Titanium clip closing system and method for gastroesophageal varicosity treatment

Abstract

The invention relates to the technical field of medical instruments, in particular to a titanium clip closing system and method for gastroesophageal varicosity treatment. Collecting the actual clamping force fed back by each force sensor at a plurality of preset time nodes, and sending the collected actual clamping force to a memory; performing dimension reduction processing on each actual clamping force in the memory, performing clustering processing on each actual clamping force according to the final low-dimensional data matrix, and constructing an actual clamping force curve graph of each titanium clip in the preset time period according to the actual clamping force set of each titanium clip in the preset time period; comparing and analyzing the actual clamping force curve graph of each titanium clip with a corresponding preset clamping force curve graph; and if the clamping state of a certain titanium clip is an abnormal state, warning information is generated. Through automatic and real-time monitoring and accurate judgment, the safety and reliability of the titanium clip in the using process can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical devices, and in particular to a titanium clip clamping system and method for the treatment of gastroesophageal varices. Background Art

[0002] Gastroesophageal varices is a common digestive system disease, mainly occurring in patients with liver cirrhosis. Gastroesophageal varices (GOV) can be divided into type 1 and type 2, and the latter extends along the greater curvature of the gastric fundus to the esophagogastric junction. Traditional treatment methods include drug treatment, endoscopic treatment, and surgical operations, etc., but these methods have certain limitations and risks. In recent years, with the progress of medical technology, minimally invasive treatment methods have received more and more attention. In the treatment of gastroesophageal varices, endoscopic titanium clip clamping technology has become a safe and effective minimally invasive treatment method. However, during the operation of the titanium clip clamping system, the monitoring and control of the clamping force of the titanium clip clamping system have always been a key technical problem. There is a lack of an objective and accurate monitoring and feedback mechanism in the existing titanium clip clamping systems, and aspects such as the monitoring accuracy of the clamping force need to be further optimized and improved. Summary of the Invention

[0003] The present invention overcomes the deficiency of low monitoring accuracy of the clamping force in the prior art, and provides a titanium clip clamping system and method for the treatment of gastroesophageal varices.

[0004] To achieve the above object, the present invention discloses a control method for a titanium clip clamping system for the treatment of gastroesophageal varices, including the following steps: Obtain a preset clamping scheme, obtain the preset clamping forces of the titanium clips at several preset time nodes in each preset clamping point according to the preset clamping scheme, and construct a preset clamping force curve graph of the titanium clips in each preset clamping point within a preset time period according to the preset clamping forces of the titanium clips at several preset time nodes; After placing each titanium clip at each preset clamping point, collect the actual clamping forces feedback by each force sensor at several preset time nodes, and send the collected actual clamping forces to a memory; After the collection is completed, perform dimensionality reduction processing on the actual clamping forces in the memory to obtain a final low-dimensional data matrix; perform clustering processing on the actual clamping forces according to the final low-dimensional data matrix to obtain the actual clamping force sets of each titanium clip within a preset time period; Construct an actual clamping force curve graph of each titanium clip within a preset time period according to the actual clamping force sets of each titanium clip within a preset time period; Compare and analyze the actual clamping force curve graphs of each titanium clip with the corresponding preset clamping force curve graphs to obtain the clamping states of each titanium clip; if the clamping state of a certain titanium clip is an abnormal state, generate a warning message and send the warning message to a preset terminal for display.

[0005] More specifically, perform dimensionality reduction processing on each actual clamping force in the memory to obtain a final low-dimensional data matrix, specifically as follows: Construct a high-dimensional data matrix based on each actual clamping force in the memory; calculate the covariance between each actual clamping force in the high-dimensional data matrix, and construct a covariance matrix according to the covariance between each actual clamping force; Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues and corresponding eigenvectors; among them, the eigenvalue represents the importance of the feature, and the eigenvector represents the direction of the feature; According to the magnitudes of the eigenvalues, select the eigenvectors corresponding to the top k largest eigenvalues as the principal components, and construct a principal component matrix according to the principal components; where k is the number of dimensions after dimensionality reduction; Multiply the high-dimensional data matrix by the principal component matrix to obtain a low-dimensional data matrix, and perform redundancy detection and correction processing on the low-dimensional data matrix to obtain a final low-dimensional data matrix; Among them, each row of the final low-dimensional data matrix represents the position coordinates of the corresponding actual clamping force in the low-dimensional space, and each column represents the eigenvalue of the corresponding actual clamping force.

