Medical waste information management method and system based on internet of things

Abstract

The application relates to the technical field of medical waste information management, and discloses a medical waste information management method and system based on the Internet of Things. A two-dimensional code is installed for each medical waste container, the identity of the container is accurately tracked, and the inefficiency and errors of manual recording are avoided. A two-dimensional code scanning device inputs medical waste information in real time and stores the information into a database, ensuring the accuracy and real-time performance of the data. A sensor collects the chemical composition, size, shape and other properties of the medical waste, solving the problem of inaccurate feature judgment. Through feature vector calculation and a weighted distance function, the system can intelligently classify medical waste, improving the classification accuracy and automation level. Real-time monitoring and dynamic updating strategy ensure that the medical waste is timely adjusted in a changing environment, reducing the risk. The system optimizes resource allocation according to the complexity and priority of the medical waste, improving the processing efficiency and intelligent management level.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical waste information management, in particular to a medical waste information management method and system based on the Internet of Things. BACKGROUND

[0002] With the emphasis on medical waste management in the medical industry, especially the problem of medical waste disposal, many countries and regions have put forward strict management standards and norms. Medical waste usually includes infectious waste, chemical waste and radioactive waste, etc. If these medical waste cannot be properly disposed of in time, it may pose a great threat to the environment and public health. Therefore, developing an efficient and intelligent medical waste management system, especially through the Internet of Things technology, for real-time monitoring, classification and disposal optimization, has become an important trend in the field of medical waste management.

[0003] Traditional medical waste management methods usually rely on manual operation and traditional identification means, such as paper records, labels, etc. These methods are not only inefficient, but also prone to human error, leading to inaccurate medical waste classification or delayed disposal. Moreover, the traditional method lacks real-time monitoring of the storage environment of medical waste, making it difficult to monitor the status of medical waste in real time, increasing the risk of accidents. With the maturity of the Internet of Things technology, more and more medical waste management systems have begun to integrate intelligent monitoring technology, using sensors to collect data, two-dimensional code technology to achieve intelligent identification, and cloud platforms to store and analyze data. Currently, research and application in the field of medical waste management are mostly focused on the implementation of information technology. Early management methods recorded medical waste information manually, including the type of medical waste, the time of generation, the disposal method, etc. However, this method has the problem of incomplete information recording and inconvenient management. In recent years, with the application of the Internet of Things technology, medical waste management has been significantly improved. Using two-dimensional code, RFID and other technologies for unique identification of medical waste containers ensures that the identity information of each container can be tracked throughout the processing flow, thereby solving the problem of information loss and misoperation in traditional methods. Scanning the two-dimensional code can easily read the basic information of the medical waste container, including the type of medical waste, the source hospital, the generation time, etc., greatly improving the efficiency of medical waste management.

[0004] Therefore, the present application aims to provide a medical waste information management method and system based on the Internet of Things, which is more intelligent, real-time responsive, and has the ability to dynamically adjust and optimize. SUMMARY

[0005] The present application provides a medical waste information management method and system based on the Internet of Things, which solves the problems mentioned in the background.

[0006] The application provides the following technical solutions: a medical waste information management method based on an Internet of Things, comprising:

[0007] A two-dimensional code is installed on each medical waste container, so that each medical waste container has a unique identifier, the unique identifier is recorded as UID, and the first medical waste container is recorded as The following operations are performed on the medical waste container

[0008] The basic information of each medical waste container is recorded in real time through a two-dimensional code scanning device, including the type of medical waste, the source hospital, and the generation time;

[0009] All the basic information is stored in a pre-constructed central database and is associated with the UID of the medical waste container;

[0010] A medical waste category set is set, recorded as , wherein m is the total number of medical waste categories;

[0011] For medical waste X, all feature values of medical waste X are obtained, and a feature vector of medical waste X is set, recorded as , wherein n is the total number of feature values of medical waste X, represents the th feature value of medical waste X;

