Thermal insulation state detection method and system of heating body thermal insulation material and storage medium

Abstract

The invention relates to the technical field of heaters, and discloses a heat preservation state detection method and system for a heating body heat preservation material and a storage medium, and the method comprises the steps: obtaining first temperature data T1 in a hot liner and second temperature data T2 of the surrounding environment of the hot liner in real time; establishing a temperature loss model: dT / dt =-K * (T1-T2); wherein K is a comprehensive heat conduction coefficient and is used for representing the thermal insulation efficiency of the thermal insulation material; performing integral transformation on the temperature loss model to obtain T (t) = T2 + (T1-T2) * e-K * t; when T1 is reduced from Ta to Tb, time is t1, and K is equal to (1 / t1) * In ((Ta-T2) / (Tb-T2)); wherein T1 > = Ta > = Tb > = T2; the K is compared with a preset warning threshold value Kx, and if the K is larger than or equal to the Kx, a heat preservation layer replacement early warning is given out. The method can improve the accuracy of thermal insulation performance evaluation of the heating body thermal insulation material.

Description

Technical Field

[0001] The present application relates to the technical field of heaters, and in particular, to a method, a system, and a storage medium for detecting the heat preservation state of a heating element heat preservation material. Background Art

[0002] Water dispensers and water heaters, as common household appliances, their energy efficiency directly affects the user experience and energy consumption. The core components of a water heater include a hot water tank and a heating rod. An insulating layer is usually installed outside the hot water tank to reduce heat loss. As the usage time increases, the heat preservation material may cause a decrease in heat preservation efficiency due to reasons such as aging, moisture, or loose structure, thereby increasing energy consumption and shortening the service life of the equipment.

[0003] Currently, the evaluation of the heat preservation efficiency of water heaters in the industry mainly relies on manual experience judgment or regular replacement, lacking scientific quantitative indicators. Traditional testing methods such as thermal imaging detection or disassembly inspection are complex to operate and cannot monitor in real time. Although some studies have proposed analysis methods based on thermodynamic models, they have not formed a standardized and automated detection system, nor have they considered the influence of factors such as room temperature fluctuations and water usage habits in the actual usage environment on the evaluation results.

[0004] In view of the above problems, there is an urgent need for a method that can evaluate the heat preservation state of the heating element heat preservation material to achieve real-time monitoring and early warning of the performance of the water heater. Summary of the Invention

[0005] In order to be able to detect the heat preservation state of the heating element heat preservation material, the present application provides a method, a system, and a storage medium for detecting the heat preservation state of a heating element heat preservation material.

[0006] In a first aspect, the present application provides a method for detecting the heat preservation state of a heating element heat preservation material, adopting the following technical solution: A method for detecting the heat preservation state of a heating element heat preservation material includes the following steps: Obtain the first temperature data T1 inside the hot water tank and the second temperature data T2 of the surrounding environment of the hot water tank in real time; Establish a temperature loss model: dT / dt = -K×(T1 - T2); where K is the comprehensive heat conduction coefficient, used to characterize the heat preservation efficiency of the heat preservation material; Perform integral transformation on the temperature loss model to obtain: T(t) = T2 + (T1 - T2)×e -K×t ; When T1 drops from Ta to Tb, and the time is t1, then: K = (1 / t1)×In((Ta - T2) / (Tb - T2)); where T1≥Ta>Tb≥T2; Compare K with a preset warning threshold Kx. If K is greater than or equal to Kx, issue a warning for replacing the heat preservation layer.

[0007] By adopting the above technical solution, a temperature loss model is established to calculate the comprehensive heat transfer coefficient K to quantify the heat preservation efficiency of the heat preservation material. Compared with the traditional method that relies on manual experience judgment or simple testing, this quantitative evaluation method based on a mathematical model is more accurate, can effectively improve the detection accuracy, and thus more accurately reflect the actual heat preservation performance of the heat preservation material. This method only needs to obtain the internal temperature data T1 of the heat tank and the ambient temperature data T2 around the heat tank in real time, without complex operations such as disassembling the heating element. It saves detection time and labor costs, reduces the damage that may be caused to the heating element due to disassembly, and makes the detection process more convenient and efficient. Through the early warning mechanism, users can be helped to timely discover the problem of the decline in the performance of the heat preservation material, arrange the replacement of the heat preservation layer in advance, avoid energy waste and equipment failures caused by the failure of the heat preservation layer, ensure the normal operation of the heating element, and extend the service life of the equipment.

[0008] Optionally, it further includes the following steps: Correct K based on a correction algorithm; The correction algorithm is: the corrected K value = the uncorrected K value × (1 + α × △T2); where α is the temperature correction coefficient and △T2 is the ambient temperature fluctuation value of the environment around the heat tank.

[0009] By adopting the above technical solution, adding the correction of K based on the correction algorithm makes the evaluation of the heat preservation efficiency of the heat preservation material more accurate, can more truly reflect the performance of the heat preservation material in a complex environment, and reduces the evaluation error caused by environmental factors.

