Auxiliary identification method and system for flap transplantation state

Abstract

The invention provides an auxiliary identification method and system for a flap transplantation state. The method comprises the following steps: acquiring physical detection information of a flap area and monitoring results of various physiological monitoring types detected by equipment such as a laser speckle instrument; identifying a first state probability of the flap area in each flap state and monitoring change information of each physiological monitoring type; identifying a state change degree corresponding to each piece of monitoring change information and a second state probability of each flap state through a monitoring analysis strategy of each physiological monitoring type; based on the first state probability of the flap region in each flap state and the second state probability of the flap region in each flap state, identifying the target state probability of the flap region in each flap state, and identifying the target state probability of the flap region in each flap state as the second state probability of the flap region in each flap state; and taking the information as flap transplantation state auxiliary identification information of the flap area. By adopting the scheme, the recognition accuracy of a worker on the flap transplantation state can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of skin flap transplantation, and in particular to an auxiliary identification method and system for skin flap transplantation status. Background Art

[0002] Skin flap transplantation is a process of transplanting a skin flap to the wound surface, while temporarily supplying nutrition from the pedicle blood supply. After the blood vessels in the wound area of ​​the flap grow into the flap and establish new blood supply, the pedicle is cut off to complete the whole process of flap transfer. In the process of flap transplantation, the recognition of the state of flap transplantation is an important means to improve the detection and analysis of flap transplantation. Therefore, how to improve the accuracy of identifying the state of flap transplantation is the current research focus.

[0003] The traditional method of identifying the status of a flap transplant is to have staff manually judge the color, temperature, and elasticity of the flap area, thereby manually analyzing the status of the flap transplant. However, manual analysis is affected by the staff's experience and the accuracy of manual detection, resulting in low accuracy in identifying the status of the flap transplant. Summary of the invention

[0004] The main purpose of the present invention is to provide an auxiliary identification method and system for the status of flap transplantation, aiming to solve the problem of low accuracy in identifying the status of flap transplantation in the prior art due to the influence of the experience of the staff and the accuracy of manual detection in manual analysis.

[0005] To achieve the above object, the present invention provides an auxiliary identification method for flap transplantation status, the method comprising:

[0006] In response to the flap area detection operation of the staff, the physical detection information of the flap area is obtained, and the monitoring results of the flap area in various physiological monitoring types are collected through real-time detection equipment; the real-time monitoring equipment includes a laser speckle instrument, a pulse monitoring instrument, and a muscle tissue thickness monitoring instrument;

[0007] Based on the physical detection information, the first state probability of the flap area in each flap state is identified through the flap state analysis strategy, and based on the monitoring results of the flap area in each physiological monitoring type and the historical detection results of the flap area in each physiological monitoring type, the monitoring change information of the flap area in each physiological monitoring type is identified;

[0008] Based on the monitoring change information of each physiological monitoring type, the degree of state change corresponding to each monitoring change information is identified through the monitoring analysis strategy of each physiological monitoring type, and based on the historical flap state of the flap area and the degree of state change corresponding to each monitoring change information, the probability of the second state of the flap area in each flap state is identified;

[0009] Based on the first state probability of the flap area in each flap state and the second state probability of the flap area in each flap state, the target state probability of the flap area in each flap state is identified, and the target state probability of the flap area in each flap state is used as auxiliary identification information of the flap transplantation state of the flap area.

[0010] Optionally, the identifying the first state probability of the flap region in each flap state by the flap state analysis strategy based on the physical detection information includes:

[0011] Splitting the physical detection information into time detection information and appearance detection information, and acquiring, based on the time detection information, sample appearance detection information of each skin flap state corresponding to the time detection information in a transplantation database;

[0012] Based on the sample appearance detection information of each flap state and the appearance detection information, the appearance detection information and the detection deviation information between the appearance detection information and each sample appearance detection information are calculated respectively, and based on the detection deviation information corresponding to each flap state, the detection analysis strategy corresponding to each flap state is used to identify the first state probability of the flap area in each flap state.

[0013] Optionally, the identifying monitoring change information of the flap area in each physiological monitoring type based on the monitoring results of the flap area in each physiological monitoring type and the historical detection results of the flap area in each physiological monitoring type includes:

[0014] For each physiological monitoring type, based on the monitoring results of the physiological monitoring type and the historical detection results of the physiological monitoring type, identifying the detection data of the physiological monitoring type and the historical detection data of the physiological monitoring type;

[0015] Based on the detection data of the physiological monitoring type and the historical detection data of the physiological monitoring type, constructing data distribution information of the physiological monitoring type, and performing curve fitting processing on the data distribution information to obtain a data change curve of the physiological monitoring type;

[0016] Based on the data change curve, the curve slope of the physiological monitoring type and the data difference value of the physiological monitoring type are identified, and the curve slope and the data difference value are used as monitoring change information of the physiological monitoring type.

