A medical model-based needle insertion learning system

Abstract

The application provides a medical model-based needle learning system, and belongs to the technical field of medical virtual learning. The system comprises a model construction module, a model optimization module and a needle practice module. The allowed needle part of a human body is determined, a basic structure model is constructed, a standard needle of the allowed needle part and a structural resistance are attached to corresponding position points of the basic model, a medical model is obtained, and the operation of a learning object in a practice operation process is compared with a corresponding needle standard, so that the practice needle operation in a virtual environment is not limited in number, the actual operation level and speed of the learning object are improved, and the actual operation risk is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical virtual learning, and particularly relates to a needle insertion learning system based on a medical model. BACKGROUND

[0002] With the development of medicine, needle insertion is a widely used treatment method, whether it is intravenous injection or other injection methods. However, in actual operation, due to various reasons, sufficient practical training cannot be carried out, which leads to the fact that students cannot fully master various skills, and thus actual operation level cannot be improved and operation risk is increased. With the continuous progress and application of medical technology, virtual simulation technology is increasingly used in medical education and practice, but there is a lack of medical scenarios for needle insertion learning.

[0003] Therefore, the present application provides a needle insertion learning system based on a medical model. SUMMARY

[0004] The present application provides a needle insertion learning system based on a medical model, which is used to determine allowed needle insertion parts of a human body, construct a basic structure model, attach standard needles of the allowed needle insertion parts and structure resistances to corresponding position points of the basic model, obtain a medical model, compare operation of a learning object in practice with corresponding needle insertion standards, and thus realize unlimited practice of needle insertion operation in a virtual environment, improve actual operation level and speed of the learning object, and reduce actual operation risk.

[0005] The present application provides a needle insertion learning system based on a medical model, which comprises:

[0006] A model construction module is configured to construct a basic structure model according to allowed needle insertion parts of a human body.

[0007] A model optimization module is configured to configure standard needles to different allowed needle insertion parts based on a part-needle mapping table, determine structure resistances of each part position point according to part types of the allowed needle insertion parts and needle insertion depths of each part position point in the allowed needle insertion parts, and attach needle types and structure resistances of the standard needles to corresponding model position points of the basic structure model to obtain a medical model.

[0008] A needle insertion practice module is configured to capture practice operation of a learning object in a virtual simulation environment composed of the medical model, compare the practice operation with needle insertion standards of operation position points of the medical model, and provide practice correction guidance.

[0009] The present application provides a needle insertion learning system based on a medical model, which comprises a model construction module.

[0010] The keyword acquisition unit acquires the learning intention of the learning object and extracts the use purpose of the learning intention;

[0011] The basic model construction unit constructs a basic structure model according to the matching of the allowed needle insertion sites of the human body and the needle insertion difficulty of each allowed needle insertion site from the use-result mapping table according to the extraction result.

[0012] The present application provides a needle insertion learning system based on a medical model, and a basic model construction unit, comprising:

[0013] The initial determination block determines the initial difficulty of each allowed needle insertion site based on the difficulty-site mapping table;

[0014] The adjustment block determines the position of each allowed needle insertion site in the human body structure, and adjusts the corresponding initial difficulty according to the number of important blood vessels and the blood vessel density of the corresponding position to obtain the needle insertion difficulty.

[0015] The present application provides a needle insertion learning system based on a medical model, and a model optimization module, comprising:

[0016] The standard needle determination unit acquires the site number of each allowed needle insertion site, and acquires the standard needle and the needle type of the corresponding allowed needle insertion site from the site-needle mapping table;

[0017] The depth determination unit determines the needle insertion useful level of each site position point in the corresponding allowed needle insertion site according to the human-body structure database, and determines the needle insertion depth of each site position point according to the level-depth mapping table;

[0018] The resistance analysis unit determines the structure resistance of the corresponding site position point based on the site type and the needle insertion depth, constructs the structure resistance surface of the corresponding allowed needle insertion site, and judges whether the structure resistance surface is qualified;

[0019] When the structure resistance surface is qualified, the structure resistance of the corresponding site position point is kept unchanged;

[0020] Otherwise, it is determined that the structure resistance of the corresponding site position point needs to be changed;

[0021] The model construction unit one-to-one corresponds each site position point to the corresponding model position point, and attaches the needle type and the final resistance corresponding to the corresponding site position point to the corresponding model position point to obtain a medical model.

