Surgeon growth visualization method based on ultrasonic scalpel big data

Abstract

The present invention relates to the field of ultrasonic scalpels. Disclosed is a surgeon growth visualization method based on ultrasonic scalpel big data, comprising: receiving a growth estimation request, the growth estimation request comprising identity information of a target surgeon; searching a first sub-system for a scalpel tip unique identifier associated with the target surgeon, the first sub-system storing identity information and scalpel tip unique identifiers of associated surgeons; searching a second sub-system for scalpel tip operation information associated with the scalpel tip unique identifier, the second sub-system storing associated scalpel tip unique identifiers and scalpel tip operation information, and the scalpel tip operation information comprising a starting time and an ending time of each operation; and collecting statistical data on the basis of the search result of the scalpel tip operation information, and displaying the collected statistical data by means of a display device. The present invention utilizes objective data to estimate growth states of surgeons performing ultrasonic scalpel surgery.

Description

A medical growth visualization method based on ultrasonic knife big data TECHNICAL FIELD

[0001] The present application relates to the field of ultrasonic knives, in particular to a medical growth visualization method based on ultrasonic knife big data. BACKGROUND

[0002] The surgical level of a doctor performing ultrasonic knife surgery is usually not objectively evaluated, and there is no horizontal comparison data between doctors.

[0003] It is difficult for a doctor to trace good and bad surgeries, such as whether the surgeries performed in a certain period of time are good or bad, which is difficult to detect or evaluate. In addition, authoritative doctors generally talk about it in the industry, but cannot be evaluated from actual surgery data, cannot know who is the authoritative operator in a certain type of surgery, and cannot obtain the way to learn from them.

[0004] The disclosure of the above background art is only used to assist in understanding the inventive concept and technical solutions of the present application, which does not necessarily belong to the prior art of the present patent application, nor necessarily give technical teaching; in the absence of explicit evidence that the above content has been disclosed before the filing date of the present patent application, the above background art should not be used to evaluate the novelty and inventiveness of the present application. SUMMARY

[0005] The purpose of the present application is to provide a medical growth visualization method based on ultrasonic knife big data, which uses ultrasonic knife head operation data to generate a growth curve based on a time axis, and visually expresses the growth of doctors and compares the growth of different doctors.

[0006] To achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0007] A medical growth visualization method based on ultrasonic knife big data, comprising the following steps:

[0008] Receiving a growth evaluation request, the growth evaluation request comprising at least the identity information of a target doctor;

[0009] Finding a knife head unique identification code associated with the identity information of the target doctor in a first subsystem, the first subsystem storing the identity information of the associated doctor and the knife head unique identification code;

[0010] Finding knife head operation information associated with the knife head unique identification code in a second subsystem, the second subsystem storing the associated knife head unique identification code and the knife head operation information, the knife head operation information comprising the start time and end time of each operation;

[0011] According to the search result of the tool head operation information, data statistics are made, and the statistical data are displayed by using a display device.

[0012] Further, the data statistics include:

[0013] Time consumption of each operation is counted.

[0014] One or more first growth trajectories are generated in a rectangular coordinate system, wherein the horizontal coordinate of a trajectory point of each first growth trajectory is the occurrence time of an operation of the same type of surgery, and the vertical coordinate is the time consumption of the corresponding operation.

[0015] Further, the growth evaluation request includes identity information of multiple target doctors.

[0016] First growth trajectories of different target doctors are generated in the same rectangular coordinate system.

[0017] Further, the method for visualizing the growth of doctors based on the large data of ultrasonic knives provided by the present application further includes:

[0018] Each surgery video is stored, and the surgery video is labeled with corresponding time information.

[0019] According to a preset rule, an abnormal trajectory point in the first growth trajectory is determined.

[0020] According to the horizontal coordinate of the abnormal trajectory point, the corresponding surgery video is viewed.

[0021] Further, the method for visualizing the growth of doctors based on the large data of ultrasonic knives provided by the present application further includes:

[0022] In a third subsystem, surgeries associated with the identity information of the target doctor are searched, and the third subsystem stores the ultrasonic knife surgery information of doctors, which includes the identity information of doctors, the surgery start time and the surgery end time.

[0023] According to the search result of the tool head operation information, the sum of operation time consumption and / or the number of operations between the surgery start time and the surgery end time of the associated surgeries are counted; and / or further including calculating the average operation time consumption in each associated surgery.

[0024] A second growth trajectory is generated in a rectangular coordinate system, wherein the horizontal coordinate of a trajectory point of the second growth trajectory is the occurrence time of an ultrasonic knife surgery, and the vertical coordinate is the sum of operation time consumption, the number of operations or the average time consumption of the corresponding operation of the ultrasonic knife surgery.

