Medical consumable full life cycle management system based on large model application

Abstract

The invention discloses a medical consumable full life cycle management system based on large model application, and relates to the field of medical consumable intelligent management. The system comprises an identity verification module which is used for realizing pre-operation and post-operation personnel verification and authority control based on multi-mode identification; the image acquisition module is used for integrating RFID (Radio Frequency Identification Device) identification, image identification and a certification OCR (Optical Character Recognition) technology, acquiring pre-operation and post-operation consumable information and constructing a corresponding information set; the consumable use state recognition module is used for constructing difference parameters based on the preoperative and postoperative information set, judging the consumable use state and outputting a use and remaining list; and the SPD integration and return management module is in butt joint with a hospital SPD system, performs state screening and tracing binding based on the remaining consumable list, automatically generates return information, and realizes consumable life cycle closed-loop management. Through the multi-modal technology, whole-course tracing and automatic management of consumables are realized, the working efficiency is improved, manual intervention is reduced, the return flow is optimized, and the data accuracy and efficient execution of the flow are ensured.

Description

Technical Field

[0001] The present invention relates to the field of intelligent management of medical consumables, and specifically to a full life cycle management system for medical consumables based on large model applications. Background Art

[0002] Orthopedic surgery in modern hospitals presents numerous challenges in managing medical consumables. Consumables used in surgery are numerous and numerous, including various orthopedic implants, tools, and other medical materials. Effectively managing the entire lifecycle of these consumables, particularly their circulation, usage status, and return procedures, is crucial to ensuring surgical quality and improving medical efficiency.

[0003] Generally, the process of orthopedic surgery in a hospital includes the following main links: surgical information notification and preparation: After the hospital determines the surgical schedule, it will notify the consumables supplier of relevant surgical information in advance. The supplier will prepare the surgical kit according to the requirements and send it to the hospital; disinfection and standby: After the surgical kit arrives at the hospital, it will enter the disinfection supply room for strict disinfection to ensure that the items are sterile and ready for use in the operation; use and record of consumables during surgery: During the operation, medical staff use consumables as needed and record the consumed items; return of unused consumables: After the operation, unused consumables and tools will be returned to the supplier, and the return information will be registered in the system.

[0004] Currently, hospitals face major challenges in this process, including cumbersome consumables management, difficulty in recording item usage during surgery, and difficulty tracing returned consumables. Traditional management models not only suffer from information lags and duplicate data entry, but also, due to the sheer number of items in surgical kits, real-time monitoring of item management and consumption is difficult, leading to disruptions in inventory management and cost control.

[0005] In this context, research on a full life cycle management system for medical consumables based on large-scale model applications is urgent. It is necessary to use digital and intelligent means to accurately and efficiently manage each consumable in orthopedic surgery, ensure the accurate traceability of the consumables' usage status, return process, and related data, thereby improving the hospital's management efficiency, reducing costs, and effectively ensuring safety and compliance during the operation. Summary of the Invention

[0006] Based on the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a medical consumables full life cycle management system based on large model applications to solve the above-mentioned technical problems.

[0007] To achieve the above objectives, the present invention provides the following technical solutions: a full life cycle management system for medical consumables based on large model applications, comprising: Identity verification module: This module uses multimodal identity recognition to verify the operator's identity before and after the operation, and grants operation permissions after passing the verification; Image acquisition module: By integrating RFID dual-frequency recognition technology, image recognition technology, and certificate OCR analysis technology, it collects information on medical consumables before and after surgery, and constructs preoperative and postoperative consumables information sets respectively; Consumables usage status identification module: Based on the preoperative consumables information set and the postoperative consumables information set, it constructs multi-dimensional difference parameters to determine the usage status of medical consumables and outputs a list of used consumables and a list of remaining consumables; SPD integration and return management module: connects to the hospital's SPD system, performs status screening and traceability binding based on the remaining consumables list, automatically generates return information, and realizes closed-loop management of the consumables life cycle.

[0008] The present invention is further configured such that the identity verification module comprises: Multimodal identification methods include: radio frequency card, finger vein, palm vein, facial recognition, and password login; Based on multi-modal identity recognition, any identity verification will open up operation permissions; Operation permissions include: entry of medical consumables before surgery and settlement of medical consumables after surgery.

