Intelligent identification and traceability method and system for medical devices based on the Internet of Things

Abstract

The present invention relates to the field of intelligent identification and monitoring technology, and is a method and system for intelligent identification and tracing of medical devices based on the Internet of Things. The specific method includes: obtaining device usage status data and storage environment data, calculating and obtaining device usage risk coefficients and device storage risk coefficients; performing intelligent identification of first defects on devices that have completed preliminary cleaning treatment, obtaining the device usage wear value and storage corrosion value; evaluating the first failure probability value of the device, and making a first round of recycling judgment based on the first failure probability value; performing intelligent identification of second defects, and evaluating the second failure probability value of the device; extracting the second failure probability value for a second round of recycling judgment, and performing final processing of the device based on the second round of recycling judgment results. The present invention solves the problems in the prior art that, during the postoperative recycling process of surgical instruments, cross infection of instruments is prone to occur, monitoring of the available status of instruments is difficult, and the efficiency of instrument recycling is low.

Description

Technical Field

[0001] The present invention relates to the field of intelligent identification and monitoring technology, and is a medical device intelligent identification and tracing method and system based on the Internet of Things. Background Art

[0002] Real-time monitoring, sterilization, and maintenance of surgical instruments to ensure their usability, accurately configure sterilization packages, and prevent cross-leakage is a time-consuming, labor-intensive, and challenging task for hospital infection control, operating rooms, and central supply rooms. However, it is crucial to surgical safety, infection control, and operating room operational efficiency. How to achieve full digital batch and rapid single-piece instrument traceability throughout the entire closed-loop flow, as well as accurate pre-operative verification, intra-operative search, post-operative recovery, and cleaning and packaging is an important industry challenge currently faced.

[0003] Among the existing disclosed invention technologies, the patent with publication number CN103606118A discloses a disinfection supply room management system and working method based on the Internet of Things, including three layers of technology: the Internet of Things perception layer, the network layer, and the application layer; the Internet of Things perception layer includes radio frequency identification technology, mainly including tags, readers (including antennas), and middleware; the network layer includes access gateway technology, and the access gateway completes the networking control and information collection of the information of each node at the end of the application, and completes forwarding functions such as sending information to each node at the end; the application layer includes a supply room management system software module based on a C / S two-layer structure.

[0004] The above patent, in the recycling process of surgical instruments, fails to take into account that surgical instruments often undergo processes such as high temperature, high pressure, and chemical cleaning. These conditions may affect the durability and performance of RFID tags, causing tag damage or reading failure, and cannot guarantee efficient identification and recycling of postoperative instruments. Summary of the invention

[0005] The technical problem to be solved by the present invention is that in the prior art, during the postoperative recovery of surgical instruments, cross infection of instruments is prone to occur, monitoring of the available status of instruments is difficult, and the efficiency of instrument recovery is low. A method and system for intelligent identification and tracing of medical instruments based on the Internet of Things are proposed.

[0006] In order to achieve the above-mentioned purpose, the technical solution of the medical device intelligent identification and tracing method based on the Internet of Things of the present invention includes the following steps:

[0007] S1: Obtain the usage status data and storage environment data of the device during this use process, and simultaneously collect the used devices and perform preliminary cleaning;

[0008] S2: Importing the usage status data and storage environment data into the device usage risk calculation strategy and the device storage risk calculation strategy respectively, and calculating and obtaining the device usage risk coefficient and the device storage risk coefficient;

[0009] S3: Performing intelligent identification of the first defects on the instruments that have completed the preliminary cleaning process, and obtaining the use wear value and storage corrosion value of the instruments;

[0010] S4: Based on S2-S3, the first failure probability value of the device is integrated and evaluated, and the first round of recycling is determined based on the first failure probability value;

[0011] S5: Based on the results of the first round of recycling, perform intelligent identification of the second defect, obtain the deformation evolution value and structural looseness value of the device, and integrate and evaluate the second failure probability value of the device;

[0012] S6: extract the second failure probability value to make a second-round recycling judgment, and perform final processing of the device according to the result of the second-round recycling judgment.

