: Determine the number of human body blocks locked by different monitoring blocks in the heating area and the human behavior characteristics locked by each human body block, and record the number of human body blocks and the human behavior characteristics of each monitoring block as a heating area sub-application data group; Based on the consistency of the number of human body blocks in each monitoring block to the preset human body block number interval, the heating area application data group is first screened and classified, and based on the consistency of the human behavior characteristics of several human body blocks in each heating area, the heating area application data group is secondly screened and classified; Randomly combine and compare the heating area application data groups after the second screening and classification, and determine the similarity of each comparison result based on the comparison results. If the similarity is less than or equal to a preset value, the heating area application data group in the comparison combination is marked as a concern; Determine the number of attention marks for each heating area application data group after the first preset number of consecutive comparisons, and if the number of attention marks is greater than or equal to the second preset number, remove the corresponding heating area application data group from the second screening and classification; Among them, the expression for calculating the similarity degree is: ; Among them, S is the similarity degree, For heating zone applications, monitor the The first equivalent number of people with the same human motion type, wherein the first equivalent number of people refers to the number of people with the same human motion type in any heating area application data group and another group, For heating zone applications, monitor the The total duration difference corresponding to the types of human body movements, is the preset continuous total time difference, x is the total number of people corresponding to the heating area application data groups that are combined and compared with each other, b is the similarity adjustment constant, and K is the similarity adjustment coefficient.

[0013] In some embodiments disclosed in the present invention, the method for determining the basic heating demand coefficients for different time periods includes: Determine the time application segment mapped by each heating area application data group in the heating area application data set, perform overlap comparison on all time application segments on the same time line, use the overlap time application segment reaching a preset overlap number as a cut-off condition, determine the head end and the tail end of the overlap time application segment, and obtain the overlap time application segment; Integrate and analyze the overlap time application segments corresponding to each heating area application data set to determine the idle time segments between the overlap time application segments; A first basic heating demand coefficient is configured for the overlapped time application segment, and a second basic heating demand coefficient is configured for the idle time segment based on the length of the idle time segment, wherein the longer the length of the idle time segment is, the lower the second basic heating demand coefficient configured for the idle time segment is; Based on the basic heating demand coefficients in different time periods, the method for driving the electric heating driving module includes: The product of the heating demand coefficient and the standard driving power is calculated to obtain the actual driving power, and the electric heating driving module is driven using the actual driving power.

[0014] In some embodiments disclosed in the present invention, there is also disclosed an electric heating energy-saving control system based on the Internet of Things remote control, including: The first module is used to divide the heating areas of the heating places, and the division method includes determining the electric heating drive modules distributed in the heating places, and the place areas where each electric heating drive module can independently control heating, and identifying the place areas as heating areas; The second module is used to shoot a video of each heating area, and use visual analysis technology to identify the human body in the heating area, analyze the human body's behavioral characteristics and the characteristics of staying in the heating area, and record the human body's behavioral characteristics and staying characteristics in the heating area, which are defined as the heating area application data group; The third module is used to classify the several heating area application data groups corresponding to each heating area by similarity, obtain several heating area application data sets, perform statistical analysis on the time characteristics of the several heating area application data sets, and determine the basic heating demand coefficients of different time periods based on the analysis results; The fourth module drives the electric heating drive module based on the basic heating demand coefficient in different time periods.

[0015] The present invention discloses an electric heating energy-saving control method and system based on remote Internet of Things, relates to an electric heating control method and system, and relates to the technical field of electric heating control. The heating areas of a heating place are divided, and each independent area is identified as a heating area; a video is taken of each heating area, and a visual analysis technology is used to identify human bodies in the area, and their behavior characteristics and residence characteristics are analyzed; multiple application data groups of each heating area are similarly classified to form a heating area application data set, and the time characteristics of these data sets are statistically analyzed, and basic heating demand coefficients for different time sections are determined according to the analysis results; based on these basic heating demand coefficients, an electric heating drive module is driven and controlled; the method realizes accurate prediction and response to heating demand by intelligently identifying and analyzing human behavior and residence characteristics, which helps to improve the energy saving and comfort of the heating system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a method step diagram of the electric heating energy-saving control method based on the Internet of Things remote control proposed by the present invention. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0018] In order to achieve the above object, the present invention adopts the following technical solutions: Electric heating energy-saving control method based on remote Internet of Things, see Figure 1 ,include: Step S100, dividing the heating areas of the heating places, the division method includes determining the electric heating drive modules distributed in the heating places, and the place areas where each electric heating drive module can independently control heating, and identifying the place areas as heating areas.

