An intelligent processing method and system for radiator salt spray test data

Abstract

The present invention relates to the field of radiator testing, and particularly to an intelligent processing method and system for radiator salt spray test data. The method includes: obtaining test data during the salt spray test and performing preprocessing, respectively generating fitting curves of salt spray concentration data and corrosion current density data, obtaining the peak points of the fitting curve of the salt spray concentration data as segmentation points, and matching the two curves through the dynamic time warping algorithm, calculating the matching degree to determine the segmentation points on the fitting curve of the corrosion current density data; calculating the correlation value based on the time difference and the shortest path distance of the matching point pairs, dynamically determining the weight distribution of the salt spray concentration data and the corrosion current density data in the forgetting gate formula, and modifying the forgetting gate formula, and using the modified LSTM model to predict the test data. The present invention improves the adaptability and prediction accuracy of the model for complex data by calculating the correlation value of the corresponding segments of the two curves and dynamically adjusting the weights of the salt spray concentration data and the corrosion current density data in the forgetting gate.

Description

Technical Field

[0001] The present invention relates to the field of radiator testing. In particular, it relates to an intelligent processing method and system for salt spray test data of radiators. Background Art

[0002] As a key heat exchange component in various devices, radiators are applied in multiple fields such as industrial equipment, automobiles, and electronics. By efficiently conducting heat, the heat generated during the operation of the device is dissipated into the surrounding environment, which is crucial for ensuring the stable operation of the device, preventing overheating failures, and extending the service life.

[0003] However, the actual working environment of radiators is often complex and changeable. Especially in coastal areas and some special industrial environments, high-salt air makes salt spray corrosion a key factor affecting their performance and lifespan. To address this challenge, salt spray tests have emerged. Salt spray tests simulate the salt spray conditions in the natural environment and conduct artificial accelerated corrosion tests on radiators to evaluate their environmental adaptability in harsh environments. With the help of salt spray tests, it is possible to predict in advance the performance of radiators during long-term use in the laboratory environment, thus ensuring that they can still maintain a stable and reliable working state in different environments and meet the operation requirements of various devices in diverse scenarios.

[0004] During the salt spray test process, in the prior art, an LSTM model is usually used to analyze and predict the data parameters of radiators. By learning the time series characteristics in the salt spray test data, the risk of a decrease in heat dissipation efficiency or poor contact can be detected in advance, and the time when the radiator reaches the critical corrosion depth in the salt spray environment can be estimated. However, the fixed forgetting gate in the LSTM model cannot distinguish between key memories and redundant memories and cannot adapt to the complex and changeable characteristics in the salt spray test data. Especially when processing data in different corrosion stages, it cannot effectively adjust the memory retention and forgetting strategies, resulting in inaccurate prediction results. Summary of the Invention

[0005] To solve the problem that the forgetting gate weight of the LSTM model is fixed and cannot adapt to the complex and changeable characteristics of salt spray test data, especially in different corrosion stages, it cannot effectively adjust the memory retention and forgetting strategies, affecting the prediction accuracy. The present invention provides solutions in the following aspects.

[0006] In a first aspect, an intelligent processing method for radiator salt spray test data includes: obtaining test data during the salt spray test and performing preprocessing, where the test data includes: data sequences of salt spray concentration data and corrosion current density data; fitting curves for the salt spray concentration data and the corrosion current density data in the test data to generate two fitting curves, and using the peak points of the fitting curves as segmentation points, matching the two fitting curves using the dynamic time warping algorithm and calculating the matching degree, determining the segmentation points on the other curve based on the matching degree to complete the corresponding segmentation of the fitting curves; obtaining the correlation values of each pair of corresponding segments according to the time difference and the shortest path distance of each pair of matching points in the corresponding segments, determining the weight distribution of the salt spray concentration data and the corrosion current density data in the forgetting gate formula based on the correlation values, and correcting the forgetting gate formula in the LSTM model to obtain the output result of the forgetting gate, and predicting the test data according to the output result of the forgetting gate.