[0006] More specifically, perform redundancy detection and correction processing on the low-dimensional data matrix to obtain a final low-dimensional data matrix, specifically as follows: Calculate the Pearson correlation coefficient between each actual clamping force in the low-dimensional data matrix; and compare the Pearson correlation coefficient between each actual clamping force in the low-dimensional data matrix with a preset coefficient threshold; If the Pearson correlation coefficient between two actual clamping forces is greater than the preset coefficient threshold, mark these two actual clamping forces as a pair of redundant actual clamping forces; Perform merging processing on the pairs of actual clamping forces marked as redundant to correct the redundant actual clamping forces; and so on, after completing the correction processing process for all pairs of redundant actual clamping forces, obtain a final low-dimensional data matrix.

[0007] More specifically, perform clustering processing on each actual clamping force according to the final low-dimensional data matrix to obtain the set of actual clamping forces of each titanium clip within a preset time period, specifically as follows: Obtain the position coordinates of each actual clamping force in the low-dimensional space in the final low-dimensional data matrix, and calculate the Hamming distance between each actual clamping force according to the position coordinates of each actual clamping force in the low-dimensional space; Cluster the two actual clamping forces with the shortest Hamming distance to obtain N sets of actual clamping forces; calculate the weighted average value of each actual clamping force in each set of actual clamping forces, and determine the clustering center of the corresponding set of actual clamping forces according to the weighted average value; Calculate the Hamming distance of each set of actual clamping forces according to the clustering center of each set of actual clamping forces, and cluster the two sets of actual clamping forces with the shortest Hamming distance to obtain sets of actual clamping forces; continue to obtain the clustering center of each set of actual clamping forces; And so on, continuously iterate and cluster each set of actual clamping forces until the number of sets of actual clamping forces obtained by clustering is equal to the number of nodes of the preset clamping points, then stop the iteration and output the initial clustering result; Evaluate and correct the initial clustering result to obtain the final clustering result, and obtain the set of actual clamping forces of each titanium clip within the preset time period according to the final clustering result.

[0008] More specifically, evaluating and correcting the initial clustering result to obtain the final clustering result is specifically as follows: Extract each set of actual clamping forces in the initial clustering result, and calculate the ARI index value of each set of actual clamping forces; Compare the ARI index values of each set of actual clamping forces with the preset index threshold; if the ARI index values of all sets of actual clamping forces are not less than the preset index threshold, directly output the initial clustering result as the final clustering result; If there is at least one case where the ARI index value of a set of actual clamping forces is less than the preset index threshold, calculate the Euclidean distance between each actual clamping force in the set of actual clamping forces with an ARI index value less than the preset index threshold and the clustering center; Screen out the actual clamping forces with Euclidean distance greater than the preset distance threshold in the set of actual clamping forces with an ARI index value less than the preset index threshold to obtain the corrected set of actual clamping forces; And so on, after correcting all sets of actual clamping forces with an ARI index value less than the preset index threshold, obtain the final state of each set of actual clamping forces, and output the final clustering result according to the final state of each set of actual clamping forces.

[0009] More specifically, compare and analyze the actual clamping force curve of each titanium clip with the corresponding preset clamping force curve, and analyze the clamping state of each titanium clip, specifically as follows: Calculate the overlap degree between the actual clamping force curve of each titanium clip and the corresponding preset clamping force curve based on the Euclidean distance algorithm; Compare the overlap degree between the actual clamping force curve graph of each titanium clip and the corresponding preset clamping force curve graph with a preset overlap degree threshold value; If the overlap degree between the actual clamping force curve graph of a certain titanium clip and the corresponding preset clamping force curve graph is greater than the preset overlap degree threshold value, mark the clamping state of this titanium clip as the normal state; If the overlap degree between the actual clamping force curve graph of a certain titanium clip and the corresponding preset clamping force curve graph is not greater than the preset overlap degree threshold value, mark the clamping state of this titanium clip as the abnormal state.

