Serialized data anomaly positioning method and device based on reagent reaction

Abstract

The invention discloses a serialized data anomaly positioning method and device based on reagent reaction, and the method comprises the steps: obtaining an original data sequence, and converting the original data sequence into a two-dimensional image; inputting the two-dimensional image into a pre-trained anomaly prediction model to obtain a prediction result output by the anomaly prediction model; under the condition that the prediction result is abnormal, performing post-processing on the prediction result to obtain a sample type and an abnormal position corresponding to an abnormal result; wherein the anomaly prediction model is obtained by training a pre-constructed deep learning network by using the image of the data sample and the corresponding result label. The problem that in the prior art, due to the fact that the robustness of a statistical model is poor, the accuracy of abnormal positioning is low is solved, the robustness of the positioning model is improved, and then the accuracy of abnormal positioning of serialized data based on reagent reaction is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and device for locating abnormalities in serialized data based on reagent reactions. Background Art

[0002] When locating the abnormal position of the serialized data, it can be done with the help of specific reagents. Add multiple reagents to the sample, obtain the sequence after the reagent reaction, and compare the sequence before the reagent reaction with the sequence after the reagent reaction. Figure 1 , curve L1 represents the sample before the reagent reaction, and curve L2 represents the sample after the reagent reaction. If the sample is abnormal at a certain position, and the sample becomes normal at that position after reacting with the reagent, then this sample is called positive for the reagent, for example Figure 1 The sample shown in the figure is positive on reagents B and D, and the shaded area is marked as the abnormal location.

[0003] In the existing technology, statistical methods are generally used to detect anomalies based on reagent reactions. During the implementation process, some sample features are calculated, such as the degree of peak protrusion, peak width, peak area, etc., and then the anomalies are detected by setting corresponding thresholds. If the sample features meet the threshold specifications, there is no anomaly, otherwise, there is an anomaly. However, since the samples present different forms, this statistical method has poor robustness. The same sample feature calculation method and threshold setting may be invalid in another group of samples.

[0004] In view of this, the present invention provides a method and device for locating anomalies in serialized data based on reagent reactions, so as to solve the problem of low anomaly locating accuracy due to poor robustness of statistical models in the prior art. Summary of the invention

[0005] To this end, an embodiment of the present invention provides a method and device for locating anomalies in serialized data based on reagent reactions, so as to solve the problem of low anomaly positioning accuracy caused by poor robustness of statistical models in the prior art, thereby improving the robustness of the positioning model and further improving the accuracy of anomaly positioning in serialized data based on reagent reactions.

[0006] In order to achieve the above purpose, the embodiment of the present invention provides the following technical solutions:

[0007] The present invention provides a method for locating anomalies in sequenced data based on reagent reactions, the method comprising:

[0008] Acquire an original data sequence, and convert the original data sequence into a two-dimensional image;

[0009] Inputting the two-dimensional image into a pre-trained abnormality prediction model to obtain a prediction result output by the abnormality prediction model;

[0010] In the case where the prediction result is abnormal, post-processing the prediction result to obtain the sample type and abnormal position corresponding to the abnormal result;

[0011] The anomaly prediction model is obtained by training a pre-built deep learning network using images of data samples and corresponding result labels.

[0012] In some embodiments, the network architecture of the deep learning network includes:

[0013] The encoder is used to extract features from the image of the input data sample. It can be a convolution or a combination of convolution and transformer. Convolution can fuse local features, and transformer can fuse global features.

[0014] The decoder is used to decode the features encoded by the encoder. Decoding generally uses upsampling methods such as interpolation and deconvolution.

[0015] The feature fusion layer is used to enrich feature information. In the decoding process, not only decoding features but also encoding features are used, which is conducive to improving the effect of semantic segmentation.

[0016] The semantic segmentation head is used to perform semantic segmentation using the fused features. It generally outputs an array of size H×W×C, where H, W, and C represent the height, width, and channel dimensions (number of categories) of the input image.

