Temperature sensing detection method for analyzing heat insulation effect of car window heat insulation film

Abstract

The invention provides a temperature sensing detection method for analyzing the heat insulation effect of a vehicle window heat insulation film, and relates to the technical field of temperature measurement, and the method comprises the steps: obtaining N sunshine scenes; based on the temperature sensing detection array, the car window pasted with the heat insulation film is tested; performing internal and external interference multi-factor global correction according to the N scene vehicle window temperature sets; carrying out naked car window temperature characteristic sampling on the car window pasted with the heat insulation film; analyzing the heat insulation effect of the heat insulation film according to the N bare car window temperature matrixes and the N scene car window temperature detection matrixes; and according to the N sunshine scenes, carrying out sensitivity correlation on the heat-insulating film performance coefficients of the N scenes, and generating a heat-insulating film detection report. According to the invention, the technical problem that the performance evaluation precision of the vehicle window heat-insulating film is poor due to the single test scene is solved, and the performance evaluation precision of the heat-insulating film is improved by carrying out comprehensive test in multiple sunshine scenes and comparing the temperature difference between a bare vehicle window and a film vehicle window.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of temperature measurement, and particularly relates to a temperature sensing detection method for heat insulation effect analysis of a car window heat insulation film. BACKGROUND

[0002] A car window heat insulation film is a film layer applied to a car window, aiming to reduce the entry of heat, ultraviolet light (UV) and visible light. Traditional performance evaluation methods of the car window heat insulation film are often carried out under specific environmental conditions, and are tested under standard laboratory conditions or a single external light condition. However, in actual use, the car window heat insulation film will face various different sunlight scenes and internal and external environmental factors, such as different sunlight angles, seasonal changes, temperature differences inside and outside the car, etc. A single test scene cannot comprehensively reflect the performance of the heat insulation film in actual use, resulting in deviation of the evaluation result, thereby affecting the evaluation accuracy of the performance of the heat insulation film.

[0003] In summary, the prior art has the technical problem of poor performance evaluation accuracy of the car window heat insulation film due to a single test scene. SUMMARY

[0004] The purpose of the present application is to provide a temperature sensing detection method for heat insulation effect analysis of a car window heat insulation film, to solve the technical problem of poor performance evaluation accuracy of the car window heat insulation film due to a single test scene in the prior art.

[0005] In view of the above problems, the present application provides a temperature sensing detection method for heat insulation effect analysis of a car window heat insulation film, wherein the temperature sensing detection method for heat insulation effect analysis of the car window heat insulation film comprises: performing sunlight characteristic mining on a car window with a heat insulation film attached according to a predetermined sunlight factor, to obtain N sunlight scenes, N being a positive integer greater than 1; testing the car window with the heat insulation film attached according to the N sunlight scenes based on a temperature sensing detection array, to obtain N scene car window temperature sets; performing internal and external interference multi-factor global correction according to the N scene car window temperature sets, to obtain N scene car window temperature detection matrices; performing bare car window temperature characteristic sampling on the car window with the heat insulation film attached according to the N sunlight scenes, to construct N bare car window temperature matrices; performing heat insulation film heat insulation effect analysis according to the N bare car window temperature matrices and the N scene car window temperature detection matrices, to obtain N scene heat insulation film performance coefficients; and performing sensitivity correlation on the N scene heat insulation film performance coefficients according to the N sunlight scenes, to generate a heat insulation film detection report.

[0006] Optionally, a user historical driving scene set of the vehicle window with the attached thermal insulation film is called; feature extraction is performed on the user historical driving scene set according to the predetermined sunlight factor, to obtain a historical driving sunlight scene set; the historical driving sunlight scene set is arranged in descending order according to the frequency evaluation, to obtain a sunlight scene arrangement set, and the sunlight scene arrangement set is screened to generate the N sunlight scenes.

[0007] Optionally, the predetermined sunlight factor includes sunlight intensity, sunlight angle and sunlight duration.

[0008] Optionally, the temperature sensing detection array includes an inner side temperature sensing array and an outer side temperature sensing array; an nth sunlight scene is extracted according to the N sunlight scenes, and inner and outer side state inspection is performed on the vehicle window with the attached thermal insulation film, to obtain a vehicle window state inspection result; when the vehicle window state inspection result is qualified, an nth test start signal is obtained, n is a positive integer, and 1≤n≤N; based on the nth test start signal, a test is performed on the vehicle window with the attached thermal insulation film according to the nth sunlight scene, and the inner side temperature sensing array of the vehicle window is read, to obtain an nth vehicle window inner side temperature set; the outer side temperature sensing array of the vehicle window is synchronously read, to obtain an nth vehicle window outer side temperature set; the nth scene vehicle window temperature set is generated by combining the nth vehicle window inner side temperature set, and the nth scene vehicle window temperature set is added to the N scene vehicle window temperature sets.

[0009] Optionally, an nth in-vehicle environment set is obtained by performing in-vehicle environment information collection according to the nth vehicle window inner side temperature set; an nth in-vehicle layout set is obtained by performing in-vehicle layout information collection according to the nth vehicle window inner side temperature set; an nth inner side sensor state set is obtained; the nth vehicle window inner side temperature set is globally corrected with respect to multiple interference factors by combining the nth in-vehicle environment set and the nth in-vehicle layout set, to obtain an nth vehicle window inner temperature detection matrix; the nth vehicle window outer side temperature set is globally corrected with respect to multiple interference factors according to an nth vehicle outer environment data set and an nth outer side sensor state set, to obtain an nth vehicle window outer temperature detection matrix; the nth scene vehicle window temperature detection matrix is constructed according to the nth vehicle window inner temperature detection matrix and the nth vehicle window outer temperature detection matrix, and the nth scene vehicle window temperature detection matrix is added to the N scene vehicle window temperature detection matrices.

[0010] Optionally, according to the nth set of in-vehicle environment, the nth set of inboard window temperature is subjected to multi-interference factor identification, to obtain an nth in-environment interference factor; according to the nth set of in-vehicle layout, the nth set of inboard window temperature is subjected to multi-interference factor identification, to obtain an nth layout interference factor; according to the nth set of inboard sensor state, the nth set of inboard window temperature is subjected to multi-interference factor identification, to obtain an nth sensor interference factor; according to the nth in-environment interference factor, the nth layout interference factor and the nth sensor interference factor, global analysis of inboard window temperature influence is performed, to obtain an nth in-interference global influence feature; according to the nth in-interference global influence feature, the nth set of inboard window temperature is corrected, to generate the nth in-window temperature detection matrix.

[0011] Optionally, according to the thermally insulated film-equipped window, bare window feature collection is performed, to obtain a bare window feature set, the bare window feature set including bare window material feature, bare window structure feature and bare window installation location feature; according to the bare window feature set and the nth sunlight scenario, bare window inboard and outboard temperature retrieval is performed, to obtain an nth bare window inboard temperature retrieval set and an nth bare window outboard temperature retrieval set; according to the nth bare window inboard temperature retrieval set, temperature measurement scene interference compensation is performed, to obtain an nth bare window inboard temperature matrix; according to the nth bare window outboard temperature retrieval set, temperature measurement scene interference compensation is performed, to obtain an nth bare window outboard temperature matrix, combined with the nth bare window inboard temperature matrix, an nth bare window temperature matrix is generated, and the nth bare window temperature matrix is added to the N bare window temperature matrices.

[0012] Optionally, according to the N scene window temperature detection matrices, thermal insulation effect analysis is performed, to obtain N scene thermal insulation coefficients; according to the N bare window temperature matrices, thermal insulation effect analysis is performed, to obtain N bare window thermal insulation coefficients; according to the N bare window thermal insulation coefficients, bare window thermal insulation interference correction is performed on the N scene thermal insulation coefficients, to generate the N scene thermal insulation film performance coefficients.