[0025] Further, any one of the preceding technical solutions or a combination of multiple technical solutions, the growth evaluation request comprises identity information of multiple target doctors; second growth trajectories of different target doctors are generated in the same rectangular coordinate system;

[0026] Alternatively, the method further comprises: storing each surgery recording video, the surgery recording video being labeled with corresponding time information; determining an abnormal trajectory point in the second growth trajectory according to a preset rule; and viewing the corresponding surgery recording video according to the abscissa of the abnormal trajectory point.

[0027] Further, any one of the preceding technical solutions or a combination of multiple technical solutions, the ultrasonic knife surgery information recorded by the third subsystem further comprises a surgery type;

[0028] The growth evaluation request further comprises a target surgery type;

[0029] According to the search result of the tool bit operation information and according to the target surgery type, corresponding ultrasonic knife surgery information is screened from the third subsystem, and the operation number and / or operation time consumption sum between the operation start time and the operation end time of each ultrasonic knife surgery that is screened out are counted; and / or further comprising calculating the average operation time of the operation of the target doctor in each ultrasonic knife surgery;

[0030] A third growth trajectory is generated in the rectangular coordinate system, wherein the abscissa of the trajectory point of the third growth trajectory is the occurrence time of each ultrasonic knife surgery that is screened out, and the ordinate is the corresponding operation time consumption sum, operation number or average time consumption.

[0031] Further, any one of the preceding technical solutions or a combination of multiple technical solutions, the tool bit operation information further comprises an operation type of each operation;

[0032] The growth evaluation request further comprises a target operation type;

[0033] The operation time consumption sum and / or operation number of the target operation type between the operation start time and the operation end time of the associated surgery are counted; and / or further comprising calculating the average operation time of the target operation type in each associated surgery;

[0034] A third growth trajectory is generated in the rectangular coordinate system, wherein the abscissa of the trajectory point of the third growth trajectory is the occurrence time of the associated surgery, and the ordinate is the operation time consumption sum, operation number or average time consumption of the target operation type in the associated surgery.

[0035] Further, any one of the preceding technical solutions or a combination of the preceding technical solutions, the growth evaluation request comprises identity information of a plurality of target doctors; and fourth growth trajectories of different target doctors are generated in the same rectangular coordinate system;

[0036] Alternatively, the method further comprises: storing each surgery recording video, the surgery recording video being marked with corresponding time information; determining an abnormal trajectory point in the third growth trajectory according to a preset rule; and viewing the corresponding surgery recording video according to the horizontal coordinate of the abnormal trajectory point.

[0037] Further, any one of the preceding technical solutions or a combination of the preceding technical solutions, the growth evaluation request further comprises a target surgery; and the ultrasonic knife surgery information corresponding to the target surgery is searched in the third subsystem;

[0038] The time consumption of each operation in the target surgery is counted.

[0039] A fourth growth trajectory is generated in the rectangular coordinate system, the horizontal axis of the fourth growth trajectory being a time axis covering the start time and the end time of the target surgery, wherein the horizontal coordinate of a trajectory point of the fourth growth trajectory is the occurrence time of each operation, and the vertical coordinate of the trajectory point is the time consumption of the operation.

[0040] Further, any one of the preceding technical solutions or a combination of the preceding technical solutions, the growth evaluation request comprises identity information of a plurality of target doctors; and fourth growth trajectories of different target doctors are generated in the same rectangular coordinate system;

[0041] Alternatively, the method further comprises: storing each surgery recording video, the surgery recording video being marked with corresponding time information; determining an abnormal trajectory point in the fourth growth trajectory according to a preset rule; and viewing the corresponding surgery recording video according to the horizontal coordinate of the abnormal trajectory point.

[0042] Further, any one of the preceding technical solutions or a combination of the preceding technical solutions, the growth evaluation request further comprises an evaluation start time, an evaluation end time, and a step unit time;

[0043] According to the search result of the knife head operation information, the number of operations in each step unit time between the evaluation start time and the evaluation end time is counted.

[0044] A fifth growth trajectory is generated in the rectangular coordinate system, wherein the horizontal coordinate of a trajectory point of the fifth growth trajectory is a step unit time, and the vertical coordinate of the trajectory point is the corresponding number of operations.

[0045] Further, any one of the preceding technical solutions or a combination thereof, the growth evaluation request comprises identity information of the target doctors; and the fifth growth track of different target doctors is generated in a same rectangular coordinate system;

[0046] Alternatively, the method further comprises: storing each surgery recording video, the surgery recording video being labeled with corresponding time information; determining an abnormal track point in the fifth growth track according to a preset rule; and viewing the corresponding surgery recording video according to the abscissa of the abnormal track point.

