Intelligent heat supply management and control system based on Internet of Things data acquisition and intelligent research and judgment

Abstract

The invention provides an intelligent heat supply management and control system based on Internet of Things data acquisition and intelligent research and judgment, and relates to the technical field of intelligent heat supply operation, and the system comprises a first indoor environment data acquisition module which detects first temperature data and first infrared radiation intensity data; the historical user information acquisition module is used for acquiring user behavior habit information; the heat supply judgment module determines whether a heat supply system needs to be started or not; a heat supply module is started, and a heat supply system is started; the indoor environment data acquisition module is used for acquiring indoor environment data; the outdoor environment data acquisition module is used for acquiring outdoor environment data; the heat supply system monitoring module is used for collecting heat supply parameters of the heat supply system; the heat load adjusting and judging module is used for determining whether the heat load of the heat supply system needs to be adjusted or not; the thermal load adjustment determining module is used for determining the adjusted thermal load; and the monitoring report module is used for generating a system monitoring report. According to the invention, the heat supply effect can be obviously improved, and the energy consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the field of smart heating operation technology, and in particular to a smart heating management and control system based on Internet of Things data collection and intelligent analysis. Background Art

[0002] In related technologies, traditional heating systems face the problem of lack of heating control means, resulting in delayed heating regulation, difficulty in achieving accurate heating, uneven heating and cooling, oversupply or undersupply, etc., affecting user experience. Smart heating is to use high-tech technologies such as the Internet of Things, big data, and artificial intelligence to carry out intelligent transformation of the heating system, realize data interconnection and sharing, and thus build a digital and intelligent heating full-process management system. The integrated application of these technologies is driving the heating industry towards a more efficient, smarter, and more environmentally friendly direction. The smart heating system can realize intelligent heating collection, automated system control, and scientific operation and supervision. With the continuous increase in urban heating demand, the smart heating industry faces diversified market demands, including the intelligent upgrade of the heating system, energy conservation and emission reduction, and improved heating efficiency. At the same time, as people's requirements for living comfort continue to increase, the smart heating industry also needs to continuously innovate and optimize products to meet the different needs of the market.

[0003] The information disclosed in the background technology section of this application is only intended to deepen the understanding of the general background technology of this application, and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0004] The present invention provides a smart heating control system based on Internet of Things data collection and intelligent analysis, which can solve the technical problem of delayed heating regulation.

[0005] According to a first aspect of the present invention, a smart heating control system based on IoT data collection and intelligent analysis is provided, comprising: The first indoor environment data acquisition module detects first indoor temperature data and first infrared radiation intensity data at the beginning of the current control cycle through sensors arranged in the heating area; The historical user information collection module obtains user behavior habit information through the cloud platform, wherein the user behavior habit information includes: user preferred temperature and user return home time; The heating module determines whether to start the heating system according to the first temperature data, the first infrared radiation intensity and the user behavior habit information at the start time of the current control cycle; Turn on the heating module. If the heating system needs to be started, turn on the heating system and provide heating to the heating area within the control cycle; The indoor environment data acquisition module acquires indoor environment data through multiple sensors arranged in the heating area at multiple moments of the current control cycle, wherein the indoor environment data includes: infrared radiation intensity data, indoor temperature data and indoor humidity data; The outdoor environment data acquisition module acquires outdoor environment data at multiple moments of the current control cycle through sensors arranged at sampling locations outside the heating area, wherein the outdoor environment data includes: outdoor temperature data, outdoor humidity data and outdoor wind speed data; The heating system monitoring module collects heating parameters of the heating system through the equipment monitoring system at multiple moments in the current control cycle, wherein the heating parameters include: heat load, thermal efficiency and system pressure; A heat load adjustment judgment module determines whether the heat load of the heating system needs to be adjusted according to the indoor environment data and the user behavior habit information; A heat load adjustment and determination module, if the heat load of the heating system needs to be adjusted, determines the adjusted heat load according to the heat load, the outdoor environment data, the indoor environment data, and the user behavior habit information, and adjusts the heating system according to the adjusted heat load; A monitoring report module generates a system monitoring report according to the thermal efficiency and the system pressure.

[0006] According to a second aspect of the present invention, a smart heating control method based on IoT data collection and intelligent analysis is provided, comprising: By means of sensors arranged in the heating area, first temperature data and first infrared radiation intensity data of the room are detected at the beginning of the current control cycle; Obtaining user behavior habit information through the cloud platform, wherein the user behavior habit information includes: user preferred temperature, user return home time; At the beginning of the current control cycle, determining whether it is necessary to start the heating system according to the first temperature data, the first infrared radiation intensity and the user behavior habit information; If the heating system needs to be started, the heating system is turned on and the heating area is heated within the control cycle; At multiple moments in the current control cycle, indoor environmental data is obtained through multiple sensors set in the heating area, wherein the indoor environmental data includes: infrared radiation intensity data, indoor temperature data and indoor humidity data; At multiple moments in the current control cycle, outdoor environmental data is obtained through sensors arranged at sampling locations outside the heating area, wherein the outdoor environmental data includes: outdoor temperature data, outdoor humidity data and outdoor wind speed data; At multiple moments in the current control cycle, the heating parameters of the heating system are collected through the equipment monitoring system, wherein the heating parameters include: heat load, thermal efficiency and system pressure; Determining whether it is necessary to adjust the heat load of the heating system according to the indoor environment data and the user behavior habit information; If it is necessary to adjust the heat load of the heating system, determine the adjusted heat load according to the heat load, the outdoor environment data, the indoor environment data, and the user behavior habit information, and adjust the heating system according to the adjusted heat load; A system monitoring report is generated according to the thermal efficiency and the system pressure.