[0010] To achieve the above object, the present invention also discloses a titanium clip clamping system for the treatment of gastroesophageal varices, which is applied to the control method of any one of the titanium clip clamping systems, and includes: A titanium clip body, which is made of titanium alloy; the length of the titanium clip body is 10 - 12 mm, and the opening angle is 135° - 180°; A force control system, which includes a force sensor, a force adjustment device, and a display; An endoscope, which is used to collect images of the lesion site; An endoscope introduction device, which is a catheter compatible with the endoscope; A control handle, which includes a handle body, a clamping trigger device, and a force adjustment knob; An image guidance system, which includes an endoscope camera interface, an image processing unit, and a display screen.

[0011] The present invention solves the technical defects existing in the background art, and the present invention has the following beneficial effects: obtaining a preset clamping scheme, obtaining the preset clamping force of the titanium clips at each preset clamping point at several preset time nodes according to the preset clamping scheme, and constructing a preset clamping force curve graph of the titanium clips at each preset clamping point within a preset time period according to the preset clamping force of the titanium clips at several preset time nodes; when each titanium clip is placed at each preset clamping point, collecting the actual clamping force feedback by each force sensor at several preset time nodes, and sending the collected actual clamping force to the memory; after the collection is completed, performing dimensionality reduction processing on each actual clamping force in the memory to obtain a final low-dimensional data matrix; performing clustering processing on each actual clamping force according to the final low-dimensional data matrix to obtain the actual clamping force set of each titanium clip within a preset time period; constructing an actual clamping force curve graph of each titanium clip within a preset time period according to the actual clamping force set of each titanium clip within a preset time period; comparing and analyzing the actual clamping force curve graph of each titanium clip with the corresponding preset clamping force curve graph to analyze the clamping state of each titanium clip; if the clamping state of a certain titanium clip is an abnormal state, generating a warning message and sending the warning message to a preset terminal for display. Through automated, real-time monitoring and accurate judgment, the safety and reliability in the use of titanium clips can be effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings.

[0013] Figure 1 It is the overall method flow chart of the control method of this titanium clip clamping system; Figure 2 It is a partial method flow chart of the control method of this titanium clip clamping system. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0014] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0015] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0016] As Figure 1 shown, the present invention discloses a control method for a titanium clip clamping system for the treatment of gastroesophageal varices, comprising the following steps: S102: Obtain a preset clamping scheme, obtain the preset clamping forces of the titanium clips at several preset time nodes in each preset clamping point according to the preset clamping scheme, and construct a preset clamping force curve graph of the titanium clips in each preset clamping point within a preset time period according to the preset clamping forces of the titanium clips at several preset time nodes; S104: After placing each titanium clip at each preset clamping point, collect the actual clamping forces fed back by each force sensor at several preset time nodes, and send the collected actual clamping forces to a memory; S106: After the collection is completed, perform dimensionality reduction processing on the actual clamping forces in the memory to obtain a final low-dimensional data matrix; perform clustering processing on the actual clamping forces according to the final low-dimensional data matrix to obtain the actual clamping force sets of each titanium clip within a preset time period; S108: Construct an actual clamping force curve graph of each titanium clip within a preset time period according to the actual clamping force sets of each titanium clip within a preset time period; S110: Compare and analyze the actual clamping force curve graphs of each titanium clip with the corresponding preset clamping force curve graphs, and analyze the clamping states of each titanium clip; if the clamping state of a certain titanium clip is an abnormal state, generate a warning message and send the warning message to a preset terminal for display.

[0017] Among them, the preset clamping scheme is formulated by medical staff, and the preset clamping scheme includes the clamping force range of the titanium clip, the placement position of the titanium clip, the clamping time, etc.

