Medical residue detection management optimization method and system based on artificial intelligence

Abstract

The invention discloses a medical residue detection management optimization method and system based on artificial intelligence. The method comprises the steps of data collection, data preprocessing, residue positioning classification, residue tracking management and residue detection management. The invention relates to the technical field of medical residue management, in particular to a medical residue detection management optimization method and system based on artificial intelligence. According to the scheme, a comprehensive detection management method combining residue positioning classification and residue tracking management is adopted, positioning classification and tracking management are organically combined, and the state change, the processing process and the circulation path of the medical residues can be tracked in real time, so that the management efficiency and accuracy are greatly improved; carrying out residue positioning and classification by adopting an integrated convolution transformer model combined with a pre-training detection model; and performing residue tracking management by adopting a reinforcement learning method combining an improved gating circulation unit and a graph neural network.

Description

Technical Field

[0001] The present invention relates to the technical field of medical residue management, and specifically to an artificial intelligence-based medical residue detection management optimization method and system. Background Art

[0002] The AI-based medical residue detection and management optimization method and system aims to efficiently and accurately detect and manage medical waste through advanced AI technology. Through deep learning, computer vision and data analysis technology, the system can monitor residues in the medical process, such as surgical instruments, drug containers, etc. in real time to ensure that they can be identified and properly handled in a timely manner. By optimizing the detection algorithm, the system can automatically identify potential residues, improve the accuracy and efficiency of medical waste management, reduce the risk of human negligence and omissions, ensure the cleanliness of the hospital, environmental safety, and comply with relevant regulatory requirements. In addition, the system can also optimize the processing process based on historical data and real-time conditions, provide medical institutions with intelligent waste management solutions, reduce operating costs and improve overall service quality.

[0003] However, in the existing medical residue detection management optimization, there are technical problems such as insufficient real-time monitoring and tracking capabilities, low dynamic decision-making efficiency, and unclear cross-regional flow information of various types of residues; in the existing residue positioning and classification methods, there are technical problems such as low classification accuracy, difficulty in adapting to different environmental changes, and inability to efficiently cope with a variety of medical wastes, and traditional classification methods often rely on a single model and have difficulty maintaining stable performance in a dynamically changing environment; in the existing residue tracking management methods, there are technical problems such as the inability to dynamically adjust treatment strategies, lack of effective cross-regional tracking capabilities, and poor timeliness of decision-making. Existing tracking management systems often find it difficult to cope with complex and changeable actual scenarios, especially in the cross-regional dependence and timing decision-making during the tracking process. There are obvious technical problems of inadequacy. Summary of the invention

[0004] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a medical residue detection management optimization method and system based on artificial intelligence. In view of the technical problems of insufficient real-time monitoring and tracking capabilities, low dynamic decision-making efficiency, and unclear cross-regional flow information of various types of residues in the existing medical residue detection management optimization, this solution creatively adopts a comprehensive detection and management method combining residue positioning classification and residue tracking management, and realizes comprehensive coverage from positioning, classification to tracking management of medical residues. By organically combining positioning classification with tracking management, it is possible to track the state changes, processing processes and flow paths of medical residues in real time, thereby greatly improving management efficiency and accuracy, realizing intelligent and automated management of medical residues, and reducing the risks of manual intervention and omissions; in view of the technical problems of low classification accuracy, difficulty in adapting to different environmental changes, and inability to efficiently cope with various medical wastes in the existing residue positioning and classification methods, and the technical problems that traditional classification methods often rely on a single model and are difficult to maintain stable performance in a dynamically changing environment, this solution creatively adopts an integrated convolutional transformer model combined with a pre-trained detection model to perform residue positioning. Classification, achieving high-precision positioning and classification in complex environments. By integrating pre-trained detection models and convolutional transformer structures, this scheme can effectively combine the spatial information and temporal information of image features to improve the robustness and adaptability of the model, especially in the high variability and uncertainty environment of medical residues. The model can achieve more accurate object positioning and type classification, significantly improving the accuracy of waste identification; In view of the inability to dynamically adjust the processing strategy, lack of effective cross-regional tracking capabilities and poor timeliness of decision-making in the existing residue tracking management methods, the existing tracking management system is often difficult to cope with complex and changeable actual scenarios, especially in the cross-regional dependence and temporal decision-making in the tracking process. There are obvious technical problems. This scheme creatively adopts a reinforcement learning method combining improved gated recurrent units and graph neural networks to carry out residue tracking management, realizing dynamic optimization and precise tracking of the entire process of medical residue processing, and combining reinforcement learning for strategy optimization. This scheme can effectively propagate information between different states and dynamically adjust the tracking strategy according to real-time data, improving the system's autonomous decision-making ability and response speed, and ensuring the efficiency and safety of medical residue management.