[0013] Specifically, S2 includes the following specific steps:

[0014] S21: extracting the usage status data of the device during use, wherein the usage status data includes: the total number of times N of the device being used during this use, single use duration data, and single use shear force data of the device;

[0015] S22: Importing the usage status data into the device usage risk calculation strategy to calculate the device usage risk coefficient. The device usage risk calculation strategy is specifically:

[0016] ;

[0017] in, is the risk factor of the device; n is the subscript,

[0018] They respectively represent the single use duration and single use shear force when the device is used for the nth time during this use; They are the mean usage time and the mean shear force used;

[0019] They are the maximum usage time and the minimum usage time respectively;

[0020] They are respectively using the maximum shear force and using the minimum shear force.

[0021] Specifically, S2 also includes the following specific steps:

[0022] S23: extracting storage environment data of the device during use, wherein the storage environment data includes: ambient temperature data and ambient humidity data during use of the device;

[0023] S24: Based on the storage environment data of the device during use, draw a temperature change curve of the ambient temperature data and ambient humidity data during the use of the device along with the data collection time point h And humidity change curve ;

[0024] S25: Importing the storage environment data into the device storage risk calculation strategy to calculate and obtain the device storage risk coefficient. The device storage risk calculation strategy is specifically:

[0025] ;

[0026] in, The risk factor for equipment storage; The total duration of this usage process;

[0027] They are the optimal storage temperature and optimal storage humidity of the equipment respectively;

[0028] They are the optimal storage temperature range and the optimal storage humidity range for the equipment.

[0029] Furthermore, the optimal storage temperature range and the optimal storage humidity range of the device are both obtained based on the factory information of the device.

[0030] Specifically, S3 includes the following specific steps:

[0031] S31: performing first defect intelligent recognition on the instrument that has completed the preliminary cleaning process, wherein the first defect intelligent recognition includes: scratch area recognition and rust area recognition;

[0032] S32: acquiring three-dimensional image data of the surface of the device through a high-resolution scanner, converting each edge point in the three-dimensional image data into a three-dimensional Hough (Hough Transform) space, calculating the position of each edge point in the parameter space, and accumulating the count in the three-dimensional Hough space;

[0033] S33: in the three-dimensional Hough space, find the point with the largest cumulative value to form a scratch straight line in the three-dimensional image, where the total number of scratch straight lines is B;

[0034] S34: Extracting the number data of scratch lines and the length data of scratch lines , Width data and depth data ;

[0035] S35: According to S34, the wear value ms of the instrument is calculated. The specific calculation formula is:

[0036] ;

[0037] in, They are the length data, width data and depth data of the bth scratch line respectively; They are the mean length, mean width and mean depth of B scratch lines respectively.

[0038] Specifically, S3 also includes the following specific steps:

[0039] S36: Extract the three-dimensional image data of the device surface and calculate the three-dimensional grayscale co-occurrence matrix of the device. The specific strategy is:

[0040] ;

[0041] in, is the joint probability of gray level i and gray level j in the gray level co-occurrence matrix; i and j are different gray levels;

[0042] Indicates the position coordinates of the voxels belonging to the ROI area in the three-dimensional image; the ROI area is the cutting surface area of ​​the instrument;

[0043] and Respectively represent the coordinates and The gray value of the voxel;

[0044] are the voxel offsets in the x, y, and z directions respectively;

[0045] is an indicator function. When the indicator condition is met, the function value is 1, otherwise it is 0.

[0046] S37: According to S36, the uniformity jy and contrast db of each rusted area are obtained, wherein the calculation formula of the uniformity is: ; The calculation formula of the contrast is: ;

[0047] S38: Importing the uniformity jy and contrast db of each rusted area into the storage rust value calculation strategy, and calculating and obtaining the storage rust value xs, wherein the storage rust value calculation strategy is specifically: , is the total number of rusted areas; They represent the uniformity and contrast of the cth rusted area respectively; Representation and The closest amount of data; Representation and The closest amount of data.

[0048] Specifically, in S4, the calculation strategy of the first failure probability value is:

[0049] ;

[0050] in, is the first failure probability value.

[0051] Specifically, in S4, the first round of recycling judgment includes: presetting the first round of equipment recycling threshold , when the first failure probability value is greater than or equal to the first round of device recovery threshold When the first failure probability value is less than the first round of device recall threshold , execute step S5.