[0022] The present application provides a needle insertion learning system based on a medical model, and a resistance analysis unit, comprising:

[0023] Resistance determination block: obtain hierarchical structure data corresponding to each position point of the allowed needle insertion site, and calculate the structural resistance:

[0024] ;

[0025] ; wherein, represents the structural resistance of the i-th position point of the allowed needle insertion site; represents the deformation resistance calculation factor of the hierarchical structure of the j-th layer under the i-th position point; represents the deformation resistance of the hierarchical structure of the j-th layer under the i-th position point; represents the friction factor of the hierarchical structure of the j-th layer under the i-th position point; represents the minimum positive pressure of the standard needle inserted into the allowed needle insertion site; represents the standard included angle between the standard needle insertion direction and the horizontal direction; represents the static friction compensation force of the hierarchical structure of the j-th layer when the standard needle does not insert into the i-th position point; represents the shear force calculation factor of the standard needle inserted into the i-th position point; represents the shear force of the standard needle inserted into the i-th position point; represents the set depth of the hierarchical structure of the j-th layer of the i-th position point; n represents n layers of hierarchical structure of the i-th position point represents the resistance fine tuning function of the i-th position point; represents the instantaneous pressure of the hierarchical structure of the j-th layer inserted into the i-th position point; represents the instantaneous pressure of the hierarchical structure of the j-th layer inserted into the i-th position point and inserted into the hierarchical structure of the j+1-th layer; represents the sum of the insertion buffer depth of the hierarchical structure of the j-th layer and the insertion buffer depth of the hierarchical structure of the j+1-th layer under the i-th position point is a preset coefficient of the number of hierarchical structures n, and the value range is [0.05, 0.10]; represents the factorial of n; represents the minimum value of all ; represents the buffer force calculation factor;

[0026] Surface acquisition block: establish a three-dimensional coordinate system, and mark all position points involved in the same allowed needle insertion site and the structural resistance of each position point in the three-dimensional coordinate system to obtain the structural resistance surface.

[0027] The application provides a needle insertion learning system based on a medical model, and the resistance analysis unit further comprises:

[0028] The first calculation block: obtaining the center point of the structural resistance surface, screening n1 curve segments penetrating the center point, and calculating the first fluctuation degree of each curve segment;

[0029] ;

[0030] ; wherein, represents the first fluctuation degree of the j1th curve segment; represents the peak point on the j1th curve segment; represents the valley point on the j1th curve segment; represents the value of the i1+1th peak point on the j1th curve segment; represents the value of the i1th peak point on the j1th curve segment; represents the value of the i2+1th valley point on the j1th curve segment; represents the value of the i2th valley point on the j1th curve segment; represents the fine tuning function of the j1th curve segment; represents the logarithmic function symbol; represents the maximum difference between adjacent peak points and valley points on the j1th curve segment;

[0031] The second calculation block: according to a preset unit area, extracting a unit area of the structural resistance surface, and extracting n1 intercepting surfaces, and calculating the second fluctuation degree of each intercepting surface;

[0032] ;

[0033] ; wherein, represents the second fluctuation degree of the j2th intercepting surface; represents the peak point on the j2th intercepting surface; represents the valley point on the j2th intercepting surface; represents the value of the i3+1th peak point on the j2th intercepting surface; represents the value of the i3th peak point on the j2th intercepting surface; represents the value of the i4+1th valley point on the j2th intercepting surface; represents the value of the i4th valley point on the j2th intercepting surface; represents the fine tuning function of the j2th intercepting surface; represents the logarithmic function symbol; represents the maximum difference between adjacent peak points and valley points on the j2th curve segment;

[0034] Adjusting block: constructing a first passing array of the intercepting surface passed by each curve segment according to all second fluctuation degrees, wherein the first passing array is constructed by the second fluctuation degree of the intercepting surface passed by the corresponding curve segment;

[0035] Fitting block: constructing a first horizontal line according to the first fluctuation degree of the corresponding curve segment, and constructing a first fitting line according to the fitting of the first passing array;

[0036] Contrast analysis block: comparing the first horizontal line with the first fitting line to determine whether the consistent fluctuation standard is met, and if met, keeping the corresponding curve segment unchanged;

[0037] If not met, adjusting the peak point and the trough point in the corresponding curve segment according to the first line segment difference between the first horizontal line and the first fitting line and the second line segment difference between the second fitting line excluding the discrete points in the first passing array and the first horizontal line, and keeping the adjusted curve segment;

[0038] Qualified block: obtaining a qualified structural resistance surface based on all kept curve segments.

[0039] The application provides a needle insertion learning system based on a medical model, a needle insertion practice module, comprising:

[0040] Practice comparison unit: when the learning object enters the virtual simulation environment, tracking the needle insertion track of the practice operation of the learning object on the medical model, comparing the needle insertion track with the standard track in the needle insertion standard, and determining the needle insertion standard degree, wherein the needle insertion standard degree is determined by the radiation range of the terminal point of the standard track and the needle insertion track;

[0041] Correction unit: when the needle insertion standard degree is less than the preset degree, extracting the abnormal track according to the comparison result and displaying the abnormal track in the virtual simulation environment, and analyzing the track jitter information of the abnormal track and the operation posture of the learning object in the practice operation process to generate the corresponding correction mode and correct the learning object, wherein the operation posture of the learning object is obtained through the media module in the virtual simulation environment;

[0042] Evaluation unit: scoring each practice operation of the learning object on the medical model of the same level, recording the practice times and the corresponding practice scores, judging whether the standard is met, if met, adjusting the related data of the corresponding hierarchical structure based on the medical model to obtain a new level medical model, and continuing to provide the learning object for needle insertion practice.