[0007] By obtaining the test data in the salt spray test and preprocessing it, fitting the curves of the salt spray concentration data and the corrosion current density data, matching the curves using the dynamic time warping algorithm and calculating the matching degree, and determining the segmentation points on the other curve, the corresponding segmentation of the curves is realized. Calculating the correlation value based on the time difference and the shortest path distance of the matching point pairs, dynamically allocating the weights of the salt spray concentration data and the corrosion current density data in the forgetting gate formula accordingly, correcting the forgetting gate formula, and optimizing the prediction accuracy of the LSTM model. The segmentation matching accuracy is improved, and the adaptability and prediction ability of the model to different corrosion stages are enhanced.

[0008] Preferably, the preprocessing includes:

[0009] Aligning the test data according to the time stamp, filling in the missing parts of the test data using the linear interpolation method to ensure the integrity of the data, and normalizing the salt spray concentration data and the corrosion current density data to unify their numerical ranges.

[0010] Preferably, the fitting curves for the salt spray concentration data and the corrosion current density data in the test data to generate two fitting curves includes:

[0011] Using the least squares method to fit curves for the data sequences of the salt spray concentration data and the corrosion current density data respectively to obtain the salt spray concentration data - time curve and the corrosion current density data - time curve.

[0012] The data sequences of the salt spray concentration data and the corrosion current density data are respectively curve-fitted by the least squares method to generate a salt spray concentration data-time curve and a corrosion current density data-time curve. Further, the dynamic time warping algorithm is used to match the two fitted curves. By accurately fitting and matching the curves, accurate data support is provided for the subsequent analysis of the relationship between the salt spray concentration data and the corrosion current density data, improving the adaptability and prediction ability of the model for different corrosion stages.

[0013] Preferably, the matching of the two fitted curves using the dynamic time warping algorithm includes:

[0014] For the fitted curve of the salt spray concentration data, the first derivative detection method is used to mark all the peak points on the curve, and the peak points are used as the segmentation points of the fitted curve, and segmentation is performed.

[0015] The dynamic time warping algorithm is used to match the data points on the two curves, calculate the distances between all the data points on the two curves, construct a distance matrix, start from the upper left corner of the distance matrix, and gradually calculate the minimum cumulative distance when reaching each data point along the shortest path to form a cumulative distance matrix. Starting from the lower right corner of the cumulative distance matrix, the path with the minimum cumulative distance is used as the optimal path. According to the principle of the closest distance, the corresponding matching points and distance values of the data points on the two curves are obtained, and the matching of the fitted curves of the salt spray concentration data and the corrosion current density data is completed.

[0016] Marking the peak points of the salt spray concentration data curve by the first derivative detection method and using them as the segmentation points can automatically identify the key feature points of the curve and ensure the rationality of segmentation. The dynamic time warping algorithm calculates the distances between all the data points on the two curves, constructs a distance matrix, and finds the optimal path through dynamic programming to achieve accurate matching of the curves, effectively solving the similarity measurement problem caused by different time series lengths or speeds, and improving the accuracy and efficiency of curve fitting and matching.

[0017] Preferably, the matching of the two fitted curves using the dynamic time warping algorithm includes:

[0018] Taking any segmentation point on the fitted curve of the salt spray concentration data as the target segmentation point, in response to the target segmentation point on the fitted curve of the salt spray concentration data corresponding to multiple matching points on the fitted curve of the corrosion current density data, the matching point corresponding to the maximum value of the matching degree is selected as the segmentation point on the corrosion current density data-time curve.

[0019] By improving the accuracy of segmented matching, ensuring the correspondence of key feature points, and making subsequent analysis more targeted. By selecting the point with the maximum matching degree as the segmentation point, the corresponding position on the corrosion current density data curve can be accurately located, enhancing the reliability of segmented matching. The adaptability of the model to the complex features of salt spray test data is enhanced, and the prediction accuracy is improved.

[0020] Preferably, the matching degree includes:

[0021] Calculate the second derivative of each matching point on the fitting curve of the corrosion current density data, calculate the Euclidean distance between the segmentation point on the fitting curve of the salt spray concentration data and the matching point on the fitting curve of the corrosion current density data, map the Euclidean distance using a negative exponential function, and normalize the product of the mapped Euclidean distance and the second derivative as the matching degree between the segmentation point and the matching point.

