A dynamic precise temperature control method for gas heating stoves based on the Internet of Things

Abstract

The present application relates to gas heating equipment technical field, disclose a kind of based on internet of things's gas heating stove dynamic precision temperature control method, including collection building space envelope structure thermal characteristic parameter and indoor heat source distribution characteristic parameter, synchronous acquisition hourly environmental temperature forecast value;Through internet of things platform receives the temperature sensing data of multiple temperature measuring points in room, generates indoor temperature field dynamic distribution atlas after space-time fusion, and constructs its future time period's evolution trend model;Obtain combustion system real-time combustion characteristic parameter and heat exchange system real-time heat exchange efficiency parameter, after atlas current state and model expected state comparison, generate combustor multi-dimensional adjustment instruction;The instruction is decomposed into gas supply, combustion air and circulating water flow rate adjustment sequence, drive gas regulating valve, variable frequency fan and circulating water pump collaborative action, and according to temperature actual change rate, the model is dynamically revised.The present application realizes the predictive precision temperature control and intelligent operation of heating stove.

Description

Technical Field

[0001] This invention relates to the field of gas heating equipment technology, specifically to a dynamic and precise temperature control method for a gas heating boiler based on the Internet of Things. Background Technology

[0002] As the primary heating equipment in homes and commercial spaces, gas-fired boilers directly impact indoor thermal comfort and energy efficiency through their temperature control accuracy. Existing gas-fired boilers typically employ a return water temperature feedback control method, adjusting the burner's operation by detecting the deviation between the boiler's return water temperature and the set temperature. This control method can only maintain a stable boiler outlet or return water temperature, failing to detect changes in the actual indoor temperature field. This results in significant temperature differences between different areas of the room, with areas closer to radiators being warmer and areas farther away being colder. Furthermore, existing control methods do not adequately consider the thermal inertia of the building envelope. When outdoor temperatures fluctuate, the indoor temperature response exhibits a significant lag. The boiler often increases its load only after the indoor temperature has already dropped, or decreases its load only after the indoor temperature has already risen, causing frequent indoor temperature fluctuations and substantial energy waste. Summary of the Invention

[0003] The purpose of this invention is to provide a dynamic and precise temperature control method for gas-fired boilers based on the Internet of Things, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for dynamic and precise temperature control of a gas-fired heating boiler based on the Internet of Things, comprising the following steps: S1. Collect the thermal characteristics parameters of the building envelope and the distribution characteristics of indoor heat sources in the building space where the heating furnace is located, and simultaneously obtain the hourly ambient temperature forecast values ​​issued by the outdoor weather station. S2. Receive temperature sensing data uploaded from multiple indoor temperature measurement points through the Internet of Things platform, generate a dynamic distribution map of indoor temperature field after spatiotemporal fusion of temperature sensing data, and construct an evolution trend model of the dynamic distribution map of indoor temperature field in the future period based on the thermal characteristic parameters of the building envelope, the characteristic parameters of indoor heat source distribution, and the hourly ambient temperature forecast value. S3. Obtain the real-time combustion characteristic parameters of the heating furnace combustion system and the real-time heat exchange efficiency parameters of the heat exchange system. Compare the current state of the dynamic distribution map of the indoor temperature field with the expected state at the corresponding moment in the evolution trend model. Based on the comparison results, combine the real-time combustion characteristic parameters and the real-time heat exchange efficiency parameters to generate multi-dimensional adjustment commands for the burner. S4. The multi-dimensional adjustment command of the burner is decomposed into a gas supply adjustment sequence, a combustion air supply adjustment sequence, and a circulating water flow rate adjustment sequence, which respectively drive the gas regulating valve, the variable frequency fan, and the circulating water pump to work together. During the operation, the updated data of the dynamic distribution map of the indoor temperature field are continuously received, and the expected state of the subsequent period in the evolution trend model is dynamically corrected according to the actual rate of change of indoor temperature.

[0005] As a preferred technical solution of the present invention, the thermal characteristic parameters of the building envelope include the comprehensive heat transfer coefficient of the building exterior wall, the solar heat gain factor of the exterior window, the thermal inertia index of the roof, and the building airtightness level. Indoor heat source distribution characteristic parameters include the heat release intensity of indoor activity areas, the heat dissipation power spectrum of household appliance clusters, and the radiation temperature field of heating radiator surfaces. The hourly ambient temperature forecast includes the time-series variation curve of outdoor dry-bulb temperature over the next 24 hours, the hourly variation pattern of solar radiation intensity, and the influence factors of wind speed and direction on heat transfer on building surfaces.

