Photoelectrochemical analysis method and system for digestive tract interstitial fluid components

Abstract

The embodiment of the invention provides a photoelectrochemical analysis method and system for components of digestive tract interstitial fluid, and belongs to the technical field of data processing. The method comprises the following steps: determining a target sample corresponding to digestive tract interstitial fluid components, and performing current measurement on the target sample by utilizing a constant potential rectifier to obtain an initial current corresponding to the target sample and a corresponding initial position of the initial current in the target sample; irradiating a target sample by using polarized light, and collecting a target image corresponding to the target sample under the irradiation of the polarized light; performing target identification on the target image to obtain a first analysis result corresponding to the target sample; performing abnormal change identification on the initial current to obtain a target current, and obtaining a target position corresponding to the target current from the initial position; determining a second analysis result corresponding to the target sample according to the target position and the target current; and fusing the first analysis result and the second analysis result to determine a target analysis result corresponding to the target sample.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a photoelectrochemical analysis method and system for the components of digestive tract interstitial fluid. Background Technology

[0002] The human gastrointestinal tract and its micro-ecosystem are highly complex, especially in the interstitial regions of closely connected digestive organs, where structure and function are intricately intertwined. The digestive tract interstitium not only houses conventional cell types such as epithelial cells and fibroblasts, but also specialized cells like nerve cells and lymphocytes. Furthermore, it contains numerous large organic molecules that play an indispensable role in maintaining the balance and normal functioning of the digestive tract. In this highly complex microenvironment, the rapid and accurate identification of the presence of these specialized cells and macromolecules is of immeasurable importance for the precise diagnosis and effective treatment of diseases. Particularly in the diagnosis of malignant tumors such as gastric cancer, colorectal cancer, duodenal cancer, and gastrointestinal stromal tumors (GIST), the ability to quickly identify these specific biomarkers can significantly improve diagnostic accuracy and provide a solid scientific basis for subsequent personalized treatment strategies. However, current technologies still face significant challenges in identifying these specialized cells and molecules.

[0003] Therefore, there is an urgent need to design a technical solution to solve at least one of the above-mentioned technical problems. Summary of the Invention

[0004] The main objective of this invention is to provide a photoelectrochemical analysis method and system for the components of the digestive tract interstitial fluid, aiming to solve the problem in related technologies where specific biomarkers cannot be accurately identified when diagnosing malignant tumors such as gastric cancer, intestinal cancer, duodenal cancer, and gastrointestinal stromal tumors (GIST), thereby reducing diagnostic accuracy.

[0005] In a first aspect, embodiments of the present invention provide a photoelectrochemical analysis method for components of digestive tract interstitial fluid, comprising:

[0006] The target sample corresponding to the components of the gastrointestinal interstitial fluid was identified, and the current of the target sample was measured using a potentiostat to obtain the initial current corresponding to the target sample and the initial position of the initial current in the target sample.

[0007] The target sample is illuminated with polarized light, and a target image of the target sample under the polarized light illumination is acquired.

[0008] The target image is subjected to target recognition to obtain the first analysis result corresponding to the target sample;

[0009] The initial current is subjected to abnormal change identification to obtain the target current, and the target position corresponding to the target current is obtained from the initial position;

[0010] The second analysis result corresponding to the target sample is determined based on the target location and the target current;

[0011] The target analysis result corresponding to the target sample is determined by combining the first analysis result and the second analysis result.

[0012] Secondly, embodiments of the present invention provide a photoelectrochemical analysis system for components of digestive tract interstitial fluid, comprising:

[0013] The data acquisition module is used to determine the target sample corresponding to the components of the digestive tract interstitial fluid, and to use a potentiostat to measure the current of the target sample to obtain the initial current corresponding to the target sample and the initial position of the initial current in the target sample.

[0014] An image acquisition module is used to illuminate the target sample with polarized light and acquire a target image of the target sample under the polarized light illumination.

[0015] The image analysis module is used to perform target recognition on the target image to obtain the first analysis result corresponding to the target sample;

[0016] Anomaly analysis module is used to identify abnormal changes in the initial current to obtain the target current, and to obtain the target position corresponding to the target current from the initial position;

[0017] The current analysis module is used to determine the second analysis result corresponding to the target sample based on the target location and the target current;

[0018] The data fusion module is used to fuse the first analysis result and the second analysis result to determine the target analysis result corresponding to the target sample.

[0019] Thirdly, embodiments of the present invention also provide a terminal device, the terminal device including a processor, a memory, a computer program stored in the memory and executable by the processor, and a data bus for realizing communication between the processor and the memory, wherein when the computer program is executed by the processor, it implements the steps of any of the photoelectrochemical analysis methods for digestive tract interstitial fluid components provided in this specification.

[0020] Fourthly, embodiments of the present invention also provide a storage medium for computer-readable storage, characterized in that the storage medium stores one or more programs, which can be executed by one or more processors to implement the steps of any of the photoelectrochemical analysis methods for digestive tract interstitial fluid components provided in this specification.

[0021] This invention provides a photoelectrochemical analysis method and system for components of the digestive tract interstitial fluid. The method includes identifying a target sample corresponding to a component of the digestive tract interstitial fluid, measuring the current in the target sample using a potentiostat to obtain the initial current and its initial position within the target sample; irradiating the target sample with polarized light and acquiring a target image of the target sample under polarized light irradiation; performing target recognition on the target image to obtain a first analytical result corresponding to the target sample; identifying abnormal changes in the initial current to obtain the target current, and obtaining the target position corresponding to the target current from the initial position; determining a second analytical result corresponding to the target sample based on the target position and target current; and fusing the first and second analytical results to determine the target analytical result corresponding to the target sample. This method, by measuring the current in the target sample using a potentiostat, can accurately obtain the initial current and its corresponding initial position, thereby highly sensitively capturing minute changes in the target sample and providing support for improving the accuracy of subsequent detection. Furthermore, irradiating the target sample with polarized light and acquiring a target image provides rich morphological information. Polarized light imaging technology can reveal the molecular arrangement and structural features of target samples, which is crucial for identifying changes at the microscopic level. Analyzing the target image using target recognition technology yields the first analytical result of the target sample. Further anomaly detection of the initial current allows for real-time monitoring of current anomalies within the target sample to obtain the target current. The second analytical result, determined by the target current and its corresponding target location, allows for timely detection of the location and intensity of anomalies. By fusing the first and second analytical results, comprehensive target analysis results can be formed by integrating image information and electrochemical data. This method not only improves the comprehensiveness of diagnosis but also enhances the accuracy of analytical results, thus supporting the development of more precise and effective treatment plans to improve patient outcomes and quality of life. It also addresses the problem in related technologies where the accurate identification of specific biomarkers is lacking in the diagnosis of malignant tumors such as gastric cancer, colorectal cancer, duodenal cancer, and gastrointestinal stromal tumors (GIST) reduces diagnostic accuracy. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A schematic flowchart of a photoelectrochemical analysis method for components of digestive tract interstitial fluid provided in an embodiment of the present invention;

[0024] Figure 2 A schematic diagram of the module structure of a photoelectrochemical analysis system for components of digestive tract interstitial fluid provided in an embodiment of the present invention;

[0025] Figure 3 This is a schematic block diagram of a terminal device provided in an embodiment of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0028] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0029] This invention provides a photoelectrochemical analysis method and system for components of the digestive tract interstitial fluid. The photoelectrochemical analysis method for these components can be applied to a terminal device, such as a tablet computer, laptop computer, desktop computer, personal digital assistant, or wearable device. This terminal device can be a server or a server cluster.