[0009] The present invention is further configured such that the image acquisition module includes: preoperative information acquisition and postoperative information acquisition; Collect information on medical consumables twice before and after surgery, and construct a preoperative medical consumables information set and a postoperative medical consumables information set respectively; The information collection method includes: using a camera to collect original images of medical consumables, performing image recognition and segmentation on the original images of medical consumables, extracting edge contour surface texture and wear status from the segmented images, constructing edge contour vectors based on the shape boundary of the consumables, and constructing a texture tensor based on the original image tensor of the medical consumables; Use RFID readers to scan the physical codes and batch identifications of medical consumables to construct structured RFID codes; Use the camera to collect the original image of the certificate, parse the text on the consumable label based on the original image, and convert it into structured features to construct the structured text features of the certificate; Combine the medical consumables segmentation image, edge contour vector, texture tensor, structured RFID code, and certificate structured text features to perform feature connection to construct a preoperative medical consumables information set; The postoperative medical consumables information set was constructed by combining medical consumables segmentation images, edge contour vectors, texture tensors, and structured RFID codes for feature connection.

[0010] The present invention is further configured such that the consumables usage status identification module includes: a multi-dimensional difference parameter construction unit and a medical consumables usage discrimination unit; Multi-dimensional difference parameter construction unit: Multi-dimensional difference parameters include: spectral domain comprehensive difference, topological perturbation mapping difference and texture perturbation energy index.

[0011] The present invention is further configured to perform Fourier transform on the segmented images based on the preoperative medical consumables information set and the postoperative consumables information set to obtain spectrum information; Based on the spectrum information of the two segmented images before and after surgery, the energy difference between corresponding pixels is extracted to construct the spectrum energy attenuation degree; The phase spectrum is calculated based on the spectral information of the two segmented images before and after surgery. The texture direction inconsistency caused by direction change, rupture, and rotation is judged according to the deviation of the phase spectrum, and the spectral phase stability offset is constructed.

[0012] The present invention is further configured to measure the spectrum noise disturbance in the high frequency region based on the frequency domain information of the preoperative medical consumables information set and the postoperative consumables information set, and construct a corresponding frequency domain noise offset; The spectral energy attenuation, spectral phase stability offset and spectral domain noise response offset are integrated and weighted to construct the spectral domain comprehensive difference.

[0013] The present invention is further configured to extract a simplified outline of the medical consumables based on the preoperative medical consumables information set and the postoperative medical consumables information set using the Douglas-Peucker algorithm to generate an ordered key point sequence; Based on the ordered key point sequence, the edge segments are divided into multiple segments according to the curvature extreme points, the edge segment lengths and edge segment direction vectors are calculated, and a parameterized set of edge segments is constructed; Comparing the edge parameterized sets of preoperative and postoperative medical consumables to calculate the length difference and angle offset; The topological perturbation mapping difference is calculated based on the cumulative weighted sum of the length difference and the angle offset.

[0014] The present invention is further configured to calculate two-dimensional second-order directional derivatives based on the preoperative medical consumables information set and the postoperative medical consumables information set, respectively, to obtain a curvature distribution map of the preoperative medical consumables and a curvature distribution map of the postoperative medical consumables; Based on the preoperative consumable surface curvature distribution map and the postoperative consumable label curvature distribution map, the curvature deviation of the preoperative and postoperative curvatures is compared pixel by pixel to obtain the curvature difference; The curvature difference of each pixel in the whole image is accumulated to obtain the texture perturbation energy index.

[0015] The present invention is further configured such that the medical consumables use discrimination unit: forms a trace fusion discrimination index by performing nonlinear weighted fusion on the multi-dimensional difference parameters; Match preoperative medical consumables with postoperative medical consumables one by one. When the minimum trace fusion judgment index is greater than the threshold, the current medical consumables are determined to be used consumables. The used consumables are integrated to build a list of used consumables.

[0016] When the trace fusion index is less than the threshold, the current medical consumables are determined to be unused consumables, and the unused consumables are integrated to build a list of remaining consumables.

[0017] The present invention is further configured to connect to the hospital SPD system for data docking, generate the medical consumables usage status based on the used consumables list and the remaining consumables list, and construct a timestamp sequence of events in the entire life cycle of medical consumables; Medical consumables are screened based on the timestamp sequence of events throughout their life cycle. If a medical consumable is unused, the current medical consumable is included in the return list. The hospital's SPD system is then connected to the manufacturer to complete the return, registering the return status and time, thus completing the full life cycle management of medical consumables. The timestamp sequence of events in the entire life cycle of medical consumables includes: medical consumable number, preoperative registration timestamp, preoperative registrant, preoperative medical consumable information set, postoperative liquidation timestamp, postoperative registrant, medical consumable usage status, return status, and return time.

