Assembly auxiliary system for disinfection supply center

Abstract

The invention provides an assembly auxiliary system for a disinfection supply center, and relates to the technical field of medical informationization, and the assembly auxiliary system comprises an image collection module which is used for automatically collecting an instrument image after an operator places a to-be-assembled instrument on an instrument placement platform; the image recognition module is used for analyzing the images collected by the image collection module and recognizing the names, the number and the specifications of the instruments; the assembly engine module is used for matching and generating an optimal assembly scheme based on standard assembly schemes of various diagnosis and treatment instruments stored in a preset database and the recognition result of the image recognition module; and the intelligent prompt module is used for providing visual assembly guidance for operators according to the optimal assembly scheme. When the auxiliary system is applied in practice, the problem of low efficiency of a traditional assembly mode can be effectively solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical informatization, in particular to a kind of for disinfection supply center's group configuration auxiliary system. BACKGROUND

[0002] Disinfection supply center is the department in hospital that bears all reusable medical instruments, instruments and supplies cleaning, disinfection, sterilization and sterile supplies. It is responsible for the cleaning, disinfection, packaging, sterilization and supply of all reusable medical instruments. In the daily work of disinfection supply center, as shown in the figure, generally involves instrument recycling, splitting, mechanical cleaning / hand washing, inspection and group configuration after cleaning, packaging, sterilization and other links. Figure 1

[0003] In the process of realizing the present application, the inventor found that in the inspection and group configuration link in the actual work, due to the diversity of instruments involved, the group configuration mode in the actual work process is still mainly manual, in general, this mode can meet the requirements of relevant technical operation specifications of disinfection supply center and the supply demand of instruments in hospital. But in essence, the efficiency of this mode depends on the individual work ability and working state of the operator, when the working staff state is not good or the supply demand increases suddenly and needs to increase new operators temporarily, it is easy to occur that due to low group configuration efficiency, the actual demand cannot be met effectively. Therefore, how to effectively improve the efficiency of group configuration operation and reduce the dependence on manual experience in the above-mentioned scene has become a technical problem to be solved. SUMMARY

[0004] In order to at least overcome the problems existing in the related art, the present application proposes a kind of group configuration auxiliary system for disinfection supply center, based on image recognition technology, the instrument to be inspected and group configuration is identified, and the operation personnel is provided with the prompt information of group configuration, to facilitate improving the actual group configuration efficiency.

[0005] The present application provides a kind of group configuration auxiliary system for disinfection supply center, the group configuration auxiliary system includes: Image acquisition module, for after the operation personnel places the instrument to be group configured on instrument placement platform, automatically acquires instrument image; Image recognition module, for analyzing the image collected by the image acquisition module, identifying the name, quantity and specification of instrument; Group configuration engine module, for matching and generating the optimal group configuration scheme based on the standard group configuration scheme of various medical instruments stored in the preset database and the identification result of the image recognition module; Intelligent prompting module, for providing visual group configuration guidance to the operator according to the optimal group configuration scheme. ​

[0006] In one possible implementation, the instrument placement platform is set on the assembly workbench and is integrated with a weight sensing unit for acquiring the weight information of the instruments to be assembled. The assembly assistance system also includes a perception fusion processing module, which is used to receive and fuse the recognition results of the image recognition module with the weight information obtained by the weight sensing unit, and to comprehensively determine and verify the name, quantity and specifications of the equipment.

[0007] In one possible implementation, the perception fusion module is configured to perform the following determination and verification process: Based on the preliminary determination of the instrument combination by image recognition, the corresponding standard total weight is retrieved from the preset database; The actual total weight measured by the weight sensing unit is compared with the standard total weight; When the weight difference is within the first preset threshold range, the image recognition result is confirmed; When the weight difference exceeds the first preset threshold range but does not exceed the second preset threshold, the image re-recognition process is triggered. When the weight difference exceeds the second preset threshold, an alarm prompt indicating a significant difference is generated and output through the intelligent prompt module to request manual review.

[0008] In one possible implementation, the image acquisition module includes a closed or semi-closed shooting chamber integrated into the assembly workbench, the shooting chamber being equipped with: Multiple cameras positioned at different angles; A shadowless light source system with adjustable brightness and color temperature; An image acquisition terminal is used to control the camera and the shadowless light source system, and to temporarily store the acquired instrument images.