[0030] The following detailed description of some embodiments of the present invention is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0031] Please refer to Figure 1 , Figure 1 This is a schematic flowchart of a photoelectrochemical analysis method for components of the digestive tract interstitial fluid provided in an embodiment of the present invention.

[0032] like Figure 1 As shown, the photoelectrochemical analysis method for the components of the digestive tract interstitial fluid includes steps S101 to S106.

[0033] Step S101: Determine the target sample corresponding to the digestive tract interstitial fluid component, and use a potentiostat to measure the current of the target sample to obtain the initial current corresponding to the target sample and the initial position of the initial current in the target sample.

[0034] For example, a target sample corresponding to the gastrointestinal interstitial fluid component is collected from a target patient or laboratory animal using an endoscope or other non-invasive or minimally invasive techniques. The target sample is then placed in the test cell of a potentiostat, ensuring full contact between the electrodes and the target sample. The potentiostat is then activated, and the initial current corresponding to the target sample is recorded. Simultaneously, the initial position of the initial current within the target sample is marked using the potentiostat's built-in positioning system.

[0035] For example, the more data the initial current is collected, the more accurate it will be. To ensure the efficiency and reliability of the collected data, a preset distance is set, and multiple initial currents are collected one by one according to the preset distance, and the initial position corresponding to each initial current is retained.

[0036] Step S102: Irradiate the target sample with polarized light and acquire the target image of the target sample under the polarized light illumination.

[0037] For example, a polarized light source, such as a polarized laser or a light source with a polarizing filter, can be used. Ensure the intensity and wavelength of the light source are suitable for the experimental requirements. Insert a polarizing filter between the polarized light source and the target sample, adjusting the polarization direction of the filter to match that of the light source. Then, fix the target sample on the slide, ensuring it is flat and free of air bubbles. The target sample can be fixed using sample clips on the microscope stage or a dedicated fixing device. Place the fixed sample on the microscope stage, adjusting its position to be within the illumination range of the polarized light source. Adjust the microscope's objective and eyepiece to ensure the sample is in optimal focus. Select an appropriate magnification to obtain the desired detail, then connect a high-resolution camera (such as a CCD or CMOS camera) to the microscope, ensuring the camera can capture a clear image of the target.

[0038] Step S103: Perform target recognition on the target image to obtain the first analysis result corresponding to the target sample.

[0039] For example, edge detection algorithms (such as Canny edge detection, Sobel operator, etc.) are used to extract edge information of the target image, thereby segmenting the target image into regions based on the edge information to obtain multiple target regions corresponding to the target image. Then, texture analysis methods (such as gray-level co-occurrence matrix, local binary mode, etc.) are used to extract texture features of the target regions. Texture features can provide microstructural information of the target regions, and color features of the target regions, such as RGB values, HSV values, etc., are extracted. Color features are also very useful in distinguishing different target types. Thus, the texture features and color features are fused to obtain the target features corresponding to the target regions. Finally, the extracted target features are matched with a pre-trained feature library to obtain the target type corresponding to the target regions, and the target regions and their corresponding target types are determined as the first analysis result corresponding to the target samples. Target types can be conventional cell types such as epithelial cells and fibroblasts in the digestive tract stroma, or special cell types such as nerve cells and lymphocytes.

[0040] In some embodiments, the step of performing target recognition on the target image to obtain a first analysis result corresponding to the target sample includes: determining an initial cluster center, and calculating a first distance between the initial cluster center and the target image based on first spatial information and first spectral information corresponding to each first pixel in the target image; optimizing the initial cluster center based on the first distance to obtain a target cluster center, and determining an initial segmentation result corresponding to the target image based on the target cluster center; determining a first rectangle containing the first segmentation result based on edge information corresponding to any first segmentation result in the initial segmentation result; determining a target interval, and determining a second rectangle corresponding to the first segmentation result based on the target interval and the first rectangle; determining a first associated region corresponding to the first segmentation result based on the first rectangle and the target image, and determining a second associated region corresponding to the first segmentation result based on the first rectangle, the second rectangle, and the target image; determining the target contrast of each second pixel in the first segmentation result in the first associated region and the second associated region based on the first associated region and the second associated region; performing image segmentation on the target image based on the target contrast to obtain a target segmentation result corresponding to the target image; and performing target recognition on the target segmentation result based on a target classification model to obtain the first analysis result.

[0041] For example, several pixels are randomly selected from the target image as initial cluster centers. The number of initial cluster centers can be determined based on a pre-defined number of categories or empirically. For each first pixel in the target image, its corresponding first spatial information (such as location coordinates) and first spectral information (such as color value) are extracted. Based on the first spatial information and first spectral information of each first pixel, the first distance between the pixel and each initial cluster center is calculated using Euclidean distance or other suitable distance metric.

[0042] For example, based on the calculated first distance, each first pixel is assigned to the initial cluster center that is closest to it. Then, the average spatial and spectral information of each cluster center is recalculated, and the position of the cluster center is updated. The above steps are repeated until the cluster centers no longer change significantly or a predetermined number of iterations is reached, and finally the target cluster centers are obtained.

[0043] For example, each pixel in the target image is assigned to its nearest target cluster center based on the target cluster center, forming an initial segmentation result. The initial segmentation result divides the target image into several target regions.

[0044] For example, edge information is extracted from any first segmentation result in the initial segmentation results, and then the minimum bounding rectangle required to contain the first segmentation result is determined based on the extracted edge information, and then the minimum bounding rectangle is determined as the first rectangle.

[0045] For example, a target interval is determined based on experimental requirements or experience. The target interval can represent the size of the expanded region in subsequent processing. A second rectangle is obtained by expanding the first rectangle by the target interval. That is, the second rectangle is the result of expanding the first rectangle by the target interval.

[0046] For example, a first rectangle is cut from the target image to obtain a first associated region corresponding to the first segmentation result. The first associated region is the part of the target image that overlaps with the first rectangle. Then, a third rectangle is obtained by subtracting the first rectangle from the second rectangle. Based on the third rectangle and the target image, a second associated region corresponding to the first segmentation result is determined. That is, the second associated region is the part of the target image that overlaps with the second rectangle but does not overlap with the first rectangle.

[0047] For example, for any second pixel in the first segmentation result, a first average similarity is calculated between it and each third pixel in the first associated region, and for any second pixel in the first segmentation result, a second average similarity is calculated between it and each fourth pixel in the second associated region. The ratio between the first average similarity and the second average similarity is then used to determine the target contrast of each second pixel between the first and second associated regions. The target contrast can be represented as whether there is a clear segmentation between the first segmentation result and its surrounding pixels. A higher target contrast indicates greater accuracy of the current first segmentation result.

[0048] For example, a preset value is determined. If the target contrast is greater than or equal to the preset value, the first segmentation result corresponding to the target contrast is determined to be accurate. If the target contrast is less than the preset value, the first segmentation result that is accurately segmented is removed from the target image, and the remaining image is re-segmented and the target contrast is recalculated until the target contrast is greater than or equal to the preset value, thereby obtaining the target segmentation result corresponding to the target image.

[0049] For example, a pre-trained target classification model is used to identify targets from the segmentation results. The target classification model can be a machine learning or deep learning-based model (such as a convolutional neural network). Based on the output of the target classification model, a first analysis result is obtained. The first analysis result may include the target segmentation results and the target type corresponding to each first segmentation result.