[0010] Optionally, it further includes the following steps: Obtain the average ambient temperature of the second temperature data T2 within the most recent first set time period; Anticorrelatively adjust the temperature correction coefficient α according to the average ambient temperature; the larger the average ambient temperature, the smaller the temperature correction coefficient α, and the smaller the average ambient temperature, the larger the temperature correction coefficient α.

[0011] By adopting the above technical solution, in winter, there are more people drinking hot water and the data error collected by the sensor is large. In summer, there are fewer people drinking hot water and the data error collected by the sensor is small, so as to avoid excessive correction. This method can effectively compensate for the sensor data error caused by the difference in usage frequency in different seasons and improve the accuracy of detection.

[0012] Optionally, the evaluation criteria for the heat preservation efficiency according to the K value are as follows: K value ≤ 0.02h -1 : It is indicated that the heat preservation efficiency is excellent and the heat preservation performance is good; 0.02h-1 <K value ≤ 0.05h -1 : It indicates good heat preservation efficiency and normal heat preservation performance; 0.05h -1 <K value ≤ 0.08h -1 : It indicates average heat preservation efficiency and prompts to pay attention to the heat preservation layer; 0.08h -1 <K value ≤ 0.12h -1 : It indicates poor heat preservation efficiency and prompts to plan to replace the heat preservation layer; 0.12h -1 <K value: It indicates dangerous heat preservation efficiency, issues a warning for replacing the heat preservation layer, and prompts to immediately replace the heat preservation layer.

[0013] By adopting the above technical solution, a clear, definite and quantitative evaluation basis is provided, changing the ambiguity of relying on manual experience judgment in the past, making the evaluation of the heat preservation performance of the water heater more scientific and objective.

[0014] Optionally, it further includes the following steps: Obtain the accumulated temperature difference value of the first temperature data T1 within the nearest first set time period; Inversely adjust the warning threshold Kx according to the accumulated temperature difference value; the larger the accumulated temperature difference value, the smaller the warning threshold Kx, and the smaller the accumulated temperature difference value.

[0015] By adopting the above technical solution, the larger the accumulated temperature difference value, the faster the performance of the heat preservation material is lost. As time goes by, the value of the warning threshold Kx is adaptively adjusted to improve the accuracy and timeliness of detection.

[0016] Optionally, it further includes the following steps: When the temperature inside the heat storage tank and the temperature on the outer surface of the heat storage tank are both decreasing; Obtain the third temperature data T3 of the outer surface temperature of the heat storage tank in real time; Calculate the first difference value X1 according to the first temperature data T1 and the third temperature data T3; Calculate the second difference value X2 according to the first temperature data T1 and the second temperature data T2; Calculate the temperature step value X3 by fusing the first difference value X1 and the second difference value X2; Calculate multiple temperature step values X3 based on the same first temperature data T1, the second temperature data T2 and the third temperature data T3 at different time points; Calculate the step change trend value J according to multiple temperature step values X3; Compare the step change trend value J with a preset step reference value Jx. If J is less than Jx, an aging state of the thermal insulation layer is issued. Inversely regulate the warning threshold Kx according to the step change trend value J; the larger the step change trend value J, the smaller the warning threshold Kx; the smaller the step change trend value J, the larger the warning threshold Kx.

[0017] By adopting the above technical solution, comparing J with the preset step reference value Jx to judge the aging state of the thermal insulation layer can more carefully and accurately grasp the actual condition of the thermal insulation layer. Inversely regulating the warning threshold Kx according to the step change trend value J makes the detection system more adaptable. When J is larger, it indicates that the temperature change trend is more obvious and the performance of the thermal insulation layer may decline faster. At this time, reducing Kx can issue a replacement warning in advance to avoid the equipment being discovered only after the thermal insulation performance has seriously declined; when J is smaller, it indicates that the thermal insulation layer is relatively stable. Appropriately increasing Kx can reduce unnecessary warnings and improve the reliability and practicality of the detection system. Evaluating the aging of the thermal insulation layer and regulating the warning threshold by integrating the temperature step value and the step change trend value provides a more comprehensive and scientific basis for equipment maintenance.

[0018] Optionally, in the step of calculating the temperature step value X3 by fusing the first difference X1 and the second difference X2, the following sub-steps are further included: X3 = m×(X1 - X3) + n×(X1 - X2), where m is the temperature difference coefficient inside the tank and n is the temperature difference coefficient outside the tank.

[0019] By adopting the above technical solution, by constructing different mathematical relationships and comprehensively considering the temperature differences on the inner and outer surfaces of the hot tank and the surrounding environment, the thermal insulation characteristics of the thermal insulation material are accurately reflected. Moreover, through the coefficients m and n, different scenario requirements can be met, enhancing the detection adaptability.

[0020] Optionally, the method further includes the following steps: Heat the hot tank within a preset control time period. Inversely control and regulate the heating power according to the comprehensive heat transfer coefficient K; the larger the comprehensive heat transfer coefficient K, the lower the heating power; the smaller the comprehensive heat transfer coefficient K, the higher the heating power.

[0021] By adopting the above technical solution, when the comprehensive heat transfer coefficient K is small, the thermal insulation performance is good, and the heating time for detection is shortened; when the comprehensive heat transfer coefficient K is large, the thermal insulation performance is reduced, and the heating time is extended, which is beneficial to improving the accuracy of the detection result.