[0007] Technical effect: According to the present invention, the heating parameters of the heating system can be continuously monitored through the equipment monitoring system, the indoor environmental data in the heating area and the outdoor environmental data at the sampling location can be continuously collected through sensors, and the user behavior habit information can be obtained through the cloud platform. Furthermore, based on the indoor environmental data, outdoor environmental data and user behavior habit information, it is determined whether the heating system needs to be started, and the heat load of the heating system is optimized and adjusted, thereby improving the comprehensiveness and accuracy of intelligent heating management and control, and can significantly improve the heating effect and reduce energy consumption. When determining the first discrimination parameter, the first discrimination parameter can be determined based on the first temperature data, the first infrared radiation intensity and the user behavior habit information. In the calculation process, the situation of whether the user is in the heating area and the difference between the temperature of the heating area and the user's preferred temperature are fully considered, and based on this, it is determined whether to start the heating system, thereby improving the accuracy of the judgment of whether to start the heating system. When determining the second discrimination parameter, the second discrimination parameter of whether the user is in the heating area can be determined according to the indoor humidity change rate, the indoor temperature change rate, the first infrared radiation intensity data and the user behavior habit information. When the user is in the heating area, the indoor temperature change condition and the indoor humidity change condition in the heating area are fully considered, so as to determine the demand for adjusting the thermal load of the heating system according to the indoor temperature change condition and the indoor humidity change condition. When the user is outside the heating area, the relationship between the time required to heat the indoor temperature in the heating area to the user's preferred temperature and the time required for the user to return home is fully considered, thereby improving the accuracy of the judgment of adjusting the thermal load of the heating system. When determining the adjusted heat load, the adjusted heat load can be determined based on the heat load, outdoor environmental data, indoor environmental data, and user behavior habit information. During the calculation process, the outdoor wind data, the indoor and outdoor temperature difference, the average indoor temperature change rate, the average indoor humidity change rate, the outdoor humidity data, and the outdoor temperature data are fully considered to affect the heat load that the heating system needs to provide. The adjusted heat load is determined based on the impact and the current heat load, so that the adjusted heat load can adapt to the indoor environmental conditions and outdoor weather conditions, thereby improving the comprehensiveness and accuracy of the adjusted heat load.

[0008] It should be understood that the above general description and the following detailed description are exemplary and explanatory only and do not limit the present invention. Other features and aspects of the present invention will become more apparent from the following detailed description of exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other embodiments can be obtained based on these drawings without creative work. Figure 1 A schematic diagram of a smart heating control system based on IoT data collection and intelligent analysis according to an embodiment of the present invention is exemplarily shown; Figure 2 The flowchart of the intelligent heating control method based on Internet of Things data collection and intelligent analysis according to an embodiment of the present invention is exemplarily shown. DETAILED DESCRIPTION

[0010] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described 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, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0011] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0012] Figure 1 A schematic diagram of a smart heating control system based on IoT data collection and intelligent analysis according to an embodiment of the present invention is exemplarily shown, and the system includes: The first indoor environment data acquisition module detects first indoor temperature data and first infrared radiation intensity data at the beginning of the current control cycle through sensors arranged in the heating area; The historical user information collection module obtains user behavior habit information through the cloud platform, wherein the user behavior habit information includes: user preferred temperature and user return home time; The heating module determines whether to start the heating system according to the first temperature data, the first infrared radiation intensity and the user behavior habit information at the start time of the current control cycle; Turn on the heating module. If the heating system needs to be started, turn on the heating system and provide heating to the heating area within the control cycle; The indoor environment data acquisition module acquires indoor environment data through multiple sensors arranged in the heating area at multiple moments of the current control cycle, wherein the indoor environment data includes: infrared radiation intensity data, indoor temperature data and indoor humidity data; The outdoor environment data acquisition module acquires outdoor environment data at multiple moments of the current control cycle through sensors arranged at sampling locations outside the heating area, wherein the outdoor environment data includes: outdoor temperature data, outdoor humidity data and outdoor wind speed data; The heating system monitoring module collects heating parameters of the heating system through the equipment monitoring system at multiple moments in the current control cycle, wherein the heating parameters include: heat load, thermal efficiency and system pressure; A heat load adjustment judgment module determines whether the heat load of the heating system needs to be adjusted according to the indoor environment data and the user behavior habit information; A heat load adjustment and determination module, if the heat load of the heating system needs to be adjusted, determines the adjusted heat load according to the heat load, the outdoor environment data, the indoor environment data, and the user behavior habit information, and adjusts the heating system according to the adjusted heat load; A monitoring report module generates a system monitoring report according to the thermal efficiency and the system pressure.