[0017] In some embodiments, the pre-built deep learning network is trained using the images of the data samples and the corresponding result labels to obtain the abnormality prediction model, specifically including:

[0018] Acquire one-dimensional data of a data sample, and convert the one-dimensional data into a two-dimensional image;

[0019] Construct a dataset using two-dimensional images of data samples and corresponding result labels;

[0020] Dividing the data set into a training set, a validation set, and a test set;

[0021] Using the training set to train a pre-built deep learning network to obtain an initial model;

[0022] The initial model is evaluated using a validation set to obtain the abnormality prediction model.

[0023] In some embodiments, the initial model is optimized using a binary cross entropy loss function.

[0024] In some embodiments, the initial model is optimized using a focal loss function.

[0025] In some embodiments, the initial model is optimized using a weighted value of Focal loss and Dice loss as a loss function.

[0026] In some embodiments, the post-processing specifically includes:

[0027] The prediction result is subjected to a sigmoid function operation to obtain a probability value of an abnormality existing at a certain position;

[0028] If the probability value reaches a preset threshold, the position is an abnormal position.

[0029] The present invention also provides a device for locating abnormalities in serialized data based on reagent reactions, the device comprising:

[0030] A data processing unit, used for acquiring an original data sequence and converting the original data sequence into a two-dimensional image;

[0031] An abnormality prediction unit, used for inputting the two-dimensional image into a pre-trained abnormality prediction model to obtain a prediction result output by the abnormality prediction model;

[0032] an abnormality locating unit, used for post-processing the prediction result to obtain a sample type and an abnormality position corresponding to the abnormal result when the prediction result is abnormal;

[0033] The anomaly prediction model is obtained by training a pre-constructed deep learning network using images of data samples and corresponding result labels.

[0034] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the program.

[0035] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and the computer program implements the steps of the above method when executed by a processor.

[0036] The method and device for locating anomalies in serialized data based on reagent reaction provided by the present invention obtains the original data sequence, converts the original data sequence into a two-dimensional image, and inputs the two-dimensional image into a pre-trained anomaly prediction model to obtain the prediction result output by the anomaly prediction model; when the prediction result is anomaly, the prediction result is post-processed to obtain the sample type and abnormal position corresponding to the abnormal result. The problem of low accuracy of abnormality positioning due to poor robustness of statistical models in the prior art is solved, thereby improving the robustness of the positioning model, and further improving the accuracy of abnormal positioning of serialized data based on reagent reaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0038] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0039] Figure 1 It is a comparison diagram of the sequence before the reagent reaction and the sequence after the reagent reaction;

[0040] Figure 2 This is one of the flow charts of the method for locating abnormalities in serialized data based on reagent reactions provided by the present invention;

[0041] Figure 3 This is the second flowchart of the method for locating abnormalities in serialized data based on reagent reactions provided by the present invention;

[0042] Figure 4 This is a structural block diagram of the device for locating abnormalities in serialized data based on reagent reactions provided by the present invention;

[0043] Figure 5 This is a structural block diagram of a computer device provided by the present invention. DETAILED DESCRIPTION

[0044] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] When locating abnormalities in serialized data, there are the following two situations: 1) The original sample does not have abnormal performance; 2) After adding a certain reagent to the original sample, the sequence before the reagent reaction and the sequence after the reagent reaction are compared, and it is found that the sample has abnormal performance. For situation 1), the method provided by the present invention can identify that the sample does not have abnormal performance on these reagents; for situation 2), the method provided by the present invention determines its positive type by comparing the difference between the sample before the reagent reaction and the sample after the reagent reaction, such as reagent A positive, reagent B positive, etc., and if it is positive on a certain reagent, it can locate the positive position in the sample. In all embodiments provided by the present invention, the sample before the reagent reaction represents the original sample, which will not be repeated.

[0046] In order to solve the problems existing in the prior art, the present invention converts 1D sequence data into 2D images through data processing, and the method provided by the present invention can not only identify the type of positive samples with the help of reagent reactions, but also locate abnormal positions. In the embodiment provided by the present invention, it is set that there are N kinds of reagents that react with the sample, then it can be identified whether the sample is positive on these N reagents, specifically, it can be positive on one or several reagents, and the specific positive position can be identified.

[0047] Combine the following Figure 2-Figure 4 The method and device for locating anomalies in serialized data based on reagent reactions provided by the present invention are introduced.