[0019] In the heating place, it is first necessary to divide the heating area according to the distribution of the electric heating drive modules; each electric heating drive module can independently control a specific area of ​​the place, and this area is identified as the heating area; this can ensure that each heating area can be accurately controlled, thereby improving the heating efficiency; for example, in a large office building, there may be multiple electric heating drive modules distributed on different floors or rooms; by dividing the heating area, it can be ensured that each floor or room can be independently heated. Control.

[0020] In some embodiments disclosed in the present invention, the electric heating energy-saving control method based on remote Internet of Things also includes: step S101, a specific network address is set for each heating area, and the user terminal determines to establish a remotely controlled heating area by selecting the network address, wherein the method of selecting the network address includes scanning a QR code corresponding to the network address, or selecting a virtual button module that establishes a mapping relationship with the network address.

[0021] This step first ensures that each heating area has a unique identifier in the Internet of Things, that is, a network address, which can be an IP address, MAC address or other form of unique identification. This network address is recorded in the system and used for subsequent remote communication and control. In order to facilitate users to easily remotely control the heating system, step S101 provides two main access methods. One is to scan the QR code corresponding to the network address. Users only need to use the code scanning function of the smart device to quickly obtain the network address information of the heating area and establish a remote connection. Another way is to select the heating area corresponding to the virtual button module on the system interface. These virtual buttons establish a mapping relationship with the network address. Users can directly connect to the specified heating area by clicking the corresponding button. Both access methods greatly improve the user experience, allowing users to remotely monitor and adjust the heating area conveniently and quickly. Once the connection is established, users can view the real-time status information of the heating area, such as temperature, humidity and human activity, through the terminal, and adjust the heating parameters according to actual needs, thereby achieving precise energy-saving control. In short, step S101 lays a solid foundation for the subsequent steps of the Internet of Things remote electric heating energy-saving control method by setting a specific network address and providing a convenient remote control access method.

[0022] Step S200, take video of each heating area, and use visual analysis technology to identify the human body in the heating area, and analyze the behavioral characteristics of the human body and the residence characteristics in the heating area, and record the behavioral characteristics and residence characteristics of the human body in the heating area, which are defined as the heating area application data group.

[0023] This step first captures and collects video data in the area in real time through high-definition cameras deployed in each heating area. Subsequently, these video data are deeply analyzed using advanced computer vision algorithms and deep learning models to automatically identify and track human bodies in the heating space. This process not only involves basic human body detection, but also extends to detailed analysis of human behavior characteristics, including but not limited to the recognition of postures such as standing, walking, sitting, and lying, as well as the recording of human movement paths and residence time in the heating area. Through these analyses, the system can build a detailed heating area application data set, which includes the activity patterns and residence characteristics of all human bodies in the heating area during a specific period of time. Such data analysis results provide a solid foundation for the accurate prediction and response of heating demand in subsequent steps, enabling the electric heating system to dynamically adjust the heating strategy according to the actual human activity situation, which not only ensures the user's comfortable experience, but also achieves efficient use and conservation of energy.

[0024] In some embodiments disclosed in the present invention, the method for analyzing the behavioral characteristics of a human body and the characteristics of staying in a heating area includes: Step S201, divide the heating area into blocks, including different types of work blocks, activity blocks, rest areas, transition blocks, service blocks and special-purpose blocks, and determine the blocks that need to be retained for monitoring based on the main purpose of heating in the heating area, and identify the blocks that need to be retained for monitoring as monitoring blocks.

[0025] First, the heating area was divided into detailed blocks. These blocks were divided into work blocks, activity blocks, rest blocks, transition blocks, service blocks and special-purpose blocks according to their functions and purposes. Such division helps to understand the usage patterns and needs of the heating areas more accurately, thus providing a basis for subsequent energy-saving control strategies.

[0026] Based on the main heating purposes of the heating area, it will be determined which blocks need to be retained for monitoring. These areas selected as monitoring blocks are usually places where heating demand is relatively stable or where human comfort requirements are high. By retaining the monitoring of these blocks, it can ensure that the heating system can respond more accurately to actual demand and improve energy efficiency.