[0009] In one possible implementation, the preset database also contains multiple combination variant templates adapted to different departments or surgical types; The combination engine module is also configured to: receive surgical notifications or medical orders from external information systems, and automatically match and load corresponding combination variant templates based on key fields in the surgical notifications or medical orders, thereby adjusting the initially matched standard combination scheme to generate the optimal combination scheme.

[0010] In one possible implementation, the intelligent prompting module is further configured as follows: While providing visual assembly guidance, it also provides a human-computer interaction interface that allows operators to fine-tune the current assembly plan; The assembly engine module is also used to record the user's fine-tuning operations, and after obtaining authorization, stores the fine-tuning results that conform to the specifications as new assembly variant templates into the preset database.

[0011] In one possible implementation, an anomaly detection and verification module is also included. The anomaly detection and verification module is used to: drive the image acquisition module and the image recognition module to continuously acquire and recognize images during or after the assembly process, and compare the identified instruments with the currently activated assembly variant template. When an instrument is detected outside the template, the number of instruments exceeds the limit, or a key instrument is missing, an alarm is issued to the operator through the intelligent prompt module.

[0012] In one possible implementation, the intelligent prompting module provides visual assembly guidance via an augmented reality device.

[0013] In one possible implementation, the image recognition module is further configured to recognize the status information of the instrument, the status information including one or more of the following: residues, rust spots, and damage status information on the instrument surface and at the joints and teeth of the instrument. The intelligent prompting module is also used to output prompts for re-cleaning, repair, or scrapping in the assembly guidance based on the status information.

[0014] One possible implementation further includes an operation recording and traceability module, which is used for: The system automatically records process data for each assembly operation, including operator identification, instrument pack identification, assembly time, and system interaction records. The process data is linked to subsequent sterilization batches and distribution department information to form a complete traceability chain.

[0015] The assembly assistance system provided in this application, designed for the actual scenarios of a sterilization supply center, integrates image recognition technology and pre-set assembly schemes to provide intelligent guidance for assembly, thereby improving the efficiency and accuracy of the assembly process in sterilization supply centers. Specifically, the image acquisition module automatically acquires images of medical instruments, and the image recognition module analyzes and identifies instrument information, providing data support for subsequent steps. The assembly engine module automatically generates the optimal assembly scheme based on standard assembly schemes in a pre-set database and actual recognition results, reducing reliance on personal experience. The intelligent prompt module provides assembly guidance in a visual manner, simplifying the operation process and enabling even beginners to quickly get started. In practical application, this system effectively solves the problems of low efficiency and error-proneness in traditional assembly methods. Attached Figure Description

[0016] Figure 1A schematic diagram illustrating the various steps in the daily operations of a disinfection supply center; Figure 2 This application provides a schematic diagram of the system configuration of an assembly auxiliary system for a disinfection supply center, as an embodiment of the present application. Figure 3 This application provides a schematic diagram of the system configuration of an assembly auxiliary system for a disinfection supply center, as an embodiment of the present application. Figure 4 This is a schematic flowchart illustrating the determination and verification process in a dispensing auxiliary system for a disinfection supply center, provided as an embodiment of this application. Detailed Implementation

[0017] To make the purpose, technical solution and advantages of this application clearer, the technical solution of this application will be described in detail below.

[0018] As described in the background section, the Central Sterile Supply Department (CSSD) is a department within a hospital responsible for cleaning, disinfecting, sterilizing, and supplying sterile medical devices, instruments, and supplies to all departments. It is responsible for the cleaning, disinfection, packaging, sterilization, and supply of all reusable medical devices.

[0019] In developing this invention, the inventors discovered that in existing practical work, the assembly and maintenance processes are still primarily conducted manually due to the diverse range of instruments involved. Generally, this method meets the technical operational requirements of the central sterile supply center and the hospital's instrument supply needs. However, the efficiency of this method ultimately depends on the individual worker's skill and condition. When staff are not in good condition or when there is a sudden surge in supply demand requiring additional personnel, the assembly efficiency may be low, failing to effectively meet the actual needs.

[0020] Based on this, and in response to the problems and needs in this actual scenario, this application proposes a dispensing assistance system for a disinfection supply center. Based on image recognition technology, the system identifies the instruments to be dispensed and provides dispensing prompts to relevant operators, thereby improving actual dispensing efficiency.

