Dynamic metering method and system for heat loss of warm air blower
By acquiring the real-time operating, environmental, and structural parameters of the heater, a heat loss fluctuation sequence and scenario adaptation correction are generated, solving the problem of inaccurate heat loss measurement of the heater and achieving efficient and safe operation and real-time early warning.
Patent Information
- Application Number
- CN202511853247.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-10
- Publication Date
- 2026-03-17
AI Technical Summary
Existing methods for measuring heat loss in warm air blowers fail to effectively consider dynamic environmental changes and structural changes, resulting in inaccurate measurements, inability to adapt to complex operating conditions, and a lack of real-time monitoring and dynamic early warning functions.
By acquiring the real-time operating parameters, environmental parameters, and structural status parameters of the heater, a heat loss fluctuation sequence and scenario adaptation correction are generated. Combined with multi-source data fusion and signal processing technology, the heat loss measurement model is dynamically adjusted to achieve real-time anomaly warning.
It improves the accuracy and adaptability of heat loss measurement for heaters, enables timely detection of equipment malfunctions, ensures efficient and safe operation, and reduces energy waste.
Smart Images
Figure CN121682602A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of heater technology, and in particular to a method and system for dynamic measurement of heat loss in heaters. Background Technology
[0002] As a commonly used heating device in both residential and commercial settings, the heat loss level of a space heater directly determines its energy efficiency and operational safety. Accurately measuring heat loss is a core prerequisite for optimizing equipment operation strategies and reducing energy waste.
[0003] Existing technologies have significant limitations. They neglect the impact of dynamic environmental changes, such as differences in indoor and outdoor temperature and humidity, air circulation paths, and external heat source interference, all of which alter the propagation and attenuation patterns of heat loss. Traditional static metering models cannot adapt to such dynamic environments. Furthermore, they do not take into account changes in the structural condition of the heater. Structural problems such as blocked air ducts, damaged heat sinks, and aging insulation layers can significantly exacerbate heat loss. Existing methods lack real-time monitoring of structural conditions and loss correlation analysis. Summary of the Invention
[0004] The purpose of this invention is to provide a method and system for dynamic measurement of heat loss in a heater, so as to solve the technical problems mentioned in the background art.
[0005] To achieve the above objectives, the present invention provides the following technical solution:
[0006] A method for dynamically measuring heat loss in a heater includes:
[0007] Obtain the real-time operating parameters, environmental parameters, and structural status parameters of the heater;
[0008] A heat loss feature set is obtained based on the real-time operating parameters, and a heat loss fluctuation sequence is generated based on the heat loss feature set.
[0009] Preliminary heat loss parameters are generated based on the heat loss fluctuation sequence and environmental correlation parameters.
[0010] Obtain the cumulative parameters of the heater's operating conditions, and generate the heat loss-scenario adaptation correction amount based on the cumulative parameters of the heater's operating conditions and the structural state parameters;
[0011] The heat loss measurement value is obtained based on the preliminary heat loss parameters and the heat loss-scenario adaptation correction amount.
[0012] Determine whether the measured heat loss value is greater than a preset threshold;
[0013] If it is less than 1, then the heat loss of the heater is considered to be in a normal state.
[0014] If the value is greater than the threshold, the heat loss of the heater is determined to be abnormal, and an early warning message is generated for real-time reminder.
[0015] Preferably, the step of obtaining the heat loss feature set based on the real-time operating parameters includes:
[0016] Based on the real-time operating parameters, key sub-parameters directly related to heat loss are obtained, including input power change data, shell surface temperature change data, and air outlet airflow velocity change data.
[0017] A trend separation method is used for key sub-parameters to obtain heat loss fluctuation characteristic data of heat loss changes from the key sub-parameters;
[0018] Effective heat loss fluctuation data is obtained based on the heat loss fluctuation characteristic data.
[0019] Based on the effective heat loss fluctuation data, obtain the fluctuation amplitude data, fluctuation period data, and fluctuation phase data of the heat loss change, and generate heat loss related feature data based on the fluctuation amplitude data, fluctuation period data, and fluctuation phase data.
[0020] The heat loss associated feature data are classified and organized, and heat loss associated feature data of the same type are grouped together to generate a heat loss feature set.
[0021] Preferably, the step of generating a heat loss fluctuation sequence based on the heat loss feature set includes:
[0022] Based on the heat loss feature set, the acquisition timestamps are obtained from the real-time operating parameters. Based on the order of the acquisition timestamps, the heat loss feature sets of each group are sorted by time, and the feature elements of the same heat loss type are arranged in order of acquisition time to obtain the classified heat loss feature sets sorted by time.
[0023] Based on the classified heat loss feature set, extract the fluctuation amplitude and fluctuation period corresponding to each feature element, obtain the amplitude range corresponding to each feature element based on the fluctuation amplitude, obtain the time length corresponding to each feature element based on the fluctuation period, and obtain the time-series fluctuation segment corresponding to each feature element based on the amplitude range and time length.
[0024] The time breakpoint between two adjacent fluctuation segments is obtained based on the time fluctuation segment corresponding to each feature element, and continuous time fluctuation segments are generated based on the fluctuation trend of the fluctuation segments before and after the breakpoint.
[0025] The continuous time-series fluctuation segments are prioritized according to the type of heat loss, and the different types of continuous fluctuation segments are superimposed on the same time axis to obtain a preliminary fluctuation sequence with multiple types of loss superposition.
[0026] Based on the preliminary fluctuation sequence of multiple loss superposition, the amplitude changes of different types of fluctuations at adjacent time points are obtained. If the amplitude change at a certain time point exceeds the fluctuation trend range of adjacent time periods, the amplitude at that point is adjusted based on the fluctuation pattern of the preceding and following time periods to obtain the heat loss fluctuation sequence of the heater heat loss change.
[0027] Preferably, the step of generating preliminary heat loss parameters based on the heat loss fluctuation sequence and environmental correlation parameters includes:
[0028] Based on each fluctuation segment in the heat loss fluctuation sequence, the segments of the rising and falling diffusion rates of the fluctuation are obtained, and the fluctuation diffusion characteristics of the spatial propagation properties of heat loss are generated.
[0029] The basic layout of the space surrounding the heater is obtained, and the coverage of heat loss in the space corresponding to each fluctuation segment is tracked. The main direction of heat loss diffusion, the size of the coverage area, and the uniformity of loss distribution in different areas are obtained and integrated to obtain the spatial influence characteristics of the fluctuation.
