Multi-dimensional feature fusion oil product detection data optimization processing method and system

Abstract

The invention provides an oil product detection data optimization processing method and system based on multi-dimensional feature fusion, and relates to the technical field of data processing.The method comprises the steps that a target oil product sample is subpackaged and detected, a plurality of subpackaged sample detection data sets are obtained, data cleaning is carried out, a plurality of preprocessed subpackaged sample detection data sets are obtained, and a plurality of preprocessed subpackaged sample detection data sets are obtained; respectively carrying out data cross enhancement processing in the set, carrying out multi-dimensional feature cross-modal fusion analysis based on multiple pieces of enhanced subpackage sample detection data to obtain an initial oil product sample detection result of the target oil product sample, carrying out equipment reliability verification, and when the equipment reliability verification is passed, carrying out data cross-modal fusion analysis on the initial oil product sample detection result of the target oil product sample; and taking the initial oil product sample detection result as a target oil product sample detection result. The technical problems of noise interference and information redundancy existing in oil product detection multi-source heterogeneous data in the prior art are solved. The technical effects of effectively improving the fusion precision of the multi-source heterogeneous oil product detection data and improving the accuracy and reliability of the oil product quality detection result are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a multi-dimensional feature fusion oil detection data optimization processing method and system. BACKGROUND

[0002] Oil is an important medium for industrial equipment lubrication, energy supply and chemical production, and its physicochemical properties directly affect the efficiency and service life of the equipment. High-quality oil can improve combustion efficiency, reduce equipment wear and tear, and reduce tail gas pollution. The existing multi-dimensional feature fusion oil detection improves the comprehensiveness of the detection quality. However, by collecting the characteristic information of oil in multiple dimensions such as spectrum, chemistry and physical parameters, comprehensive evaluation of oil degradation, pollution and performance changes is realized, and there are problems such as complex oil detection data sources, and high redundancy and noise interference between multi-dimensional features, which affect the accuracy and reliability of the oil quality detection results.

[0003] The noise interference and information redundancy of the existing technology oil detection multi-source heterogeneous data cause the technical problem of low accuracy and reliability of the oil quality detection results. SUMMARY

[0004] The purpose of the present application is to provide a multi-dimensional feature fusion oil detection data optimization processing method and system to solve the technical problem of low accuracy and reliability of the oil quality detection results caused by the noise interference and information redundancy of the existing technology oil detection multi-source heterogeneous data.

[0005] In view of the above problems, the present application provides a multi-dimensional feature fusion oil detection data optimization processing method and system.

[0006] The first aspect of the present application provides a multi-dimensional feature fusion oil detection data optimization processing method, which comprises: using an automatic sample splitting device to split a target oil sample according to a preset splitting requirement and send it to multiple detection devices for detection to obtain multiple split sample detection data sets, wherein the preset splitting requirement is to divide at least two split samples for each detection device; traversing the multiple split sample detection data sets for data cleaning preprocessing to obtain multiple preprocessed split sample detection data sets; performing intra-set data cross-enhancement processing on the multiple preprocessed split sample detection data sets respectively to determine multiple enhanced split sample detection data; performing multi-dimensional feature cross-modal fusion analysis based on the multiple enhanced split sample detection data to obtain an initial oil sample detection result of the target oil sample; performing equipment reliability verification according to the initial oil sample detection result, and when the equipment reliability verification is passed, taking the initial oil sample detection result as the target oil sample detection result.

[0007] Optionally, a first pre-processed sub-packed sample detection data set is extracted from a plurality of pre-processed sub-packed sample detection data sets; data non-missing and non-repeated cross correlation is performed on the first pre-processed sub-packed sample detection data set to obtain a first data cross correlation group set; cross enhancement processing is performed on each data cross correlation group in the first data cross correlation group set to obtain a first initial enhanced sub-packed sample detection data group set; a mean value of the first initial enhanced sub-packed sample detection data group set is calculated to obtain a first enhanced sub-packed sample detection data, and the first enhanced sub-packed sample detection data is added to the plurality of enhanced sub-packed sample detection data.

[0008] Optionally, similarity analysis is respectively performed on the same type of data in each data cross correlation group in the first data cross correlation group set to obtain a first similarity group set; in-group similarity normalization processing is respectively performed on the first similarity group set, and the processing result is added to an initially empty matrix to obtain a first cross enhancement processing matrix set; cross enhancement is respectively performed on the first data cross correlation group set by using the first cross enhancement processing matrix set to obtain the first initial enhanced sub-packed sample detection data group set.

[0009] Optionally, a convolutional enhancement network layer is pre-constructed; the first cross enhancement processing matrix set and the corresponding first data cross correlation group in the first data cross correlation group set are transmitted to the convolutional enhancement network layer for analysis to obtain the first initial enhanced sub-packed sample detection data group set.

[0010] Optionally, based on a feature extraction mechanism, different modal data of each enhanced sub-packed sample detection data in the plurality of enhanced sub-packed sample detection data are feature extracted according to different detection types to construct a plurality of multi-modal features; a multi-modal coupling feature is obtained by using an attention mechanism to cross-modal weight coupling the plurality of multi-modal features; and the initial oil product sample detection result is obtained by performing detection analysis on the multi-modal coupling feature.

[0011] Optionally, the feature extraction mechanism is principal component analysis on spectral data, statistical feature extraction on physical data, and standardized index analysis on chemical features.