[0021] In one embodiment, such as Figure 2 As shown, the assembly and dispensing auxiliary system for a disinfection supply center includes: Image acquisition module 10 is used to automatically acquire images of the instruments after the operator places the instruments to be assembled on the instrument placement platform; As will be readily understood by those skilled in the art, the instruments to be assembled here refer to reusable medical instruments that have been disassembled into their smallest possible form after the disassembly process and have undergone mechanical and / or manual cleaning. In practice, they are generally placed in a standard stainless steel cleaning basket. During system use, the operator places the stainless steel cleaning basket containing the instruments on the instrument placement platform so that the image acquisition module 10 can acquire images.

[0022] In one specific implementation, the image acquisition module in this embodiment includes a closed or semi-closed imaging chamber integrated into the assembly workbench. The imaging chamber is equipped with multiple high-resolution cameras arranged at different angles, such as a top vertical-view camera and a 45° side-view camera, used to simultaneously or sequentially acquire images of diagnostic and therapeutic instruments placed on the instrument placement platform within the chamber from multiple perspectives, thereby overcoming blind spots caused by instrument stacking, obstruction, or reflection under a single perspective.

[0023] The imaging chamber is also equipped with an adjustable brightness and color temperature shadowless light source system, such as an LED shadowless light source system, to effectively eliminate shadows and highlight interference on the instrument surface through uniform lighting, thereby improving image quality and recognition stability. In addition, the chamber also includes an image acquisition terminal, which can be implemented in practice through an embedded industrial control computer or a dedicated image processing terminal. This terminal is used to control the shooting sequence, resolution, and brightness and color temperature parameters of the shadowless light source system of the aforementioned camera equipment, and to temporarily store the acquired instrument images.

[0024] In actual operation, after the operator puts the equipment to be assembled into the shooting chamber, they can send a trigger signal to the image acquisition terminal through the touch screen, foot switch, etc. The image acquisition terminal then coordinates the light source system and stabilizes the illumination, and then controls each camera to complete image acquisition, and transmits the multi-view image data to the image recognition and processing module for subsequent analysis.

[0025] like Figure 1 As shown, the system also includes an image recognition module 20, which is used to analyze the images acquired by the image acquisition module and identify the name, quantity and specifications of the instruments.

[0026] Based on the development of existing related information technologies, the image recognition module here can be implemented using machine vision and artificial intelligence technologies. For example, as a specific implementation method, the image recognition module 20 can be implemented in the following way: First, a meta-recognition model based on a deep convolutional neural network is pre-built, such as one based on network architectures like ResNet, EfficientNet, or YOLO series. Then, by using a dataset containing labeled images of various disassembled medical devices under different angles, lighting conditions, and various stacking scenarios, the meta-recognition model is trained to obtain a recognition model for actual deployment. The dataset here needs to include labeled images under conditions such as strong light, weak light, and side light. The model trained in this way has strong generalization ability, which is conducive to adapting to the complex environment in the actual operation of the disinfection supply center, such as disordered placement of instruments and severe reflection interference, thus enabling it to effectively recognize instrument images under actual working conditions.

[0027] During system operation, after the image acquisition module obtains multi-view images of the instruments to be assembled, the recognition model deployed in the system performs the following processing steps on the input images in sequence: The system utilizes object detection algorithms to locate and segment individual medical devices in an image, such as an improved YOLOv7 or Mask R-CNN. For each segmented device, visual features such as shape, texture details, and joint structures are extracted. An embedded classifier is then used to determine its name and specifications, such as a thyroid retractor (medium size) or a bent needle holder (14cm). Finally, based on all detected devices, the system automatically counts the number of each type of device, generating a structured recognition result that is then output.

[0028] like Figure 1 As shown, the system also includes a combination engine module 30, which is used to match and generate the optimal combination scheme based on the standard combination schemes of various diagnostic and therapeutic devices stored in the preset database and the recognition results of the image recognition module.

[0029] Specifically, the pre-set database here contains standard assembly schemes for surgical instrument kits commonly used in various clinical departments of the hospital (including configuration list information), such as "laparoscopic cholecystectomy instrument kit" and "orthopedic internal fixation instrument kit". The list of each scheme clearly defines the name, specifications and corresponding quantity of the required instruments.

[0030] The assembly engine module interacts with the image recognition and processing module and the preset database. Based on the standard assembly schemes of various diagnostic and therapeutic devices stored in the preset database and the recognition results output by the image recognition and processing module (including device name, specifications and quantity), it performs intelligent matching and logical comparison to generate the optimal assembly scheme for the current operating scenario.