[0006] As a preferred technical solution of the present invention, spatiotemporal fusion of the temperature sensing data specifically includes: By associating the spatial coordinates of multiple indoor temperature measurement points with the temperature values ​​uploaded by each measurement point, a spatial interpolation surface for indoor temperature is constructed. By temporally superimposing spatial interpolation surfaces from multiple consecutive time sections, the variation characteristics of the indoor temperature field in the time dimension can be extracted. Based on the variation characteristics of the spatial interpolation surface and the time dimension, a dynamic distribution map of the indoor temperature field containing temperature gradient information and temperature change rate information is generated.

[0007] As a preferred embodiment of the present invention, the model for constructing the evolution trend of the dynamic distribution map of the indoor temperature field over a future period specifically includes: The attenuation and delay characteristics of the building space to external temperature disturbances are determined based on the thermal characteristic parameters of the building envelope. The contribution weight and influence radius of the indoor heating element to the temperature field are determined based on the characteristic parameters of the indoor heat source distribution. By coupling attenuation characteristics, delay characteristics, contribution weights, and influence radius with hourly ambient temperature forecasts, a sequence of expected temperature values ​​for each indoor spatial location over a continuous future period is generated, forming an evolution trend model.

[0008] As a preferred technical solution of the present invention, the real-time combustion characteristic parameters include the flame spectral distribution characteristics in the combustion chamber, the residual oxygen concentration in the flue gas emission channel, and the fluctuation amplitude of the gas pressure before and after the burner nozzle. Real-time heat exchange efficiency parameters include the temperature gradient distribution of the medium on both sides of the plate heat exchanger, the online estimate of the fouling thermal resistance on the heat exchanger surface, and the dynamic response relationship between the circulating water flow rate and the return water temperature.

[0009] As a preferred embodiment of the present invention, generating the multi-dimensional adjustment command for the burner specifically includes: The current temperature value at key locations in the dynamic distribution map of the indoor temperature field is compared point by point with the expected temperature value at the corresponding location and time in the evolution trend model to obtain the overall deviation of the temperature field. The overall deviation of the temperature field is decomposed into an average temperature deviation component and a temperature gradient deviation component. The burner's basic load adjustment direction is determined based on the average temperature deviation component, and the flame spatial distribution pattern adjustment strategy is determined based on the temperature gradient deviation component combined with real-time combustion characteristic parameters. By integrating the base load adjustment direction and flame spatial distribution pattern adjustment strategy, a multi-dimensional adjustment command is generated that simultaneously includes the total gas supply adjustment and the flame spatial distribution adjustment.

[0010] As a preferred embodiment of the present invention, the gas supply regulation sequence includes three control parameters: the rotation direction, rotation angle, and rotation speed of the stepper motor of the gas regulating valve. The combustion air supply regulation sequence includes three control parameters: the target speed of the variable frequency fan, the speed change slope, and the opening of the duct damper. The circulating water flow rate regulation sequence includes three control parameters: the start-stop frequency of the circulating water pump, the operating duty cycle, and the water flow direction switching. When the gas regulating valve, variable frequency fan and circulating water pump work together, the ratio of gas to air is kept within the range of full combustion, and the heat exchange efficiency on both sides of the heat exchanger is kept within the set range.

[0011] As a preferred embodiment of the present invention, dynamically correcting the expected state in subsequent periods of the evolution trend model specifically includes: Record the actual rate of temperature rise at key indoor locations after the execution of multi-dimensional adjustment commands of the burner, compare the actual rate of temperature rise with the preset rate of temperature rise in the evolution trend model, and obtain the thermal response correction coefficient of the building space. Record the actual temperature distribution when the indoor temperature field reaches a stable state after the execution of the multi-dimensional adjustment command of the burner. Compare the actual temperature distribution with the preset temperature distribution in the evolution trend model to obtain the heat source contribution correction coefficient. By substituting the thermal response correction coefficient and the heat source contribution correction coefficient into the evolution trend model, the expected temperature value sequence for each spatial location in subsequent time periods is refitted.

[0012] As a preferred embodiment of the present invention, the method further includes a behavioral pattern self-learning step: Long-term collection of personnel activity trajectory data and corresponding temperature adjustment operation records in various indoor areas; extraction of personnel presence probability distribution in each time period from personnel activity trajectory data; extraction of personnel preferred temperature setting values ​​in each time period from temperature adjustment operation records. By correlating the probability distribution of human presence with the human preferred temperature setpoint, a human thermal comfort demand curve that changes over time is generated. The expected state for the corresponding period in the evolution trend model is weighted and corrected based on the thermal comfort demand curve of the personnel, so that the operation strategy of the heating boiler matches the actual needs of the personnel.