[0013] Optionally, the nth scene window temperature detection matrix is subjected to inboard and outboard feature alignment comparison, to obtain an nth scene window temperature comparison matrix; a thermal insulation effect evaluation component is activated, the thermal insulation effect evaluation component including M thermal insulation effect evaluation models, M being a positive integer greater than 1; the nth scene window temperature comparison matrix is input into the M thermal insulation effect evaluation models, to obtain M thermal insulation effect evaluation coefficients; a central value of the M thermal insulation effect evaluation coefficients is calculated, to generate an nth scene thermal insulation coefficient, and the nth scene thermal insulation coefficient is added to the N scene thermal insulation coefficients.

[0014] Optionally, according to the N sunlight scenarios, N expected thermal insulation film performance coefficients are set; according to the N expected thermal insulation film performance coefficients, sensitivity evaluation is performed on the N scene thermal insulation film performance coefficients, and N thermal insulation film performance sensitivity coefficients are obtained; the N bare car window temperature matrices, the N scene car window temperature detection matrices, the N scene thermal insulation film performance coefficients and the N thermal insulation film performance sensitivity coefficients are sorted, and the thermal insulation film detection report is generated.

[0015] The technical solutions provided in the present application have at least the following beneficial effects: By mining the sunlight characteristics of the car window with the thermal insulation film according to the predetermined sunlight factor, N sunlight scenarios are obtained, N is a positive integer greater than 1; based on the temperature sensing detection array, the car window with the thermal insulation film is tested according to the N sunlight scenarios, and N scene car window temperature sets are obtained; according to the N scene car window temperature sets, global correction of internal and external interference is performed, and N scene car window temperature detection matrices are obtained; according to the N sunlight scenarios, the temperature characteristics of the bare car window are sampled, and N bare car window temperature matrices are constructed; according to the N bare car window temperature matrices and the N scene car window temperature detection matrices, thermal insulation effect of the thermal insulation film is analyzed, and N scene thermal insulation film performance coefficients are obtained; according to the N scene thermal insulation film performance coefficients, sensitivity correlation is performed according to the N sunlight scenarios, and a thermal insulation film detection report is generated. That is, by determining multiple sunlight scenarios, and detecting the temperature of the car window with the thermal insulation film through the temperature sensor under different sunlight scenarios, combining the global correction method, the accuracy and reliability of the data are improved, at the same time, the temperature of the bare car window is detected through the temperature sensor under different sunlight scenarios, the temperatures of the car window with and without the film are compared, and the influence of different sunlight scenarios on the performance of the thermal insulation film is analyzed, which provides comprehensive evaluation of the thermal insulation effect, and improves the performance evaluation accuracy of the car window thermal insulation film.

[0016] The above description is only a summary of the technical solutions of the present application, in order to more clearly understand the technical means of the present application, the specific embodiments of the present application can be implemented according to the content of the description, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent through the following description. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the accompanying drawings in the following description are only exemplary and, for those skilled in the art, other drawings can be obtained without creative effort based on the provided drawings.

[0018] Figure 1 Flowchart for the temperature sensing detection method for analyzing the heat insulation effect of the car window heat insulation film.

[0019] Figure 2 Flowchart for obtaining N scene car window temperature detection matrices in the temperature sensing detection method for analyzing the heat insulation effect of the car window heat insulation film. DETAILED DESCRIPTION

[0020] The application provides a temperature sensing detection method for analyzing the heat insulation effect of a car window heat insulation film, which solves the technical problem in the prior art that the performance evaluation precision of the car window heat insulation film is poor due to a single test scene. By determining multiple sunshine scenes and detecting the temperature of the car window with the heat insulation film attached under different sunshine scenes through a temperature sensor, combining a global correction method, the precision and reliability of the data are improved, and by detecting the temperature of the bare car window under different sunshine scenes through the temperature sensor, comparing the temperatures of the car window with and without the film, and analyzing the influence of different sunshine scenes on the performance of the heat insulation film, comprehensive heat insulation effect evaluation is provided, and the performance evaluation precision of the car window heat insulation film is improved.

[0021] The technical solutions in the application will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the application, rather than all the embodiments of the application. It should be understood that the application is not limited by the exemplary embodiments described herein. Based on the embodiments of the application, all other embodiments obtained by those skilled in the art without creative effort fall within the scope of the application. In addition, it should be noted that, for convenience of description, only parts related to the application are shown in the drawings, rather than all parts.

[0022] Embodiment, please refer to the accompanying Figure 1 The application provides a temperature sensing detection method for analyzing the heat insulation effect of a car window heat insulation film, which specifically comprises the following steps: S100: According to a predetermined sunshine factor, the sunshine characteristics of the car window with the heat insulation film attached are mined to obtain N sunshine scenes, and N is a positive integer greater than 1.

[0023] Further, the S100 comprises: S110: retrieve a set of user historical driving scenarios of the vehicle with the thermal insulation film window; S120: perform feature extraction on the set of user historical driving scenarios according to the predetermined solar factor, to obtain a set of historical driving solar scenarios; S130: arrange the set of historical driving solar scenarios in descending order according to the frequency evaluation, to obtain a set of solar scenarios, and screen the set of solar scenarios to generate the N solar scenarios.

[0024] The predetermined solar factor includes solar intensity, solar angle, and solar duration.

[0025] Specifically, a set of user historical driving scenarios of the vehicle with the thermal insulation film window is retrieved, including various driving scenarios experienced by the user during driving, such as road conditions, weather, vehicle speed, and solar radiation information. The thermal insulation film window is a vehicle window with a thermal insulation film attached to the window, which blocks part of the heat from entering the vehicle, improves the comfort inside the vehicle, and reduces the air conditioning load.

[0026] The predetermined solar factor is a set of solar characteristic parameters preset when analyzing the thermal insulation performance of the window, usually including solar intensity, angle, and duration. According to the predetermined solar factor (such as solar intensity, angle, and duration), relevant solar information is extracted from the historical driving scenarios. Each historical driving scenario has different solar characteristics, such as strong sunlight in some scenarios, and other scenarios may be on cloudy days or at night. By extracting the solar-related features from the user's historical data, a set of historical driving solar scenarios can be obtained. For each scenario in the set of historical driving scenarios, analyze the solar-related features, including solar intensity (such as the intensity of solar radiation around the vehicle), solar angle (the angle of solar incidence in each scenario), and solar duration (the duration of the window exposed to sunlight in each scenario).

[0027] By performing feature extraction on all historical driving scenarios, a set of historical driving solar scenarios containing solar intensity, solar angle, and solar duration is finally obtained, with each scenario corresponding to a set of solar data. That is, all solar-related scenarios in the set of user historical driving scenarios are extracted, and scenarios unrelated to solar are eliminated, such as cloudy days, night, etc. For example, if the historical data shows that the user often drives under strong sunlight at noon, the characteristics of this scenario will include high solar intensity and vertical solar angle.

[0028] The extracted historical driving sunshine scene set is analyzed for frequency, and the frequency of each sunshine scene in the historical driving record is evaluated. The frequency of different sunshine scenes in the user's historical driving scene set is evaluated, that is, the number of times each scene appears in the historical driving sunshine scene set is counted, and a frequency score is obtained. The frequency count can be directly used to evaluate the scene frequency. The higher the frequency, the higher the frequency of the scene in actual use. According to the frequency of each scene in the historical driving sunshine scene set, the sunshine scene arrangement set is sorted in descending order. The top N sunshine scenes from the sunshine scene arrangement set represent the most common sunshine environment encountered by the user during actual driving.