[0030] From the environmental parameters, we extract the main direction and branch paths of airflow, the regional differences in humidity distribution, the location of heat source interference relative to the heater, and the constraints of spatial geometry. We then integrate this information to form environmental spatial characteristics.
[0031] By aligning the spatial impact characteristics of fluctuations with the spatial characteristics of the environment in a spatiotemporal manner, a spatial correlation characteristic between fluctuations and the environment is generated.
[0032] Based on each wave segment, combined with the wave-environment spatial correlation characteristics, the loss and diffusion characteristics of the wave under the airflow path in the current environment and the loss and diffusion characteristics in the high humidity area are obtained. The loss and diffusion characteristics under the airflow path and the loss and diffusion characteristics in the high humidity area are organized into wave-environment adaptation characteristics.
[0033] Loss fluctuation characteristics are obtained based on the fluctuation-environment adaptation characteristics corresponding to each fluctuation segment. The loss fluctuation characteristics include the initial loss intensity and the loss change during the diffusion process. The loss fluctuation characteristics of all fluctuation segments are integrated in chronological order to generate preliminary heat loss parameters that can continuously reflect the heat loss changes of the heater.
[0034] Preferably, the step of obtaining the cumulative parameters of the heater's operating conditions and generating the heat loss-scenario adaptation correction amount based on the cumulative parameters of the heater's operating conditions and the structural state parameters includes:
[0035] The historical operation records of the heater are obtained, and the start and stop events of each operation are obtained according to the historical operation records of the heater to count the cumulative number of start and stop. The duration of each continuous operation is recorded and accumulated to obtain the cumulative continuous operation duration. The intermittent operation interval parameter is obtained according to the time interval between two adjacent start and stop. The cumulative number of start and stop, the cumulative continuous operation duration, and the intermittent operation interval parameter are integrated into the cumulative parameters of the heater operating condition.
[0036] Based on the cumulative number of start-stops, obtain the correlation parameters between the number of start-stops and the change in heat loss, and based on the cumulative continuous running time, obtain the heat loss trend parameters caused by the change in thermal inertia of the heat dissipation components. Based on the correlation parameters and the heat loss trend parameters, generate the operating condition influence trend characteristics.
[0037] The airflow unobstructedness, heat sink integrity, and insulation layer effectiveness of the heater are obtained, and these factors are integrated into structural state characteristics.
[0038] The working condition influence trend characteristics are mapped to the structural state characteristics to generate working condition-structure correlation influence characteristics;
[0039] Obtain spatial information of the current application scenario of the heater, and obtain scenario-based correlation influence features adapted to the current scenario based on the spatial information and the working condition-structure correlation influence features;
[0040] Based on the combined influence of each operating condition and structure in the scenario-based correlation influence features, the correction direction of the combined influence on heat loss measurement is obtained, and a heat loss-scenario adaptation correction amount that can compensate for the combined influence of operating conditions and structures is generated.
[0041] Preferably, the step of obtaining the heat loss measurement value based on the preliminary heat loss parameters and the heat loss-scenario adaptation correction includes:
[0042] Based on the preliminary heat loss parameters, conduction loss data, convection loss data, and radiation loss data are obtained, and the conduction loss data, convection loss data, and radiation loss data are integrated into loss type composition data.
[0043] Based on the heat loss-scenario adaptation correction amount, obtain correction content data for different loss types, wherein the correction content data includes conduction loss correction data, convection loss correction data and radiation loss correction data, and generate categorized correction data based on the conduction loss correction data, convection loss correction data and radiation loss correction data;
[0044] Each type of loss data in the loss type composition data is matched with the corresponding correction data in the categorized correction data to generate loss-correction correlation features;
[0045] The adjusted loss data obtained based on the loss-correction correlation characteristics includes adjusted conduction loss data, adjusted convection loss data, and adjusted radiation loss data.
[0046] The heat loss measurement value is generated based on the adjusted conduction loss data, adjusted convection loss data, and adjusted radiation loss data.
[0047] The present invention also provides a dynamic heat loss metering system for a heater, comprising:
[0048] The parameter acquisition module is used to acquire the real-time operating parameters, environmental parameters, and structural status parameters of the heater.
[0049] A heat loss fluctuation sequence generation module is used to obtain a heat loss feature set based on the real-time operating parameters and generate a heat loss fluctuation sequence based on the heat loss feature set.
[0050] A heat loss parameter generation module is used to generate preliminary heat loss parameters based on the heat loss fluctuation sequence and environmental correlation parameters.
[0051] The correction amount generation module is used to obtain the cumulative parameters of the heater operating conditions and generate the heat loss-scenario adaptation correction amount based on the cumulative parameters of the heater operating conditions and the structural state parameters.
[0052] The heat loss measurement value acquisition module is used to acquire the heat loss measurement value based on the preliminary heat loss parameters and the heat loss-scenario adaptation correction amount.
[0053] The judgment module is used to determine whether the heat loss measurement value is greater than a preset threshold.
[0054] If it is less than 1, then the heat loss of the heater is considered to be in a normal state.
[0055] If the value is greater than the threshold, the heat loss of the heater is determined to be abnormal, and an early warning message is generated for real-time reminder.
[0056] Preferably, the heat loss fluctuation sequence generation module includes:
[0057] The first acquisition unit is used to acquire key sub-parameters directly related to heat loss based on the real-time operating parameters. The key sub-parameters include input power change data, shell surface temperature change data, and air outlet airflow velocity change data.
[0058] The second acquisition unit is used to extract heat loss fluctuation characteristic data of heat loss change from the key sub-parameters by using a trend separation method.
[0059] The third acquisition unit is used to acquire effective heat loss fluctuation data based on the heat loss fluctuation characteristic data.
[0060] The fourth acquisition unit is used to acquire the fluctuation amplitude data, fluctuation period data, and fluctuation phase data of the heat loss change based on the effective heat loss fluctuation data, and to generate heat loss related feature data based on the fluctuation amplitude data, fluctuation period data, and fluctuation phase data.
[0061] The fifth acquisition unit is used to classify and organize the heat loss associated feature data, and group the heat loss associated feature data of the same type into a group to generate a heat loss feature set.
[0062] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described dynamic measurement method for heat loss of a heater.
[0063] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method for dynamic measurement of heat loss of a heater.
[0064] The beneficial effects of this application are as follows: This invention effectively compensates for the interference of operating condition changes, structural status and environmental factors on measurement by multi-source data fusion, accurate extraction of heat loss characteristics and scenario-based dynamic correction, which greatly improves the accuracy and adaptability of heat loss measurement of heaters; real-time abnormal early warning can detect abnormal heat loss of equipment in time, avoid energy waste and ensure the efficient and safe operation of heaters; and provides reliable data support for energy efficiency management, fault diagnosis and maintenance of heaters. Attached Figure Description
[0065] Figure 1 This is a schematic diagram of a method flow according to an embodiment of this application.