[0013] As a preferred embodiment of the present invention, the method further includes a system health self-diagnosis step: Continuously monitor the flame stability characteristics of the combustion system, the heat exchange efficiency decay characteristics of the heat exchange system, and the pressure fluctuation characteristics of the water circulation system; The flame stability characteristic value is compared with the stable combustion characteristic template to generate a combustion system health score; The heat exchange efficiency decay characteristic value is compared with the heat exchanger cleanliness status threshold to generate a heat exchange system health score. The pressure fluctuation characteristic values ​​are compared with the standard pressure spectrum of the water circulation system to generate a health score for the water circulation system. When the health score of any system falls below the set threshold, a maintenance prompt signal is issued for the corresponding system and the upper limit of the operating parameters of the heating boiler is automatically adjusted.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention collects thermal characteristic parameters of building envelope and indoor heat source distribution characteristics, and combines them with outdoor ambient temperature forecast values ​​to construct an evolution trend model of indoor temperature field. This enables the heating boiler to predict future changes in indoor temperature and adjust its operating status in advance, overcoming the lag problem of traditional feedback control and realizing the transformation from passive response to active prediction control mode.

[0015] 2. This invention generates a dynamic distribution map of indoor temperature field by spatiotemporally fusing temperature sensing data from multiple indoor temperature measuring points. This allows for accurate understanding of the temperature distribution and rate of temperature change in various indoor areas. As a result, the heating boiler focuses not only on the temperature value of a single measuring point but also on the entire indoor temperature field as the control target, effectively improving the uniformity of indoor temperature distribution and avoiding local overheating or undercooling.

[0016] 3. This invention decomposes the burner adjustment command into a gas supply adjustment sequence, an air supply adjustment sequence, and a circulating water flow rate adjustment sequence, thereby achieving coordinated control of the gas regulating valve, variable frequency fan, and circulating water pump. This enables precise matching of the burner's output heat with the indoor heat load demand, while maintaining a full combustion ratio of gas and air, thus improving temperature control accuracy and ensuring combustion efficiency.

[0017] 4. This invention introduces a behavior pattern self-learning function. By collecting personnel activity trajectories and temperature adjustment operation records over a long period of time, it generates personnel thermal comfort demand curves and performs weighted corrections on the evolution trend model, so that the operation strategy of the heating boiler matches the actual living habits of users, achieving energy-saving operation while meeting personalized comfort needs.

[0018] 5. This invention includes a system health self-diagnosis step, which continuously monitors the operating characteristic parameters of the combustion system, heat exchange system, and water circulation system. When a decline in system health is detected, a maintenance prompt is issued in a timely manner and the upper limit of operating parameters is automatically adjusted, which effectively prevents equipment failure, extends the service life of the heating boiler, and improves the operational reliability of the system. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall process of a dynamic and precise temperature control method for a gas-fired heating boiler based on the Internet of Things according to the present invention. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1 This embodiment provides a specific implementation process for a dynamic and precise temperature control method for gas-fired boilers based on the Internet of Things.

[0022] First, step S1 is executed to collect the thermal characteristic parameters of the building envelope and the indoor heat source distribution characteristics of the building space where the heating boiler is located. Simultaneously, hourly ambient temperature forecasts from the outdoor weather station are obtained. Taking a high-rise residential community in Beijing as an example, this building is located on the 15th floor with a floor area of ​​120 square meters. The specific thermal characteristic parameters of the building envelope include: the comprehensive heat transfer coefficient of the building's exterior walls is 0.45 W / (m²). 2The building's solar heat gain factor is 0.62, the roof thermal inertia index is 3.2, and the building's airtightness level is 7. Specific parameters of the indoor heat source distribution include: heat release intensity in indoor activity areas is 80W / person in the living room and 60W / person in the bedroom; the heat dissipation power spectrum of household appliances is obtained through smart socket monitoring, with the refrigerator continuously dissipating 150W, the television dissipating 200W, and the computer dissipating 120W; the radiation temperature field of the heating radiator surface is obtained through an infrared thermometer array, with an average surface temperature of 45℃; and the temporal variation curve of outdoor dry-bulb temperature, the hourly variation law of solar radiation intensity, and the influence factors of wind speed and direction on building surface heat transfer are simultaneously obtained from the China Meteorological Data Network for the next 24 hours.

[0023] Step S2: Temperature sensing data uploaded from multiple indoor temperature measurement points is received through the IoT platform. The temperature sensing data is then spatiotemporally fused to generate a dynamic distribution map of the indoor temperature field. A total of eight wireless temperature measurement nodes are deployed indoors, located in the living room, master bedroom, secondary bedroom, study, dining room, kitchen, bathroom, and corridor. The sampling frequency is once every 30 seconds. The specific spatiotemporal fusion process is as follows: the spatial coordinates of the eight temperature measurement points are associated with the temperature values ​​uploaded by each point, and an inverse distance weighted interpolation method is used to construct a spatial interpolation surface for the indoor temperature. The spatial interpolation surfaces of 12 consecutive time segments, one every 30 seconds for a total of 6 minutes, are then overlaid temporally to extract the change characteristics of the indoor temperature field in the time dimension. Based on the spatial interpolation surface and the change characteristics in the time dimension, a dynamic distribution map of the indoor temperature field containing temperature gradient information and temperature change rate information is generated. This map clearly shows that the temperature gradient change in the living room area is relatively gentle, while the temperature gradient change near the exterior windows is more drastic.