[0050] In some embodiments, determining the target contrast of each second pixel in the first segmentation result corresponding to the first associated region and the second associated region based on the first associated region and the second associated region includes: obtaining second spatial information and second spectral information corresponding to each second pixel in the first segmentation result; obtaining third spatial information and third spectral information corresponding to each third pixel in the first associated region, and obtaining fourth spatial information and fourth spectral information corresponding to each fourth pixel in the second associated region; determining a second distance between the second pixel and the first associated region based on the second spatial information, the second spectral information, the third spatial information, and the third spectral information; determining a third distance between the second pixel and the second associated region based on the second spatial information, the second spectral information, the fourth spatial information, and the fourth spectral information; and determining the target contrast of the second pixel between the first associated region and the second associated region based on the second distance and the third distance.

[0051] For example, for each second pixel in the first segmentation result, its second spatial information (e.g., position coordinates) and second spectral information (e.g., color value) are extracted. For each third pixel in the first associated region, the corresponding third spatial information and third spectral information are extracted. For each fourth pixel in the second associated region, the corresponding fourth spatial information and fourth spectral information are extracted.

[0052] For example, for each second pixel, a second distance is calculated using Euclidean distance or other suitable distance metric methods between the second spatial information and the second spectral information and the third spatial information and the third spectral information of all third pixels in the first associated region.

[0053] For example, for each second pixel, a third distance is calculated using Euclidean distance or other suitable distance metric to compare its second spatial information and second spectral information with the fourth spatial information and fourth spectral information of all fourth pixels in the second associated region.

[0054] For example, the target contrast of each second pixel between the first associated region and the second associated region is determined based on the second distance and the third distance. The target contrast can be expressed as the difference or ratio between the second distance and the third distance.

[0055] Step S104: Identify abnormal changes in the initial current to obtain the target current, and obtain the target position corresponding to the target current from the initial position.

[0056] For example, anomaly detection models are determined, such as statistical methods (e.g., mean-variance models, autoregressive models), machine learning methods (e.g., support vector machines, random forests, deep learning models), and rule-based methods (e.g., threshold detection). The initial current is then input into the trained anomaly detection model to obtain the target current corresponding to the initial current; that is, the target current is either a current that is significantly different from the initial current or a current whose distribution trend is significantly different from most of the currents in the initial current. Finally, the target position corresponding to each target current is obtained from the initial position.

[0057] In some implementations, the step of identifying abnormal changes in the initial current to obtain the target current includes: determining a first sub-current from the initial current, and obtaining a second sub-current from the initial current after excluding the first sub-current; calculating the current similarity between the first sub-current and the second sub-current; determining the current change degree corresponding to each of the first sub-currents in the initial current based on the current similarity; and identifying abnormal changes in the initial current based on the current change degree to obtain the target current.

[0058] For example, a current data point is selected from the initial current data as the first sub-current, and then, after excluding the first sub-current from the initial current, any one of the remaining current data points is obtained as the second sub-current.

[0059] For example, the current similarity between the first sub-current and the second sub-current is calculated using similarity calculation methods such as cosine similarity, Euclidean distance, and correlation coefficient. Then, the average value is calculated based on the current similarity between the first sub-current and each second sub-current to obtain the current change degree corresponding to each first sub-current in the determined initial current. The current change degree represents the distance information between the first sub-current and other sub-currents.

[0060] For example, a threshold for the degree of current change is set. The threshold can be determined based on empirical values, statistical analysis, or other standards. The degree of current change of each first sub-current is compared with the set threshold. If the degree of current change is less than the threshold, it is determined that the part of the current corresponding to the first sub-current has an abnormal change, thereby identifying the first sub-current whose current change is less than the threshold as the target current.

[0061] In some implementations, calculating the current similarity between the first sub-current and the second sub-current includes: performing a nearest neighbor set calculation on the first sub-current to obtain first nearest neighbor data, and performing a nearest neighbor set calculation on the second sub-current to obtain second nearest neighbor data; performing a nearest neighbor set calculation on each third sub-current in the first nearest neighbor data to obtain third nearest neighbor data, and performing a nearest neighbor set calculation on each fourth sub-current in the second nearest neighbor data to obtain fourth nearest neighbor data; performing an intersection calculation on the third nearest neighbor data and the first sub-current to obtain first reciprocal data corresponding to the first sub-current; and performing an intersection calculation on the fourth nearest neighbor data and the second sub-current to obtain the second sub-current corresponding to... The second reciprocal data; determining the target shared data between the first sub-current and the second sub-current based on the first reciprocal data and the second reciprocal data; calculating the intersection of the second nearest neighbor data and the first sub-current to obtain the intersection result corresponding to the first sub-current and the second sub-current; determining the reciprocal state between the first sub-current and the second sub-current based on the intersection result; determining the nearest neighbor value between the first sub-current and the second sub-current based on the reciprocal state; fusing the target shared data, the reciprocal state, and the nearest neighbor value to determine the current similarity between the first sub-current and the second sub-current; wherein, the current similarity is obtained according to the following formula:

[0062]

[0063]

[0064] Where sim(x1,x2) represents the current similarity between the first sub-current x1 and the second sub-current x2, share 12 This represents the target shared data between the first sub-current x1 and the second sub-current x2, where num represents the number of times the target shared data is obtained. The intersection result between the first sub-current x1 and the second sub-current x2 represents the intersection result between the two sub-currents. 12 Not equal to empty set At that time, The function value is 1 if the intersection result between the first sub-current x1 and the second sub-current x2 is inter. 12 Equal to empty set At that time, The function value is 0; dis(x1,x2) represents the distance information between the first sub-current x1 and the second sub-current x2, and e represents the natural constant. The intersection result between the first sub-current x1 and the second sub-current x2 represents the intersection result between the two sub-currents. 12 Not equal to empty set At that time, The function value is max, if the intersection result between the first sub-current x1 and the second sub-current x2 is inter 12 Equal to empty set At that time, The function value is 1, and max represents the maximum distance between any two currents in the initial current.

[0065] For example, the K-Nearest Neighbors (KNN) algorithm or other approximate nearest neighbor search methods are used to calculate the nearest neighbor set for the first sub-current, finding current data similar to the first sub-current to form the first nearest neighbor data. Similarly, the nearest neighbor set calculation is performed for the second sub-current, finding current data similar to the second sub-current to form the second nearest neighbor data.

[0066] For example, the K-Nearest Neighbors (KNN) algorithm or other approximate nearest neighbor search methods are used to calculate the nearest neighbor set for each third sub-current in the first nearest neighbor data, finding current data similar to each third sub-current to form the third nearest neighbor data. Similarly, the K-Nearest Neighbors (KNN) algorithm or other approximate nearest neighbor search methods are used to calculate the nearest neighbor set for each fourth sub-current in the second nearest neighbor data, finding current data similar to each fourth sub-current to form the fourth nearest neighbor data.

[0067] For example, the intersection of the third nearest neighbor data and the first sub-current is calculated. Specifically, the current data of the third nearest neighbor data of the third sub-current in the first nearest neighbor data that includes the first sub-current is found. This yields the current data corresponding to the condition where the third sub-current and the first sub-current in the first nearest neighbor data are each other's nearest neighbors. These current data, when the third sub-current and the first sub-current in the first nearest neighbor data are each other's nearest neighbors, together form the first mutually inverse data. In other words, the first mutually inverse data is the current data corresponding to the condition where the third sub-current and the first sub-current in the first nearest neighbor data are each other's nearest neighbors.

