Orthopedic implant dynamic inventory management method based on pre-recognition analysis

Abstract

The invention provides an orthopedic implant dynamic inventory management method based on pre-recognition analysis, which comprises the following steps of: after a doctor issues an operation notice, recognizing keywords in required orthopedic implant information on the operation notice, constructing orthopedic implant library checking cues, and traversing an orthopedic implant dynamic inventory database by using an LLM (Logistics Language Model); based on the orthopedic implant library checking prompt word, retrieving and judging whether the orthopedic implant inventory of the orthopedic implant dynamic inventory database meets the orthopedic implant information or not: if so, generating corresponding inventory sufficient information and feeding back the information to the order receiving system to inform a doctor that the operation reservation succeeds; and if not, corresponding inventory shortage information is generated and output, and a supplier is notified to replenish goods. Therefore, consumables of a surgical patient can be ensured to be in place on time during the surgery, the risk that the implant cannot be in place during the surgery due to manual judgment is greatly reduced, and the safety of the patient is guaranteed. By monitoring the dynamic inventory of the implants, excessive or insufficient inventory is avoided, and the requirement for surgical consumables is met.

Description

Technical Field

[0001] The present invention relates to the field of medical management technology, and in particular to an orthopedic implant dynamic inventory management method based on pre-identification analysis, an orthopedic implant dynamic inventory management system based on pre-identification analysis, an electronic device and a computer-readable storage medium. Background Art

[0002] Implants are essential consumables for orthopedic surgery. When such consumables are circulated in hospitals, many departments are involved and it takes a long time. At the same time, the number of surgical patients that require similar consumables is constantly changing every day. Therefore, without dynamic inventory display, it is difficult for each department to implement accurate dynamic monitoring of consumables.

[0003] Traditionally, the inventory management of orthopedic implants is mainly based on statistical management of inventory bases, but this method is prone to expiration or shortage of consumables due to inadequate personnel assessment or dynamic fluctuations in surgical volume. Although the inventory management of orthopedic implants in operating rooms currently adopts a model that combines information and manual labor, these methods still have the following shortcomings: The current inventory can only display the physical inventory, and cannot obtain the scheduled inventory. Therefore, the surgeon cannot accurately determine the consumables preparation status for the next day's surgery and needs to confirm before seeing the patient, or make a temporary replacement during the operation. Equipment suppliers cannot accurately obtain the hospital's demand for consumables for the next day, so they must increase the amount of consumables delivered, increasing the workload of personnel at all levels; Due to the lack of early warning identification, departments such as procurement, acceptance, and sterilization supply centers do not arrange the processing time for urgently needed implants reasonably. They can only display the physical inventory and cannot obtain the inventory scheduled for consumption. Therefore, if an expedited process is taken, manual communication is required, which leads to low efficiency. Summary of the invention

[0004] In order to solve the technical problems existing in the prior art, the present invention provides the following technical solutions: On the one hand, a method for dynamic inventory management of orthopedic implants based on pre-identification analysis is provided, the method being implemented by an electronic device, the method comprising: S1. The surgeon places an order through the terminal and sends the surgical notification form for bone implantation to the order receiving system in advance; S2. Receive and identify the orthopedic implant information required on the surgical notification form through the order receiving system, including the surgical time and surgical type of the orthopedic implant and the orthopedic implant list, wherein the orthopedic implant list includes the brand, name, specification model and quantity of the orthopedic implant; S3, extracting the operation time, the operation type and the keywords of the orthopedic implant information in the orthopedic implant information, and combining the preset orthopedic implant library search logic, forming an orthopedic implant library search prompt word and sending it to the consumables platform; wherein the consumables platform is pre-deployed with an LLM large language model and an orthopedic implant dynamic inventory database; S4, the consumables platform inputs the orthopedic implant library query prompt word into the LLM large language model, traverses the orthopedic implant dynamic inventory database through the LLM large language model, and retrieves and determines whether the orthopedic implant inventory in the orthopedic implant dynamic inventory database meets the orthopedic implant information based on the orthopedic implant library query prompt word: If the conditions are met, corresponding sufficient inventory information is generated and fed back to the order receiving system; If not, the corresponding insufficient inventory information is generated and output; S5. If the order receiving system receives the sufficient inventory information, it generates a corresponding surgery reservation success notification according to the sufficient inventory information and sends it to the terminal; If the consumables platform receives the insufficient inventory information, a corresponding orthopedic implant replenishment order is generated according to the insufficient inventory information and sent to the supplier backend.

[0005] Preferably, the orthopedic implant dynamic inventory database is constructed in the following manner: When the orthopedic implant passes through an application node, the application node scans the QR code on the orthopedic implant, collects the supply or consumption information of the orthopedic implant (including the quantity and time of the corresponding supply or consumption) and uploads it to the consumables platform; On the consumables platform, based on preset implant keywords and supply management logic, corresponding supply management prompt words are constructed and input into the LLM large language model, and the quantity and time information of the supply or consumption of the orthopedic implant is identified and output through the LLM large language model; The quantity and time information of the supply or consumption of the orthopedic implants are saved or updated in a SQL database preset on the consumables platform to obtain the orthopedic implant dynamic inventory database.

[0006] Preferably, after identifying and outputting the quantity and time information of the supply or consumption of the orthopedic implant through the LLM large language model, the method further comprises: According to the time information, the daily supply or consumption quantity of each orthopedic implant is counted, and a corresponding inventory table is generated.