[0012] Optionally, M abnormal items in the initial oil product sample detection result are extracted, wherein M is a positive integer; M historical detection log sets are obtained by extracting M detection equipment corresponding to the M abnormal items according to the same type of oil product historical detection log; M clustering trend features are determined by performing clustering trend identification on the M historical detection log sets; and if the similarity between M abnormal features of the M abnormal items and the M clustering trend features does not satisfy a preset similarity threshold, the device reliability verification is passed.

[0013] Optionally, the mean values of the M historical detection log sets are calculated to obtain M mean shift centers; the M historical detection log sets are subjected to cluster trend identification by using a mean shift algorithm in combination with the M mean shift centers to determine M cluster trend features.

[0014] Optionally, the multiple split sample detection data sets are subjected to structural analysis to be converted into standardized data structures to obtain multiple split sample detection standardized data sets; the multiple split sample detection standardized data sets are subjected to outlier elimination or baseline correction respectively to obtain the multiple pretreated split sample detection data sets.

[0015] In a second aspect of the present application, a multi-dimensional feature fusion oil product detection data optimization processing system is provided, which comprises: a detection data obtaining module configured to split a target oil product sample into multiple split samples according to preset split requirements by using an automatic sample splitting device and send the split samples into multiple detection devices for detection to obtain multiple split sample detection data sets, wherein the preset split requirements are to divide at least two split samples for each detection device; a data cleaning module configured to clean and pretreat the multiple split sample detection data sets to obtain multiple pretreated split sample detection data sets; a data processing module configured to perform intra-set data cross-enhancement processing on the multiple pretreated split sample detection data sets respectively to determine multiple enhanced split sample detection data; an initial detection result obtaining module configured to perform multi-dimensional feature cross-modal fusion analysis based on the multiple enhanced split sample detection data to obtain an initial oil product sample detection result of the target oil product sample; and an oil product detection result obtaining module configured to perform equipment reliability verification according to the initial oil product sample detection result, and when the equipment reliability verification is passed, take the initial oil product sample detection result as a target oil product sample detection result.

[0016] The one or more technical solutions provided in the present application have at least the following technical effects or advantages: The method provided by the embodiment of the application is used to divide the target oil sample according to preset division requirements and send it into multiple detection devices for detection to obtain multiple division sample detection data sets, wherein the preset division requirements are to divide at least two division samples for each detection device; data cleaning preprocessing is performed on the multiple division sample detection data sets to obtain multiple preprocessed division sample detection data sets; the multiple preprocessed division sample detection data sets are subjected to cross-enhancement processing within the sets respectively to determine multiple enhanced division sample detection data; multi-dimensional feature cross-modal fusion analysis is performed based on the multiple enhanced division sample detection data to obtain an initial oil sample detection result of the target oil sample; device reliability verification is performed according to the initial oil sample detection result, and when the device reliability verification is passed, the initial oil sample detection result is taken as a target oil sample detection result. The technical effect of effectively improving the fusion precision of multi-source heterogeneous oil detection data and further improving the accuracy and reliability of oil quality detection results is achieved.

[0017] The above description is only a summary of the technical solutions of the application. In order to more clearly understand the technical means of the application, the specific embodiments of the application can be implemented according to the content of the description, and in order to make the above and other purposes, characteristics and advantages of the application more obvious and easy to understand, the following specific embodiments of the application are described. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the application, nor is it intended to limit the scope of the application. Other features of the application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only exemplary, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0019] Figure 1 The flowchart of the multi-dimensional feature fusion oil detection data optimization processing method provided by the application.

[0020] Figure 2 The structure diagram of the multi-dimensional feature fusion oil detection data optimization processing system provided by the application.

[0021] Explanation of reference signs: detection data obtaining module 11, data cleaning module 12, data processing module 13, initial detection result obtaining module 14, oil detection result obtaining module 15. DETAILED DESCRIPTION

[0022] This application provides a method and system for optimizing oil product testing data through multi-dimensional feature fusion. This method addresses the technical problem of low accuracy and reliability in oil product quality testing results due to noise interference and information redundancy in existing multi-source heterogeneous oil product testing data. The goal is to effectively improve the fusion accuracy of multi-source heterogeneous oil product testing data, thereby enhancing the accuracy and reliability of oil product quality testing results.

[0023] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, not all of them.

[0024] Example 1, as Figure 1 As shown, this application provides a method for optimizing oil product testing data through multi-dimensional feature fusion. The method includes: An automated sampling device is used to package the target oil sample according to a preset packaging requirement, and then send it to multiple testing devices for testing to obtain multiple sets of test data for the packaged samples. The preset packaging requirement is that each testing device must divide at least two packaged samples.

[0025] Specifically, the target oil sample is an oil medium to be detected, such as industrial equipment lubricating oil, fuel oil, synthetic oil, etc., wherein the industrial equipment lubricating oil is, for example, gear oil, hydraulic oil, grease diluent, etc. The fuel oil is, for example, diesel oil, gasoline, aviation kerosene, etc. The target oil sample is obtained from an actual equipment operation site or an oil storage tank. A preset dispensing requirement is determined according to an oil detection target. The preset dispensing requirement refers to the setting of dispensing volume, quantity and detection channel allocation rules during sample dispensing, including but not limited to: at least two dispensing samples are divided for each detection device to avoid accidental errors and ensure the stability of the detection results, and the volumes of the dispensing samples are consistent. The oil detection target includes spectral data detection, physical characteristic data detection and chemical group data detection, including oil moisture content determination, kinematic viscosity determination, acid value determination, dissolved gas component content determination, medium loss factor and volume resistivity determination, and antioxidant content determination. The automatic sample dispensing device is an intelligent liquid sample dispensing device with quantitative dispensing and multi-channel dispensing functions, including a sample input part, a dispensing control part, a dispensing execution part and a sample conveying part. The sample input part is used to receive the target oil sample. The dispensing control part accurately controls sample dispensing according to the preset dispensing requirement. The dispensing execution part performs specific dispensing operations to dispense the sample into different containers. The sample conveying part conveys the dispensed sample to each detection device.