[0027] Step S202, based on the type of the monitoring block, determine the locking mode of the human behavior characteristics, and record the start time of the human behavior characteristics entering the locking mode as the locking mode time section.

[0028] Further, according to the type of monitoring block, the locking mode of human behavior characteristics is determined. Different block types correspond to different human behavior characteristics, so the locking mode will also be different. For example, in the work block, you may pay more attention to the sitting or standing time of the human body; while in the activity block, you may pay more attention to the movement frequency and path of the human body.

[0029] Once the lock mode is determined, the system will start recording the human behavior characteristics of entering the mode and record the time at this time as the lock mode time period. The purpose of this step is to collect detailed data on the human behavior patterns in the heating area so as to accurately predict and respond to heating needs in the future.

[0030] In some embodiments disclosed in the present invention, the method for determining a human behavior characteristic to enter a lock mode includes: Step S2021, use the on-site camera in the heating area to shoot the on-site video, pre-process the on-site video, and use the human body detection algorithm to analyze the pre-processed on-site video, delineate the human body blocks, and identify and mark different parts of the human body.

[0031] Live videos are captured using on-site cameras in the heating area. These videos are pre-processed to remove noise and enhance image quality for subsequent analysis. Next, the pre-processed live videos are analyzed using a human body detection algorithm to identify the human body in the video and demarcate the human body. This step marks different parts of the human body, such as the head and limbs, providing a basis for subsequent analysis.

[0032] Step S2022, setting a human body block coordinate system for the human body block, and determining the part coordinates of each marked part in real time, and calculating the inter-part distance parameters of different part coordinates relative to the coordinates of the central part of the human body, and constructing an inter-part distance parameter group.

[0033] After defining the human body blocks, the next step is to set a coordinate system for the human body blocks. This coordinate system will use a central part of the human body (such as the center of the torso) as the origin and determine the coordinates of each marked part in real time. Then, the distance parameters between the coordinates of different parts relative to the coordinates of the central part of the human body are calculated, and these parameters constitute the distance parameter group between parts. The purpose of this step is to quantify the relative position relationship between the various parts of the human body and provide a basis for subsequent judgment of the type of human body movement.

[0034] Step S2023, for different types of human body movements, a reference part distance parameter interval group is set, and the reference part distance parameter interval group includes several reference part distance parameter intervals. The reference part distance parameter interval group to which the part distance parameter group belongs is judged in real time. If the time length is greater than or equal to the preset value, the human body behavior characteristics are determined to enter the locking mode.

[0035] For different types of human body movements, a group of reference part distance parameter intervals will be set. These interval groups contain several reference part distance parameter intervals, each of which corresponds to a specific human body movement type. The system will determine in real time the reference part distance parameter interval group to which the current part distance parameter group belongs. If the time length is greater than or equal to the preset value (that is, the human body maintains a certain type of movement for a long enough time), it is determined that the human behavior characteristics have entered the lock mode.

[0036] Step S203, analyze the residence time of the human behavior characteristics of the non-locked mode in different monitoring blocks. If the residence time is greater than or equal to the preset value and the human behavior characteristics meet the preset repetitive behavior judgment rules, then start recording the time of the human behavior characteristics at this time and record it as the behavior attention time segment.

[0037] During the monitoring process of the heating area, the system not only pays attention to the behavioral characteristics of the human body in the locked mode, but also conducts a detailed analysis of the behavioral characteristics in the non-locked mode. Step S203 first monitors and calculates the residence time of the human body in the non-locked mode in different monitoring blocks. When it is detected that the residence time of an individual in a certain monitoring block reaches or exceeds the preset threshold, the system will further analyze the behavioral characteristics of the individual.

[0038] To determine whether these behavioral features are repetitive, the system applies a set of preset repetitive behavior judgment rules. These rules may be based on the periodicity, regularity, similarity or other relevant characteristics of the behavior, and aim to identify those behavioral patterns that are predictable and stable from complex human behaviors.

[0039] Once the system determines that a certain human behavior characteristic meets the preset repetitive behavior judgment rules, it will start recording the time period of the behavior characteristic, which is called the behavior focus time segment. The purpose of recording the behavior focus time segment is to provide more accurate data support for subsequent heating demand prediction and response. By understanding the repetitive behavior patterns of the human body in the heating area, the system can adjust the heating strategy more intelligently to improve energy efficiency and user comfort.