[0007] Preferably, the retrieval based on the orthopedic implant database search prompt word determines whether the orthopedic implant inventory of the orthopedic implant dynamic inventory database meets the orthopedic implant information, including: Retrieve the next day's inventory of the orthopedic implant that meets the operation time and the operation type in the orthopedic implant information from the orthopedic implant dynamic inventory database based on the orthopedic implant search prompt word through the LLM large language model; Determine whether the next day's inventory of the orthopedic implant meets the orthopedic implant information required this time.

[0008] Preferably, the step S2, receiving and identifying the orthopedic implant information required on the surgical notification form through the order receiving system, comprises: The order receiving system receives and parses the surgical notification form to obtain attribute information and parsed text content therein; Reading the operation time and operation type of the orthopedic implant from the attribute information; Collecting keyword text features of a number of orthopedic implants, constructing a keyword feature set, and inputting the keyword feature set into the preset LLM large language model, and generating an LLM large language model-A capable of identifying different orthopedic implants through model training; Construct quantitative statistical logic and input the LLM large language model-A, traverse the parsed text content through the LLM large language model-A, identify the orthopedic implants and their quantitative statistical results in the parsed text content, and obtain the required orthopedic implant information.

[0009] On the other hand, a dynamic inventory management system for orthopedic implants based on pre-identification analysis is provided, and the dynamic inventory management system for orthopedic implants based on pre-identification analysis is used to implement the above-mentioned dynamic inventory management method for orthopedic implants based on pre-identification analysis, and the system includes: The terminal is used for the surgeon to issue a surgical notice of bone placement (or bone implant) to the order receiving system in advance by placing an order; An order receiving system is used to receive and identify the orthopedic implant information required on the surgical notification form through the order receiving system, including the operation time, operation type and orthopedic implant list of the orthopedic implant, wherein the orthopedic implant list includes the brand, name, specification model and quantity of the orthopedic implant; and extract the operation time, operation type and keywords of the orthopedic implant information in the orthopedic implant information, and combine the preset orthopedic implant library query logic to form an orthopedic implant library query prompt word and send the consumables platform; The consumables platform is used to input the orthopedic implant library query prompt word into the LLM large language model, traverse the orthopedic implant dynamic inventory database through the LLM large language model, and retrieve and determine whether the orthopedic implant inventory in the orthopedic implant dynamic inventory database meets the orthopedic implant information based on the orthopedic implant library query prompt word: If the conditions are met, corresponding sufficient inventory information is generated and fed back to the order receiving system; If not, the corresponding insufficient inventory information is generated and output; The order receiving system is further configured to: if the inventory sufficient information is received, generate a corresponding surgery reservation success notification according to the inventory sufficient information and send the notification to the terminal; The consumables platform is further used to: if the insufficient inventory information is received, generate a corresponding orthopedic implant replenishment order according to the insufficient inventory information and send it to the supplier backend; The supplier backend is used to respond to the orthopedic implant replenishment order and provide feedback.

[0010] On the other hand, an electronic device is provided, comprising: a processor; and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the above-mentioned methods for dynamic inventory management of orthopedic implants based on pre-identification analysis is implemented.

[0011] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned methods for dynamic inventory management of orthopedic implants based on pre-identification analysis.

[0012] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: After the doctor issues the surgery notice, the present invention identifies the keywords in the orthopedic implant information required thereon and constructs the orthopedic implant library query prompt words, allowing the LLM large language model to traverse the orthopedic implant dynamic inventory database, and based on the orthopedic implant library query prompt words, retrieve and determine whether the orthopedic implant inventory in the orthopedic implant dynamic inventory database meets the orthopedic implant information: if it meets, then generate the corresponding sufficient inventory information and feedback to the order receiving system to notify the doctor that the surgery is scheduled successfully; if it does not meet, then generate the corresponding insufficient inventory information and output it to notify the supplier to replenish the stock. Therefore, it can ensure that the intraoperative consumables of the surgical patient are in place on time, greatly reduce the risk of the implant not being in place during the operation due to manual judgment, and ensure patient safety. By monitoring the dynamic inventory of implants, excessive or insufficient inventory is avoided, and the demand for surgical consumables is guaranteed.

[0013] At the same time, through the early warning system, it is ensured that clinical departments and suppliers give priority to the early warning implants, thereby shortening the circulation time, improving work efficiency, and reducing the labor intensity of medical staff. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of 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 work.

[0015] Figure 1 It is a flow chart of a dynamic inventory management method for orthopedic implants based on pre-identification analysis provided by an embodiment of the present invention; Figure 2 This is a schematic diagram of an application process of an LLM large language model provided by an embodiment of the present invention; Figure 3 It is a block diagram of a dynamic inventory management system for orthopedic implants based on pre-identification analysis provided by an embodiment of the present invention; Figure 4 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0017] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0018] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0019] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are consistent.

[0020] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0021] The embodiment of the present invention provides a method for dynamic inventory management of orthopedic implants based on pre-identification analysis, which can be implemented by an electronic device, which can be a terminal or a server. Figure 1 The flowchart of the dynamic inventory management method of orthopedic implants based on pre-identification analysis is shown, and the processing flow of the method may include the following steps: S1. The surgeon places an order through the terminal and sends the surgical notification form for bone implantation to the order receiving system in advance; S2. Receive and identify the orthopedic implant information required on the surgical notification form through the order receiving system, including the surgical time and surgical type of the orthopedic implant and the orthopedic implant list, wherein the orthopedic implant list includes the brand, name, specification model and quantity of the orthopedic implant; S3, extracting the operation time, the operation type and the keywords of the orthopedic implant information in the orthopedic implant information, and combining the preset orthopedic implant library search logic, forming an orthopedic implant library search prompt word and sending it to the consumables platform; wherein the consumables platform is pre-deployed with an LLM large language model and an orthopedic implant dynamic inventory database; S4, the consumables platform inputs the orthopedic implant library query prompt word into the LLM large language model, traverses the orthopedic implant dynamic inventory database through the LLM large language model, and retrieves and determines whether the orthopedic implant inventory in the orthopedic implant dynamic inventory database meets the orthopedic implant information based on the orthopedic implant library query prompt word: If the conditions are met, corresponding sufficient inventory information is generated and fed back to the order receiving system; If not, the corresponding insufficient inventory information is generated and output; S5. If the order receiving system receives the sufficient inventory information, it generates a corresponding surgery reservation success notification according to the sufficient inventory information and sends it to the terminal; If the consumables platform receives the insufficient inventory information, a corresponding orthopedic implant replenishment order is generated according to the insufficient inventory information and sent to the supplier backend.