[0052] Specifically, S5 includes the following specific steps:

[0053] S51: perform 3D laser scanning on the instrument and perform polynomial fitting on the instrument blade profile curve through edge detection algorithm;

[0054] S52: extracting the fitted instrument blade surface contour curve, and marking U data collection points on the instrument blade surface contour curve;

[0055] S53: Importing the curvature data collected by the U data collection points into the deformation evolution value calculation strategy to obtain the deformation evolution value xb. The deformation evolution value calculation strategy is specifically:

[0056] ;

[0057] in, represents the curvature data collected at the u-th data collection point on the instrument blade surface profile curve; It indicates the curvature data collected at the u-th data collection point on the blade surface profile curve of the instrument when the instrument leaves the factory.

[0058] Specifically, S5 also includes the following specific steps:

[0059] S54: using a fixed frequency exciter to excite vibration at the connection of the instrument, and collecting the vibration frequency of the connection of the instrument through an acceleration sensor, wherein the acceleration sensors are evenly distributed at the connection of the instrument, and the total number of the acceleration sensors is V;

[0060] S55: Import the vibration frequency data collected by the acceleration sensor into the structural looseness value calculation strategy to obtain the structural looseness value jg, wherein the structural looseness value calculation strategy is specifically:

[0061]

[0062] in, Represents the vibration frequency data collected by the vth acceleration sensor;

[0063] It indicates the vibration frequency data collected by the vth acceleration sensor when the device leaves the factory.

[0064] Specifically, in S5, the fusion evaluation strategy of the second failure probability value is as follows:

[0065] ;

[0066] in, is the second failure probability value.

[0067] Specifically, in S6, the second-round recycling judgment includes: presetting a second-round instrument recycling threshold, when the second failure probability value is greater than or equal to the second-round instrument recycling threshold, executing the final processing of the instrument, arranging the second failure probability values ​​in order from large to small, synchronizing the traceability of instrument purchase information, and recalling and replenishing the instruments according to the arrangement order; when the second failure probability value is less than the second-round instrument recycling threshold, tracing the instrument usage type information, configuring the instrument sterilization package according to the instrument usage type information, and sterilizing the instruments that need to be recycled.

[0068] In addition, the medical device intelligent identification and tracing system based on the Internet of Things of the present invention includes the following modules:

[0069] Data acquisition module, risk coefficient calculation module, first defect recognition module, second defect recognition module, and instrument final processing module;

[0070] The data acquisition module is used to acquire the usage status data and storage environment data of the equipment during use, and simultaneously collect the used equipment and perform preliminary cleaning processing;

[0071] The risk coefficient calculation module imports the usage status data and the storage environment data into the device usage risk calculation strategy and the device storage risk calculation strategy respectively, and calculates and obtains the device usage risk coefficient and the device storage risk coefficient;

[0072] The first defect recognition module is used to perform intelligent first defect recognition on the equipment that has completed the preliminary cleaning process, and obtain the use wear value and storage corrosion value of the equipment;

[0073] The second defect recognition module performs intelligent recognition of the second defect according to the first round of recycling judgment results;

[0074] The instrument final processing module performs the instrument final processing according to the second round of recycling judgment result.

[0075] A storage medium stores instructions, and when a computer reads the instructions, the computer executes the medical device intelligent identification and tracing method based on the Internet of Things.

[0076] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned intelligent identification and tracing method of medical devices based on the Internet of Things when executing the computer program.

[0077] Compared with the prior art, the technical effects of the present invention are as follows:

[0078] 1. The present invention uses the Internet of Things technology to obtain real-time device usage status data, such as the number of uses, usage duration, shear force, etc., as well as storage environment data, such as temperature and humidity. These data are used in real time to evaluate the use and storage risks of the device through the risk coefficient calculation module, and potential problems can be discovered in time and early warnings can be issued.

[0079] 2. The present invention can monitor the wear value, corrosion value, deformation evolution value and structural looseness value of the instrument in real time by intelligently identifying the first defect and the second defect of the instrument. This detailed defect analysis helps to timely discover and repair potential problems of the instrument, thereby ensuring that the instrument always remains in a usable state.