[0013] According to the embodiment of the present invention, the intelligent heating control system based on Internet of Things data collection and intelligent analysis can continuously monitor the heating parameters of the heating system through the equipment monitoring system, continuously collect indoor environmental data in the heating area and outdoor environmental data at the sampling location through sensors, and obtain user behavior habit information through the cloud platform. Furthermore, based on the indoor environmental data, outdoor environmental data and user behavior habit information, it is determined whether the heating system needs to be started, and the thermal load of the heating system is optimized and adjusted, which improves the comprehensiveness and accuracy of intelligent heating control, can significantly improve the heating effect and reduce energy consumption.

[0014] According to one embodiment of the present invention, in the first indoor environment data acquisition module, a sensor disposed in the heating area detects first indoor temperature data and first infrared radiation intensity data at the beginning of the current control cycle.

[0015] For example, by installing a temperature sensor in the heating area (for example, a residential building), the first indoor temperature data at the start of the control cycle is collected, and by installing an infrared sensor in the heating area, the first indoor infrared radiation intensity data at the start of the control cycle is collected. Since human activities will generate infrared radiation, the first infrared radiation intensity data can be used to analyze whether the user is located in the heating area at the start of the control cycle.

[0016] According to an embodiment of the present invention, in the historical user information collection module, user behavior habit information is obtained through a cloud platform, wherein the user behavior habit information includes: user preferred temperature and user return home time.

[0017] For example, smart home devices (such as smart door locks and smart bracelets) can collect historical user home times from multiple historical dates. The cloud platform will store multiple historical user home times and determine the user's home time based on the average of multiple historical user home times. When the user adjusts the smart heating system through the cloud platform, the cloud platform will store the user's adjustment records and determine the user's preferred temperature based on the user's adjustment records.

[0018] According to one embodiment of the present invention, in the heating judgment module, at the beginning of the current control cycle, it is determined whether the heating system needs to be started based on the first temperature data, the first infrared radiation intensity and the user behavior habit information.

[0019] According to an embodiment of the present invention, at the beginning of the current control cycle, determining whether to start the heating system according to the first temperature data, the first infrared radiation intensity and the user behavior habit information includes: Determining a first discrimination parameter according to the first temperature data, the first infrared radiation intensity and the user behavior habit information; When the first discrimination parameter is less than a set threshold, it is determined that the heating system does not need to be started; When the first discrimination parameter is greater than or equal to the set threshold, it is determined that the heating system needs to be started.

[0020] According to an embodiment of the present invention, determining a first discrimination parameter according to the first temperature data, the first infrared radiation intensity and the user behavior habit information includes: determining the first discrimination parameter according to formula (1): , (1) Among them, if is a conditional function, is the first infrared radiation intensity, To preset the infrared radiation intensity threshold, For the user's preferred temperature, is the first temperature data, is the start time of the management cycle, The time for users to go home, is the preset time difference threshold.

[0021] According to one embodiment of the present invention, in formula (1), the conditional function The value of includes the following two cases, when satisfying When the condition is met, it means that the user is located in the heating area at the beginning of the management period. The value of this condition function is , The difference between the user's preferred temperature and the first temperature data at the start of the management cycle. , it means that at the beginning of the management period, the user is outside the heating area. The value of this condition function is the value of the inner condition function. The inner condition function is .

[0022] According to one embodiment of the present invention, in formula (1), the inner condition function The value of includes the following two cases, when satisfying When the condition is met, the time difference between the user's home time and the start time of the management cycle is greater than the preset time difference threshold, indicating that the user still needs some time to reach the heating area, and no preheating is required. The value of the inner condition function is 0. When the condition is met, the time difference between the user's home time and the start time of the management cycle is less than the preset time difference threshold, indicating that the user is about to reach the heating area and may need preheating. The value of the inner condition function .

[0023] According to one embodiment of the present invention, the value of the first discrimination parameter is Or 0, when the first discrimination parameter is less than the set threshold, it indicates that the heating area does not need heating or preheating, and it is determined that there is no need to start the heating system. When the first discrimination parameter is greater than or equal to the set threshold, it indicates that the heating area needs heating or preheating, and it is determined that the heating system needs to be started.

[0024] In this way, the first discrimination parameter can be determined based on the first temperature data, the first infrared radiation intensity and the user behavior habit information. During the calculation process, whether the user is in the heating area and the difference between the temperature of the heating area and the user's preferred temperature are fully considered, and based on this, whether to start the heating system is determined, thereby improving the accuracy of the judgment of whether to start the heating system.

[0025] According to one embodiment of the present invention, in starting the heating module, if the heating system needs to be started, the heating system is started and the heating area is heated within the control cycle.

[0026] According to one embodiment of the present invention, in the indoor environment data acquisition module, at multiple moments in the current control cycle, indoor environment data is acquired through multiple sensors arranged in the heating area, wherein the indoor environment data includes: infrared radiation intensity data, indoor temperature data and indoor humidity data.