[0026] The target oil sample is stirred uniformly, and then input into the automatic sample dispensing device for dispensing. The dispensing is performed according to the preset dispensing requirement, and the sample is dispensed into different containers, such as chromatographic vials, colorimetric tubes, beakers, viscosity tubes, etc. The dispensed samples are sent to multiple detection devices for detection. For example, after sample dispensing, the moisture content is determined by a micro-moisture tester; the kinematic viscosity of the dispensed sample is determined by a full-automatic kinematic viscosity instrument; after manual chemical pretreatment, the acid value of the dispensed sample is determined by a full-automatic potentiometric titrator; the dissolved gas component content of the dispensed sample is determined by a gas chromatograph; the medium loss factor, i.e. volume resistivity, is determined by a test cell method; after manual chemical pretreatment, the antioxidant content of the dispensed sample is determined by a spectrophotometer. Multiple detection devices are used to detect multiple dispensed samples to obtain a set of detection data of the dispensed samples. Each detection data corresponds to a dispensed sample.

[0027] Through automatic sample dispensing, the dispensing and detection tasks of the oil sample can be efficiently and accurately completed. At least two dispensed samples are divided for each detection device to avoid accidental errors and ensure that the detection data of at least two dispensed samples are mapped to each other, thereby improving the detection efficiency and the reliability of the results.

[0028] Data cleaning and pretreatment are performed on the set of dispensed sample detection data to obtain a set of pretreated dispensed sample detection data.

[0029] Further, the plurality of sub-packed sample detection data sets are traversed for structured parsing, converted into standardized data structures, and a plurality of sub-packed sample detection standardized data sets are obtained; the plurality of sub-packed sample detection standardized data sets are respectively subjected to outlier rejection or baseline correction, and the plurality of pre-processed sub-packed sample detection data sets are obtained.

[0030] Specifically, different detection equipment can output different formats of data, and there can be multiple data formats in the plurality of sub-packed sample detection data sets, such as CSV, TDMS, text log, binary spectrum file, database record, etc. The plurality of sub-packed sample detection data sets are traversed, and each sub-packed sample detection data is subjected to structured parsing processing. According to the data source and data type, the corresponding parsing method is used to parse the original detection data into processable intermediate data structures, such as data tables, arrays, etc. The parsed data is subjected to field mapping and unified processing, including the same field naming, data type conversion, timestamp format standardization, etc. The sub-packed sample detection data generated by different detection equipment is converted into a unified standard data structure, such as CSV format, to ensure that the fields in each data set have consistent naming and format.

[0031] Through structured parsing, a plurality of sub-packed sample detection standardized data sets are obtained, and the plurality of sub-packed sample detection standardized data sets are respectively subjected to data cleaning and preprocessing. Specifically, by using statistical methods such as mean, standard deviation, quartile, etc. to identify outliers, any data point exceeding 3 times the standard deviation of the mean is regarded as an outlier and is subjected to outlier rejection. Alternatively, the plurality of sub-packed sample detection standardized data sets are subjected to baseline correction. For spectral detection data, wavelet denoising or Savitzky-Golay filter is used for smoothing / denoising. For physical or time series data, high-pass filtering or polynomial fitting is used to remove slow drift for long time series, and moving average or Savitzky-Golay smoothing is used. If there is high-frequency electrical noise, a low-pass filter or wavelet denoising can be used. For example, the detection data of an infrared spectrum analyzer has baseline drift. By fitting a baseline, such as a polynomial fit, and subtracting the baseline from the original spectrum data, the corrected spectrum data can be obtained, ensuring the stability and accuracy of the characteristic data.

[0032] By respectively subjecting the plurality of sub-packed sample detection standardized data sets to outlier rejection or baseline correction, the quality of the original detection data is optimized, the quality and consistency of the detection data are improved, and the accuracy and stability of the oil product detection results are further improved.

[0033] The plurality of pre-processed sub-packed sample detection data sets are respectively subjected to intra-set data cross-enhancement processing, and a plurality of enhanced sub-packed sample detection data are determined.

[0034] Further, the plurality of pre-processed sub-packaged sample detection data sets are respectively subjected to intra-set data cross-enhancement processing to determine a plurality of enhanced sub-packaged sample detection data, including: extracting a first pre-processed sub-packaged sample detection data set from the plurality of pre-processed sub-packaged sample detection data sets; performing data non-missing non-repeated cross-correlation on the first pre-processed sub-packaged sample detection data set to obtain a first data cross-correlation group set; performing cross-enhancement processing on each data cross-correlation group in the first data cross-correlation group set to obtain a first initial enhanced sub-packaged sample detection data group set; calculating the mean of the first initial enhanced sub-packaged sample detection data group set to obtain a first enhanced sub-packaged sample detection data, and adding the first enhanced sub-packaged sample detection data to the plurality of enhanced sub-packaged sample detection data.