[0068] For example, if the first sub-current is x1, and the first nearest neighbor data of the first sub-current is x11, x12, and x13, then the third nearest neighbor data corresponding to the third sub-current x11 is obtained by performing nearest neighbor calculation on the third sub-current x11, which are x111, x112, and x113. If x111 is the same as x1, then when the intersection calculation of the third nearest neighbor data x111, x112, and x113 with the first sub-current x1 is performed, it can be determined that the first sub-current and the first nearest neighbor data x11 are each other's nearest neighbors, and x11 can be determined as a data in the first mutually inverse data; if x111, x112, and x113 are all different from x1, then the first mutually inverse data is an empty set.

[0069] Similarly, the intersection of the fourth nearest neighbor data and the second sub-current is calculated. That is, the current data that includes the second sub-current among the nearest neighbors of the fourth sub-current in the second nearest neighbor data is found, thereby obtaining the second reciprocal data corresponding to the second sub-current. In other words, the second reciprocal data is the current data corresponding to the fourth sub-current and the second sub-current being each other's nearest neighbors in the second nearest neighbor data.

[0070] For example, the intersection calculation is performed based on the first and second reciprocal data to determine the intersection calculation result as the target shared data between the first and second sub-currents. The target shared data represents the reciprocal data shared by the first and second sub-currents.

[0071] For example, the intersection of the second nearest neighbor data and the first sub-current is calculated to determine whether the first sub-current exists in the second nearest neighbor data, obtaining the intersection result of the first sub-current and the second sub-current. Then, based on the intersection result, the reversible state between the first sub-current and the second sub-current is determined. When the intersection result is not an empty set, that is, when the first sub-current exists in the second nearest neighbor data, the reversible state is the adjacent state where the first sub-current is the nearest neighbor of the second sub-current; when the intersection result is an empty set, that is, when the first sub-current does not exist in the second nearest neighbor data, the reversible state is the non-adjacent state where the first sub-current is the nearest neighbor of the second sub-current.

[0072] For example, the nearest neighbor value between the first subcurrent and the second subcurrent is determined based on the reversible state. The nearest neighbor value represents the degree of closeness between the first subcurrent and the second subcurrent in their nearest neighbor relationship. When the reversible state is an adjacent state where the first subcurrent is the nearest neighbor of the second subcurrent, the nearest neighbor value is 1; when the reversible state is a non-adjacent state where the first subcurrent is the nearest neighbor of the second subcurrent, the nearest neighbor value is 0.

[0073] For example, the current similarity between the first sub-current and the second sub-current is determined by fusing target shared data, reciprocal states, and nearest neighbor values ​​according to the following formula, taking into account three factors:

[0074]

[0075] Where sim(x1,x2) represents the current similarity between the first sub-current x1 and the second sub-current x2, share 12 This represents the target shared data between the first sub-current x1 and the second sub-current x2, where num represents the number of target shared data points obtained. The inter represents the intersection result between the first sub-current x1 and the second sub-current x2. 12 Not equal to empty set At that time, The function value is 1, if the intersection result between the first sub-current x1 and the second sub-current x2 is inter 12 Equal to empty set At that time, The function value is 0; dis(x1,x2) represents the distance information between the first subcurrent x1 and the second subcurrent x2, and e represents the natural constant. The inter represents the intersection result between the first sub-current x1 and the second sub-current x2. 12 Not equal to empty set At that time, The function value is max, if the intersection result between the first sub-current x1 and the second sub-current x2 is inter 12 Equal to empty set At that time, The function value is 1, and max represents the maximum distance between any two currents in the initial current.

[0076] For example, the number of currents contained in the target shared data is obtained through the num function, and then... Determine when the intersection result is inter 12 When it is an empty set, then it is determined. The result is equal to 0 when the intersection result is inter. 12 If it is not an empty set, then it is determined. The result is equal to 1. Therefore, num(share) 12 )and Perform summation.

[0077] For example, the distance information dis(x1,x2) between the first sub-current x1 and the second sub-current x2 is obtained through Euclidean distance calculation, and then... Determine when the intersection result is inter 12 When it is an empty set, then it is determined. The result is equal to 1 when the intersection result is inter 12 If it is not an empty set, then it is determined. The result equals max, where max represents the maximum distance between any two currents in the initial current. Finally, the current similarity between the first sub-current x1 and the second sub-current x2 is obtained according to the above formula.

[0078] Step S105: Determine the second analysis result corresponding to the target sample based on the target location and the target current.

[0079] For example, the target location is mapped to the target segmentation result to obtain the corresponding associated region of the target location in the target segmentation result, thereby obtaining all the current in the associated region from the initial current, and then determining the degree of abnormality corresponding to the associated region based on the ratio between the first quantity of the target current and the second quantity of the total current, thereby determining the second analysis result corresponding to the target sample based on the associated region and the degree of abnormality of the associated region.

[0080] In some embodiments, determining the second analysis result corresponding to the target sample based on the target location and the target current includes: determining a target coordinate system, then transforming the target segmentation result to the target coordinate system to obtain a target transformed segmentation result, and transforming the target location to the target coordinate system to obtain a target transformed location; transforming the initial location to the target coordinate system to obtain an initial transformed location; determining, based on the target transformed segmentation result and the target transformed location, that each second segmentation result in the target transformed segmentation result contains a first related current corresponding to the target current; determining, based on the target transformed segmentation result and the initial transformed location, that each second segmentation result in the target transformed segmentation result contains a second related current corresponding to the initial current; determining a preset current, comparing the first related current and the preset current to obtain a first comparison result; comparing the second related current and the preset current to obtain a second comparison result; and determining the second analysis result corresponding to the target sample based on the first comparison result and the second comparison result.

[0081] For example, a target coordinate system is determined, and then the target segmentation result is transformed to the target coordinate system to obtain the target transformed segmentation result. The target position is also transformed to the target coordinate system to obtain the target transformed position, and the initial position is transformed to the target coordinate system to obtain the initial transformed position. This ensures that the target segmentation result, the target position, and the initial position are in the same reference system, providing support for subsequent data alignment.

[0082] For example, it is determined whether the target transition position exists in the second segmentation result of the target transition segmentation result. If the target transition position exists in the second segmentation result of the target transition segmentation result, the target current corresponding to the target transition position is determined as the first related current corresponding to the second segmentation result; if the target transition position does not exist in the second segmentation result of the target transition segmentation result, the target current corresponding to the target transition position cannot be determined as the first related current corresponding to the second segmentation result. That is, the first related current is the target current contained in the second segmentation result.

[0083] For example, it is determined whether the initial transition position exists in the second segmentation result of the target transition segmentation result. If the initial transition position exists in the second segmentation result of the target transition segmentation result, the initial current corresponding to the initial transition position is determined as the second related current corresponding to the second segmentation result; if the initial transition position does not exist in the second segmentation result of the target transition segmentation result, the initial current corresponding to the initial transition position cannot be determined as the second related current corresponding to the second segmentation result. That is, the second related current is the initial current contained in the second segmentation result.

[0084] For example, a preset current is determined based on expert experience or experimental experience. A first relevant current is then compared with the preset current to obtain a first comparison result, whereby the first relevant current is greater than, less than, or equal to any of the preset currents. A second relevant current is then compared with the preset current to obtain a second comparison result, whereby the second relevant current is greater than, less than, or equal to any of the preset currents.

[0085] For example, after obtaining the first quantity corresponding to the first relevant current and the second quantity corresponding to the second relevant current, the second quantity is subtracted from the first quantity to determine the third quantity corresponding to the normal current in the second segmentation result. If the first quantity is greater than the third quantity, and the first comparison result is that the first relevant current is greater than the preset current, and the second comparison result is that the second relevant current is less than the preset current, then it can be known that the number of abnormal currents in the second segmentation result is greater than the number of normal currents, and thus it is determined that the second analysis result is that the target area corresponding to the second segmentation result is seriously abnormal; if the first quantity is less than the third quantity, then it can be known that the number of abnormal currents in the second segmentation result is less than the number of normal currents, and thus it is determined that the second analysis result is that the target area corresponding to the second segmentation result has become abnormal, and so on.