[0027] For example, during the control cycle, the heating system provides heat to the heating area, and collects infrared radiation intensity data, indoor temperature data, and indoor humidity data in the heating area through multiple infrared sensors, temperature sensors, and humidity sensors installed in the heating area.

[0028] According to one embodiment of the present invention, in the outdoor environment data acquisition module, at multiple moments in the current control cycle, outdoor environment data is acquired through sensors arranged at sampling locations outside the heating area, wherein the outdoor environment data includes: outdoor temperature data, outdoor humidity data and outdoor wind data.

[0029] For example, during the control period, outdoor temperature data, outdoor humidity data and outdoor wind data are collected by setting temperature sensors, humidity sensors and wind sensors at the sampling location. The sampling location is an area outside the heating area, and the sampling location is not affected by the temperature, humidity and wind in the heating area. The outdoor temperature data, outdoor humidity data and outdoor wind data collected at the sampling location can fully reflect the outdoor weather conditions.

[0030] According to one embodiment of the present invention, in the heating system monitoring module, at multiple moments in the current control cycle, the heating parameters of the heating system are collected through the equipment monitoring system, wherein the heating parameters include: heat load, thermal efficiency and system pressure.

[0031] For example, during the management and control cycle, the heat load, thermal efficiency and system pressure of the heating system are collected and monitored through the equipment monitoring system.

[0032] According to an embodiment of the present invention, in the heat load adjustment judgment module, it is determined whether the heat load of the heating system needs to be adjusted according to the indoor environment data and the user behavior habit information.

[0033] According to an embodiment of the present invention, determining whether it is necessary to adjust the heat load of the heating system according to the indoor environment data and the user behavior habit information includes: Fitting the indoor temperature data to the time in the current control cycle to obtain an indoor temperature function of the indoor temperature in the current control cycle; Determining an indoor temperature derivative function according to the indoor temperature function; Determine the indoor temperature change rate at multiple moments in the current control cycle according to the indoor temperature derivative function; Fitting the indoor humidity data to the time in the current control cycle to obtain an indoor humidity function of the indoor humidity in the current control cycle; Determining an indoor humidity derivative function according to the indoor humidity function; Determine the indoor humidity change rate at multiple moments in the current control cycle according to the indoor humidity derivative function; Determine whether the heat load of the heating system needs to be adjusted based on the infrared radiation intensity data, the indoor humidity change rate, the indoor temperature change rate and the user behavior habit information.

[0034] According to an embodiment of the present invention, determining whether it is necessary to adjust the heat load of the heating system according to the infrared radiation intensity data, the indoor humidity change rate, the indoor temperature change rate and the user behavior habit information includes: determining the second discrimination parameter at the i-th moment of the control cycle according to formula (2): , (2) in, is the i-th moment of the current control cycle, is the indoor humidity change rate at the i-th moment of the current control cycle, is the indoor temperature change rate at the i-th moment of the current control cycle, is the indoor temperature data at the i-th moment of the current control cycle, is the infrared radiation intensity at the i-th moment of the current control cycle, To preset the infrared radiation intensity threshold, For the user's preferred temperature, The time for users to go home, is the preset temperature change rate threshold, is the preset humidity change rate threshold, The user prefers temperature; If the second discrimination parameter at the i-th moment of the control cycle is greater than the set threshold, it is determined to adjust the heat load of the heating system at the i+1-th moment of the control cycle.

[0035] According to one embodiment of the present invention, in formula (2), when When , it means that the user is located in the heating area at the i-th moment of the management period, and the value of the second discrimination parameter is , is the average indoor temperature change rate at the first i moments of the control cycle, It indicates the relative difference between the preset temperature change rate threshold and the average indoor temperature change rate i moments before the control cycle. The larger the ratio is, the greater the difference between the preset temperature change rate threshold and the average indoor temperature change rate i moments before the control cycle. When the average indoor temperature change rate i moments before the control cycle is much larger than the preset temperature change rate threshold, human discomfort may occur due to the excessively fast heating rate, and the heat load of the heating system needs to be adjusted. When the average indoor temperature change rate i moments before the control cycle is much smaller than the preset temperature change rate threshold, it indicates that the heating effect is not good, and the heat load of the heating system needs to be adjusted. is the average indoor humidity change rate at the first i moments of the control cycle, It represents the difference between the preset temperature change rate threshold and the average indoor humidity change rate at the i-th moment before the control period. The smaller the ratio, The larger it is, the greater the average indoor humidity change rate before the control period i moments is. The humidity drop in the heating area caused by the heating system during the heating process is abnormal, which may cause discomfort and require adjustment of the heat load of the heating system.