[0017] Optionally, the monitoring change information based on each of the physiological monitoring types, identifying the state change degree corresponding to each monitoring change information through the monitoring analysis strategy of each physiological monitoring type, includes:

[0018] For each physiological monitoring type, querying the monitoring and analysis strategy of the physiological monitoring type in the data monitoring database;

[0019] Based on the monitoring and analysis strategy, identifying the sub-state change ratio value corresponding to each curve slope range, and based on the curve slope of the physiological monitoring type and the sub-state change ratio value corresponding to each curve slope range, identifying the target sub-state change ratio value of the physiological monitoring type;

[0020] Based on the data difference value of the physiological monitoring type, multiply the sub-state change ratio value corresponding to the curve slope range to obtain the state change degree corresponding to the physiological monitoring type.

[0021] Optionally, the identifying the second state probability of the flap area in each flap state based on the historical flap state of the flap area and the state change degree corresponding to each monitoring change information includes:

[0022] Obtaining the state change degree interval between each flap state and the state sorting information between each flap state;

[0023] Based on the historical flap state of the flap area, the range of state change between each flap state, the state sorting information between each flap state, and the state change degree of each physiological monitoring type, the second state probability of the flap state belonging to each physiological monitoring type is identified through the state identification strategy of each physiological monitoring type.

[0024] Optionally, the identifying the target state probability of the flap region in each flap state based on the first state probability of the flap region in each flap state and the second state probability of the flap region in each flap state includes:

[0025] Obtaining a first probability weight value of the first state probability and a second probability weight value of the second state probability;

[0026] For each flap state, the first state probability of the flap state and the second state probability of the flap state are weighted and summed using the first probability weight value of the first state probability and the second probability weight value of the second state probability to obtain the target state probability of the flap state.

[0027] In addition, to achieve the above-mentioned purpose, the present invention also provides an auxiliary identification system for skin flap transplantation status, the auxiliary identification system for skin flap transplantation status comprising:

[0028] An acquisition module, for acquiring physical detection information of the skin flap area in response to a flap area detection operation by a staff member, and collecting monitoring results of the skin flap area in various physiological monitoring types through a real-time detection device; the real-time monitoring device includes a laser speckle instrument, a pulse monitoring instrument, and a muscle tissue thickness monitoring instrument;

[0029] A first identification module is used to identify the first state probability of the flap area in each flap state based on the physical detection information and the flap state analysis strategy, and to identify the monitoring change information of the flap area in each physiological monitoring type based on the monitoring results of the flap area in each physiological monitoring type and the historical detection results of the flap area in each physiological monitoring type;

[0030] a second identification module, for identifying the degree of state change corresponding to each monitoring change information based on the monitoring change information of each physiological monitoring type through the monitoring analysis strategy of each physiological monitoring type, and identifying the second state probability of the flap area in each flap state based on the historical flap state of the flap area and the degree of state change corresponding to each monitoring change information;

[0031] A determination module is used to identify the target state probability of the flap area in each flap state based on the first state probability of the flap area in each flap state and the second state probability of the flap area in each flap state, and use the target state probability of the flap area in each flap state as auxiliary identification information of the flap transplantation state of the flap area.

[0032] Optionally, the first identification module is specifically used to:

[0033] Splitting the physical detection information into time detection information and appearance detection information, and acquiring, based on the time detection information, sample appearance detection information of each skin flap state corresponding to the time detection information in a transplantation database;

[0034] Based on the sample appearance detection information of each flap state and the appearance detection information, the appearance detection information and the detection deviation information between the appearance detection information and each sample appearance detection information are calculated respectively, and based on the detection deviation information corresponding to each flap state, the detection analysis strategy corresponding to each flap state is used to identify the first state probability of the flap area in each flap state.

[0035] Optionally, the first identification module is specifically used to:

[0036] For each physiological monitoring type, based on the monitoring results of the physiological monitoring type and the historical detection results of the physiological monitoring type, identifying the detection data of the physiological monitoring type and the historical detection data of the physiological monitoring type;

[0037] Based on the detection data of the physiological monitoring type and the historical detection data of the physiological monitoring type, constructing data distribution information of the physiological monitoring type, and performing curve fitting processing on the data distribution information to obtain a data change curve of the physiological monitoring type;

[0038] Based on the data change curve, the curve slope of the physiological monitoring type and the data difference value of the physiological monitoring type are identified, and the curve slope and the data difference value are used as monitoring change information of the physiological monitoring type.

[0039] Optionally, the second identification module is specifically used to:

[0040] For each physiological monitoring type, querying the monitoring and analysis strategy of the physiological monitoring type in the data monitoring database;

[0041] Based on the monitoring and analysis strategy, identifying the sub-state change ratio value corresponding to each curve slope range, and based on the curve slope of the physiological monitoring type and the sub-state change ratio value corresponding to each curve slope range, identifying the target sub-state change ratio value of the physiological monitoring type;

[0042] Based on the data difference value of the physiological monitoring type, multiply the sub-state change ratio value corresponding to the curve slope range to obtain the state change degree corresponding to the physiological monitoring type.

[0043] Optionally, the second identification module is specifically used to:

[0044] Obtaining the state change degree interval between each flap state and the state sorting information between each flap state;

[0045] Based on the historical flap state of the flap area, the range of state change between each flap state, the state sorting information between each flap state, and the state change degree of each physiological monitoring type, the second state probability of the flap state belonging to each physiological monitoring type is identified through the state identification strategy of each physiological monitoring type.