[0022] The present invention relates to dynamic inventory management and early warning reminders of orthopedic non-sterile packaged implants. Specifically, each orthopedic implant (implant for short) is configured with a QR code as a unique identification code in the overall circulation. From the order of orthopedic implants to the supplier's distribution, the hospital's temporary work department's acceptance, the disinfection and sterilization distribution of the disinfection supply center, the warehousing in the operating room, the use, and the return to the warehouse of multiple departments and links, the information of the implant inventory of different brands and specifications is dynamically managed to ensure that the intraoperative consumables of surgical patients are in place on time, greatly reduce the implants that cannot be in place during the operation due to manual judgment, and ensure patient safety. At the same time, through the design of the early warning system, it is ensured that various clinical departments and suppliers give priority to the processing of early warning implants, thereby shortening the circulation time, improving work efficiency, and reducing the labor intensity of medical staff.

[0023] Each application node, such as issuance, operating room storage, use, return to the warehouse, etc., can use its terminal or other scanning device to scan the QR code and report the current application status of the implant (information such as usage quantity, time, location, etc. For example, when entering the warehouse, the type of implant, storage quantity, storage time and storage location will all be uploaded to the platform for storage; after use, the platform will update its saved data).

[0024] Combination Figure 3 As shown, this system includes a consumables platform, which is mainly used to manage the application status information of implants reported by each node and to count their usage. There is also an order-taking system, which can receive and identify the surgical notices for bone implant surgery issued by surgeons in various surgical departments (the surgical notices also record the patient information to be operated on next, including the patient's PID number, name, gender, age, surgical site, surgical name, surgical room, surgical time, etc., which are recorded by the system and the surgical schedule notification is sent to the circulating nurse in the operating room), to determine the information of the surgical plants needed next and make preparations. In the order-taking system, the surgical notices sent by the front-end surgeons through terminals such as PDA, EDA or PC can be parsed to identify the orthopedic implants required for the implant surgery to be prepared by the surgeon, including the names and quantities of different types of orthopedic implants, as well as the surgical time, surgical type, etc. After extracting this information, the order-taking system can construct the prompt words required by the LLM large language model and input them into the consumables platform. The platform will then assist in searching and determining whether the inventory of the corresponding implants in the consumables platform can meet the needs of the next implantation surgery.

[0025] The present invention mainly arranges the surgery for the next day, and needs to check the inventory to determine whether the inventory of the corresponding orthopedic implants recorded in the platform meets the requirements of the implant surgery for the next day. Combined with the large language model to assist in the retrieval and judgment, whether the inventory meets the demand information on the surgery notice issued by the current surgeon, the inventory retrieval and judgment can be performed quickly, the efficiency of surgery order acceptance is improved, and nurses are not required to check the inventory, which greatly improves the efficiency of surgery and reduces the amount of manual labor.

[0026] The platform and order-taking system can be deployed on the backend server. The backend can communicate with each terminal and can also communicate with the supplier backend. The specific communication method can refer to the communication mode of the existing hospital backend, which will not be described in detail in this embodiment.

[0027] The consumables platform can use its large language model (self-designed or through API interface request to call a third-party model such as Baidu's Wenxin Yiyan) to search the library. If the inventory is found to be insufficient, the demand information of the corresponding insufficient bone implants can be fed back to the supplier's backend to notify the supplier to replenish this type of implant. Therefore, early warning can be given to avoid delays in the progress of bone implant surgery due to insufficient consumables.

[0028] Combination Figure 2 As shown, preferably, the orthopedic implant dynamic inventory database is constructed in the following manner: When the orthopedic implant passes through an application node, the application node scans the QR code on the orthopedic implant, collects the supply or consumption information of the orthopedic implant (including the quantity and time of the corresponding supply or consumption) and uploads it to the consumables platform; On the consumables platform, based on preset implant keywords and supply management logic, corresponding supply management prompt words are constructed and input into the LLM large language model, and the quantity and time information of the supply or consumption of the orthopedic implant is identified and output through the LLM large language model; The quantity and time information of the supply or consumption of the orthopedic implants are saved or updated in a SQL database preset on the consumables platform to obtain the orthopedic implant dynamic inventory database.

[0029] Each application node scans the code to report the supply or consumption information of orthopedic implants (including the quantity and time of the corresponding supply or consumption) and uploads it to the consumables platform, which can be understood in combination with the above. The data reported by each node includes the quantity information of each orthopedic implant consumed, increased or unchanged at the corresponding node and the corresponding status time, such as inventory type, quantity and time, address and other status information. If used in the operating room, the corresponding record of the consumption, usage time and usage department of this type of orthopedic implant is recorded (it can also be bound to the corresponding surgical order and patient medical record for easy billing).