[0036] According to one embodiment of the present invention, in formula (2), when When , it means that the user is outside the heating area at the i-th moment of the management period, and the value of the second discrimination parameter is, , is the difference between the user's preferred temperature and the indoor temperature data at the i-th moment of the management cycle, is the time required to raise the temperature in the heating area to the user's preferred temperature, determined according to the average indoor temperature change rate at the previous i moments of the control cycle. Indicates the time required for users to reach the heating area. It is the relative difference between the time required for the user to reach the heating area and the time required to raise the temperature in the heating area to the user's preferred temperature. The larger the ratio, the greater the difference between the time required for the user to reach the heating area and the time required to raise the temperature in the heating area to the user's preferred temperature. When the time required to raise the temperature in the heating area to the user's preferred temperature is much longer than the time required for the user to reach the heating area, the temperature in the heating area has not reached the user's preferred temperature when the user arrives at the heating area, affecting the user's usage experience. When the time required to raise the temperature in the heating area to the user's preferred temperature is much shorter than the time required for the user to reach the heating area, it will cause a waste of heating resources. Therefore, when the difference between the time required for the user to reach the heating area and the time required to raise the temperature in the heating area to the user's preferred temperature is too large, the thermal load of the heating system needs to be adjusted.

[0037] According to one embodiment of the present invention, if the second discrimination parameter at the i-th moment of the control cycle is greater than the set threshold, it means that the thermal load at the i-th moment of the control cycle does not meet the user's needs, and it is determined to adjust the thermal load of the heating system at the i+1-th moment of the control cycle.

[0038] In this way, the second discrimination parameter of whether the user is in the heating area can be determined according to the indoor humidity change rate, the indoor temperature change rate, the first infrared radiation intensity data and the user behavior habit information. When the user is in the heating area, the indoor temperature change condition and the indoor humidity change condition in the heating area are fully considered, so as to determine the demand for adjusting the thermal load of the heating system according to the indoor temperature change condition and the indoor humidity change condition. When the user is outside the heating area, the relationship between the heating time required to raise the indoor temperature in the heating area to the user's preferred temperature and the time required for the user to return home is fully considered, thereby improving the accuracy of the judgment on the thermal load of the heating system.

[0039] According to one embodiment of the present invention, in the heat load adjustment determination module, if the heat load of the heating system needs to be adjusted, the adjusted heat load is determined according to the heat load, the outdoor environment data, the indoor environment data, and the user behavior habit information, and the heating system is adjusted according to the adjusted heat load.

[0040] According to an embodiment of the present invention, if it is necessary to adjust the heat load of the heating system, the adjusted heat load is determined according to the heat load, the outdoor environment data, the indoor environment data, and the user behavior habit information, and the heating system is adjusted according to the adjusted heat load, including: determining the heat load of the heating system at the i+1th moment of the control cycle according to formula (3): , (3) in, is the heat load of the heating system at the i-th moment of the current control cycle, is the outdoor wind data at the i-th moment of the current control cycle, is the outdoor humidity data at the i-th moment of the current control cycle, is the outdoor temperature data at the i-th moment of the current control cycle, To preset the outdoor wind data threshold, To preset the outdoor humidity data threshold, It is the preset indoor and outdoor temperature difference threshold.

[0041] According to one embodiment of the present invention, in formula (3), when When , it means that the user is located in the heating area at the i-th moment of the management cycle, and the heat load of the heating system at the i+1-th moment of the management cycle The value of , is the indoor and outdoor temperature difference at the i-th moment of the control cycle, is the ratio of the indoor and outdoor temperature difference at the i-th moment of the control cycle to the preset indoor and outdoor temperature difference threshold. The closer the ratio is to 1, the smaller the impact of the change in indoor and outdoor temperature difference on the heat load of the heating system. It is the ratio of the average indoor temperature change rate at the first i moments of the control period to the preset temperature change rate threshold. The closer the ratio is to 1, the smaller the impact of the change in the average indoor temperature change rate on the heat load of the heating system. is the ratio of the outdoor wind data at the i-th moment of the control period to the preset outdoor wind data threshold. The closer the ratio is to 1, the smaller the impact of the change of outdoor wind data on the heat load of the heating system. is the ratio of the outdoor humidity data at the i-th moment of the control cycle to the preset outdoor humidity data threshold. The closer the ratio is to 1, the smaller the impact of the change in outdoor humidity data on the heat load of the heating system. is the ratio of the average indoor humidity change rate at the first i moments of the control period to the preset humidity change rate threshold. The closer the ratio is to 1, the smaller the impact of the change in the average indoor humidity change rate on the heat load of the heating system. is the ratio of the outdoor temperature data at the i-th moment of the control cycle to the preset outdoor temperature data threshold. The closer the ratio is to 1, the smaller the impact of the change in outdoor temperature data on the heat load of the heating system. It indicates that the outdoor wind data and the indoor and outdoor temperature difference are positively correlated with the heat load of the heating system, and the average indoor temperature change rate, the average indoor humidity change rate, the outdoor humidity data and the outdoor temperature data are negatively correlated with the heat load of the heating system. For example, when the outdoor wind data is larger, the exchange of indoor and outdoor air will be accelerated, taking away the indoor heat, resulting in an increase in the heat load that the heating system needs to provide. When the indoor and outdoor temperature difference is larger, the heat transfer speed from the high temperature area to the low temperature area is faster, the indoor heat loss is faster, and the more heat load the heating system needs to provide. The larger the average indoor temperature change rate, the outdoor humidity data and the outdoor temperature data, the less heat demand, and the more heat load the heating system needs to provide. The larger the average indoor humidity change rate, the abnormal decrease in indoor humidity and the need to reduce the heat load of the heating system. Therefore, the items related to the outdoor wind data and the indoor and outdoor temperature difference are placed in the numerator, indicating that the outdoor wind data and the indoor and outdoor temperature difference are larger relative to the preset outdoor wind data threshold and the preset indoor and outdoor temperature difference threshold, that is, and The larger the value of , the greater the heat load that the heating system needs to provide. The items related to the average indoor temperature change rate, the average indoor humidity change rate, the outdoor humidity data, and the outdoor temperature data are placed in the denominator, that is, , , and The smaller the value, the greater the heat load the heating system needs to provide.