[0022] Preferably, the correlation value includes:

[0023] Taking any pair of segments as the target segment, calculate the shortest Euclidean distance between all segmentation points and matching points in the target segment, take the reciprocal of the shortest Euclidean distance, and normalize it to obtain the similarity weight; calculate the ratio of the standard deviation of the time difference between each pair of matching points in the target segment to the mean of the time difference between each pair of matching points in the target segment to obtain the coefficient of variation of the time difference; calculate the average value between the matching degree of the left segmentation point and the matching point and the matching degree of the right segmentation point and the matching point in the target segment to obtain the comprehensive matching degree;

[0024] Take the product of multiplying the similarity weight, the coefficient of variation of the time difference, and the comprehensive matching degree respectively as the correlation value of the target segment.

[0025] Preferably, the determining the weight allocation of the salt spray concentration data and the corrosion current density data in the forget gate formula based on the correlation value includes:

[0026] Perform a weighted average of the correlation values of all segments as the weight of the salt spray concentration data in the forget gate , and take 1 minus the weight of the salt spray concentration data in the forget gate as the weight of the corrosion current density data in the forget gate formula ;

[0027] In response to the weight of the salt spray concentration data in the forget gate approaching 1, the LSTM model prediction focuses more on the influence of the salt spray concentration data on the corrosion current density data. In response to the weight of the salt spray concentration data in the forget gate approaching 0, the LSTM model prediction pays more attention to the historical trend of the corrosion current density data.

[0028] Dynamically allocate weights through the correlation of different segments, so as to more flexibly cope with the complex changes of salt spray test data. When the weight of the salt spray concentration data approaches 1, the model focuses on the influence of salt spray concentration data on corrosion current density data, and can more sensitively capture the driving effect of external environmental changes on corrosion; while when approaches 0, the model pays more attention to the historical trend of corrosion current density data, which is beneficial to maintaining the stability of prediction when the data fluctuates or the environment changes. The adaptive weight adjustment not only improves the prediction accuracy of the model for different corrosion stages, but also enhances the overall generalization ability and robustness of the model, ensuring that the prediction results are closer to the actual corrosion process.

[0029] Preferably, the output result of the forgetting gate includes:

[0030] Multiply the salt spray concentration data at the current moment of the forgetting gate, the corresponding weight influence matrix, and the weight of the salt spray concentration data in the forgetting gate respectively, and use the product as the contribution value of the salt spray concentration data at the current moment; multiply the corrosion current density data at the current moment of the forgetting gate, the corresponding weight influence matrix, and the weight of the corrosion current density data in the forgetting gate respectively, and use the product as the contribution value of the corrosion current density data at the current moment; calculate the product between the hidden state at the previous moment and the weight matrix of the hidden state at the current moment to obtain the contribution value of the hidden state at the previous moment; sum up the contribution value of the salt spray concentration data, the contribution value of the corrosion current density data, the contribution value of the hidden state at the previous moment, and the bias term respectively, and use an activation function to obtain the output result of the corrected forgetting gate at the current moment.

[0031] In a second aspect, an intelligent processing method and system for radiator salt spray test data includes: a processor and a memory, and the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned intelligent processing method for radiator salt spray test data is implemented.

[0032] The present invention has the following effects:

[0033] 1. The present invention determines the peak point of the salt spray concentration data curve as the segmentation point through the derivative detection method, and matches the two curves through the DTW algorithm, calculates the matching degree between the matching point and the segmentation point, so as to find the segmentation point on the corrosion current density data curve with the highest matching degree with the segmentation point of the salt spray concentration data curve. It effectively improves the accuracy of segmentation matching, ensures the maximum similarity of local segmentation features, and provides a more reliable data basis for subsequent analysis.