[0048] Please refer to Figure 2 , Figure 2 This is one of the flow charts of the method for locating anomalies in serialized data based on reagent reactions provided by the present invention.

[0049] In a specific embodiment, the method for locating abnormalities in sequenced data based on reagent reactions provided by the present invention comprises the following steps:

[0050] S210: Acquire an original data sequence, and convert the original data sequence into a two-dimensional image; that is, the acquired original data is one-dimensional sequence data, and in order to facilitate subsequent model processing, the one-dimensional sequence data is converted into a two-dimensional image, and the two-dimensional image is used as the input of the model.

[0051] S220: Input the two-dimensional image into a pre-trained anomaly prediction model to obtain a prediction result output by the anomaly prediction model; wherein the anomaly prediction model is obtained by training a pre-constructed deep learning network using images of data samples and corresponding result labels; it should be understood that the so-called samples in model training are data samples used as training.

[0052] S230: When the prediction result is that an abnormality exists, post-process the prediction result to obtain the sample type and abnormal position corresponding to the abnormal result; the prediction result can be two types, one is that there is no abnormality, in which case there is no need to analyze and locate the abnormal result, and the other is that there is an abnormality, in which case post-processing is required to determine the abnormal sample type and the position of the abnormality.

[0053] In step S230, the post-processing specifically includes:

[0054] The prediction result is subjected to a sigmoid function operation to obtain a probability value of an abnormality existing at a certain position;

[0055] If the probability value reaches a preset threshold, the position is an abnormal position.

[0056] In a specific usage scenario, first, according to the algorithm input adjustment method, the 1D sequence is converted into an image, and then the trained algorithm is used for calculation, and finally an output of 224×224×N is obtained. The output at this time is a floating point number, which needs to be post-processed to obtain the positive sample type and the abnormal position. The post-processing method is as follows: 1) The obtained output is subjected to a sigmoid function operation, and the output range is limited between 0 and 1, indicating the probability of an abnormality at a certain position; 2) A threshold is set, such as 0.5, and the position greater than the threshold indicates an abnormal position, and the position less than the threshold indicates a normal position; 3) Result analysis: if all output values ​​are less than 0.5, it means that the sample is not abnormal. If there is a position where the output is greater than 0.5 on some channels, the sample has a positive performance on the reagent represented by this channel, and the positive position is the position where the output is greater than 0.5.

[0057] The artificial intelligence algorithm used in the present invention may be a semantic segmentation algorithm Unet, TransUnet, etc. In some embodiments, the network architecture of the deep learning network includes:

[0058] The encoder is used to extract features from the image of the input data sample; the encoder is a key part of the deep learning model, and its main function is to extract useful features from the input data, which can be used for subsequent tasks such as segmentation. The encoder extracts features through convolution or a combination of convolution and transformer, where convolution is used to fuse local features of the image and transformer is used to fuse global features of the image. In other words, the encoder can be a convolution or a combination of convolution and transformer, where convolution can fuse local features and transformer can fuse global features.

[0059] The decoder is used to decode the features encoded by the encoder. The decoder is another key component in the deep learning model and usually works with the encoder. Its main function is to use the features extracted by the encoder to obtain the prediction results after multiple calculations. Decoding generally uses upsampling methods such as interpolation and deconvolution.

[0060] The feature fusion layer is used to fuse the encoding features and the decoding features. The feature fusion layer can combine features at different levels to create a richer and more comprehensive feature representation. For example, in image processing, fusing low-level texture features and high-level semantic features can help the model better understand the image content. This feature fusion layer is used to enrich feature information. In the decoding process, not only the decoding features but also the encoding features are used, which is conducive to improving the effect of semantic segmentation.

[0061] The semantic segmentation head is used to perform semantic segmentation using the fused features. It generally outputs an array of size H×W×C, where H, W, and C represent the height, width, and channel dimensions (number of categories) of the input image. The semantic segmentation head is the part of the deep learning model specifically used for semantic segmentation tasks. In semantic segmentation, the purpose of the model is to assign a category label to each pixel in the image, thereby achieving semantic segmentation of the image. The semantic segmentation head usually receives the output from the feature fusion layer and generates a segmentation map with pixel-level classification.