[0043] The application provides a needle insertion learning system based on a medical model, an evaluation unit, comprising:

[0044] Obtaining the first human body model of different age stages in the current population census result of the place, and performing the first update on the medical model;

[0045] Obtaining the part change model of different parts of the human body, and performing the second update on the medical model;

[0046] Based on the model after the first update and the second update, as a new level of medical model.

[0047] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be achieved and obtained by the structure particularly pointed out in the written description and the accompanying drawings.

[0048] The technical solutions of the present application will be further described in detail below by the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0049] The accompanying drawings are used to provide a further understanding of the present application, and constitute a part of the specification, together with the embodiments of the present application, used to explain the present application, and do not constitute a limitation on the present application. In the drawings:

[0050] Figure 1 The structure diagram of a medical model based needle learning system in an embodiment of the present application;

[0051] Figure 2 The structure diagram of the first array in an embodiment of the present application;

[0052] Figure 3 The specific implementation diagram of the medical model in an embodiment of the present application. DETAILED DESCRIPTION

[0053] The preferred embodiments of the present application are described below in conjunction with the accompanying drawings, and it should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and do not limit the present application.

[0054] Embodiment 1:

[0055] The embodiment of the present application provides a medical model based needle learning system, as shown in the figure, comprising: Figure 1

[0056] Model construction module: constructing a basic structure model according to the allowed needle part of the human body;

[0057] ​The model optimization module: based on the part-needle mapping table, configure standard needles to different allowed needle parts, and determine the structural resistance of each part position point by combining the part type of the allowed needle part and the needle depth of each part position point in the allowed needle part, and attach the needle type and structural resistance of the standard needle to the corresponding model position point of the basic structure model to obtain a medical model.

[0058] The needle practice module: captures the practice operation of the learning object in the virtual simulation environment composed of the medical model, compares it with the needle standard of the operation position point of the medical model, and gives practice correction guidance.

[0059] In this embodiment, the allowed needle part is matched with the corresponding allowed needle part from the use-result mapping table according to the learning intention of the learning object, such as the back of the hand, the head, and the leg. For example, if the use is to treat leg numbness, the relevant acupoints need to be connected.

[0060] In this embodiment, the basic construction model is constructed by collecting and constructing the allowed needle part according to the needle difficulty of the allowed needle part, that is, the standard human body is used as the basis to build the corresponding acupoint construction model on the standard human body.

[0061] In this embodiment, the part-needle mapping table is a comprehensive table of allowed needle parts and corresponding needle types, such as the standard needle corresponding to the back of the hand is a venous infusion needle.

[0062] In this embodiment, the standard needle is determined by experts in advance, and the needles used in different parts may be different.

[0063] In this embodiment, the part type of the allowed needle part is the division of the whole human body, including the limb part, the trunk part, and the head.

[0064] In this embodiment, the part position point is a refinement of the allowed needle part, such as all points of the blood vessel needle part of the back of the hand corresponding to the limb part.

[0065] In this embodiment, the needle depth is determined according to the needle useful level of the allowed needle part. There are multiple needle useful levels for the same allowed needle part, such as the arm can be used for skin test, blood sampling, and intravenous injection.

[0066] In this embodiment, the structural resistance of the part position point is determined by the allowed needle part and the needle depth. Since the hierarchical structure of the allowed needle part is different, the structural resistance is also different.

[0067] In this embodiment, the medical model is a corresponding model that integrates all the allowed needle insertion sites to be constructed in a virtual simulation environment, the input is the relevant data of the learning object in the needle insertion practice operation process, and the output is the score of the learning object in the needle insertion practice operation process and the practice correction guidance to the learning object.

[0068] In this embodiment, the learning object is an object that needs to learn needle insertion, including medical students and nursing students.

[0069] In this embodiment, the virtual simulation environment is a three-dimensional dynamic real scene of the computer simulated medical model.

[0070] In this embodiment, the practice operation is the posture of the learning object and the trajectory of the needle in the practice of needle insertion on the medical model in the virtual simulation environment.

[0071] In this embodiment, the needle insertion standard is derived from the successful needle insertion of each allowed needle insertion site, such as infusion on the back of the hand, one-time needle insertion hitting the blood vessel without causing secondary damage to the blood vessel, which is considered successful needle insertion.

[0072] In this embodiment, the practice correction guidance is the adjustment of the hand posture and the adjustment of the needle insertion accuracy of the learning object in the practice operation process.