[0029] By calling the user's historical driving scene set and combining the feature extraction of the sunshine factor, various sunshine conditions encountered by the user in different driving environments can be covered, and the top N sunshine scenes most commonly encountered by the user can be selected, accurately simulating common scenes in actual driving, and avoiding the problem of overly idealized or single environment in testing.

[0030] S200: Based on the temperature sensing detection array, the N sunshine scenes are used to test the vehicle window with the attached thermal insulation film, and an N scene vehicle window temperature set is obtained.

[0031] Further, the S200 of the present application comprises: S210: The temperature sensing detection array comprises an inner side temperature sensing array and an outer side temperature sensing array; S220: According to the N sunshine scenes, the nth sunshine scene is extracted, and the inner and outer state of the vehicle window with the attached thermal insulation film is tested to obtain a vehicle window state test result. When the vehicle window state test result is qualified, an nth test start signal is obtained, n is a positive integer, and 1≤n≤N; S230: Based on the nth test start signal, the Nth sunshine scene is used to test the vehicle window with the attached thermal insulation film, and the inner side temperature sensing array is read to obtain an Nth inner side temperature set; S240: The outer side temperature sensing array is read synchronously to obtain an Nth outer side temperature set, combined with the Nth inner side temperature set, an Nth scene vehicle window temperature set is generated, and the Nth scene vehicle window temperature set is added to the N scene vehicle window temperature set.

[0032] Specifically, the temperature sensing detection array is a set of sensors for measuring temperature, which usually includes an inner side temperature sensing array and an outer side temperature sensing array, respectively used for detecting the temperature change inside and outside the vehicle window. The inner side temperature sensing array is installed on the inner side of the vehicle window for real-time detection of the temperature of the vehicle interior environment, usually placed on the inner surface of the vehicle window, and can sense the change of the temperature inside the vehicle. The outer side temperature sensing array is installed on the outer side of the vehicle window for detecting the temperature of the environment outside the vehicle window, especially the temperature change related to the weather or sunshine outside.

[0033] According to N sunlight scenarios, any one of them is selected as the nth sunlight scenario, which contains specific information such as intensity, angle, and duration, and can simulate specific environmental conditions. According to this sunlight scenario, the inside and outside state of the window with thermal insulation film is inspected to confirm whether the integrity and function of the window meet the requirements, including checking whether the window is damaged, whether the thermal insulation film is correctly attached, and whether there are stains, etc. When the window state inspection result is qualified, it means that the window state is qualified and meets the test requirements, at this time a test start signal is sent, indicating that the following test can start. The nth test start signal refers to the window state being qualified under the nth sunlight scenario, corresponding to the nth sunlight scenario.

[0034] Under the indication of the nth test start signal, the window with thermal insulation film is tested through the nth sunlight scenario, that is, the information such as intensity, angle, and duration corresponding to the nth sunlight scenario is input into the simulated environment, for example, specific light is used to simulate the intensity and angle of sunlight, and heating or cooling equipment is used to simulate different environmental temperatures. The conditions of the nth sunlight scenario are applied in the simulated environment, including adjusting the intensity and angle of the light, and the environmental temperature to simulate the real sunlight scenario. The window is placed in the simulated environment to ensure that the inside and outside temperature sensor arrays of the window have been correctly installed and can work normally.

[0035] After applying the conditions of the nth sunlight scenario, the test process is started. At the same time, the inside and outside temperatures of the window are detected through the temperature sensor array. The inside temperature sensor array of the window reads the temperature data of the inner surface of the window. By reading the data of multiple sensors, the nth inside temperature set of the window is obtained; the outside temperature sensor array of the window reads the temperature data of the outer surface of the window at the same time, and the nth outside temperature set of the window is obtained. The nth inside temperature set of the window and the nth outside temperature set of the window are combined to generate a complete nth scene window temperature set, which records the temperature difference between the inside and outside of the window under specific sunlight conditions, serving as the data basis for evaluating the performance of the window thermal insulation film.

[0036] For example, suppose the nth sunshine scenario is midday in a high-latitude region during summer, with a solar intensity of 700 W / m², a solar angle of 45°, and a sunshine duration of 3 hours. First, the condition of the vehicle windows is inspected to ensure they are undamaged and the heat insulation film is intact and correctly installed. If the inspection is successful, a test start signal is issued, and the test begins. The temperature values ​​detected by the inner window temperature sensor array in this scenario are 22℃, 23℃, and 24℃ (data collected by multiple sensors); the temperature values ​​detected by the outer window temperature sensor array in this scenario are, for example, 45℃, 46℃, and 47℃. Combining these temperature data, a window temperature set for the nth scenario is generated: Window temperature set for the nth scenario = {Inner temperature: 22℃, 23℃, 24℃, Outer temperature: 45℃, 46℃, 47℃}.

[0037] For N sunlight scenarios, the above steps are performed to obtain corresponding window temperature sets, i.e., N scenario window temperature sets, including the outer window temperature set and the inner window temperature set for each scenario. These are used to compare the temperature differences between the inner and outer sides of the window under different sunlight scenarios, evaluate the effectiveness of the heat insulation film, and check for any abnormal data. Through simulation testing, the performance of the window under specific sunlight scenarios is accurately evaluated in a controlled environment. Repeated and consistent testing under various sunlight conditions provides accurate and reliable data to evaluate the effectiveness of the heat insulation film.

[0038] By using a temperature sensor array to simultaneously read the temperature inside and outside the car window, it is possible to comprehensively and accurately obtain the temperature changes of the window, ensuring that temperature data is obtained at the same point in time. By conducting temperature tests under different sunlight scenarios, covering different environmental conditions, the test results are ensured to be more representative and widely applicable. The performance of the window heat insulation film under different environmental conditions is comprehensively evaluated, which helps to optimize the film material or installation method and improve its effect in practical applications.

[0039] S300: Perform global correction of multiple factors of internal and external interference based on the N scene window temperature sets to obtain N scene window temperature detection matrices.

[0040] Further details are attached. Figure 2 As shown, S300 of this application includes: S310: Collecting in-vehicle environmental information according to the nth set of inboard window temperature, obtaining the nth set of in-vehicle environment; S320: Collecting in-vehicle layout information according to the nth set of inboard window temperature, obtaining the nth set of in-vehicle layout; S330: Obtaining the nth set of inboard sensor state, combining the nth set of in-vehicle environment and the nth set of in-vehicle layout to globally correct the nth set of inboard window temperature with multiple interference factors, obtaining the nth window in-temperature detection matrix; S340: Globally correcting the nth set of outboard window temperature with multiple interference factors according to the nth set of out-of-vehicle environment data and the nth set of outboard sensor state, obtaining the nth window out-temperature detection matrix; S350: Constructing the nth scene window temperature detection matrix according to the nth window in-temperature detection matrix and the nth window out-temperature detection matrix, and adding the nth scene window temperature detection matrix to the N scene window temperature detection matrices.

[0041] S331: Identifying multiple interference factors of the nth set of inboard window temperature according to the nth set of in-vehicle environment, obtaining the nth in-environment interference factor; S332: Identifying multiple interference factors of the nth set of inboard window temperature according to the nth set of in-vehicle layout, obtaining the nth layout interference factor; S333: Identifying multiple interference factors of the nth set of inboard window temperature according to the nth set of inboard sensor state, obtaining the nth sensor interference factor; S334: Globally analyzing the influence of inboard window temperature according to the nth in-environment interference factor, the nth layout interference factor and the nth sensor interference factor, obtaining the nth global influence feature of in-interference; S335: Correcting the nth set of inboard window temperature according to the nth global influence feature of in-interference, generating the nth window in-temperature detection matrix.