[0005] The technical solution adopted by the present invention is as follows: The medical residue detection management optimization method based on artificial intelligence provided by the present invention comprises the following steps:

[0006] Step S1: data collection;

[0007] Step S2: data preprocessing;

[0008] Step S3: residue location and classification;

[0009] Step S4: Residue tracking management;

[0010] Step S5: Residue detection management.

[0011] Further, in step S1, the data collection is used to collect multi-source data related to medical residues, specifically, to collect data of monitoring operating rooms, wards, and waste treatment areas through camera data collection, and to obtain image data of medical equipment and waste container areas in real time through the medical system, to collect temperature, humidity, and air quality environmental data through sensors, and to collect dynamic data of medical residues through tag tracking, so as to obtain the original data set for medical residue detection management;

[0012] The medical residue detection and management raw data set specifically includes camera data, environmental data, and tag tracking dynamic data.

[0013] Further, in step S2, the data preprocessing is used to clean, denoise and format the collected data, specifically performing data preprocessing operations on the original data set of medical residue detection management to obtain an optimized data set of medical residue detection management, and specifically includes the following steps:

[0014] Step S21: data cleaning, specifically, performing data cleaning by removing invalid data and filling data to obtain cleaned and optimized data;

[0015] Step S22: De-noising optimization, specifically, performing image denoising on the image data, performing sensor data denoising on the sensor environment data, and performing signal denoising on the tag tracking data to obtain denoising optimization data;

[0016] Step S23: data standardization, specifically normalizing the resolution and format of the image data, normalizing the sensor data by Z-score, and normalizing the tag tracking data by time alignment to obtain standardized data;

[0017] Step S24: data enhancement, specifically, performing comprehensive data enhancement through image data enhancement and sensor data enhancement to obtain enhanced and optimized data;

[0018] Step S25: data integration, specifically, obtaining integrated optimized data by performing multi-distance data fusion on the enhanced optimized data;

[0019] Step S26: feature engineering optimization, specifically, performing manual feature screening and extraction on the integrated optimization data to obtain a medical residue detection management optimization data set.

[0020] Further, in step S3, the residue location and classification is used to locate and classify medical residues using an artificial intelligence algorithm, identify different types of waste, and mark their locations. Specifically, based on the medical residue detection management optimization data set, an integrated convolution transformer model combined with a pre-trained detection model is used to perform residue location and classification to obtain residue location and classification reference data, which specifically includes the following steps:

[0021] Step S31: constructing a pre-trained detection model, specifically constructing a pre-trained integrated detection model, and adjusting the weight of the target detection model for the integrated detection model in combination with the medical residue detection management optimization data set through transfer learning and pre-trained integration methods, to obtain a pre-trained detection model;

[0022] The integrated detection model specifically refers to an integrated model of YOLOv8 and Faster-R-CNN;

[0023] Step S32: constructing an improved convolution transformer model, specifically constructing a standard convolution neural network and a transformer model, and improving the hierarchy of the convolution transformer model by introducing a bidirectional attention mechanism and a multi-scale feature hierarchical transformation module to obtain an improved convolution transformer model;

[0024] Step S33: constructing an integrated classifier, specifically introducing a comprehensive integration method combining an adaptive integration method and a dynamic weighted integration method, using a standard extreme gradient boosting model as a model classifier, and constructing a process classifier;

[0025] Step S34: residue positioning and classification, specifically, performing model training and model use of residue positioning and classification through the pre-trained detection model, the improved convolution transformer model and the integrated classifier to obtain residue positioning and classification reference data.