[0030] However, because of the need to manage a large amount of node status information of orthopedic implants, administrators need to spend a lot of time to conduct inventory and statistics. In order to reduce the workload, LLM is used here to automatically count and update orthopedic implants. The administrator can pre-build and configure the corresponding implant keywords (keywords for the names of different implants, such as "tibial fixation plate") and supply management logic (for example, if the status of an implant is consumption, its quantity is reduced by one; otherwise, it is increased by one; if the status is disinfection and sterilization, the quantity remains unchanged; etc., the administrator configures the corresponding supply management logic), build the corresponding supply management prompt words and input them into the LLM large language model. You can refer to the following steps: Step 1: Build supply management prompts ‌Determine implant keywords‌: First, identify the keywords for orthopedic implants that need to be tracked, such as "hip prosthesis", "spinal screw", etc.

[0031] ‌Design supply management logic‌: Based on the business logic, design the structure of prompt words, including key information such as the name of the implant, supplier, quantity, time, etc.

[0032] ‌Build prompt word template‌: Based on the above logic, build a fillable prompt word template, for example: "{implant name} supplied {quantity} pieces by {supplier} on {date}, or {quantity} pieces consumed on {date}." Step 2: Input prompt words into the LLM large language model ‌Fill in prompt words‌: Based on the actual supply or consumption situation, fill in the prompt word template with specific implant name, supplier, quantity, time and other information.

[0033] ‌Input model‌: Input the filled prompt words into the LLM large language model, and use the model's text understanding and generation capabilities to recognize and process this information.

[0034] Step 3: Identify and output quantity and time information ‌Model processing‌: The LLM large language model will parse the input prompt words and identify key information, such as the name, quantity, time, etc. of the implant.

[0035] ‌Output information‌: The model outputs the recognized information in a structured format, such as JSON or table, for subsequent processing.

[0036] Step 4: Save or update to SQL database ‌Connect to database‌: Establish a connection with the SQL database preset on the consumables platform.

[0037] ‌Data Mapping‌: Map the information output by the LLM large language model with the table structure in the SQL database to ensure that data can be inserted or updated correctly.

[0038] ‌Execute database operations‌: According to the mapping relationship, the quantity and time information of the supply or consumption of the identified orthopedic implants are saved or updated in the SQL database.

[0039] ‌Verify data‌: Check whether the data in the database accurately reflects the information output by the LLM large language model to ensure the integrity and consistency of the data.

[0040] Step 5: Get the dynamic inventory database of orthopedic implants Through the above steps, an SQL database containing dynamic inventory information of orthopedic implants is obtained. This database can reflect the supply and consumption of implants in real time and provide strong data support for consumables management.

[0041] Preferably, after identifying and outputting the quantity and time information of the supply or consumption of the orthopedic implant through the LLM large language model, the method further comprises: According to the time information, the daily supply or consumption quantity of each orthopedic implant is counted, and a corresponding inventory table is generated.

[0042] The present invention mainly counts and determines whether the inventory of the next day meets the needs of the surgery the next day. Therefore, the daily supply or consumption of each orthopedic implant can be counted and a corresponding inventory table can be generated. It is convenient to predict whether the inventory of the implants the next day meets the needs of the surgery.

[0043] Therefore, the following functions can be realized: The system can accurately judge the number of implants at each time and in each circulation link, such as: the number in stock, the number in use, and the number being cleaned and packaged in the supply room.

[0044] The system can automatically predict whether the implants will meet clinical needs the next day based on the surgeon's orders. If there is insufficient inventory, the system will automatically send data to the supplier to remind delivery.

[0045] When the patient enters the operating room, the system finds that the implant is not ready, and the system issues a warning that the consumables are not in place and whether the patient needs to be picked up.

[0046] Preferably, the retrieval based on the orthopedic implant database search prompt word determines whether the orthopedic implant inventory of the orthopedic implant dynamic inventory database meets the orthopedic implant information, including: Retrieve the next day's inventory of the orthopedic implant that meets the operation time and the operation type in the orthopedic implant information from the orthopedic implant dynamic inventory database based on the orthopedic implant search prompt word through the LLM large language model; Determine whether the next day's inventory of the orthopedic implant meets the orthopedic implant information required this time.

[0047] The present invention also uses LLM to assist in determining whether the inventory of the corresponding implants meets the amount of implants required for the next day on the surgical order. Therefore, based on the application of the previous prompt words, this article also uses LLM to search the inventory table for the next day to determine whether the amount of the corresponding implants recorded in the inventory table for the next day meets the orthopedic implants required in the doctor's order. You can refer to the following steps: To retrieve the next-day inventory of orthopedic implants that meet specific surgery time and surgery type in the orthopedic implant dynamic inventory database through the LLM large language model based on the orthopedic implant search prompt word, and determine whether the inventory meets the needs of this surgery, you can follow the following steps: ‌First, build a clear query prompt. ‌The prompt needs to include the surgery time, surgery type, and a clear instruction to request the next day's inventory. For example: "Please retrieve the next day's inventory of orthopedic implants required for {specific surgery type} at {specific surgery time}."‌ ‌Next, the constructed search prompt words are input into the LLM large language model. ‌The model will parse the prompt words and search the dynamic inventory database of orthopedic implants according to the instructions therein. ‌ ‌Then, the model will output the retrieved next-day inventory information. ‌The information should be directly related to the input surgery time and surgery type to ensure the accuracy of the retrieval results.

[0048] ‌Finally, based on the next day's inventory information output by the model, determine whether the inventory meets the orthopedic implant information required for this operation. ‌If the inventory is sufficient, the operation can be prepared; if the inventory is insufficient, timely measures need to be taken, such as transferring goods, emergency procurement, etc., to ensure the smooth progress of the operation.