[0046] Optionally, the determining module is specifically used to:

[0047] Obtaining a first probability weight value of the first state probability and a second probability weight value of the second state probability;

[0048] For each flap state, the first state probability of the flap state and the second state probability of the flap state are weighted and summed using the first probability weight value of the first state probability and the second probability weight value of the second state probability to obtain the target state probability of the flap state.

[0049] In a third aspect, the present application provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any one of the methods in the first aspect are implemented.

[0050] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0051] In a fifth aspect, the present application provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0052] The present invention provides an auxiliary identification method for the state of a flap transplantation, the method comprising: in response to a flap area detection operation by a staff member, obtaining physical detection information of the flap area, and collecting monitoring results of the flap area in various physiological monitoring types through a real-time detection device; the real-time monitoring device comprises a laser speckle instrument, a pulse monitoring instrument, and a muscle tissue thickness monitoring instrument; based on the physical detection information, through the flap state analysis strategy, identifying the first state probability of the flap area in each flap state, and based on the monitoring results of the flap area in each physiological monitoring type and the historical detection results of the flap area in each physiological monitoring type, identifying the first state probability of the flap area in each physiological monitoring type monitoring change information of each physiological monitoring type; based on the monitoring change information of each physiological monitoring type, through the monitoring analysis strategy of each physiological monitoring type, identify the degree of state change corresponding to each monitoring change information, and based on the historical flap state of the flap area and the degree of state change corresponding to each monitoring change information, identify the second state probability of the flap area in each flap state; based on the first state probability of the flap area in each flap state and the second state probability of the flap area in each flap state, identify the target state probability of the flap area in each flap state, and use the target state probability of the flap area in each flap state as the auxiliary identification information of the flap transplantation state of the flap area. This scheme obtains the monitoring results of each physiological monitoring type through various physical detection methods of the flap area, and performs various physiological monitoring on the flap area, thereby comprehensively analyzing the flap state of the user's flap area and improving the comprehensiveness of the state recognition of the flap state of the flap area. Then, this scheme analyzes each physical detection information separately to obtain the first state probability of the flap area in each flap state, and analyzes the monitoring results of each physiological monitoring type separately to obtain the second state probability of the flap area in each flap state. Finally, by combining the first state probability of the flap area in each flap state and the second state probability of the flap area in each flap state, the target state probability of the flap area in each flap state is analyzed to obtain the flap transplantation state auxiliary identification information of the flap area, thereby improving the accuracy and practicality of the obtained flap transplantation state auxiliary identification information, thereby effectively assisting and improving the recognition accuracy of the flap transplantation state by the staff. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1is a flow chart of an auxiliary identification method for skin flap transplantation status provided by an embodiment of the present invention;

[0055] Figure 2 It is a structural schematic diagram of the auxiliary identification of the flap transplantation state provided by an embodiment of the present invention;

[0056] Figure 3 An internal structure diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The auxiliary identification method and system method of the flap transplantation state provided by the embodiment of the present invention is applied to the auxiliary identification method and system of the flap transplantation state. Unless otherwise defined, all technical and scientific terms used in this article have the same meaning as those generally understood by technicians in the technical field of this application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned drawings and any variations thereof are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0058] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0059] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0060] The auxiliary identification method of the flap transplantation state provided in the embodiment of the present application can be applied to the auxiliary identification application environment of the flap transplantation state. The method is applied to the terminal corresponding to the portable smart wearable devices such as smart bracelets, smart watches, and smart wristbands. Among them, the terminal obtains the physical detection information through various physical detection methods of the flap area, and performs various physiological monitoring on the flap area to obtain the monitoring results of each physiological monitoring type, thereby comprehensively analyzing the flap state of the user's flap area, and improving the comprehensiveness of the state recognition of the flap state of the flap area. Then, this scheme analyzes each physical detection information separately, obtains the first state probability of the flap area in each flap state, and analyzes the monitoring results of each physiological monitoring type separately, and obtains the second state probability of the flap area in each flap state. Finally, by combining the first state probability of the flap area in each flap state and the second state probability of the flap area in each flap state, the target state probability of the flap area in each flap state is analyzed, and the flap transplantation state auxiliary identification information of the flap area is obtained, which improves the accuracy and practicality of the obtained flap transplantation state auxiliary identification information, thereby effectively assisting and improving the recognition accuracy of the flap transplantation state by the staff.

[0061] In one embodiment, Figure 1 As shown, a flap transplantation state auxiliary identification method is provided, and the method is applied to a terminal as an example for explanation, including the following steps:

[0062] Step S101, in response to the flap area detection operation of the staff, the physical detection information of the flap area is obtained, and the monitoring results of the flap area in various physiological monitoring types are collected through real-time detection equipment.

[0063] Among them, the real-time monitoring equipment includes laser speckle instruments, pulse monitoring instruments, and muscle tissue thickness monitoring instruments.