[0062] The method provided by the present invention converts the problem of locating anomalies in serialized data into a semantic segmentation problem. The algorithm used is not limited to a specific semantic segmentation method, and can be any semantic segmentation algorithm, such as a pure convolution algorithm Unet, an algorithm combining convolution and transformer TransUnet, etc. The network architecture mainly includes an encoder, a decoder, a feature fusion layer, and a semantic segmentation head. The encoder is mainly used for feature extraction, which can be a convolution or a combination of convolution and transformer. Convolution can fuse local features, and transformer can fuse global features; the decoder mainly decodes the encoded features, and decoding generally uses upsampling methods such as interpolation and deconvolution; the feature fusion layer is used to enrich feature information. In the decoding process, not only decoding features but also encoding features are used, which is conducive to improving the effect of semantic segmentation; the semantic segmentation head generally outputs an array of size H×W×C, where H, W, and C represent the height, width, and channel dimensions (number of categories) of the input.

[0063] In some embodiments, the pre-built deep learning network is trained using the images of the data samples and the corresponding result labels to obtain the abnormality prediction model, such as Figure 3 As shown, the specific steps include:

[0064] S310: Obtain one-dimensional data of the data sample and convert the one-dimensional data into a two-dimensional image; in a specific usage scenario, for each reagent, convert the reagent reaction sequence ( Figure 1 The middle curve L1) and the sequence after reagent reaction ( Figure 1 The middle curve L2) is drawn on the same graph, the area between the two sequences is filled with white, and the other positions are filled with black. The image size is set to 224×224. Since there are N kinds of reagents that react with the sample, the above image can be obtained after reacting with each reagent. All images are spliced ​​together on the channel to obtain a sample input of size 224×224×N.

[0065] S320: Construct a data set using the two-dimensional image of the data sample and the corresponding result label; when labeling the sample, there are two labeling methods. One labeling method is to label which reagent the sample is positive for, for example Figure 1 Reagents B and D are marked as positive in the sample. Another mark is to locate the position where a certain reagent is positive, which is represented by an interval. For example, reagent B is added at this time and reagent B is positive. The marked interval is [a0, a1], which means that in the sample, reagent B is positive in the interval from a0 to a1.

[0066] Furthermore, the above annotations need to be converted into a format that can adapt to the algorithm. The dimension of the annotation is the same as the input, which is also 224×224×N. First, the annotation is initialized to 0, and then the positive position is updated to 1. For example, if the labeled sample is positive under reagent D, and the positive position is [b0, b1], since the labeled channel dimension represents N reagents, first locate the channel where reagent D is located, between b0 and b1, and update the area between the sample before the reagent reaction and the sample after the reagent reaction to 1. It should be noted that if the sample is negative under N reagents, then the annotation does not need to be updated.

[0067] S330: Divide the data set into a training set, a validation set, and a test set, and the ratio may be 6:3:1 or 8:1:1.

[0068] S340: Using the training set to train a pre-built deep learning network to obtain an initial model;

[0069] S350: Evaluate the initial model using the validation set to obtain the abnormality prediction model.

[0070] Since a sample can be positive on one or more reagents, this method is a multi-classification task. In the process of optimizing the initial model, a binary cross entropy loss function can be used, as shown in formula (1):

[0071]

[0072] Among them, p t represents the prediction probability, p represents the probability of the algorithm predicting the positive class, y represents whether it is marked as foreground, 1 represents the mark as foreground, CE(p t ) represents the binary cross entropy loss function, log(p t ) represents taking the logarithm of the predicted probability.

[0073] Considering that the area occupied by the abnormal region is relatively small and some abnormal locations are difficult to detect, formula (1) is expanded to introduce two parameters α t ,γ,α t is the positive and negative sample number imbalance parameter, γ is the sample difficulty parameter, and is improved to the Focal loss function, as shown in the following formula (2):

[0074] FL(p t )= -α t (1-p t ) γ log (p t ) (2)

[0075] Among them, α tIt can suppress the impact caused by the excessive proportion of normal areas, and γ can increase the optimization of samples that are difficult to detect in abnormal locations.