[0086] In some implementations, determining the second analysis result corresponding to the target sample based on the first comparison result and the second comparison result includes: when the first comparison result is that the first related current is greater than or equal to the preset current, and the second comparison result is that the second related current is less than the preset current, then determining the second analysis result corresponding to the second segmentation result based on the first related current; or, when the first comparison result is that the first related current is less than the preset current, and the second comparison result is that the second related current is greater than or equal to the preset current, then determining the second analysis result corresponding to the second segmentation result based on the second related current; or, when the first comparison result is that the first related current is greater than or equal to the preset current, and the second comparison result is that the second related current is greater than or equal to the preset current, then determining the second analysis result corresponding to the second segmentation result based on the first related current and the second related current.

[0087] For example, when the first comparison result is that the first related current is greater than or equal to the preset current, and the second comparison result is that the second related current is less than the preset current, the second analysis result corresponding to the second segmentation result is determined based on the first related current.

[0088] For example, if the first relevant current is greater than or equal to the preset current, it indicates that there is a special substance in the second segmentation result that causes the current in that region to increase. Then, the current difference between the first relevant current and the preset current is obtained, and the anomaly level corresponding to the second segmentation result is determined based on the current difference. Thus, the second segmentation result and the anomaly level corresponding to the second segmentation result are determined as the second analysis result.

[0089] For example, when the first comparison result is that the first related current is less than the preset current, and the second comparison result is that the second related current is greater than or equal to the preset current, the second analysis result corresponding to the second segmentation result is determined based on the second related current.

[0090] For example, when the first comparison result shows that the first related current is less than the preset current, and the second comparison result shows that the second related current is greater than or equal to the preset current, it indicates that the abnormal current in the target sample already occupies a large proportion, while the first related current less than the preset current is relatively small. Therefore, based on the current difference between the second related current and the preset current, the abnormality level corresponding to the second segmentation result is determined, and the second segmentation result and the abnormality level corresponding to the second segmentation result are determined as the second analysis result. Furthermore, if the first related current in the second segmentation result is empty, it indicates that the abnormality level corresponding to the second segmentation result is the highest level, suggesting that the sample of the second segmentation result is filled with a large number of special molecules.

[0091] For example, when the first comparison result is that the first related current is greater than or equal to the preset current, and the second comparison result is that the second related current is greater than or equal to the preset current, the second analysis result corresponding to the second segmentation result is determined based on the first related current and the second related current.

[0092] For example, when the first comparison result is that the first related current is greater than or equal to the preset current, and the second comparison result is that the second related current is greater than or equal to the preset current, then the current measurement results in the second segmentation result of the specification are all abnormal, and there are also abnormalities in the abnormal currents. Then, the difference between the first related current and the second related current is compared. If the first related current is greater than the second related current, then the second segmentation result of the specification has begun to deteriorate; if the first related current is less than or equal to the second related current, then the second segmentation result of the specification has already deteriorated significantly, and then the second analysis result is obtained.

[0093] For example, when the first comparison result is that the first related current is less than the preset current, and the second comparison result is that the second related current is less than the preset current, then the second analysis result is that the target area corresponding to the second segmentation result is normal.

[0094] Step S106: Combine the first analysis result and the second analysis result to determine the target analysis result corresponding to the target sample.

[0095] For example, the first analysis result and the second analysis result are combined to obtain the target analysis result corresponding to the target sample.

[0096] For example, the first analysis result is the target region and the target type corresponding to the target region, and the second analysis result is the second segmentation result and the anomaly level corresponding to the second segmentation result. Then, the second segmentation result is aligned with the target region in the first analysis result, so that the first analysis result and the second analysis result are merged to obtain the target region, the target type corresponding to the target region, and the anomaly level corresponding to the target region.

[0097] Please see Figure 2 , Figure 2This application provides an embodiment of a photoelectrochemical analysis system 200 for digestive tract interstitial fluid components. The system includes a data acquisition module 201, an image acquisition module 202, an image analysis module 203, an anomaly analysis module 204, a current analysis module 205, and a data fusion module 206. The data acquisition module 201 is used to determine the target sample corresponding to the digestive tract interstitial fluid components and to measure the current of the target sample using a potentiostat to obtain the initial current corresponding to the target sample and the initial position of the initial current in the target sample. The image acquisition module 202 is used to irradiate the target sample with polarized light. The system describes a target sample and acquires a target image corresponding to the target sample under polarized light illumination; an image analysis module 203 is used to perform target recognition on the target image to obtain a first analysis result corresponding to the target sample; an anomaly analysis module 204 is used to identify anomalies in the initial current to obtain a target current, and obtain a target position corresponding to the target current from the initial position; a current analysis module 205 is used to determine a second analysis result corresponding to the target sample based on the target position and the target current; and a data fusion module 206 is used to fuse the first analysis result and the second analysis result to determine a target analysis result corresponding to the target sample.

[0098] In some implementations, during the process of obtaining the first analysis result corresponding to the target sample by performing target recognition on the target image, the image analysis module 203 performs the following:

[0099] Determine the initial cluster center, and calculate the first distance between the target image and the initial cluster center based on the first spatial information and the first spectral information corresponding to each first pixel in the target image;

[0100] The initial cluster center is optimized based on the first distance to obtain the target cluster center, and the initial segmentation result corresponding to the target image is determined based on the target cluster center.

[0101] Determine the first rectangle containing the first segmentation result based on the edge information corresponding to any first segmentation result in the initial segmentation results;

[0102] Determine the target interval, and determine the second rectangle corresponding to the first segmentation result based on the target interval and the first rectangle;

[0103] A first associated region corresponding to the first segmentation result is determined based on the first rectangle and the target image, and a second associated region corresponding to the first segmentation result is determined based on the first rectangle, the second rectangle, and the target image;

[0104] The target contrast of each second pixel in the first segmentation result is determined in the first associated region and the second associated region based on the first associated region and the second associated region;

[0105] The target image is segmented based on the target contrast to obtain the target segmentation result corresponding to the target image;

[0106] The first analysis result is obtained by performing target identification on the target segmentation result based on the target classification model.

[0107] In some implementations, the image analysis module 203 performs the following steps during the process of determining the target contrast of each second pixel in the first segmentation result corresponding to the first associated region and the second associated region based on the first associated region and the second associated region:

[0108] Obtain the second spatial information and second spectral information corresponding to each second pixel in the first segmentation result;

[0109] Obtain the third spatial information and third spectral information corresponding to each third pixel in the first associated region, and obtain the fourth spatial information and fourth spectral information corresponding to each fourth pixel in the second associated region;

[0110] The second distance between the second pixel and the first associated region is determined based on the second spatial information, the second spectral information, the third spatial information, and the third spectral information;

[0111] A third distance between the second pixel and the second associated region is determined based on the second spatial information, the second spectral information, the fourth spatial information, and the fourth spectral information;

[0112] The target contrast of the second pixel between the first associated region and the second associated region is determined based on the second distance and the third distance.

[0113] In some implementations, during the process of identifying abnormal changes in the initial current to obtain the target current, the anomaly analysis module 204 performs the following:

[0114] A first sub-current is determined from the initial current, and a second sub-current is obtained from the initial current after excluding the first sub-current;

[0115] Calculate the current similarity between the first sub-current and the second sub-current;

[0116] The degree of current change corresponding to each of the first sub-currents in the initial current is determined based on the current similarity.