[0064] In this embodiment, the terminal responds to the physical detection information upload operation obtained by the staff through various physical detection equipment to the flap area, and obtains the physical detection information of the flap area. Among them, the physical detection information includes the detection information corresponding to each physical detection type, and the physical detection type includes a time detection type and an appearance detection type. The time detection type is used to detect the transplantation duration of the flap area, and the appearance detection type includes the detection information corresponding to each appearance detection device. The appearance detection device includes but is not limited to a color detection device, a temperature detection device, an elasticity detection type, and a perception detection information. Among them, the detection information of the color detection device includes the color distribution information in the flap area. The color distribution information includes the color data of each position point. The detection information of the temperature detection device includes the temperature distribution information in the flap area, and the temperature distribution information includes the temperature data of each position point. The detection information of the elasticity detection device includes the elasticity distribution information in the flap area, and the elasticity distribution information includes the elasticity data of each position point. The detection information of the perception detection device includes the perception distribution information in the flap area, and the perception distribution information includes the perception data of each position point. The perception data is the contact perception data of each position point of the user receiving the flap transplant, and the contact perception data includes but is not limited to pain, insensitivity, touch, etc. The contact perception data of different position points is the contact perception information of the user when the staff touches each position point through the perception detection device, and the contact perception information can be the contact perception data of different position points obtained after the user selects the contact perception data on the perception detection device. Then, the terminal performs physiological monitoring on the user through various physiological monitoring instruments set on the user's body surface, and obtains monitoring results of various physiological monitoring types. The physiological monitoring instrument includes but is not limited to a laser speckle instrument, a pulse monitoring instrument, a muscle tissue thickness monitoring instrument, etc. The physiological monitoring types corresponding to the pulse monitoring instrument include the pulse monitoring type around the flap area and the pulse monitoring type in the flap area, etc. The physiological monitoring type of the muscle tissue thickness monitoring instrument is the muscle tissue thickness monitoring type. The laser speckle instrument is a detection device based on laser speckle technology, which can monitor the microcirculation parameters such as the blood vessel diameter, blood vessel density, blood flow rate and blood perfusion in the flap area in real time, thereby detecting the blood flow in the flap area.

[0065] Step S102, based on the physical detection information, through the flap state analysis strategy, identify the first state probability of the flap area in each flap state, and based on the monitoring results of the flap area in each physiological monitoring type, and the historical detection results of the flap area in each physiological monitoring type, identify the monitoring change information of the flap area in each physiological monitoring type.

[0066] In this embodiment, the terminal identifies the first state probability of the flap area in each flap state through a flap state analysis strategy based on physical detection information. The flap state analysis strategy includes a detection and analysis strategy corresponding to each flap state. The specific detection and analysis strategy will be described in detail later.

[0067] Then, the terminal identifies the monitoring change information of the flap area in each physiological monitoring type based on the monitoring results of the flap area in each physiological monitoring type and the historical detection results of the flap area in each physiological monitoring type. The monitoring change information includes the slope of the curve of each physiological detection type and the data difference value. The specific identification process will be described in detail later.

[0068] Step S103, based on the monitoring change information of each physiological monitoring type, identifies the state change degree corresponding to each monitoring change information through the monitoring analysis strategy of each physiological monitoring type, and identifies the second state probability of the flap area in each flap state based on the historical flap state of the flap area and the state change degree corresponding to each monitoring change information.

[0069] In this embodiment, the terminal identifies the degree of state change corresponding to each monitoring change information based on the monitoring change information of each physiological monitoring type through the monitoring analysis strategy of each physiological monitoring type, and identifies the second state probability of the flap area in each flap state based on the historical flap state of the flap area and the degree of state change corresponding to each monitoring change information. Among them, the degree of state change corresponding to each monitoring change information represents the degree of change of the flap state that can be identified based on the state change information. Among them, each flap state is sorted from good to bad, namely, normal flap state, slightly abnormal flap state, abnormal flap state, partially necrotic flap state, mostly necrotic flap state, necrotic flap state, and diseased flap state. The state change degree interval between each flap state is the same, and each state change degree interval is evenly split and processed to obtain the state probability of each flap state corresponding to different state change degrees. For example, if the state change interval is 10, the state change corresponding to the normal state is (0,5), the state change corresponding to the slightly abnormal flap state is (5,15), and the state change corresponding to the abnormal flap state is (15,25). Every time the state change degree changes by 1, the probability of the flap state changes by 10%.

[0070] When the user's historical flap state is a normal flap state, the terminal determines that when the user's peripheral pulse monitoring type state change degree in the flap area is 14, the user is in a slightly abnormal flap state, and the slightly abnormal flap state is 90%.

[0071] Step S104, based on the first state probability of the flap area in each flap state and the second state probability of the flap area in each flap state, identifies the target state probability of the flap area in each flap state, and uses the target state probability of the flap area in each flap state as auxiliary identification information of the flap transplantation state of the flap area.

[0072] In this embodiment, the terminal identifies the target state probability of the flap region in each flap state based on the first state probability of the flap region in each flap state and the second state probability of the flap region in each flap state, and uses the target state probability of the flap region in each flap state as auxiliary identification information of the flap transplantation state of the flap region. Among them, the probability weight values ​​of the first state probability and the second state probability are different. When the flap state only includes the first state probability or the second state probability, the terminal directly uses the first state probability or the second state probability as the target state probability of the flap state. When the flap state includes both the first state probability and the second state probability, the target state probability of the flap state is the probability value obtained by weighted summation of the two state probabilities.