[0018] More specifically. As Figure 2 shown, perform dimensionality reduction processing on the actual clamping forces in the memory to obtain a final low-dimensional data matrix, specifically: S202: Construct a high-dimensional data matrix according to the actual clamping forces in the memory; calculate the covariance between the actual clamping forces in the high-dimensional data matrix, and construct a covariance matrix according to the covariance between the actual clamping forces; S204: Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues and corresponding eigenvectors; among them, the eigenvalue represents the importance of the feature, and the eigenvector represents the direction of the feature; S206: According to the magnitudes of the eigenvalues, select the eigenvectors corresponding to the first k largest eigenvalues as the principal components, and construct a principal component matrix according to the principal components; where k is the number of dimensions after dimensionality reduction; S208: Multiply the high-dimensional data matrix by the principal component matrix to obtain a low-dimensional data matrix, and perform redundancy detection and correction processing on the low-dimensional data matrix to obtain a final low-dimensional data matrix; Among them, each row of the final low-dimensional data matrix represents the position coordinates of the corresponding actual clamping force in the low-dimensional space, and each column represents the eigenvalue of the corresponding actual clamping force.

[0019] It should be noted that by using the principal component analysis method to reduce the dimension of each actual clamping force in the memory, a final low-dimensional data matrix is obtained. By reducing the dimension of the data, the size of the data set is reduced, thereby achieving data compression, which helps to reduce the storage space requirement and improve the data processing speed.

[0020] More specifically, performing redundancy detection and correction processing on the low-dimensional data matrix to obtain a final low-dimensional data matrix is specifically as follows: Calculate the Pearson correlation coefficient between each actual clamping force in the low-dimensional data matrix; and compare the Pearson correlation coefficient between each actual clamping force in the low-dimensional data matrix with a preset coefficient threshold; If the Pearson correlation coefficient between two actual clamping forces is greater than the preset coefficient threshold, then mark these two actual clamping forces as a pair of redundant actual clamping forces; Perform a merging process on the pairs of redundant actual clamping forces marked as such to correct the redundant actual clamping forces; and so on. After completing the correction process for all pairs of redundant actual clamping forces, a final low-dimensional data matrix is obtained.

[0021] It should be noted that first, a high-dimensional data matrix containing different actual clamping forces is constructed, and the covariance between these clamping forces is calculated to construct a covariance matrix. Then, through eigenvalue decomposition, the most important eigenvectors are extracted from the covariance matrix as the principal components, and a principal component matrix is constructed. Next, the high-dimensional data matrix is projected onto the principal component matrix to obtain a low-dimensional data matrix.

[0022] Principal component analysis is a dimensionality reduction technique that projects high-dimensional data into a low-dimensional space through linear transformation. It achieves data compression and simplification by extracting the main directions of variation in the original data, namely the principal components. However, even the principal components may introduce redundancy in the reduced data matrix for the following reasons: When principal component analysis extracts the principal components, it mainly focuses on the global data variation trend. However, for local data features, there may be some subtle correlations that may not be fully expressed in the principal components, resulting in redundancy in the reduced data matrix. Also, although principal component analysis ensures the linear independence between the principal components through orthogonal transformation, in the original high-dimensional space, different features may have partial overlap or high similarity, and this overlap may not be completely eliminated during the dimensionality reduction process, thus forming redundancy in the low-dimensional matrix. In addition, in the original data, there may be some noise or outliers, and these noises may be amplified during the dimensionality reduction process of principal component analysis, thus forming redundancy in the low-dimensional data matrix. Therefore, although principal component analysis is an effective dimensionality reduction method, in practical applications, the low-dimensional data matrix may still have redundancy. To solve this problem, the present invention also proposes a method for redundancy detection and correction processing. The specific steps include: calculating the Pearson correlation coefficients between the actual clamping forces in the low-dimensional data matrix and comparing these coefficients with a preset threshold. If the correlation coefficient between two actual clamping forces exceeds the threshold, they are considered to have redundancy. For the redundant pairs of actual clamping forces, merge processing is performed to correct the redundancy problem. In this way, the redundant information in the low-dimensional data matrix can be removed to obtain the final low-dimensional data matrix.

[0023] In summary, through covariance matrix and eigenvalue decomposition, the key information in the actual clamping forces is effectively extracted, achieving dimensionality reduction of the data; by selecting the eigenvectors corresponding to the first k largest eigenvalues, the number of dimensions after dimensionality reduction can be controlled, realizing compression and simplification of the data; redundancy detection and correction processing further improves the quality and usability of the data after dimensionality reduction, avoiding the influence of redundant information on subsequent analysis; the finally generated low-dimensional data matrix can provide a clearer and more accurate data basis for subsequent data analysis and processing. The dimensionality reduction processing method based on the high-dimensional data matrix and its redundancy detection and correction processing provide an effective technical means for data analysis and processing of actual clamping forces, helping to improve the quality and efficiency of data processing, thereby effectively improving the monitoring accuracy of the clamping force.