[0042] According to one embodiment of the present invention, in formula (3), when , it means that at the beginning of the management cycle, the user is outside the heating area, but the user is about to arrive at the heating area. The heat load of the heating system at the i+1th moment of the management cycle is The value of ,according to Determine the heat load and The heat load is determined similarly, in this case The meaning of each parameter in will not be repeated here, but The heat load of the heating system at the i+1th moment of the determined control cycle is the heat load of the heating system when the user is outside the heating area. Therefore, The denominator of is the average indoor temperature change rate, outdoor humidity data, and items related to outdoor temperature data. There is no need to consider the impact of changes in indoor humidity data on the human body.

[0043] In this way, the adjusted heat load can be determined based on the heat load, outdoor environmental data, indoor environmental data, and user behavior habit information. During the calculation process, the outdoor wind data, indoor and outdoor temperature difference, average indoor temperature change rate, average indoor humidity change rate, outdoor humidity data, and outdoor temperature data are fully considered to affect the heat load that the heating system needs to provide. The adjusted heat load is determined based on this impact and the current heat load, so that the adjusted heat load can adapt to the indoor environmental conditions and outdoor weather conditions, thereby improving the comprehensiveness and accuracy of the adjusted heat load.

[0044] According to an embodiment of the present invention, in a monitoring report module, a system monitoring report is generated according to the thermal efficiency and the system pressure.

[0045] For example, the thermal efficiency of a heating system indicates the ratio of the heat effectively transferred to the user segment by the heating system to the heat obtained by the heating system from the heat source. When the thermal efficiency is too low, it means that the energy consumption of the heating system is higher and there may be a fault. The system pressure of the heating system includes: water supply pressure, return water pressure and make-up water pressure. The system pressure must be maintained within a certain range to ensure that hot water can flow smoothly to each user end while avoiding system leakage and damage.

[0046] According to an embodiment of the present invention, a system monitoring report is generated according to the thermal efficiency and the system pressure, including: Determining a pressure difference between the system pressure and a standard system pressure; determining a first ratio of the pressure difference to a standard system pressure; determining a second ratio of a preset standard thermal efficiency to the thermal efficiency; Determining a heating system operation status score according to the first ratio and the second ratio; If the heating system operation status score is greater than a set threshold, the staff is notified to inspect and maintain the heating system.

[0047] For example, determine the difference between the system pressure and the standard system pressure, and determine the pressure difference based on the absolute value of the difference; determine a first ratio based on the ratio of the pressure difference to the standard system pressure, the larger the first ratio is, the greater the difference between the system pressure and the standard system pressure is, and the greater the possibility of abnormal operation of the heating system is; determine a second ratio based on the ratio of the standard thermal efficiency to the thermal efficiency, the larger the second ratio is, the smaller the thermal efficiency is relative to the standard thermal efficiency, and the greater the possibility of abnormal operation of the heating system is; determine the heating system operation status score by taking a weighted sum of the first ratio and the second ratio; if the heating system operation status score is greater than the set threshold, it indicates that the heating system is operating abnormally, and notify the staff to inspect and maintain the heating system.

[0048] According to the smart heating control system based on IoT data collection and intelligent analysis according to the embodiment of the present invention, the heating parameters of the heating system can be continuously monitored through the equipment monitoring system, the indoor environment data in the heating area and the outdoor environment data at the sampling location can be continuously collected through sensors, and the user behavior habit information can be obtained through the cloud platform. Further, based on the indoor environment data, outdoor environment data and user behavior habit information, it is judged whether the heating system needs to be started, and the heat load of the heating system is optimized and adjusted, which improves the comprehensiveness and accuracy of smart heating control, can significantly improve the heating effect, and reduce energy consumption. When determining the first discrimination parameter, the first discrimination parameter can be determined based on the first temperature data, the first infrared radiation intensity and the user behavior habit information. In the calculation process, the situation of whether the user is in the heating area and the difference between the temperature of the heating area and the user's preferred temperature are fully considered, and based on this, it is determined whether to start the heating system, which improves the accuracy of the judgment of whether to start the heating system. When determining the second discrimination parameter, the second discrimination parameter of whether the user is in the heating area can be determined according to the indoor humidity change rate, the indoor temperature change rate, the first infrared radiation intensity data and the user behavior habit information. When the user is in the heating area, the indoor temperature change condition and the indoor humidity change condition in the heating area are fully considered, so as to determine the demand for adjusting the thermal load of the heating system according to the indoor temperature change condition and the indoor humidity change condition. When the user is outside the heating area, the relationship between the time required to heat the indoor temperature in the heating area to the user's preferred temperature and the time required for the user to return home is fully considered, thereby improving the accuracy of the judgment of adjusting the thermal load of the heating system. When determining the adjusted heat load, the adjusted heat load can be determined based on the heat load, outdoor environmental data, indoor environmental data, and user behavior habit information. During the calculation process, the outdoor wind data, the indoor and outdoor temperature difference, the average indoor temperature change rate, the average indoor humidity change rate, the outdoor humidity data, and the outdoor temperature data are fully considered to affect the heat load that the heating system needs to provide. The adjusted heat load is determined based on the impact and the current heat load, so that the adjusted heat load can adapt to the indoor environmental conditions and outdoor weather conditions, thereby improving the comprehensiveness and accuracy of the adjusted heat load.