[0073] Based on the above scheme, through various physical detection methods of the flap area, the physical detection information obtained, and various physiological monitoring of the flap area, the monitoring results of each physiological monitoring type are obtained, so as to comprehensively analyze the flap state of the user's flap area, and improve the comprehensiveness of the state recognition of the flap state of the flap area. Then, this scheme analyzes each physical detection information separately to obtain the first state probability of the flap area in each flap state, and analyzes the monitoring results of each physiological monitoring type separately to obtain the second state probability of the flap area in each flap state. Finally, by combining the first state probability of the flap area in each flap state and the second state probability of the flap area in each flap state, the target state probability of the flap area in each flap state is analyzed to obtain the auxiliary identification information of the flap transplantation state of the flap area, which improves the accuracy and practicality of the obtained auxiliary identification information of the flap transplantation state, thereby effectively assisting and improving the recognition accuracy of the flap transplantation state by the staff.

[0074] Optionally, based on the physical detection information, a flap state analysis strategy is used to identify the probability of the flap area in the first state in each flap state, including: splitting the physical detection information into time detection information and appearance detection information, and based on the time detection information, obtaining sample appearance detection information of each flap state corresponding to the time detection information in a transplant database; based on the sample appearance detection information of each flap state and the appearance detection information, respectively calculating the detection deviation information between the appearance detection information and each sample appearance detection information, and based on the detection deviation information corresponding to each flap state, identifying the probability of the flap area in the first state in each flap state through the detection analysis strategy corresponding to each flap state.

[0075] In this embodiment, the terminal splits the physical detection information into time detection information and appearance detection information, and based on the time detection information, obtains the sample appearance detection information of each flap state corresponding to the time detection information in the transplant database. The sample appearance detection information is the sample detection information range of each appearance detection information. Then, based on the sample appearance detection information of each flap state and the appearance detection information, the terminal calculates the detection deviation information between the appearance detection information and each sample appearance detection information. The detection deviation information is the detection deviation value range between each appearance detection information and the sample appearance detection information.

[0076] Then, based on the detection deviation information corresponding to each flap state, the terminal identifies the first state probability of the flap area in each flap state through the detection analysis strategy corresponding to each flap state. The detection analysis strategy corresponding to each flap state includes a preset detection deviation value range corresponding to different state probabilities. The terminal obtains the first state probability corresponding to each flap state by identifying the preset detection deviation value range to which the detection deviation value range between each appearance detection information and the sample appearance detection information belongs.

[0077] Based on the above scheme, by screening the appearance detection information within the sample of each flap state and the appearance detection information of the user, the state probability corresponding to each flap state is identified, thereby improving the accuracy of identifying the first state probability of the flap area in each flap state.

[0078] Optionally, based on the monitoring results of the flap area in each physiological monitoring type and the historical detection results of the flap area in each physiological monitoring type, the monitoring change information of the flap area in each physiological monitoring type is identified, including: for each physiological monitoring type, based on the monitoring results of the physiological monitoring type and the historical detection results of the physiological monitoring type, identifying the detection data of the physiological monitoring type and the historical detection data of the physiological monitoring type; based on the detection data of the physiological monitoring type and the historical detection data of the physiological monitoring type, constructing data distribution information of the physiological monitoring type, and performing curve fitting on the data distribution information to obtain a data change curve of the physiological monitoring type; based on the data change curve, identifying the slope of the curve of the physiological monitoring type and the data difference value of the physiological monitoring type, and using the slope of the curve and the data difference value as the monitoring change information of the physiological monitoring type.

[0079] In this embodiment, the terminal identifies the detection data of the physiological monitoring type and the historical detection data of the physiological monitoring type for each physiological monitoring type based on the monitoring results of the physiological monitoring type and the historical detection results of the physiological monitoring type. The detection data is the detection data that can be processed by user data in each inspection result, such as the pulse frequency value, the maximum muscle thickness value, etc.

[0080] Then, the terminal constructs data distribution information of the physiological monitoring type based on the detection data of the physiological monitoring type and the historical detection data of the physiological monitoring type, and performs curve fitting processing on the data distribution information to obtain a data change curve of the physiological monitoring type. The curve fitting processing method can obtain a data change curve by curve fitting the data distribution information of the physiological monitoring type through the least squares method.

[0081] Finally, the terminal identifies the curve slope of the physiological monitoring type and the data difference value of the physiological monitoring type based on the data change curve, and uses the curve slope and the data difference value as the monitoring change information of the physiological monitoring type. The data difference value is the deviation value between the historical detection data and the detection data.

[0082] Based on the above scheme, the data distribution information obtained after distribution processing of historical test data and test data, the slope of the identification curve, and the data difference value are improved, thereby improving the comprehensiveness and accuracy of the identification of monitoring change information of physiological monitoring types.