[0042] Specifically, in-vehicle environmental information is collected according to the nth set of inboard window temperature, and the nth set of in-vehicle environment is constructed. In-vehicle environmental data includes humidity, air flow speed, temperature of other areas in the vehicle except near the window, working state of in-vehicle devices (such as air conditioner), etc., which will affect the temperature of the inboard window. The nth set of in-vehicle environment refers to the environmental information in the vehicle under the nth sunlight scene, including humidity, air flow speed, working state of in-vehicle devices (such as air conditioner), etc. At the same time, the layout information in the vehicle is collected, and the nth set of in-vehicle layout is constructed, including the position of the seat, the arrangement of the instrument panel, the number and position of passengers, etc. The in-vehicle layout has an important influence on the temperature distribution of the window, because it will change the air circulation, heat transfer, etc.

[0043] For each temperature sensor on the inside of the window, its status data is acquired, forming the nth inner-side sensor status set. The status of each temperature sensor can affect the accuracy of its temperature readings, therefore these statuses need to be monitored to ensure normal sensor operation. For example, a sensor may be aging or malfunctioning, resulting in inaccurate measurements, requiring correction based on the status data. The sensor status set contains status data related to the inner-side window temperature sensors, including information such as the operating status, accuracy, and sensitivity of each sensor, used to determine data reliability.

[0044] Based on the nth in-vehicle environment set, multiple interfering factors are identified for the nth window interior temperature set. By analyzing environmental factors such as humidity, airflow, air quality, and temperatures in other areas of the vehicle, the main interfering factors affecting the window interior temperature are identified. For example, high temperatures, the use of various in-vehicle devices, and the window's sealing performance can all affect the window interior temperature. Multiple related variables (such as in-vehicle temperature, humidity, and airflow speed) are compressed into several principal components to reduce data complexity while retaining most of the information. Specifically, the original data of the nth in-vehicle environment set is standardized to ensure they have the same dimensions, avoiding the influence of different dimensions on the analysis results for certain features (such as temperature). The covariance matrix between each variable is calculated to represent the linear relationship between them. Eigenvalue decomposition is performed on the covariance matrix to obtain eigenvalues ​​and eigenvectors. Eigenvalues ​​represent the importance of each principal component, and eigenvectors represent the direction of each principal component. The top few principal components with larger eigenvalues ​​are selected, typically those that can explain most of the data variance, to identify the main interfering factors. By analyzing the relationship between principal components and the interior window temperature dataset, and by analyzing the correlation between each principal component and the interior window temperature, the most significant interfering factors can be identified. For example, if principal component 1 is mainly determined by the interior temperature and humidity and is highly correlated with the interior window temperature, then interior temperature and humidity are considered the main interfering factors. Based on the results, the environmental factors that have the greatest impact on the interior window temperature are identified as interior humidity, airflow speed, or a combination thereof.

[0045] After analyzing in-vehicle environmental data and window interior temperature data, several interfering factors were identified. For example, a higher in-vehicle temperature may lead to an increase in the window interior temperature, especially when the air conditioning is not turned on or the air conditioning is not directed correctly, which may exacerbate this phenomenon. The speed and distribution of airflow inside the vehicle also affect the temperature sensor readings; areas with weaker airflow may have higher temperatures. The identified interfering factors were quantified, and the nth internal environmental interfering factor was obtained based on the degree of influence of each factor on the window interior temperature. Multi-interfering factor identification refers to identifying multiple environmental factors that may affect the window interior temperature through data analysis.

[0046] Similarly, according to the above process, the n-th window inner side temperature set is subjected to multi-interference factor identification according to the n-th in-vehicle layout set, and the n-th layout interference factor is obtained. Different in-vehicle layouts (such as seat position, instrument panel position, etc.) can affect air flow and temperature distribution, thereby affecting the temperature data of the vehicle window. For example, the front seats can block the air flow, causing local temperature changes. By correlating and analyzing each layout factor in the n-th in-vehicle layout set with the n-th window inner side temperature set data, it is identified which in-vehicle layout factors have a significant impact on the window inner side temperature. If the seat is close to the window, it will affect the air flow around the window, causing local temperature to be too high or too low. The direction and speed of the air outlet of the air conditioner can affect the temperature distribution in the vehicle, thereby affecting the temperature of the window, especially when the air conditioner air directly blows on the window. The placement of items in the vehicle can affect the air flow in the vehicle, thereby indirectly affecting the distribution of the window temperature.

[0047] For each window inner side sensor, its working state information is collected to obtain the n-th inner side sensor state set, including whether the sensor is working normally, whether the sensor has drift, failure or inaccurate reading, the response time of the sensor (i.e. the response speed of the sensor when the temperature changes), the accuracy of the sensor (whether there is deviation, sensor error), etc. The relationship between the n-th inner side sensor state set and the n-th window inner side temperature set is analyzed to identify which sensor state problems may affect the accuracy of the window inner side temperature data. For example, the sensor may have systematic errors due to aging or quality problems, causing the temperature reading to be too high or too low. Alternatively, the sensor may not respond quickly enough to temperature changes, which may cause the temperature data recorded by the sensor to lag in a rapidly changing temperature environment. After long-term use, the accuracy of the sensor may decrease, causing its reading to drift. Similarly, according to the foregoing steps, the n-th inner side sensor state set is compared and analyzed with the n-th window inner side temperature set to identify the interference factors of the sensor. Through the above analysis, it is identified which sensor states have interfered with the window inner side temperature data in the n-th scenario. If the sensor has drifted in some areas, causing the temperature reading in these areas to be too high, this part can be marked as the n-th sensor interference factor; if the response time of the sensor is slow, causing the window inner side temperature data to lag, it is classified as a sensor response delay interference; if the accuracy of the sensor is low, causing its temperature reading to deviate from the true value, the sensor is identified as an interference source.

[0048] The nth interior environmental disturbance factor, the nth layout disturbance factor, and the nth sensing disturbance factor are combined into a multivariate analysis framework, and the effects of multiple factors are combined together to evaluate how they comprehensively affect the temperature inside the window. A mathematical model is established with the nth temperature inside the window set as the dependent variable and the interior environmental disturbance factor, the layout disturbance factor, and the sensing disturbance factor as the independent variables to analyze the influence weight of each factor on the temperature inside the window. In the regression model, the regression coefficient of each independent variable represents the influence of the independent variable on the dependent variable (the temperature inside the window). The size and sign (positive or negative) of the regression coefficient indicate the direction and strength of the influence of the independent variable on the dependent variable. Assuming that the influence of each independent variable on the dependent variable is linear, a multiple regression model is used to fit the best regression equation by the least squares method, that is, T = a + b*E + c*L + d*S + ϵ, where T is the nth temperature inside the window, which is the dependent variable, that is, the temperature data inside the window in the nth test scenario; a is a constant term, which is the intercept term, representing the expected value of the temperature inside the window when all independent variables are 0, that is, the baseline temperature, representing the temperature inside the window without any disturbance factors; b, c, and d are regression coefficients, respectively corresponding to the influence intensity of the interior environmental disturbance factor, the layout disturbance factor, and the sensor disturbance factor on the temperature inside the window; E represents the factor set of the interior environment in the nth test scenario; L represents the interior layout factor in the nth test scenario; S represents the sensor state factor in the nth test scenario; and ϵ is the error term in the regression model, representing the difference between the actual temperature inside the window and the predicted value of the regression equation, which is usually caused by other factors (such as the thermal conductivity of the window glass, weather changes, etc.) that are not considered in the regression model. The error term can be regarded as the part that the model cannot explain, that is, in addition to the known disturbance factors, there are other factors affecting the window temperature, which are not included in the independent variables of the regression model.