[0049] In this process, the accuracy of the LLM large language model and the real-time performance of the dynamic inventory database of orthopedic implants are key factors. Ensuring that the model can correctly understand and process the prompt words and that the database can update the inventory information in real time are important prerequisites for ensuring the smooth progress of this process.

[0050] Preferably, the step S2, receiving and identifying the orthopedic implant information required on the surgical notification form through the order receiving system, comprises: The order receiving system receives and parses the surgical notification form to obtain attribute information and parsed text content therein; Reading the operation time and operation type of the orthopedic implant from the attribute information; Collecting keyword text features of a number of orthopedic implants, constructing a keyword feature set, and inputting the keyword feature set into the preset LLM large language model, and generating an LLM large language model-A capable of identifying different orthopedic implants through model training; Construct quantitative statistical logic and input the LLM large language model-A, traverse the parsed text content through the LLM large language model-A, identify the orthopedic implants and their quantitative statistical results in the parsed text content, and obtain the required orthopedic implant information.

[0051] The order-taking system needs to request the consumables platform to call the LLM. Each time a request is made, the order-taking system can send an LLM call request to the consumables platform, and the consumables platform responds to the request and develops the LLM to the order-taking system.

[0052] You can train LLM to learn the keyword text features of different orthopedic implants in advance (for example, hip prosthesis used for hip implant surgery, here we extract "hip prosthesis"), so that LLM can be trained to build a model that can implement different orthopedic implants, that is, LLM large language model-A. You can refer to the following steps: In order to read the operation time and operation type of orthopedic surgery from the attribute information, and use the LLM large language model to identify and parse orthopedic implants and their quantity statistics, the following steps can be followed: Step 1: Read the operation attribute information ‌Obtain attribute information‌: First, obtain attribute information including orthopedic surgery time and surgery type from relevant medical records or systems.

[0053] ‌Parse attribute information‌: Use text parsing technology to extract specific surgery time and surgery type from attribute information. This may require the use of regular expressions, natural language processing (NLP) technology, or specialized medical information extraction tools.

[0054] Step 2: Build keyword feature set ‌Collect keyword text features‌: Collect keywords related to orthopedic implants. These keywords should be able to represent different types of implants, such as "hip prosthesis", "spinal screw", etc.

[0055] ‌Build feature set‌: The collected keyword text features are organized into a feature set, which will be used to train the LLM large language model so that it can identify different orthopedic implants.

[0056] Step 3: Train the LLM large language model-A ‌Input keyword feature set‌: Input the constructed keyword feature set into the preset LLM large language model.

[0057] ‌Model Training‌: The LLM large language model is trained using the keyword feature set to enable it to accurately identify orthopedic implants in text. During the training process, a large amount of labeled data may be required to optimize the performance of the model.

[0058] ‌Generate LLM large language model-A‌: After training, the LLM large language model-A that can recognize different orthopedic implants is obtained.

[0059] Step 4: Build quantity statistics logic ‌Design statistical logic‌: Based on business requirements, design a set of quantitative statistical logic to calculate the number of each orthopedic implant in the text. This may require considering the description method of the implant, the unit, and possible quantifiers.

[0060] ‌Input statistical logic‌: Input the designed quantitative statistical logic into the LLM large language model-A, so that it can count the number of each implant while identifying the implant.

[0061] Step 5: Traverse and parse the text content ‌Get Parsed Text‌: Get text content containing orthopedic implant information from relevant medical records or systems, such as surgical notes, implant lists, etc.

[0062] ‌Traversing the text content‌: Use the LLM large language model-A to traverse and parse the text content, identify the orthopedic implants, and calculate the number of each implant based on quantitative statistical logic.

[0063] ‌Output orthopedic implant information‌: Output the identified orthopedic implants and their quantity statistics in a structured format, such as JSON, table, etc., for subsequent processing and analysis.

[0064] Through the above steps, the operation time and operation type of orthopedic surgery can be read from the attribute information, and the LLM large language model can be used to identify and parse orthopedic implants and their quantity statistics. This helps to realize the automated processing and analysis of medical information and improve the efficiency and quality of medical services.

[0065] S3, extracting the operation time, the operation type and the keywords of the orthopedic implant information in the orthopedic implant information, and combining the preset orthopedic implant library search logic, forming an orthopedic implant library search prompt word and sending it to the consumables platform; wherein the consumables platform is pre-deployed with an LLM large language model and an orthopedic implant dynamic inventory database; The orthopedic implant dynamic inventory database mainly stores the inventory quantity of each consumable dynamically, for example, using a MySQL database to dynamically record and update the inventory quantity of each consumable.

[0066] The method of using the LLM large language model for database search and extraction first requires the construction of prompt words, then the prompt words are input into the LLM for learning, and then the LLM is used for automatic database search. 1. Information Extraction ‌Surgery Time‌: Identify and extract the surgery time from orthopedic implant information, which is usually a date or timestamp format.

[0067] ‌Surgery Type‌: Identify and extract the specific type of surgery, such as "hip replacement", "spinal fusion", etc.

[0068] ‌Orthopedic implant information keywords‌: Extract keywords related to orthopedic implants, such as the name, model, manufacturer, etc. of the implant.

[0069] This step can be achieved through natural language processing (NLP) technology, especially named entity recognition (NER) technology, which can effectively identify specific entity information from text.

[0070] 2. Create a search prompt ‌Combined with preset logic‌: According to the preset orthopedic implant database search logic, the extracted surgery time, surgery type and orthopedic implant information keywords are combined to form a database search prompt word. This logic may include specific format requirements, keyword priority sorting, etc.