[0073] In this embodiment, the specific embodiment of the medical model is as follows:

[0074] Silicone simulated human skin: simulates the surface of the human skin to make the mechanism solve the human touch and skin color, movable rack: functions against the silicone simulated human skin, when there is a needle insertion, it will push the rack and the skin apart, the rack will also push the bottom gear while moving down. Module fixing frame: responsible for fixing the stepper motor and rack guide. Gear: meshed with the rack, sensing the activity of the rack. Stepper motor: connected with the gear, sensing the rotation of the gear to identify the activity state of the rack, then setting different muscle tissue force feedback according to different scenes and positions, the stepper motor can provide different values of resistance, also can create a momentary empty feeling of unloading, also can reset after completing the operation. This structure is a separate module, which will be installed in different parts of the simulated human body according to the needs, providing different needle insertion practice for medical students, simulating the needle insertion hand feeling feedback close to the human body, wherein the specific components involved are shown in Figure 3 .

[0075] The working principle and beneficial effects of the technical solution are as follows: by determining the allowed needle insertion part of the human body, a basic structure model is constructed, the standard needle of the allowed needle insertion part and the structural resistance are added to the corresponding position points of the basic model, a medical model is obtained, and by comparing the operation of the learning object in the practice operation process with the corresponding needle insertion standard, the practice needle insertion operation in the virtual environment is realized unlimited times, the actual operation level and speed of the learning object are improved, and the actual operation risk is reduced.

[0076] Embodiment 2:

[0077] The embodiment of the application provides a needle insertion learning system based on a medical model, a model construction module, comprising:

[0078] The keyword acquisition unit acquires the learning intention of the learning object and extracts the use purpose of the learning intention.

[0079] The basic model construction unit matches the allowed needle insertion part of the human body and the needle insertion difficulty of each allowed needle insertion part from the use-purpose mapping table according to the extraction result, and constructs a basic structure model.

[0080] In this embodiment, the learning intention is the needle insertion method and purpose that the learning object wants to practice, for example, the learning intention is infusion, and the allowed needle insertion parts are the back of the hand, the wrist and the head.

[0081] In this embodiment, the use-purpose mapping table is a collection table of the allowed needle insertion parts and the corresponding needle insertion methods and purposes.

[0082] In this embodiment, the needle insertion difficulty is obtained according to the position of the allowed needle insertion part in the human body structure and according to the number of important blood vessels and the blood vessel density of the corresponding position.

[0083] The working principle and beneficial effects of the technical solution are as follows: by obtaining the corresponding allowed needle insertion part according to the learning intention of the learning object, the needle insertion difficulty of the allowed needle insertion part is determined, and the basic structure model is constructed to realize the establishment of the medical model.

[0084] Embodiment 3:

[0085] The embodiment of the application provides a needle insertion learning system based on a medical model, a basic model construction unit, comprising:

[0086] The initial determination block determines the initial difficulty of each allowed needle insertion part based on the difficulty-part mapping table.

[0087] The adjustment block determines the position of each allowed needle insertion part in the human body structure, and adjusts the corresponding initial difficulty according to the number of important blood vessels and the blood vessel density of the corresponding position to obtain the needle insertion difficulty.

[0088] In this embodiment, the difficulty-position mapping table is a preliminary judgment of the difficulty of needle insertion, for example, the difficulty of the hand is 1, and the difficulty of the head is 5, and the head is an important human organ, and a certain damage will cause danger to the human body.

[0089] In this embodiment, the initial difficulty is determined according to the importance of the allowed position to the human body.

[0090] In this embodiment, the higher the blood vessel density of the allowed needle insertion position, the greater the cost when the needle insertion anomaly occurs, and the higher the difficulty of needle insertion.

[0091] The working principle and beneficial effects of the above technical solutions are: by determining the initial difficulty of the allowed needle insertion position, adjusting the initial difficulty according to the number of important blood vessels and the blood vessel density of the corresponding position, obtaining the difficulty of needle insertion of the allowed needle insertion position, and establishing a basic structure model according to the level of needle insertion difficulty.

[0092] Embodiment 4:

[0093] The embodiment of the application provides a needle insertion learning system based on a medical model, a model optimization module, comprising:

[0094] A standard needle determination unit: obtains the position number of each allowed needle insertion position, and obtains the standard needle corresponding to the allowed needle insertion position and the needle type of the standard needle from the position-needle mapping table;

[0095] A depth determination unit: determines the needle insertion useful level of each position point in the corresponding allowed needle insertion position according to the human-body-structure database, and determines the needle insertion depth of each position point according to the level-depth mapping table;

[0096] A resistance analysis unit: determines the structure resistance of the corresponding position point based on the position type and the needle insertion depth, constructs the structure resistance surface of the corresponding allowed needle insertion position, and judges whether the structure resistance surface is qualified;

[0097] When the structure resistance surface is qualified, the structure resistance of the corresponding position point is kept unchanged;

[0098] Otherwise, it is determined that the structure resistance of the corresponding position point needs to be changed;

[0099] A model construction unit: one-to-one correspondence is established between each position point and the corresponding model position point, and the needle type corresponding to the corresponding position point and the final resistance are attached to the corresponding model position point, to obtain a medical model.