[0018] The present invention provides a full life cycle management system for medical consumables based on large-scale model applications. The system includes an identity authentication module: performing pre-operative and post-operative identity authentication on operators through multimodal identity recognition means, and granting operation permissions after passing the authentication; an image acquisition module: integrating RFID dual-frequency recognition technology, image recognition technology, and certificate OCR analysis technology to collect information on medical consumables before and after surgery, and respectively constructing a pre-operative consumables information set and a post-operative consumables information set; a consumables usage status identification module: constructing multi-dimensional difference parameters based on the pre-operative consumables information set and the post-operative consumables information set, determining the usage status of medical consumables, and outputting a list of used consumables and a list of remaining consumables; an SPD integration and return management module: connecting to the hospital SPD system, performing status screening and traceability binding based on the remaining consumables list, automatically generating return information, and realizing closed-loop management of the consumables life cycle. The beneficial effects produced include:

[0019] Precise Management and Real-Time Monitoring: By combining multimodal identity recognition, image recognition, RFID, and OCR technologies, this system enables full traceability of medical consumables, ensuring precise management and real-time monitoring of every consumable, from its use to its return. This not only improves the hospital's management efficiency in orthopedic surgery, but also reduces manual errors and ensures data accuracy.

[0020] Reduced manual intervention and improved work efficiency: The system automates data collection and processing, reducing the complexity of traditional manual record-keeping and operations. This significantly improves efficiency and accuracy, particularly in the consumables usage and returns process. Medical staff and suppliers can more quickly obtain accurate information on consumables usage status and remaining supplies, shortening processing time and reducing workload.

[0021] Enhanced returns management and supply chain transparency: The system automatically generates return information and connects to the hospital's SPD system, ensuring that unused consumables are returned to suppliers smoothly. Detailed records are kept of all return processes, ensuring timely and accurate returns. Furthermore, the system supports transparent supply chain management, providing hospitals with traceable return data and reducing potential audit risks.

[0022] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in 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 those skilled in the art, other drawings can be obtained based on these drawings without inventive efforts. In the drawings: Figure 1 The figure is a schematic structural diagram of a full life cycle management system for medical consumables based on large model applications, showing an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following describes the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art will readily appreciate the other advantages and benefits of the present invention from the disclosure herein. The present invention may also be implemented or applied through various other specific embodiments, and the various details in this specification may be modified or altered based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are intended only to illustrate the present invention and are not intended to limit the scope of protection of the present invention.

[0025] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0026] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the embodiments of the present invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring the embodiments of the present invention.

[0027] Example

[0028] A full life cycle management system for medical consumables based on large model applications, such as Figure 1 Shown, including: Identity verification module: This module uses multimodal identity recognition to verify the operator's identity before and after the operation, and grants operation permissions after passing the verification; Image acquisition module: By integrating RFID dual-frequency recognition technology, image recognition technology, and certificate OCR analysis technology, it collects information on medical consumables before and after surgery, and constructs preoperative and postoperative consumables information sets respectively; Consumables usage status identification module: Based on the preoperative consumables information set and the postoperative consumables information set, it constructs multi-dimensional difference parameters to determine the usage status of medical consumables and outputs a list of used consumables and a list of remaining consumables; SPD integration and return management module: connects to the hospital's SPD system, performs status screening and traceability binding based on the remaining consumables list, automatically generates return information, and realizes closed-loop management of the consumables life cycle.

[0029] The present invention is further configured such that the identity verification module comprises: Multimodal identification methods include: radio frequency card, finger vein, palm vein, facial recognition, and password login; Based on multi-modal identity recognition, any identity verification will open up operation permissions; Operational permissions include pre-operative medical consumable entry and post-operative medical consumable liquidation. Specifically, the identity verification module uses multimodal identification technology to verify the operator's identity before and after surgery, ensuring the correct allocation and management of operational permissions. Among these multimodal identification methods: RFID card: RFID technology reads the information on a worn RFID card to quickly verify the operator's identity. This method is suitable for scenarios requiring rapid access to specific areas or operational permissions. Finger vein recognition: Utilizing the unique characteristics of finger veins for authentication, it offers high security and accuracy, making it difficult to forge or copy, and suitable for operations requiring high security. Palm vein recognition: Similar to finger vein recognition, palm vein recognition scans the veins in the palm of your hand for authentication, providing more personalized and accurate verification, particularly suitable for high-security environments such as operating rooms. Facial recognition: Analyzing facial features provides contactless authentication, ensuring convenient and accurate identification. It primarily uses iris recognition to verify identity information. Password login: A traditional and simple authentication method, users enter a personal password for authentication, suitable for rapid identity verification and emergency operations. After successful authentication using any of the multimodal identity verification methods, operational permissions are granted through the permission verification system. All operational permissions are rigorously verified to ensure that only authorized personnel can perform the relevant operations. The authentication module simultaneously records the operator's identity information, operation time, and operation content, further improving system security and traceability. During the specific deployment process, any one or more identity verification methods can be selected.