[0012] The medical waste is all non-illegal medical waste;

[0013] The first data in the medical waste category set is recorded as , and a feature center value set of is set, all feature center values of are obtained, all feature center values of are added to the feature center value set of , and is obtained, wherein is the total number of feature center values of ;

[0014] The weighted distance function is used to calculate the distance between each feature value of medical waste X and all feature center values of , and the weighted distance function is specifically:

[0015] ;

[0016] wherein,​​ For the first Feature weights; for The The central value of each feature;

[0017] Select the category with the smallest distance to the feature vector of medical waste. ,Right now:

[0018] ;

[0019] in, The predicted category is the classification result predicted based on the feature vector of medical waste.

[0020] A real-time monitoring and dynamic updating strategy is adopted for medical waste.

[0021] Optionally, the real-time monitoring and dynamic updating strategy for medical waste specifically includes:

[0022] The environment containing containers for storing medical waste shall be designated as the medical waste storage site;

[0023] Sensors are installed at medical waste storage sites, including temperature sensors, humidity sensors, and gas concentration sensors.

[0024] Sensors are used to collect relevant data in medical waste storage sites in real time, and the data is uploaded to a cloud server through an Internet of Things platform. The relevant data includes temperature, humidity, air quality and concentration of harmful gases.

[0025] Obtain historical data on medical waste storage sites;

[0026] By using a weighted average dynamic update method that combines historical and real-time data, the environmental conditions corresponding to medical waste storage sites are adjusted.

[0027] Early warning of abnormal environmental conditions.

[0028] Optionally, the method of adjusting the environmental status of medical waste storage sites using a weighted average dynamic update method, combining historical and real-time data, specifically includes:

[0029] Set the storage location for the data collected by the sensor as follows: ,in For the number of sensors, For the first Real-time data from each sensor;

[0030] The new state is calculated by weighted average and denoted as follows: Specifically:

[0031] ;

[0032] wherein, is the weight of the th sensor.

[0033] Optionally, the prewarning of the abnormal environment state specifically comprises:

[0034] According to the category and storage environment of the medical waste, an environment data threshold set is set, denoted as , wherein is the threshold of the th sensor data;

[0035] By comparing the real-time collected environment data with the environment data threshold, a weighted difference method is used to determine whether an abnormal state occurs:

[0036] The obtained current environment data is denoted as ;

[0037] The difference calculation formula is: ;

[0038] The total abnormal index is calculated: ;

[0039] , wherein is the weight of the th environment data; is the absolute value of the difference between the th environment data and the environment data threshold; is the total abnormal index;

[0040] A total abnormal index threshold is set, denoted as ;

[0041] If , a prewarning is triggered, and a processing optimization based on data analysis is implemented.

[0042] Optionally, the processing optimization based on data analysis specifically comprises:

[0043] A complexity index of the medical waste container is calculated, specifically:

[0044] ;

[0045] , wherein is the complexity index of the medical waste container ; is the weight of the th feature;

[0046] Computing a processing priority of a medical waste container ;

[0047] ;

[0048] wherein, is a processing priority of a medical waste container ; , , is a weight factor; is a positive number, which is a parameter for controlling exponential decay;

[0049] According to the processing priority of the medical waste container, the medical waste container with a high processing priority is arranged at the front end of the processing queue, and the medical waste container with a low processing priority is arranged at the rear end of the processing queue;

[0050] Resource allocation and adjustment are performed on the medical waste treatment.

[0051] Optionally, the resource allocation and adjustment on the medical waste treatment specifically include:

[0052] A total processing resource amount is obtained, denoted as ;

[0053] According to the processing priority, the resource is allocated, specifically as follows:

[0054] ;

[0055] wherein, is a processing priority of a medical waste container ; is a processing resource amount allocated to a medical waste container ;

[0056] According to the change in the medical waste treatment process, a dynamic adjustment processing strategy is implemented.