[0100] In this embodiment, the position number is the number of all allowed needle insertion positions, which is sorted according to the difficulty of needle insertion.

[0101] In this embodiment, the human-structure database is a hierarchical structure of all allowed needle insertion sites and related data, and also includes the useful hierarchical structure of needle insertion and related data, such as the useful hierarchical structure of the back of the hand is the blood vessel layer, and the skin layer and muscle layer are on the blood vessel layer.

[0102] In this embodiment, the hierarchical-depth mapping table is the depth of the corresponding hierarchical structure of the allowed needle insertion site, such as the muscle layer of the leg has a depth of 1-3 cm.

[0103] In this embodiment, the structure resistance surface is an integration of the structure resistance curve of all position points of the allowed needle insertion site.

[0104] In this embodiment, the qualified degree judgment of the structure resistance surface is a judgment of the fluctuation degree of the section of the structure resistance curve segment passing through the center point of the structure resistance surface and the section of the structure resistance surface.

[0105] In this embodiment, the model position point is the corresponding point of the site position point in the virtual simulation environment.

[0106] The working principle and beneficial effects of the above technical solution are: by matching the corresponding standard needle for each allowed needle insertion site, obtaining the useful hierarchical structure of needle insertion and the depth of needle insertion of the allowed needle insertion site, deriving the structure resistance surface of the corresponding allowed needle insertion site, judging and adjusting the qualified degree of the structure resistance surface, and finally attaching it to the basic structure model, realizing the dataization of the standard operation, improving the actual operation level and speed of the learning object, and reducing the operation risk.

[0107] Embodiment 5:

[0108] The embodiment of the present application provides a needle insertion learning system based on a medical model, a resistance analysis unit, comprising:

[0109] The resistance determination block: obtains the hierarchical structure data of each site position point in the corresponding allowed needle insertion site, and calculates the structure resistance:

[0110] ;

[0111] ; wherein, Ri represents the structure resistance of the i-th site position point of the allowed needle insertion site; Rij represents the deformation resistance calculation factor of the hierarchical structure of the j-th layer under the i-th site position point; Rij represents the deformation resistance of the hierarchical structure of the j-th layer under the i-th site position point; Rij represents the friction factor of the hierarchical structure of the j-th layer under the i-th site position point; Pmin represents the minimum positive pressure of the standard needle inserted into the allowed needle insertion site; a represents the standard included angle between the insertion direction of the standard needle and the horizontal direction; Static friction compensation force of the hierarchical structure of the jth layer when the standard needle does not pierce the ith site position point; Shear force calculation factor of the ith site position point when the standard needle pierces the ith site position point; Shear force of the ith site position point when the standard needle pierces the ith site position point; Set depth of the hierarchical structure of the jth layer of the ith site position point; n represents that the ith site position point has n layers of hierarchical structure Resistance fine-tuning function of the ith site position point; Instantaneous pressure of the ith site position point when the hierarchical structure of the jth layer is pierced; Instantaneous pressure of the ith site position point when the hierarchical structure of the jth layer is pierced and the hierarchical structure of the j+1th layer is pierced; Sum of the piercing buffer depth of the hierarchical structure of the jth layer and the piercing buffer depth of the hierarchical structure of the j+1th layer of the ith site position point Preset coefficient of the number of hierarchical structures n, and the value range is [0.05, 0.10]; Factorial of n; Minimum value of all ; Buffer force calculation factor;

[0112] The curved surface acquisition block: a three-dimensional coordinate system is established, and all site position points involved in the same allowed needle piercing site and the structural resistance of each site position point are marked in the three-dimensional coordinate system, and the structural resistance curved surface is obtained.

[0113] In this embodiment, the hierarchical structure data includes the corresponding hierarchical category and hierarchical depth.

[0114] The working principle and beneficial effects of the above technical solution are: the structural resistance of the allowed needle piercing position point is calculated by calculating the hierarchical structure and related data of the allowed needle piercing site, and the structural resistance curved surface is obtained by combining all the allowed needle piercing position points, the data of the standard operation is realized, the actual operation level and speed of the learning object are improved, and the operation risk is reduced.