[0030] The present invention is further configured such that the image acquisition module includes: preoperative information acquisition and postoperative information acquisition; Collect information on medical consumables twice before and after surgery, and construct a preoperative medical consumables information set and a postoperative medical consumables information set respectively; The information collection method includes: using a camera to collect original images of medical consumables, performing image recognition and segmentation on the original images of medical consumables, extracting edge contour surface texture and wear status from the segmented images, constructing edge contour vectors based on the shape boundary of the consumables, and constructing a texture tensor based on the original image tensor of the medical consumables; Use RFID readers to scan the physical codes and batch identifications of medical consumables to construct structured RFID codes; Use the camera to collect the original image of the certificate, parse the text on the consumable label based on the original image, and convert it into structured features to construct the structured text features of the certificate; Combine the medical consumables segmentation image, edge contour vector, texture tensor, structured RFID code, and certificate structured text features to perform feature connection to construct a preoperative medical consumables information set; The postoperative medical consumables information set is constructed by combining segmented images of medical consumables, edge contour vectors, texture tensors, and structured RFID codes for feature concatenation. Specifically, the image acquisition module is responsible for collecting and processing image data of medical consumables, ensuring accurate recording of preoperative and postoperative consumable information. This module consists of two main components: preoperative information acquisition and postoperative information acquisition. Through these two information acquisition processes, the system can comprehensively and accurately identify and manage the usage status and changes of medical consumables. Preoperative information acquisition: Before surgery, the system uses a high-definition camera to capture raw images of medical consumables. At this point, the system performs image recognition, segmentation, edge extraction, and texture analysis on the medical consumables to generate a preoperative consumables information set, recording basic information such as the consumables' appearance, shape, and status. Postoperative information acquisition: After the surgery, the system again captures images of the used consumables. The postoperative image data is also identified and segmented, recording the consumables' usage during the surgery, including information such as wear and damage. This information is compared with the preoperative data to determine the consumables' usage status and generate a postoperative consumables information set. A camera is used to capture images from the medical consumables image acquisition area to obtain the original image of the medical consumables. Image recognition and segmentation are performed on the original image. The collected medical consumables image is preprocessed and segmented using an image recognition algorithm. Image segmentation refers to separating the medical consumables area in the image from the background and extracting specific consumables information. Edge contour extraction and surface texture analysis are performed on the segmented image. The edge contour refers to the shape boundary of the medical consumables. After extracting this edge information, an edge contour vector is constructed. The texture tensor reflects the usage status of the consumables by analyzing the surface texture in the image, such as tiny scratches, wear, and bumps. The edge contour vector and texture tensor are two important feature information that help in the subsequent determination of the consumables status. The system uses an RFID reader to scan the RFID tag of the medical consumables, extracts the physical code and batch identification from it, and constructs a structured RFID code. The system uses a camera to capture the original image of the certificate and applies OCR technology to parse the text information in the image, such as the certificate number, production date, and expiration date, and converts it into structured text features. These information collection technologies are all existing technologies and will not be elaborated on here. Based on the feature connection of the collected medical consumables information, Xingheng has a structured pre-operative / post-operative information set. By comparing the differences between the pre-operative / post-operative information sets, it can accurately judge the usage status and damage status of the consumables, providing accurate data for the traceability and management of consumables.

[0031] The present invention is further configured such that the consumables usage status identification module includes: a multi-dimensional difference parameter construction unit and a medical consumables usage discrimination unit; Multidimensional difference parameter construction unit: Multidimensional difference parameters include: spectral domain comprehensive difference, topological perturbation mapping difference and texture perturbation energy index. Specifically, the consumables usage status identification module is mainly used to identify the usage status of medical consumables, and accurately evaluate whether the consumables are in use through a series of complex difference parameters and discrimination mechanisms. The consumables usage status identification module is mainly composed of two units: a multidimensional difference parameter construction unit and a medical consumables usage discrimination unit. The main function of the multidimensional difference parameter construction unit is to differentiate indicators from multiple dimensions, which are used to quantify the state changes of medical consumables during the use process. The spectral domain comprehensive difference converts the image from the time domain to the frequency domain through Fourier transform. By comparing the image frequency information, the difference in frequency components before and after use of the consumables is calculated, which is mainly used to evaluate the macroscopic changes of the image. Including changes in shape and loss of surface details; topological perturbation mapping difference evaluates shape changes caused by external forces or wear during use by performing topological analysis on the surface structure of medical consumables. By describing the topological characteristics of the surface changes of objects, it can more accurately judge the deformation, damage and other states of consumables; texture perturbation energy index quantitatively analyzes the texture features in the image, calculates the texture changes caused by wear, scratches or other external forces, and then evaluates the usage status of consumables through perturbation energy. The medical consumables usage discrimination unit discriminates the usage status of consumables by integrating multi-dimensional difference parameters. Based on the calculated spectral domain comprehensive difference, topological perturbation mapping difference and texture perturbation energy index, combined with the preset discrimination model, it makes a judgment on whether the medical consumables are used and classifies them according to the judgment results.