[0035] Specifically, a pre-processed sub-packaged sample detection data set is randomly selected from the plurality of pre-processed sub-packaged sample detection data sets as a first pre-processed sub-packaged sample detection data set, and the first pre-processed sub-packaged sample detection data set does not represent any order. By traversing the first pre-processed sub-packaged sample detection data set, each data point in the first pre-processed sub-packaged sample detection data set is extracted, and then each data point in the first pre-processed sub-packaged sample detection data set is cross-correlated with other data points that have not been combined with it in a non-missing non-repeated manner. Through the way of two-by-two combination, a first data cross-correlation group set is formed, which contains the detection data of a plurality of sub-packaged samples. Non-missing means that each sample participates in cross-correlation at least once, ensuring that all data in the first pre-processed sub-packaged sample detection data set are cross-correlated. Non-repeated means that each sample combination is not repeated, avoiding redundant enhancement. For example, the first pre-processed sub-packaged sample detection data set is D1={d1, d2, d3…dn}, where di represents the i-th data point, corresponding to the pre-processed sample detection measurement value under a certain detection device. For data point di, it is paired with di+1, di+2…dn in turn to generate new data groups (d1, d1), (d1, d3)…(d1, dn). Then, take data point d2, since d2 has been paired with d1, it is paired with d3, d4…dn in turn to generate new data groups. Repeat the above steps to process each data point in the first pre-processed sub-packaged sample detection data set, ensuring that each data point is combined with all other data points once and only once, thereby realizing non-missing and non-repeated cross-correlation of data, ensuring the comprehensiveness and efficiency of cross-correlation. In order to ensure the accuracy and efficiency of cross-correlation, a unique index label can be assigned to each data point to control the pairing process of the data points.

[0036] For each data cross-association group in the first data cross-association group set, cross-enhancement processing is performed based on the similarity between the data cross-association groups. Based on the cross-enhancement processing results, a first initial enhanced sub-packaged sample detection data set is obtained. The average value of the first initial enhanced sub-packaged sample detection data set is calculated to generate the first enhanced sub-packaged sample detection data. The above steps are repeated to perform intra-set data cross-enhancement processing on multiple preprocessed sub-packaged sample detection data sets to obtain multiple enhanced sub-packaged sample detection data sets. The enhanced data is then added to the multiple enhanced sub-packaged sample detection data sets.

[0037] Through cross-correlation and enhancement processing, the resulting data not only retains the original information but also undergoes feature enhancement and redundancy optimization. Mean calculation reduces the impact of noise or occasional errors in individual samples on the overall features, thereby effectively improving the stability and quality of oil product repackaging sample testing data.

[0038] Furthermore, cross-enhancement processing is performed on each data cross-association group in the first data cross-association group set to obtain a first initial enhanced packaged sample detection data set, including: performing similarity analysis on the same type of data in each data cross-association group in the first data cross-association group set to obtain a first similarity group set; performing intra-group similarity normalization processing on the first similarity group set respectively, and adding the processing results into an initially empty matrix to obtain a first cross-enhancement processing matrix set; and using the first cross-enhancement processing matrix set to perform cross-enhancement on the first data cross-association group set respectively to obtain the first initial enhanced packaged sample detection data set.

[0039] Specifically, for each data cross-association group in the first data cross-association set, similarity analysis is performed on the data of the same type within it to quantify the degree of similarity between two data points. Similarity analysis can be calculated using Euclidean distance, cosine similarity, or Pearson correlation coefficient. Data of the same type refers to data in each data cross-association group where two data points belong to the same detection index. For example, if one data point represents viscosity and another data point also represents viscosity, then these two data points are of the same type. Through similarity calculation, the similarity values ​​of the data of the same type in each data cross-association group are obtained, forming the first similarity set. Using a normalization formula, each similarity value in the first similarity set is normalized, mapping the similarity value to the 0-1 interval. The normalized similarity values ​​are then added to an initially empty matrix, forming the first cross-enhancement processing matrix set. This provides a weight matrix for the cross-enhancement of the first data cross-association set, allowing the enhancement processing to adaptively adjust according to sample similarity. The first set of cross-enhancement processing matrices is used to perform cross-enhancement on the first set of cross-association data groups. Specifically, the normalized similarity in the first set of cross-enhancement processing matrices is used as weights to perform weighted enhancement on each cross-association group in the first set of cross-association data groups. The weights are controlled by the similarity matrix, with similar samples having lower weights and dissimilar samples having higher weights. Through weighted enhancement, an enhanced feature vector set for each cross-association group is obtained, which is the first initial enhanced set of sample detection data groups. Cross-enhancement processing amplifies the differences between data points, making abnormal data more prominent and improving the overall quality of oil sample detection data.

[0040] Furthermore, the first data cross-association group set is cross-enhanced using the first cross-enhancing processing matrix set to obtain the first initial enhanced packaged sample detection data set, including: pre-constructing a convolutional enhancement network layer; and transmitting the corresponding first data cross-association group in the first cross-enhancing processing matrix set and the first data cross-association group set to the convolutional enhancement network layer for analysis to obtain the first initial enhanced packaged sample detection data set.