[0040] In some embodiments disclosed in the present invention, the method for determining whether a human behavior characteristic meets a preset repetitive behavior determination rule includes: Step S2031, real-time judgment of the reference inter-part distance parameter interval group to which the inter-part distance parameter group belongs, analysis that the reference inter-part distance parameter interval group transformed within the first preset time length is less than or equal to the preset value, and the duration of the reference inter-part distance parameter interval group to which the repeated mapping belongs is greater than or equal to the second preset time length, then it is determined that the human behavior characteristics meet the preset repetitive behavior judgment rules.

[0041] Step S204: identifying the lock mode time segment and the focus time segment as stay characteristics in the heating area.

[0042] Step S300, similarity classification is performed on several heating area application data groups corresponding to each heating area to obtain several heating area application data sets, and time characteristics of several heating area application data sets are statistically analyzed, and based on the analysis results, basic heating demand coefficients for different time periods are determined.

[0043] By applying data mining and machine learning techniques, we first conduct in-depth similarity classification of the application data groups collected in different time periods in each heating area; the goal of this classification is to identify and summarize the common characteristics of heating demand in the same heating area in different time periods; specifically, we extract key features from each data group, such as human activity density, distribution of residence time, and behavior patterns, and then use clustering algorithms to divide the data groups into several heating area application data sets based on the similarity of these features; we can gain a clearer insight into the internal connections and common laws of heating demand in different time periods in the same heating area; we use mathematical methods and statistical software to conduct detailed statistical analysis of these classified data sets; we pay special attention to the time characteristics of the data, and accurately grasp the actual heating demand of the heating area in each time period by calculating the basic heating demand coefficient in different time periods.

[0044] In some embodiments disclosed in the present invention, a method for similarity classification of a plurality of heating area application data sets corresponding to each heating area includes: Step S301, determining the number of human body blocks locked by different monitoring blocks in the heating area and the human behavior characteristics locked by each human body block, and recording the number of human body blocks and human behavior characteristics of each monitoring block as a heating area sub-application data group.

[0045] Step S302, based on the consistency of the number of human body blocks in each monitoring block to the preset human body block number interval, the heating area application data group is screened and classified for the first time, and based on the consistency of the human behavior characteristics of several human body blocks in each heating area, the heating area application data group is screened and classified for the second time.

[0046] Step S303, randomly combine and compare the heating area application data groups after the second screening and classification, and determine the similarity of each comparison result based on the comparison results. If the similarity is less than or equal to a preset value, the heating area application data group in the comparison combination is marked as a focus.

[0047] Step S304, determining the number of attention marks for each heating area application data group after continuous comparison for a first preset number of times, if the number of attention marks is greater than or equal to a second preset number of times, the corresponding heating area application data group is eliminated in the second screening and classification.

[0048] Among them, the expression for calculating the similarity degree is: .

[0049] Among them, S is the similarity degree, For heating zone applications, monitor the The first equivalent number of people with the same human motion type, wherein the first equivalent number of people refers to the number of people with the same human motion type in any heating area application data group and another group, For heating zone applications, monitor the The total duration difference corresponding to the types of human body movements, is the preset continuous total time difference, x is the total number of people corresponding to the heating area application data groups that are combined and compared with each other, b is the similarity adjustment constant, and K is the similarity adjustment coefficient.

[0050] In some embodiments disclosed in the present invention, the method for determining the basic heating demand coefficients for different time periods includes: Step S305, determine the time application segment mapped by each heating area application data group in the heating area application data set, compare and overlap all the time application segments on the same time line, use the overlapping time application segments reaching a preset overlap number as the cut-off condition, determine the head and tail ends of the overlapping time application segments, and obtain the overlapping time application segments.

[0051] Step S306, integrating and analyzing the overlap time application segments corresponding to each heating area application data set to determine the idle time segments between the overlap time application segments.

[0052] Step S307, configuring a first basic heating demand coefficient for the overlapping time application segment, and configuring a second basic heating demand coefficient for the idle time segment based on the length of the idle time segment, wherein the longer the length of the idle time segment, the lower the second basic heating demand coefficient configured for the idle time segment.

[0053] Step S400: driving the electric heating driving module based on the basic heating demand coefficients in different time periods.

[0054] Based on the basic heating demand coefficients in different time periods, the method for driving the electric heating driving module includes: Step S401, calculating the product of the heating demand coefficient and the standard driving power to obtain the actual driving power, and using the actual driving power to drive the electric heating driving module.