[0117] The initial current is identified for abnormal changes based on the current change rate, and the target current is obtained.

[0118] In some implementations, during the process of calculating the current similarity between the first sub-current and the second sub-current, the anomaly analysis module 204 performs the following:

[0119] The first nearest neighbor data is obtained by performing a nearest neighbor set calculation on the first sub-current, and the second nearest neighbor data is obtained by performing a nearest neighbor set calculation on the second sub-current.

[0120] The third nearest neighbor data is obtained by performing a nearest neighbor set calculation on each third sub-current in the first nearest neighbor data, and the fourth nearest neighbor data is obtained by performing a nearest neighbor set calculation on each fourth sub-current in the second nearest neighbor data.

[0121] The first reciprocal data corresponding to the first sub-current is obtained by intersecting the third nearest neighbor data and the first sub-current.

[0122] The second reciprocal data corresponding to the second sub-current is obtained by intersecting the fourth nearest neighbor data and the second sub-current.

[0123] The target shared data between the first sub-current and the second sub-current is determined based on the first reciprocal data and the second reciprocal data.

[0124] The intersection result of the first sub-current and the second sub-current is obtained by calculating the intersection of the second nearest neighbor data and the first sub-current.

[0125] The reciprocal state between the first sub-current and the second sub-current is determined based on the intersection result;

[0126] The nearest neighbor value between the first sub-current and the second sub-current is determined based on the reciprocal state;

[0127] The current similarity between the first sub-current and the second sub-current is determined by fusing the target shared data, the reciprocal state, and the nearest neighbor value.

[0128] The current similarity is obtained according to the following formula:

[0129]

[0130] Where sim(x1,x2) represents the current similarity between the first sub-current x1 and the second sub-current x2, share 12This represents the target shared data between the first sub-current x1 and the second sub-current x2, where num represents the number of times the target shared data is obtained. The intersection result between the first sub-current x1 and the second sub-current x2 represents the intersection result between the two sub-currents. 12 Not equal to empty set At that time, The function value is 1 if the intersection result between the first sub-current x1 and the second sub-current x2 is inter. 12 Equal to empty set At that time, The function value is 0; dis(x1,x2) represents the distance information between the first sub-current x1 and the second sub-current x2, and e represents the natural constant. The intersection result between the first sub-current x1 and the second sub-current x2 represents the intersection result between the two sub-currents. 12 Not equal to empty set At that time, The function value is max, if the intersection result between the first sub-current x1 and the second sub-current x2 is inter 12 Equal to empty set At that time, The function value is 1, and max represents the maximum distance between any two currents in the initial current.

[0131] In some implementations, the current analysis module 205 performs the following steps during the process of determining the second analysis result corresponding to the target sample based on the target location and the target current:

[0132] Once the target coordinate system is determined, the target segmentation result is transformed to the target coordinate system to obtain the target transformed segmentation result, and the target position is transformed to the target coordinate system to obtain the target transformed position;

[0133] The initial position is transformed to the target coordinate system to obtain the initial transformed position;

[0134] Based on the target conversion segmentation result and the target conversion position, it is determined that each second segmentation result in the target conversion segmentation result contains a first related current corresponding to the target current;

[0135] Based on the target conversion segmentation result and the initial conversion position, it is determined that each second segmentation result in the target conversion segmentation result contains a second related current corresponding to the initial current;

[0136] Determine a preset current, and compare the first relevant current with the preset current to obtain a first comparison result;

[0137] A second comparison result is obtained by comparing the second relevant current and the preset current;

[0138] The second analysis result corresponding to the target sample is determined based on the first comparison result and the second comparison result.

[0139] In some implementations, the current analysis module 205 performs the following steps during the process of determining the second analysis result corresponding to the target sample based on the first comparison result and the second comparison result:

[0140] When the first comparison result is that the first related current is greater than or equal to the preset current, and the second comparison result is that the second related current is less than the preset current, then the second analysis result corresponding to the second segmentation result is determined based on the first related current;

[0141] or,

[0142] When the first comparison result is that the first related current is less than the preset current, and the second comparison result is that the second related current is greater than or equal to the preset current, then the second analysis result corresponding to the second segmentation result is determined based on the second related current;

[0143] or,

[0144] When the first comparison result is that the first related current is greater than or equal to the preset current, and the second comparison result is that the second related current is greater than or equal to the preset current, then the second analysis result corresponding to the second segmentation result is determined based on the first related current and the second related current.

[0145] In some embodiments, the photoelectrochemical analysis system 200 for the components of the digestive tract interstitial fluid can be applied to terminal devices.

[0146] It should be noted that those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the photoelectrochemical analysis system 200 for the digestive tract interstitial fluid components described above can be referred to the corresponding process in the aforementioned embodiments of the photoelectrochemical analysis method for digestive tract interstitial fluid components, and will not be repeated here.

[0147] Please see Figure 3 , Figure 3 This is a schematic block diagram of the structure of a terminal device provided in an embodiment of the present invention.

[0148] like Figure 3As shown, the terminal device 300 includes a processor 301 and a memory 302, which are connected via a bus 303, such as an I2C (Inter-integrated Circuit) bus.

[0149] Specifically, processor 301 provides computing and control capabilities to support the operation of the entire terminal device. Processor 301 can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0150] Specifically, the memory 302 can be a Flash chip, a read-only memory (ROM) disk, an optical disk, a USB flash drive, or a portable hard drive, etc.

[0151] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the embodiments of the present invention, and does not constitute a limitation on the terminal device to which the embodiments of the present invention are applied. A specific server may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0152] The processor is used to run a computer program stored in a memory, and when executing the computer program, implements any of the photoelectrochemical analysis methods for digestive tract interstitial fluid components provided in the embodiments of the present invention.

[0153] In one embodiment, the processor is configured to run a computer program stored in memory, and when executing the computer program, perform the following steps:

[0154] The target sample corresponding to the components of the gastrointestinal interstitial fluid was identified, and the current of the target sample was measured using a potentiostat to obtain the initial current corresponding to the target sample and the initial position of the initial current in the target sample.

[0155] The target sample is illuminated with polarized light, and a target image of the target sample under the polarized light illumination is acquired.

[0156] The target image is subjected to target recognition to obtain the first analysis result corresponding to the target sample;

[0157] The initial current is subjected to abnormal change identification to obtain the target current, and the target position corresponding to the target current is obtained from the initial position;

[0158] The second analysis result corresponding to the target sample is determined based on the target location and the target current;

[0159] The target analysis result corresponding to the target sample is determined by combining the first analysis result and the second analysis result.

[0160] In some embodiments, during the process of obtaining the first analysis result corresponding to the target sample by performing target recognition on the target image, the processor 301 executes:

[0161] Determine the initial cluster center, and calculate the first distance between the target image and the initial cluster center based on the first spatial information and the first spectral information corresponding to each first pixel in the target image;

[0162] The initial cluster center is optimized based on the first distance to obtain the target cluster center, and the initial segmentation result corresponding to the target image is determined based on the target cluster center.

[0163] Determine the first rectangle containing the first segmentation result based on the edge information corresponding to any first segmentation result in the initial segmentation results;

[0164] Determine the target interval, and determine the second rectangle corresponding to the first segmentation result based on the target interval and the first rectangle;

[0165] A first associated region corresponding to the first segmentation result is determined based on the first rectangle and the target image, and a second associated region corresponding to the first segmentation result is determined based on the first rectangle, the second rectangle, and the target image;

[0166] The target contrast of each second pixel in the first segmentation result is determined in the first associated region and the second associated region based on the first associated region and the second associated region;

[0167] The target image is segmented based on the target contrast to obtain the target segmentation result corresponding to the target image;

[0168] The first analysis result is obtained by performing target identification on the target segmentation result based on the target classification model.