[0026] Further, in step S4, the residue tracking management is used to track the entire process of medical residues from generation, processing to destruction, specifically, based on the medical residue detection management optimization data set and the residue positioning classification reference data, a reinforcement learning method combining an improved gated recurrent unit and a graph neural network is used to perform residue tracking management to obtain residue tracking management reference data;

[0027] Step S41: constructing an improved gated recurrent subnet, specifically constructing a standard bidirectional gated recurrent unit, and constructing a long-short-term dependency improvement module after the output layer of the bidirectional gated recurrent unit, wherein the long-short-term dependency improvement module specifically includes a single-layer standard long-short-term memory layer and a single-layer standard gated recurrent unit layer, and by constructing the long-short-term dependency improvement module, the output feature of the gated recurrent subnet is updated to obtain updated time series feature data;

[0028] Step S42: constructing a graph neural subnet, specifically by constructing a medical residue dynamic tracking state graph, and by constructing a standard graph neural subnet, combining the updated time series feature data, performing spatiotemporal feature extraction, and obtaining spatiotemporal feature data;

[0029] Step S43: constructing a reinforcement learning model, specifically, constructing a reinforcement learning model by constructing state space parameters and action space parameters, combining with reward function improvement, and according to the spatiotemporal feature data to obtain reinforcement learning decision output data;

[0030] The reward function improvement specifically includes a medical residue processing efficiency parameter, a medical residue processing safety parameter, and a medical residue utilization rate parameter;

[0031] Step S44: training the residue tracking management model, specifically, performing model training through the improved gated recurrent subnetwork, the graph neural subnetwork and the reinforcement learning model to obtain the residue tracking management model;

[0032] Step S45: residue tracking management, specifically using the residue tracking management model, performing residue tracking management according to the medical residue detection management optimization data set and the residue positioning classification reference data, and obtaining residue tracking management reference data.

[0033] Further, in step S5, the residue detection management is used to continuously monitor and manage medical residues, specifically by reading the image detection information of medical residues based on the residue positioning classification reference data, and reading the tracking detection information of the residues in combination with the residue tracking management reference data. By combining the image detection information and the tracking detection information, comprehensive detection management of medical residues is performed, and a rule-based real-time monitoring system for residues is constructed, which automatically generates alarms and assists management personnel in recording, generating and executing medical residue cleaning tasks, thereby obtaining comprehensive residue detection management reference data.

[0034] The medical residue detection management optimization system based on artificial intelligence provided by the present invention includes a data acquisition module, a data preprocessing module, a residue positioning and classification module, a residue tracking and management module, and a residue detection and management module;

[0035] The data acquisition module is used for data acquisition, and obtains the original data set of medical residue detection management through data acquisition, and sends the original data set of medical residue detection management to the data preprocessing module;

[0036] The data preprocessing module is used for data preprocessing, and obtains a medical residue detection management optimization data set through data preprocessing, and sends the medical residue detection management optimization data set to the residue positioning classification module and the residue tracking management module;

[0037] The residue positioning and classification module is used for residue positioning and classification, obtains residue positioning and classification reference data through residue positioning and classification, and sends the residue positioning and classification reference data to the residue tracking management module and the residue detection management module;

[0038] The residue tracking management module is used for residue tracking management, obtains residue tracking management reference data through residue tracking management, and sends the residue tracking management reference data to the residue detection management module;

[0039] The residue detection management module is used for residue detection management, and through the residue detection management, residue comprehensive detection management reference data is obtained.

[0040] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0041] (1) In view of the technical problems of insufficient real-time monitoring and tracking capabilities, low dynamic decision-making efficiency, and unclear cross-regional flow information of various types of residues in the existing medical residue detection and management optimization, this solution creatively adopts a comprehensive detection and management method that combines residue positioning and classification with residue tracking management, achieving comprehensive coverage from positioning, classification to tracking management of medical residues. By organically combining positioning and classification with tracking management, it is possible to track the status changes, processing processes and flow paths of medical residues in real time, thereby greatly improving management efficiency and accuracy, realizing intelligent and automated management of medical residues, and reducing the risks of manual intervention and omissions;

[0042] (2) In view of the technical problems of low classification accuracy, difficulty in adapting to different environmental changes, and inability to efficiently deal with various medical wastes in existing residue positioning and classification methods, as well as the technical problems that traditional classification methods often rely on a single model and are difficult to maintain stable performance in a dynamically changing environment, this solution creatively adopts an integrated convolutional transformer model combined with a pre-trained detection model to perform residue positioning and classification, achieving high-precision positioning and classification in complex environments. By integrating the pre-trained detection model and the convolutional transformer structure, this solution can effectively combine the spatial information and temporal information of image features to improve the robustness and adaptability of the model. In particular, in an environment with high variability and uncertainty of medical residues, the model can achieve more accurate object positioning and type classification, significantly improving the accuracy of waste identification;