[0076] The present invention can also use the optimization target Dice loss function, and its calculation formula is shown in the following formula (3):

[0077]

[0078] Among them, P represents the mask predicted by the algorithm, and Y represents the real labeled mask.

[0079] During the training process, Focal loss and Dice loss are combined in different proportions. The proportion can be adjusted according to the actual situation to obtain the final optimization target. Then the optimizer is used for back propagation to continuously update the algorithm parameters until convergence.

[0080] In the above specific implementation, the method for locating anomalies in serialized data based on reagent reactions provided by the present invention obtains the original data sequence, converts the original data sequence into a two-dimensional image, and inputs the two-dimensional image into a pre-trained anomaly prediction model to obtain the prediction result output by the anomaly prediction model; when the prediction result is an abnormality, the prediction result is post-processed to obtain the sample type and abnormal position corresponding to the abnormal result. The problem of low accuracy of abnormality positioning due to poor robustness of the statistical model in the prior art is solved, thereby improving the robustness of the positioning model, and further improving the accuracy of abnormal positioning of serialized data of reagent reactions.

[0081] In addition to the above method, the present invention also provides a device for locating abnormalities in serialized data based on reagent reactions, such as Figure 4 As shown, the device comprises:

[0082] The data processing unit 410 is used to obtain an original data sequence and convert the original data sequence into a two-dimensional image;

[0083] An abnormality prediction unit 420, used for inputting the two-dimensional image into a pre-trained abnormality prediction model to obtain a prediction result output by the abnormality prediction model;

[0084] An anomaly locating unit 430 is used to post-process the prediction result to obtain a sample type and an abnormal position corresponding to the abnormal result when the prediction result is abnormal;

[0085] The anomaly prediction model is obtained by training a pre-built deep learning network using images of data samples and corresponding result labels.

[0086] In some embodiments, the network architecture of the deep learning network includes:

[0087] The encoder is used to extract features from the image of the input data sample. It can be a convolution or a combination of convolution and transformer. Convolution can fuse local features, and transformer can fuse global features.

[0088] The decoder is used to decode the features encoded by the encoder. Decoding generally uses upsampling methods such as interpolation and deconvolution.

[0089] The feature fusion layer is used to enrich feature information. In the decoding process, not only decoding features but also encoding features are used, which is conducive to improving the effect of semantic segmentation.

[0090] The semantic segmentation head is used to perform semantic segmentation using the fused features. It generally outputs an array of size H×W×C, where H, W, and C represent the height, width, and channel dimensions (number of categories) of the input image.

[0091] In some embodiments, the pre-built deep learning network is trained using the images of the data samples and the corresponding result labels to obtain the abnormality prediction model, specifically including:

[0092] Acquire one-dimensional data of a data sample, and convert the one-dimensional data into a two-dimensional image;

[0093] Construct a dataset using two-dimensional images of data samples and corresponding result labels;

[0094] Dividing the data set into a training set, a validation set, and a test set;

[0095] Using the training set to train a pre-built deep learning network to obtain an initial model;

[0096] The initial model is evaluated using a validation set to obtain the abnormality prediction model.

[0097] In some embodiments, the initial model is optimized using a binary cross entropy loss function.

[0098] In some embodiments, the initial model is optimized using a focal loss function.

[0099] In some embodiments, the initial model is optimized using a weighted value of Focal loss and Dice loss as a loss function.

[0100] In some embodiments, the post-processing specifically includes:

[0101] The prediction result is subjected to a sigmoid function operation to obtain a probability value of an abnormality existing at a certain position;

[0102] If the probability value reaches a preset threshold, the position is an abnormal position.

[0103] In the above specific implementation, the reagent reaction-based serialized data anomaly location device provided by the present invention obtains the original data sequence, converts the original data sequence into a two-dimensional image, and inputs the two-dimensional image into a pre-trained anomaly prediction model to obtain the prediction result output by the anomaly prediction model; when the prediction result is an abnormality, the prediction result is post-processed to obtain the sample type and abnormal position corresponding to the abnormal result. The problem of low anomaly location accuracy caused by poor robustness of the statistical model in the prior art is solved, thereby improving the robustness of the location model, and further improving the accuracy of the reagent reaction-based serialized data anomaly location.