[0066] Figure 2 This is a schematic diagram of the system structure according to an embodiment of this application.
[0067] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0068] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
[0069] like Figure 1 As shown, this application provides a method for dynamic measurement of heat loss in a heater, including:
[0070] S1. Obtain the real-time operating parameters, environmental parameters, and structural status parameters of the heater;
[0071] S2. Obtain a heat loss feature set based on the real-time operating parameters, and generate a heat loss fluctuation sequence based on the heat loss feature set;
[0072] S3. Generate preliminary heat loss parameters based on the heat loss fluctuation sequence and environmental correlation parameters;
[0073] S4. Obtain the cumulative parameters of the heater's operating conditions, and generate the heat loss-scenario adaptation correction amount based on the cumulative parameters of the heater's operating conditions and the structural state parameters.
[0074] S5. Obtain the heat loss measurement value based on the preliminary heat loss parameters and the heat loss-scenario adaptation correction amount.
[0075] S6. Determine whether the heat loss measurement value is greater than the preset threshold.
[0076] If it is less than 1, then the heat loss of the heater is considered to be in a normal state.
[0077] If the value is greater than the threshold, the heat loss of the heater is determined to be abnormal, and an early warning message is generated for real-time reminder.
[0078] As described in steps S1-S6 above, this invention accurately predicts the heat loss of the heater by comprehensively analyzing the real-time operating parameters, environmental parameters, and structural status parameters of the heater, and generates early warning information based on the actual heat loss situation, ensuring that the heater operates efficiently under normal conditions, and promptly detecting and warning of abnormal heat loss, thereby improving the working efficiency and safety of the equipment.
[0079] First, to comprehensively obtain the operating status of the heater, a complete understanding of the equipment's operating conditions, external environment, and structural changes is necessary. Existing technologies often rely on data from a single sensor, ignoring the diversity of structural states and environmental conditions, making it difficult to accurately reflect the true state of heat loss under complex operating conditions. This invention integrates multiple data sources, utilizing high-precision sensors and data acquisition devices to acquire operating parameters (such as fan power, speed, and output airflow), environmental parameters (such as external temperature, humidity, air pressure, and airflow), and structural parameters (such as internal pressure and vibration frequency) in real time. This data is then transmitted to the central control system via wired or wireless transmission, ensuring the real-time nature and accuracy of the data.
[0080] This invention acquires a heat loss feature set based on real-time operating parameters and generates a heat loss fluctuation sequence. Heat loss fluctuations reflect the energy loss of the heater under different operating conditions and are crucial for subsequent prediction. Existing technologies often rely on static models or simple regression methods, failing to fully consider the dynamic changes in operating parameters, leading to prediction errors. This invention employs signal processing methods such as Fourier transform and Kalman filtering to extract feature parameters such as temperature change rate and power fluctuations, constructing a heat loss feature set. Then, time series analysis is used to transform this set into a heat loss fluctuation sequence, capturing heat loss variation patterns under different operating conditions and updating and correcting the model in real time to ensure its adaptability.
[0081] Preliminary heat loss parameters are generated by combining heat loss fluctuation sequences with environmental parameters. Environmental factors (such as temperature and humidity) have a significant impact on heat loss, and traditional methods often ignore or simply handle these factors, leading to prediction bias. This invention uses regression analysis, machine learning, and other methods to deeply integrate environmental parameters with heat loss fluctuation sequences to train and generate preliminary heat loss parameters. These parameters incorporate the correction effect of environmental factors, improving the accuracy and real-time performance of predictions.
[0082] This invention acquires accumulated operating parameters of a space heater and combines them with structural state parameters to generate a heat loss-scenario adaptation correction. Space heaters operate differently in various scenarios, requiring dynamic adjustments to heat loss to adapt to actual conditions. Existing methods often rely on fixed models, neglecting the impact of scenario variations. This invention acquires operating parameters (such as runtime and cumulative power) from accumulated historical data, combines them with structural state parameters (such as equipment aging and maintenance records), and uses models such as multinomial regression or neural networks to calculate the heat loss-scenario adaptation correction. This allows the correction to automatically adjust according to the actual working environment, more accurately reflecting actual energy loss.
[0083] By combining preliminary heat loss parameters with heat loss-scenario adaptation correction values, a heat loss measurement value is obtained, providing a basis for subsequent status judgment and early warning. Preliminary parameters are mostly based on design standard presets and cannot fully reflect actual scenarios, while the correction value is a dynamically adjusted value generated according to the current environment. This invention integrates the two through weighted averaging or other mathematical models to obtain a heat loss measurement value that reflects the true energy efficiency of the heater under specific environments.
[0084] By determining whether the measured heat loss exceeds a preset threshold, the operating status of the heater is identified and anomaly warnings are issued. An abnormally high heat loss usually indicates equipment efficiency issues or operational abnormalities, requiring timely detection. Existing monitoring systems often rely on basic parameters to determine status, lacking precise monitoring and dynamic early warning of heat loss. This invention sets a dynamic threshold based on equipment design standards and actual operating data, considering environmental changes and equipment specifications. The controller or central system compares the measured value with the threshold in real time. If the measured value exceeds the threshold, the system determines it as an abnormal state and generates an early warning message, thus promptly alerting potential problems and ensuring the efficient and safe operation of the heater.
[0085] In one embodiment, the step of obtaining the heat loss feature set based on the real-time operating parameters includes:
[0086] S201. Obtain key sub-parameters directly related to heat loss based on the real-time operating parameters. The key sub-parameters include input power change data, shell surface temperature change data, and air outlet airflow velocity change data.
[0087] S202. Use trend separation to extract heat loss fluctuation characteristic data from key sub-parameters.
[0088] S203. Obtain effective heat loss fluctuation data based on the heat loss fluctuation characteristic data;
[0089] S204. Obtain the fluctuation amplitude data, fluctuation period data, and fluctuation phase data of the heat loss change based on the effective heat loss fluctuation data, and generate heat loss related feature data based on the fluctuation amplitude data, fluctuation period data, and fluctuation phase data.
[0090] S205. Classify and organize the heat loss associated feature data, and group the heat loss associated feature data of the same type into a group to generate a heat loss feature set.