[0057] Optionally, the dynamic adjustment processing strategy specifically includes:

[0058] An abnormality index of a medical waste container at a time point is obtained, denoted as ;

[0059] An abnormality index of a medical waste container at a time point is obtained, denoted as ;

[0060] A priority change amount of a medical waste container at a time point is calculated, specifically as follows:

[0061] ;

[0062] wherein, medical waste container at time priority change amount; dynamic adjustment coefficient;

[0063] all medical waste containers at time t are sequentially calculated;

[0064] The priority change amount of the medical waste container is sorted from large to small to obtain a sorting order;

[0065] According to the sorting order, the medical waste in the medical waste container is sequentially processed.

[0066] A medical waste information management system based on the Internet of Things, comprising:

[0067] The scanning module: through a two-dimensional code scanning device, the basic information of each medical waste container is real-time input, including the type of medical waste, the source hospital, the generation time;

[0068] The information storage module: all information is stored in the central database, and is associated with the UID of the medical waste container;

[0069] The calculation module: for executing data calculation.

[0070] The present application has the following beneficial effects:

[0071] 1. By installing a two-dimensional code and assigning a unique identifier, the problem of tracing the identity of medical waste containers is solved. The two-dimensional code technology assigns a unique identifier to each medical waste container, so that the information of each container can be accurately tracked throughout its life cycle. In this way, medical waste containers no longer rely on manual records, avoiding data loss or misoperation, and improving the accuracy and efficiency of information management. Through the two-dimensional code scanning device, the basic information of medical waste containers is recorded in real time and stored in the central database, solving the problem of low efficiency and easy error of traditional manual data recording. All the basic information of medical waste can be recorded and updated to the database at the same time when the container is stored, ensuring the real-time and accuracy of the data, and providing reliable data support for the subsequent medical waste treatment, classification and monitoring. By setting the medical waste category set and calculating the medical waste feature vector, combined with the weighted distance function for intelligent classification, the error problem that may occur in the medical waste classification process is solved. The weighted distance calculation of the feature vector of medical waste and the center value of all categories can accurately classify medical waste according to its specific characteristics, thereby improving the accuracy and automation level of medical waste classification, reducing human intervention and errors. Through real-time monitoring and dynamic updating strategy, the storage environment and state of medical waste are continuously tracked, solving the problem that the storage environment of medical waste may change but cannot be responded in time. Through real-time collection and dynamic update of the storage environment of medical waste and the state of medical waste, the strategy can be adjusted quickly when an exception occurs, ensuring that medical waste is always in a safe and compliant storage condition, reducing the risk of environmental risk. Through the optimization and weighted calculation of the feature vector of medical waste, the overall intelligent level of the medical waste management system is improved, realizing the automation and precision of medical waste management. Through the calculation of the complexity index and priority ranking, resources can be reasonably allocated according to the urgency and characteristics of medical waste, and precise medical waste treatment scheduling is implemented, optimizing the use efficiency of medical waste resources.