[0032] The present invention is further configured to perform Fourier transform on the segmented images based on the preoperative medical consumables information set and the postoperative consumables information set to obtain spectrum information; Based on the spectrum information of the two segmented images before and after surgery, the energy difference between corresponding pixels is extracted to construct the spectrum energy attenuation degree; The phase spectrum is calculated based on the spectral information of the two segmented images before and after surgery. The texture direction inconsistency caused by direction change, rupture, and rotation is judged based on the deviation of the phase spectrum, and the spectral phase stability offset is constructed. Specifically, in the calculation of spectral energy attenuation: the spectral energy attenuation is to quantify the degree of damage to the image during use by comparing the energy changes of corresponding pixels in the preoperative and postoperative spectral information. By calculating the energy difference between the preoperative and postoperative images in the spectral domain, the overall change of the image can be judged. Using Fourier transform to convert images from time domain to spectrum is an existing technology and will not be elaborated on here. Spectral energy attenuation calculation logic: ,in, is the spectrum energy attenuation; It is the spectrum information of the preoperative segmentation image; It is the spectral information of the postoperative segmentation image; The power enhancement coefficient is used to adjust the contrast of spectral energy. Its value range is between 1.2 and 2. The larger the value, the more sensitive it is to the area with higher spectral energy. Indicates the position in the preoperative spectrum The amplitude value of Indicates the position in the spectrum after surgery The amplitude value of . In the calculation of spectral phase stability offset: Spectral phase stability offset refers to the calculation of the phase deviation of the image in the frequency domain based on the phase spectrum information of the preoperative and postoperative images, and then the determination of the directional change of the image texture. The magnitude of the phase deviation is directly related to whether the image has undergone a directional change. Calculating the phase spectrum through spectral information is an existing technology and will not be elaborated on here. The calculation logic of spectral phase stability offset is as follows: ,in, is the spectrum phase stability offset; is the phase spectrum of the preoperative segmentation image; is the phase spectrum of the postoperative segmentation image; It is the double angle formula of the sine function, which is used to ensure the symmetry of directional perturbations and to enhance the full-transmission invariant recognition capability.

[0033] The present invention is further configured to measure the spectrum noise disturbance in the high frequency region based on the frequency domain information of the preoperative medical consumables information set and the postoperative medical consumables information set, and construct a corresponding frequency domain noise offset; The spectral energy attenuation, spectral phase stability offset, and spectral domain noise response offset are weighted and combined to construct the spectral domain comprehensive difference. Specifically, in the calculation of the frequency domain noise response offset: the high-frequency region of the spectrum information corresponds to the texture details, edge mutations, and local anomalies in the image. Damage and scratches on consumables usually appear in the high-frequency part of the spectrum. By analyzing the differences in the high-frequency part, the consumable damage information is obtained. The frequency domain noise response offset calculation logic is as follows: ,in, is the corresponding offset of frequency domain noise; is the high-frequency starting threshold, which is the set frequency domain range used to eliminate the low-frequency part and focus only on the high-frequency area. The value range is between 0.7 and 0.85. It is the maximum ratio value of the image frequency and is generally set to 75% of the maximum length of the frequency axis to ensure that only the high-frequency area of ​​the image is counted. It is the frequency activation factor used to enhance the disturbance sensitivity of the high-frequency area. The value is between 0.005 and 0.01. The higher the value, the more sensitive it is to high-frequency disturbances. The specific value is adjusted according to the actual application scenario. In the calculation of spectral domain comprehensive difference: the spectral domain comprehensive difference provides a highly sensitive, stable and robust image status discrimination mechanism for the consumables usage status by performing weighted nonlinear fusion of the frequency domain energy changes, phase disturbances and high-frequency noise responses of the pre-operative / post-operative images, which can effectively improve the accuracy and automation of consumables identification. The frequency domain comprehensive difference calculation logic: ,in, is the frequency domain comprehensive difference; The energy difference weight coefficient ranges from 0 to 1. The higher the value, the more important this parameter is. The recommended value is 0.4. Phase difference weight coefficient, the value range is between 0 and 1. The higher the value, the more important this parameter is. The recommended value is 0.4. The high-frequency noise difference weight coefficient ranges from 0 to 1. The higher the value, the more important this parameter is. The recommended value is 0.2. 、 、 The sum is equal to 1.