[0047] Further, any one of the preceding technical solutions or a combination thereof, the growth evaluation request further comprises an evaluation start time, an evaluation end time, and identity information of the target doctors;

[0048] According to the searching result of the tool bit operation information, a total time consumption and an operation frequency of operations of each target doctor between the evaluation start time and the evaluation end time are counted;

[0049] An average time consumption of operations of each target doctor is calculated.

[0050] A comparison interface of the average time consumption of each target doctor is generated.

[0051] Further, any one of the preceding technical solutions or a combination thereof, the tool bit operation information further comprises an operation type of each operation;

[0052] The growth evaluation request further comprises a target operation type;

[0053] Data statistics are performed according to the target operation type.

[0054] Further, any one of the preceding technical solutions or a combination thereof, the medical growth visualization method based on the ultrasonic knife big data provided by the present application further comprises representing the counted data in the form of a graph or a table.

[0055] Further, any one of the preceding technical solutions or a combination thereof, the medical growth visualization method based on the ultrasonic knife big data provided by the present application further comprises:

[0056] The first subsystem is pre-established, comprising: reading an ultrasonic knife bit unique identification code and obtaining identity information of a doctor using the ultrasonic knife bit, wherein a single ultrasonic knife bit is used by only one doctor;

[0057] The pre-establishment of the second subsystem comprises: recording the ultrasonic knife head operation information by using a storage module of the ultrasonic knife device; the storage module also stores the unique identification code of the ultrasonic knife head; and storing the unique identification code of the knife head and the knife head operation information read from the storage module in the second subsystem in association.

[0058] The pre-establishment of the third subsystem comprises: recording the ultrasonic knife operation information of each doctor, which comprises the identity information of the doctor, the operation start time and the operation end time.

[0059] The technical scheme provided by the application has the following beneficial effects:

[0060] a. The growth state of the ultrasonic knife operation doctor is evaluated by using objective data;

[0061] b. The horizontal comparison between different doctors is realized by using objective data, so as to estimate the high-low ranking of the operation level of the doctor in the industry;

[0062] c. The phenomenon that the past operation is not good is traced back and found, and the operation level of the doctor can be improved by timely review;

[0063] d. The authoritative doctor is evaluated by using objective data, so as to provide a basis and a way for learning from the doctor. BRIEF DESCRIPTION OF DRAWINGS

[0064] In order to more clearly illustrate the technical scheme in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiment or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0065] Fig. 1 is a basic flowchart of the ultrasonic knife big data-based doctor growth visualization method provided by an exemplary embodiment of the present application;

[0066] Fig. 2 is a flowchart of generating a first growth track based on ultrasonic knife big data provided by an exemplary embodiment of the present application;

[0067] Fig. 3 is a flowchart of generating a second growth track based on ultrasonic knife big data provided by an exemplary embodiment of the present application;

[0068] Fig. 4 is a flowchart of generating a first third growth track based on ultrasonic knife big data provided by an exemplary embodiment of the present application;

[0069] Fig. 5 is a flowchart of generating another third growth track based on ultrasonic knife big data provided by an exemplary embodiment of the present application;

[0070] FIG. 6 is a flowchart of generating a fourth growth track based on the ultrasonic knife big data according to an example embodiment of the present application;

[0071] FIG. 7 is a flowchart of generating a fifth growth track based on the ultrasonic knife big data according to an example embodiment of the present application;

[0072] FIG. 8 is a flowchart of generating a plurality of doctor comparison interfaces based on the ultrasonic knife big data according to an example embodiment of the present application. DETAILED DESCRIPTION

[0073] In order to make the technical personnel in the art better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0074] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, device, product or apparatus including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or apparatuses.

[0075] In an embodiment of the present application, a doctor growth visualization method based on ultrasonic knife big data is provided, as shown in FIG. 1, the method comprises the following steps:

[0076] Receiving a growth evaluation request, the growth evaluation request comprising at least the identity information of a target doctor;

[0077] Finding a knife head unique identification code associated with the identity information of the target doctor in a first subsystem, the first subsystem storing the identity information of the associated doctor and the knife head unique identification code;

[0078] Finding knife head operation information associated with the knife head unique identification code in a second subsystem, the second subsystem storing the knife head unique identification code and the knife head operation information, the knife head operation information comprising the start time and end time of each operation;

[0079] According to the searching result of the tool head operation information, data statistics are made, and the statistical data are displayed by using a display device. The specific display mode can be any form of directly displaying data, such as a pie chart, a column chart, a table, or a curve chart.

[0080] And different statistical results can be obtained according to different data statistical modes, so as to display different growth records.