[0022] In a second aspect, the present application provides a thermal insulation state detection system for a heating body thermal insulation material, adopting the following technical solution: An insulation state detection system for a heating element insulation material, based on the above-mentioned insulation state detection method for a heating element insulation material, includes the following modules: A temperature detection module, including a temperature sensor, for detecting the first temperature data T1 inside the hot water tank and the second temperature data T2 of the surrounding environment of the hot water tank; A data acquisition module, including a data acquisition unit and a storage unit, for acquiring and recording the temperature data of the temperature sensor, the heating power of the heating rod, and the corresponding time data; A control module, including a microprocessor, a relay control unit, and a human-machine interaction unit, for controlling the working state of the heating rod according to the data of the data acquisition module according to a preset control strategy and executing the detection process; and, An evaluation module, including a data analysis unit, a heat preservation efficiency calculation unit, and a state evaluation unit, for calculating the comprehensive heat transfer coefficient K value according to the data acquired by the data acquisition module: when T1 drops from Ta to Tb and the time is t1, then: K = 1 / t1 × In((Ta - T2) / (Tb - T2)); where, T1 ≥ Ta > Tb ≥ T2; evaluating the state of the insulation system according to the heat preservation efficiency K value: comparing K with a preset warning threshold Kx, if K is greater than or equal to Kx, a warning for replacing the insulation layer is issued.

[0023] In a third aspect, the present application provides a storage medium, adopting the following technical solution: A storage medium, in which a program is stored, and when the program is executed by a processor, the steps of the insulation state detection method for the heating element insulation material described in any one of the above are implemented.

[0024] In summary, the present application includes at least one of the following beneficial technical effects: By collecting the temperature data inside, on the outer surface, and of the surrounding environment of the hot water tank in real time, establishing a temperature loss model to calculate the comprehensive heat transfer coefficient K, and combining a correction algorithm, considering factors such as environmental temperature fluctuations and temperature difference accumulation values, the heat preservation efficiency of the insulation material can be accurately quantified, which is greatly improved compared with traditional methods, with an accuracy increase of more than 50% compared with traditional methods, accurately reflecting the actual performance of the insulation material, and providing a reliable basis for evaluating the state of the insulation layer.

[0025] Comparing the K value with a preset warning threshold, combining the step change trend value J with a preset step reference value to judge the aging state of the insulation layer and issue a warning, and at the same time giving replacement suggestions and adaptively adjusting the warning threshold according to different evaluation results, helping users and maintenance personnel plan maintenance and replacement plans in advance, avoiding equipment failures and energy waste, reducing usage costs, and extending the service life of the equipment.

[0026] Adjust and control the heating power in inverse correlation with the comprehensive heat transfer coefficient K, realizing intelligent heating during the detection process. When the heat preservation performance is good, shorten the heating time; when the heat preservation performance is poor, extend the heating time. This not only improves the accuracy of the detection results but also optimizes the energy utilization efficiency and reduces energy consumption.

[0027] Provide multiple ways to calculate the temperature step value X3, meet the requirements of different scenarios by setting different coefficients, and can also adjust the temperature correction coefficient α according to different seasons, usage frequencies, etc., so that the detection method can better adapt to various complex environments and usage conditions, enhancing the adaptability and flexibility of the detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a step diagram of a method for detecting the heat preservation state of a heating body heat preservation material.

[0029] Figure 2 It is a step diagram for judging the heat preservation effect by combining the monitoring of the outer surface temperature of the heat storage tank. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] The following details the embodiments of the present application, and the examples of the embodiments are shown in the drawings.

[0031] In the description of this specification, the descriptions referring to the terms "certain embodiments", "one embodiment", "some embodiments", "schematic embodiments", "examples", "specific examples" or "some examples" mean that the specific features, structures, materials or characteristics described in connection with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiments or examples. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0032] The embodiments of the present application disclose a method for detecting the heat preservation state of a heating body heat preservation material. Referring to Figure 1 , the method includes the following steps: Obtain the first temperature data T1 inside the heat storage tank and the second temperature data T2 of the surrounding environment of the heat storage tank in real time.

[0033] Based on the thermodynamic heat transfer theory, establish a temperature loss model for the water heater. According to Newton's law of cooling, when there is a temperature difference between an object and the environment, the cooling rate of the object is proportional to the temperature difference, which can be expressed as: dT / dt = -K × (T1 - T2); where K is the comprehensive heat transfer coefficient, used to characterize the heat preservation efficiency of the heat preservation material.

[0034] Perform integral transformation on the temperature loss model to obtain: T(t) = T2 + (T1 - T2) × e -K×t .

[0035] When T1 naturally cools from Ta to Tb, and the time is t1, then: K = (1 / t1) × In((Ta - T2) / (Tb - T2)); where, T1 ≥ Ta > Tb ≥ T2. Ta is the highest temperature, generally 96°C; Tb is the termination temperature, generally 90°C. During the natural cooling stage, turn off the heating rod.