[0043] (3) In view of the problems in the existing residue tracking and management methods, such as the inability to dynamically adjust the processing strategy, the lack of effective cross-regional tracking capabilities and the poor timeliness of decision-making, the existing tracking and management systems are often difficult to cope with complex and changeable actual scenarios, especially in the cross-regional dependency and timing decision-making during the tracking process. This solution creatively adopts a reinforcement learning method that combines an improved gated recurrent unit and a graph neural network to carry out residue tracking management, realizing dynamic optimization and precise tracking of the entire process of medical residue processing, and combining reinforcement learning for strategy optimization. This solution can effectively transmit information between different states and dynamically adjust the tracking strategy according to real-time data, thereby improving the system's autonomous decision-making ability and response speed, and ensuring the efficiency and safety of medical residue management. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic diagram of the process of the medical residue detection management optimization method based on artificial intelligence provided by the present invention;

[0045] Figure 2 A schematic diagram of the medical residue detection management optimization system based on artificial intelligence provided by the present invention;

[0046] Figure 3 This is a schematic diagram of the process of data preprocessing in step S2;

[0047] Figure 4 A schematic diagram of the process for locating and classifying residues in step S3;

[0048] Figure 5 This is a schematic diagram of the process of residue tracking management in step S4.

[0049] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0051] In the description of the present invention, it should be understood that terms such as “upper”, “lower”, “front”, “back”, “left”, “right”, “top”, “bottom”, “inside” and “outside” indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0052] Example 1, see Figure 1 The present invention provides an artificial intelligence-based medical residue detection management optimization method, which comprises the following steps:

[0053] Step S1: data collection;

[0054] Step S2: data preprocessing;

[0055] Step S3: residue location and classification;

[0056] Step S4: Residue tracking management;

[0057] Step S5: Residue detection management.

[0058] By executing the above operations, in view of the technical problems in the existing medical residue detection and management optimization, such as insufficient real-time monitoring and tracking capabilities, low dynamic decision-making efficiency, and unclear cross-regional flow information of various types of residues, this solution creatively adopts a comprehensive detection and management method that combines residue positioning and classification with residue tracking management, and achieves comprehensive coverage from positioning, classification to tracking management of medical residues. By organically combining positioning and classification with tracking management, it can track the status changes, processing procedures and flow paths of medical residues in real time, thereby greatly improving management efficiency and accuracy, realizing intelligent and automated management of medical residues, and reducing the risks of manual intervention and omissions.

[0059] Example 2, see Figure 1 and Figure 2 In step S1, the data collection is used to collect multi-source data related to medical residues, specifically, to collect data from monitoring operating rooms, wards, and waste treatment areas through camera data collection, and to obtain image data of medical equipment and waste container areas in real time through the medical system, to collect temperature, humidity, and air quality environmental data through sensors, and to collect dynamic data of medical residues through tag tracking to obtain the original data set for medical residue detection management;

[0060] The medical residue detection and management raw data set specifically includes camera data, environmental data, and tag tracking dynamic data.

[0061] Example 3, see Figure 1 , Figure 2 and Figure 3 This embodiment is based on the above embodiment. In step S2, the data preprocessing is used to clean, denoise and format the collected data. Specifically, the data preprocessing operation is performed on the original data set of medical residue detection management to obtain an optimized data set of medical residue detection management. Specifically, the following steps are included:

[0062] Step S21: data cleaning, specifically, performing data cleaning by removing invalid data and filling data to obtain cleaned and optimized data;

[0063] Step S22: De-noising optimization, specifically, performing image denoising on the image data, performing sensor data denoising on the sensor environment data, and performing signal denoising on the tag tracking data to obtain denoising optimization data;

[0064] Step S23: data standardization, specifically normalizing the resolution and format of the image data, normalizing the sensor data by Z-score, and normalizing the tag tracking data by time alignment to obtain standardized data;

[0065] Step S24: data enhancement, specifically, performing comprehensive data enhancement through image data enhancement and sensor data enhancement to obtain enhanced and optimized data;

[0066] Step S25: data integration, specifically, obtaining integrated optimized data by performing multi-distance data fusion on the enhanced optimized data;

[0067] Step S26: feature engineering optimization, specifically, performing manual feature screening and extraction on the integrated optimization data to obtain a medical residue detection management optimization data set.