[0024] Based on the thermal characteristics of the building envelope, the distribution characteristics of indoor heat sources, and hourly ambient temperature forecasts, a dynamic distribution map of the indoor temperature field is constructed, and an evolution trend model is established for the future period. Specifically, this includes: determining the attenuation characteristic of the building space to external temperature disturbances as 0.65 and the delay characteristic as 1.8 hours based on the thermal characteristics of the building envelope; determining the contribution weights of indoor heat sources to the temperature field based on the distribution characteristics of indoor heat sources, with human activities contributing 0.3, household appliances contributing 0.2, and heating radiators contributing 0.5, with influence radii of 1.5 meters, 1.0 meter, and 2.5 meters, respectively; and coupling the attenuation characteristic, delay characteristic, contribution weights, and influence radius with hourly ambient temperature forecasts to generate a sequence of expected temperature values ​​for each indoor spatial location every 30 minutes over the next 24 hours, thus forming the evolution trend model.

[0025] Step S3: Obtain real-time combustion characteristic parameters of the heating boiler combustion system and real-time heat exchange efficiency parameters of the heat exchange system. Real-time combustion characteristic parameters include the flame spectral distribution characteristics within the combustion chamber, acquired by a spectral sensor installed at the observation window of the combustion chamber; the flame spectrum exhibits a continuous distribution within the visible light band of 450nm to 700nm. The residual oxygen concentration in the flue gas emission channel is measured to be 4.2% by a zirconia oxygen sensor. The fluctuation amplitude of the gas pressure before and after the burner nozzle is monitored by a pressure transmitter; the inlet pressure is stable within the range of 2.0kPa±0.05kPa. Real-time heat exchange efficiency parameters include the temperature gradient distribution of the media on both sides of the plate heat exchanger; the primary side inlet water temperature is 75℃ and the return water temperature is 55℃; the secondary side inlet water temperature is 45℃ and the return water temperature is 55℃. The online estimate of the fouling thermal resistance on the heat exchanger surface is obtained by comparing the heat exchange coefficient under clean conditions with the current heat exchange coefficient; the estimated value is 0.015m. 2 • K / W; The dynamic response relationship between circulating water flow rate and return water temperature is shown to be that the return water temperature decreases at a rate of 0.3℃ / (L / min) when the flow rate increases.

[0026] The current temperature values ​​at key locations in the dynamic distribution map of the indoor temperature field are compared point-by-point with the expected temperature values ​​at the corresponding locations and times in the evolution trend model to obtain the overall deviation of the temperature field. The current temperature in the living room is 21.3℃, and the expected temperature is 22.0℃, with a deviation of -0.7℃; the current temperature in the master bedroom is 20.8℃, and the expected temperature is 21.5℃, with a deviation of -0.7℃; the current temperature in the second bedroom is 20.5℃, and the expected temperature is 21.0℃, with a deviation of -0.5℃. The overall deviation of the temperature field is decomposed into an average temperature deviation component and a temperature... The temperature gradient deviation component has an average temperature deviation of -0.63℃. The temperature gradient deviation is mainly manifested in the temperature difference between the living room and bedroom being 0.2℃ larger than expected. Based on the average temperature deviation component, it is determined that the burner's base load needs to be increased by about 8%. Based on the temperature gradient deviation component and real-time combustion characteristic parameters, it is determined that the flame spatial distribution pattern needs to be shifted towards the front area to enhance radiative heat transfer in the living room area. The base load adjustment direction and flame spatial distribution pattern adjustment strategy are integrated to generate a multi-dimensional adjustment command that simultaneously includes the total gas supply adjustment and the flame spatial distribution adjustment.

[0027] Step S4: Decompose the multi-dimensional adjustment command of the burner into a gas supply adjustment sequence, a combustion air supply adjustment sequence, and a circulating water flow rate adjustment sequence. The gas supply adjustment sequence includes three control parameters: the stepper motor of the gas regulating valve rotates clockwise, the rotation angle is 15 degrees, and the rotation speed is 3 degrees / second. The combustion air supply adjustment sequence includes three control parameters: the target speed of the variable frequency fan is increased from 1800 rpm to 2000 rpm, the speed change slope is 100 rpm / second, and the air duct damper opening is maintained at 85%. The circulating water flow rate regulation sequence includes three control parameters: the start-stop frequency of the circulating water pump to maintain continuous operation, the duty cycle to be increased from 60% to 70%, and the water flow direction to maintain positive circulation. These parameters drive the gas regulating valve, the variable frequency fan, and the circulating water pump to work together. When the gas regulating valve, the variable frequency fan, and the circulating water pump work together, the ratio of gas to air is maintained within the fully combusted range of 1:10.5. The flue gas analyzer shows that the residual oxygen concentration is maintained between 4.0% and 4.5%, and the heat exchange efficiency on both sides of the heat exchanger is maintained above 85%.