[0034] The present invention is further configured to extract a simplified outline of the medical consumables based on the preoperative medical consumables information set and the postoperative medical consumables information set using the Douglas-Peucker algorithm to generate an ordered key point sequence; Based on the ordered key point sequence, the edge segments are divided into multiple segments according to the curvature extreme points, the edge segment lengths and edge segment direction vectors are calculated, and a parameterized set of edge segments is constructed; Comparing the edge parameterized sets of preoperative and postoperative medical consumables to calculate the length difference and angle offset; The topological perturbation mapping difference is calculated based on the cumulative weighted sum of the length difference and the angle offset. Specifically, the edge contours in the segmented images of medical consumables before and after surgery are digitized and parameterized to form a quantifiable structural difference index; through: contour simplification modeling, key point extraction and curvature segmentation, edge segment parameter calculation, structural comparison, a topological perturbation mapping difference reflecting the degree of structural change is finally calculated. The edge contour vectors in the preoperative / postoperative medical consumables information set are used as input data and directly input into the Douglas-Peucker algorithm for contour simplification and ordered key point extraction. This method is an existing technology and will not be elaborated on here. In the simplified contour point set, the curvature extreme points are identified as segmentation points, and the contour is split into several edge segments. The length of each edge segment and the direction vector of the edge segment are calculated. This is an existing technology and will not be elaborated on here. The difference of each edge segment in each segmented image before / after surgery is calculated, and the edge segment differences are summarized to obtain the final topological perturbation mapping difference. The topological perturbation mapping difference calculation logic is as follows: ,in, is the topological perturbation mapping difference; is the total number of edge segments; is the length difference, and the first The length of the edge segment and the first The difference between the lengths of the edge segments is obtained; is the angle offset, by calculating the first The edge angle and the first The difference between the angles of the edge segments is obtained; 、 is the weight factor, the value range is between 0 and 1, and the sum of the two weights must be equal to 1. The recommended weight is is 0.6, weight is 0.4.

[0035] The present invention is further configured to calculate two-dimensional second-order directional derivatives based on the preoperative medical consumables information set and the postoperative medical consumables information set, respectively, to obtain a curvature distribution map of the preoperative medical consumables and a curvature distribution map of the postoperative medical consumables; Based on the preoperative consumable surface curvature distribution map and the postoperative consumable label curvature distribution map, the curvature deviation of the preoperative and postoperative curvatures is compared pixel by pixel to obtain the curvature difference; The curvature difference of each pixel in the whole image is accumulated to obtain the texture perturbation energy index. Specifically, the curvature distribution map of preoperative medical consumables and the curvature distribution map of postoperative medical consumables are respectively based on the texture tensors constructed by the preoperative medical consumables information set and the postoperative medical consumables information set. By performing a two-dimensional second-order directional derivative operation on the texture tensor, the local curvature value of each pixel is calculated, and then a complete curvature distribution map is formed at the image scale. The texture tensor is used to characterize the spatial gradient structure of the surface texture in the consumables image. The second-order derivative calculation can use the principal curvature estimation method of the Hessian matrix, which is a prior art and will not be elaborated here. The specific calculation formula for constructing the texture perturbation energy index by accumulating the curvature difference of each pixel in the whole image is as follows: ,in, is the texture perturbation energy index; is the texture tensor in the preoperative medical consumables information set, representing the texture response value of each pixel in the preoperative medical consumables segmentation image; is the texture tensor in the postoperative medical consumables information set, representing the texture response value of each pixel in the postoperative medical consumables segmentation image; It is a two-dimensional second-order directional derivative used to estimate the local curvature; by counting the curvature difference of each pixel at each position, the texture perturbation energy index of the two medical consumables before and after surgery is obtained.

[0036] The present invention is further configured such that the medical consumables use discrimination unit: forms a trace fusion discrimination index by performing nonlinear weighted fusion on the multi-dimensional difference parameters; Match preoperative medical consumables with postoperative medical consumables one by one. When the minimum trace fusion judgment index is greater than the threshold, the current medical consumables are determined to be used consumables. The used consumables are integrated to build a list of used consumables. When the trace fusion judgment index is less than the threshold, the current medical consumables are determined to be unused consumables, and the unused consumables are integrated to build a list of remaining consumables. Specifically, the trace fusion judgment index intelligently determines whether the consumables have been used by integrating the multi-dimensional difference parameters obtained by calculation; the calculation logic of the trace fusion judgment index is: ,in, It is the behavior integration index; 、 、 It is a weighting coefficient used to control the weight of each indicator in the fusion result. The value range is between 0 and 1. It is necessary to satisfy that the sum of the three weighting coefficients is equal to 1. Take 0.4, Take 0.35, Take 0.25. Use the minimum difference strategy to match the consumable status and define the consumable list and remaining supplies list The threshold value range of the used consumables list is between 0.35 and 0.55. When the trace fusion judgment index is greater than the threshold, it means that the current medical consumables cannot find a matching object in the postoperative medical consumables information set, which proves that the current medical consumables have been used or damaged or seriously deformed. They can be judged as used consumables and included in the used consumables list; when the trace fusion judgment index is less than or equal to the threshold and a similar object is successfully matched in the postoperative medical consumables information set, it proves that the consumables have not been used and remain in a consistent state, they are judged as remaining consumables and included in the remaining consumables list.