[0091] As described in steps S201-S205 above, this invention extracts key sub-parameters directly related to heat loss from real-time operating parameters, including input power variation data, casing surface temperature variation data, and outlet airflow velocity variation data. Traditional methods often rely on static or simplified models, ignoring the effects of multivariate coupling, leading to prediction bias. Through multidimensional data monitoring and dynamic correlation analysis, key parameters are extracted based on the actual operating status of the equipment: input power variation reflects the impact of load changes on heat loss; casing surface temperature variation indirectly characterizes internal heat loss; and outlet airflow velocity variation is directly related to heat exchange efficiency and the degree of heat loss. These data are collected by sensors and transmitted to the analysis platform via a data interface.
[0092] Subsequently, trend separation technology is used to extract heat loss fluctuation characteristic data from key sub-parameters. In actual operation, equipment status and environmental factors are intertwined, and traditional methods, relying on simple statistics or global averaging, are difficult to effectively separate complex fluctuations. This invention applies methods such as moving average or wavelet transform to perform time-domain decomposition on data such as power, temperature, and flow rate, distinguishing between long-term trends and short-term fluctuations, thereby obtaining fluctuation characteristic data that can reveal the pattern and periodicity of heat loss changes.
[0093] To improve data quality, we further screen effective heat loss fluctuation data from the fluctuation characteristics. Since some fluctuations originate from external interference or equipment malfunctions, traditional methods lack effective cleaning mechanisms and are easily affected by noise. Through statistical analysis or model-based judgment methods, such as calculating standard deviation and bias, invalid data deviating from the normal range is eliminated, ensuring that the retained data accurately reflects the heat loss fluctuations under normal equipment operating conditions.
[0094] Based on the acquisition of effective fluctuation data, key features such as fluctuation amplitude, period, and phase are extracted. Existing methods often simplify the processing and fail to comprehensively capture multidimensional fluctuation information. This paper employs Fourier transform or time-domain analysis techniques to extract spectral information from the fluctuation data to identify periodicity, calculates the fluctuation amplitude through extreme value differences, determines the phase based on the signal's time relationship, and then generates heat loss-related feature data.
[0095] Finally, the heat loss-related feature data are classified and organized to construct a heat loss feature set. Traditional methods lack flexibility in feature organization and are difficult to adapt to complex operating conditions. Using clustering algorithms such as K-means, the data is classified based on feature similarity. Each category represents the heat loss pattern of the equipment under specific operating conditions, helping analysts understand its changing patterns. The generated heat loss feature set provides reliable data support for equipment operation management, energy efficiency optimization, and fault early warning.
[0096] In one embodiment, the step of generating a heat loss fluctuation sequence based on the heat loss feature set includes:
[0097] S301. Obtain the collection timestamp from the real-time operating parameters according to the heat loss feature set. Based on the order of the collection timestamps, sort the heat loss feature sets of each group by time and arrange the feature elements of the same heat loss type in order of collection time to obtain the classified heat loss feature set sorted by time.
[0098] S302. Extract the fluctuation amplitude and fluctuation period corresponding to each feature element according to the classification heat loss feature set, obtain the amplitude range corresponding to each feature element according to the fluctuation amplitude, obtain the time length corresponding to each feature element according to the fluctuation period, and obtain the time-series fluctuation segment corresponding to each feature element based on the amplitude range and time length.
[0099] S303. Obtain the time breakpoint between two adjacent fluctuation segments based on the time fluctuation segment corresponding to each feature element, and generate continuous time fluctuation segments based on the fluctuation trend of the fluctuation segments before and after the breakpoint.
[0100] S304. The continuous time-series fluctuation segments are prioritized according to the type of heat loss, and the different types of continuous fluctuation segments are superimposed on the same time axis to obtain a preliminary fluctuation sequence with multiple types of loss superposition.
[0101] S305. Based on the preliminary fluctuation sequence of multiple types of losses superimposed, obtain the amplitude changes of different types of fluctuations at adjacent time points, and determine if the amplitude change at a certain time point exceeds the fluctuation trend range of adjacent time periods. Adjust the amplitude at that point based on the fluctuation pattern of the preceding and following time periods to obtain the heat loss fluctuation sequence of the heater's heat loss change.
[0102] As described in steps S301-S305 above, the present invention generates a heat loss fluctuation sequence to accurately reflect the fluctuation changes caused by the superposition of multiple heat loss types during the operation of the heater, providing basic data support for equipment performance analysis, fault diagnosis and energy efficiency optimization. At the same time, it realizes high-precision extraction and dynamic analysis of heat loss fluctuation characteristics, providing a strong basis for the optimization of intelligent control system.
[0103] The process involves obtaining heat loss feature sets from real-time operating parameters of the heater and sorting them by acquisition timestamps to ensure temporal continuity. The fluctuation amplitude and period of each feature element are extracted, and the amplitude range is defined based on the fluctuation amplitude. Combined with the time length, time-series fluctuation segments are generated. Time breakpoints between adjacent fluctuation segments are analyzed to generate continuous time-series fluctuation segments. Different types of fluctuation segments are superimposed according to heat loss type priority to obtain a preliminary fluctuation sequence of multiple loss types. The amplitude of abrupt fluctuations is adjusted to ultimately obtain an accurate heat loss fluctuation sequence. This achieves accurate extraction and dynamic optimization of complex heat loss fluctuation features, ensuring high precision and real-time performance of the analysis results. In terms of detailed implementation steps and technical effects, heat loss feature set data (including acquisition timestamps of each feature element, which can be sensor-recorded time points or time data synchronously generated internally by the system) is obtained from the real-time operating parameters of the heater. Based on the timestamps, each group of classified heat loss feature sets is sorted in chronological order. This step ensures that subsequent time-series fluctuation segments are arranged in the correct time, helping to accurately capture the operating status of the equipment at different time points, avoiding misjudgments due to time discrepancies in subsequent analysis, and ensuring the timeliness and accuracy of the analysis results.
[0104] After completing the time sorting, the fluctuation amplitude and period of each feature element are extracted from the set of classified heat loss features. The amplitude range is calculated based on the statistical characteristics of the fluctuation amplitude to achieve accurate division of the fluctuation segment. From a physical perspective, these data reflect the energy loss fluctuation caused by different types of heat loss during the operation of the heater, which can better restore the actual operating state of the equipment and provide accurate input for subsequent fluctuation analysis.
[0105] Based on the fluctuation amplitude and period information, analyze the time discontinuity of adjacent fluctuation segments, combine the fluctuation trend before and after the discontinuity to generate continuous time-series fluctuation segments, identify and adjust unreasonable discontinuities and abrupt changes caused by faults or data acquisition problems, ensure smooth transition of fluctuation segments and accurate reflection of change patterns, and guarantee the continuity and scientific nature of time-series fluctuation segments.