[0049] The regression coefficients are determined by minimizing the difference between the actual data and the model predicted value (i.e., minimizing the sum of squares of the error term), so that the fitting effect of the model is as good as possible. The regression coefficients are calculated by inputting the data using a data analysis tool. The calculation result of the regression coefficients will show the influence degree of each independent variable (interior environmental disturbance, layout disturbance, and sensor disturbance) on the temperature inside the window. According to the influence degree (i.e., weight) obtained from the regression coefficients, the disturbance factors are weighted and synthesized to obtain the global interior disturbance influence feature. The nth global interior disturbance influence feature is a comprehensive measure of the change in the temperature inside the window, representing the overall influence of different disturbance factors (such as the interior environment, the interior layout, and the sensor state) on the temperature inside the window.

[0050] Based on the n-th inside interference global influence feature, the n-th inside temperature set of the vehicle window is corrected, the inside interference global influence feature is combined with the original inside temperature set of the vehicle window, correction is performed, so that the measured value of the inside temperature of the vehicle window is more real and accurate. That is, the n-th inside temperature set of the vehicle window is subtracted from the n-th inside interference global influence feature to obtain the n-th inside temperature detection matrix of the vehicle window. The inside interference global influence feature is a coefficient obtained according to the foregoing regression equation, which quantifies the influence of environmental factors, layout factors, sensor state factors and the like on the inside temperature of the vehicle window.

[0051] The corrected n-th inside temperature set of the vehicle window has eliminated the influence of interference factors on the temperature data, but in order to more systematically analyze the temperature distribution, the corrected data is arranged into a matrix. For example, each row represents temperature data at different time points, and each column represents data measured by different position sensors.

[0052] Similarly, according to the n-th outside temperature set of the vehicle window, outside environmental information is collected, combined with external meteorological data, and the n-th outside environmental data set of the vehicle window is obtained. For example, if the external temperature is high and the wind speed is low, the temperature outside the vehicle window may be higher than under conditions of lower temperature and high wind speed. At the same time, the state data of each sensor corresponding to the temperature collected outside the vehicle window is obtained, and the n-th outside sensor state set is obtained. Different states of each sensor will affect the measured temperature value. The n-th outside temperature set of the vehicle window is corrected based on the n-th outside environmental data set (such as temperature, humidity, wind speed, sunshine, etc.) and the n-th outside sensor state set, and a regression model is used to correct the temperature data outside the vehicle window. For example, some external environmental factors (such as high humidity) may cause the sensor reading to be low, and some sensors may cause measurement error of the temperature data due to calibration problems.

[0053] That is, the n-th outside temperature set of the vehicle window is identified according to the n-th outside environmental data set; the n-th outside temperature set of the vehicle window is identified according to the n-th outside sensor state set; the n-th outside temperature influence global analysis is performed according to the interference factors obtained by identifying the two interference factors, and the n-th outside interference global influence feature is obtained; the n-th outside temperature set of the vehicle window is corrected according to the n-th outside interference global influence feature, and the n-th outside temperature detection matrix of the vehicle window is obtained. Since the process is similar to the foregoing process, for the sake of brevity of the specification, it will not be described in detail here. Similar to the temperature data of the inside of the vehicle window, the corrected temperature data outside the vehicle window is arranged into a matrix to form the n-th outside temperature detection matrix of the vehicle window. Each row in the matrix may represent temperature data at different time points, and each column represents data measured by different position sensors.

[0054] In the nth scenario, the corrected temperature data matrix of the inner side and the outer side of the window (i.e., the nth window inner temperature detection matrix and the nth window outer temperature detection matrix) is obtained respectively. The nth window inner temperature detection matrix and the nth window outer temperature detection matrix are merged by column or by row to form a new matrix. The nth scenario window temperature detection matrix contains the temperature data of the inner and outer windows, arranged by time, and reflects the overall temperature state of the window. For N sunlight scenarios, the above steps are performed to obtain the window temperature detection matrix corresponding to each scenario, i.e., N scenario window temperature detection matrices. The N scenario window temperature detection matrices are a set of window temperature detection matrices under N sunlight scenarios, covering the data of all test scenarios, forming a complete data set.

[0055] By combining the inner and outer temperature data and correcting the interference factors, the obtained window temperature detection matrix is more accurate, reflecting the temperature performance of the window heat insulation film in the real environment. The correction of the inner and outer interference factors can eliminate various external influences, ensuring that the temperature data is more representative and usable. Constructing temperature detection matrices of multiple test scenarios helps to analyze and optimize the performance of the heat insulation film in different environments, and improve its applicability in various scenarios.

[0056] S400: According to the N sunlight scenarios, the bare window temperature characteristics of the window with the attached heat insulation film are sampled to construct N bare window temperature matrices.

[0057] Further, the S400 of the present application comprises: S410: Collecting bare window features according to the window with the attached heat insulation film to obtain a bare window feature set, the bare window feature set comprising bare window material features, bare window structure features and bare window installation location features; S420: Retrieving the inner and outer side temperatures of the bare window according to the bare window feature set and the nth sunlight scenario to obtain the nth bare window inner side temperature retrieval set and the nth bare window outer side temperature retrieval set; S430: Compensating for the temperature measurement scene interference according to the nth bare window inner side temperature retrieval set to obtain the nth bare window inner side temperature matrix; S440: Compensating for the temperature measurement scene interference according to the nth bare window outer side temperature retrieval set to obtain the nth bare window outer side temperature matrix, combining the nth bare window inner side temperature matrix to generate the nth bare window temperature matrix, and adding the nth bare window temperature matrix to the N bare window temperature matrices.

[0058] Specifically, the bare window refers to a window without any additional materials such as thermal insulation film, curtains, etc., i.e., a bare window without thermal insulation film, which serves as a control group for evaluating the thermal insulation effect of the window thermal insulation film. The characteristics of the bare window are collected, including material, structure, and installation location, to form a bare window feature set. The bare window material characteristics are the properties of the window material itself, such as glass material, thickness, surface coating, etc., which determine the thermal conductivity of the window; the bare window structure characteristics are the physical structure of the window, such as window frame shape, thickness, window opening area, etc., which affect the heat transfer path; the bare window installation location characteristics are the installation location of the window, specifically whether the window is close to the engine, the orientation of the window in the vehicle body (front window, side window, rear window), which all have an impact on the temperature distribution of the window. For example, a certain window uses double-layer glass, which means it has a lower thermal conductivity, so external heat is difficult to quickly conduct to the inside of the vehicle, and the inside temperature of the window may be relatively low.

[0059] Based on the feature set of the bare window and the nth sunlight scene, the bare window is tested, and the inside and outside temperature sensor arrays of the window are read to obtain the nth bare window inside temperature retrieval set and the nth bare window outside temperature retrieval set. Similarly, according to the nth bare window inside temperature retrieval set, the temperature measurement scene interference compensation is performed, i.e., similar to the aforementioned, according to the nth inside sensor state set, the interference factors of the nth bare window inside temperature retrieval set are corrected based on the nth vehicle interior environment set and the nth vehicle interior layout set to obtain the nth bare window inside temperature matrix. The interference factors of the nth bare window outside temperature retrieval set are corrected based on the nth vehicle exterior environment data set and the nth outside sensor state set to obtain the nth bare window outside temperature matrix. The temperature measurement scene interference compensation and the inside interference multi-factor global correction and the outside interference multi-factor global correction are similar, all of which are to exclude the influence of other factors on the temperature, so that the temperature data more accurately reflects the temperature changes inside and outside the window.