[0034] 2. By calculating the correlation values between corresponding segments of two curves, the present invention dynamically determines the weight distribution of salt spray concentration data and corrosion current density data in the forgetting gate formula, enabling the LSTM model to more accurately capture the relationship between salt spray concentration data and corrosion current density data, especially in different corrosion stages. Through the weight adjustment strategy, the model can better adapt to the complex and variable characteristics of salt spray test data, improving the adaptability and prediction accuracy of the model for different corrosion stages. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 FIG. is a flowchart of the method for steps S1 - S3 in an intelligent processing method for radiator salt spray test data according to an embodiment of the present invention.

[0036] Figure 2 FIG. is a structural block diagram of an intelligent processing system for radiator salt spray test data according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention.

[0038] Refer to Figure 1 , an intelligent processing method for radiator salt spray test data includes steps S1 - S3, specifically as follows:

[0039] During the radiator salt spray test, in order to evaluate the degree of performance loss of the radiator in the salt spray environment and predict the time when the radiator reaches the critical corrosion depth in the salt spray environment, it is necessary to align the timestamps of the data sequences of salt spray concentration data and corrosion current density data changing with time for subsequent synchronous analysis. The specific steps are as follows:

[0040] S1: Obtain the test data during the salt spray test and perform pre - processing. Among them, the test data includes: data sequences of salt spray concentration data and corrosion current density data.

[0041] Align the test data according to the timestamps, use the linear interpolation method to fill in the missing parts in the test data to ensure the integrity of the data, and normalize the salt spray concentration data and corrosion current density data to make their numerical ranges unified.

[0042] By filling in the missing values through linear interpolation and normalization processing, the data becomes more complete and consistent. Timestamp alignment and data pre - processing ensure the synchronization and comparability between different data parameters, which helps to more accurately evaluate the corrosion situation and performance loss of the radiator.

[0043] S2: Fit curves for the salt spray concentration data and corrosion current density data in the test data to generate two fitted curves. Use the peak points of the fitted curves as segmentation points, and use the dynamic time warping algorithm to match the two fitted curves and calculate the matching degree. Based on the matching degree, determine the segmentation points on the other curve to complete the segmented correspondence of the fitted curves.

[0044] Using the least squares method, fit curves for the data sequences of the salt spray concentration data and corrosion current density data respectively to obtain the salt spray concentration data-time curve and the corrosion current density data-time curve.

[0045] For the fitted curve of the salt spray concentration data, use the first derivative detection method to mark all the peak points on the curve, and use the peak points as the segmentation points of the fitted curve and conduct segmentation;

[0046] It should be noted that in the salt spray test, the curve of the salt spray concentration data changing with time usually shows the characteristics of undulating fluctuations. In the initial stage of corrosion, the salt spray concentration data often gradually increases. However, after reaching a specific threshold, due to the formation of corrosion products and the influence of other relevant factors, the salt spray concentration data may show a certain degree of fluctuation. Taking the study on the cyclic salt spray accelerated corrosion behavior of high-strength aluminum alloy as an example, the change trend of the corrosion current density data shows a fluctuating increase first and then a decrease, which indicates that the change of the salt spray concentration data is not fixed but complex and variable. Based on this characteristic, the peak points on the salt spray concentration data curve can be accurately determined through the extreme value detection algorithm, and these peak points can be used as segmentation points to conduct a more detailed segmented analysis of the salt spray concentration data curve.

[0047] At the same time, the curve of the corrosion current density data changing with time mostly shows a monotonically increasing trend. However, this change trend will also vary due to different salt spray concentration data. Specifically, under the condition of lower salt spray concentration data, the corrosion current density data will show a gradually increasing trend over time; while when the salt spray concentration data is at a higher level, the rising speed of the corrosion current density data will be significantly accelerated. It should be noted that although the corrosion current density data curve as a whole shows a monotonically increasing characteristic, it will not have obvious peak points like the salt spray concentration data curve.

[0048] To achieve the piecewise correspondence of two curves, the dynamic time warping (DTW) algorithm was introduced in the experiment. Through this algorithm, the piecewise points on the salt spray concentration data curve can be accurately matched with the corresponding positions on the corrosion current density data curve. Specifically, by using the DTW algorithm to match the two fitted curves, the corresponding piecewise positions on the corrosion current density data curve can be determined, thus realizing the piecewise correspondence between the salt spray concentration data curve and the corrosion current density data curve. This process not only fully considers the complex relationship between the salt spray concentration data and the corrosion current density data, but also lays a solid foundation for the subsequent corrosion behavior analysis and prediction.