[0024] More specifically, clustering processing is performed on each actual clamping force according to the final low-dimensional data matrix to obtain the set of actual clamping forces of each titanium clip within a preset time period, specifically: Obtain the position coordinates of each actual clamping force in the low-dimensional space in the final low-dimensional data matrix, and calculate the Hamming distance between each actual clamping force according to the position coordinates of each actual clamping force in the low-dimensional space; Perform clustering processing on the two actual clamping forces with the shortest Hamming distance to obtain N sets of actual clamping forces; calculate the weighted average value of each actual clamping force in each set of actual clamping forces, and determine the clustering center of the corresponding set of actual clamping forces according to the weighted average value; Calculate the Hamming distance of each set of actual clamping forces according to the clustering center of each set of actual clamping forces, and perform clustering processing on the two sets of actual clamping forces with the shortest Hamming distance to obtain sets of actual clamping forces; continue to obtain the clustering center of each set of actual clamping forces; And so on, continuously perform iterative clustering processing on each set of actual clamping forces until the number of sets of actual clamping forces obtained by clustering is equal to the number of nodes of the preset clamping points, then stop the iteration, and output the initial clustering result; Perform evaluation and correction processing on the initial clustering result to obtain the final clustering result, and obtain the set of actual clamping forces of each titanium clip within the preset time period according to the final clustering result.

[0025] It should be noted that by calculating the Hamming distance of the position coordinates of the actual clamping force in the low-dimensional space, iterative clustering processing is performed, and finally the set of actual clamping forces of each titanium clip within the preset time period is determined. The specific steps include: First, obtain the position coordinates of each actual clamping force in the low-dimensional space, and calculate the Hamming distance between the actual clamping forces based on these coordinates. Then, perform clustering through the shortest Hamming distance to form multiple sets of actual clamping forces, and calculate the weighted average value of each set as the clustering center. Next, calculate the Hamming distance again according to the clustering center and perform clustering processing, gradually reducing the number of sets of actual clamping forces until the number of preset clamping point nodes (i.e., the number of titanium clips in the work) is reached. Finally, evaluate and correct the initial clustering result to obtain the final clustering result. By obtaining the position coordinates of the actual clamping force in the low-dimensional space, the relationship and distribution between each clamping force can be more accurately located and understood. And using the Hamming distance for clustering processing can identify similar or related sets of actual clamping forces, which helps to group and classify the data, helps to find the optimal set of clamping forces, and improves the accuracy of clustering. Through the final clustering result, the set of actual clamping forces of each titanium clip within the preset time period can be determined, which helps to optimize the monitoring of the clamping force. In summary, this iterative clustering analysis method based on the Hamming distance can realize the monitoring of the clamping force of each titanium clip through steps such as precise positioning, effective clustering, gradual refinement, evaluation and correction, etc., has high accuracy and reliability, and provides strong support for clinical decision-making.

[0026] More specifically, the initial clustering result is evaluated and corrected to obtain the final clustering result, specifically as follows: Extract each actual clamping force set in the initial clustering result, and calculate the ARI index value of each actual clamping force set; Among them, the ARI index, full name "Adjusted Rand Index", is a statistic for measuring the similarity between two clusters, and is used to evaluate the effect of clustering analysis. The value range of the ARI index is [-1, 1]. Among them: 1 means that the two data partitions are exactly the same, that is, their clustering results are exactly the same; 0 means that the similarity of the two data partitions is the same as the expected value of random label assignment, that is, there is no better consistency than random; -1 means that the two data partitions are exactly opposite, that is, their clustering results are completely inconsistent. The ARI index takes into account the consistency between the two data partitions and the randomness of label assignment, so it can more accurately evaluate the similarity of the two clustering results; Compare the ARI index value of each actual clamping force set with the preset index threshold; if the ARI index values of all actual clamping force sets are not less than the preset index threshold, directly output the initial clustering result as the final clustering result; If there is at least one case where the ARI index value of an actual clamping force set is less than the preset index threshold, calculate the Euclidean distance between each actual clamping force in the actual clamping force set with an ARI index value less than the preset index threshold and the aggregation center; Screen out the actual clamping forces with Euclidean distances greater than the preset distance threshold in the actual clamping force set with an ARI index value less than the preset index threshold to obtain the corrected actual clamping force set; And so on. After all the actual clamping force sets with ARI index values less than the preset index threshold are corrected, obtain the final state of each actual clamping force set, and output the final clustering result according to the final state of each actual clamping force set.