[0036] Considering the ambient temperature fluctuation, a correction algorithm is used to correct the K value; Based on the correction algorithm, K is corrected; The correction algorithm is: the corrected K value = the uncorrected K value × (1 + α × △T2); where, α is the temperature correction coefficient, and △T2 is the ambient temperature fluctuation value of the environment around the hot water tank.

[0037] Compare K with the preset warning threshold Kx. If K is greater than or equal to Kx, a warning for replacing the thermal insulation layer is issued.

[0038] By establishing a temperature loss model and calculating the comprehensive heat transfer coefficient K to quantify the heat preservation efficiency of the heat preservation material, compared with the traditional method that relies on manual experience judgment or simple testing, it has significant advantages. The traditional method often has problems such as strong subjectivity and low accuracy, while this method is based on a rigorous mathematical model for quantitative evaluation, which is more accurate, can effectively improve the detection accuracy, and thus more accurately reflects the actual heat preservation performance of the heat preservation material. In addition, this method only needs to obtain the internal temperature data T1 of the hot water tank and the ambient temperature data T2 around the hot water tank in real time, without complex operations such as disassembling the heating body, greatly saving the detection time and labor costs. At the same time, it avoids the damage that may be caused to the heating body due to disassembly, making the detection process more convenient and efficient.

[0039] The method further includes the following steps: Obtain the average ambient temperature of the second temperature data T2 within the most recent first set time period. In the actual life scenario, seasonal changes have a significant impact on the usage frequency of the heating body and the accuracy of sensor data. Taking winter and summer as examples, in winter, the weather is cold, people's demand for hot water increases significantly, there are more people drinking hot water, and the usage frequency of the heating body is significantly improved.

[0040] Inversely adjust the temperature correction coefficient α according to the average ambient temperature; the larger the average ambient temperature, the smaller the temperature correction coefficient α, and the smaller the average ambient temperature, the larger the temperature correction coefficient α.

[0041] Suppose in winter, the average ambient temperature in a certain area is 5°C. According to the adjustment rules obtained from long-term experiments and data analysis, the temperature correction coefficient α is set to 0.8 at this time. When detecting the heat preservation performance of a certain heating body, if the adjustment of α by the average ambient temperature is not considered and the default α value of 1.0 is used to calculate the corrected K value, it may overestimate the degree of performance degradation of the heat preservation material, give incorrect replacement suggestions, and cause unnecessary economic losses. However, when the adjusted α value of 0.8 is used in the calculation, the obtained corrected K value is more in line with the actual situation, and the evaluation of the heat preservation material performance is more accurate. Suppose in summer, when the average ambient temperature in this area reaches 30°C, according to the inverse correlation adjustment rule, the temperature correction coefficient α is adjusted to 0.5. When detecting another heating body, due to the small sensor data error caused by seasonal factors, the calculation is carried out with the adjusted α value, avoiding overcorrection caused by too large α value, making the detection result more reliable, providing more accurate heat preservation material performance information for users, and helping users make more reasonable decisions, such as whether to replace the heat preservation layer, etc.

[0042] Based on a large amount of experimental data, this application has established an evaluation standard for the heat preservation efficiency of water heaters. The evaluation standard for heat preservation efficiency according to the K value is as follows: K value ≤ 0.02h -1 : It is indicated that the heat preservation efficiency is excellent and the heat preservation performance is good; 0.02h -1 <K value ≤ 0.05h -1 : It is indicated that the heat preservation efficiency is good and the heat preservation performance is normal; 0.05h -1 <K value ≤ 0.08h -1 : It is indicated that the heat preservation efficiency is average and attention should be paid to the heat preservation layer; 0.08h -1 <K value ≤ 0.12h -1 : It is indicated that the heat preservation efficiency is poor and it is recommended to plan to replace the heat preservation layer; 0.12h -1 <K value: It is indicated that the heat preservation efficiency is dangerous, a heat preservation layer replacement warning is issued, and it is recommended to immediately replace the heat preservation layer.

[0043] The above method provides a clear, definite and quantitative judgment basis, changes the fuzziness of relying on manual experience judgment in the past, and makes the evaluation of the heat preservation performance of water heaters more scientific and objective.

[0044] Taking a household electric water heater with a volume of 50L as an example, the initial ambient temperature T2 around the hot water tank is 25°C, the initial temperature T1 inside the hot water tank is 20°C, the set maximum temperature Ta is 75°C, and the termination temperature Tb is 50°C.

[0045] During the test, the heating time was 35 minutes and the power consumption was 2.3 kWh; the cooling stage lasted t1 for 24 hours, during which the ambient temperature fluctuated by ±2°C. According to the calculation formula, the K value was 0.0462h -1 After considering the ambient temperature fluctuation correction, the K value is 0.0478h -1 .

[0046] Compared with the evaluation standards, the insulation efficiency of this water heater is at the "good" level, the insulation performance is normal, and there is no need to replace the insulation layer.

[0047] The system also calculated that the water heater loses about 124W of heat per hour. Under standard conditions of use, it is estimated that the monthly electricity bill will increase by about 11.2 yuan due to insufficient insulation.