[0055] The electric heating drive module is driven and controlled based on the basic heating demand coefficient in different time periods; this means that the heating system will automatically adjust the heating intensity and time according to actual demand, thereby achieving the dual goals of energy saving and comfort.

[0056] In some embodiments disclosed in the present invention, the standard driving power of each heating area is determined. The method for determining the standard driving power of each heating area includes: Step S500, obtaining temperature values ​​collected by the outdoor temperature sensor and the indoor sensor, recording them as outdoor temperature values ​​and indoor temperature values, and constructing outdoor temperature value curves and indoor temperature value curves based on the outdoor temperature values ​​and indoor temperature values ​​at different time nodes.

[0057] Step S600, calculating the negative temperature difference between the outdoor temperature value curve and the indoor temperature value curve at different time nodes, and constructing a negative temperature difference curve.

[0058] Step S700: a standard temperature line is set for the indoor temperature value curve, a standard temperature difference between the indoor temperature value curve and the standard temperature line at different time nodes is calculated, and a standard temperature difference curve is constructed.

[0059] Step S800, based on the standard temperature difference curve, determines the initial driving power for heating the heating area at different time nodes, and based on the negative temperature difference curve, determines the first power correction coefficient for the initial driving power at different time nodes, and calculates the product of the first power correction coefficient and the initial driving power to obtain the standard driving power.

[0060] In some embodiments disclosed in the present invention, the method for determining the initial driving power for heating a heating area at different time nodes includes: In step S801 , a plurality of standard temperature difference intervals are set, and each standard temperature difference interval is correspondingly set with a specific initial driving power.

[0061] Step S802 : comparing the standard temperature difference curve with the standard temperature difference interval, and configuring corresponding initial driving powers for different curve segments based on the standard temperature difference intervals to which different curve segments in the standard temperature difference curve respectively conform.

[0062] The method for determining the first power correction coefficient for the initial driving power at different time nodes includes: In step S803, a plurality of negative temperature difference intervals are set, and a specific first power correction coefficient is set corresponding to each negative temperature difference interval.

[0063] Step S804 , comparing the negative temperature difference curve with the negative temperature difference interval, and determining first power correction coefficients of different curve sections based on the negative temperature difference intervals that different curve sections in the negative temperature difference curve respectively conform to.

[0064] In some embodiments disclosed in the present invention, there is also disclosed an electric heating energy-saving control system based on the Internet of Things remote control, including: The first module is used to divide the heating areas of the heating places, and the division method includes determining the electric heating drive modules distributed in the heating places, and the place areas where each electric heating drive module can independently control heating, and identifying the place areas as heating areas; The second module is used to shoot a video of each heating area, and use visual analysis technology to identify the human body in the heating area, analyze the human body's behavioral characteristics and the characteristics of staying in the heating area, and record the human body's behavioral characteristics and staying characteristics in the heating area, which are defined as the heating area application data group; The third module is used to classify the several heating area application data groups corresponding to each heating area by similarity, obtain several heating area application data sets, perform statistical analysis on the time characteristics of the several heating area application data sets, and determine the basic heating demand coefficients of different time periods based on the analysis results; The fourth module drives the electric heating drive module based on the basic heating demand coefficient in different time periods.

[0065] The present invention discloses an electric heating energy-saving control method and system based on remote Internet of Things, relates to an electric heating control method and system, and relates to the technical field of electric heating control. The heating areas of a heating place are divided, and each independent area is identified as a heating area; a video is taken of each heating area, and a visual analysis technology is used to identify human bodies in the area, and their behavior characteristics and residence characteristics are analyzed; multiple application data groups of each heating area are similarly classified to form a heating area application data set, and the time characteristics of these data sets are statistically analyzed, and basic heating demand coefficients for different time sections are determined according to the analysis results; based on these basic heating demand coefficients, an electric heating drive module is driven and controlled; the method realizes accurate prediction and response to heating demand by intelligently identifying and analyzing human behavior and residence characteristics, which helps to improve the energy saving and comfort of the heating system.