[0027] It should be noted that first, each actual clamping force set is extracted from the initial clustering result, and their ARI index values are calculated. Then, these index values are compared with a preset threshold to evaluate the quality of the clustering result. If the ARI index values of all actual clamping force sets are not lower than the preset threshold, the initial clustering result is considered good enough to be directly output as the final result. If there is at least one ARI index value of an actual clamping force set lower than the preset threshold, it indicates that there may be problems with this clustering result and further optimization is needed. At this time, the Euclidean distance between these actual clamping forces and the cluster center is calculated, and the points with distances greater than the preset threshold are screened out to correct the clustering result. This process may be repeated multiple times until the ARI index values of all actual clamping force sets are not lower than the preset threshold. Through this clustering result optimization method based on the ARI index, the quality of the clustering result can be improved, the consistency and accuracy of the clustering result can be ensured, the clustering result can be made more stable and reliable, and the quality and effect of cluster analysis can be effectively improved.

[0028] More specifically, the actual clamping force curve graphs of each titanium clip are compared and analyzed with the corresponding preset clamping force curve graphs, and the clamping states of each titanium clip are obtained through analysis, specifically as follows: Calculate the overlap degree between the actual clamping force curve graph of each titanium clip and the corresponding preset clamping force curve graph based on the Euclidean distance algorithm; Compare the overlap degree between the actual clamping force curve graph of each titanium clip and the corresponding preset clamping force curve graph with a preset overlap degree threshold; If the overlap degree between the actual clamping force curve graph of a certain titanium clip and the corresponding preset clamping force curve graph is greater than the preset overlap degree threshold, mark the clamping state of this titanium clip as the normal state; If the overlap degree between the actual clamping force curve graph of a certain titanium clip and the corresponding preset clamping force curve graph is not greater than the preset overlap degree threshold, mark the clamping state of this titanium clip as the abnormal state.

[0029] It should be noted that by comparing the overlap degree between the actual clamping force curve graph and the preset clamping force curve graph, the automatic judgment of the clamping state of the titanium clip and the abnormal warning are realized. Specifically, first, the Euclidean distance algorithm is used to calculate the overlap degree between the actual clamping force curve graph and the preset clamping force curve graph of each titanium clip. Then, the calculated overlap degree is compared with the preset overlap degree threshold to judge the clamping state of the titanium clip. If the overlap degree is greater than the threshold, it is considered that the clamping state of the titanium clip is normal; if the overlap degree is not greater than the threshold, it is considered that the clamping state of the titanium clip is abnormal, and a warning message is generated and sent to the preset terminal for display. Through the automated calculation and comparison process, the workload of manual monitoring is reduced, the monitoring efficiency is improved, the clamping state of the titanium clip can be monitored and judged in real time, abnormal situations can be detected in time, and a time window for subsequent processing is provided. When an abnormal clamping state is detected, the system will automatically generate a warning message and send it to the preset terminal for display, facilitating relevant personnel to take corresponding measures in time. To sum up, through automated and real-time monitoring and accurate judgment, the safety and reliability in the process of using the titanium clip can be effectively improved. Analyzing and judging based on the actual clamping force data makes the decision-making process more data-driven and reduces the deviation of subjective judgment.

[0030] To achieve the above object, the present invention also discloses a titanium clip clamping system for the treatment of gastroesophageal varices, which is applied to the control method of any one of the above-mentioned titanium clip clamping systems, and includes: A titanium clip body, which is made of titanium alloy; the length of the titanium clip body is 10 - 12 mm, and the opening angle is 135° - 180°; A force control system, which includes a force sensor, a force adjusting device, and a display; An endoscope, which is used to collect images of the lesion site; An endoscope introduction device, which is a catheter compatible with the endoscope; A control handle, which includes a handle main body, a clamping trigger device, and a force adjusting knob; An image guidance system, which includes an endoscope camera interface, an image processing unit, and a display screen.