[0060] After the inside and outside temperature correction, the temperature data of the inside and outside of the window are combined to form a complete bare window temperature matrix, which shows the comprehensive temperature distribution of the bare window under the corresponding sunlight conditions, covering the temperature changes inside and outside. For N sunlight scenes, the above steps are performed to obtain N bare window temperature matrices corresponding to N sunlight scenes, i.e., a bare window temperature matrix set under test scenarios, which contains temperature data of multiple scenes. By compensating for the interference factors of the inside and outside temperature measurements, the measured bare window temperature data is ensured to be accurate and reliable. Combined with the characteristics of the bare window and the external sunlight data, the temperature performance of the bare window under different environments can be comprehensively evaluated, and the heat transfer process can be revealed. Through the temperature analysis of the bare window, baseline data for comparing the effect of thermal insulation film is provided.

[0061] S500: Perform heat insulation film heat insulation effect analysis according to the N naked car window temperature matrices and the N scene car window temperature detection matrices, and obtain N scene heat insulation film performance coefficients.

[0062] Further, the S500 of the present application comprises: S510: Perform heat insulation effect analysis according to the N scene car window temperature detection matrices, and obtain N scene heat insulation coefficients; S520: Perform heat insulation effect analysis according to the N naked car window temperature matrices, and obtain N naked car window heat insulation coefficients; S530: Perform naked car window heat insulation interference correction on the N scene heat insulation coefficients according to the N naked car window heat insulation coefficients, and generate the N scene heat insulation film performance coefficients.

[0063] Further, the present application further comprises the following steps: S511: Perform inside-out feature alignment comparison on the nth scene car window temperature detection matrix, and obtain an nth scene car window temperature comparison matrix; S512: Activate a heat insulation effect evaluation component, wherein the heat insulation effect evaluation component comprises M heat insulation effect evaluation models, and M is a positive integer greater than 1; S513: Input the nth scene car window temperature comparison matrix into the M heat insulation effect evaluation models, and obtain M heat insulation effect evaluation coefficients; S514: Calculate the central value of the M heat insulation effect evaluation coefficients, generate an nth scene heat insulation coefficient, and add the nth scene heat insulation coefficient to the N scene heat insulation coefficients.

[0064] Specifically, the inside-out feature alignment comparison on the nth scene car window temperature detection matrix is to compare the inside-out temperature matrices of the nth scene car window, align the temperature data inside and outside the car window according to time and space, and ensure that the data is at the same time and position, and then effectively analyze the inside-out temperature difference. The inside-out feature alignment comparison is to align the inside-out temperature data of the car window in the nth scene, and ensure that the inside and outside temperatures correspond to the same position and time point data when compared. The purpose of alignment is to eliminate errors caused by different collection positions or time differences, and ensure the accuracy of subsequent analysis.

[0065] The activation of the thermal insulation effect evaluation component includes M thermal insulation effect evaluation models (M is greater than 1). The thermal insulation performance of the vehicle window is comprehensively evaluated according to the internal and external temperature data of the vehicle window, environmental data, and other factors. Each thermal insulation effect evaluation model calculates a thermal insulation effect score based on the input vehicle window temperature data, vehicle window material characteristics, external environmental data, and other factors. The M models evaluate the thermal insulation performance of the vehicle window from different dimensions, and finally generate a comprehensive evaluation coefficient. The thermal insulation effect evaluation component is a component for evaluating the thermal insulation effect of the vehicle window. By analyzing the temperature difference between the inside and outside of the vehicle window and other related factors, the thermal insulation performance of the vehicle window material is calculated. It contains multiple evaluation models for quantitative evaluation of the thermal insulation effect of the vehicle window in different scenarios. M represents the number of evaluation models, usually multiple models, which analyze the thermal insulation effect of the vehicle window from different angles through different algorithms.

[0066] The activation component means starting the thermal insulation effect evaluation component, receiving data from the vehicle window temperature comparison matrix, and analyzing these data through M thermal insulation effect evaluation models. The nth scene vehicle window temperature comparison matrix is input as input data into the M thermal insulation effect evaluation models. Each model evaluates the thermal insulation effect of the vehicle window according to different algorithms (which may include heat conduction models, radiation transmission models, artificial intelligence prediction models, etc.). For example, model 1 is a physical model based on heat conduction theory, which calculates the internal and external temperature difference and estimates the heat flow of the vehicle window; model 2 is a prediction model based on machine learning, which analyzes historical data to determine the thermal insulation performance of the vehicle window thermal insulation film; model 2 is a statistical analysis model that evaluates the thermal insulation effect of the thermal insulation film according to the temperature change trend of the vehicle window. The M thermal insulation effect evaluation models include physical basic models, which evaluate the thermal insulation performance of the vehicle window based on the principles of thermodynamics and heat transfer, usually through the calculation of physical quantities such as heat flow, temperature gradient, and thermal radiation to obtain results. The M thermal insulation effect evaluation models also include machine learning models, which are trained through historical data to predict and evaluate the thermal insulation effect of the vehicle window. Machine learning models can handle more complex nonlinear relationships and are particularly suitable for complex multi-factor problems. The M thermal insulation effect evaluation models also include statistical analysis models that analyze the distribution and trend of vehicle window temperature through statistical methods to evaluate the thermal insulation effect of the vehicle window. These models are some models that already exist, and direct access to these models and transfer learning through the temperature data of the vehicle window can make them adapt to the relationship between the multiple characteristics of the vehicle window and the thermal insulation effect.

[0067] Different models evaluate the heat insulation performance of the vehicle window from different perspectives. Each heat insulation effect evaluation model outputs an evaluation coefficient, which represents the heat insulation effect of the vehicle window under the model and is a quantitative representation of the model evaluation result, usually a numerical value. The heat insulation effect evaluation coefficient is a numerical value given by each heat insulation effect evaluation model, which reflects the heat insulation ability of the vehicle window in a specific environment, usually a unitless quantity, which can be expressed as temperature difference, heat flow change or similar physical quantity, depending on the model. By using multiple heat insulation effect evaluation models, the heat insulation effect of the vehicle window can be comprehensively evaluated from different perspectives, and each model may focus on different physical phenomena or data characteristics to ensure the multidimensionality and accuracy of the evaluation result.

[0068] By inputting the vehicle window temperature comparison matrix of the nth scene into the M models, the models will derive the heat insulation effect evaluation coefficient corresponding to each model according to the temperature difference inside and outside the vehicle window, the material characteristics of the vehicle window and the external environmental conditions. The heat insulation evaluation coefficients obtained by the M models are calculated by central value, usually using mean or median. The central value is the heat insulation coefficient of the nth scene, which represents the overall heat insulation performance of the vehicle window heat insulation film. For example, assuming that the heat insulation scores of the M models are [0.8, 0.75, 0.9, 0.85], then the heat insulation coefficient of the nth scene is the mean of these scores, i.e. (0.8+0.75+0.9+0.85) / 4=0.825, which is the heat insulation coefficient of the vehicle window heat insulation film in this scene.

[0069] For N sunlight scenes, the heat insulation effect of the heat insulation film is analyzed to obtain N scene heat insulation film performance coefficients, i.e. the heat insulation effect of the vehicle window heat insulation film under different sunlight scenes, which helps to comprehensively evaluate the performance of the heat insulation film under different environments. By comparing and analyzing the temperature data of the bare vehicle window and the vehicle window with the heat insulation film, the heat insulation effect of the heat insulation film under different scenes can be accurately evaluated. By comprehensively analyzing the heat insulation effect of the vehicle window under different environmental conditions through multiple evaluation models, more accurate and comprehensive heat insulation performance scores can be obtained, thereby obtaining the performance coefficients of the heat insulation film under different sunlight scenes, i.e. the heat insulation effect of the heat insulation film.

[0070] S600: According to the N scene heat insulation film performance coefficients, the sensitivity correlation is generated, and a heat insulation film detection report is generated.