[0048] In order to further improve the accuracy and timeliness of detection, the following steps are also included: The accumulated value of the temperature difference of the first temperature data T1 in the most recent first set time period is obtained; the first set time period is, for example, one month, and the system will continue to record the first temperature data T1 inside the heat bulb during this month. The value of T1 will be collected at multiple time points every day, and then the temperature difference between adjacent time points will be calculated, and finally all the temperature differences in this month will be accumulated to obtain the accumulated value of the temperature difference.

[0049] The warning threshold Kx is adjusted inversely according to the accumulated value of the temperature difference; the larger the accumulated value of the temperature difference, the smaller the warning threshold Kx, and the smaller the accumulated value of the temperature difference. When the accumulated value of the temperature difference is larger, it means that the temperature fluctuation inside the heat tank is more drastic during the first set time period. The drastic fluctuation of the temperature inside the heat tank often implies that the performance of the insulation material is rapidly declining, and its insulation effect is not as good as before. Because under normal circumstances, materials with good insulation performance can effectively suppress the change of the temperature inside the heat tank and keep the temperature relatively stable. Therefore, at this time, appropriately reducing the warning threshold Kx can allow the system to more keenly capture the deterioration of the performance of the insulation material, issue an early warning in time, and remind the user to take corresponding measures. On the contrary, when the accumulated value of the temperature difference is small, it means that the temperature inside the heat tank is relatively stable and the performance of the insulation material is still good. At this time, the warning threshold Kx can be appropriately increased to avoid misjudgment due to some small fluctuations and reduce unnecessary warnings.

[0050] Assume that we have a water heater whose insulation material is in normal condition. In the first month of testing, the accumulated temperature difference of the first temperature data T1 recorded is small, for example, 10°C. According to the preset adjustment rules, the warning threshold Kx is set to 0.06h. -1。In the following month, due to certain reasons such as the aging of the thermal insulation material or external force damage, the temperature fluctuation inside the hot water tank intensifies. The cumulative temperature difference in this month reaches 30°C, which is significantly greater than that in the previous month. According to the principle of inverse correlation adjustment, the warning threshold Kx is adjusted to 0.04h -1 。At this time, in subsequent detections, if the calculated comprehensive heat transfer coefficient K reaches 0.05h -1 ,although according to the previous warning threshold of 0.06h -1 ,the system will not issue a warning, but since the warning threshold has now been adjusted to 0.04h -1 ,and the K value is greater than Kx, the system will promptly issue a warning for replacing the thermal insulation layer.

[0051] Refer to Figure 2 。To more accurately and comprehensively evaluate the actual condition of the thermal insulation layer, the following steps are also included: When both the temperature inside the hot water tank and the temperature on the outer surface of the hot water tank are decreasing; Obtain the third temperature data T3 of the outer surface temperature of the hot water tank in real time.

[0052] Calculate the first difference X1 based on the first temperature data T1 and the third temperature data T3. This difference reflects the temperature gradient between the inside and the outer surface of the hot water tank, and reflects the ease of heat transfer from the inside of the hot water tank to the outer surface.

[0053] Calculate the second difference X2 based on the first temperature data T1 and the second temperature data T2; this difference reflects the temperature gradient between the inside of the hot water tank and the surrounding environment, and reflects the heat exchange situation between the entire hot water tank system and the external environment.

[0054] Calculate the temperature gradient value X3 by fusing the first difference X1 and the second difference X2. The fusion calculation method can adopt the weighted average algorithm. The temperature gradient value X3 comprehensively considers the temperature relationship between the inside and the outer surface of the hot water tank, and between the inside of the hot water tank and the surrounding environment, and can more comprehensively reflect the role of the thermal insulation layer in the heat transfer process.

[0055] Calculate multiple temperature gradient values X3 at different time points based on the same first temperature data T1, second temperature data T2, and third temperature data T3. By analyzing these temperature gradient values X3 at different time points, the change of the temperature gradient value over time can be understood.

[0056] Calculate the gradient change trend value J based on multiple temperature gradient values X3. The gradient change trend value J reflects the change trend of the temperature gradient value over a period of time, and can intuitively reflect the change of the thermal insulation layer performance over time.

[0057] Compare the step change trend value J with the preset step reference value Jx. The preset step reference value Jx is obtained by summarizing a large amount of experimental data and practical application experience, and it represents the change trend range of the temperature step value when the thermal insulation layer is in a normal state. If J is less than Jx, it indicates that the change trend of the temperature step value deviates from the normal range, which may mean that the thermal insulation layer has aged. At this time, the system will promptly issue a warning message about the aging state of the thermal insulation layer.

[0058] Adjust the warning threshold Kx in an inverse correlation with the step change trend value J; the larger the step change trend value J, the smaller the warning threshold Kx; the smaller the step change trend value J, the larger the warning threshold Kx. When J is larger, it indicates that the temperature change trend is more obvious, and the possibility of the performance degradation of the thermal insulation layer is greater. At this time, reduce the warning threshold Kx. The purpose of this is to make the system more sensitive to monitor the performance change of the thermal insulation layer, be able to issue a replacement warning in advance, and avoid discovering problems only after the thermal insulation performance of the equipment has seriously declined, thereby reducing the energy waste and equipment failure risks caused by the aging of the thermal insulation layer. On the contrary, when J is smaller, it indicates that the thermal insulation layer is relatively stable and the temperature change trend is relatively gentle. At this time, appropriately increase the warning threshold Kx to reduce unnecessary warnings, improve the reliability and practicality of the detection system, and avoid bringing unnecessary troubles to users.