[0083] Optionally, based on the monitoring change information of each physiological monitoring type, the monitoring and analysis strategy of each physiological monitoring type is used to identify the degree of state change corresponding to each monitoring change information, including: for each physiological monitoring type, querying the monitoring and analysis strategy of the physiological monitoring type in the data monitoring database; based on the monitoring and analysis strategy, identifying the sub-state change ratio value corresponding to each curve slope range, and based on the curve slope of the physiological monitoring type and the sub-state change ratio value corresponding to each curve slope range, identifying the target sub-state change ratio value of the physiological monitoring type; based on the data difference value of the physiological monitoring type, multiplying it by the sub-state change ratio value corresponding to the curve slope range to obtain the degree of state change corresponding to the physiological monitoring type.

[0084] In this embodiment, the terminal queries the monitoring and analysis strategy of the physiological monitoring type in the data monitoring database for each physiological monitoring type. The monitoring and analysis strategy includes sub-state change ratio values ​​corresponding to different curve slope ranges, and the sub-state change ratio value is a ratio value for identifying the corresponding relationship between the data difference value and the state change degree of the user. The larger the slope, the larger the corresponding sub-state change ratio value, and the smaller the slope, the smaller the corresponding sub-state change ratio value.

[0085] Then, based on the monitoring and analysis strategy, the terminal identifies the sub-state change ratio value corresponding to each curve slope range, and identifies the target sub-state change ratio value of the physiological monitoring type based on the curve slope of the physiological monitoring type and the sub-state change ratio value corresponding to each curve slope range. Finally, the terminal multiplies the data difference value of the physiological monitoring type by the sub-state change ratio value corresponding to the curve slope range to obtain the state change degree corresponding to the physiological monitoring type.

[0086] Based on the above scheme, the sub-state change ratio value is identified by the slope, and then the state change degree corresponding to the physiological monitoring type is identified by the sub-state change ratio value, thereby improving the recognition accuracy and efficiency of the state change degree.

[0087] Optionally, based on the historical flap states of the flap area and the degree of state change corresponding to each monitored change information, the second state probability of the flap area in each flap state is identified, including: obtaining the state change degree interval between each flap state and the state ranking information between each flap state; based on the historical flap states of the flap area, the state change degree range between each flap state, the state ranking information between each flap state, and the state change degree of each physiological monitoring type, the second state probability of the flap state belonging to each physiological monitoring type is identified through the state identification strategy of each physiological monitoring type.

[0088] In this embodiment, the terminal obtains the state change degree interval between each flap state and the state sorting information between each flap state. The state sorting information is sorted from good to bad as normal flap state, slightly abnormal flap state, abnormal flap state, partially necrotic flap state, mostly necrotic flap state, necrotic flap state, and diseased flap state.

[0089] Then, based on the historical flap state of the flap area, the range of the state change degree between each flap state, the state sorting information between each flap state, and the state change degree of each physiological monitoring type, the terminal identifies the second state probability of the flap state belonging to each physiological monitoring type through the state identification strategy of each physiological monitoring type. Among them, the state change degree is a state change degree with the historical flap state as the state change degree of 0, a flap state that is better than the historical flap state as a negative value, and a flap state that is worse than the historical flap state as a positive value.

[0090] The identification process of the second state probability of each flap state is detailed in the embodiment of step S103, and redundant description is omitted here.

[0091] Based on the above scheme, by identifying the state change degree interval between the flap states and the state sorting information between each flap state, the second state probability of the flap state belonging to each physiological monitoring type is identified, thereby improving the recognition accuracy and comprehensiveness of the second state probability of the flap state belonging to each physiological monitoring type.

[0092] Optionally, based on the first state probability of the flap area in each flap state and the second state probability of the flap area in each flap state, the target state probability of the flap area in each flap state is identified, including: obtaining a first probability weight value of the first state probability and a second probability weight value of the second state probability; for each flap state, weighted summing the first state probability of the flap state and the second state probability of the flap state by using the first probability weight value of the first state probability and the second probability weight value of the second state probability to obtain the target state probability of the flap state.

[0093] In this embodiment, the terminal obtains a first probability weight value of the first state probability and a second probability weight value of the second state probability. Finally, for each flap state, the terminal performs weighted summation processing on the first state probability of the flap state and the second state probability of the flap state by using the first probability weight value of the first state probability and the second probability weight value of the second state probability to obtain the target state probability of the flap state.

[0094] Based on the above scheme, by combining the first state probability of the flap area in each flap state and the second state probability of the flap area in each flap state, the target state probability of the flap area in each flap state is analyzed to obtain the auxiliary identification information of the flap transplantation state of the flap area, thereby improving the accuracy and practicality of the obtained auxiliary identification information of the flap transplantation state, thereby effectively assisting in improving the staff's recognition accuracy of the flap transplantation state.

[0095] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0096] Based on the same inventive concept, the embodiment of the present application also provides an auxiliary identification of the flap transplantation state for realizing the auxiliary identification method of the flap transplantation state involved above. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method, so the specific limitations in the auxiliary identification embodiments of one or more flap transplantation states provided below can refer to the limitations of the auxiliary identification method of flap transplantation state in the above text, and will not be repeated here.