[0106] Based on the priority of the impact of heat loss type on equipment operation, different types of continuous time-series fluctuation segments are superimposed to obtain a preliminary fluctuation sequence of multiple types of loss superposition. The key is to reasonably handle the superposition effect, avoid information loss or error amplification, fully reflect the overall fluctuation characteristics of the heater under the superposition of various heat losses, and realize the key fluctuation analysis.
[0107] The amplitude changes of different types of fluctuations at adjacent time points in the preliminary fluctuation sequence are judged. If the amplitude changes abruptly at a certain time point and exceeds the fluctuation trend range of the adjacent time period, the amplitude at that point is adjusted based on the fluctuation pattern of the preceding and following time periods. This eliminates abrupt changes caused by sensor errors, external interference, etc., improves the stability and continuity of the fluctuation sequence, and reduces the analysis error caused by abnormal data fluctuations.
[0108] In one embodiment, the step of generating preliminary heat loss parameters based on the heat loss fluctuation sequence and environmental correlation parameters includes:
[0109] S401. Based on each fluctuation segment in the heat loss fluctuation sequence, obtain the segments of the rising diffusion rate and the falling diffusion rate of the fluctuation, and generate the fluctuation diffusion characteristics of the spatial propagation characteristics of heat loss.
[0110] S402. Obtain the basic layout of the space surrounding the heater, track the coverage of heat loss in the space corresponding to each fluctuation segment, obtain the main direction of heat loss diffusion, the size of the coverage area and the uniformity of loss distribution in different areas, and integrate them to obtain the fluctuation space influence characteristics.
[0111] S403. Extract the main direction and branch paths of airflow, regional differences in humidity distribution, the location of heat source interference relative to the heater, and the constraints of spatial geometry from the environmental parameters. Integrate and associate this information to form environmental spatial characteristics.
[0112] S404. Spatiotemporally align the spatial impact characteristics of fluctuations with the spatial characteristics of the environment to generate spatial correlation characteristics between fluctuations and the environment.
[0113] S405. Based on each wave segment, combined with the wave-environment spatial correlation characteristics, obtain the loss and diffusion characteristics of the wave under the airflow path in the current environment and the loss and diffusion characteristics in the high humidity area, and organize the loss and diffusion characteristics under the airflow path and the loss and diffusion characteristics in the high humidity area into wave-environment adaptation characteristics.
[0114] S406. Obtain loss fluctuation characteristics based on the fluctuation-environment adaptation characteristics corresponding to each fluctuation segment. The loss fluctuation characteristics include the initial loss intensity and the loss change during the diffusion process. Integrate the loss fluctuation characteristics of all fluctuation segments in chronological order to generate preliminary heat loss parameters that can continuously reflect the heat loss changes of the heater.
[0115] As described in steps S401-S406 above, this invention analyzes the propagation characteristics of each wave segment in the heat loss wave sequence. The diffusion rate of the rising edge of the wave reflects the rate at which heat energy is released from the device to the surrounding space, while the convergence mode of the falling edge reflects the spatial distribution changes during the heat energy decay process. Through time series analysis, combined with temperature data collected by devices such as infrared sensors and thermal imagers, and fitted according to models such as the heat conduction equation, wave diffusion characteristics, including diffusion rate, decay rate, and spatial distribution pattern, can be extracted.
[0116] The process involves acquiring basic layout information of the space surrounding the heater, tracking the spatial coverage of heat loss corresponding to each fluctuation segment, identifying its main diffusion direction, the size of the affected area, and the uniformity of loss distribution in each area, thereby integrating these findings to form the spatial impact characteristics of the fluctuations. This process can be achieved using physical measurements, CFD simulations, or infrared imaging technology to accurately characterize the spatial propagation path and distribution of heat loss.
[0117] Simultaneously, the main direction and branch structure of airflow paths, regional differences in humidity distribution, the orientation of heat sources relative to the heater, and constraint parts in the spatial geometry are extracted from environmental parameters to comprehensively construct environmental spatial characteristics. These parameters are obtained through CFD simulation, sensor monitoring, or numerical analysis methods, and then integrated using data fusion technology into a spatial feature set describing the impact of the environment on heat loss.
[0118] To achieve effective coupling between heat loss and environmental factors, the spatial impact characteristics of fluctuations are spatiotemporally aligned with environmental spatial characteristics to generate fluctuation-environment spatial correlation features. This step ensures that each fluctuation segment precisely corresponds to its environmental conditions through synchronous matching of timestamps and spatial coordinates, providing a consistent data foundation for subsequent analysis.
[0119] Based on each wave segment and its corresponding spatial correlation characteristics, we further analyze its specific diffusion behavior under the current environment, including the loss propagation characteristics along the airflow path and its diffusion performance in high humidity areas, thus forming a wave-environment adaptation characteristic. This characteristic can reflect the actual impact of different environmental conditions on the heat loss process.
[0120] Finally, based on the fluctuation-environment adaptation characteristics of each fluctuation segment, key parameters such as the initial loss intensity and loss changes during the diffusion process are extracted. The loss description information of all segments is then integrated in chronological order to generate preliminary heat loss parameters that continuously reflect the dynamic changes in the heat loss of the heater. This parameter model provides reliable data support for subsequent heat loss measurement and anomaly early warning.
[0121] In one embodiment, the step of obtaining the cumulative parameters of the heater's operating conditions and generating the heat loss-scenario adaptation correction amount based on the cumulative parameters of the heater's operating conditions and the structural state parameters includes:
[0122] S501. Obtain the historical operation record of the heater, and obtain the start and stop events of each operation according to the historical operation record of the heater to count the cumulative number of start and stop, record the duration of each continuous operation and accumulate them to obtain the cumulative continuous operation duration, obtain the intermittent operation interval parameter according to the time interval between two adjacent start and stop, and integrate the cumulative number of start and stop, the cumulative continuous operation duration, and the intermittent operation interval parameter into the cumulative operating condition parameter of the heater.
[0123] S502. Obtain the correlation parameters between the number of start-stops and the change in heat loss based on the cumulative number of start-stops, and obtain the heat loss trend parameters caused by the change in thermal inertia of the heat dissipation components based on the cumulative continuous running time. Generate the operating condition influence trend characteristics based on the correlation parameters and the heat loss trend parameters.
[0124] S503. Obtain the airflow unobstructedness, heat sink integrity, and insulation layer effectiveness of the heater, and integrate the airflow unobstructedness, heat sink integrity, and insulation layer effectiveness into structural state characteristics.