[0041] Specifically, a pre-constructed convolutional augmentation network layer is constructed. First, the network architecture is designed, including an input layer, convolutional layers, activation layers, fully connected layers, and an output layer. The output layer receives the input data, which consists of a first set of cross-enhancement processing matrices and a first set of data cross-correlation groups. The convolutional layers use convolution operations to extract local features from the data. Multiple convolutional layers can be designed, each containing multiple convolutional kernels to capture features at different scales and orientations. The activation layers use the ReLU activation function to increase the non-linearity of the convolutional augmentation network layer. Pooling layers reduce the dimensionality of features, decreasing computation while preserving important features. The fully connected layers integrate the features extracted by the convolutional layers, and the output layer outputs the final augmented data. Then, the parameters of the convolutional augmentation network layer are initialized, including weight initialization and bias initialization. Weight initialization uses methods such as Xavier initialization or He initialization to initialize the weights of the convolutional kernels and fully connected layers, while bias initialization is set to 0. Next, a loss function and optimizer are defined. For example, the mean squared error loss function is chosen to measure the difference between the output of the convolutional augmentation network layer and the target data. The Adam optimizer is selected to update the network parameters and minimize the loss function. Historical data, including both input and target data, is used as training data and fed into the convolutional augmentation network layer for forward propagation. The network output is calculated, and the difference between the network output and the target data is calculated using the loss function. The gradient is calculated using the backpropagation algorithm to update the network parameters. The above steps are repeated until the network converges or reaches the predetermined number of training epochs, resulting in the constructed convolutional augmentation network layer. This convolutional augmentation network layer is a lightweight forward network used for feature enhancement. Although it is a lightweight forward network, it still needs to be trained with historical data to determine the weights of the convolutional kernels and fully connected layers, enabling the convolutional augmentation network layer to have feature enhancement capabilities. After training, the network can be directly used for online or batch feature enhancement.

[0042] The first cross-enhancement processing matrix set and the corresponding first data cross-association group in the first data cross-association group set are transmitted to the convolutional enhancement network layer for analysis. The convolutional enhancement network layer outputs enhanced data. Each cross-association group is processed by the convolutional enhancement network to obtain enhanced data, forming the first initial enhanced sample detection data set. By pre-constructing the convolutional enhancement network layer, automated data processing is achieved, reducing manual intervention and improving processing efficiency.

[0043] Based on the detection data of the multiple enhanced repackaged samples, multi-dimensional feature cross-modal fusion analysis is performed to obtain the initial oil sample detection results of the target oil sample.

[0044] Furthermore, based on the multiple enhanced repackaging sample detection data, multi-dimensional feature cross-modal fusion analysis is performed to obtain the initial oil sample detection result of the target oil sample, including: based on the feature extraction mechanism, extracting features from different modal data of each enhanced repackaging sample detection data according to different detection types to construct multiple multi-modal features; using an attention mechanism to perform cross-modal weighted coupling of the multiple multi-modal features to obtain multi-modal coupled features; and performing detection analysis on the multi-modal coupled features to obtain the initial oil sample detection result.

[0045] Furthermore, the feature extraction mechanism involves performing principal component analysis on spectral data, extracting statistical features from physical data, and performing standardized index analysis on chemical features.

[0046] Specifically, based on a feature extraction mechanism, features are extracted from different modal data of each enhanced packaged sample detection data according to different detection types. The feature extraction mechanism involves principal component analysis (PCA) of the spectral data. PCA is a dimensionality reduction technique that projects high-dimensional data into a low-dimensional space while retaining the main variation information. By performing PCA on the spectral data, the main component features are extracted, reducing data dimensionality while preserving key feature information. Statistical features are extracted from the physical data, calculating statistics such as mean, variance, standard deviation, maximum, and minimum values ​​to reflect changes in physical properties. Standardization index analysis is performed on the chemical features, normalizing the chemical data to a uniform numerical range. By extracting and integrating features of different detection types from each enhanced packaged sample detection data, multiple multimodal features are formed.

[0047] An attention mechanism is used to perform cross-modal weighted coupling of the multiple multimodal features. Specifically, an attention mechanism is employed to analyze multiple multimodal features. Based on the correlation between each modal feature and the global feature distribution, the attention mechanism automatically calculates the importance weight of each modality. These importance weights can be generated through a self-attention mechanism. Each modal feature is multiplied by its corresponding importance weight and then summed in a weighted manner to obtain the fused multimodal coupled features. These multimodal coupled features integrate spectral, physical, and chemical data from oil product testing.

[0048] The multimodal coupling features are then detected to obtain the initial oil sample detection results. Detection of multimodal coupling features can employ neural networks, discriminative models, or regression models. The detection model is constructed according to actual needs, and the multimodal coupling features are detected. For example, a regression / classification detection model based on a multilayer perceptron: the input layer receives the multimodal coupling feature vectors; the feature interaction layer uses a fully connected structure to perform nonlinear interactions on features of different dimensions; the pattern recognition layer extracts oil state change patterns; the output layer selects different structures according to the task type; the regression output is used to output oil index values; and the Softmax classification outputs oil grade or state recognition. The activation function uses ReLU or LeakyReLU to improve feature representation ability. The optimizer is Adam. For index prediction, the mean squared error is used as the loss function; for state prediction, cross-entropy loss is used. Historical oil detection results are used as label data for supervised training. After training, the multimodal coupling features are input into the detection model for detection and analysis to obtain the initial oil sample detection results. The initial oil sample detection results include several detection indicators and a normal / abnormal label for each indicator.

[0049] By employing feature extraction mechanisms and cross-modal weighted coupling, the system strengthens the feature representation that plays a crucial role in determining oil quality, while filtering out redundant information and cross-interference, effectively improving the fusion accuracy of multi-source heterogeneous oil testing data. Through a detection model, the fused high-dimensional feature data is optimized for detection, enabling precise analysis and processing of oil testing data. This, in turn, improves the accuracy and reliability of initial oil sample testing results, reduces the false positive rate, and meets the high-precision requirements of industrial-grade oil quality testing.

[0050] The equipment reliability is verified based on the initial oil sample test results. When the equipment reliability verification is passed, the initial oil sample test results are used as the target oil sample test results.

[0051] Furthermore, based on the initial oil sample test results, equipment reliability verification is performed. When the equipment reliability verification passes, the initial oil sample test results are used as the target oil sample test results. This includes: extracting M anomalies from the initial oil sample test results, where M is a positive integer; extracting historical test logs of the same type of oil from the M test devices corresponding to the M anomalies to obtain M sets of historical test logs; identifying clustering trends in the M sets of historical test logs to determine M clustering trend features; and determining whether the similarity between the M anomaly features of the M anomalies and the M clustering trend features meets a preset similarity threshold. If not, the equipment reliability verification passes.