[0097] Further references Figure 2 , as a response to the above Figure 1 The present application provides an embodiment of an auxiliary identification system 200 for flap transplantation status, and an auxiliary identification method for flap transplantation status. The system includes an acquisition module 210, a first identification module 220, a second identification module 230 and a determination module 240, wherein:

[0098] The acquisition module 210 is used to obtain the physical detection information of the skin flap area in response to the flap area detection operation of the staff, and collect the monitoring results of the skin flap area in various physiological monitoring types through real-time detection equipment; the real-time monitoring equipment includes a laser speckle instrument, a pulse monitoring instrument, and a muscle tissue thickness monitoring instrument;

[0099] A first identification module 220 is used to identify the first state probability of the flap region in each flap state based on the physical detection information and the flap state analysis strategy, and to identify the monitoring change information of the flap region in each physiological monitoring type based on the monitoring results of the flap region in each physiological monitoring type and the historical detection results of the flap region in each physiological monitoring type;

[0100] A second identification module 230 is used to identify the state change degree corresponding to each monitoring change information based on the monitoring change information of each physiological monitoring type through the monitoring analysis strategy of each physiological monitoring type, and identify the second state probability of the flap area in each flap state based on the historical flap state of the flap area and the state change degree corresponding to each monitoring change information;

[0101] Determination module 240 is used to identify the target state probability of the flap area in each flap state based on the first state probability of the flap area in each flap state and the second state probability of the flap area in each flap state, and use the target state probability of the flap area in each flap state as auxiliary identification information of the flap transplantation state of the flap area.

[0102] Optionally, the first identification module 220 is specifically configured to:

[0103] Splitting the physical detection information into time detection information and appearance detection information, and acquiring, based on the time detection information, sample appearance detection information of each skin flap state corresponding to the time detection information in a transplantation database;

[0104] Based on the sample appearance detection information of each flap state and the appearance detection information, the appearance detection information and the detection deviation information between the appearance detection information and each sample appearance detection information are calculated respectively, and based on the detection deviation information corresponding to each flap state, the detection analysis strategy corresponding to each flap state is used to identify the first state probability of the flap area in each flap state.

[0105] Optionally, the first identification module 220 is specifically configured to:

[0106] For each physiological monitoring type, based on the monitoring results of the physiological monitoring type and the historical detection results of the physiological monitoring type, identifying the detection data of the physiological monitoring type and the historical detection data of the physiological monitoring type;

[0107] Based on the detection data of the physiological monitoring type and the historical detection data of the physiological monitoring type, constructing data distribution information of the physiological monitoring type, and performing curve fitting processing on the data distribution information to obtain a data change curve of the physiological monitoring type;

[0108] Based on the data change curve, the curve slope of the physiological monitoring type and the data difference value of the physiological monitoring type are identified, and the curve slope and the data difference value are used as monitoring change information of the physiological monitoring type.

[0109] Optionally, the second identification module 230 is specifically configured to:

[0110] For each physiological monitoring type, querying the monitoring and analysis strategy of the physiological monitoring type in the data monitoring database;

[0111] Based on the monitoring and analysis strategy, identifying the sub-state change ratio value corresponding to each curve slope range, and based on the curve slope of the physiological monitoring type and the sub-state change ratio value corresponding to each curve slope range, identifying the target sub-state change ratio value of the physiological monitoring type;

[0112] Based on the data difference value of the physiological monitoring type, multiply the sub-state change ratio value corresponding to the curve slope range to obtain the state change degree corresponding to the physiological monitoring type.

[0113] Optionally, the second identification module 230 is specifically configured to:

[0114] Obtaining the state change degree interval between each flap state and the state sorting information between each flap state;

[0115] Based on the historical flap state of the flap area, the range of state change between each flap state, the state sorting information between each flap state, and the state change degree of each physiological monitoring type, the second state probability of the flap state belonging to each physiological monitoring type is identified through the state identification strategy of each physiological monitoring type.

[0116] Optionally, the determining module 240 is specifically configured to:

[0117] Obtaining a first probability weight value of the first state probability and a second probability weight value of the second state probability;

[0118] For each flap state, the first state probability of the flap state and the second state probability of the flap state are weighted and summed using the first probability weight value of the first state probability and the second probability weight value of the second state probability to obtain the target state probability of the flap state.

[0119] Each module in the auxiliary identification system of the skin flap transplantation status can be implemented in whole or in part by software, hardware and a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0120] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input system connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, an auxiliary identification method and a system method for the state of a flap transplantation are implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input system of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse, etc.

[0121] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0122] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of any one of the methods in the first aspect are implemented.

[0123] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0124] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of any one of the methods in the first aspect.