[0080] 3. The present invention realizes the digital traceability of the whole process of the equipment through the Internet of Things and intelligent identification technology. Each equipment can be tracked through real-time data and detailed records at each stage of use, cleaning and recycling. The system provides batch tracing and single piece tracking functions for equipment, ensuring accurate verification before surgery, accurate search during surgery and efficient recycling after surgery.

[0081] 4. The present invention can accurately determine which instruments need to be recycled through the first and second rounds of recycling, ensuring the effectiveness of the recycling process. The accuracy of cleaning and packaging is guaranteed by detailed instrument status data and intelligent defect recognition, further improving the efficiency and accuracy of the entire process. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0083] Figure 1 It is a flow chart of the method for intelligent identification and tracing of medical devices based on the Internet of Things of the present invention;

[0084] Figure 2 It is a structural schematic diagram of the medical device intelligent identification and tracing system based on the Internet of Things of the present invention;

[0085] Figure 3 It is a schematic diagram of the structure of the final processing module of the device of the present invention. DETAILED DESCRIPTION

[0086] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0087] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0088] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0089] Embodiment 1:

[0090] like Figure 1 As shown, the medical device intelligent identification and tracing method based on the Internet of Things in an embodiment of the present invention is as follows: Figure 1 As shown, the specific steps include:

[0091] S1: Obtain the usage status data and storage environment data of the device during this use process, and simultaneously collect the used devices and perform preliminary cleaning;

[0092] S2: Import the usage status data and storage environment data into the device usage risk calculation strategy and the device storage risk calculation strategy respectively, and calculate and obtain the device usage risk coefficient and the device storage risk coefficient.

[0093] S2 includes the following specific steps:

[0094] S21: extracting the usage status data of the device during use, wherein the usage status data includes: the total number of times N of the device being used during this use, single use duration data, and single use shear force data of the device;

[0095] S22: Importing the usage status data into the device usage risk calculation strategy to calculate the device usage risk coefficient. The device usage risk calculation strategy is specifically:

[0096] ;

[0097] in, is the risk factor of the device; n is the subscript,

[0098] They respectively represent the single use duration and single use shear force when the device is used for the nth time during this use; They are the mean usage time and the mean shear force used;

[0099] They are the maximum usage time and the minimum usage time respectively;

[0100] They are respectively using the maximum shear force and using the minimum shear force.

[0101] S2 also includes the following specific steps:

[0102] S23: extracting storage environment data of the device during use, wherein the storage environment data includes: ambient temperature data and ambient humidity data during use of the device;

[0103] S24: Based on the storage environment data of the device during use, draw a temperature change curve of the ambient temperature data and ambient humidity data during the use of the device along with the data collection time point h And humidity change curve ;

[0104] S25: Importing the storage environment data into the device storage risk calculation strategy to calculate and obtain the device storage risk coefficient. The device storage risk calculation strategy is specifically:

[0105] ;

[0106] in, The risk factor for equipment storage; The total duration of this usage process;

[0107] They are the optimal storage temperature and optimal storage humidity of the equipment respectively;

[0108] They are the optimal storage temperature range and the optimal storage humidity range for the equipment.

[0109] S3: Performing intelligent identification of the first defects on the instruments that have completed the preliminary cleaning process, and obtaining the use wear value and storage corrosion value of the instruments;

[0110] S3 includes the following specific steps:

[0111] S31: performing first defect intelligent recognition on the instrument that has completed the preliminary cleaning process, wherein the first defect intelligent recognition includes: scratch area recognition and rust area recognition;

[0112] S32: acquiring three-dimensional image data of the surface of the device through a high-resolution scanner, converting each edge point in the three-dimensional image data into a three-dimensional Hough space, calculating a position of each edge point in the parameter space, and accumulating counts in the three-dimensional Hough space;

[0113] S33: in the three-dimensional Hough space, find the point with the largest cumulative value to form a scratch straight line in the three-dimensional image, where the total number of scratch straight lines is B;

[0114] S34: Extracting the number data of scratch lines and the length data of scratch lines , Width data and depth data ;

[0115] S35: According to S34, the wear value ms of the instrument is calculated. The specific calculation formula is:

[0116] ;

[0117] in, They are the length data, width data and depth data of the bth scratch line respectively; They are the mean length, mean width and mean depth of B scratch lines respectively.