[0081] The first subsystem is pre-established by the following method: the ultrasonic knife head is typically marked with a tool head unique identification code, the tool head unique identification code can be recorded by an electronic tag in the form of RFID or a visual form, after each use of the ultrasonic knife, the ultrasonic knife head unique identification code is read, and the identity information of the doctor using the ultrasonic knife head is obtained, wherein a single ultrasonic knife head is used by only one doctor; the tool head unique identification code and the identity information of the doctor are stored in the first subsystem in association, the unique identification code of the ultrasonic knife head used in the operation can be read by the doctor or the nurse after the operation, and the identity information of the doctor of the operation is recorded in the first subsystem, obviously, the start and end time of the operation and the like can also be recorded;

[0082] The second subsystem is pre-established by the following method: the ultrasonic knife head operation information is recorded by using a storage module of the ultrasonic knife device, the storage module also stores the ultrasonic knife head unique identification code, the tool head operation information includes the start time and the end time of each operation, and the tool head operation information can also include the operation type (coagulation operation or cutting operation); the tool head unique identification code and the tool head operation information are stored in the second subsystem in association, the information record in the storage module of the ultrasonic knife device can be read by the doctor or the nurse after the operation, and the tool head unique identification code is recorded in the second subsystem.

[0083] Next, taking a trajectory chart as an example, the growth process of the doctor's operation of the ultrasonic knife is visualized from different angles according to different data statistical modes.

[0084] As shown in FIG. 2, in a specific embodiment, the data statistics include: the time consumption of each operation occurring in a specified period of time is counted;

[0085] One or more first growth trajectories are generated in a rectangular coordinate system, wherein the horizontal coordinate of each trajectory point of each first growth trajectory is the occurrence time of the corresponding operation, and the vertical coordinate of the trajectory point is the time consumption of the operation.

[0086] In one embodiment, the tool head operation information further comprises an operation type of each operation; the growth evaluation request further comprises a target operation type; and data statistics are performed according to the target operation type. For example, the operation type comprises a coagulation operation and a cutting operation, and the data of the operation can be classified according to the operation type, and in the first growth track and other growth tracks mentioned in the following embodiments, the relevant data of one growth track can be limited to the same type of operation. However, the present application is not limited to mixing the relevant data of different types of operations in one growth track, for example, the type of the operation is a stomach ultrasonic knife operation, and there are n1 cutting operations and n2 coagulation operations in the operation, then the track points of the time consumption of each operation of n1 cutting operations can be separately represented, or the track points of the time consumption of each operation of n2 coagulation operations can be separately represented, or the track points of the time consumption of each operation of n1 cutting operations and n2 coagulation operations can be represented.

[0087] In one embodiment, the growth evaluation request comprises identity information of a plurality of target doctors; and the first growth tracks of different target doctors are generated in the same orthogonal coordinate system. Along the time axis, the change of the time consumption of each operation of the doctor can be obviously felt from the ups and downs of the visualization of the first growth track, and if the first growth track gradually shows a downward trend, it can be said that the level of the ultrasonic knife operation of the doctor is improving to some extent. By comparing the first growth tracks of a plurality of target doctors, the doctor can compare the gap with other doctors of the same type of operation.

[0088] In one embodiment, by observing the first growth track, or according to a preset rule, for example, compared with the adjacent track points, the difference of the longitudinal axis value exceeds the preset threshold, then an abnormal track point in the first growth track is determined, so as to determine that the time consumption of the operation corresponding to July 1, 2023 to July 3, 2023 has a sharp increase, and then the operation video of this period can be viewed from the database storing the operation video (the operation video is marked with corresponding time information).

[0089] Of course, the abnormal track point can also be a track point with a sharp decrease in the time consumption of the operation.

[0090] In one embodiment of the present application, in addition to pre-establishing the first subsystem and the second subsystem, a third subsystem is also pre-established in the following manner: after the doctor completes the operation, the ultrasonic knife operation information of each doctor is recorded, which comprises the identity information of the doctor, the start time of the operation and the end time of the operation;

[0091] It should be noted that the first subsystem, the second subsystem and the third subsystem can be independent systems that can communicate with each other, or can be integrated in the same general system.

[0092] An embodiment of the present application is shown in FIG. 3, in which the third subsystem searches for operations associated with the identity information of the target doctor;

[0093] According to the search result of the tool head operation information, the total operation time and / or the number of operations between the start time and the end time of the associated operation are counted; and / or further comprising calculating the average operation time of each associated operation;

[0094] A second growth trajectory is generated in a rectangular coordinate system, wherein the horizontal coordinate of the trajectory point of the second growth trajectory is the occurrence time of each associated operation, and the vertical coordinate is the total operation time, the number of operations or the average operation time corresponding to the each associated operation.