[0125] S504. Generate working condition-structure correlation influence features by matching the trend characteristics of working condition influence with the characteristics of structural state.
[0126] S505. Obtain spatial information of the current application scenario of the heater, and obtain scenario-based correlation influence features adapted to the current scenario based on the spatial information and the working condition-structure correlation influence features.
[0127] S506. Based on the combined influence of each operating condition and structure in the scenario-based correlation influence features, obtain the correction direction of the combined influence on heat loss measurement, and generate a heat loss-scenario adaptation correction amount that can compensate for the combined influence of operating conditions and structures.
[0128] As described in steps S501-S506 above, this invention analyzes the historical operation records of the heater, extracts the timestamps of start-up and stop events, counts the cumulative number of start-ups and stoppages and the cumulative continuous running time, and calculates intermittent operating parameters based on adjacent start-up and stop intervals, integrating them into cumulative operating condition parameters. These parameters can reflect the long-term operating mode of the equipment and its impact on the trend of heat loss.
[0129] Based on the analysis of the cumulative number of start-stop cycles and their correlation with heat loss fluctuations, the loss variation patterns caused by high-frequency start-stop cycles are identified. Simultaneously, the cumulative effect of thermal inertia of heat dissipation components is assessed based on continuous operating time, generating characteristic parameters that characterize the trend of operating conditions. By monitoring airflow unobstructedness, heat sink integrity, and insulation layer effectiveness using sensors or detection equipment, key structural parameters affecting heat conduction, convection, and radiation are obtained and integrated into structural state characteristics, providing a basis for generating correction parameters.
[0130] By conducting a correlation analysis between the above-mentioned operating condition influence trend characteristics and structural state characteristics, the influence mechanism of the two combined on heat loss is examined, such as the combined effect when high-frequency start-stop and air duct blockage coexist, thus forming the operating condition-structure correlation influence characteristics.
[0131] By further combining spatial information of the current application scenario of the heater, including environmental parameters such as airflow organization, humidity distribution and heat source location, the working condition-structure correlation influence characteristics are integrated with the actual scenario conditions to generate scenario-based correlation influence characteristics, so as to reflect the coupling influence of load and structural state under different environments.
[0132] Finally, based on the contextualized impact characteristics, the direction and extent of the effects of various operating conditions and structural combinations on heat loss are analyzed, and a dynamically adaptable heat loss-scenario adjustment is generated. This adjustment can be adaptively adjusted according to the actual operating mode, structural health status, and environmental conditions, effectively improving the accuracy of heat loss measurement and the practicality of the system.
[0133] In one embodiment, the step of obtaining the heat loss measurement value based on the preliminary heat loss parameters and the heat loss-scenario adaptation correction includes:
[0134] S601. Obtain conduction loss data, convection loss data and radiation loss data based on the preliminary heat loss parameters, and integrate the conduction loss data, convection loss data and radiation loss data into loss type composition data;
[0135] S602. Obtain correction content data for different loss types based on the heat loss-scenario adaptation correction amount, wherein the correction content data includes conduction loss correction data, convection loss correction data and radiation loss correction data, and generate categorized correction data based on the conduction loss correction data, convection loss correction data and radiation loss correction data.
[0136] S603. Match each type of loss data in the loss type composition data with the corresponding correction data in the categorized correction data to generate loss-correction correlation features.
[0137] S604. Adjusted loss data obtained based on the loss-correction correlation characteristics, wherein the adjusted loss data includes adjusted conduction loss data, adjusted convection loss data, and adjusted radiation loss data;
[0138] S605. Generate and obtain heat loss measurement values based on the adjusted conduction loss data, adjusted convection loss data, and adjusted radiation loss data.
[0139] As described in steps S601-S605 above, this invention extracts key data from real-time operating parameters and independently calculates various types of losses using dedicated models: conduction losses are calculated based on the temperature difference between the outer casing surface and the ambient temperature using the heat conduction equation; convection losses are calculated based on the airflow velocity at the outlet and airflow characteristics, combined with fluid dynamics and heat transfer coefficients; and radiation losses are estimated using the Stefan-Boltzmann law based on surface temperature, emissivity, and ambient temperature. Finally, the three types of loss data are integrated into loss type composition data, providing a complete description of heat loss characteristics for subsequent analysis.
[0140] In practical applications, heat loss is significantly affected by factors such as ambient temperature, humidity, airflow, and spatial structure. Traditional methods, by ignoring scenario differences, struggle to accurately reflect changes in loss under actual operating conditions. Therefore, this paper generates scenario-adaptive correction values for different loss types using historical operating data, environmental sensor monitoring information, and structural state parameters. These correction values comprehensively consider environmental characteristics such as airflow path, humidity distribution, and heat source location, as well as structural conditions such as airflow duct unobstructedness, heat sink integrity, and insulation layer effectiveness, thereby achieving precise correction for various types of losses.
[0141] The loss type data is matched with the scenario adaptation correction amount on a category-by-category basis: conduction loss data corresponds to conduction correction amount, convection loss data corresponds to convection correction amount, and radiation loss data corresponds to radiation correction amount. By establishing loss-correction correlation features, independent adjustments can be made to each type of loss, avoiding the accuracy loss caused by uniform correction, and providing a reliable basis for the accurate generation of the final heat loss measurement value.
[0142] Obtain adjusted loss data, namely, conduction loss, convection loss, and radiation loss data after correction based on environmental and operating conditions; traditional heat loss models are not sufficiently adjusted for external environment and operating conditions, or the adjustments are based on coarse estimates, affecting the accuracy of the results; substitute the loss-correction correlation features generated in step S603 into the corresponding calculation formulas (wherein, conduction loss, convection loss, and radiation loss can all be calculated using a unified formula: The calculation yielded that, This indicates the adjusted conduction loss data. This represents the raw data of conduction loss, convection loss, or radiation loss (the raw data of conduction loss, convection loss, and radiation loss are obtained based on the preliminary heat loss parameters). This represents the conduction loss correction data, convection loss correction data, or radiation loss correction data (obtained based on the heat loss-scenario adaptation correction amount). The conduction loss, convection loss, and radiation loss are calculated separately to obtain the final adjustment value for each loss type, generating adjusted loss data. This data integrates all correction factors and can reflect the real changes of each loss type in actual operation.