[0118] S3 also includes the following specific steps:

[0119] S36: Extract the three-dimensional image data of the device surface and calculate the three-dimensional grayscale co-occurrence matrix of the device. The specific strategy is:

[0120] ;

[0121] in, is the joint probability of gray level i and gray level j in the gray level co-occurrence matrix; i and j are different gray levels;

[0122] Indicates the position coordinates of the voxels belonging to the ROI area in the three-dimensional image; the ROI area is the cutting surface area of ​​the instrument;

[0123] and Respectively represent the coordinates and The gray value of the voxel;

[0124] are the voxel offsets in the x, y, and z directions respectively;

[0125] is an indicator function. When the indicator condition is met, the function value is 1, otherwise it is 0.

[0126] S37: According to S36, the uniformity jy and contrast db of each rusted area are obtained, wherein the calculation formula of the uniformity is: ; The calculation formula of the contrast is: ;

[0127] S38: Importing the uniformity jy and contrast db of each rusted area into the storage rust value calculation strategy, and calculating and obtaining the storage rust value xs, wherein the storage rust value calculation strategy is specifically: , is the total number of rusted areas; They represent the uniformity and contrast of the cth rusted area respectively; Representation and The closest amount of data; Representation and The closest amount of data.

[0128] S4: Based on S2-S3, the first failure probability value of the device is integrated and evaluated, and the first round of recycling is determined based on the first failure probability value;

[0129] In S4, the calculation strategy of the first failure probability value is specifically:

[0130] ;

[0131] in, is the first failure probability value.

[0132] In S4, the first round of recycling judgment includes: presetting the first round of equipment recycling threshold , when the first failure probability value is greater than or equal to the first round of device recovery threshold When the first failure probability value is less than the first round of device recall threshold , execute step S5.

[0133] S5: Based on the results of the first round of recycling, perform intelligent identification of the second defect, obtain the deformation evolution value and structural looseness value of the device, and integrate and evaluate the second failure probability value of the device;

[0134] S5 includes the following specific steps:

[0135] S51: perform 3D laser scanning on the instrument and perform polynomial fitting on the instrument blade profile curve through edge detection algorithm;

[0136] S52: extracting the fitted instrument blade surface contour curve, and marking U data collection points on the instrument blade surface contour curve;

[0137] S53: Importing the curvature data collected by the U data collection points into the deformation evolution value calculation strategy to obtain the deformation evolution value xb. The deformation evolution value calculation strategy is specifically:

[0138] ;

[0139] in, represents the curvature data collected at the u-th data collection point on the instrument blade surface profile curve; It indicates the curvature data collected at the u-th data collection point on the blade surface profile curve of the instrument when the instrument leaves the factory.

[0140] S5 also includes the following specific steps:

[0141] S54: using a fixed frequency exciter to excite vibration at the connection of the instrument, and collecting the vibration frequency of the connection of the instrument through an acceleration sensor, wherein the acceleration sensors are evenly distributed at the connection of the instrument, and the total number of the acceleration sensors is V;

[0142] S55: Import the vibration frequency data collected by the acceleration sensor into the structural looseness value calculation strategy to obtain the structural looseness value jg, wherein the structural looseness value calculation strategy is specifically:

[0143]

[0144] in, Represents the vibration frequency data collected by the vth acceleration sensor;

[0145] It indicates the vibration frequency data collected by the vth acceleration sensor when the device leaves the factory.

[0146] In S5, the fusion evaluation strategy of the second failure probability value is as follows:

[0147] ;

[0148] in, is the second failure probability value.

[0149] S6: extract the second failure probability value to make a second-round recycling judgment, and perform final processing of the device according to the result of the second-round recycling judgment.

[0150] In S6, the second-round recycling judgment includes: presetting a second-round instrument recycling threshold, when the second failure probability value is greater than or equal to the second-round instrument recycling threshold, executing the final processing of the instrument, arranging the second failure probability values ​​in order from large to small, synchronizing the traceability of instrument purchase information, and recalling and replenishing the instruments according to the arrangement order; when the second failure probability value is less than the second-round instrument recycling threshold, tracing the instrument usage type information, configuring the instrument sterilization package according to the instrument usage type information, and sterilizing the instruments that need to be recycled.