[0049] Use the dynamic time warping algorithm to match the data points on the two curves, calculate the distances between all data points on the two curves, construct a distance matrix, start from the upper left corner of the distance matrix, and gradually calculate the minimum cumulative distance when reaching each data point along the shortest path to form a cumulative distance matrix. Start from the lower right corner of the cumulative distance matrix, take the path of the minimum cumulative distance as the optimal path, and according to the principle of the closest distance, obtain the corresponding matching points and distance values of the data points on the two curves, and complete the matching of the fitted curves of the salt spray concentration data and the corrosion current density data.

[0050] Taking any piecewise point on the fitted curve of the salt spray concentration data as the target piecewise point, in response to the target piecewise point on the fitted curve of the salt spray concentration data corresponding to multiple matching points on the fitted curve of the corrosion current density data, the matching point corresponding to the maximum value of the matching degree is selected as the piecewise point on the corrosion current density data-time curve.

[0051] The matching degree includes:

[0052] Calculate the second derivative of each matching point on the corrosion current density data-time curve, calculate the Euclidean distance between the piecewise point on the salt spray concentration data-time curve and the matching point on the corrosion current density data-time curve, map the Euclidean distance using the negative exponential function, and normalize the product of the mapped Euclidean distance and the second derivative as the matching degree between the piecewise point and the matching point.

[0053] Specifically, the matching degree satisfies the following relational expression:

[0054] ;

[0055] In the formula, represents the matching degree between the piecewise point and the matching point , represents the second derivative of the th matching point of the piecewise point on the corrosion current density data-time curve, denotes the exponential function with the natural number as the base, denotes the Euclidean distance between the segmentation point and the matching point in the DTW matching algorithm, denotes the normalization function.

[0056] That is to say, denotes the correlation and reliability of the matching points on the corrosion current density data curve, is the second derivative of the matching points on the corrosion current density data curve, reflecting the degree of change in the corrosion rate. The larger it is, the greater the curvature of the corrosion current density data at this point and the more drastic the change in the corrosion rate. Such points usually correspond to the key turning points in the corrosion process and are closely related to the change in corrosion behavior caused by the change in salt spray concentration data.

[0057] is the Euclidean distance between the segmentation point of the salt spray concentration data curve and the matching point of the corrosion current density data curve. The smaller the Euclidean distance, the closer the two points are in space and the more consistent the change trend of the corrosion current density data is with the change trend of the salt spray concentration data.

[0058] When is larger and is smaller, is naturally larger, indicating that the matching point is both a key point with a significant change in corrosion rate on the corrosion current density data curve and has a high similarity and correlation with the segmentation point on the salt spray concentration data curve in the time series. Therefore, it is suitable as the corresponding segmentation point. Considering both the change in corrosion rate and the proximity of the two points, it is used to evaluate whether the matching point is suitable as the segmentation point.

[0059] S3: According to the time difference and the shortest path distance of each pair of matching points in the corresponding segmentations, obtain the correlation values of each pair of corresponding segments. Based on the correlation values, determine the weight allocation of the salt spray concentration data and the corrosion current density data in the forgetting gate formula, and correct the forgetting gate formula. Predict the experimental data according to the output result of the corrected forgetting gate in the LSTM model.

[0060] The correlation values include:

[0061] Taking any pair of segments as the target segment, calculate the shortest Euclidean distance between all segment points and matching points in the target segment, take the reciprocal of the shortest Euclidean distance, and perform normalization to obtain the similarity weight; calculate the ratio between the standard deviation of the time differences of each pair of matching points in the target segment and the mean of the time differences of each pair of matching points in the target segment to obtain the coefficient of variation of the time differences; calculate the average value between the matching degree of the left segment point and the matching point and the matching degree of the right segment point and the matching point in the target segment to obtain the comprehensive matching degree.