[0104] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0105] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0106] Corresponding to the above embodiment, the embodiment of the present invention further provides a computer storage medium, which contains one or more program instructions, wherein the one or more program instructions are used to execute the above method.

[0107] The present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the above method.

[0108] In the embodiment of the present invention, the processor may be an integrated circuit chip having the ability to process signals. The processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0109] The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor for execution, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The processor reads the information in the storage medium and completes the steps of the above method in combination with its hardware.

[0110] The storage medium may be a memory, which may be, for example, a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memory.

[0111] Among them, the non-volatile memory can be a read-only memory (ROM), a programmable ROM (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.

[0112] The volatile memory may be a random access memory (RAM) which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DRRAM).

[0113] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0114] Those skilled in the art will appreciate that in one or more of the above examples, the functions described in the present invention can be implemented using a combination of hardware and software. When software is used, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any media that facilitates the transmission of computer programs from one place to another. Storage media can be any available media that can be accessed by general or special-purpose computers.

[0115] The above specific implementation methods further illustrate the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific implementation methods of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for locating abnormalities in serialized data based on reagent reactions, characterized in that: The method comprises: Acquire an original data sequence, and convert the original data sequence into a two-dimensional image; Inputting the two-dimensional image into a pre-trained abnormality prediction model to obtain a prediction result output by the abnormality prediction model; In the case where the prediction result is abnormal, post-processing the prediction result to obtain the sample type and abnormal position corresponding to the abnormal result; The anomaly prediction model is obtained by training a pre-constructed deep learning network using images of data samples and corresponding result labels.

2. The method for locating anomalies in serialized data based on reagent reaction according to claim 1, characterized in that: The network architecture of a deep learning network includes: An encoder is used to extract features from an image of an input data sample. The encoder extracts features through convolution or a combination of convolution and transformer, wherein convolution is used to fuse local features of an image and transformer is used to fuse global features of an image; A decoder, used to decode the features encoded by the encoder through an upsampling method; Feature fusion layer; The semantic segmentation head is used to perform semantic segmentation using the fused features. The semantic segmentation head outputs an array of size H×W×C, where H, W, and C represent the height, width, and channel dimensions of the input image, respectively.

3. The method for locating anomalies in serialized data based on reagent reaction according to claim 2, characterized in that: The pre-built deep learning network is trained using the images of the data samples and the corresponding result labels to obtain the anomaly prediction model, which specifically includes: Acquire one-dimensional data of a data sample, and convert the one-dimensional data into a two-dimensional image; Construct a dataset using two-dimensional images of data samples and corresponding result labels; Dividing the data set into a training set, a validation set, and a test set; Using the training set to train a pre-built deep learning network to obtain an initial model; The initial model is evaluated using a validation set to obtain the abnormality prediction model.

4. The method for locating anomalies in serialized data based on reagent reaction according to claim 3, characterized in that: The initial model is optimized using a binary cross entropy loss function.

5. The method for locating anomalies in serialized data based on reagent reaction according to claim 3, characterized in that: The initial model is optimized using the Focalloss loss function.

6. The method for locating anomalies in serialized data based on reagent reaction according to claim 3, characterized in that: The initial model is optimized using the weighted value of Focal loss and Dice loss as the loss function.

7. The method for locating anomalies in serialized data based on reagent reaction according to claim 1, characterized in that: The post-processing specifically includes: The prediction result is subjected to a sigmoid function operation to obtain a probability value of an abnormality existing at a certain position; If the probability value reaches a preset threshold, the position is an abnormal position.

8. A device for locating abnormalities in serialized data based on reagent reaction, characterized in that: The device comprises: A data processing unit, used for acquiring an original data sequence and converting the original data sequence into a two-dimensional image; An abnormality prediction unit, used for inputting the two-dimensional image into a pre-trained abnormality prediction model to obtain a prediction result output by the abnormality prediction model; an abnormality locating unit, used for post-processing the prediction result to obtain a sample type and an abnormality position corresponding to the abnormal result when the prediction result is abnormal; The anomaly prediction model is obtained by training a pre-built deep learning network using images of data samples and corresponding result labels.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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