[0031] In several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed with each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0032] The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units; they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0033] In addition, each functional unit in the embodiments of the present invention can be all integrated in a processing unit, or each unit can be separately used as a unit, or two or more units can be integrated in a unit; the above-mentioned integrated units can be implemented in the form of hardware, or in the form of hardware plus software functional units.

[0034] Those of ordinary skill in the art can understand that all or part of the steps to implement the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments; and the foregoing storage medium includes: removable storage devices, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical disks and other various media that can store program codes.

[0035] Alternatively, if the above-mentioned integrated units of the present invention are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention essentially or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the various embodiments of the present invention. And the foregoing storage medium includes: removable storage devices, ROM, RAM, magnetic disks or optical disks and other various media that can store program codes.

[0036] The above are only the specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A control method for a titanium clip closure system for treating gastroesophageal varices, characterized in that: The following steps are involved: Obtaining a preset clamping scheme, obtaining a preset clamping force of the titanium clamp at each preset clamping point at a number of preset time nodes according to the preset clamping scheme, and constructing a preset clamping force curve graph of the titanium clamp at each preset clamping point within a preset time period according to the preset clamping force of the titanium clamp at a number of preset time nodes; After each titanium clamp is placed at each preset clamping point, the actual clamping force fed back by each force sensor is collected at a number of preset time nodes, and the collected actual clamping force is sent to the memory; After the acquisition is completed, the actual clamping forces in the memory are subjected to dimensionality reduction processing to obtain the final low-dimensional data matrix; Clustering is performed on each actual clamping force according to the final low-dimensional data matrix to obtain the actual clamping force set of each titanium clamp within a preset time period; Constructing an actual clamping force curve graph of each titanium clamp within a preset time period according to an actual clamping force set of each titanium clamp within a preset time period; Compare and analyze the actual clamping force curve of each titanium clip with the corresponding preset clamping force curve to obtain the clamping state of each titanium clip; If the clamping state of a titanium clip is abnormal, a warning message is generated and sent to a preset terminal for display.

2. The control method of the titanium clip closure system for treating gastroesophageal varices according to claim 1, characterized in that: The actual clamping forces in the memory are subjected to dimensionality reduction processing to obtain the final low-dimensional data matrix, which is: A high-dimensional data matrix is ​​constructed according to each actual clamping force in the memory; Calculate the covariance between the actual clamping forces in the high-dimensional data matrix, and construct a covariance matrix according to the covariance between the actual clamping forces; Performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and corresponding eigenvectors; wherein the eigenvalues ​​represent the importance of the features, and the eigenvectors represent the direction of the features; According to the size of the eigenvalue, the eigenvectors corresponding to the first k largest eigenvalues ​​are selected as principal components, and a principal component matrix is ​​constructed according to the principal components; wherein k is the number of dimensions after dimensionality reduction; Multiplying the high-dimensional data matrix by the principal component matrix to obtain a low-dimensional data matrix, performing redundancy detection and correction processing on the low-dimensional data matrix to obtain a final low-dimensional data matrix; Among them, each row of the final low-dimensional data matrix represents the position coordinates of the corresponding actual clamping force in the low-dimensional space, and each column represents the eigenvalue of the corresponding actual clamping force.

3. The control method of the titanium clip closure system for treating gastroesophageal varices according to claim 2, characterized in that: The low-dimensional data matrix is ​​subjected to redundancy detection and correction processing to obtain a final low-dimensional data matrix, which is specifically: Calculating the Pearson correlation coefficient between each actual clamping force in the low-dimensional data matrix; and comparing the Pearson correlation coefficient between each actual clamping force in the low-dimensional data matrix with a preset coefficient threshold; If the Pearson correlation coefficient between two actual clamping forces is greater than a preset coefficient threshold, the two actual clamping forces are marked as a redundant actual clamping force pair; The actual clamping force pairs marked as redundant are merged to correct the redundant actual clamping force; In this way, after completing the correction process for all redundant actual clamping force pairs, the final low-dimensional data matrix is ​​obtained.