[0066] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A remote electric heating energy-saving control method based on the Internet of Things, characterized in that: include: Dividing the heating areas of the heating places, the division method includes determining the electric heating drive modules distributed in the heating places, and the place areas where each electric heating drive module can independently control heating, and identifying the place areas as heating areas; Take video of each heating area, and use visual analysis technology to identify the human body in the heating area, analyze the human body's behavioral characteristics and the characteristics of staying in the heating area, and record the human body's behavioral characteristics and staying characteristics in the heating area, which are defined as the heating area application data group; Perform similarity classification on several heating area application data groups corresponding to each heating area to obtain several heating area application data sets, perform statistical analysis on the time characteristics of the several heating area application data sets, and determine the basic heating demand coefficients of different time periods based on the analysis results; The electric heating drive module is driven based on the basic heating demand coefficient in different time periods.

2. The electric heating energy-saving control method based on the Internet of Things remote control according to claim 1 is characterized in that: Also includes: A specific network address is set for each heating area. The user terminal determines the heating area for remote control by selecting the network address. The method of selecting the network address includes scanning the QR code corresponding to the network address, or selecting a virtual button module that establishes a mapping relationship with the network address.

3. The electric heating energy-saving control method based on the Internet of Things remote control according to claim 1 is characterized in that: The method also includes determining a standard driving power of each heating zone. The method for determining the standard driving power of each heating zone includes: Obtain temperature values ​​collected by an outdoor temperature sensor and an indoor temperature sensor, record them as outdoor temperature values ​​and indoor temperature values, and construct an outdoor temperature value curve and an indoor temperature value curve based on the outdoor temperature values ​​and indoor temperature values ​​at different time nodes; Calculate the negative temperature difference between the outdoor temperature value curve and the indoor temperature value curve at different time nodes, and construct a negative temperature difference curve; A standard temperature line is set for the indoor temperature value curve, the standard temperature difference between the indoor temperature value curve and the standard temperature line at different time nodes is calculated, and a standard temperature difference curve is constructed; Based on the standard temperature difference curve, the initial driving power for heating the heating area at different time nodes is determined, and based on the negative temperature difference curve, the first power correction coefficient for the initial driving power at different time nodes is determined, and the product of the first power correction coefficient and the initial driving power is calculated to obtain the standard driving power.

4. The electric heating energy-saving control method based on remote Internet of Things according to claim 3 is characterized in that: The method for determining the initial driving power for heating the heating area at different time nodes includes: A number of standard temperature difference intervals are set, and each standard temperature difference interval corresponds to a specific initial driving power; Comparing the standard temperature difference curve with the standard temperature difference interval, and configuring corresponding initial driving powers for different curve sections based on the standard temperature difference intervals that different curve sections in the standard temperature difference curve respectively conform to; The method for determining the first power correction coefficient for the initial driving power at different time nodes includes: A number of negative temperature difference intervals are set, and each negative temperature difference interval is correspondingly set with a specific first power correction coefficient; The negative temperature difference curve is compared with the negative temperature difference interval, and the first power correction coefficients of the different curve sections are determined based on the negative temperature difference intervals that the different curve sections in the negative temperature difference curve respectively conform to.

5. The electric heating energy-saving control method based on remote Internet of Things according to claim 1 is characterized in that: Methods for analyzing the behavioral characteristics of a human body and the characteristics of staying in a heating area include: Divide the heating area into blocks, including different types of work blocks, activity blocks, rest blocks, transition blocks, service blocks and special-purpose blocks. Based on the main heating purposes of the heating area, determine the blocks that need to be monitored, and identify the blocks that need to be monitored as monitoring blocks; Based on the type of the monitoring block, determine the locking mode of the human behavior characteristics, and record the start time of the human behavior characteristics entering the locking mode as the locking mode time segment; The residence time of the human behavior characteristics of non-locked mode in different monitoring blocks is analyzed. If the residence time is greater than or equal to the preset value, and the human behavior characteristics meet the preset repetitive behavior judgment rules, the human behavior characteristics at this time will be recorded and recorded as the behavior attention time segment; The lock mode time period and the attention time period are identified as stay characteristics in the heating area.

6. The electric heating energy-saving control method based on remote Internet of Things according to claim 5 is characterized in that: Methods for judging human behavior characteristics and entering the lock mode include: Use the on-site camera in the heating area to shoot the on-site video, pre-process the on-site video, and use the human body detection algorithm to analyze the pre-processed on-site video, delineate the human body blocks, and identify and mark different parts of the human body; A human body block coordinate system is set for the human body block, and the coordinates of each marked part are determined in real time, and the distance parameters between the coordinates of different parts relative to the coordinates of the central part of the human body are calculated to construct a distance parameter group between the parts; According to different types of human body movements, a reference part distance parameter interval group is set, and the reference part distance parameter interval group includes several reference part distance parameter intervals. The reference part distance parameter interval group to which the part distance parameter group belongs is judged in real time. If the time length is greater than or equal to the preset value, the human body behavior characteristics are determined to enter the locking mode.