[0037] The present invention is further configured to connect to the hospital SPD system for data docking, generate the medical consumables usage status based on the used consumables list and the remaining consumables list, and construct a timestamp sequence of events in the entire life cycle of medical consumables; Medical consumables are screened based on the timestamp sequence of events throughout their life cycle. If a medical consumable is unused, the current medical consumable is included in the return list. The hospital's SPD system is then connected to the manufacturer to complete the return, registering the return status and time, thus completing the full life cycle management of medical consumables. The timestamp sequence of events in the entire life cycle of medical consumables includes: medical consumable number, preoperative registration timestamp, preoperative registrant, preoperative medical consumable information set, postoperative liquidation timestamp, postoperative registrant, medical consumable usage status, return status, and return time. Specifically, the SPD system is a standard information system for hospitals to manage medical consumables. It has complete functions for consumables purchase, sale, inventory, usage registration, and batch supervision. The system connects to the SPD database or interface through API / Webhook or local middleware to achieve data docking, and obtains basic information on medical consumables from the hospital SPD system. Based on the basic information of medical consumables, the timestamp sequence of the entire life cycle of medical consumables can be expanded according to the actual application scenarios, such as adding batch number, manufacturer, expiration date, and consumables storage registration information to improve the information registration of the entire life cycle of medical consumables. The rules for determining the usage status of medical consumables in the timestamp sequence of events throughout their life cycle are as follows: If the current medical consumable number can be found in the list of used consumables, the current usage status of the medical consumables in the hospital SPD system is changed to used; if the current medical consumable number cannot be found in the list of used consumables but can be found in the list of remaining consumables, the current usage status of the medical consumables in the hospital SPD system is changed to unused; if the current medical consumable number cannot be found in both the list of used consumables and the list of remaining consumables, it is determined to be abnormal data, specially marked, and fed back to the system administrator. Return information is automatically generated based on the timestamp sequence of events throughout the life cycle of medical consumables: If the current consumable is determined to be unused, it is included in the return list, and the return module is called through the system and SPD interface: a return request is automatically initiated to the manufacturer or supply platform through the return API of the SPD system, and the return status and timestamp are written. If the return fails or is interrupted, the status will also be recorded as unreturned for background review and processing.

[0038] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using 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 the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application 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. The 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, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., infrared, wireless, microwave, etc.) method. 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 drive.

[0039] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0040] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0041] 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.

[0042] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein 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 beyond the scope of this application.

[0043] Those skilled in the art will 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.

[0044] In the several embodiments provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, 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.

[0045] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0046] In addition, each functional unit in each embodiment of the present application 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.

[0047] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0048] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A full life cycle management system for medical consumables based on large model applications, characterized in that: include: Identity verification module: This module uses multimodal identity recognition to verify the operator's identity before and after the operation, and grants operation permissions after passing the verification; Image acquisition module: By integrating RFID dual-frequency recognition technology, image recognition technology, and certificate OCR analysis technology, it collects information on medical consumables before and after surgery, and constructs preoperative and postoperative consumables information sets respectively; Consumables usage status identification module: Based on the preoperative consumables information set and the postoperative consumables information set, it constructs multi-dimensional difference parameters to determine the usage status of medical consumables and outputs a list of used consumables and a list of remaining consumables; SPD integration and return management module: connects to the hospital's SPD system, performs status screening and traceability binding based on the remaining consumables list, automatically generates return information, and realizes closed-loop management of the consumables life cycle.

2. A medical consumables full life cycle management system based on large model application according to claim 1, characterized in that: The identity verification module includes: Multimodal identification methods include: radio frequency card, finger vein, palm vein, facial recognition, and password login; Based on multi-modal identity recognition, any identity verification will open up operation permissions; Operation permissions include: entry of preoperative medical consumables and settlement of postoperative medical consumables.