[0095] If the vertical coordinate is the number of operations corresponding to the operation, for example, the second growth trajectory records that the number of operations (such as cutting operations) of a surgery of doctor A at 9:30 on May 27, 2024 is 25, and the number of operations (such as cutting operations) of a surgery at 14:30 on May 27, 2024 is 32. Along the time axis, the change of the number of operations in each surgery of the doctor can be obviously felt from the ups and downs of the visualization of the second growth trajectory. If the second growth trajectory gradually shows a downward trend, it can be said that the level of the doctor's ultrasonic knife surgery is improving to some extent.

[0096] If the vertical coordinate is the total operation time corresponding to the operation, that is, the total of the time of all single operations in this surgery, which can be distinguished as the total time of cutting operations or the total time of coagulation operations, or the total time of cutting operations and coagulation operations in this surgery. Along the time axis, the change of the total operation time in each surgery of the doctor can be obviously felt from the ups and downs of the visualization of the second growth trajectory. If the second growth trajectory gradually shows a downward trend, it can be said that the level of the doctor's ultrasonic knife surgery is improving to some extent.

[0097] If the vertical coordinate is the average operation time corresponding to the operation, that is, the total of the time of all single operations in this surgery divided by the number of operations, which can be distinguished as the average time of cutting operations or the average time of coagulation operations, or the average time of cutting operations and coagulation operations in this surgery. Along the time axis, the change of the average operation time in each surgery of the doctor can be obviously felt from the ups and downs of the visualization of the second growth trajectory. If the second growth trajectory gradually shows a downward trend, it can be said that the level of the doctor's ultrasonic knife surgery is improving to some extent.

[0098] If the growth evaluation request includes the identity information of multiple target doctors, the second growth trajectories of different target doctors are generated in the same rectangular coordinate system. By comparing the second growth trajectories of multiple target doctors, the doctor can compare with other doctors who perform the same type of surgery.

[0099] Likewise, by observing the second growth trajectory, or according to a preset rule, such as the difference of the longitudinal axis value exceeds a preset threshold compared with adjacent trajectory points, an abnormal trajectory point in the second growth trajectory is determined, so as to determine that the number of intraoperative operations or the total operation time or the average operation time of a single operation corresponding to July 1, 2023 to July 10, 2023 has a sharp increase or a sharp drop, and then the surgical video of this period can be viewed from the database storing the surgical video (the surgical video is marked with corresponding time information).

[0100] In an embodiment of the present application, the ultrasonic knife operation information recorded by the third subsystem further includes a surgical type;

[0101] The growth evaluation request further includes a target surgical type;

[0102] As shown in FIG. 4, according to the search result of the tool head operation information and according to the target surgical type, corresponding ultrasonic knife operation information is screened from the third subsystem, and the number of operations and / or the total operation time between the operation start time and the operation end time of each ultrasonic knife operation is counted; and / or further includes calculating the average operation time of the target doctor in each ultrasonic knife operation;

[0103] A third growth trajectory is generated in a rectangular coordinate system, wherein the horizontal coordinate of the trajectory point of the third growth trajectory is the occurrence time of each screened ultrasonic knife operation, and the longitudinal coordinate is the corresponding total operation time, operation number or average operation time. Compared with the second growth trajectory, the third growth trajectory is for the target surgical type.

[0104] In another embodiment, the tool head operation information further includes an operation type of each operation;

[0105] The growth evaluation request further includes a target operation type;

[0106] As shown in FIG. 5, the total operation time and / or the number of operations of the target operation type between the operation start time and the operation end time of the associated surgery is counted; and / or further includes calculating the average operation time of the target operation type in each associated surgery;

[0107] A third growth trajectory is generated in a rectangular coordinate system, wherein the horizontal coordinate of the trajectory point of the third growth trajectory is the occurrence time of the associated surgery, and the longitudinal coordinate is the total operation time, operation number or average operation time of the target operation type in the associated surgery. Compared with the second growth trajectory, the third growth trajectory is for the target operation type.

[0108] If the growth evaluation request includes the identity information of multiple target doctors, third growth trajectories of different target doctors are generated in the same rectangular coordinate system. By comparing the third growth trajectories of multiple target doctors, the doctors can compare with other doctors in a horizontal manner.

[0109] Similarly, by observing the third growth trajectory, or according to a preset rule, such as the difference of the longitudinal axis value compared with the adjacent trajectory point exceeds a preset threshold, an abnormal trajectory point in the third growth trajectory is determined, so as to determine that the average time consumption of the intraoperative operation corresponding to July 1, 2023 to July 3, 2023 has a sharp increase or a sharp drop, and the surgical video in this period can be viewed from the database storing the surgical video (the surgical video is marked with corresponding time information).