[0068] Example 4, see Figure 1 , Figure 2 and Figure 4 This embodiment is based on the above embodiment. In step S3, the residue positioning and classification is used to locate and classify medical residues using an artificial intelligence algorithm, identify different types of waste, and mark their locations. Specifically, based on the medical residue detection management optimization data set, an integrated convolution transformer model combined with a pre-trained detection model is used to perform residue positioning and classification to obtain residue positioning and classification reference data, which specifically includes the following steps:

[0069] Step S31: constructing a pre-trained detection model, specifically constructing a pre-trained integrated detection model, and adjusting the weight of the target detection model for the integrated detection model in combination with the medical residue detection management optimization data set through transfer learning and pre-trained integration methods, to obtain a pre-trained detection model;

[0070] The integrated detection model specifically refers to an integrated model of YOLOv8 and Faster-R-CNN;

[0071] Step S32: constructing an improved convolution transformer model, specifically constructing a standard convolution neural network and a transformer model, and improving the hierarchy of the convolution transformer model by introducing a bidirectional attention mechanism and a multi-scale feature hierarchical transformation module to obtain an improved convolution transformer model;

[0072] The bidirectional attention mechanism is used to simultaneously focus on local and global features of the image through information propagation in two directions;

[0073] The multi-scale feature hierarchical transformation module is used to optimize the classification and positioning of multi-scale residues by combining local perception and global perception;

[0074] The calculation formula for the hierarchical improvement of the convolution transformer model is:

[0075] ;

[0076] In the formula, F TBF is the feature output of the improved convolution transformer model, MSTF(·) is the multi-scale feature level transformation module operation function, Bi-Att(·) is the bidirectional attention mechanism operation function, Transformer(·) is the transformer model operation function, and F Conv is the feature output of a standard convolutional neural network;

[0077] Step S33: constructing an integrated classifier, specifically introducing a comprehensive integration method combining an adaptive integration method and a dynamic weighted integration method, using a standard extreme gradient boosting model as a model classifier, and constructing a process classifier;

[0078] The comprehensive integration method constructs adaptive weights for dynamic integration of model outputs, and the calculation formula is:

[0079] ;

[0080] Where P F is the comprehensive integrated output of the process classifier, k is the total number of classifiers, i is the classifier index, Per(·) is the classifier performance evaluation function, M i is the symbolic representation of the i-th classifier model, P i is the classification result of the i-th classifier model;

[0081] Step S34: residue positioning and classification, specifically, performing model training and model use of residue positioning and classification through the pre-trained detection model, the improved convolution transformer model and the integrated classifier to obtain residue positioning and classification reference data.

[0082] By performing the above operations, in view of the technical problems of low classification accuracy, difficulty in adapting to different environmental changes, and inability to efficiently cope with various medical wastes in the existing residue positioning and classification methods, and the technical problems that traditional classification methods often rely on a single model and are difficult to maintain stable performance in a dynamically changing environment, this scheme creatively adopts an integrated convolutional transformer model combined with a pre-trained detection model to perform residue positioning and classification, and achieves high-precision positioning and classification in complex environments. By integrating the pre-trained detection model and the convolutional transformer structure, this scheme can effectively combine the spatial information and temporal information of image features to improve the robustness and adaptability of the model, especially in the high variability and uncertainty environment of medical residues, the model can achieve more accurate object positioning and type classification, and significantly improve the accuracy of waste identification.

[0083] Example 5, see Figure 1 , Figure 2 and Figure 5 This embodiment is based on the above embodiment. In step S4, the residue tracking management is used to track the entire process of medical residues from generation, processing to destruction. Specifically, based on the medical residue detection management optimization data set and the residue positioning classification reference data, a reinforcement learning method combining an improved gated recurrent unit and a graph neural network is used to perform residue tracking management to obtain residue tracking management reference data;

[0084] Step S41: constructing an improved gated recurrent subnet, specifically constructing a standard bidirectional gated recurrent unit, and constructing a long-short-term dependency improvement module after the output layer of the bidirectional gated recurrent unit, wherein the long-short-term dependency improvement module specifically includes a single-layer standard long-short-term memory layer and a single-layer standard gated recurrent unit layer, and by constructing the long-short-term dependency improvement module, the output feature of the gated recurrent subnet is updated to obtain updated time series feature data;