[0059] Suppose there is a household water heater with a well - performing thermal insulation layer in its initial state. One day, the monitoring system finds that both the temperature inside the hot water tank and the temperature on the outer surface of the hot water tank start to drop, and then starts to execute the above - mentioned detection steps.

[0060] At the first time point t3, the temperature inside the hot water tank T1 = 80 °C, the temperature on the outer surface of the hot water tank T3 = 70 °C, and the ambient temperature around the hot water tank T2 = 20 °C are obtained. Calculate the first difference X1 = T1 - T3 = 80 - 70 = 10 °C, and the second difference X2 = T1 - T2 = 80 - 20 = 60 °C. Assume that the weighted average fusion calculation method is used, and the temperature step value X3 = 0.5×X1 + 0.5×X2 = 35 °C.

[0061] At the second time point t4, the temperature inside the hot water tank T2 = 78 °C, the temperature on the outer surface of the hot water tank T3 = 68 °C, and the ambient temperature around the hot water tank T2 = 20 °C. Calculate the first difference X1 = T1 - T3 = 78 - 68 = 10 °C, the second difference X2 = T1 - T2 = 78 - 20 = 58 °C, and the temperature step value X3 = 0.5×X1 + 0.5×X2 = 34 °C.

[0062] Over time, the above calculation process is repeated at multiple different time points to obtain a series of temperature step values X3. Based on these temperature step values, the step change trend value J is calculated. Assuming that the preset step reference value is 0.5 and the calculated step change trend value J = 0.3, since J < Jx, the system issues a warning message about the aging state of the thermal insulation layer.

[0063] At the same time, assume that the initial warning threshold K-0 = 0.06h -1 , since J is small, indicating that the thermal insulation layer is relatively stable. According to the inverse correlation adjustment rule, the warning threshold K is increased to 0.07h -1 to reduce unnecessary warnings.

[0064] By comprehensively considering the temperature step value and the step change trend value to evaluate the aging state of the thermal insulation layer and adjust the warning threshold, it provides a more comprehensive and scientific basis for equipment maintenance. This method can more carefully and accurately grasp the actual condition of the thermal insulation layer, make the detection system have stronger adaptability, help users detect problems with the thermal insulation layer in time, reasonably arrange the maintenance and replacement plan of the equipment, ensure the normal operation of the equipment, and reduce energy consumption and maintenance costs.

[0065] In the step of calculating the temperature step value X3 by fusing the first difference X1 and the second difference X2, the following calculation method is included: X3 = m×(X1 - X3) + n×(X1 - X2), where m is the temperature difference coefficient inside the tank and n is the temperature difference coefficient outside the tank. In different application scenarios, the influence degrees of the temperature changes inside and outside the hot tank on the heat preservation performance may be different. The temperature difference coefficient m inside the tank is used to adjust the weight of the temperature difference between the inside and the outer surface of the hot tank in the calculation, and the temperature difference coefficient n outside the tank is used to adjust the weight of the temperature difference between the inside of the hot tank and the surrounding environment. The initial values of m and n are both 0.5. By flexibly adjusting the values of m and n, the influence of certain factors can be highlighted or weakened, making the temperature step value X3 more in line with the actual heat preservation situation and further improving the accuracy of the heat preservation performance evaluation.

[0066] To further improve the accuracy and efficiency of the detection results, the method further includes the following steps: During the preset control time period, the hot tank is heated; the purpose is to simulate the working state of the hot tank in actual use and let the heat preservation material show its true heat preservation performance under the condition of temperature difference.

[0067] According to the principle of heat conduction, the larger the K value, the worse the heat insulation performance of the thermal insulation material, and the faster the heat loss; the smaller the K value, the better the heat insulation performance, and the slower the heat loss. Therefore, during the heating process, the heating power will be controlled and adjusted in an inverse correlation with the K value. Specifically, when the comprehensive heat conduction coefficient K is larger, it means that the heat insulation performance of the thermal insulation material is poor and heat is easily lost. At this time, the heating power is reduced. Because even if the power is reduced, due to the poor heat insulation performance, the hot water tank can reach the required temperature range for detection relatively quickly, and extending the heating time can allow the hot water tank to be detected in a more stable state, making the heat transfer process more sufficient, so as to obtain more accurate temperature data. On the contrary, when the comprehensive heat conduction coefficient K is smaller, it indicates that the heat insulation performance of the thermal insulation material is good and heat loss is slow. At this time, the heating power is increased. This can quickly heat the hot water tank to the required temperature for detection, shorten the heating time for detection, and improve the detection efficiency.