[0071] ‌Build query statements‌: Based on the extracted information and preset logic, build one or more query statements for retrieving relevant information in the orthopedic implant dynamic inventory database.

[0072] 3. Interact with the Consumables Platform ‌Send query request‌: Send the constructed query prompt word or query statement to the consumables platform. This is usually achieved through a network request, such as an HTTP request.

[0073] Platform processing: After receiving the query request, the consumables platform uses the pre-deployed LLM large language model to parse the request and access the orthopedic implant dynamic inventory database to obtain relevant information.

[0074] ‌Return results‌: The consumables platform returns the query results to the requester, which can include inventory status, available quantity of implants, location information, etc.

[0075] 4. Automating the process To automate the above steps, a software program or script can be developed that automatically performs tasks such as information extraction, forming query prompts, sending requests, and receiving and processing returned results. This program can be integrated into the hospital's information system so that it can be automatically triggered and run when needed.

[0076] Example Process ‌Information Extraction‌: Extract "Operation time: 2023-04-01", "Operation type: knee replacement", and "Orthopedic implant information keywords: artificial knee joint, model XYZ" from the orthopedic surgery records.

[0077] ‌Build a query statement: According to the preset logic, construct a query statement: "Query the inventory status of artificial knee joints (model XYZ) required for knee replacement surgery performed on 2023-04-01".

[0078] ‌Send Request‌: Send the query statement to the consumables platform through a network request.

[0079] ‌Platform processing and return‌: The consumables platform parses the request, accesses the database, and returns information such as "Artificial knee joints (model XYZ) are in sufficient stock and are located on shelf B, floor C, area A of the warehouse."

[0080] Through such a process, automatic database lookup of orthopedic implant information can be achieved, improving the efficiency and accuracy of hospital consumables management.

[0081] S4, the consumables platform inputs the orthopedic implant library query prompt word into the LLM large language model, traverses the orthopedic implant dynamic inventory database through the LLM large language model, and retrieves and determines whether the orthopedic implant inventory in the orthopedic implant dynamic inventory database meets the orthopedic implant information based on the orthopedic implant library query prompt word: If the conditions are met, corresponding sufficient inventory information is generated and fed back to the order receiving system; If not, the corresponding insufficient inventory information is generated and output; LLM will search the database based on the search keywords and logic in the prompt words and output the corresponding search results.

[0082] The consumables platform uses the LLM large language model to query the dynamic inventory database of orthopedic implants, and generates corresponding inventory information based on the query results and feeds it back to the order-taking system as follows: 1. Receive the search prompt: The consumables platform receives orthopedic implant library query prompts sent by other systems (such as surgery scheduling systems or physician workstations).

[0083] 2. Input to the LLM large language model: The consumables platform takes the library query prompt word as input and passes it to the LLM large language model pre-deployed on the platform.

[0084] 3. Traverse the dynamic inventory database of orthopedic implants: The LLM large language model parses the query prompt words and understands key elements such as surgery type, surgery time, orthopedic implant information, etc.

[0085] Based on these elements, the LLM large language model constructs query statements and traverses the orthopedic implant dynamic inventory database to find matching orthopedic implant inventory records.

[0086] 4. Determine the inventory level: After finding a matching orthopedic implant in the database, the LLM large language model checks whether the inventory quantity of the implant meets the requirements in the query prompt.

[0087] This may involve comparing the stock quantity to the quantity required for the surgery, or checking if the stock status is "Available".

[0088] 5. Generate inventory information: If the inventory quantity meets the demand, the LLM large language model will generate a message of sufficient inventory, including the name, model, inventory quantity, available status, etc. of the implant.

[0089] If stock levels do not meet demand, the LLM large language model generates a low-stock message, which may also include suggested alternative implants or a restock reminder.

[0090] 6. Feedback to the order-taking system: The consumables platform feeds back the inventory information (whether sufficient or insufficient) generated by the LLM large language model to the order-taking system.

[0091] The order-taking system can use this information to decide whether to proceed with the surgical arrangement or notify relevant personnel to take appropriate measures (such as ordering more implants, adjusting the surgical plan, etc.).

[0092] Example Process ‌Stock search prompt‌: "Query the inventory status of artificial hip joints (model ABC) required for hip replacement surgery performed on April 10, 2023." ‌LLM Large Language Model Processing‌: The prompt words are parsed to identify the surgery date, surgery type, and implant information.

[0093] Build the query: "SELECT * FROM Orthopedic Implant Inventory WHERE Procedure Type = 'Hip Replacement' AND Implant Model = 'ABC' AND Inventory Status = 'Available'".

[0094] Execute a query to check the stock level.

[0095] ‌Inventory judgment and feedback‌: If the inventory is sufficient, the message is generated: "Artificial hip joints (model ABC) are in sufficient stock for hip replacements on April 10, 2023." If the inventory is insufficient, a message is generated: "Insufficient inventory of artificial hip joints (model ABC) to meet the demand for hip replacements on April 10, 2023. It is recommended to take alternative measures or replenish the stock as soon as possible." ‌The order-taking system receives information‌: Based on the feedback information, the order-taking system can update the surgical schedule or notify relevant personnel accordingly.

[0096] Through such a process, the consumables platform can efficiently use the LLM large language model and the dynamic inventory database of orthopedic implants to query and manage the inventory of orthopedic implants to ensure that the operation can proceed smoothly.