[0115] Embodiment 6:

[0116] The embodiment of the application provides a needle piercing learning system based on a medical model, and the resistance analysis unit further comprises:

[0117] The first calculation block: the center point of the structural resistance curved surface is obtained, n1 curve segments penetrating the center point are screened, and the first fluctuation degree of each curve segment is calculated;

[0118] ;

[0119] ; wherein, represents the first fluctuation degree of the j1th curve segment; represents the peak point on the j1th curve segment; represents the valley point on the j1th curve segment; represents the value of the i1+1th peak point on the j1th curve segment; represents the value of the i1th peak point on the j1th curve segment; represents the value of the i2+1th valley point on the j1th curve segment; represents the value of the i2th valley point on the j1th curve segment; represents the fine-tuning function of the j1th curve segment; represents the logarithmic function symbol; represents the maximum difference between adjacent peak points and valley points on the j1th curve segment;

[0120] The second calculation block: according to the preset unit area, the unit area extraction is carried out on the structure resistance surface, and n1 intercepting surfaces are extracted, and the second fluctuation degree of each intercepting surface is calculated;

[0121] ;

[0122] ; wherein, represents the second fluctuation degree of the j2th intercepting surface; represents the peak point on the j2th intercepting surface; represents the valley point on the j2th intercepting surface; represents the value of the i3+1th peak point on the j2th intercepting surface; represents the value of the i3th peak point on the j2th intercepting surface; represents the value of the i4+1th valley point on the j2th intercepting surface; represents the value of the i4th valley point on the j2th intercepting surface; represents the fine-tuning function of the j2th intercepting surface; represents the logarithmic function symbol; represents the maximum difference between adjacent peak points and valley points on the j2th curve segment;

[0123] The adjustment block: according to all the second fluctuation degrees, a first passing array of the intercepting surface passed by each curve segment is constructed, wherein the first passing array is constructed by the second fluctuation degree of the intercepting surface passed by the corresponding curve segment;

[0124] The fitting block: according to the first fluctuation degree of the corresponding curve segment, a first horizontal line is constructed, and according to the first passing array, a first fitting line is constructed by fitting;

[0125] Comparative analysis block: comparative analysis of the first horizontal line and the first fitting line to determine whether the consistent fluctuation standard is met, and if so, the corresponding curve segment is retained unchanged;

[0126] If not, the high peak points and the low valley points in the corresponding curve segment are adjusted according to the first line segment difference between the first horizontal line and the first fitting line and the second line segment difference between the second fitting line after removing the discrete points in the first array and the first horizontal line, and the adjusted curve segment is retained;

[0127] Qualified block: based on all retained curve segments, a qualified structural resistance curve surface is obtained.

[0128] In this embodiment, the structural resistance curve surface is three-dimensional and will have fluctuations of different degrees, that is, it is materialized through three-dimensional coordinates.

[0129] In this embodiment, the center point generally refers to the point of the center of the curve surface.

[0130] In this embodiment, the first horizontal line is a horizontal line established with the first fluctuation degree as the vertical coordinate.

[0131] In this embodiment, the fitting line is obtained by fitting the fluctuation degree in the first array.

[0132] In this embodiment, the consistent fluctuation standard refers to whether the standard deviation of the first horizontal line and the first fitting line is less than a preset threshold value, and the value of the preset threshold value is generally 0.3, and if so, it is considered to meet the consistent fluctuation standard.

[0133] In this embodiment, as shown in Figure 2 For example, the first curve segment y01 passes through the intercepting surfaces x1, x2, x3, x4 and x5, and then the first array is At this time, the first fitting line is obtained based on the first array, such as y02.

[0134] In this embodiment, during the comparison of y01 and y02, there will be an unsafe overlapping line segment, and at this time, the line segment that does not completely overlap is considered as the first line segment difference, for example, the discrete points in the first array are At this time, after removing , the second fitting line is obtained based on the remaining At this time, the second line segment difference is determined to exist, and the acquisition method is consistent with the first line segment difference.

[0135] In this embodiment, the peak point refers to a point higher than the preset upper threshold, and the valley point refers to a point lower than the preset lower threshold. For example, the peak point on the first curve segment 1 is 01, and the value of the peak point 01 can be adjusted from b1 to b2 according to the first line segment difference and the second line segment difference, and the purpose of the adjustment is to make the line segment as smooth as possible.

[0136] Because there are different differences at the same point in the line segment difference, there will be a maximum difference. At this time, the maximum difference is used to adjust the peak point to a smaller value and adjust the valley point to a larger value.

[0137] ; wherein max1 is the maximum value based on the first line segment difference; is the maximum value based on the second line segment difference.

[0138] ; wherein ave1 is the average difference value based on the first line segment difference; ave is the average difference value based on the second line segment difference, is the value of the valley point; is the value of the adjusted valley point.

[0139] The working principle and beneficial effects of the above technical solution are: by judging and adjusting the fluctuation degree of the structural resistance surface data in the resistance analysis unit, a qualified structural resistance surface is obtained, the data of the standard operation is realized, the actual operation level and speed of the learning object are improved, and the operation risk is reduced.