[0151] Embodiment 2:

[0152] like Figure 2 , 3 As shown, the medical device intelligent identification and tracing system based on the Internet of Things in the embodiment of the present invention is as follows: Figure 2 As shown, it includes the following modules:

[0153] Data acquisition module, risk coefficient calculation module, first defect recognition module, second defect recognition module, and instrument final processing module;

[0154] The data acquisition module is used to acquire the usage status data and storage environment data of the equipment during use, and simultaneously collect the used equipment and perform preliminary cleaning processing;

[0155] The risk coefficient calculation module imports the usage status data and the storage environment data into the device usage risk calculation strategy and the device storage risk calculation strategy respectively, and calculates and obtains the device usage risk coefficient and the device storage risk coefficient;

[0156] The first defect recognition module is used to perform intelligent first defect recognition on the equipment that has completed the preliminary cleaning process, and obtain the use wear value and storage corrosion value of the equipment;

[0157] The second defect recognition module performs intelligent recognition of the second defect according to the first round of recycling judgment results;

[0158] The instrument final processing module performs the instrument final processing according to the second round of recycling judgment result.

[0159] Furthermore, the first defect identification module includes: a first failure assessment unit and a first-round recycling judgment unit, wherein the first failure assessment unit is used to integrate and evaluate the first failure probability value of the instrument; the first-round recycling judgment unit performs a first-round recycling judgment according to the first failure probability value;

[0160] The second defect identification module includes: a second failure assessment unit and a secondary recycling judgment unit, wherein the second failure assessment unit is used to assess the deformation evolution value and structural looseness value of the device and integrate the second failure probability value of the assessed device; the secondary recycling judgment unit is used to extract the second failure probability value for secondary recycling judgment;

[0161] like Figure 3 As shown, the instrument final processing module also includes: a sterilization package configuration unit, a traceability recall unit, and an inventory replenishment unit, wherein the sterilization package configuration unit is used to sterilize the instruments that need to be recycled; the traceability recall unit is used to track the usage type information and purchase information of the instruments that need to be recalled, and execute the instrument recall processing; the inventory replenishment unit is used to monitor and manage the inventory status of the instruments, and replenish the recalled instruments.

[0162] Embodiment three:

[0163] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0164] The processor executes the above-mentioned medical device intelligent identification and tracing method based on the Internet of Things by calling the computer program stored in the memory.

[0165] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the medical device intelligent identification and tracing method based on the Internet of Things provided by the above method embodiment. The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0166] Embodiment 4:

[0167] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0168] When the computer program runs on a computer device, the computer device executes the above-mentioned medical device intelligent identification and tracing method based on the Internet of Things.

[0169] For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0170] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0171] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0172] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0173] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0174] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0175] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0176] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0177] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0178] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0179] In summary, compared with the prior art, the technical effects of the present invention are as follows:

[0180] 1. The present invention uses the Internet of Things technology to obtain real-time device usage status data, such as the number of uses, usage duration, shear force, etc., as well as storage environment data, such as temperature and humidity. These data are used in real time to evaluate the use and storage risks of the device through the risk coefficient calculation module, and potential problems can be discovered in time and early warnings can be issued.

[0181] 2. The present invention can monitor the wear value, corrosion value, deformation evolution value and structural looseness value of the instrument in real time by intelligently identifying the first defect and the second defect of the instrument. This detailed defect analysis helps to timely discover and repair potential problems of the instrument, thereby ensuring that the instrument always remains in a usable state.

[0182] 3. The present invention realizes the digital traceability of the whole process of the equipment through the Internet of Things and intelligent identification technology. Each equipment can be tracked through real-time data and detailed records at each stage of use, cleaning and recycling. The system provides batch tracing and single piece tracking functions for equipment, ensuring accurate verification before surgery, accurate search during surgery and efficient recycling after surgery.

[0183] 4. The present invention can accurately determine which instruments need to be recycled through the first and second rounds of recycling, ensuring the effectiveness of the recycling process. The accuracy of cleaning and packaging is guaranteed by detailed instrument status data and intelligent defect recognition, further improving the efficiency and accuracy of the entire process.