[0071] Further, the S600 of the present application comprises: S610: setting N expected thermal insulation film performance coefficients according to the N sunshine scenarios; S620: performing sensitivity evaluation on the N scene thermal insulation film performance coefficients according to the N expected thermal insulation film performance coefficients, to obtain N thermal insulation film performance sensitivity coefficients; S630: collating the N bare car window temperature matrices, the N scene car window temperature detection matrices, the N scene thermal insulation film performance coefficients and the N thermal insulation film performance sensitivity coefficients, to generate the thermal insulation film detection report.

[0072] Specifically, according to the characteristics of the N sunshine scenarios, corresponding expected thermal insulation film performance coefficients are set, to obtain N expected thermal insulation film performance coefficients, which reflect the ideal thermal insulation effect that the thermal insulation film should achieve under different scenarios. That is, the performance level that the thermal insulation film should achieve according to the thermal insulation demand under each sunshine scenario usually includes reflectivity, light transmittance, thermal insulation performance, etc. The expected coefficient is the ideal performance requirement of the thermal insulation film in design.

[0073] According to the N expected thermal insulation film performance coefficients, sensitivity evaluation is performed on the N scene thermal insulation film performance coefficients, that is, the N expected thermal insulation film performance coefficients are compared with the N scene thermal insulation film performance coefficients, to calculate the difference in thermal insulation film performance under different scenarios. Sensitivity evaluation is used to analyze the difference between the expected thermal insulation film performance coefficient and the actual thermal insulation film performance coefficient, and to evaluate the influence of these differences on the thermal insulation effect under different scenarios. The difference between the expected thermal insulation film performance coefficient and the actual thermal insulation film performance coefficient under each sunshine scenario is calculated. Sensitivity coefficient=(scene thermal insulation film performance coefficient-expected thermal insulation film performance coefficient) / expected thermal insulation film performance coefficient×100%, which is calculated for each scene by this formula, to obtain N thermal insulation film performance sensitivity coefficients. For example, if the expected thermal insulation film performance coefficient under a certain scene is 0.8, and the actual test performance coefficient is 0.75, then the sensitivity coefficient is (0.75-0.8) / 0.8×100%=-6.25%, indicating that the actual performance is 6.25% lower than the expected performance. The thermal insulation film performance sensitivity coefficients of the N scenes are summarized to comprehensively evaluate the sensitivity of the thermal insulation film performance under different scenarios.

[0074] For each sunlight scenario, set the expected performance coefficient and measure the actual performance coefficient, use the appropriate sensitivity analysis method to evaluate the change of the performance coefficient of the heat insulation film under each sunlight scenario, obtain the sensitivity evaluation value under each sunlight scenario, reflect the change range and sensitivity of the film performance under different scenarios. The N bare car window temperature matrices corresponding to the N sunlight scenarios, the N scene car window temperature detection matrices, the N scene heat insulation film performance coefficients and the N heat insulation film performance sensitivity coefficients and other information are sorted together to generate a comprehensive heat insulation film detection report, which shows the performance evaluation, sensitivity analysis results and optimization suggestions of the heat insulation film under different scenarios, helping users to fully understand and optimize the effect of the heat insulation film. The bare car window temperature matrix is the temperature data matrix on the car window without heat insulation film under different sunlight scenarios (such as different times, weather, etc.), which represents the temperature change of the car window surface without heat insulation film. The scene car window temperature detection matrix records the temperature detection data of the heat insulation film car window under different sunlight scenarios (such as different times, weather, etc.). The scene heat insulation film performance coefficient is a coefficient for quantitatively measuring the performance of the heat insulation film under different sunlight scenarios, which reflects the heat insulation effect of the heat insulation film under different environmental conditions. The heat insulation film performance sensitivity coefficient measures the performance sensitivity of the heat insulation film under different scenarios, indicating the difference between the performance of the heat insulation film under specific environmental conditions (such as sunlight intensity, temperature, etc.) and the expected value.

[0075] The sorted data is integrated into a comprehensive report, i.e. the heat insulation film detection report, which includes the temperature data of the bare car window and the heat insulation film car window under different sunlight conditions, showing the effect of the heat insulation film. According to different scenarios, the performance coefficients of the heat insulation film (such as heat transmittance, thermal resistance, light transmittance, etc.) are listed. The sensitivity coefficients of the heat insulation film under different sunlight scenarios are displayed, highlighting which scenarios have the best performance of the heat insulation film and which scenarios have the worst performance of the heat insulation film. Finally, the analysis results are comprehensively analyzed to generate the heat insulation film detection report for comprehensive evaluation of the performance of the heat insulation film. By sorting the bare car window temperature matrix, the scene car window temperature detection matrix, the scene heat insulation film performance coefficient and the heat insulation film performance sensitivity coefficient, the generated heat insulation film detection report can fully reflect the performance of the heat insulation film under different environmental conditions, help understand the effect of the heat insulation film, propose optimization schemes, and finally improve the performance and application effect of the car window heat insulation film.

[0076] In summary, the temperature sensing detection method for analyzing the heat insulation effect of the car window heat insulation film provided by the present application has the following beneficial effects: The solar characteristics of the vehicle window with the attached thermal insulation film are mined according to a predetermined solar factor, N solar scenes are obtained, N is a positive integer greater than 1; the vehicle window with the attached thermal insulation film is tested according to the N solar scenes based on a temperature sensing detection array, N scene window temperature sets are obtained; global correction of internal and external interference multi-factors is performed according to the N scene window temperature sets, N scene window temperature detection matrices are obtained; naked window temperature characteristics of the vehicle window with the attached thermal insulation film are sampled according to the N solar scenes, N naked window temperature matrices are constructed; thermal insulation effect analysis of the thermal insulation film is performed according to the N naked window temperature matrices and the N scene window temperature detection matrices, N scene thermal insulation film performance coefficients are obtained; sensitivity correlation of the N scene thermal insulation film performance coefficients is performed according to the N solar scenes, and a thermal insulation film detection report is generated. That is, by determining multiple solar scenes, detecting the temperature of the vehicle window with the attached thermal insulation film through the temperature sensor under different solar scenes, combining the global correction method, the accuracy and reliability of the data are improved, at the same time, the temperature of the naked window is detected through the temperature sensor under different solar scenes, the temperatures of the window with and without the film are compared, and the influence of different solar scenes on the performance of the thermal insulation film is analyzed, comprehensive thermal insulation effect evaluation is provided, and the performance evaluation accuracy of the window thermal insulation film is improved.

[0077] The above description of disclosed embodiments enables one of ordinary skill in the art to make or use the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0078] Obviously, for those skilled in the art, some improvements and modifications can be made to the application without departing from the principles of the application, and these improvements and modifications also fall within the protection scope of the application.

Claims

1. A temperature sensing detection method for analyzing the heat insulation effect of a heat insulation film for a vehicle window, characterized by, The method comprises the following steps: According to the predetermined solar factor, the solar characteristics of the vehicle window with the attached thermal insulation film are excavated to obtain N solar scenes, N being a positive integer greater than 1; Based on the temperature sensing detection array, the vehicle window with the attached thermal insulation film is tested according to the N solar scenes to obtain N scene window temperature sets; According to the N scene window temperature sets, internal and external interference multi-factor global correction is performed to obtain N scene window temperature detection matrices; According to the N solar scenes, the bare window temperature characteristics of the vehicle window with the attached thermal insulation film are sampled to construct N bare window temperature matrices; According to the N bare window temperature matrices and the N scene window temperature detection matrices, thermal insulation film thermal insulation effect analysis is performed to obtain N scene thermal insulation film performance coefficients; According to the N solar scenes, the sensitivity correlation of the N scene thermal insulation film performance coefficients is performed to generate a thermal insulation film detection report.