[0062] Multiply the similarity weight, the coefficient of variation of the time differences, and the comprehensive matching degree respectively, and use the product as the correlation value of the target segment.

[0063] Specifically, the correlation value satisfies the following relational expression:

[0064] ;

[0065] In the formula, represents the correlation value between the th pair of segments, represents the shortest Euclidean distance in the th pair of segments, represents the total number of pairs of matching points in the th pair of segments, represents the time difference between the th pair of matching points in the th pair of segments, represents the mean of the time differences between all pairs of matching points in the th pair of segments, represents the matching degree between the left segment point and the matching point in the th pair of segments, represents the matching degree between the right segment point and the matching point in the th pair of segments, represents the normalization function, represents the exponential function with the natural number as the base.

[0066] That is to say, the larger the value, the greater the distance between the corresponding segments of the two curves, and the lower their similarity. Therefore, is negatively correlated with the correlation value is the The coefficient of variation of the time difference of the matching point pairs in the corresponding segment of the curve can reflect the degree of dispersion of the time difference of the matching point pairs. Since the influence of the salt spray concentration data on the corrosion current density data has a lag, the time difference between the matching point pairs is the length of the lag period for the salt spray concentration data to cause changes in the corrosion current density data. The more consistent the lag period length in the corresponding segment, the smaller the coefficient of variation. At this time, the greater the influence of the salt spray concentration data on the change of the corrosion current density data, the stronger the correlation. The greater it is, the greater the influence of the salt spray concentration data on the change of the corrosion current density data in the corresponding segment, and the stronger the correlation.

[0067] Based on the correlation value, determine the weight allocation of the salt spray concentration data and the corrosion current density data in the forget gate formula, including:

[0068] Perform a weighted average of the correlation values of all segments as the weight of the salt spray concentration data in the forget gate , and use 1 minus the weight of the salt spray concentration data in the forget gate as the weight of the corrosion current density data in the forget gate formula .

[0069] It should be noted that the influence degree of the salt spray concentration data at different time periods on the change of the corrosion current density data is different. Therefore, based on the weight matrix obtained from model training, the forget gate parameters are adjusted secondarily according to the correlation between the salt spray concentration data and the corrosion current density data. In response to approaching 1, the model pays more attention to the influence of the salt spray concentration data on the corrosion process. In response to approaching 0, the model relies more on the historical trend of the corrosion current density data for prediction.

[0070] Modify the forget gate formula in the LSTM model to obtain the output result of the forget gate, including:

[0071] Multiply the salt spray concentration data at the current moment of the forget gate, the corresponding weight influence matrix, and the weight of the salt spray concentration data in the forget gate respectively, and use the product as the contribution value of the salt spray concentration data at the current moment; multiply the corrosion current density data at the current moment of the forget gate, the corresponding weight influence matrix, and the weight of the corrosion current density data in the forget gate respectively, and use the product as the contribution value of the corrosion current density data at the current moment; calculate the product between the hidden state at the previous moment and the weight matrix of the hidden state at the current moment to obtain the contribution value of the hidden state at the previous moment; sum up the contribution value of the salt spray concentration data, the contribution value of the corrosion current density data, the contribution value of the hidden state at the previous moment, and the bias term, and use the activation function to obtain the output result of the corrected forget gate at the current moment.

[0072] Specifically, the output result of the corrected forget gate satisfies the following relational expression:

[0073] ;

[0074] In the formula, represents the output result of the forget gate, represents the activation function, represents the weight of the salt fog concentration data, represents the weight influence matrix of the salt fog concentration data, represents the salt fog concentration data, represents the weight of the corrosion current density data, is the weight influence matrix of the corrosion current density data, represents the corrosion current density data, represents the weight matrix of the previous moment's hidden state, represents the previous moment's hidden state, represents the bias term of the forget gate.

[0075] Based on the improved forget gate formula, use the LSTM model to predict the corrosion current density data and calculate the corrosion depth. When the corrosion depth reaches the preset depth corrosion threshold, mark the corresponding time as the time when the radiator reaches the critical corrosion depth in the salt fog environment.