[0028] During the operation, the system continuously receives updated data on the dynamic distribution map of the indoor temperature field, acquiring the temperature changes at various points indoors every 30 seconds. It records the actual rate of temperature rise at key indoor locations after the burner's multi-dimensional adjustment commands are executed. In the living room area, the temperature rose from 21.3℃ to 22.5℃ within 15 minutes after adjustment, at a rate of 0.08℃ / minute. Comparing this actual rate of rise with the preset rate of 0.06℃ / minute in the evolution trend model yields a thermal response correction coefficient of 1.33 for the building space. The system also records that the indoor temperature field stabilizes after the burner's multi-dimensional adjustment commands are executed. The actual temperature distribution during the initial state was stable at 22.8℃ in the living room, 21.5℃ in the master bedroom, and 21.2℃ in the secondary bedroom. Comparing the actual temperature distribution with the preset temperature distributions of 22.0℃ in the living room, 21.5℃ in the master bedroom, and 21.0℃ in the secondary bedroom in the evolution trend model, the heat source contribution correction coefficients were found to be 1.04 for the living room area and 1.00 for the bedroom area. Substituting the thermal response correction coefficients and the heat source contribution correction coefficients into the evolution trend model, the expected temperature value sequence for each spatial location in subsequent time periods was refitted, making the evolution trend model more closely match the actual thermal response characteristics of the building space.

[0029] Through the implementation of this embodiment, the heating boiler can dynamically adjust its operating parameters according to the characteristics of the building space and changes in the indoor and outdoor environment, thereby achieving precise temperature control.

[0030] Example 2 Based on Example 1, this embodiment further refines the construction and correction process of the evolution trend model and introduces a behavior pattern self-learning function.

[0031] Taking a villa in Tianjin as an example, the building is a two-story structure with a floor area of ​​280 square meters. The thermal characteristics of the building envelope differ from those in Example 1. In step S2, when constructing the evolution trend model, the attenuation characteristic of the building space to external temperature disturbances is determined to be 0.58 and the delay characteristic is 2.5 hours based on the thermal characteristics of the building envelope. Due to its large shape coefficient, the villa has a weaker attenuation capacity to external temperature fluctuations but a longer delay time. The contribution weight of indoor heat sources to the temperature field is determined based on the indoor heat source distribution characteristics. The contribution weight of the densely populated living room on the first floor is 0.4, the contribution weight of the less populated bedroom area on the second floor is 0.2, the contribution weight of the kitchen area on the first floor (where household appliances are mainly concentrated) is 0.1, and the contribution weight of the heating radiators distributed in various rooms is 0.5. The influence radius is set to 2.0 meters to 3.5 meters depending on the size of the room. The attenuation characteristic, delay characteristic, contribution weight, and influence radius are coupled with the hourly ambient temperature forecast value to generate a sequence of expected temperature values ​​for each indoor space location in the future period.

[0032] When generating the multi-dimensional adjustment command for the burner in step S3, this embodiment pays special attention to the adjustment strategy of the flame spatial distribution pattern. The flame spectral distribution characteristics in the real-time combustion characteristic parameters show that the flame center is biased to the right side of the combustion chamber, which is related to the uneven gas distribution of the burner nozzle. According to the temperature gradient deviation component, the temperature in the bedroom area on the second floor is 0.8℃ lower than expected, while the temperature in the living room on the first floor is as expected. It is determined that the flame spatial distribution pattern needs to be shifted to the upper area. Combined with the gas pressure fluctuation amplitude in the real-time combustion characteristic parameters, it is determined that the flame should be extended to the upper part by adjusting the opening of the burner damper to enhance the radiative heat transfer to the second floor area.

[0033] This embodiment focuses on implementing a behavior pattern self-learning step, continuously collecting data on the activity trajectories of people in various indoor areas and corresponding temperature adjustment operation records. Infrared sensors and smart door magnets deployed indoors are used to acquire these activity trajectories, and a smart temperature control panel records user temperature adjustment operations. After 30 days of continuous collection, the probability distribution of people's presence at different times is extracted from the activity trajectory data. The probability of people being present in the first-floor dining room is 0.9 from 7:00 AM to 8:00 AM, and the probability of people being present in the second-floor master bedroom is 1.0 from 10:00 PM to 6:00 AM the next day. The preferred temperature settings for each time period are extracted from the temperature adjustment operation records. For example, the preferred temperature setting in the living room is... The preferred temperature is 22.5℃, the preferred temperature for the master bedroom is 21.0℃, and the preferred temperature for the secondary bedroom is 20.5℃. The probability distribution of the presence of people is correlated with the set values ​​of their preferred temperatures to generate a thermal comfort demand curve that changes over time. This curve peaks at 23.0℃ at 7:30 AM and reaches a secondary peak of 22.0℃ at 9:00 PM. Based on the thermal comfort demand curve, the expected state for the corresponding time period in the evolution trend model is weighted and corrected to match the operating strategy of the heating boiler with the actual needs of people. During periods with a high probability of people being present, priority is given to ensuring that the temperature meets the standard, while during periods with a low probability of people being present, the expected temperature is appropriately reduced to achieve energy saving.