[0110] One embodiment of the present application considers the total time consumption of the ultrasonic knife operation to evaluate the growth of the doctor, as shown in FIG. 6, a third subsystem is established in advance, including: recording the ultrasonic knife operation information of each doctor, including the identity information of the doctor, the start time of the operation and the end time of the operation; the growth evaluation request also includes a target operation, such as using date, patient or other landmark label information as the retrieval key of the target operation; the ultrasonic knife operation information corresponding to the target operation is searched in the third subsystem; the time consumption of each operation in the target operation is counted; a fourth growth trajectory is generated in a rectangular coordinate system, and the coordinate horizontal axis is a time axis covering the start time and the end time of the target operation, wherein the horizontal coordinate of the trajectory point of the fourth growth trajectory is the occurrence time of each operation, and the vertical coordinate is the time consumption of the operation.

[0111] The fourth growth trajectory can review each operation in the target operation by the form of the trajectory point.

[0112] If the growth evaluation request includes the identity information of multiple target doctors, fourth growth trajectories of different target doctors are generated in the same rectangular coordinate system. By comparing the fourth growth trajectories of multiple target doctors, the doctors can compare with other doctors in a horizontal manner.

[0113] Similarly, by observing the fourth growth trajectory, or according to a preset rule, such as the difference of the longitudinal axis value compared with the adjacent trajectory point exceeds a preset threshold, an abnormal trajectory point in the fourth growth trajectory is determined, so as to determine that the time consumption of a certain operation or a certain number of operations in a surgery has a sharp increase; or the preset rule is that the longitudinal axis value of the trajectory point exceeds a preset limit value, so as to determine that the time consumption of a certain operation or a certain number of operations in a surgery exceeds the limit, and the surgical video in this period can be viewed from the database storing the surgical video (the surgical video is marked with corresponding time information).

[0114] As shown in FIG. 8, in one embodiment, the growth evaluation request further comprises an evaluation start time, an evaluation end time and identity information of a plurality of target doctors;

[0115] According to the search result of the tool bit operation information, the total time consumption and the operation times of the operations of each target doctor between the evaluation start time and the evaluation end time are counted;

[0116] The average time consumption of the operations of each target doctor is calculated;

[0117] A comparison interface of the average time consumption of each target doctor is generated, which can be in the form of a table plus text, or in the form of a pie chart or a column chart.

[0118] For example, the 2023 year can be selected as the evaluation time period, and a ranking list of the target doctors is generated according to the average time consumption from low to high, and the differences between the doctors are compared horizontally.

[0119] As shown in FIG. 7, in one embodiment, the growth evaluation request further comprises an evaluation start time, an evaluation end time and a step unit time;

[0120] According to the search result of the tool bit operation information, the operation times in each step unit time between the evaluation start time and the evaluation end time are counted;

[0121] A fifth growth trajectory is generated in a rectangular coordinate system, wherein the horizontal coordinate of a trajectory point of the fifth growth trajectory is the step unit time, and the vertical coordinate is the corresponding operation times.

[0122] For example, the step unit time is in days, and the fifth growth trajectory can be the operation times of each day in the selected evaluation time period, so as to represent the workload.

[0123] In one embodiment, the growth evaluation request comprises identity information of a plurality of target doctors; and the fifth growth trajectories of different target doctors are generated in the same rectangular coordinate system, so as to compare the workloads of the doctors horizontally.

[0124] After the above data statistics and before the generation of various growth trajectories, the data can be classified and counted according to the operation types, so that the corresponding information of the target operation types expected to be understood is separately reflected on the trajectory diagram. However, the present application does not exclude the implementation mode of mixed statistics without operation type classification.

[0125] In the above embodiment in which the growth evaluation request comprises identity information of a plurality of target doctors, all doctors can be selected as target doctors, and the data of different target doctors are compared horizontally, from which the data representing the authoritative level is selected to determine the target doctor representing the authority as a learning object;

[0126] The request for acquiring the surgery video is sent, and then the corresponding video can be retrieved from the database storing the surgery video in the above embodiment for learning and observation, or the video viewing permission is sent to the requester with the consent of the learning object.

[0127] It should be noted that the relational terms herein such as first and second and the like are used solely to distinguish one entity or action from another, without necessarily requiring or implying any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0128] The above description is merely one specific implementation of the application. It should be noted that, for one of ordinary skill in the art, without departing from the principles of the application, a number of improvements and refinements can be made, and these improvements and refinements should also be considered as the protection scope of the application.