[0031] like Figure 1As shown, the system also includes an intelligent prompting module 40, which provides operators with visual assembly guidance based on the optimal assembly scheme. For example, the intelligent prompting module 40 can be implemented based on a flat panel display device. The flat panel display device can be installed on one side of the workbench with an adjustable bracket, and the display viewing angle can be adjusted to be at the operator's line of sight, so that the user can view the assembly information and guidance in real time without occupying the actual workbench operating space.

[0032] The assembly assistance system in this application, designed for the practical scenarios of a sterilization supply center, integrates image recognition technology and pre-set assembly schemes to provide intelligent guidance for assembly, thereby improving the efficiency and accuracy of the assembly process in the sterilization supply center. Specifically, the image acquisition module automatically acquires images of medical devices, while the image recognition module analyzes and identifies device information, providing data support for subsequent steps. The assembly engine module automatically generates the optimal assembly scheme based on standard assembly schemes in a pre-set database and actual recognition results, reducing reliance on personal experience. The intelligent prompt module provides assembly guidance in a visual manner, simplifying the operation process and enabling even beginners to quickly get started. In practical application, this system effectively solves the problems of low efficiency and error-proneness associated with traditional assembly methods.

[0033] In real-world scenarios, when multiple adverse conditions such as stacked instruments, obstruction, or glare (which is common with stainless steel instruments) occur simultaneously, relying solely on image recognition can still lead to false identifications. To address this, other perceptual dimensions can be introduced for fusion recognition to reduce false identifications. Considering the characteristics of the scenario in this application, assembly operations are typically performed on a fixed instrument placement platform, and the theoretical total weight of each instrument or combination is clearly defined, providing a physical basis and business rationale for introducing weight perception for cross-validation. Therefore, in some embodiments, this application introduces weight perception information to improve recognition accuracy.

[0034] Specifically, in these embodiments, the instrument placement platform is set on the assembly workbench and is integrated with a weight sensing unit for obtaining the total weight information of the instruments to be assembled. like Figure 2 As shown, the assembly assistance system also includes a perception fusion processing module 50, used to receive and fuse the recognition results of the image recognition module with the weight information acquired by the weight sensing unit (e.g., ...). Figure 3 The “weight information” illustration in the diagram is used to comprehensively determine and verify the name, quantity, and specifications of the instruments in order to improve the accuracy of identification.

[0035] For example, as a specific implementation method, such as Figure 3 and Figure 4 As shown, the perception fusion module 50 is configured to perform the following determination and verification process: Based on the preliminary determination of the instrument combination by image recognition, the corresponding standard total weight is retrieved from the preset database. It is easy to understand that the standard total weight here is the sum of the standard individual weights of each instrument in the instrument combination. The standard individual weight is the nominal weight of a single instrument in a clean, dry state with no missing accessories. It is measured in advance by a high-precision weighing device and stored in the preset database. Next, the actual total weight measured by the weight sensor unit is compared with the standard total weight. It is easy to understand that the tare weight of the equipment basket will be taken into account when making the actual comparison calculation. When the weight difference is within the first preset threshold range, the image recognition result is confirmed; when the weight difference exceeds the first preset threshold range but does not exceed the second preset threshold, the image re-recognition process is triggered; when the weight difference exceeds the second preset threshold, an alarm prompt indicating a significant difference is generated and output through the intelligent prompt module to request manual review.

[0036] In light of the technical scenario described in this application, those skilled in the art will readily understand that the first preset threshold is used to cover reasonable deviations such as normal residual fluctuations after instrument cleaning and drying, manufacturing tolerances, and sensor measurement errors, thus avoiding unnecessary intervention triggered by minor differences in the system. When the weight deviation exceeds the first threshold but does not reach the second threshold, it is determined that there may be a slight omission, and image re-recognition is automatically triggered to achieve self-correction. Once the deviation exceeds the second threshold, it indicates that there is a high-risk anomaly such as missing key instruments or serious mismatch, and an alarm is immediately triggered and manual review is forced. This two-level threshold judgment logic can balance efficiency and recognition accuracy in practical scenarios.

[0037] Based on the embodiments described above, different departments or surgical types often have different configurations for the same type of instrument pack, and relying solely on the standard configuration scheme is insufficient to meet actual needs. Therefore, in some embodiments, a preset database also stores multiple configuration variant templates adapted to different departments or surgical types; the configuration engine module is also configured to: receive surgical notifications or medical orders from external information systems (such as surgical scheduling systems or electronic medical order systems), and based on key fields in the surgical notifications or medical orders (such as surgical name, department, surgeon, etc.), automatically match and load the corresponding configuration variant templates, thereby adjusting the initially matched standard configuration scheme to generate an optimal configuration scheme that better suits clinical practice.