[0076] It should be noted that the prediction process of the LSTM model includes:

[0077] Preprocess the data sequences of the obtained salt fog concentration data and corrosion current density data, and use them as input data to train the preset LSTM model. The process of training the model is well-known to those skilled in the art and will not be described in detail;

[0078] The LSTM model structure is: forget gate: determines how much information of the previous moment's cell state is retained, input gate: controls the degree of update of the current input to the cell state, and output gate: determines the hidden state at the current moment.

[0079] Replace the forget gate formula in the existing trained LSTM model with the corrected forget gate of the present invention, input the data sequences of the salt fog concentration data and corrosion current density data to be predicted into the LSTM model with the corrected forget gate, and the output result is: corrosion current density data. Calculate the corrosion depth according to the prediction result, so as to judge the time to reach the critical corrosion depth in the salt fog environment.

[0080] Exemplarily, the preset depth corrosion threshold is half of the thickness of the radiator surface material. Specifically, the implementer can set it according to the specifications of the radiator material.

[0081] Specifically, the corrosion depth satisfies the following relational expression:

[0082] ;

[0083] In the formula, represents the corrosion depth, represents the corrosion current density data, represents the time of the corrosion test, represents the molar mass of the radiator material, represents the oxidation state of the radiator material in the corrosion reaction, represents the Faraday constant, represents the density of the radiator material.

[0084] It should be noted that the corrosion current density data (unit: ); the molar mass (unit: ); the oxidation state of the material in the corrosion reaction (i.e., the number of electrons lost), for example, aluminum is usually +3 in corrosion, then ; the Faraday constant ( ); the density of the radiator material (unit: ), for example, aluminum ( ).

[0085] It should be noted that the corrosion depth is not the focus of the present invention, and there are various ways to calculate the corrosion depth, which will not be described in detail here.

[0086] The present invention also provides an intelligent processing system for radiator salt spray test data. As Figure 2 shown, the system includes a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement an intelligent processing method for radiator salt spray test data according to the first aspect of the present invention. The system also includes a communication bus, a communication interface, and other components well known to those skilled in the art. Their settings and functions are known in the art, so they will not be described in detail here.

[0087] It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the appended claims.

Claims

1. An intelligent processing method for radiator salt spray test data, characterized in that Including: Obtain the test data during the salt spray test and perform preprocessing. Among them, the test data includes: the data sequences of salt spray concentration data and corrosion current density data; Fit curves for the salt spray concentration data and the corrosion current density data in the test data to generate two fitted curves, and use the peak points of the fitted curves as segmentation points. Use the dynamic time warping algorithm to match the two fitted curves and calculate the matching degree. Based on the matching degree, determine the segmentation points on the other curve to complete the corresponding segmentation of the fitted curves; According to the time differences and the shortest path distances of each pair of matching points in the corresponding segments, obtain the correlation values of each corresponding segment. Based on the correlation values, determine the weight distribution of the salt spray concentration data and the corrosion current density data in the forgetting gate formula, and correct the forgetting gate formula in the LSTM model to obtain the output result of the forgetting gate. Predict the test data according to the output result of the forgetting gate; The output result of the forget gate includes: multiplying the salt spray concentration data at the current moment of the forget gate, the corresponding weight influence matrix, and the weight of the salt spray concentration data in the forget gate respectively to obtain the contribution value of the salt spray concentration data at the current moment; multiplying the corrosion current density data at the current moment of the forget gate, the corresponding weight influence matrix, and the weight of the corrosion current density data in the forget gate respectively to obtain the contribution value of the corrosion current density data at the current moment; calculating the product between the hidden state at the previous moment and the weight matrix of the hidden state at the current moment to obtain the contribution value of the hidden state at the previous moment; summing up the contribution value of the salt spray concentration data, the contribution value of the corrosion current density data, the contribution value of the hidden state at the previous moment, and the bias term respectively, and using the activation function to obtain the output result of the corrected forget gate at the current moment.