[0049] Figure 2 The flowchart of a smart heating control system method based on IoT data collection and intelligent analysis according to an embodiment of the present invention is exemplarily shown, and the method includes: By means of sensors arranged in the heating area, first temperature data and first infrared radiation intensity data of the room are detected at the beginning of the current control cycle; Obtaining user behavior habit information through the cloud platform, wherein the user behavior habit information includes: user preferred temperature, user return home time; At the beginning of the current control cycle, determining whether it is necessary to start the heating system according to the first temperature data, the first infrared radiation intensity and the user behavior habit information; If the heating system needs to be started, the heating system is turned on and the heating area is heated within the control cycle; At multiple moments in the current control cycle, indoor environmental data is obtained through multiple sensors set in the heating area, wherein the indoor environmental data includes: infrared radiation intensity data, indoor temperature data and indoor humidity data; At multiple moments in the current control cycle, outdoor environmental data is obtained through sensors arranged at sampling locations outside the heating area, wherein the outdoor environmental data includes: outdoor temperature data, outdoor humidity data and outdoor wind speed data; At multiple moments in the current control cycle, the heating parameters of the heating system are collected through the equipment monitoring system, wherein the heating parameters include: heat load, thermal efficiency and system pressure; Determining whether it is necessary to adjust the heat load of the heating system according to the indoor environment data and the user behavior habit information; If it is necessary to adjust the heat load of the heating system, determine the adjusted heat load according to the heat load, the outdoor environment data, the indoor environment data, and the user behavior habit information, and adjust the heating system according to the adjusted heat load; A system monitoring report is generated according to the thermal efficiency and the system pressure.

[0050] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0051] It should be understood by those skilled in the art that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The purpose of the present invention has been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and explained in the embodiments, and the embodiments of the present invention may be deformed or modified in any way without departing from the principles.

Claims

1. A smart heating control system based on IoT data collection and intelligent analysis, characterized in that: include: The first indoor environment data acquisition module detects first indoor temperature data and first infrared radiation intensity data at the beginning of the current control cycle through sensors arranged in the heating area; The historical user information collection module obtains user behavior habit information through the cloud platform, wherein the user behavior habit information includes: user preferred temperature and user return home time; The heating module determines whether to start the heating system according to the first temperature data, the first infrared radiation intensity and the user behavior habit information at the start time of the current control cycle; Turn on the heating module. If the heating system needs to be started, turn on the heating system and provide heating to the heating area within the control cycle; The indoor environment data acquisition module acquires indoor environment data through multiple sensors arranged in the heating area at multiple moments of the current control cycle, wherein the indoor environment data includes: infrared radiation intensity data, indoor temperature data and indoor humidity data; The outdoor environment data acquisition module acquires outdoor environment data at multiple moments of the current control cycle through sensors arranged at sampling locations outside the heating area, wherein the outdoor environment data includes: outdoor temperature data, outdoor humidity data and outdoor wind speed data; The heating system monitoring module collects heating parameters of the heating system through the equipment monitoring system at multiple moments in the current control cycle, wherein the heating parameters include: heat load, thermal efficiency and system pressure; A heat load adjustment judgment module determines whether the heat load of the heating system needs to be adjusted according to the indoor environment data and the user behavior habit information; A heat load adjustment and determination module, if the heat load of the heating system needs to be adjusted, determines the adjusted heat load according to the heat load, the outdoor environment data, the indoor environment data, and the user behavior habit information, and adjusts the heating system according to the adjusted heat load; A monitoring report module generates a system monitoring report according to the thermal efficiency and the system pressure.

2. According to the smart heating control system based on Internet of Things data collection and intelligent analysis according to claim 1, it is characterized in that: At the beginning of the current control cycle, determining whether to start the heating system according to the first temperature data, the first infrared radiation intensity and the user behavior habit information includes: Determining a first discrimination parameter according to the first temperature data, the first infrared radiation intensity and the user behavior habit information; When the first discrimination parameter is less than a set threshold, it is determined that the heating system does not need to be started; When the first discrimination parameter is greater than or equal to the set threshold, it is determined that the heating system needs to be started.