Claims

1. [Corrected according to detailed rule 91, December 17, 2024] A method for visualizing physician growth based on ultrasonic scalpel big data, characterized in that, Includes the following steps: Receive a growth assessment request, which includes at least the target doctor's identity information; The first subsystem searches for the unique identification code of the blade associated with the identity information of the target doctor. The first subsystem stores the identity information of the associated doctor and the unique identification code of the blade. The second subsystem searches for the cutter head operation information associated with the cutter head's unique identification code. The second subsystem stores the associated cutter head unique identification code and cutter head operation information, including the start and end times of each operation. Based on the search results of the cutter head operation information, data statistics are performed, and the statistical data is displayed using a display device.

2. [Corrected according to detailed rule 91, 17.12.2024] The method for visualizing doctor growth based on ultrasonic scalpel big data according to claim 1 is characterized in that, The statistical data includes: Calculate the time taken for each operation; One or more first growth trajectories are generated in a rectangular coordinate system, where the x-coordinate of the trajectory points of each first growth trajectory is the occurrence time of the operation corresponding to the same surgical type, and the y-coordinate is the time consumed by the corresponding operation.

3. [Corrected according to detailed rule 91, 17.12.2024] The method for visualizing doctor growth based on ultrasonic scalpel big data according to claim 2 is characterized in that, The growth assessment request includes the identity information of multiple target doctors; Generate the first growth trajectory of different target doctors in the same rectangular coordinate system.

4. [Corrected according to detailed rule 91, 17.12.2024] The method for visualizing doctor growth based on ultrasonic scalpel big data according to claim 2 is characterized in that, Also includes: Each surgical video recording is stored, and the surgical video recording is labeled with corresponding time information; Based on preset rules, abnormal trajectory points in the first growth trajectory are determined; Based on the x-coordinate of the abnormal trajectory point, view the corresponding surgical video recording.

5. [Corrected according to detailed rule 91, 17.12.2024] The method for visualizing doctor growth based on ultrasonic scalpel big data according to claim 1 is characterized in that, Also includes: The third subsystem searches for surgeries associated with the target doctor's identity information. The third subsystem stores the doctor's ultrasonic scalpel surgery information, which includes the doctor's identity information, surgery start time, and surgery end time. Based on the search results of the blade operation information, the total operation time and / or number of operations between the start and end times of the associated surgeries are calculated; and / or the average operation time in each associated surgery is also calculated. A second growth trajectory is generated in a rectangular coordinate system, wherein the abscissa of the trajectory point of the second growth trajectory is the occurrence time of the associated surgery, and the ordinate is the total operation time, number of operations, or average operation time corresponding to each associated surgery.

6. [Corrected according to detailed rule 91, 17.12.2024] The method for visualizing doctor growth based on ultrasonic scalpel big data according to claim 5 is characterized in that, The growth assessment request includes the identity information of multiple target doctors; Generate second growth trajectories for different target doctors within the same Cartesian coordinate system; Alternatively, the method may further include: storing various surgical video recordings, each surgical video recording being labeled with corresponding time information; determining abnormal trajectory points in the second growth trajectory according to preset rules; and viewing the corresponding surgical video recordings based on the horizontal coordinates of the abnormal trajectory points.

7. [Correction 17.12.2024 based on detailed rule 91] The method for visualizing doctor growth based on ultrasonic scalpel big data according to claim 5 is characterized in that, The ultrasonic scalpel surgery information recorded by the third subsystem also includes the surgery type; The growth assessment request also includes the target surgical type; Based on the search results of the blade operation information and the target surgical type, the corresponding ultrasonic scalpel surgical information is filtered from the third subsystem, and the number of operations and / or the total operation time between the start time and end time of each filtered ultrasonic scalpel surgical procedure are counted; and / or the average operation time of the target doctor in each ultrasonic scalpel surgical procedure is calculated. A third growth trajectory is generated in a rectangular coordinate system, wherein the horizontal coordinate of the trajectory points of the third growth trajectory is the occurrence time of each selected ultrasonic scalpel surgery, and the vertical coordinate is the total operation time, number of operations, or average operation time.

8. [Correction to Article 91 of Detailed Rules 17.12.2024] The method for visualizing physician growth based on ultrasonic scalpel big data according to claim 5 is characterized in that, The cutter head operation information also includes the operation type for each operation; The growth assessment request also includes the target operation type; The total operation time and / or number of operations of the target operation type between the start time and end time of the associated surgeries are calculated; and / or the average operation time of the target operation type in each associated surgery is also calculated. A third growth trajectory is generated in a rectangular coordinate system, wherein the abscissa of the trajectory point of the third growth trajectory is the occurrence time of the associated surgery, and the ordinate is the total operation time, number of operations, or average operation time of the target operation type in the associated surgery.