[0068] For the first water heater, the calculated comprehensive heat conduction coefficient K1 = 0.02 h -1 , indicating that its heat insulation performance is good. The preset control time period is 30 minutes. When starting to heat, the detection system adjusts the heating power to a relatively high 2000 W according to the value of K1. Due to the good heat insulation performance and slow heat loss, the internal temperature of the hot water tank rises rapidly. Within 10 minutes, the internal temperature of the hot water tank reaches the required 90 °C for detection. At this time, the heating can be ended in advance and the detection stage can be entered. This not only shortens the heating time for detection, but also reduces energy consumption. At the same time, because the hot water tank heats up rapidly in a short time, the internal temperature distribution is relatively uniform, which is beneficial to improving the accuracy of the detection results.

[0069] For the second water heater, its comprehensive heat conduction coefficient K2 = 0.08 h -1 , indicating that the heat insulation performance is poor. It is also heated within the preset 30-minute control time period. The detection system adjusts the heating power to a relatively low 1000 W according to the value of K2. Due to the poor heat insulation performance and fast heat loss, the internal temperature of the hot water tank rises slowly. During the entire 30-minute heating process, the internal temperature of the hot water tank gradually rises and finally reaches 90 °C. Although the heating time is longer, by extending the heating time and allowing the hot water tank to be continuously heated at a lower power, the heat transfer process inside the hot water tank is more sufficient, and the temperature of each part of the hot water tank is more stable, so as to obtain more accurate temperature data, which is beneficial to improving the accuracy of the detection results.

[0070] By heating the hot water tank within the preset control time period and controlling and adjusting the heating power in an inverse correlation with the comprehensive heat conduction coefficient K, the heating strategy can be flexibly adjusted according to the different performance states of the thermal insulation material. Shorten the heating time when the heat insulation performance is good to improve the detection efficiency; extend the heating time when the heat insulation performance is poor to ensure the accuracy of the detection.

[0071] The embodiment of the present application also discloses a thermal insulation state detection system for a heating element thermal insulation material, including a temperature detection module, a data acquisition module, a control module, and an evaluation module. The modules are connected through a data bus to achieve data interaction and collaborative work.

[0072] The temperature detection module includes at least three temperature sensors, which are used to detect the first temperature data T1 inside the water tank, the second temperature data T2 of the surrounding environment of the water tank, and the third temperature data T3 of the outer surface temperature of the water tank. The sensor installed inside the water tank is used to measure the water temperature, that is, the first temperature data T1. The sensor installed on the outer surface of the water tank is used to measure the surface temperature, that is, the second temperature data T2. The sensor installed in the surrounding environment of the water heater is used to measure the ambient temperature, that is, the third temperature data T3. All sensors use PT100 platinum resistance temperature sensors with an accuracy of ±0.1°C.

[0073] The data acquisition module includes a data acquisition unit and a storage unit; it is used to collect and record the temperature data of the temperature sensors, the heating power of the heating rod, and the corresponding time data. The data acquisition unit uses a 16-bit ADC converter to collect temperature data at a frequency of 1Hz; the storage unit can store at least 30 days of historical data.

[0074] The control module includes a microprocessor, a relay control unit, and a human-machine interaction unit; it is used to control the working state of the heating rod according to the data of the data acquisition module according to a preset control strategy and execute the detection process. The microprocessor uses the ARM Cortex-M4 architecture with an operating frequency of 120MHz; the relay control unit is used to control the on and off of the heating rod; the human-machine interaction unit includes an LCD display screen and operation buttons.

[0075] The evaluation module includes a data analysis unit, a heat preservation efficiency calculation unit, and a state evaluation unit, which are responsible for implementing the core algorithm; it is used to calculate the comprehensive heat transfer coefficient K value according to the data collected by the data acquisition module: when T1 drops from Ta to Tb, and the time is t1, then: K = (1 / t1) × In((Ta - T2) / (Tb - T2)). Where, T1 ≥ Ta > Tb ≥ T2; evaluate the state of the thermal insulation system according to the heat preservation efficiency K value: compare K with a preset warning threshold Kx, if K is greater than or equal to Kx, then issue a warning for replacing the thermal insulation layer.

[0076] The embodiment of the present application also discloses a storage medium, in which a program is stored, and when the program is executed by a processor, the steps of the thermal insulation state detection method for the heating element thermal insulation material described in any one of the above are implemented.

[0077] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for detecting the heat preservation state of a heating element heat preservation material, characterized in that, The steps are as follows: Obtain the first temperature data T1 inside the hot water tank and the second temperature data T2 of the environment around the hot water tank in real time; Establish a temperature loss model: dT / dt = -K × (T1 - T2); where K is the comprehensive heat conduction coefficient, which is used to characterize the heat preservation efficiency of the heat preservation material; Integrate and transform the temperature loss model to obtain: T(t) = T2 + (T1 - T2) × e -K×t ; When T1 drops from Ta to Tb and the time is t1, then: K = (1 / t1) × In((Ta - T2) / (Tb - T2)); where T1 ≥ Ta > Tb ≥ T2; Compare K with a preset warning threshold Kx. If K is greater than or equal to Kx, issue a warning for replacing the heat preservation layer.

2. The method for detecting the heat preservation state of the heat preservation material of the heating element according to claim 1, characterized in that The method further includes the following steps: Correct the value of K based on a correction algorithm; The correction algorithm is: the corrected K value = the uncorrected K value × (1 + α × △T2); where α is the temperature correction coefficient and △T2 is the ambient temperature fluctuation value of the environment around the hot water tank.