[0169] In some implementations, during the process of determining the target contrast of each second pixel in the first segmentation result corresponding to the first associated region and the second associated region based on the first associated region and the second associated region, the processor 301 performs the following:

[0170] Obtain the second spatial information and second spectral information corresponding to each second pixel in the first segmentation result;

[0171] Obtain the third spatial information and third spectral information corresponding to each third pixel in the first associated region, and obtain the fourth spatial information and fourth spectral information corresponding to each fourth pixel in the second associated region;

[0172] The second distance between the second pixel and the first associated region is determined based on the second spatial information, the second spectral information, the third spatial information, and the third spectral information;

[0173] A third distance between the second pixel and the second associated region is determined based on the second spatial information, the second spectral information, the fourth spatial information, and the fourth spectral information;

[0174] The target contrast of the second pixel between the first associated region and the second associated region is determined based on the second distance and the third distance.

[0175] In some implementations, during the process of identifying abnormal changes in the initial current to obtain the target current, the processor 301 performs the following:

[0176] A first sub-current is determined from the initial current, and a second sub-current is obtained from the initial current after excluding the first sub-current;

[0177] Calculate the current similarity between the first sub-current and the second sub-current;

[0178] The degree of current change corresponding to each of the first sub-currents in the initial current is determined based on the current similarity.

[0179] The initial current is identified for abnormal changes based on the current change rate, and the target current is obtained.

[0180] In some implementations, during the process of calculating the current similarity between the first sub-current and the second sub-current, the processor 301 performs the following:

[0181] The first nearest neighbor data is obtained by performing a nearest neighbor set calculation on the first sub-current, and the second nearest neighbor data is obtained by performing a nearest neighbor set calculation on the second sub-current.

[0182] The third nearest neighbor data is obtained by performing a nearest neighbor set calculation on each third sub-current in the first nearest neighbor data, and the fourth nearest neighbor data is obtained by performing a nearest neighbor set calculation on each fourth sub-current in the second nearest neighbor data.

[0183] The first reciprocal data corresponding to the first sub-current is obtained by intersecting the third nearest neighbor data and the first sub-current.

[0184] The second reciprocal data corresponding to the second sub-current is obtained by intersecting the fourth nearest neighbor data and the second sub-current.

[0185] The target shared data between the first sub-current and the second sub-current is determined based on the first reciprocal data and the second reciprocal data.

[0186] The intersection result of the first sub-current and the second sub-current is obtained by calculating the intersection of the second nearest neighbor data and the first sub-current.

[0187] The reciprocal state between the first sub-current and the second sub-current is determined based on the intersection result;

[0188] The nearest neighbor value between the first sub-current and the second sub-current is determined based on the reciprocal state;

[0189] The current similarity between the first sub-current and the second sub-current is determined by fusing the target shared data, the reciprocal state, and the nearest neighbor value.

[0190] The current similarity is obtained according to the following formula:

[0191]

[0192] Where sim(x1,x2) represents the current similarity between the first sub-current x1 and the second sub-current x2, share 12 This represents the target shared data between the first sub-current x1 and the second sub-current x2, where num represents the number of times the target shared data is obtained. The intersection result between the first sub-current x1 and the second sub-current x2 represents the intersection result between the two sub-currents. 12 Not equal to empty set At that time, The function value is 1 if the intersection result between the first sub-current x1 and the second sub-current x2 is inter. 12 Equal to empty set At that time, The function value is 0; dis(x1,x2) represents the distance information between the first sub-current x1 and the second sub-current x2, and e represents the natural constant. The intersection result between the first sub-current x1 and the second sub-current x2 represents the intersection result between the two sub-currents. 12 Not equal to empty set At that time, The function value is max, if the intersection result between the first sub-current x1 and the second sub-current x2 is inter 12 Equal to empty set At that time, The function value is 1, and max represents the maximum distance between any two currents in the initial current.

[0193] In some implementations, during the process of determining the second analysis result corresponding to the target sample based on the target location and the target current, the processor 301 executes:

[0194] Once the target coordinate system is determined, the target segmentation result is transformed to the target coordinate system to obtain the target transformed segmentation result, and the target position is transformed to the target coordinate system to obtain the target transformed position;

[0195] The initial position is transformed to the target coordinate system to obtain the initial transformed position;

[0196] Based on the target conversion segmentation result and the target conversion position, it is determined that each second segmentation result in the target conversion segmentation result contains a first related current corresponding to the target current;

[0197] Based on the target conversion segmentation result and the initial conversion position, it is determined that each second segmentation result in the target conversion segmentation result contains a second related current corresponding to the initial current;

[0198] Determine a preset current, and compare the first relevant current with the preset current to obtain a first comparison result;

[0199] A second comparison result is obtained by comparing the second relevant current and the preset current;

[0200] The second analysis result corresponding to the target sample is determined based on the first comparison result and the second comparison result.

[0201] In some implementations, during the process of determining the second analysis result corresponding to the target sample based on the first comparison result and the second comparison result, the processor 301 performs the following:

[0202] When the first comparison result is that the first related current is greater than or equal to the preset current, and the second comparison result is that the second related current is less than the preset current, then the second analysis result corresponding to the second segmentation result is determined based on the first related current;

[0203] or,

[0204] When the first comparison result is that the first related current is less than the preset current, and the second comparison result is that the second related current is greater than or equal to the preset current, then the second analysis result corresponding to the second segmentation result is determined based on the second related current;

[0205] or,

[0206] When the first comparison result is that the first related current is greater than or equal to the preset current, and the second comparison result is that the second related current is greater than or equal to the preset current, then the second analysis result corresponding to the second segmentation result is determined based on the first related current and the second related current.

[0207] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the terminal device described above can be referred to the corresponding process in the aforementioned embodiments of the photoelectrochemical analysis method for digestive tract interstitial fluid components, and will not be repeated here.

[0208] This invention also provides a storage medium for computer-readable storage, wherein the storage medium stores one or more programs that can be executed by one or more processors to implement the steps of any of the photoelectrochemical analysis methods for digestive tract interstitial fluid components provided in the specification of this invention.

[0209] The storage medium can be an internal storage unit of the terminal device described in the foregoing embodiments, such as the hard drive or memory of the terminal device. Alternatively, the storage medium can be an external storage device of the terminal device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the terminal device.

[0210] It will be understood by those skilled in the art that all or some of the steps, systems, or apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware embodiments, the division between functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0211] It should be understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. It should be noted that, herein, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0212] The sequence numbers of the above embodiments of the present invention are merely for descriptive purposes and do not represent the superiority or inferiority of the embodiments. The above descriptions are only specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A photoelectrochemical analysis method for digestive tract interstitial fluid components, characterized in that: The method comprises: Determine a target sample corresponding to a digestive tract interstitial fluid component, and use a constant potentiostat to measure the current of the target sample to obtain an initial current corresponding to the target sample and an initial position corresponding to the initial current in the target sample; irradiating the target sample with polarized light, and collecting a target image corresponding to the target sample under the irradiation of the polarized light; Performing target recognition on the target image to obtain a first analysis result corresponding to the target sample; Identifying abnormal changes in the initial current to obtain a target current, and obtaining a target position corresponding to the target current from the initial position; Determine a second analysis result corresponding to the target sample according to the target position and the target current; The first analysis result and the second analysis result are integrated to determine a target analysis result corresponding to the target sample.