[0125] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0126] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0127] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0128] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for assisting in identifying the state of skin flap transplantation, characterized in that: The method comprises: In response to the flap area detection operation of the staff, the physical detection information of the flap area is obtained, and the monitoring results of the flap area in various physiological monitoring types are collected through real-time detection equipment; the real-time monitoring equipment includes a laser speckle instrument, a pulse monitoring instrument, and a muscle tissue thickness monitoring instrument; Based on the physical detection information, the first state probability of the flap area in each flap state is identified through the flap state analysis strategy, and based on the monitoring results of the flap area in each physiological monitoring type and the historical detection results of the flap area in each physiological monitoring type, the monitoring change information of the flap area in each physiological monitoring type is identified; Based on the monitoring change information of each physiological monitoring type, the degree of state change corresponding to each monitoring change information is identified through the monitoring analysis strategy of each physiological monitoring type, and based on the historical flap state of the flap area and the degree of state change corresponding to each monitoring change information, the probability of the second state of the flap area in each flap state is identified; Based on the first state probability of the flap area in each flap state and the second state probability of the flap area in each flap state, the target state probability of the flap area in each flap state is identified, and the target state probability of the flap area in each flap state is used as auxiliary identification information of the flap transplantation state of the flap area.

2. The method according to claim 1, characterized in that: The step of identifying the first state probability of the flap region in each flap state based on the physical detection information and using the flap state analysis strategy includes: Splitting the physical detection information into time detection information and appearance detection information, and acquiring, based on the time detection information, sample appearance detection information of each skin flap state corresponding to the time detection information in a transplantation database; Based on the sample appearance detection information of each flap state and the appearance detection information, the appearance detection information and the detection deviation information between the appearance detection information and each sample appearance detection information are calculated respectively, and based on the detection deviation information corresponding to each flap state, the detection analysis strategy corresponding to each flap state is used to identify the first state probability of the flap area in each flap state.

3. The method according to claim 2, characterized in that The identifying monitoring change information of the skin flap area in each physiological monitoring type based on the monitoring results of the skin flap area in each physiological monitoring type and the historical detection results of the skin flap area in each physiological monitoring type includes: For each physiological monitoring type, based on the monitoring results of the physiological monitoring type and the historical detection results of the physiological monitoring type, identifying the detection data of the physiological monitoring type and the historical detection data of the physiological monitoring type; Based on the detection data of the physiological monitoring type and the historical detection data of the physiological monitoring type, constructing data distribution information of the physiological monitoring type, and performing curve fitting processing on the data distribution information to obtain a data change curve of the physiological monitoring type; Based on the data change curve, the curve slope of the physiological monitoring type and the data difference value of the physiological monitoring type are identified, and the curve slope and the data difference value are used as monitoring change information of the physiological monitoring type.

4. The method according to claim 3, characterized in that The monitoring change information based on each physiological monitoring type, identifying the state change degree corresponding to each monitoring change information through the monitoring analysis strategy of each physiological monitoring type, includes: For each physiological monitoring type, querying the monitoring and analysis strategy of the physiological monitoring type in the data monitoring database; Based on the monitoring and analysis strategy, identifying the sub-state change ratio value corresponding to each curve slope range, and based on the curve slope of the physiological monitoring type and the sub-state change ratio value corresponding to each curve slope range, identifying the target sub-state change ratio value of the physiological monitoring type; Based on the data difference value of the physiological monitoring type, multiply the sub-state change ratio value corresponding to the curve slope range to obtain the state change degree corresponding to the physiological monitoring type.

5. The method according to claim 4, characterized in that The identifying the second state probability of the flap region in each flap state based on the historical flap state of the flap region and the state change degree corresponding to each monitoring change information includes: Obtaining the state change degree interval between each flap state and the state sorting information between each flap state; Based on the historical flap state of the flap area, the range of state change between each flap state, the state sorting information between each flap state, and the state change degree of each physiological monitoring type, the second state probability of the flap state belonging to each physiological monitoring type is identified through the state identification strategy of each physiological monitoring type.

6. The method according to claim 5, characterized in that The step of identifying the target state probability of the flap region in each flap state based on the first state probability of the flap region in each flap state and the second state probability of the flap region in each flap state comprises: Obtaining a first probability weight value of the first state probability and a second probability weight value of the second state probability; For each flap state, the first state probability of the flap state and the second state probability of the flap state are weighted and summed using the first probability weight value of the first state probability and the second probability weight value of the second state probability to obtain the target state probability of the flap state.

7. An auxiliary identification system for flap transplantation status, characterized in that: The system comprises: An acquisition module, for acquiring physical detection information of the skin flap area in response to a flap area detection operation by a staff member, and collecting monitoring results of the skin flap area in various physiological monitoring types through a real-time detection device; the real-time monitoring device includes a laser speckle instrument, a pulse monitoring instrument, and a muscle tissue thickness monitoring instrument; A first identification module is used to identify the first state probability of the flap area in each flap state based on the physical detection information and the flap state analysis strategy, and to identify the monitoring change information of the flap area in each physiological monitoring type based on the monitoring results of the flap area in each physiological monitoring type and the historical detection results of the flap area in each physiological monitoring type; a second identification module, for identifying the degree of state change corresponding to each monitoring change information based on the monitoring change information of each physiological monitoring type through the monitoring analysis strategy of each physiological monitoring type, and identifying the second state probability of the flap area in each flap state based on the historical flap state of the flap area and the degree of state change corresponding to each monitoring change information; A determination module is used to identify the target state probability of the flap area in each flap state based on the first state probability of the flap area in each flap state and the second state probability of the flap area in each flap state, and use the target state probability of the flap area in each flap state as auxiliary identification information of the flap transplantation state of the flap area.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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