3. The method for detecting the heat preservation state of the heat preservation material of the heating element according to claim 2, characterized in that, The method further includes the following steps: Obtain the average ambient temperature of the second temperature data T2 within the most recent first set time period; Inversely and correlatively adjust the temperature correction coefficient α according to the average ambient temperature; the larger the average ambient temperature, the smaller the temperature correction coefficient α, and the smaller the average ambient temperature, the larger the temperature correction coefficient α.

4. The insulation state detection method of the heating element insulation material according to claim 1, characterized in that The evaluation criteria for the heat preservation efficiency according to the value of K are as follows: The value of K ≤ 0.02h -1 : It indicates excellent heat preservation efficiency and good heat preservation performance; 0.02h -1 <K value ≤ 0.05h -1 : It indicates good heat preservation efficiency and normal heat preservation performance; 0.05 h -1 0.05 h < K value ≤ 0.08 h -1 : It indicates that the heat preservation efficiency is average and prompts to pay attention to the heat preservation layer; 0.08 h -1 <K value ≤ 0.12 h -1 : It indicates poor heat preservation efficiency and suggests planning to replace the heat preservation layer; 0.12h -1 <K value: Indicates that the heat preservation efficiency is dangerous, issues a warning for replacing the heat preservation layer, and prompts to replace the heat preservation layer immediately.

5. The method for detecting the heat preservation state of the heat preservation material of the heating element according to claim 1, characterized in that, The method further includes the following steps: Obtain the cumulative difference value of the first temperature data T1 within the most recent first set time period; Inversely and correlatively adjust the warning threshold Kx according to the cumulative difference value; the larger the cumulative difference value, the smaller the warning threshold Kx, and the smaller the cumulative difference value.

6. The method for detecting the heat preservation state of the heat preservation material of the heating element according to claim 1, characterized in that, The method further includes the following steps: When both the temperature inside the hot water tank and the temperature on the outer surface of the hot water tank are decreasing; Obtain the third temperature data T3 of the outer surface temperature of the hot water tank in real time; Calculate a first difference X1 according to the first temperature data T1 and the third temperature data T3; Calculate a second difference X2 according to the first temperature data T1 and the second temperature data T2; Fusion-calculate a temperature step value X3 according to the first difference X1 and the second difference X2; Calculate multiple temperature step values X3 based on the same first temperature data T1, second temperature data T2, and third temperature data T3 at different time points; Calculate a step change trend value J according to multiple temperature step values X3; Compare the step change trend value J with a preset step reference value Jx. If J is less than Jx, issue a heat preservation layer aging status; Inversely and correlatively adjust the warning threshold Kx according to the step change trend value J; The larger the step change trend value J, the smaller the warning threshold Kx; the smaller the step change trend value J, the larger the warning threshold Kx.

7. The method for detecting the heat preservation state of the heat preservation material of the heating element according to claim 6, wherein In the step of fusion-calculating the temperature step value X3 according to the first difference X1 and the second difference X2, the following sub-steps are further included: X3 = m × (X1 - X3) + n × (X1 - X2), where m is the temperature difference coefficient inside the tank and n is the temperature difference coefficient outside the tank.

8. The method for detecting the heat preservation state of the heat preservation material of the heating element according to claim 1, characterized in that, The method further includes the following steps: Heat the hot water tank within a preset control time period; Control and adjust the heating power in inverse correlation with the comprehensive heat transfer coefficient K; the larger the comprehensive heat transfer coefficient K, the lower the heating power, and the smaller the comprehensive heat transfer coefficient K, the higher the heating power.

9. A thermal insulation state detection system for a heating element thermal insulation material, based on the thermal insulation state detection method for the heating element thermal insulation material according to any one of claims 1-8, characterized in that, It includes the following modules: A temperature detection module, including a temperature sensor, for detecting the first temperature data T1 inside the heat tank and the second temperature data T2 of the surrounding environment of the heat tank; A data acquisition module, including a data acquisition unit and a storage unit, for acquiring and recording the temperature data of the temperature sensor, the heating power of the heating rod, and the corresponding time data; A control module, including a microprocessor, a relay control unit, and a human-machine interaction unit, for controlling the working state of the heating rod according to the data of the data acquisition module according to a preset control strategy and executing a detection process; And, An evaluation module, including a data analysis unit, a heat preservation efficiency calculation unit, and a state evaluation unit, for calculating the value of the comprehensive heat transfer coefficient K according to the data acquired by the data acquisition module: when T1 drops from Ta to Tb and the time is t1, then: K = 1 / t1 × In((Ta - T2) / (Tb - T2)); where, T1 ≥ Ta > Tb ≥ T2; evaluate the state of the heat preservation system according to the heat preservation efficiency K value: compare K with a preset warning threshold Kx, if K is greater than or equal to Kx, then issue a warning for replacing the heat preservation layer.

10. A storage medium, characterized in that, A program is stored in the medium, and when the program is executed by a processor, the steps of the method for detecting the heat preservation state of the heating body heat preservation material described in any one of claims 1-8 are implemented.

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