[0052] Specifically, after obtaining the initial oil sample test results, M anomalies are extracted from the initial oil sample test results, where M is a positive integer. These anomalies are determined based on the detection indicators and anomaly markers for each detection quality in the initial oil sample test results. When an indicator exceeds a preset threshold, or its corresponding marker is an anomaly marker, it is identified as an anomaly. Each anomaly is associated with a corresponding detection equipment marker. Based on the M anomalies, the corresponding M detection equipment are located, and historical oil detection logs for the same type of oil tested by that equipment are extracted from the oil testing database, resulting in a set of M historical detection logs. In other words, based on the anomalies in the initial oil sample test results, the corresponding detection equipment is determined, and the historical detection logs of these equipment are extracted. This ensures that the log data is the same type of oil being tested. The historical detection logs include detection time, detection type, and detection result value. During extraction, the latest record within the time window is prioritized, and abnormal data during equipment maintenance or calibration is filtered out to ensure the representativeness and stability of the log data.

[0053] Clustering trend identification is performed on M sets of historical detection logs to analyze the result change patterns of each detection device during historical detection processes. Specifically, each historical detection log is standardized. A mean-shift clustering algorithm or a time series clustering algorithm based on dynamic time warping is used to perform cluster analysis on each detection data in the historical detection logs, identifying the main detection trends and drift patterns of the detection devices within the historical period. Through clustering, M clustering trend features are obtained, including centrality features, slope of change, and fluctuation range, which are used to characterize the stability characteristics of the detection devices during historical detection processes.

[0054] After obtaining M clustering trend features, the similarity between each anomaly feature of the M outliers and the clustering trend features of their corresponding detection devices is calculated. This similarity calculation can be based on methods such as Euclidean distance or cosine similarity. The anomaly features corresponding to the outliers are extracted and their similarity is calculated with each clustering trend feature. If the similarity between an anomaly feature and any clustering trend feature is higher than a preset similarity threshold, it indicates that the anomaly is similar to the historical drift characteristics of the detection device, possibly stemming from a problem with the device itself. Conversely, if the similarity is lower than the preset similarity threshold, it indicates that the anomaly does not conform to the historical behavior pattern of the device and is more likely due to changes in the actual characteristics of the oil. The preset similarity threshold is used to determine whether the similarity between the anomaly features and the clustering trend features is within a reasonable range. The preset similarity threshold can be determined through statistical analysis of the similarity distribution in historical stable period data, or adaptively adjusted according to the dynamic changes in the similarity distribution during the detection task. In static scenarios, the standard deviation of the average similarity during the stable period minus a preset multiple is used as the threshold. In dynamic scenarios, the adaptive threshold is calculated based on the kernel density estimation or quantile interval of the historical distribution, thereby realizing the adaptive judgment of reliability under changes in equipment status.

[0055] Finally, the reliability of the testing equipment is verified based on the similarity judgment results. When the similarity between the abnormal features of M anomalies and the clustering trend features of their corresponding equipment does not reach a preset similarity threshold, the reliability of the testing equipment is deemed to have passed the verification, indicating that the testing equipment is operating normally, and the initial oil sample test result is used as the target oil sample test result. If one or more anomalies are highly similar to the equipment's clustering trend features, the equipment reliability verification is deemed to have failed, the corresponding equipment is automatically marked as abnormal equipment, and equipment maintenance and oil sample retesting procedures are performed to ensure the accuracy and reliability of the final oil test results. By identifying and verifying anomalies, potential problems with the equipment can be discovered in a timely manner, the testing process can be optimized, the reliability and accuracy of the target oil sample test results can be improved, the false judgment rate can be reduced, and the high-precision requirements of industrial-grade oil quality testing can be met.

[0056] Furthermore, determining M clustering trend features includes: calculating the mean of the M historical detection log sets to obtain M mean drift centers; using the mean drift algorithm in combination with the M mean drift centers to identify clustering trends in the M historical detection log sets and determine the M clustering trend features.

[0057] Specifically, the mean of the detection index is calculated for each of the M historical detection log sets, resulting in M ​​mean drift centers. These M mean drift centers reflect the overall detection level and central trend of each detection device within the historical detection period. Using the mean drift algorithm in conjunction with the M mean drift centers, clustering trends are identified for each historical detection log set. The kernel function type and bandwidth parameters, such as a Gaussian kernel function, are determined based on the distribution characteristics of the detection index. Using the mean drift centers as initial estimation points, the density distribution of the data within the M historical detection log sets is estimated. In each iteration, the sample points are gradually moved to the point of maximum density according to the kernel density weighted average principle until all sample points converge to stable cluster centers. After iterative convergence, samples whose points converge to the same position in the clustering results are grouped into the same cluster. The center position, sample distribution variance, slope of change, and fluctuation characteristics of each cluster are extracted to obtain the clustering trend characteristics of each detection device. These clustering trend characteristics are used to describe the typical behavior patterns and performance change laws of the detection devices in historical testing cycles, reflecting the long-term stability and drift trend of the device's testing results. The clustering trend characteristics determined for all M detection devices are then organized to obtain M sets of clustering trend characteristics.

[0058] By extracting stable trend features from the equipment's historical testing logs, accurate and quantifiable references are provided for comparing the similarity between anomalies and the equipment's historical behavior, thereby achieving effective and reliable equipment reliability verification and improving the reliability and validity of oil testing data.