[0085] The calculation formula for updating the timing characteristic data is:

[0086] ;

[0087] In the formula, It is the updated time series feature data after long-term and short-term dependency improvement, a t is the update time sequence weight, BiGRU(·) is the bidirectional gated recurrent unit operation function, x t is the raw data input, used to represent the medical residue detection management optimization dataset and the residue location classification reference data, h t-1 is the output timing characteristic data of the standard bidirectional gated recurrent unit at the previous moment, h tis the output time series feature data of the standard bidirectional gated recurrent unit at time t, t is the time index, f(x t ) is the output feature data of the long-term and short-term dependency improvement module;

[0088] Step S42: constructing a graph neural subnet, specifically by constructing a medical residue dynamic tracking state graph, and by constructing a standard graph neural subnet, combining the updated time series feature data, performing spatiotemporal feature extraction, and obtaining spatiotemporal feature data;

[0089] Step S43: constructing a reinforcement learning model, specifically, constructing a reinforcement learning model by constructing state space parameters and action space parameters, combining with reward function improvement, and according to the spatiotemporal feature data to obtain reinforcement learning decision output data;

[0090] The reward function improvement specifically includes a medical residue processing efficiency parameter, a medical residue processing safety parameter, and a medical residue utilization rate parameter;

[0091] The improved calculation formula of the reward function is:

[0092] ;

[0093] In the formula, R t is the improved reward function, c1 is the medical residue treatment efficiency weight, Efficiency(t) is the medical residue treatment efficiency parameter, c2 is the medical residue treatment safety weight, Risk(t) is the medical residue treatment safety parameter, c3 is the medical residue utilization weight, and ResourceUtilization(t) is the medical residue utilization parameter;

[0094] Step S44: training the residue tracking management model, specifically, performing model training through the improved gated recurrent subnetwork, the graph neural subnetwork and the reinforcement learning model to obtain the residue tracking management model;

[0095] Step S45: residue tracking management, specifically using the residue tracking management model, performing residue tracking management according to the medical residue detection management optimization data set and the residue positioning classification reference data, and obtaining residue tracking management reference data.

[0096] By performing the above operations, the existing residue tracking management methods have the problems of being unable to dynamically adjust processing strategies, lacking effective cross-regional tracking capabilities, and poor timeliness of decision-making. Existing tracking management systems are often difficult to cope with complex and changeable actual scenarios, especially in the cross-regional dependencies and timing decisions during the tracking process. There are obvious technical problems of insufficient technical issues. This solution creatively adopts a reinforcement learning method that combines improved gated recurrent units and graph neural networks to carry out residue tracking management, realizes dynamic optimization and precise tracking of the entire process of medical residue processing, and combines reinforcement learning for strategy optimization. This solution can effectively transmit information between different states and dynamically adjust tracking strategies according to real-time data, thereby improving the system's autonomous decision-making ability and response speed, and ensuring the efficiency and safety of medical residue management.

[0097] Example 6, see Figure 1 and Figure 2 This embodiment is based on the above embodiment. In step S5, the residue detection management is used to continuously monitor and manage medical residues. Specifically, the image detection information of the medical residues is read according to the residue positioning classification reference data, and the tracking detection information of the residues is read in combination with the residue tracking management reference data. By combining the image detection information and the tracking detection information, comprehensive detection management of medical residues is performed, and a rule-based real-time monitoring system for residues is constructed. Alarms are automatically generated and management personnel are assisted to record, generate and execute medical residue cleaning tasks, and comprehensive residue detection management reference data is obtained.