2. The method according to claim 1, characterized in that The performing target recognition on the target image to obtain a first analysis result corresponding to the target sample includes: Determine an initial cluster center, and calculate a first distance between the initial cluster center and the first cluster center according to first spatial information and first spectral information corresponding to each first pixel in the target image; Optimizing the initial cluster center according to the first distance to obtain a target cluster center, and determining an initial segmentation result corresponding to the target image according to the target cluster center; Determine a first rectangle required to contain the first segmentation result according to edge information corresponding to any first segmentation result in the initial segmentation results; Determine a target interval, and determine a second rectangle corresponding to the first segmentation result according to the target interval and the first rectangle; Determine a first associated area corresponding to the first segmentation result according to the first rectangle and the target image, and determine a second associated area corresponding to the first segmentation result according to the first rectangle, the second rectangle and the target image; Determine, according to the first associated area and the second associated area, a target contrast corresponding to each second pixel in the first segmentation result in the first associated area and the second associated area; Performing image segmentation on the target image according to the target contrast to obtain a target segmentation result corresponding to the target image; The first analysis result is obtained by performing target recognition on the target segmentation result according to the target classification model.

3. The method according to claim 2, characterized in that The determining, according to the first associated area and the second associated area, a target contrast corresponding to each second pixel in the first segmentation result in the first associated area and the second associated area includes: Obtaining second spatial information and second spectral information corresponding to each of the second pixels in the first segmentation result; Obtaining third spatial information and third spectral information corresponding to each third pixel in the first associated area, and obtaining fourth spatial information and fourth spectral information corresponding to each fourth pixel in the second associated area; determining a second distance between the second pixel and the first associated area according to the second spatial information, the second spectral information, the third spatial information, and the third spectral information; determining a third distance between the second pixel and the second associated area according to the second spatial information, the second spectral information, the fourth spatial information, and the fourth spectral information; The target contrast corresponding to the second pixel between the first associated area and the second associated area is determined according to the second distance and the third distance.

4. The method according to claim 1, characterized in that: The step of identifying abnormal changes in the initial current to obtain a target current includes: determining a first sub-current from the initial current, and obtaining a second sub-current from the initial current after excluding the first sub-current; calculating a current similarity corresponding to the first sub-current and the second sub-current; Determining a current variation degree corresponding to each of the first sub-currents in the initial current according to the current similarity; The abnormal change of the initial current is identified according to the current change degree to obtain the target current.

5. The method according to claim 4, characterized in that The calculating the current similarity corresponding to the first sub-current and the second sub-current includes: Performing a neighbor set calculation on the first sub-current to obtain first neighbor data, and performing a neighbor set calculation on the second sub-current to obtain second neighbor data; Performing a neighbor set calculation on each third sub-current in the first neighbor data to obtain third neighbor data, and performing a neighbor set calculation on each fourth sub-current in the second neighbor data to obtain fourth neighbor data; Performing intersection calculation on the third neighbor data and the first sub-current to obtain first reciprocal data corresponding to the first sub-current; Performing intersection calculation on the fourth neighbor data and the second sub-current to obtain second reciprocal data corresponding to the second sub-current; determining target shared data between the first sub-current and the second sub-current according to the first reciprocal data and the second reciprocal data; Performing an intersection calculation based on the second nearest neighbor data and the first sub-current to obtain an intersection result corresponding to the first sub-current and the second sub-current; determining a reciprocal state between the first sub-current and the second sub-current according to the intersection result; determining a neighbor value between the first sub-current and the second sub-current according to the reciprocal state; fusing the target shared data, the reciprocal state, and the neighbor value to determine the current similarity corresponding to the first sub-current and the second sub-current; The current similarity is obtained according to the following formula: Wherein, sim(x1,x2) represents the current similarity between the first sub-current x1 and the second sub-current x2, share 12 represents the target shared data between the first sub-current x1 and the second sub-current x2, num represents the number corresponding to the target shared data obtained, represents the intersection result inter between the first sub-current x1 and the second sub-current x2 12 Not equal to the empty set When The function value of is 1, if the intersection result inter between the first sub-current x1 and the second sub-current x2 12 Equal to the empty set When The function value of is 0; dis(x1,x2) represents the distance information between the first sub-current x1 and the second sub-current x2, e represents a natural constant, represents the intersection result inter between the first sub-current x1 and the second sub-current x2 12 Not equal to the empty set When The function value of is max, if the intersection result inter between the first sub-current x1 and the second sub-current x2 is 12 Equal to the empty set When The function value of is 1, and max represents the maximum distance between any two currents in the initial current.

6. The method according to claim 2, characterized in that The determining a second analysis result corresponding to the target sample according to the target position and the target current includes: Determine a target coordinate system, then transform the target segmentation result into the target coordinate system to obtain a target transformed segmentation result, and transform the target position into the target coordinate system to obtain a target transformed position; Converting the initial position into the target coordinate system to obtain an initial converted position; Determine, according to the target conversion segmentation result and the target conversion position, that each second segmentation result in the target conversion segmentation result contains a first related current corresponding to the target current; Determine, according to the target conversion segmentation result and the initial conversion position, a second related current corresponding to the initial current in each of the second segmentation results in the target conversion segmentation result; Determine a preset current, and obtain a first comparison result by comparing the first related current with the preset current; Obtaining a second comparison result by comparing the second related current with the preset current; The second analysis result corresponding to the target sample is determined according to the first comparison result and the second comparison result.

7. The method according to claim 6, characterized in that The determining the second analysis result corresponding to the target sample according to the first comparison result and the second comparison result includes: When the first comparison result is that the first relevant current is greater than or equal to the preset current, and the second comparison result is that the second relevant current is less than the preset current, determining the second analysis result corresponding to the second segmentation result according to the first relevant current; or, When the first comparison result is that the first related current is less than the preset current, and the second comparison result is that the second related current is greater than or equal to the preset current, determining the second analysis result corresponding to the second segmentation result according to the second related current; or, When the first comparison result is that the first relevant current is greater than or equal to the preset current and the second comparison result is that the second relevant current is greater than or equal to the preset current, the second analysis result corresponding to the second segmentation result is determined according to the first relevant current and the second relevant current.

8. A photoelectrochemical analysis system for digestive tract interstitial fluid components, characterized in that: include: A data acquisition module is used to determine a target sample corresponding to a digestive tract interstitial fluid component, and to measure the current of the target sample using a constant potential instrument to obtain an initial current corresponding to the target sample and an initial position corresponding to the initial current in the target sample; An image acquisition module, used to illuminate the target sample with polarized light and acquire a target image corresponding to the target sample under the polarized light; An image analysis module, used to perform target recognition on the target image to obtain a first analysis result corresponding to the target sample; An abnormality analysis module, used for identifying abnormal changes in the initial current to obtain a target current, and obtaining a target position corresponding to the target current from the initial position; a current analysis module, configured to determine a second analysis result corresponding to the target sample according to the target position and the target current; A data fusion module is used to fuse the first analysis result and the second analysis result to determine a target analysis result corresponding to the target sample.

9. A terminal device, characterized in that: The terminal device includes a processor and a memory; The memory is used to store computer programs; The processor is used to execute the computer program and implement the photoelectrochemical analysis method of the digestive tract interstitial fluid components according to any one of claims 1 to 7 when executing the computer program.

10. A computer storage medium for computer storage, characterized in that: The computer storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the photoelectrochemical analysis method of the digestive tract interstitial fluid components described in any one of claims 1 to 7.

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