[0038] In practice, offline information transmission also occurs. Therefore, in some embodiments, the intelligent prompting module is configured to provide both visual assembly guidance and a human-computer interaction interface (such as adding or deleting instruments on a touchscreen) that allows operators to fine-tune the current assembly plan. This addresses special surgical needs or temporary adjustments not covered by the standard template, thus achieving a closed-loop business process. The assembly engine module also records the user's fine-tuning operations and, upon authorization, stores the compliant fine-tuning results as new assembly variant templates in a preset database. This approach adapts to the needs of real-world scenarios and, through relevant system function settings, facilitates the accumulation and subsequent reuse of experiential knowledge, promoting continuous system optimization and enabling the system to adapt to diverse clinical scenarios.

[0039] Furthermore, in some embodiments, the system also includes an anomaly detection and verification module (not shown in the figure). This module is used to: drive the image acquisition module and image recognition module to continuously acquire and recognize images during or after assembly, comparing the identified devices with the currently activated assembly variant template; and to issue an alarm to the operator via an intelligent prompting module when devices outside the template, the number of devices exceeds the limit, or critical devices are missing. This configuration can compensate for assembly deviations caused by human error or process disturbances, strengthen closed-loop verification capabilities, and ensure the integrity, compliance, and clinical safety of the final device package.

[0040] Given the increasing maturity of augmented reality (AR) technology, as an optional implementation, the intelligent prompting module can also provide visual assembly guidance through AR devices. Specifically, the AR device acquires real-time images of the operator's field of vision through its built-in image acquisition unit and registers them with a reference image acquired by the system's image acquisition module, achieving spatial tracking and registration of the instrument placement platform and the instruments to be assembled. Subsequently, on the AR device's display unit, the virtual instrument model corresponding to the optimal assembly scheme, assembly sequence animation, or text prompts are precisely overlaid and anchored to the corresponding positions of the real instruments. This approach deeply integrates digital guidance with the physical operating space, achieving "what you see is what you assemble," significantly improving assembly efficiency and accuracy.

[0041] Furthermore, in the technical scenario of this application, the existing inspection process involves visual inspection or using a magnifying glass with a light source to examine each dried instrument, tool, and item. This requires that the surface of the instrument, as well as its joints and teeth, be clean, free of bloodstains, dirt, scale, rust, and other residues, and that it be fully functional and undamaged. Considering the system configuration of the assembly assistance system in this application (such as high-resolution multi-angle image acquisition, controllable shadowless light source, and AI image analysis capabilities), the inspection functions can be integrated to simultaneously complete the intelligent assessment of the instrument's condition during the assembly process. This replaces or partially replaces traditional manual inspection processes such as visual inspection, achieving integrated assembly and quality inspection.

[0042] Based on this, in some embodiments, such as Figure 3 As shown, the image recognition module is also configured to recognize the status information of the instrument, which includes one or more of the following: residues (such as bloodstains, dirt, scale), rust spots, and damage status information on the instrument surface and joints and teeth; the intelligent prompting module is also used to output prompts for re-cleaning, repair, or scrapping in the assembly guide based on the status information. In practice, the system utilizes a high-resolution camera inside the imaging chamber and a controllable shadowless light source to capture images of instruments for assembly and identification while simultaneously imaging detailed surfaces. The image recognition module, based on a trained deep learning model, analyzes for residual water stains, blood stains, scale, or other residues that could indicate substandard cleaning quality, or signs of rust, rust spots, or functional damage (such as chipped cutting edges). If the system detects substandard cleaning quality, it provides a prompt through its intelligent alert module suggesting "re-cleaning." If rust, rust spots, or functional damage are detected, it indicates "requiring repair or scrapping." This reduces or completely eliminates manual visual inspection steps, facilitating simultaneous instrument assembly and condition inspection. It significantly shortens the operation time for inspection and assembly, improves overall workflow efficiency, and reduces the exposure time and frequency of human contact with instruments after cleaning and before packaging. This effectively reduces the risk of secondary contamination or cross-infection, further ensuring the safety and compliance of sterile supplies.