3. The medical consumables full life cycle management system based on large model application according to claim 1 is characterized in that: The image acquisition module includes: preoperative information acquisition and postoperative information acquisition; Collect information on medical consumables twice before and after surgery, and construct a preoperative medical consumables information set and a postoperative medical consumables information set respectively; The information collection method includes: using a camera to collect original images of medical consumables, performing image recognition and segmentation on the original images of medical consumables, extracting edge contour surface texture and wear status from the segmented images, constructing edge contour vectors based on the shape boundary of the consumables, and constructing a texture tensor based on the original image tensor of the medical consumables; Use RFID readers to scan the physical codes and batch identifications of medical consumables to construct structured RFID codes; Use the camera to collect the original image of the certificate, parse the text on the consumable label based on the original image, and convert it into structured features to construct the structured text features of the certificate; Combine the medical consumables segmentation image, edge contour vector, texture tensor, structured RFID code, and certificate structured text features to perform feature connection to construct a preoperative medical consumables information set; The postoperative medical consumables information set was constructed by combining medical consumables segmentation images, edge contour vectors, texture tensors, and structured RFID codes for feature connection.

4. The medical consumables full life cycle management system based on large model application according to claim 1 is characterized in that: The consumables usage status identification module includes: a multi-dimensional difference parameter construction unit and a medical consumables usage determination unit; Multi-dimensional difference parameter construction unit: Multi-dimensional difference parameters include: spectral domain comprehensive difference, topological perturbation mapping difference and texture perturbation energy index.

5. A medical consumables full life cycle management system based on large model application according to claim 4, characterized in that: Based on the preoperative medical consumables information set and the postoperative medical consumables information set, Fourier transform is performed on the segmented images to obtain spectrum information; Based on the spectrum information of the two segmented images before and after surgery, the energy difference between corresponding pixels is extracted to construct the spectrum energy attenuation degree; The phase spectrum is calculated based on the spectral information of the two segmented images before and after surgery. The texture direction inconsistency caused by direction change, rupture, and rotation is judged according to the deviation of the phase spectrum, and the spectral phase stability offset is constructed.

6. A medical consumables full life cycle management system based on large model application according to claim 5, characterized in that: Based on the frequency domain information of the preoperative medical consumables information set and the postoperative consumables information set, the spectrum noise disturbance in the high-frequency region is measured and the corresponding frequency domain noise offset is constructed; The spectral energy attenuation, spectral phase stability offset and spectral domain noise response offset are integrated and weighted to construct the spectral domain comprehensive difference.

7. The medical consumables full life cycle management system based on large model application according to claim 4 is characterized in that: Based on the preoperative and postoperative medical consumables information sets, the Douglas-Peucker algorithm is used to extract the simplified outline of the medical consumables and generate an ordered sequence of key points. Based on the ordered key point sequence, the edge segments are divided into multiple segments according to the curvature extreme points, the edge segment lengths and edge segment direction vectors are calculated, and the edge segment parameterization set is constructed; Comparing the edge segment parameterized sets of preoperative and postoperative medical consumables to calculate the length difference and angle offset; The topological perturbation mapping difference is calculated based on the cumulative weighted sum of the length difference and the angle offset.

8. The medical consumables full life cycle management system based on large model application according to claim 4 is characterized in that: Based on the preoperative medical consumables information set and the postoperative medical consumables information set, two-dimensional second-order directional derivatives are calculated respectively to obtain the curvature distribution map of the preoperative medical consumables and the curvature distribution map of the postoperative medical consumables; Based on the preoperative consumable surface curvature distribution map and the postoperative consumable label curvature distribution map, the curvature deviation of the preoperative and postoperative curvatures is compared pixel by pixel to obtain the curvature difference; The curvature difference of each pixel in the whole image is accumulated to obtain the texture perturbation energy index.

9. The medical consumables full life cycle management system based on large model application according to claim 4 is characterized in that: Medical consumables usage discrimination unit: It forms a behavior fusion index by performing nonlinear weighted fusion on multi-dimensional difference parameters; Match preoperative medical consumables with postoperative medical consumables one by one. When the minimum trace fusion judgment index is greater than the threshold, the current medical consumables are determined to be used consumables. The used consumables are integrated to build a list of used consumables. When the trace fusion index is less than the threshold, the current medical consumables are determined to be unused consumables, and the unused consumables are integrated to build a list of remaining consumables.

10. The medical consumables full life cycle management system based on large model application according to claim 1, characterized in that: The SPD integration and return management module includes: Connect to the hospital's SPD system for data docking, generate the medical consumables usage status based on the used consumables list and the remaining consumables list, and build a timestamp sequence of medical consumables life cycle events; Medical consumables are screened based on the timestamp sequence of events throughout their life cycle. If a medical consumable is unused, the current medical consumable is included in the return list. The hospital's SPD system is then connected to the manufacturer to complete the return, registering the return status and time, thus completing the full life cycle management of medical consumables. The timestamp sequence of events in the entire life cycle of medical consumables includes: medical consumable number, preoperative registration timestamp, preoperative registrant, preoperative medical consumable information set, postoperative liquidation timestamp, postoperative registrant, medical consumable usage status, return status, and return time.

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