[0059] Example 2, based on the same inventive concept as the oil product testing data optimization processing method using multi-dimensional feature fusion in the aforementioned examples, such as... Figure 2 As shown, this application provides a multi-dimensional feature fusion-based oil product testing data optimization processing system, wherein the multi-dimensional feature fusion-based oil product testing data optimization processing system includes: The detection data acquisition module 11 is used to use an automated sampling device to package the target oil sample according to preset packaging requirements and send it to multiple testing devices for testing to obtain multiple sets of packaged sample detection data. The preset packaging requirements are that each testing device should divide at least two packages. The data cleaning module 12 is used to traverse the multiple sets of packaged sample detection data to perform data cleaning and preprocessing to obtain multiple sets of preprocessed packaged sample detection data. The data processing module 13 is used to perform intra-set data cross-enhancement processing on the multiple sets of preprocessed packaged sample detection data to determine multiple enhanced packaged sample detection data. The initial detection result acquisition module 14 is used to perform multi-dimensional feature cross-modal fusion analysis based on the multiple enhanced packaged sample detection data to obtain the initial oil sample detection result of the target oil sample. The oil detection result acquisition module 15 is used to perform equipment reliability verification based on the initial oil sample detection result. When the equipment reliability verification is passed, the initial oil sample detection result is used as the target oil sample detection result.

[0060] Furthermore, the data processing module 13 is also configured to: extract a first pre-processed and packaged sample detection data set from multiple pre-processed and packaged sample detection data sets; perform cross-association on the first pre-processed and packaged sample detection data set without data omission or duplication to obtain a first data cross-association group set; perform cross-enhancement processing on each data cross-association group in the first data cross-association group set to obtain a first initial enhanced packaged sample detection data set; calculate the mean of the first initial enhanced packaged sample detection data set to obtain the first enhanced packaged sample detection data, and add the first enhanced packaged sample detection data into the multiple enhanced packaged sample detection data sets.

[0061] Furthermore, the data processing module 13 is also used to: perform similarity analysis on the same type of data in each data cross-association group in the first data cross-association group set to obtain a first similarity group set; perform intra-group similarity normalization processing on the first similarity group set respectively, and add the processing results into an initially empty matrix to obtain a first cross-enhancement processing matrix set; and use the first cross-enhancement processing matrix set to perform cross-enhancement on the first data cross-association group set respectively to obtain the first initial enhanced packaged sample detection data group set.

[0062] Furthermore, the data processing module 13 is also used to: pre-build a convolutional enhancement network layer; transmit the first data cross-association group corresponding to the first cross-enhancement processing matrix set and the first data cross-association group set to the convolutional enhancement network layer for analysis, and obtain the first initial enhanced packaged sample detection data set.

[0063] Furthermore, the initial detection result acquisition module 14 is also used to: extract features from different modal data of each enhanced repackaged sample detection data according to different detection types based on the feature extraction mechanism, and construct multiple multimodal features; use an attention mechanism to perform cross-modal weighted coupling on the multiple multimodal features to obtain multimodal coupling features; and perform detection analysis on the multimodal coupling features to obtain the initial oil sample detection result.

[0064] Furthermore, the initial detection result acquisition module 14 is also used for: the feature extraction mechanism is to perform principal component analysis on spectral data, perform statistical feature extraction on physical data, and perform standardized index analysis on chemical features.

[0065] Furthermore, the oil product testing result acquisition module 15 is also used to: extract M anomalies from the initial oil product sample testing results, where M is a positive integer; extract historical testing logs of the same type of oil from the M testing devices corresponding to the M anomalies to obtain a set of M historical testing logs; perform clustering trend identification on the set of M historical testing logs to determine M clustering trend features; determine whether the similarity between the M anomaly features of the M anomalies and the M clustering trend features meets a preset similarity threshold; if not, the device reliability verification is passed.

[0066] Furthermore, the oil product testing result acquisition module 15 is also used to: calculate the mean of the M historical testing log sets to obtain M mean drift centers; and use the mean drift algorithm in combination with the M mean drift centers to perform clustering trend identification on the M historical testing log sets to determine M clustering trend features.

[0067] Furthermore, the data cleaning module 12 is also used to: traverse the multiple sub-packaged sample test data sets for structured parsing, transform them into standardized data structures, and obtain multiple sub-packaged sample test standardized data sets; and perform outlier removal or baseline correction on the multiple sub-packaged sample test standardized data sets respectively to obtain the multiple pre-processed sub-packaged sample test data sets.

[0068] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not 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.

[0069] Obviously, those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for optimizing oil product testing data through multi-dimensional feature fusion, characterized in that, The method includes: The target oil sample is packaged according to the preset packaging requirements using an automated sampling device and sent to multiple testing devices for testing to obtain multiple sets of test data for the packaged samples. The preset packaging requirements are that each testing device must divide at least two packaged samples. The data cleaning and preprocessing of the multiple sets of test data for the sub-packaged samples are traversed to obtain multiple sets of preprocessed test data for the sub-packaged samples. Perform intra-set data cross-enhancement processing on the multiple pre-processed and packaged sample test data sets to determine multiple enhanced packaged sample test data sets; Based on the detection data of the multiple enhanced repackaged samples, multi-dimensional feature cross-modal fusion analysis is performed to obtain the initial oil sample detection results of the target oil sample; The equipment reliability is verified based on the initial oil sample test results. When the equipment reliability verification is passed, the initial oil sample test results are used as the target oil sample test results.