[0140] Embodiment 7:

[0141] The embodiment of the application provides a needle insertion learning system based on a medical model, and a needle insertion practice module, which comprises:

[0142] A practice comparison unit: when the learning object enters the virtual simulation environment, the needle insertion track of the practice operation of the learning object on the medical model is tracked, compared with the standard track in the needle insertion standard, and the needle insertion standard degree is determined, wherein the needle insertion standard degree is determined by the radiation range of the end point of the standard track and the needle insertion track;

[0143] A correction unit: when the needle insertion standard degree is less than the preset degree, the abnormal track is extracted according to the comparison result and displayed in the virtual simulation environment, and the track jitter information of the abnormal track and the operation posture of the learning object in the practice operation process are analyzed to generate a corresponding correction mode and guide the correction of the learning object, wherein the operation posture of the learning object is obtained through a media module in the virtual simulation environment.

[0144] The evaluation unit scores each practice operation of the learning object on the medical model of the same level, records the number of practice operations and the corresponding practice scores, judges whether the standard is met, and if the standard is met, adjusts the relevant data of the medical model based on the corresponding hierarchical structure to obtain a new medical model of the level, and continues to provide the learning object with needle insertion practice.

[0145] In this embodiment, the needle insertion trajectory is the relevant data trajectory of the learning object during needle insertion practice.

[0146] In this embodiment, the standard trajectory is the relevant data trajectory of one-time needle insertion into the corresponding useful level without causing other Shanghai situations.

[0147] In this embodiment, the needle insertion standard degree is obtained by comparing the needle insertion trajectory with the standard trajectory, for example, the standard trajectory is one-time needle insertion into the corresponding level, and the needle insertion trajectory is adjusted after being inserted into the corresponding level without reaching the useful level, and finally inserted into the useful level, and the corresponding needle insertion standard degree is 0.5.

[0148] In this embodiment, the standard trajectory end point is the corresponding point of inserting into the useful level.

[0149] In this embodiment, the abnormal trajectory is a trajectory different from the standard trajectory, for example, the needle insertion trajectory is adjusted after being inserted into the corresponding level without reaching the useful level, and finally inserted into the useful level, which is an abnormal trajectory.

[0150] In this embodiment, the correction method is to adjust the operation posture of the learning object and the insertion angle.

[0151] In this embodiment, the trajectory jitter information is abnormal information obtained by comparing the needle insertion trajectory with the standard trajectory, for example, the needle insertion trajectory is adjusted after being inserted into the corresponding level without reaching the useful level, and finally inserted into the useful level, and the trajectory jitter information is the trajectory deviation degree of the first time without inserting into the useful level.

[0152] The working principle and beneficial effects of the above technical solution are: the abnormal operation of the learning object is captured by capturing the data of the virtual simulation environment, and the corresponding correction method is generated, and the data of the medical model is expanded and updated, the exercisable content of the learning object is expanded, the actual operation level and speed of the learning object are improved, and the operation risk is reduced.

[0153] Embodiment 8:

[0154] The embodiment of the application provides a needle insertion learning system based on a medical model, an evaluation unit, comprising:

[0155] Obtaining the first human body model in different age groups from the current population census results of the place, and performing the first update on the medical model;

[0156] Obtaining the part change model of different parts of the human body, and performing the second update on the medical model;

[0157] Based on the first update and the second updated model, the medical model is updated as a new level.

[0158] In this embodiment, the first update is an update according to the different age groups to obtain the different corresponding allowed needle insertion site hierarchical structure data.

[0159] In this embodiment, the second update is an update according to the changes of the hierarchical structure data of different parts of the human body caused by the body type.

[0160] The working principle and beneficial effects of the above technical solution are: the medical model is updated through the acquisition of relevant data of different age groups and different body types, the data of the medical structure is widely corresponded, the exercisable content of the learning object is expanded, the actual operation level and speed of the learning object is improved, and the operation risk is reduced.

[0161] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A medical model-based needle insertion learning system, characterized by, The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. The application relates to a medical model construction method and device.

2. The medical model-based needle learning system of claim 1, wherein, The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. The application relates to a medical model construction method and device.

3. The medical model-based needle learning system of claim 2, wherein, The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. The application relates to a medical model construction method and device.