[0034] In the dynamic correction process of step S4, this embodiment introduces refined processing of thermal response correction coefficient and heat source contribution correction coefficient. The actual temperature rise rate at key indoor locations is recorded after the burner's multi-dimensional adjustment command is executed. In the second-floor master bedroom, the temperature rises from 20.2℃ to 21.5℃ within 20 minutes after adjustment, with a rise rate of 0.065℃ / minute. The actual rise rate is compared with the preset rise rate of 0.05℃ / minute in the evolution trend model, yielding a thermal response correction coefficient of 1.30 for the building space. The actual temperature distribution when the indoor temperature field reaches a stable state after the burner's multi-dimensional adjustment command is executed is also recorded. This actual temperature distribution is compared with the preset temperature distribution in the evolution trend model, yielding a heat source contribution correction coefficient. The thermal response correction coefficient and heat source contribution correction coefficient are substituted into the evolution trend model to refit the expected temperature value sequence for each spatial location in subsequent time periods. Through the organic combination of behavioral pattern self-learning and thermal response characteristic correction, the evolution trend model can more accurately predict indoor temperature changes, making the heating boiler's temperature control strategy more intelligent.

[0035] Example 3 Based on Examples 1 and 2, this embodiment focuses on implementing the system health self-diagnosis steps and provides a detailed explanation of the dynamic switching between multiple modes.

[0036] Taking a high-rise residential building in Shenyang as an example, the area experiences cold winters and a heating season lasting up to 5 months. During the long-term operation of the heating boiler, the flame stability characteristic value of the combustion system, the heat exchange efficiency decay characteristic value of the heat exchange system, and the pressure fluctuation characteristic value of the water circulation system were continuously monitored. The flame stability characteristic value was monitored by a flame spectrum sensor to obtain the fluctuation frequency of the flame spectrum over time. Under normal circumstances, the fluctuation frequency of the flame spectrum in the range of 450nm to 700nm should be less than 2Hz. In this embodiment, the fluctuation frequency was monitored to be 3.5Hz, indicating a decrease in combustion stability. The heat exchange efficiency decay characteristic value was obtained by comparing the relationship between the inlet and outlet water temperature difference of the heat exchanger and the circulating water flow rate. When the heat exchanger was newly installed, the inlet and outlet water temperature difference was 20℃ at the rated flow rate. Currently, the inlet and outlet water temperature difference is 16℃, and the heat exchange efficiency has decreased by about 20%. The pressure fluctuation characteristic value of the water circulation system was monitored by a pressure sensor. Under normal operation, the system pressure fluctuation range should be within 0.05MPa. In this embodiment, the pressure fluctuation amplitude reached 0.12MPa, indicating that there may be airlock or leakage in the water circulation system.

[0037] The flame stability characteristic value was compared with the stable combustion characteristic template, which is stored in the local controller and includes parameters such as the flame spectral characteristic range and fluctuation frequency threshold under fully combusted conditions. The comparison result showed that the flame fluctuation frequency exceeded the threshold by 30%, resulting in a combustion system health score of 65. The heat exchange efficiency decay characteristic value was compared with the heat exchanger cleanliness threshold, which is set to a heat exchange efficiency decay of no more than 10%. The current decay of 20% exceeds the threshold, resulting in a heat exchange system health score of 55. The pressure fluctuation characteristic value was compared with the standard pressure spectrum of the water circulation system. The standard pressure spectrum includes pressure fluctuation characteristics under normal operation, slight airlock, and serious leakage conditions. The current characteristic has a high degree of matching with the slight airlock condition, resulting in a water circulation system health score of 70.

[0038] When the health score of any system falls below the set threshold (60 points in this embodiment), and the health score of the heat exchange system is 55 points, which is below the threshold, the system issues a maintenance prompt signal to remind the user to check whether the heat exchanger needs cleaning. At the same time, it automatically adjusts the upper limit of the operating parameters of the heating boiler, reducing the maximum outlet water temperature from 85℃ to 75℃ and the maximum operating power from 28kW to 22kW, to avoid continued high-load operation under reduced heat exchange efficiency, which could lead to increased energy consumption or equipment damage.