2. The intelligent processing method for the salt spray test data of a radiator according to claim 1, wherein The preprocessing includes: Align the test data according to the timestamp, use the linear interpolation method to fill in the missing parts in the test data to ensure the integrity of the data, and normalize the salt spray concentration data and the corrosion current density data to unify their numerical ranges.

3. The intelligent processing method for radiator salt spray test data according to claim 1, wherein, The fitting of curves for the salt spray concentration data and the corrosion current density data in the test data to generate two fitted curves includes: Use the least squares method to fit curves for the data sequences of the salt spray concentration data and the corrosion current density data respectively to obtain the salt spray concentration data-time curve and the corrosion current density data-time curve.

4. An intelligent processing method for radiator salt spray test data according to claim 1, characterized in that The use of the dynamic time warping algorithm to match the two fitted curves includes: Use the first derivative detection method for the fitted curve of the salt spray concentration data, mark all the peak points on the curve, and use the peak points as the segmentation points of the fitted curve and perform segmentation; Use the dynamic time warping algorithm to match the data points on the two curves, calculate the distances between all the data points on the two curves, construct a distance matrix, start from the upper left corner of the distance matrix, and gradually calculate the minimum cumulative distance when reaching each data point along the shortest path to form a cumulative distance matrix. Start from the lower right corner of the cumulative distance matrix, use the path of the minimum cumulative distance as the optimal path, and obtain the corresponding matching points and distance values of the data points on the two curves according to the principle of the closest distance to complete the matching of the fitted curves of the salt spray concentration data and the corrosion current density data.

5. The intelligent processing method for radiator salt spray test data according to claim 1, wherein, The use of the dynamic time warping algorithm to match the two fitted curves includes: Take any segmentation point on the fitted curve of the salt spray concentration data as the target segmentation point. In response to the fact that there are multiple corresponding matching points on the fitted curve of the salt spray concentration data on the fitted curve of the corrosion current density data, select the matching point corresponding to the maximum value of the matching degree as the segmentation point on the corrosion current density data-time curve.

6. The intelligent processing method for radiator salt spray test data according to claim 1, characterized in that, The matching degree includes: Calculate the second derivative of each matching point on the fitting curve of the corrosion current density data, calculate the Euclidean distance between the segmentation points on the fitting curve of the salt spray concentration data and the matching points on the fitting curve of the corrosion current density data, map the Euclidean distance using a negative exponential function, and normalize the product of the mapped Euclidean distance and the second derivative as the matching degree between the segmentation point and the matching point.

7. An intelligent processing method for radiator salt spray test data according to claim 1, characterized in that The correlation value includes: Taking any pair of segments as the target segment, calculate the shortest Euclidean distance between all segmentation points and matching points in the target segment, take the reciprocal of the shortest Euclidean distance, and normalize it to obtain the similarity weight; calculate the ratio between the standard deviation of the time difference of each matching point pair in the target segment and the mean value of the time difference of each matching point pair in the target segment to obtain the coefficient of variation of the time difference; calculate the average value between the matching degree of the left segmentation point and the matching point and the matching degree of the right segmentation point and the matching point in the target segment to obtain the comprehensive matching degree. Take the product of multiplying the similarity weight, the coefficient of variation of the time difference, and the comprehensive matching degree respectively as the correlation value of the target segment.

8. The intelligent processing method for radiator salt spray test data according to claim 1, characterized in that The determination of the weight allocation of the salt spray concentration data and the corrosion current density data in the forgetting gate formula based on the correlation value includes: Perform a weighted average of all segmented correlation values as the weight of the salt spray concentration data in the forgetting gate , and use 1 minus the weight of the salt spray concentration data in the forgetting gate as the weight of the corrosion current density data in the forgetting gate formula ; The weight of the salt spray concentration data in the forget gate When approaching 1, the LSTM model prediction focuses more on the influence of the salt spray concentration data on the corrosion current density data, and the weight of the salt spray concentration data in the forget gate When approaching 0, the LSTM model prediction pays more attention to the historical trend of the corrosion current density data.

9. An intelligent processing system for radiator salt spray test data, characterized in that, Including: A processor and a memory, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the intelligent processing method for radiator salt spray test data according to any one of claims 1-8 is implemented.

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