[0043] In some embodiments, to meet the requirements of hospital medical informatization and quality traceability, the system also includes an operation record and traceability module. The operation record and traceability module is used to: automatically record the process data of each assembly operation. The process data includes operator identification (e.g., obtained by the operator logging into the system), instrument pack identification (e.g., obtained by scanning the instrument pack barcode in the subsequent packaging process), assembly time, and system interaction records (implemented based on the system log function); and bind the process data with the subsequent sterilization batch and distribution department information to form a complete traceability chain, so as to achieve overall process traceability, accountability, and risk control.

[0044] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0045] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0046] It should be noted that in the description of this application, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this application, unless otherwise stated, "a plurality of" means at least two.

[0047] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this application pertain.

[0048] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0049] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A dispensing auxiliary system for a disinfection supply center, characterized in that, include: The image acquisition module is used to automatically acquire images of the instruments after the operator places the instruments to be assembled on the instrument placement platform; The image recognition module is used to analyze the images acquired by the image acquisition module and identify the name, quantity, and specifications of the instruments. The assembly engine module is used to match and generate the optimal assembly scheme based on the standard assembly schemes of various diagnostic and therapeutic devices stored in the preset database and the recognition results of the image recognition module. The intelligent prompt module is used to provide operators with visual assembly guidance based on the optimal assembly scheme.

2. The assembly assistance system according to claim 1, wherein, The instrument placement platform is set on the assembly workbench and is integrated with a weight sensing unit to obtain the weight information of the instruments to be assembled. The assembly assistance system also includes a perception fusion processing module, which is used to receive and fuse the recognition results of the image recognition module with the weight information obtained by the weight sensing unit, and to comprehensively determine and verify the name, quantity and specifications of the equipment.

3. The assembly assistance system according to claim 2, wherein, The perception fusion module is configured to perform the following determination and verification process: Based on the preliminary determination of the instrument combination by image recognition, the corresponding standard total weight is retrieved from the preset database; The actual total weight measured by the weight sensing unit is compared with the standard total weight; When the weight difference is within the first preset threshold range, the image recognition result is confirmed; When the weight difference exceeds the first preset threshold range but does not exceed the second preset threshold, the image re-recognition process is triggered. When the weight difference exceeds the second preset threshold, an alarm prompt indicating a significant difference is generated and output through the intelligent prompt module to request manual review.

4. The assembly assistance system according to claim 2, wherein, The image acquisition module includes a closed or semi-closed shooting chamber integrated into the assembly workbench, and the shooting chamber is equipped with: Multiple cameras positioned at different angles; A shadowless light source system with adjustable brightness and color temperature; An image acquisition terminal is used to control the camera and the shadowless light source system, and to temporarily store the acquired instrument images.

5. The assembly assistance system according to claim 1, wherein, The preset database also contains multiple combination variant templates adapted to different departments or surgical types; The combination engine module is also configured to: receive surgical notifications or medical orders from external information systems, and automatically match and load corresponding combination variant templates based on key fields in the surgical notifications or medical orders, thereby adjusting the initially matched standard combination scheme to generate the optimal combination scheme.

6. The assembly assistance system according to claim 5, wherein, The intelligent prompt module is also configured to: While providing visual assembly guidance, it also provides a human-computer interaction interface that allows operators to fine-tune the current assembly plan; The assembly engine module is also used to record the user's fine-tuning operations, and after obtaining authorization, stores the fine-tuning results that conform to the specifications as new assembly variant templates into the preset database.

7. The assembly assistance system according to claim 5, wherein, It also includes an anomaly detection and verification module, which is used to: drive the image acquisition module and the image recognition module to continuously acquire and recognize images during or after the assembly process, and compare the identified instruments with the currently activated assembly variant template; When an instrument is detected outside the template, the number of instruments exceeds the limit, or a key instrument is missing, an alarm is issued to the operator through the intelligent prompt module.

8. The assembly assistance system according to any one of claims 1 to 7, wherein, The intelligent prompting module provides visual assembly guidance through augmented reality devices.

9. The assembly assistance system according to any one of claims 1 to 7, wherein, The image recognition module is also configured to recognize the status information of the instrument, which includes one or more of the following: residues, rust spots, and damage status information on the surface of the instrument and at the joints and teeth of the instrument. The intelligent prompting module is also used to output prompts for re-cleaning, repair, or scrapping in the assembly guidance based on the status information.

10. The assembly assistance system according to any one of claims 1 to 7, wherein, It also includes an operation recording and traceability module, which is used for: The system automatically records process data for each assembly operation, including operator identification, instrument pack identification, assembly time, and system interaction records. The process data is linked to subsequent sterilization batches and distribution department information to form a complete traceability chain.

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