3. According to the intelligent heating control system based on Internet of Things data collection and intelligent judgment according to claim 2, it is characterized in that: Determining a first discrimination parameter according to the first temperature data, the first infrared radiation intensity and the user behavior habit information includes: According to the formula Determine the first discriminant parameter , where if is a conditional function, is the first infrared radiation intensity, To preset the infrared radiation intensity threshold, For the user's preferred temperature, is the first temperature data, is the start time of the management cycle, The time for users to go home, is the preset time difference threshold.

4. According to claim 1, the intelligent heating control system based on Internet of Things data collection and intelligent analysis is characterized in that: Determining whether it is necessary to adjust the heat load of the heating system according to the indoor environment data and the user behavior habit information includes: Fitting the indoor temperature data to the time in the current control cycle to obtain an indoor temperature function of the indoor temperature in the current control cycle; Determining an indoor temperature derivative function according to the indoor temperature function; Determine the indoor temperature change rate at multiple moments in the current control cycle according to the indoor temperature derivative function; Fitting the indoor humidity data to the time in the current control cycle to obtain an indoor humidity function of the indoor humidity in the current control cycle; Determining an indoor humidity derivative function according to the indoor humidity function; Determine the indoor humidity change rate at multiple moments in the current control cycle according to the indoor humidity derivative function; Determine whether the heat load of the heating system needs to be adjusted based on the infrared radiation intensity data, the indoor humidity change rate, the indoor temperature change rate and the user behavior habit information.

5. According to the intelligent heating control system based on Internet of Things data collection and intelligent judgment according to claim 4, it is characterized in that: Determining whether it is necessary to adjust the heat load of the heating system according to the infrared radiation intensity data, the indoor humidity change rate, the indoor temperature change rate and the user behavior habit information includes: According to the formula Determine the second discriminant parameter at the i-th moment of the control cycle ,in, is the i-th moment of the current control cycle, is the indoor humidity change rate at the i-th moment of the current control cycle, is the indoor temperature change rate at the i-th moment of the current control cycle, is the indoor temperature data at the i-th moment of the current control cycle, is the infrared radiation intensity at the i-th moment of the current control cycle, To preset the infrared radiation intensity threshold, For the user's preferred temperature, The time for users to go home, is the preset temperature change rate threshold, is the preset humidity change rate threshold, The user prefers temperature; If the second discrimination parameter at the i-th moment of the control cycle is greater than the set threshold, it is determined to adjust the heat load of the heating system at the i+1-th moment of the control cycle.

6. The intelligent heating control system based on IoT data collection and intelligent analysis according to claim 5 is characterized in that: If it is necessary to adjust the heat load of the heating system, determine the adjusted heat load according to the heat load, the outdoor environment data, the indoor environment data, and the user behavior habit information, and adjust the heating system according to the adjusted heat load, including: According to the formula Determine the heat load of the heating system at the i+1th moment of the control cycle ,in, is the heat load of the heating system at the i-th moment of the current control cycle, is the outdoor wind data at the i-th moment of the current control cycle, is the outdoor humidity data at the i-th moment of the current control cycle, is the outdoor temperature data at the i-th moment of the current control cycle, To preset the outdoor wind data threshold, To preset the outdoor humidity data threshold, It is the preset indoor and outdoor temperature difference threshold.

7. The intelligent heating control system based on IoT data collection and intelligent analysis according to claim 1 is characterized in that: Generate a system monitoring report based on the thermal efficiency and the system pressure, including: Determining a pressure difference between the system pressure and a standard system pressure; determining a first ratio of the pressure difference to a standard system pressure; determining a second ratio of a preset standard thermal efficiency to the thermal efficiency; Determining a heating system operation status score according to the first ratio and the second ratio; If the heating system operation status score is greater than a set threshold, the staff is notified to inspect and maintain the heating system.

8. A smart heating control method based on IoT data collection and intelligent analysis, characterized in that: include: By means of sensors arranged in the heating area, first temperature data and first infrared radiation intensity data of the room are detected at the beginning of the current control cycle; Obtaining user behavior habit information through the cloud platform, wherein the user behavior habit information includes: user preferred temperature, user return home time; At the beginning of the current control cycle, determining whether it is necessary to start the heating system according to the first temperature data, the first infrared radiation intensity and the user behavior habit information; If the heating system needs to be started, the heating system is turned on and the heating area is heated within the control cycle; At multiple moments in the current control cycle, indoor environmental data is obtained through multiple sensors set in the heating area, wherein the indoor environmental data includes: infrared radiation intensity data, indoor temperature data and indoor humidity data; At multiple moments in the current control cycle, outdoor environmental data is obtained through sensors arranged at sampling locations outside the heating area, wherein the outdoor environmental data includes: outdoor temperature data, outdoor humidity data and outdoor wind speed data; At multiple moments in the current control cycle, the heating parameters of the heating system are collected through the equipment monitoring system, wherein the heating parameters include: heat load, thermal efficiency and system pressure; Determining whether it is necessary to adjust the heat load of the heating system according to the indoor environment data and the user behavior habit information; If it is necessary to adjust the heat load of the heating system, determine the adjusted heat load according to the heat load, the outdoor environment data, the indoor environment data, and the user behavior habit information, and adjust the heating system according to the adjusted heat load; A system monitoring report is generated according to the thermal efficiency and the system pressure.

Images