2. The method for optimizing oil product testing data through multi-dimensional feature fusion as described in claim 1, characterized in that, The multiple pre-processed and packaged sample test data sets are subjected to in-set data cross-enhancement processing to determine multiple enhanced packaged sample test data sets, including: Extract the first pre-processed and packaged sample test data set from multiple pre-processed and packaged sample test data sets; Perform a cross-correlation of the first pre-processed and packaged sample detection data set without omissions or duplications to obtain a first data cross-correlation group set; Perform cross-enhancement processing on each data cross-association group in the first data cross-association group set to obtain the first initial enhanced sample detection data group set; Calculate the mean of the first initial enhanced subpackaged sample test data set to obtain the first enhanced subpackaged sample test data, and add the first enhanced subpackaged sample test data into the plurality of enhanced subpackaged sample test data.

3. The method for optimizing oil product testing data through multi-dimensional feature fusion as described in claim 2, characterized in that, Cross-enhancement processing is performed on each data cross-association group in the first data cross-association group set to obtain a first initial enhanced sample detection data set, including: Similarity analysis is performed on the same type of data in each data cross-association group in the first data cross-association group set to obtain the first similarity group set; The first similarity group set is subjected to intra-group similarity normalization, and the processing results are added to an initially empty matrix to obtain the first cross-enhancement processing matrix set. The first set of cross-enhancement processing matrices is used to perform cross-enhancement on the first set of cross-correlation data groups to obtain the first set of enhanced subpackaged sample detection data groups.

4. The method for optimizing oil product testing data by multi-dimensional feature fusion as described in claim 3, characterized in that, The first set of cross-enhancement processing matrices is used to perform cross-enhancement on the first set of cross-correlation data groups to obtain the first set of enhanced packaged sample detection data groups, including: Pre-constructed convolutional augmentation network layers; The first data cross-association group corresponding to the first cross-enhancement processing matrix set and the first data cross-association group set is transmitted to the convolutional enhancement network layer for analysis to obtain the first initial enhanced packaged sample detection data set.

5. The method for optimizing oil product testing data through multi-dimensional feature fusion as described in claim 1, characterized in that, Based on the multidimensional feature cross-modal fusion analysis of the multiple enhanced repackaged sample detection data, the initial oil sample detection results of the target oil sample are obtained, including: Based on the feature extraction mechanism, features are extracted from different modal data of each enhanced sub-packaged sample detection data according to different detection types to construct multiple multimodal features; The attention mechanism is used to perform cross-modal weighted coupling on the multiple multimodal features to obtain multimodal coupled features; The multimodal coupling characteristics are detected and analyzed to obtain the detection results of the initial oil sample.

6. The method for optimizing oil product testing data through multi-dimensional feature fusion as described in claim 5, characterized in that, The feature extraction mechanism involves principal component analysis for spectral data, statistical feature extraction for physical data, and standardized index analysis for chemical features.

7. The method for optimizing oil product testing data through multi-dimensional feature fusion as described in claim 1, characterized in that, Based on the initial oil sample test results, equipment reliability verification is performed. When the equipment reliability verification passes, the initial oil sample test results are used as the target oil sample test results, including: Extract M outliers from the initial oil sample test results, where M is a positive integer; Extract historical detection logs of the same type of oil from the M detection devices corresponding to the M anomalies to obtain a set of M historical detection logs. Clustering trend identification is performed on the M historical detection log sets to determine M clustering trend features; Determine whether the similarity between the M abnormal features of the M anomalies and the M clustering trend features meets a preset similarity threshold. If not, the device reliability verification is passed.

8. The method for optimizing oil product testing data by multi-dimensional feature fusion as described in claim 7, characterized in that, Calculate the mean of the M historical detection log sets to obtain M mean drift centers; The mean shift algorithm is used in conjunction with the M mean shift centers to identify clustering trends in the M historical detection log sets, thereby determining M clustering trend features.

9. The method for optimizing oil product testing data by multi-dimensional feature fusion as described in claim 1, characterized in that, The multiple sets of test data for packaged samples are traversed for data cleaning and preprocessing to obtain multiple preprocessed sets of test data for packaged samples, including: The multiple sets of test data for the subpackaged samples are traversed and structured parsed to transform them into a standardized data structure, thereby obtaining a standardized set of test data for the multiple subpackaged samples. Outlier removal or baseline correction is performed on the multiple standardized test datasets of the subpackaged samples to obtain the multiple preprocessed test datasets of the subpackaged samples.

10. A multi-dimensional feature fusion-based oil product testing data optimization processing system, characterized in that, The steps for implementing the oil product testing data optimization processing method based on multi-dimensional feature fusion according to any one of claims 1 to 9 include: The detection data acquisition module is used to use an automated sampling device to package the target oil sample according to the preset packaging requirements and send it to multiple testing devices for testing to obtain multiple sets of test data for packaged samples. The preset packaging requirements are that each testing device should divide at least two packaged samples. The data cleaning module is used to traverse the multiple sets of test data for packaged samples to perform data cleaning and preprocessing, and obtain multiple sets of preprocessed test data for packaged samples. The data processing module is used to perform intra-set data cross-enhancement processing on the multiple pre-processed and packaged sample test data sets respectively to determine multiple enhanced packaged sample test data. The initial detection result acquisition module is used to perform multi-dimensional feature cross-modal fusion analysis based on the detection data of the multiple enhanced sub-packaged samples to obtain the initial oil sample detection result of the target oil sample; The oil product testing result acquisition module is used to verify the reliability of the equipment based on the initial oil product sample testing results. When the equipment reliability verification is passed, the initial oil product sample testing results are used as the target oil product sample testing results.

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