[0143] Based on the adjusted conduction, convection, and radiation loss data, an overall heat loss measurement value is generated. This value comprehensively reflects the heat loss of the heater under its current operating condition, serving as the basis for further evaluation and optimization. Traditional methods, lacking precise corrections and relying solely on preliminary heat loss estimates, cannot effectively adapt to different operating conditions and environmental changes, leading to significant measurement errors. By integrating the adjusted loss data and weighting the adjusted conduction, convection, and radiation losses according to the weights and relative influences of different loss types, a weighted synthesis of the heat loss is generated. This final heat loss measurement value comprehensively considers all actual factors, including heater operating parameters, environmental factors, and structural status, accurately reflecting the true changes in the heater's heat loss and providing reliable data support for subsequent heater energy efficiency assessments, operational optimization, and maintenance decisions.
[0144] like Figure 2 As shown, the present invention also provides a dynamic metering system for heat loss of a heater, comprising:
[0145] Parameter acquisition module 1 is used to acquire the real-time operating parameters, environmental related parameters, and structural status parameters of the heater.
[0146] The heat loss fluctuation sequence generation module 2 is used to obtain a heat loss feature set based on the real-time operating parameters and generate a heat loss fluctuation sequence based on the heat loss feature set.
[0147] Heat loss parameter generation module 3 is used to generate preliminary heat loss parameters based on the heat loss fluctuation sequence and environmental correlation parameters.
[0148] The correction amount generation module 4 is used to obtain the cumulative parameters of the heater operating conditions and generate the heat loss-scenario adaptation correction amount based on the cumulative parameters of the heater operating conditions and the structural state parameters.
[0149] The heat loss measurement value acquisition module 5 is used to acquire the heat loss measurement value based on the preliminary heat loss parameters and the heat loss-scenario adaptation correction amount.
[0150] Module 6 is used to determine whether the heat loss measurement value is greater than a preset threshold.
[0151] If it is less than 1, then the heat loss of the heater is considered to be in a normal state.
[0152] If the value is greater than the threshold, the heat loss of the heater is determined to be abnormal, and an early warning message is generated for real-time reminder.
[0153] In one embodiment, the heat loss fluctuation sequence generation module 2 includes:
[0154] The first acquisition unit is used to acquire key sub-parameters directly related to heat loss based on the real-time operating parameters. The key sub-parameters include input power change data, shell surface temperature change data, and air outlet airflow velocity change data.
[0155] The second acquisition unit is used to extract heat loss fluctuation characteristic data of heat loss change from the key sub-parameters by using a trend separation method.
[0156] The third acquisition unit is used to acquire effective heat loss fluctuation data based on the heat loss fluctuation characteristic data.
[0157] The fourth acquisition unit is used to acquire the fluctuation amplitude data, fluctuation period data, and fluctuation phase data of the heat loss change based on the effective heat loss fluctuation data, and to generate heat loss related feature data based on the fluctuation amplitude data, fluctuation period data, and fluctuation phase data.
[0158] The fifth acquisition unit is used to classify and organize the heat loss correlation feature data, and group heat loss correlation feature data of the same type into a group to generate a heat loss feature set. The present invention also provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described dynamic heat loss measurement method for a heater.
[0159] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method for dynamic measurement of heat loss of a heater.
[0160] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
[0161] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.
[0162] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent results or equivalent process transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.
Claims
1. A method for dynamically metering heat loss of a fan heater, characterized by, The method comprises the following steps: acquiring real-time operation parameters, environment-related parameters and structure state parameters of the warm air blower; acquiring a heat loss feature set according to the real-time operation parameters, and generating a heat loss fluctuation sequence according to the heat loss feature set; generating preliminary heat loss parameters according to the heat loss fluctuation sequence and the environment-related parameters; acquiring warm air blower working condition cumulative parameters, and generating a heat loss-scene adaptation correction amount according to the warm air blower working condition cumulative parameters and the structure state parameters; acquiring a heat loss measurement value according to the preliminary heat loss parameters and the heat loss-scene adaptation correction amount; judging whether the heat loss measurement value is greater than a preset threshold value; if the heat loss measurement value is less than the preset threshold value, judging that the heat loss of the warm air blower is in a normal state; if the heat loss measurement value is greater than the preset threshold value, judging that the heat loss of the warm air blower is in an abnormal state, and generating a warning information for real-time reminding.
2. The method according to claim 1, wherein, The step of acquiring the heat loss feature set according to the real-time operation parameters comprises the following steps: acquiring key sub-parameters directly related to the heat loss according to the real-time operation parameters, wherein the key sub-parameters include input power change data, shell surface temperature change data and air outlet air flow velocity change data; acquiring heat loss fluctuation feature data of the heat loss change from the key sub-parameters by using a trend separation method; acquiring effective heat loss fluctuation data according to the heat loss fluctuation feature data; acquiring fluctuation amplitude data, fluctuation period data and fluctuation phase data of the heat loss change according to the effective heat loss fluctuation data, and generating heat loss-related feature data according to the fluctuation amplitude data, the fluctuation period data and the fluctuation phase data; classifying and arranging the heat loss-related feature data, grouping the heat loss-related feature data of the same type into one group, and generating the heat loss feature set.
3. The method of claim 1, wherein, The step of generating the heat loss fluctuation sequence according to the heat loss feature set comprises the following steps: acquiring collection time stamps from the real-time operation parameters according to the heat loss feature set, time-sorting each group of classified heat loss feature sets based on the sequence of the collection time stamps, and arranging the feature elements of the same heat loss type in sequence according to the collection time to obtain time-sorted classified heat loss feature sets; extracting the fluctuation amplitude and the fluctuation period corresponding to each feature element according to the classified heat loss feature sets, acquiring the amplitude range corresponding to each feature element according to the fluctuation amplitude, acquiring the time length corresponding to each feature element according to the fluctuation period, and acquiring the time sequence fluctuation segment corresponding to each feature element based on the amplitude range and the time length; acquiring the time breakpoints between adjacent two fluctuation segments according to the time sequence fluctuation segment corresponding to each feature element, and generating continuous time sequence fluctuation segments according to the fluctuation trends of the fluctuation segments before and after the breakpoints. superimposing the continuous fluctuation segments of different types on the same time axis according to the priority of the heat loss types to obtain a preliminary fluctuation sequence of multi-type loss superposition; acquiring the amplitude change of different types of fluctuations at adjacent time points according to the preliminary fluctuation sequence of multi-type loss superposition, and adjusting the amplitude of a certain time point based on the fluctuation law of the adjacent time periods if the amplitude mutation of the certain time point exceeds the fluctuation trend range of the adjacent time periods to obtain the heat loss fluctuation sequence of the heat loss change of the warm air blower.