[0098] Embodiment 7, see Figure 1 and Figure 2 , this embodiment is based on the above embodiment, and the medical residue detection management optimization system based on artificial intelligence provided by the present invention includes a data acquisition module, a data preprocessing module, a residue positioning and classification module, a residue tracking management module and a residue detection management module;

[0099] The data acquisition module is used for data acquisition, and obtains the original data set of medical residue detection management through data acquisition, and sends the original data set of medical residue detection management to the data preprocessing module;

[0100] The data preprocessing module is used for data preprocessing, and obtains a medical residue detection management optimization data set through data preprocessing, and sends the medical residue detection management optimization data set to the residue positioning classification module and the residue tracking management module;

[0101] The residue positioning and classification module is used for residue positioning and classification, obtains residue positioning and classification reference data through residue positioning and classification, and sends the residue positioning and classification reference data to the residue tracking management module and the residue detection management module;

[0102] The residue tracking management module is used for residue tracking management, obtains residue tracking management reference data through residue tracking management, and sends the residue tracking management reference data to the residue detection management module;

[0103] The residue detection management module is used for residue detection management, and through the residue detection management, residue comprehensive detection management reference data is obtained.

[0104] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0105] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

[0106] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. The medical residue detection management optimization method based on artificial intelligence is characterized by: The method comprises the following steps: Step S1: data collection, obtaining the original data set of medical residue detection management; Step S2: data preprocessing to obtain a medical residue detection management optimization data set; Step S3: residue location and classification, used to locate and classify medical residues using artificial intelligence algorithms, identify different types of waste, and mark their locations. Specifically, based on the medical residue detection management optimization data set, an integrated convolution transformer model combined with a pre-trained detection model is used to perform residue location and classification to obtain residue location and classification reference data, specifically including the following steps: Step S31: construct a pre-trained detection model; Step S32: construct an improved convolution transformer model; Step S33: construct an integrated classifier; Step S34: residue location and classification; The calculation formula of the improved convolution transformer model is: ； In the formula, F TBF is the feature output of the improved convolution transformer model, MSTF(·) is the multi-scale feature level transformation module operation function, Bi-Att(·) is the bidirectional attention mechanism operation function, Transformer(·) is the transformer model operation function, and F Conv is the feature output of a standard convolutional neural network; Step S4: residue tracking management, used to track the entire process of medical residues from generation, processing to destruction, specifically, based on the medical residue detection management optimization data set and the residue positioning classification reference data, a reinforcement learning method combining an improved gated recurrent unit and a graph neural network is used to perform residue tracking management to obtain residue tracking management reference data; Step S5: Residue detection management, obtaining residue comprehensive detection management reference data.

2. The medical residue detection management optimization method based on artificial intelligence according to claim 1 is characterized in that: In step S1, the data collection is used to collect multi-source data related to medical residues, specifically, to monitor the data collection of operating rooms, wards, and waste treatment areas through camera data collection, and to obtain real-time image data of medical equipment and waste container areas through the medical system. Through sensors, temperature, humidity and air quality environmental data are collected, and through tag tracking, medical residue dynamic data is collected to obtain the original data set for medical residue detection management.

3. The method for optimizing medical residue detection management based on artificial intelligence according to claim 2 is characterized in that: In step S2, the data preprocessing is used to clean, denoise and format the collected data, specifically, to perform data preprocessing operations on the original data set of medical residue detection management to obtain an optimized data set of medical residue detection management.

4. The method for optimizing medical residue detection management based on artificial intelligence according to claim 3 is characterized in that: In step S3, the residue location and classification is used to locate and classify medical residues using an artificial intelligence algorithm, identify different types of waste, and mark their locations. Specifically, based on the medical residue detection management optimization data set, an integrated convolution transformer model combined with a pre-trained detection model is used to perform residue location and classification to obtain residue location and classification reference data, which specifically includes the following steps: Step S31: constructing a pre-trained detection model, specifically constructing a pre-trained integrated detection model, and adjusting the weight of the target detection model for the integrated detection model in combination with the medical residue detection management optimization data set through transfer learning and pre-trained integration methods, to obtain a pre-trained detection model; The integrated detection model specifically refers to an integrated model of YOLOv8 and Faster-R-CNN; Step S32: constructing an improved convolution transformer model, specifically constructing a standard convolution neural network and a transformer model, and improving the hierarchy of the convolution transformer model by introducing a bidirectional attention mechanism and a multi-scale feature hierarchical transformation module to obtain an improved convolution transformer model; Step S33: constructing an integrated classifier, specifically introducing a comprehensive integration method combining an adaptive integration method and a dynamic weighted integration method, using a standard extreme gradient boosting model as a model classifier, and constructing a process classifier; Step S34: residue positioning and classification, specifically, performing model training and model use of residue positioning and classification through the pre-trained detection model, the improved convolution transformer model and the integrated classifier to obtain residue positioning and classification reference data.