7. The electric heating energy-saving control method based on remote Internet of Things according to claim 6 is characterized in that: Methods for determining whether human behavior characteristics conform to preset repetitive behavior determination rules include: The reference inter-part distance parameter interval group to which the inter-part distance parameter group belongs is judged in real time, and it is analyzed that the reference inter-part distance parameter interval group transformed within the first preset time length is less than or equal to the preset value, and the duration of the reference inter-part distance parameter interval group to which the repeated mapping belongs is greater than or equal to the second preset time length, then it is determined that the human behavior characteristics meet the preset repetitive behavior judgment rules.

8. The electric heating energy-saving control method based on remote Internet of Things according to claim 5 is characterized in that: The method for similarity classification of a plurality of heating area application data sets corresponding to each heating area includes: Determine the number of human body blocks locked by different monitoring blocks in the heating area and the human behavior characteristics locked by each human body block, and record the number of human body blocks and the human behavior characteristics of each monitoring block as a heating area sub-application data group; Based on the consistency of the number of human body blocks in each monitoring block to the preset human body block number interval, the heating area application data group is first screened and classified, and based on the consistency of the human behavior characteristics of several human body blocks in each heating area, the heating area application data group is secondly screened and classified; Randomly combine and compare the heating area application data groups after the second screening and classification, and determine the similarity of each comparison result based on the comparison results. If the similarity is less than or equal to a preset value, the heating area application data group in the comparison combination is marked as a concern; Determine the number of attention marks for each heating area application data group after the first preset number of consecutive comparisons, and if the number of attention marks is greater than or equal to the second preset number, remove the corresponding heating area application data group from the second screening and classification; Among them, the expression for calculating the similarity degree is: ; Among them, S is the similarity degree, For heating zone applications, monitor the The first equivalent number of people with the same human motion type, wherein the first equivalent number of people refers to the number of people with the same human motion type in any heating area application data group and another group, is the total duration difference corresponding to the i-th human motion type in the monitoring block between the heating area application data groups, is the preset continuous total time difference, x is the total number of people corresponding to the heating area application data groups that are combined and compared with each other, b is the similarity adjustment constant, and K is the similarity adjustment coefficient.

9. The electric heating energy-saving control method based on remote Internet of Things according to claim 1 is characterized in that: Methods for determining the basic heating demand coefficient for different time periods include: Determine the time application segment mapped by each heating area application data group in the heating area application data set, perform overlap comparison on all time application segments on the same time line, use the overlap time application segment reaching a preset overlap number as a cut-off condition, determine the head end and the tail end of the overlap time application segment, and obtain the overlap time application segment; Integrate and analyze the overlap time application segments corresponding to each heating area application data set to determine the idle time segments between the overlap time application segments; A first basic heating demand coefficient is configured for the overlapped time application segment, and a second basic heating demand coefficient is configured for the idle time segment based on the length of the idle time segment, wherein the longer the length of the idle time segment is, the lower the second basic heating demand coefficient configured for the idle time segment is; Based on the basic heating demand coefficients in different time periods, the method for driving the electric heating driving module includes: The product of the heating demand coefficient and the standard driving power is calculated to obtain the actual driving power, and the electric heating driving module is driven using the actual driving power.

10. The electric heating energy-saving control system based on the Internet of Things is characterized by: include: The first module is used to divide the heating areas of the heating places, and the division method includes determining the electric heating drive modules distributed in the heating places, and the place areas where each electric heating drive module can independently control heating, and identifying the place areas as heating areas; The second module is used to shoot a video of each heating area, and use visual analysis technology to identify the human body in the heating area, analyze the human body's behavioral characteristics and the characteristics of staying in the heating area, and record the human body's behavioral characteristics and staying characteristics in the heating area, which are defined as the heating area application data group; The third module is used to classify the several heating area application data groups corresponding to each heating area by similarity, obtain several heating area application data sets, perform statistical analysis on the time characteristics of the several heating area application data sets, and determine the basic heating demand coefficients of different time periods based on the analysis results; The fourth module drives the electric heating drive module based on the basic heating demand coefficient in different time periods.

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