[0039] This embodiment also implements a multi-mode dynamic switching step, which detects in real time whether there are people indoors and the changing trend of outdoor ambient temperature. The presence of people indoors is detected by infrared sensors, and the changing trend of outdoor ambient temperature is obtained through a meteorological data interface. When no one is detected indoors and the outdoor ambient temperature continues to drop, the expected temperature change trajectory is switched to anti-freeze protection mode. In this mode, the indoor temperature is maintained above the anti-freeze threshold of 5°C to 8°C. The heating boiler is only started for intermittent operation when the temperature is below 5°C. When someone is detected indoors and the outdoor ambient temperature continues to rise, for example, when the outdoor temperature rises from -5°C to 5°C at noon in spring, the expected temperature change trajectory is switched to energy-saving comfort mode. In this mode, the temperature value of the corresponding period in the expected temperature change trajectory is automatically reduced according to the change of outdoor ambient temperature, reducing energy consumption while ensuring comfort. When someone is detected indoors and the outdoor ambient temperature fluctuates drastically, for example, when the outdoor temperature drops by 8°C in 2 hours during a cold wave, the expected temperature change trajectory is switched to constant temperature and stable flow mode. In this mode, the output power of the burner is limited to a rate of change of less than 1kW / min to avoid indoor temperature fluctuations caused by drastic changes in external temperature.

[0040] Through the implementation of this embodiment, the heating boiler not only has precise temperature control capabilities, but also has self-health diagnosis and multi-mode adaptive switching functions, which improves the reliability and intelligence level of the system. Under the severe cold climate conditions of Shenyang, the system operates stably throughout the heating season, and users report that the indoor temperature fluctuations are small. Moreover, the system can provide early warning of potential faults, avoiding heating interruptions caused by equipment failures.

[0041] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A method for dynamic and precise temperature control of a gas-fired boiler based on the Internet of Things, characterized in that, Includes the following steps: S1. Collect the thermal characteristics parameters of the building envelope and the distribution characteristics of indoor heat sources in the building space where the heating furnace is located, and simultaneously obtain the hourly ambient temperature forecast values ​​issued by the outdoor weather station. S2. Receive temperature sensing data uploaded from multiple indoor temperature measurement points through the Internet of Things platform, generate a dynamic distribution map of indoor temperature field after spatiotemporal fusion of temperature sensing data, and construct an evolution trend model of the dynamic distribution map of indoor temperature field in the future period based on the thermal characteristic parameters of the building envelope, the characteristic parameters of indoor heat source distribution, and the hourly ambient temperature forecast value. S3. Obtain the real-time combustion characteristic parameters of the heating furnace combustion system and the real-time heat exchange efficiency parameters of the heat exchange system. Compare the current state of the dynamic distribution map of the indoor temperature field with the expected state at the corresponding moment in the evolution trend model. Based on the comparison results, combine the real-time combustion characteristic parameters and the real-time heat exchange efficiency parameters to generate multi-dimensional adjustment commands for the burner. S4. The multi-dimensional adjustment command of the burner is decomposed into a gas supply adjustment sequence, a combustion air supply adjustment sequence, and a circulating water flow rate adjustment sequence, which respectively drive the gas regulating valve, the variable frequency fan, and the circulating water pump to work together. During the operation, the updated data of the dynamic distribution map of the indoor temperature field are continuously received, and the expected state of the subsequent period in the evolution trend model is dynamically corrected according to the actual rate of change of indoor temperature.

2. The method according to claim 1, characterized in that, The thermal performance parameters of the building envelope include the overall heat transfer coefficient of the building exterior walls, the solar heat gain factor of the exterior windows, the thermal inertia index of the roof, and the building airtightness level. Indoor heat source distribution characteristic parameters include the heat release intensity of indoor activity areas, the heat dissipation power spectrum of household appliance clusters, and the radiation temperature field of heating radiator surfaces. The hourly ambient temperature forecast includes the time-series variation curve of outdoor dry-bulb temperature over the next 24 hours, the hourly variation pattern of solar radiation intensity, and the influence factors of wind speed and direction on heat transfer on building surfaces.

3. The method according to claim 1, characterized in that, The spatiotemporal fusion of the temperature sensing data specifically includes: By associating the spatial coordinates of multiple indoor temperature measurement points with the temperature values ​​uploaded by each measurement point, a spatial interpolation surface for indoor temperature is constructed. By temporally superimposing spatial interpolation surfaces from multiple consecutive time sections, the variation characteristics of the indoor temperature field in the time dimension can be extracted. Based on the variation characteristics of the spatial interpolation surface and the time dimension, a dynamic distribution map of the indoor temperature field containing temperature gradient information and temperature change rate information is generated.