[0184] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. The intelligent identification and tracing method of medical devices based on the Internet of Things is characterized by: The method comprises the following specific steps: S1: Obtain the usage status data and storage environment data of the equipment during this round of use, and simultaneously collect the used equipment and perform preliminary cleaning; S2: Importing the usage status data and storage environment data into the device usage risk calculation strategy and the device storage risk calculation strategy respectively, and calculating and obtaining the device usage risk coefficient and the device storage risk coefficient; S3: Performing intelligent identification of the first defects on the instruments that have completed the preliminary cleaning process, and obtaining the use wear value and storage corrosion value of the instruments; S4: Based on S2-S3, the first failure probability value of the device is integrated and evaluated, and the first round of recycling is determined based on the first failure probability value; S5: Based on the results of the first round of recycling, perform intelligent identification of the second defect, obtain the deformation evolution value and structural looseness value of the device, and integrate and evaluate the second failure probability value of the device; S6: extracting the second failure probability value to perform a second round of recycling judgment, and performing final treatment of the device according to the second round of recycling judgment result; S2 includes the following specific steps: S21: extracting the usage status data of the device during use, wherein the usage status data includes: the total number of times N of the device being used in this round of use, the single use duration data, and the single use shear force data of the device; S22: Importing the usage status data into the device usage risk calculation strategy to calculate the device usage risk coefficient. The device usage risk calculation strategy is specifically: ； in, is the risk factor of the device; n is the subscript, They respectively represent the single use duration and single use shear force when the instrument is used for the nth time during this round of use; They are the mean usage time and the mean shear force used; They are the maximum usage time and the minimum usage time respectively; They are the maximum shear force used and the minimum shear force used respectively; S2 also includes the following specific steps: S23: extracting storage environment data of the device during use, wherein the storage environment data includes: ambient temperature data and ambient humidity data during use of the device; S24: Based on the storage environment data of the device during use, draw a temperature change curve of the ambient temperature data and ambient humidity data during the use of the device along with the data collection time point h And humidity change curve ; S25: Import the storage environment data into the device storage risk calculation strategy to calculate and obtain the device storage risk coefficient. The device storage risk calculation strategy is specifically: ； in, The risk factor for equipment storage; The total duration of this round of use; They are the optimal storage temperature and optimal storage humidity of the equipment respectively; They are the optimal storage temperature range and the optimal storage humidity range of the device respectively; S3 includes the following specific steps: S31: performing first defect intelligent recognition on the instrument that has completed the preliminary cleaning process, wherein the first defect intelligent recognition includes: scratch area recognition and rust area recognition; S32: acquiring three-dimensional image data of the surface of the device through a high-resolution scanner, converting each edge point in the three-dimensional image data into a three-dimensional Hough space, calculating a position of each edge point in the parameter space, and accumulating counts in the three-dimensional Hough space; S33: in the three-dimensional Hough space, find the point with the largest cumulative value to form a scratch straight line in the three-dimensional image, where the total number of scratch straight lines is B; S34: Extracting the number data of scratch lines and the length data of scratch lines , Width data and depth data ; S35: According to S34, the wear value ms of the instrument is calculated. The specific calculation formula is: ； in, They are the length data, width data and depth data of the bth scratch line respectively; They are the mean length, width and depth of the B scratch lines respectively; S3 also includes the following specific steps: S36: Extract the three-dimensional image data of the device surface and calculate the three-dimensional gray-level co-occurrence matrix of the device. The specific strategy is as follows: ； in, is the joint probability of gray level i and gray level j in the gray level co-occurrence matrix; i and j are different gray levels; Indicates the position coordinates of the voxels belonging to the ROI area in the three-dimensional image; the ROI area is the cutting surface area of ​​the instrument; and Respectively represent the coordinates and The gray value of the voxel; are the voxel offsets in the x, y, and z directions respectively; is an indicator function. When the indicator condition is met, the function value is 1, otherwise it is 0. S37: According to S36, obtaining the uniformity jy and contrast db of each rusted area; S38: Importing the uniformity jy and contrast db of each rusted area into the storage rust value calculation strategy to calculate and obtain the storage rust value xs, wherein the storage rust value calculation strategy is specifically: , is the total number of rusted areas; They represent the uniformity and contrast of the cth rusted area respectively; Representation and The closest amount of data; Representation and The closest amount of data.