5. The method for optimizing medical residue detection management based on artificial intelligence according to claim 4 is characterized in that: In step S4, the residue tracking management is used to track the entire process of medical residues from generation, processing to destruction. Specifically, based on the medical residue detection management optimization data set and the residue positioning classification reference data, a reinforcement learning method combining an improved gated recurrent unit and a graph neural network is used to perform residue tracking management to obtain residue tracking management reference data; Step S41: constructing an improved gated recurrent subnet, specifically constructing a standard bidirectional gated recurrent unit, and constructing a long-short-term dependency improvement module after the output layer of the bidirectional gated recurrent unit, wherein the long-short-term dependency improvement module specifically includes a single-layer standard long-short-term memory layer and a single-layer standard gated recurrent unit layer, and by constructing the long-short-term dependency improvement module, the output feature of the gated recurrent subnet is updated to obtain updated time series feature data; The calculation formula for updating the timing characteristic data is: ； In the formula, It is the updated time series feature data after long-term and short-term dependency improvement, a t is the update time sequence weight, BiGRU(·) is the bidirectional gated recurrent unit operation function, x t is the raw data input, used to represent the medical residue detection management optimization dataset and the residue location classification reference data, h t-1 is the output timing characteristic data of the standard bidirectional gated recurrent unit at the previous moment, h t is the output time series feature data of the standard bidirectional gated recurrent unit at time t, t is the time index, f(x t ) is the output feature data of the long-term and short-term dependency improvement module; Step S42: constructing a graph neural subnet, specifically by constructing a medical residue dynamic tracking state graph, and by constructing a standard graph neural subnet, combining the updated time series feature data, performing spatiotemporal feature extraction, and obtaining spatiotemporal feature data; Step S43: constructing a reinforcement learning model, specifically, constructing a reinforcement learning model by constructing state space parameters and action space parameters, combining with reward function improvement, and according to the spatiotemporal feature data to obtain reinforcement learning decision output data; The reward function improvement specifically includes a medical residue processing efficiency parameter, a medical residue processing safety parameter, and a medical residue utilization rate parameter; Step S44: training the residue tracking management model, specifically, performing model training through the improved gated recurrent subnetwork, the graph neural subnetwork and the reinforcement learning model to obtain the residue tracking management model; Step S45: residue tracking management, specifically using the residue tracking management model, performing residue tracking management according to the medical residue detection management optimization data set and the residue positioning classification reference data, and obtaining residue tracking management reference data.

6. The method for optimizing medical residue detection management based on artificial intelligence according to claim 5 is characterized in that: In step S5, the residue detection management is used to continuously monitor and manage medical residues, specifically by reading the image detection information of medical residues based on the residue positioning classification reference data, and reading the tracking detection information of the residues in combination with the residue tracking management reference data. By combining the image detection information and the tracking detection information, comprehensive detection management of medical residues is performed, and a rule-based real-time monitoring system for residues is constructed, which automatically generates alarms and assists management personnel in recording, generating and executing medical residue cleaning tasks, thereby obtaining comprehensive residue detection management reference data.

7. A medical residue detection management optimization system based on artificial intelligence, used to implement the medical residue detection management optimization method based on artificial intelligence as described in any one of claims 1 to 6, characterized in that: It includes a data acquisition module, a data preprocessing module, a residue positioning and classification module, a residue tracking and management module, and a residue detection and management module.

8. The medical residue detection management optimization system based on artificial intelligence according to claim 7 is characterized by: The data acquisition module is used for data acquisition, and obtains the original data set of medical residue detection management through data acquisition, and sends the original data set of medical residue detection management to the data preprocessing module; The data preprocessing module is used for data preprocessing, and obtains a medical residue detection management optimization data set through data preprocessing, and sends the medical residue detection management optimization data set to the residue positioning classification module and the residue tracking management module; The residue positioning and classification module is used for residue positioning and classification, obtains residue positioning and classification reference data through residue positioning and classification, and sends the residue positioning and classification reference data to the residue tracking management module and the residue detection management module; The residue tracking management module is used for residue tracking management, obtains residue tracking management reference data through residue tracking management, and sends the residue tracking management reference data to the residue detection management module; The residue detection management module is used for residue detection management, and through the residue detection management, residue comprehensive detection management reference data is obtained.

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