4. The method according to claim 1, characterized in that, The model for the evolution trend of the dynamic distribution map of the indoor temperature field over a future period specifically includes: The attenuation and delay characteristics of the building space to external temperature disturbances are determined based on the thermal characteristic parameters of the building envelope. The contribution weight and influence radius of the indoor heating element to the temperature field are determined based on the characteristic parameters of the indoor heat source distribution. By coupling attenuation characteristics, delay characteristics, contribution weights, and influence radius with hourly ambient temperature forecasts, a sequence of expected temperature values ​​for each indoor spatial location over a continuous future period is generated, forming an evolution trend model.

5. The method according to claim 1, characterized in that, The real-time combustion characteristic parameters include the flame spectral distribution characteristics in the combustion chamber, the residual oxygen concentration in the flue gas emission channel, and the fluctuation amplitude of the gas pressure before and after the burner nozzle. Real-time heat exchange efficiency parameters include the temperature gradient distribution of the medium on both sides of the plate heat exchanger, the online estimate of the fouling thermal resistance on the heat exchanger surface, and the dynamic response relationship between the circulating water flow rate and the return water temperature.

6. The method according to claim 1, characterized in that, Generating the multi-dimensional adjustment command for the burner specifically includes: The current temperature value at key locations in the dynamic distribution map of the indoor temperature field is compared point by point with the expected temperature value at the corresponding location and time in the evolution trend model to obtain the overall deviation of the temperature field. The overall deviation of the temperature field is decomposed into an average temperature deviation component and a temperature gradient deviation component. The burner's basic load adjustment direction is determined based on the average temperature deviation component, and the flame spatial distribution pattern adjustment strategy is determined based on the temperature gradient deviation component combined with real-time combustion characteristic parameters. By integrating the base load adjustment direction and flame spatial distribution pattern adjustment strategy, a multi-dimensional adjustment command is generated that simultaneously includes the adjustment of total gas supply and the adjustment of flame spatial distribution.

7. The method according to claim 1, characterized in that, The gas supply regulation sequence includes three control parameters: the rotation direction, rotation angle, and rotation speed of the stepper motor of the gas regulating valve. The combustion air supply regulation sequence includes three control parameters: the target speed of the variable frequency fan, the speed change slope, and the opening of the duct damper. The circulating water flow rate regulation sequence includes three control parameters: the start-stop frequency of the circulating water pump, the operating duty cycle, and the water flow direction switching. When the gas regulating valve, variable frequency fan and circulating water pump work together, the ratio of gas to air is kept within the range of full combustion, and the heat exchange efficiency on both sides of the heat exchanger is kept within the set range.

8. The method according to claim 1, characterized in that, The dynamic correction of the expected state in subsequent periods of the evolution trend model specifically includes: Record the actual rate of temperature rise at key indoor locations after the execution of multi-dimensional adjustment commands of the burner, compare the actual rate of temperature rise with the preset rate of temperature rise in the evolution trend model, and obtain the thermal response correction coefficient of the building space. Record the actual temperature distribution when the indoor temperature field reaches a stable state after the execution of the multi-dimensional adjustment command of the burner. Compare the actual temperature distribution with the preset temperature distribution in the evolution trend model to obtain the heat source contribution correction coefficient. By substituting the thermal response correction coefficient and the heat source contribution correction coefficient into the evolution trend model, the expected temperature value sequence for each spatial location in subsequent time periods is refitted.

9. The method according to claim 1, characterized in that, The method also includes a behavioral pattern self-learning step: Long-term collection of personnel activity trajectory data and corresponding temperature adjustment operation records in various indoor areas; extraction of personnel presence probability distribution in each time period from personnel activity trajectory data; extraction of personnel preferred temperature setting values ​​in each time period from temperature adjustment operation records. By correlating the probability distribution of human presence with the preferred temperature setpoint, a human thermal comfort demand curve that changes over time is generated. The expected state for the corresponding period in the evolution trend model is weighted and corrected based on the thermal comfort demand curve of the personnel, so that the operation strategy of the heating boiler matches the actual needs of the personnel.

10. The method according to claim 1, characterized in that, The method also includes a system health self-diagnosis step: Continuously monitor the flame stability characteristics of the combustion system, the heat exchange efficiency decay characteristics of the heat exchange system, and the pressure fluctuation characteristics of the water circulation system; The flame stability characteristic value is compared with the stable combustion characteristic template to generate a combustion system health score; The heat exchange efficiency decay characteristic value is compared with the heat exchanger cleanliness status threshold to generate a heat exchange system health score. The pressure fluctuation characteristic values ​​are compared with the standard pressure spectrum of the water circulation system to generate a health score for the water circulation system. When the health score of any system falls below the set threshold, a maintenance prompt signal is issued for the corresponding system and the upper limit of the operating parameters of the heating boiler is automatically adjusted.

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