2. The method for intelligent identification and tracing of medical devices based on the Internet of Things according to claim 1 is characterized in that: In S4, the calculation strategy of the first failure probability value is specifically: ； in, is the first failure probability value.

3. The method for intelligent identification and tracing of medical devices based on the Internet of Things according to claim 2 is characterized in that: In S4, the first round of recycling judgment includes: presetting the first round of equipment recycling threshold , when the first failure probability value is greater than or equal to the first round of device recovery threshold When the first failure probability value is less than the first round of device recall threshold , execute step S5.

4. The method for intelligent identification and tracing of medical devices based on the Internet of Things according to claim 3 is characterized in that: S5 includes the following specific steps: S51: perform 3D laser scanning on the instrument and perform polynomial fitting on the instrument blade profile curve through edge detection algorithm; S52: extracting the fitted instrument blade surface contour curve, and marking U data collection points on the instrument blade surface contour curve; S53: Importing the curvature data collected by the U data collection points into the deformation evolution value calculation strategy to obtain the deformation evolution value xb. The deformation evolution value calculation strategy is specifically as follows: ； in, represents the curvature data collected at the u-th data collection point on the instrument blade surface profile curve; It indicates the curvature data collected at the u-th data collection point on the blade surface profile curve of the instrument when the instrument leaves the factory.

5. The method for intelligent identification and tracing of medical devices based on the Internet of Things according to claim 4 is characterized in that: S5 also includes the following specific steps: S54: using a fixed frequency exciter to excite vibration at the connection of the instrument, and collecting the vibration frequency of the connection of the instrument through an acceleration sensor, wherein the acceleration sensors are evenly distributed at the connection of the instrument, and the total number of the acceleration sensors is V; S55: Import the vibration frequency data collected by the acceleration sensor into the structural looseness value calculation strategy to obtain the structural looseness value jg, wherein the structural looseness value calculation strategy is specifically: ； in, Represents the vibration frequency data collected by the vth acceleration sensor; It indicates the vibration frequency data collected by the vth acceleration sensor when the device leaves the factory.

6. The method for intelligent identification and tracing of medical devices based on the Internet of Things according to claim 5 is characterized in that: In S5, the fusion evaluation strategy of the second failure probability value is as follows: ； in, is the second failure probability value.

7. The method for intelligent identification and tracing of medical devices based on the Internet of Things according to claim 6 is characterized in that: In S6, the second-round recycling judgment includes: presetting a second-round instrument recycling threshold, when the second failure probability value is greater than or equal to the second-round instrument recycling threshold, executing the final processing of the instrument, arranging the second failure probability values ​​in order from large to small, synchronizing the traceability of instrument purchase information, and recalling and replenishing the instruments according to the arrangement order; when the second failure probability value is less than the second-round instrument recycling threshold, tracing the instrument usage type information, configuring the instrument sterilization package according to the instrument usage type information, and sterilizing the instruments that need to be recycled.

8. An intelligent identification and tracing system for medical devices based on the Internet of Things, which is used to implement the intelligent identification and tracing method for medical devices based on the Internet of Things as described in any one of claims 1 to 7, characterized in that: The system includes the following modules: Data acquisition module, risk coefficient calculation module, first defect recognition module, second defect recognition module, and instrument final processing module; The data acquisition module is used to acquire the usage status data and storage environment data of the equipment during use, and simultaneously collect the used equipment and perform preliminary cleaning processing; The risk coefficient calculation module imports the usage status data and the storage environment data into the device usage risk calculation strategy and the device storage risk calculation strategy respectively, and calculates and obtains the device usage risk coefficient and the device storage risk coefficient; The first defect recognition module is used to perform intelligent first defect recognition on the equipment that has completed the preliminary cleaning process, and obtain the use wear value and storage corrosion value of the equipment; The second defect recognition module performs intelligent recognition of the second defect according to the first round of recycling judgment results; The instrument final processing module performs the instrument final processing according to the second round of recycling judgment result.

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