2. The temperature sensing detection method for analyzing the heat insulation effect of a heat insulation film for a vehicle window according to claim 1, wherein Based on the temperature sensing detection array, the vehicle window with the attached thermal insulation film is tested according to the N solar scenes to obtain N scene window temperature sets, comprising: The temperature sensing detection array comprises an inner side temperature sensing array and an outer side temperature sensing array of the vehicle window; According to the N solar scenes, the nth solar scene is extracted, and the internal and external side state of the vehicle window with the attached thermal insulation film is inspected to obtain a vehicle window state inspection result. When the vehicle window state inspection result is qualified, the nth test start signal is obtained, n is a positive integer, and 1≤n≤N; Based on the nth test start signal, the vehicle window with the attached thermal insulation film is tested according to the nth solar scene, and the inner side temperature sensing array of the vehicle window is read to obtain the nth inner side temperature set of the vehicle window; The outer side temperature sensing array of the vehicle window is read synchronously to obtain the nth outer side temperature set of the vehicle window, the nth scene window temperature set is generated by combining the nth inner side temperature set of the vehicle window, and the nth scene window temperature set is added to the N scene window temperature sets.

3. The temperature sensing detection method for analyzing the heat insulation effect of a heat insulation film for a vehicle window according to claim 2, wherein According to the N scene window temperature sets, internal and external interference multi-factor global correction is performed to obtain N scene window temperature detection matrices, comprising: According to the nth inner side temperature set, the nth in-vehicle environment set is obtained by collecting in-vehicle environmental information; According to the nth inner side temperature set, the nth in-vehicle layout set is obtained by collecting in-vehicle layout information; The nth inner side sensor state set is obtained, and the nth inner side temperature set of the vehicle window is corrected by combining the nth in-vehicle environment set and the nth in-vehicle layout set to obtain the nth inner temperature detection matrix of the vehicle window; According to the nth outer side temperature set, the nth outer temperature detection matrix of the vehicle window is obtained by correcting the external interference multi-factor global correction according to the nth in-vehicle environment data set and the nth outer side sensor state set; According to the nth inner temperature detection matrix of the vehicle window and the nth outer temperature detection matrix of the vehicle window, the nth scene window temperature detection matrix is constructed, and the nth scene window temperature detection matrix is added to the N scene window temperature detection matrices.

4. The temperature sensing detection method for analyzing the heat insulation effect of a heat insulation film for a vehicle window according to claim 3, wherein The nth inner side sensor state set is obtained, and the nth inner side temperature set of the vehicle window is corrected by combining the nth in-vehicle environment set and the nth in-vehicle layout set to obtain the nth inner temperature detection matrix of the vehicle window, comprising: According to the nth vehicle interior environment set, the nth vehicle window inner side temperature set is subjected to multi-interference factor identification, and an nth interior environment interference factor is obtained; According to the nth vehicle interior layout set, the nth vehicle window inner side temperature set is subjected to multi-interference factor identification, and an nth layout interference factor is obtained; According to the nth inner side sensor state set, the nth vehicle window inner side temperature set is subjected to multi-interference factor identification, and an nth sensing interference factor is obtained; According to the nth interior environment interference factor, the nth layout interference factor and the nth sensing interference factor, a global analysis of the influence of the window inner side temperature is performed, and an nth interior interference global influence feature is obtained; According to the nth interior interference global influence feature, the nth vehicle window inner side temperature set is corrected to generate the nth vehicle window inner temperature detection matrix.

5. The temperature sensing detection method for thermal analysis of a window film thermal effectiveness as defined in claim 1, wherein, According to the N sunlight scenarios, the bare window temperature characteristics of the vehicle window with the attached thermal insulation film are sampled to construct N bare window temperature matrices, including: According to the vehicle window with the attached thermal insulation film, bare window characteristics are collected to obtain a bare window characteristic set, which includes bare window material characteristics, bare window structure characteristics and bare window installation location characteristics; According to the bare window characteristic set and the nth sunlight scenario, bare window inner and outer side temperature retrieval is performed to obtain an nth bare window inner side temperature retrieval set and an nth bare window outer side temperature retrieval set; According to the nth bare window inner side temperature retrieval set, temperature measurement scene interference compensation is performed to obtain an nth bare window inner side temperature matrix; According to the nth bare window outer side temperature retrieval set, temperature measurement scene interference compensation is performed to obtain an nth bare window outer side temperature matrix, which is combined with the nth bare window inner side temperature matrix to generate an nth bare window temperature matrix, and the nth bare window temperature matrix is added to the N bare window temperature matrices.

6. The temperature sensing detection method for thermal analysis of a window film thermal effectiveness as defined in claim 1, wherein, According to the N bare window temperature matrices and the N scene window temperature detection matrices, thermal insulation film thermal insulation effect analysis is performed to obtain N scene thermal insulation film performance coefficients, including: According to the N scene window temperature detection matrices, thermal insulation effect analysis is performed to obtain N scene thermal insulation coefficients; According to the N bare window temperature matrices, thermal insulation effect analysis is performed to obtain N bare window thermal insulation coefficients; According to the N bare window thermal insulation coefficients, the N scene thermal insulation coefficients are subjected to bare window thermal insulation interference correction to generate the N scene thermal insulation film performance coefficients.

7. The temperature sensing detection method for thermal analysis of a window film thermal effectiveness as defined in claim 6, wherein, According to the N scene window temperature detection matrices, thermal insulation effect analysis is performed to obtain N scene thermal insulation coefficients, including: The nth scene window temperature detection matrix is subjected to inner and outer feature alignment comparison to obtain an nth scene window temperature comparison matrix; An thermal insulation effect evaluation component is activated, which includes M thermal insulation effect evaluation models, M being a positive integer greater than 1; The nth scene window temperature comparison matrix is input into the M thermal insulation effect evaluation models to obtain M thermal insulation effect evaluation coefficients; The central value of the M thermal insulation effect evaluation coefficients is calculated to generate an nth scene thermal insulation coefficient, and the nth scene thermal insulation coefficient is added to the N scene thermal insulation coefficients.

8. The temperature sensing detection method for thermal analysis of a window film thermal effectiveness as defined in claim 1, wherein, According to the N sunlight scenarios, the N scene thermal insulation film performance coefficients are subjected to sensitivity correlation to generate a thermal insulation film detection report, including: According to the N sunshine scenarios, N expected thermal insulation film performance coefficients are set; According to the N expected thermal insulation film performance coefficients, sensitivity evaluation is performed on the N scene thermal insulation film performance coefficients to obtain N thermal insulation film performance sensitivity coefficients; The N bare car window temperature matrices, the N scene car window temperature detection matrices, the N scene thermal insulation film performance coefficients, and the N thermal insulation film performance sensitivity coefficients are sorted to generate the thermal insulation film detection report.

9. The temperature sensing detection method for thermal analysis of a window film thermal effectiveness as defined in claim 1, wherein, According to a predetermined sunshine factor, sunshine characteristics of a car window with a thermal insulation film are mined to obtain N sunshine scenarios, including: A user historical driving scene set of the car window with the thermal insulation film is called; According to the predetermined sunshine factor, feature extraction is performed on the user historical driving scene set to obtain a historical driving sunshine scene set; According to the historical driving sunshine scene set, frequency evaluation is performed in descending order to obtain a sunshine scene arrangement set, and the sunshine scene arrangement set is screened to generate the N sunshine scenarios.

10. The temperature sensing detection method for thermal analysis of a window film thermal effectiveness as defined in claim 1, wherein, The predetermined sunshine factor includes sunshine intensity, sunshine angle, and sunshine duration.

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