9. [Correction to Article 91 of Detailed Rules 17.12.2024] The method for visualizing physician growth based on ultrasonic scalpel big data according to claim 7 or 8 is characterized in that, The growth assessment request includes the identity information of multiple target doctors; Generate third growth trajectories for different target doctors in the same Cartesian coordinate system; Alternatively, the method may further include: storing various surgical video recordings, each surgical video recording being labeled with corresponding time information; determining abnormal trajectory points in the third growth trajectory according to preset rules; and viewing the corresponding surgical video recordings based on the horizontal coordinates of the abnormal trajectory points.

10. [Corrected according to Rule 91, 17.12.2024] The method for visualizing doctor growth based on ultrasonic scalpel big data according to claim 5 is characterized in that, The growth assessment request also includes a target surgery; the third subsystem is used to search for ultrasonic scalpel surgery information corresponding to the target surgery. The time consumed by each operation in the target surgery was calculated. A fourth growth trajectory is generated in a rectangular coordinate system, with its horizontal axis representing the time axis covering the start and end times of the target surgery. The horizontal coordinate of the trajectory points of the fourth growth trajectory represents the time of each operation, and its vertical coordinate represents the time consumed by that operation.

11. [Corrected to 17.12.2024 according to detailed rule 91] The method for visualizing doctor growth based on ultrasonic scalpel big data according to claim 10 is characterized in that, The growth assessment request includes the identity information of multiple target doctors; and generates a fourth growth trajectory for different target doctors in the same Cartesian coordinate system. Alternatively, the method may further include: storing various surgical video recordings, each surgical video recording being labeled with corresponding time information; determining abnormal trajectory points in the fourth growth trajectory according to preset rules; and viewing the corresponding surgical video recordings based on the horizontal coordinates of the abnormal trajectory points.

12. [Corrected according to Rule 91, 17.12.2024] The method for visualizing doctor growth based on ultrasonic scalpel big data according to claim 1 is characterized in that, The growth assessment request also includes the assessment start time, assessment end time, and step unit time; Based on the search results of the cutter head operation information, the number of operations within each step unit time between the evaluation start time and the evaluation end time is counted. A fifth growth trajectory is generated in a rectangular coordinate system, wherein the x-coordinate of the trajectory point of the fifth growth trajectory is the step unit time, and its y-coordinate is the corresponding number of operations.

13. [Corrected to 17.12.2024 according to detailed rule 91] The method for visualizing doctor growth based on ultrasonic scalpel big data according to claim 12 is characterized in that, The growth assessment request includes the identity information of multiple target doctors; and generates fifth growth trajectories for different target doctors in the same Cartesian coordinate system. Alternatively, the method may further include: storing each surgical video recording, the surgical video recording being labeled with corresponding time information; determining abnormal trajectory points in the fifth growth trajectory according to preset rules; and viewing the corresponding surgical video recording based on the horizontal coordinate of the abnormal trajectory points.

14. [Corrected to 17.12.2024 according to detailed rule 91] The method for visualizing doctor growth based on ultrasonic scalpel big data according to claim 1 is characterized in that, The growth assessment request also includes the assessment start time, assessment end time, and identity information of multiple target doctors; Based on the search results of the blade operation information, the total time spent and number of operations performed by each target doctor between the assessment start time and the assessment end time are calculated. Calculate the average time taken for each target doctor's operation; Generate a comparison interface for the total time spent, number of operations, and / or average time spent by each target doctor.

15. [Corrected according to Rule 91, 17.12.2024] Doctors' data based on ultrasonic scalpel big data as described in claim 2, 7, 10, 12, or 14 The long visualization method is characterized by, The cutter head operation information also includes the operation type for each operation; The growth assessment request also includes the target operation type; Data statistics are performed according to the target operation type.

16. [Corrected according to Rule 91, 17.12.2024] The method for visualizing physician growth based on ultrasonic scalpel big data according to claim 1, 2, 5, 7, 8, 10, 12, or 14 is characterized in that, It also includes representing the statistical data in the form of graphs or tables.

17. [Corrected according to Rule 91, 17.12.2024] The method for visualizing physician growth based on ultrasonic scalpel big data according to any one of claims 1 to 8 or any one of claims 10 to 14 is characterized in that, Also includes: The first subsystem is pre-established, including: reading the unique identification code of the ultrasonic scalpel head and obtaining the identity information of the doctor using the ultrasonic scalpel head, wherein a single ultrasonic scalpel head is used by only one doctor; The second subsystem is pre-established, including: recording ultrasonic scalpel tip operation information using the storage module of the ultrasonic scalpel device; the storage module also stores a unique identification code for the ultrasonic scalpel tip; and associating the unique identification code and tip operation information read from the storage module in the second subsystem; and A third subsystem is pre-established, which includes recording information on each doctor's ultrasonic scalpel surgery, including the doctor's identity information, surgery start time, and surgery end time.

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