4. The medical model-based needle learning system of claim 1, wherein, The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. The application relates to a medical model construction method and device. 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The application relates to a medical model construction The standard needle determination unit obtains the site number of each allowed needle insertion site, and obtains the standard needle corresponding to the allowed needle insertion site and the needle type of the standard needle from a site-needle mapping table. The depth determination unit determines the needle insertion useful level of each site position point in the corresponding allowed needle insertion site according to the human-body-structure database, and determines the needle insertion depth of each site position point according to a level-depth mapping table. The resistance analysis unit determines the structure resistance of the corresponding site position point based on the site type and the needle insertion depth, constructs a structure resistance surface of the corresponding allowed needle insertion site, and judges whether the structure resistance surface is qualified. When the structure resistance surface is qualified, the structure resistance of the corresponding site position point is kept unchanged. Otherwise, it is determined that the structure resistance of the corresponding site position point needs to be changed. The model construction unit one-to-one corresponds each site position point to a corresponding model position point, and adds the needle type corresponding to the corresponding site position point and the final resistance to the corresponding model position point, to obtain a medical model.

5. The medical model-based needle learning system of claim 4, wherein, The resistance analysis unit comprises: The resistance determination block obtains the hierarchical structure data of each site position point in the corresponding allowed needle insertion site, and calculates the structure resistance: ； ; wherein, represents the structural resistance of the i-th site location point allowing needle penetration; represents the deformation resistance calculation factor of the j-th hierarchical structure under the i-th site location point; represents the deformation resistance of the j-th hierarchical structure under the i-th site location point; represents the friction factor of the j-th hierarchical structure under the i-th site location point; represents the minimum positive pressure of the standard needle penetrating the allowed needle penetration site; represents the standard angle between the standard needle penetration direction and the horizontal direction; represents the static friction compensation force of the j-th hierarchical structure when the standard needle does not penetrate the i-th site location point; represents the shear force calculation factor of the standard needle penetrating the i-th site location point; represents the shear force of the standard needle penetrating the i-th site location point; represents the set depth of the j-th hierarchical structure of the i-th site location point; n represents the total number of n hierarchical structures of the i-th site location point; represents the resistance fine-tuning function of the i-th site location point; represents the instantaneous pressure of the j-th hierarchical structure penetrating out of the i-th site location point; represents the instantaneous pressure of the j-th hierarchical structure penetrating out of the i-th site location point and penetrating into the j+1-th hierarchical structure; represents the sum of the penetration buffer depth of the j-th hierarchical structure and the penetration buffer depth of the j+1-th hierarchical structure under the i-th site location point is a preset coefficient of the number of hierarchical structures n, and the value range is [0.05, 0.10]; represents the factorial of n; represents the minimum value of all ; represents the buffer force calculation factor; The surface acquisition block establishes a three-dimensional coordinate system, and labels all site position points involved in the same allowed needle insertion site and the structure resistance of each site position point in the three-dimensional coordinate system, to obtain a structure resistance surface.

6. The medical model-based needle learning system of claim 4, wherein, The resistance analysis unit further comprises: The first calculation block obtains the center point of the structure resistance surface, screens n1 curve segments passing through the center point, and calculates the first fluctuation degree of each curve segment; ； ; wherein represents the first fluctuation degree of the j1th curve segment; represents the peak point on the j1th curve segment; represents the valley point on the j1th curve segment; represents the value of the i1+1th peak point on the j1th curve segment; the value of the i1th peak point on the j1th curve segment; represents the value of the i2+1th valley point on the j1th curve segment; represents the value of the i2th valley point on the j1th curve segment; represents the fine tuning function of the j1th curve segment; represents the logarithmic function symbol; represents the maximum difference between adjacent peak and valley points on the j1th curve segment; The second calculation block extracts a unit area of the structure resistance surface according to a preset unit area, extracts n1 intercepting surfaces, and calculates the second fluctuation degree of each intercepting surface; ； ; wherein represents the second fluctuation degree of the j2th intercept surface; represents the peak point on the j2th intercept surface; represents the valley point on the j2th intercept surface; represents the value of the i3+1th peak point on the j2th intercept surface; represents the value of the i3th peak point on the j2th intercept surface; represents the value of the i4+1th valley point on the j2th intercept surface; represents the value of the i4th valley point on the j2th intercept surface; represents the fine tuning function of the j2th intercept surface; represents the logarithmic function symbol; represents the maximum difference between adjacent peak points and valley points on the j2th curve segment; The adjustment block constructs a first passing array of the intercepting surface passed by each curve segment according to all second fluctuation degrees, wherein the first passing array is constructed by the second fluctuation degrees of the intercepting surface passed by the corresponding curve segment; The fitting block constructs a first horizontal line according to the first fluctuation degree of the corresponding curve segment, and constructs a first fitting line according to the first passing array; The comparative analysis block compares and analyzes the first horizontal line and the first fitting line to determine whether the consistent fluctuation standard is met, and if so, the corresponding curve segment is kept unchanged; If not, the high peak points and low valley points in the corresponding curve segment are adjusted according to the first line segment difference between the first horizontal line and the first fitting line and the second line segment difference between the second fitting line after removing the discrete points in the first passing array and the first horizontal line, and the adjusted curve segment is kept; The qualified block obtains a qualified structure resistance surface based on all kept curve segments.

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