[0097] S5. If the order receiving system receives the sufficient inventory information, it generates a corresponding surgery reservation success notification according to the sufficient inventory information and sends it to the terminal; If the consumables platform receives the insufficient inventory information, a corresponding orthopedic implant replenishment order is generated according to the insufficient inventory information and sent to the supplier backend.

[0098] After checking the warehouse and finding that there is sufficient inventory, the consumables information can be output and bound to the patient information to be operated on recorded in the surgery notice, which will facilitate the operating room circulating nurse to check the patient information and his orthopedic implant list later).

[0099] When the order-taking system receives sufficient inventory information, it will generate a corresponding successful surgery reservation notification based on this information and send it to the terminal immediately. This means that once the consumables platform confirms that the required orthopedic implants are in sufficient stock, the order-taking system can respond quickly, generate and send a successful surgery reservation message, and ensure that the surgery can be arranged smoothly.

[0100] On the contrary, if the consumables platform receives insufficient inventory information, it will generate a corresponding orthopedic implant replenishment order based on this information and send it to the supplier's backend in a timely manner. In this way, the supplier can quickly understand which orthopedic implants are out of stock, so as to replenish them in time and ensure that the inventory level meets the surgical needs.

[0101] In short, the order-taking system intelligently generates and sends corresponding notifications or replenishment orders based on the inventory information provided by the consumables platform to ensure the smooth scheduling of surgery and the effectiveness of inventory management.

[0102] like Figure 3 As shown, on the other hand, a dynamic inventory management system for orthopedic implants based on pre-identification analysis is provided, and the dynamic inventory management system for orthopedic implants based on pre-identification analysis is used to implement the above-mentioned dynamic inventory management method for orthopedic implants based on pre-identification analysis, and the system includes: The terminal is used for the surgeon to issue the surgical notice of bone implantation to the order receiving system in advance by placing an order; An order receiving system is used to receive and identify the orthopedic implant information required on the surgical notification form through the order receiving system, including the operation time, operation type and orthopedic implant list of the orthopedic implant, wherein the orthopedic implant list includes the brand, name, specification model and quantity of the orthopedic implant; and extract the operation time, operation type and keywords of the orthopedic implant information in the orthopedic implant information, and combine the preset orthopedic implant library query logic to form an orthopedic implant library query prompt word and send the consumables platform; The consumables platform is used to input the orthopedic implant library query prompt word into the LLM large language model, traverse the orthopedic implant dynamic inventory database through the LLM large language model, and retrieve and determine whether the orthopedic implant inventory in the orthopedic implant dynamic inventory database meets the orthopedic implant information based on the orthopedic implant library query prompt word: If the conditions are met, corresponding sufficient inventory information is generated and fed back to the order receiving system; If not, the corresponding insufficient inventory information is generated and output; The order receiving system is further configured to: if the inventory sufficient information is received, generate a corresponding surgery reservation success notification according to the inventory sufficient information and send the notification to the terminal; The consumables platform is further used to: if the insufficient inventory information is received, generate a corresponding orthopedic implant replenishment order according to the insufficient inventory information and send it to the supplier backend; The supplier backend is used to respond to the orthopedic implant replenishment order and provide feedback.

[0103] Please understand this system in conjunction with the corresponding interaction steps in the above method, and will not be repeated here.

[0104] Figure 4 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 4 As shown, the electronic device may include the above Figure 3 The dynamic inventory management system of orthopedic implants based on pre-identification analysis is shown. Optionally, the electronic device 410 may include a first processor 2001.

[0105] Optionally, the electronic device 410 may further include a memory 2002 and a transceiver 2003 .

[0106] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0107] Combine the following Figure 4 The components of the electronic device 410 are described in detail: The first processor 2001 is the control center of the electronic device 410, and may be a processor or a general term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or may be application specific integrated circuits (ASICs), or may be one or more integrated circuits configured to implement the embodiments of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (field programmable gate arrays, FPGAs).

[0108] Optionally, the first processor 2001 can execute various functions of the electronic device 410 by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002 .

[0109] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 4 CPU0 and CPU1 are shown in FIG.

[0110] In a specific implementation, as an embodiment, the electronic device 410 may also include multiple processors, such as Figure 4The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0111] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled to be executed by the first processor 2001. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0112] Optionally, the memory 2002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001, or may exist independently and access the first processor 2001 through the interface circuit ( Figure 4 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0113] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0114] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 4 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0115] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently and communicate with the first processor 2001 through the interface circuit ( Figure 4 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0116] It should be noted that Figure 4 The structure of the electronic device 410 shown in the figure does not constitute a limitation on the router, and the actual knowledge structure recognition device may include more or fewer components than those shown in the figure, or combine certain components, or arrange the components differently.

[0117] In addition, the technical effects of the electronic device 410 can refer to the technical effects of the dynamic inventory management method for orthopedic implants based on pre-identification analysis described in the above method embodiment, which will not be repeated here.

[0118] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0119] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0120] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), 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 the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the 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 system. 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 by wired (such as infrared, wireless, microwave, etc.). 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 (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0121] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0122] In the present invention, "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 items 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 multiple.

[0123] It should be understood that in various embodiments of the present invention, 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 invention.

[0124] 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 to be beyond the scope of the present invention.

[0125] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0126] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, 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 the system or unit, which can be electrical, mechanical or other forms.

[0127] 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.

[0128] 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.