4. The method of claim 1, wherein, The step of generating the preliminary heat loss parameter according to the heat loss fluctuation sequence and the environment-related parameter comprises: According to each fluctuation segment in the heat loss fluctuation sequence, a segment of diffusion speed of fluctuation rise and diffusion speed of fluctuation fall is obtained, and a fluctuation diffusion characteristic of heat loss space propagation is generated; A basic layout of a space around the air heater is obtained, and a coverage range of heat loss corresponding to each fluctuation segment in the space is tracked, a main direction of heat loss diffusion, a size of coverage area, and a loss distribution uniformity of different areas are obtained, and are integrated to obtain a fluctuation space influence characteristic; From the environment-related parameter, a main trend of airflow path and branch path, a regional difference of humidity distribution, a positional relationship of heat source interference position relative to the air heater, and a constraint part of space geometry structure are extracted, and these information is associated and integrated to form an environment space characteristic; The fluctuation space influence characteristic and the environment space characteristic are spatio-temporally aligned to generate a fluctuation-environment space-related characteristic; Based on each fluctuation segment, the fluctuation-environment space-related characteristic is combined to obtain a loss diffusion characteristic of the fluctuation under the airflow path and a loss diffusion characteristic of the fluctuation in a high humidity area under the current environment, and the loss diffusion characteristic under the airflow path and the loss diffusion characteristic in the high humidity area are arranged into a fluctuation-environment adaptation characteristic; According to the fluctuation-environment adaptation characteristic corresponding to each fluctuation segment, a loss fluctuation characteristic is obtained, wherein the loss fluctuation characteristic comprises a starting loss intensity and a loss change in a diffusion process, and all loss fluctuation characteristics of the fluctuation segments are integrated in time sequence to generate a preliminary heat loss parameter capable of continuously reflecting a heat loss change of the air heater.
5. The method of claim 1, wherein, The step of obtaining the air heater working condition cumulative parameter and generating the heat loss-scene adaptation correction amount according to the air heater working condition cumulative parameter and the structure state parameter comprises: A historical operation record of the air heater is obtained, and a start-stop event of each operation is obtained according to the historical operation record of the air heater to count a cumulative start-stop number, a time length of each continuous operation is recorded and accumulated to obtain a cumulative continuous operation time length, an intermittent operation interval parameter is obtained according to a time interval between adjacent two start-stop, and the cumulative start-stop number, the cumulative continuous operation time length, and the intermittent operation interval parameter are integrated into the air heater working condition cumulative parameter; A correlation parameter of start-stop number and heat loss change is obtained according to the cumulative start-stop number, a heat loss trend parameter caused by heat dissipation component heat inertia change is obtained according to the cumulative continuous operation time length, and a working condition influence trend characteristic is generated according to the correlation parameter and the heat loss trend parameter; A duct patency of the air heater, a fin integrity, and an effectiveness of a thermal insulation layer are obtained, and the duct patency, the fin integrity, and the effectiveness of the thermal insulation layer are integrated into a structure state characteristic; The working condition influence trend characteristic and the structure state characteristic are correspondingly generated into a working condition-structure-related influence characteristic; A space information of a current application scene of the air heater is obtained, and a scenario-related influence characteristic adapted to the current scene is obtained according to the space information and the working condition-structure-related influence characteristic. According to the combined influence of each working condition and structure in the scenario correlation influence feature, a combined influence correction direction for heat loss metering is obtained, and a heat loss-scenario adaptation correction amount capable of compensating for the combined influence of working conditions and structure is generated.
6. The method of claim 1, wherein, The step of obtaining the heat loss metering value according to the preliminary heat loss parameter and the heat loss-scenario adaptation correction amount comprises: According to the preliminary heat loss parameter, conduction loss data, convection loss data and radiation loss data are obtained, and the conduction loss data, the convection loss data and the radiation loss data are integrated into loss type composition data; According to the heat loss-scenario adaptation correction amount, correction content data of different loss types are obtained, wherein the correction content data comprises conduction loss correction data, convection loss correction data and radiation loss correction data, and type-specific correction data is generated according to the conduction loss correction data, the convection loss correction data and the radiation loss correction data; Each type of loss data in the loss type composition data is matched with the corresponding correction data in the type-specific correction data to generate a loss-correction correlation feature; According to the loss-correction correlation feature, adjustment loss data is obtained, wherein the adjustment loss data comprises adjustment conduction loss data, adjustment convection loss data and adjustment radiation loss data; According to the adjustment conduction loss data, the adjustment convection loss data and the adjustment radiation loss data, the heat loss metering value is obtained.
7. A dynamic metering system for heat loss of a fan heater, characterized by, Comprise: The parameter acquisition module is used for acquiring real-time operation parameters, environment correlation parameters and heater structure state parameters of the heater; The heat loss fluctuation sequence generation module is used for obtaining a heat loss feature set according to the real-time operation parameters, and generating a heat loss fluctuation sequence according to the heat loss feature set; The heat loss parameter generation module is used for generating a preliminary heat loss parameter according to the heat loss fluctuation sequence and the environment correlation parameters; The correction amount generation module is used for acquiring heater working condition cumulative parameters, and generating a heat loss-scenario adaptation correction amount according to the heater working condition cumulative parameters and the structure state parameters; The heat loss metering value acquisition module is used for obtaining a heat loss metering value according to the preliminary heat loss parameter and the heat loss-scenario adaptation correction amount; The judgment module is used for judging whether the heat loss metering value is greater than a preset threshold value; If less, it is judged that the heat loss of the heater is in a normal state; If greater, it is judged that the heat loss of the heater is in an abnormal state, and a warning information is generated for real-time reminding.
8. The dynamic heat loss metering system for a fan heater of claim 7, wherein, The heat loss fluctuation sequence generation module comprises: The first acquisition unit is used for obtaining heat loss directly correlated key sub-parameters according to the real-time operation parameters, wherein the key sub-parameters comprise input power change data, shell surface temperature change data and outlet air flow velocity change data; The second acquisition unit is used for obtaining heat loss fluctuation feature data of heat loss change from the key sub-parameters by adopting a trend separation method; The third acquisition unit is used for obtaining effective heat loss fluctuation data according to the heat loss fluctuation feature data. A fourth acquisition unit is configured to acquire fluctuation amplitude data, fluctuation period data and fluctuation phase data of the thermal loss variation according to the effective thermal loss fluctuation data, and generate thermal loss correlation feature data according to the fluctuation amplitude data, the fluctuation period data and the fluctuation phase data. A fifth acquisition unit is configured to classify and arrange the thermal loss correlation feature data, and group thermal loss correlation feature data of the same category into a group to generate a thermal loss feature set. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to implement the steps of the method in any one of claims 1 to 6.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 6.