4. The control method of the titanium clip closure system for treating gastroesophageal varices according to claim 1, characterized in that: The actual clamping forces are clustered according to the final low-dimensional data matrix to obtain the actual clamping force set of each titanium clamp within the preset time period, specifically: Obtaining the position coordinates of each actual clamping force in the low-dimensional space in the final low-dimensional data matrix, and calculating the Hamming distance between each actual clamping force according to the position coordinates of each actual clamping force in the low-dimensional space; Clustering the two actual clamping forces with the shortest Hamming distance to obtain N actual clamping force sets; calculating the weighted average of each actual clamping force in each actual clamping force set, and determining the clustering center of the corresponding actual clamping force set according to the weighted average; The Hamming distance of each actual clamping force set is calculated according to the clustering center of each actual clamping force set, and the two actual clamping force sets with the shortest Hamming distance are clustered to obtain actual clamping force sets; continue to obtain the aggregation center of each actual clamping force set; In this way, the clustering process is continuously iterated for each actual clamping force set until the number of sets of actual clamping force sets obtained by clustering is equal to the number of nodes of the preset clamping points, then the iteration is stopped and the initial clustering result is output; The initial clustering results are evaluated and corrected to obtain final clustering results, and the actual clamping force set of each titanium clamp within a preset time period is obtained according to the final clustering results.

5. The control method of the titanium clip closure system for treating gastroesophageal varices according to claim 4, characterized in that: The initial clustering result is evaluated and corrected to obtain the final clustering result, which is specifically: Extracting each actual clamping force set in the initial clustering result, and calculating the ARI index value of each actual clamping force set; Compare the ARI index value of each actual clamping force set with the preset index threshold; if the ARI index values ​​of all actual clamping force sets are not less than the preset index threshold, directly output the initial clustering result as the final clustering result; If there is a situation where the ARI index value of at least one actual clamping force set is less than the preset index threshold, then the Euclidean distance between each actual clamping force in the actual clamping force set whose ARI index value is less than the preset index threshold and the aggregation center is calculated; The actual clamping force whose Euclidean distance is greater than a preset distance threshold is screened out from the actual clamping force set whose ARI index value is less than a preset index threshold, to obtain a corrected actual clamping force set; Similarly, after all actual clamping force sets with ARI index values ​​less than the preset index threshold are corrected, the final states of each actual clamping force set are obtained, and the final clustering results are output according to the final states of each actual clamping force set.

6. The control method of the titanium clip closure system for treating gastroesophageal varices according to claim 1, characterized in that: The actual clamping force curve of each titanium clip is compared and analyzed with the corresponding preset clamping force curve, and the clamping state of each titanium clip is obtained by analysis, specifically: Calculate the overlap between the actual clamping force curve graph of each titanium clip and the corresponding preset clamping force curve graph based on the Euclidean distance algorithm; Compare the overlap between the actual clamping force curve graph of each titanium clip and the corresponding preset clamping force curve graph with a preset overlap threshold; If the overlap between the actual clamping force curve of a titanium clip and the corresponding preset clamping force curve is greater than the preset overlap threshold, the clamping state of the titanium clip is marked as a normal state; If the degree of overlap between the actual clamping force curve graph of a titanium clip and the corresponding preset clamping force curve graph is not greater than the preset overlap threshold, the clamping state of the titanium clip is marked as an abnormal state.

7. A titanium clip closure system for treating gastroesophageal varices, applied to the control method of the titanium clip closure system according to any one of claims 1 to 6, characterized in that: include: A titanium clip body, wherein the titanium clip body is made of titanium alloy; the length of the titanium clip body is 10-12 mm, and the opening angle is 135°-180°; A force control system, the force control system comprising a force sensor, a force adjustment device and a display; An endoscope, which is used to collect images of lesion sites; An endoscope introduction device, wherein the endoscope introduction device is a catheter compatible with an endoscope; A control handle, the control handle comprising a handle body, a clamping trigger device and a force adjustment knob; An image guidance system includes an endoscope camera interface, an image processing unit and a display screen.

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