[0129] 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 invention, 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, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0130] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A dynamic inventory management method for orthopedic implants based on pre-identification analysis, characterized in that: The method comprises: S1. The surgeon places an order through the terminal and sends the surgical notification form for bone implantation to the order receiving system in advance; S2. Receive and identify the orthopedic implant information required on the surgical notification form through the order receiving system, including the surgical time and surgical type of the orthopedic implant and the orthopedic implant list, wherein the orthopedic implant list includes the brand, name, specification model and quantity of the orthopedic implant; S3, extracting the operation time, the operation type and the keywords of the orthopedic implant information in the orthopedic implant information, and combining the preset orthopedic implant library search logic, forming an orthopedic implant library search prompt word and sending it to the consumables platform; wherein the consumables platform is pre-deployed with an LLM large language model and an orthopedic implant dynamic inventory database; S4, the consumables platform inputs the orthopedic implant library query prompt word into the LLM large language model, traverses the orthopedic implant dynamic inventory database through the LLM large language model, and retrieves and determines whether the orthopedic implant inventory in the orthopedic implant dynamic inventory database meets the orthopedic implant information based on the orthopedic implant library query prompt word: If the conditions are met, corresponding sufficient inventory information is generated and fed back to the order receiving system; If not, the corresponding insufficient inventory information is generated and output; S5. If the order receiving system receives the sufficient inventory information, it generates a corresponding surgery reservation success notification according to the sufficient inventory information and sends it to the terminal; If the consumables platform receives the insufficient inventory information, a corresponding orthopedic implant replenishment order is generated according to the insufficient inventory information and sent to the supplier backend.

2. The method for dynamic inventory management of orthopedic implants based on pre-identification analysis according to claim 1, characterized in that: The construction method of the orthopedic implant dynamic inventory database is as follows: When the orthopedic implant passes through a certain application node, the application node scans the QR code on the orthopedic implant, collects the supply or consumption information of the orthopedic implant including the quantity and time of the corresponding supply or consumption, and uploads it to the consumables platform; On the consumables platform, based on preset implant keywords and supply management logic, corresponding supply management prompt words are constructed and input into the LLM large language model, and the quantity and time information of the supply or consumption of the orthopedic implant is identified and output through the LLM large language model; The quantity and time information of the supply or consumption of the orthopedic implants are saved or updated in a SQL database preset on the consumables platform to obtain the orthopedic implant dynamic inventory database.

3. The method for dynamic inventory management of orthopedic implants based on pre-identification analysis according to claim 2, characterized in that: After the quantity and time information of the supply or consumption of the orthopedic implant is identified and outputted through the LLM large language model, the method further includes: According to the time information, the daily supply or consumption quantity of each orthopedic implant is counted, and a corresponding inventory table is generated.

4. The method for dynamic inventory management of orthopedic implants based on pre-identification analysis according to claim 3, characterized in that: The step of searching based on the orthopedic implant database search prompt word to determine whether the orthopedic implant inventory in the orthopedic implant dynamic inventory database satisfies the orthopedic implant information includes: Retrieve the next day's inventory of the orthopedic implant that meets the operation time and the operation type in the orthopedic implant information from the orthopedic implant dynamic inventory database based on the orthopedic implant search prompt word through the LLM large language model; Determine whether the next day's inventory of the orthopedic implant meets the orthopedic implant information required this time.

5. The method for dynamic inventory management of orthopedic implants based on pre-identification analysis according to claim 1, characterized in that: The step S2, receiving and identifying the orthopedic implant information required on the surgical notification form through the order receiving system, comprises: The order receiving system receives and parses the surgical notification form to obtain attribute information and parsed text content therein; Reading the operation time and operation type of the orthopedic implant from the attribute information; Collecting keyword text features of a number of orthopedic implants, constructing a keyword feature set, and inputting the keyword feature set into the preset LLM large language model, and generating an LLM large language model-A capable of identifying different orthopedic implants through model training; Construct quantitative statistical logic and input the LLM large language model-A, traverse the parsed text content through the LLM large language model-A, identify the orthopedic implants and their quantitative statistical results in the parsed text content, and obtain the required orthopedic implant information.

6. A dynamic inventory management system for orthopedic implants based on pre-identification analysis, wherein the dynamic inventory management system for orthopedic implants based on pre-identification analysis is used to implement the dynamic inventory management method for orthopedic implants based on pre-identification analysis as claimed in any one of claims 1 to 5, characterized in that: The system comprises: The terminal is used for the surgeon to issue the surgical notice of bone implantation to the order receiving system in advance by placing an order; An order receiving system is used to receive and identify the orthopedic implant information required on the surgical notification form, including the operation time, operation type and orthopedic implant list of the orthopedic implant, wherein the orthopedic implant list includes the brand, name, specification model and quantity of the orthopedic implant; and extract the operation time, operation type and keywords of the orthopedic implant information from the orthopedic implant information, and combine the preset orthopedic implant library search logic to form an orthopedic implant library search prompt and send the consumables platform; The consumables platform is used to input the orthopedic implant library query prompt word into the LLM large language model, traverse the orthopedic implant dynamic inventory database through the LLM large language model, and retrieve and determine whether the orthopedic implant inventory in the orthopedic implant dynamic inventory database meets the orthopedic implant information based on the orthopedic implant library query prompt word: If the conditions are met, corresponding sufficient inventory information is generated and fed back to the order receiving system; If not, the corresponding insufficient inventory information is generated and output; The order receiving system is further configured to: if the inventory sufficient information is received, generate a corresponding surgery reservation success notification according to the inventory sufficient information and send the notification to the terminal; The consumables platform is further used to: if the insufficient inventory information is received, generate a corresponding orthopedic implant replenishment order according to the insufficient inventory information and send it to the supplier backend; The supplier backend is used to respond to the orthopedic implant replenishment order and provide feedback.

7. An electronic device, characterized in that: The electronic device comprises: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the method according to any one of claims 1 to 5.

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