[0072] 2、By setting temperature sensors, humidity sensors and gas concentration sensors in medical waste storage sites, real-time environmental data is collected, solving the problem of real-time monitoring of medical waste storage environment. Various sensors can accurately monitor key data such as temperature, humidity and harmful gas concentration in the environment, ensuring the safety of the medical waste storage environment. Real-time collection of these data provides comprehensive data support for subsequent environmental state analysis and dynamic adjustment. Through the Internet of Things platform, data is uploaded to the cloud server, solving the problem of centralized management and analysis of medical waste environmental monitoring data. Internet of Things technology enables sensor-collected data to be uploaded to the cloud server in a timely manner, ensuring centralized storage and management of data, avoiding data silos and information lag, and improving data accessibility and security. In addition, the cloud server can also process large amounts of data and provide necessary computing resources for subsequent analysis and optimization. Through the dynamic updating method of weighted average, combined with historical data and real-time data, the state of the medical waste storage environment is adjusted, solving the problem of delayed response to changes in the medical waste storage environment. The weighted average method can accurately adjust the storage environment state according to the weighted fusion of historical data and real-time data. Historical data provides long-term environmental change trends, while real-time data ensures the accuracy of the current environmental state. Through dynamic updating, the adjustment of the medical waste storage environment becomes more flexible and accurate, avoiding potential safety risks caused by environmental changes. By using the weighted average method to combine historical data and real-time data, the adjustment of the medical waste storage environment state is more scientific and accurate, solving the problem of inconsistent adjustment methods and insignificant adjustment effects when the storage environment fluctuates. By using the weighted average method to assign different weights to different data sources, the adjustment strategy can be more accurately controlled according to the actual changes in the environment, making the medical waste storage conditions more stable and controllable, and reducing the impact of external environmental factors on medical waste treatment.

[0073] 3、By setting the environmental data collected by the sensor and obtaining the current environmental state of medical waste, the problem of medical waste storage environment monitoring data unable to reflect environmental changes in real time is solved. The sensor monitors various environmental parameters of the medical waste storage site in real time, such as temperature, humidity, and gas concentration, etc. These data can accurately reflect the current storage environment state. Through real-time collection of environmental data, the system can understand the status of medical waste storage at any time, thereby providing timely and effective data basis for subsequent dynamic adjustment. The new environmental state is calculated by the weighted average method, which solves the problem of how to consider the data of each sensor and make reasonable decisions. The data collected by each sensor may have some errors or biases, and the use of the weighted average method can integrate the data of each sensor according to the importance of the sensor, thereby avoiding the bias that may be caused by single sensor data. The weight of different sensors is set according to its influence on the overall environmental monitoring, ensuring that the most critical environmental factors contribute more to the final state calculation, so that the update of the environmental state is more scientific and reasonable. The medical waste storage environment state is adjusted by the weighted average dynamic updating method, which solves the problem of lack of flexibility when medical waste storage environment responds to changing conditions. Traditional methods often cannot quickly adjust when the environmental state changes, while the weighted average method can combine historical data and real-time data in real time, dynamically adjust the processing strategy according to the latest environmental state, avoid abnormal fluctuations that may occur in the storage environment at different time periods, and improve the stability and controllability of the medical waste storage environment. The weighted average method improves the accuracy and timeliness of medical waste storage environment adjustment, and solves the problems of storage environment state adjustment lag and inaccuracy. After using the weighted average method, the system can more accurately adjust the environmental state, especially in the case of rapid changes in environmental conditions, which can avoid slow response or incorrect adjustment of the system, improve the safety of medical waste storage, and reduce the negative impact of adverse environmental conditions on the medical waste management process.

[0074] 4、By setting the threshold set of environmental data and collecting data in real time and comparing with the threshold, the problem of not being able to find out in time when the storage environment of medical waste appears abnormal situation is solved. Different types of medical waste have different storage environment requirements, for example, some medical waste is more sensitive to temperature, humidity or gas concentration, so setting reasonable threshold is the key step to ensure that the storage environment of medical waste meets the standard. By comparing the real-time collected environmental data with the set threshold, the system can identify any abnormalities in the environment in time, avoiding the potential safety hazards caused by the storage of medical waste in unsuitable environment. By using the weighted difference method to determine the occurrence of abnormal state, the problem of how to accurately determine the abnormality of environmental data is solved. The weighted difference method combines the weight of each sensor data, making the judgment of environmental data more accurate. The measurement values of different sensors have different influences on the overall environment, and the setting of weight can highlight the influence of important sensor data, thereby improving the accuracy of judgment. By calculating the difference between the real-time collected environmental data and the threshold, it further accurately judges whether an abnormality occurs, avoiding false alarms caused by errors of some single sensors. By calculating the total abnormality index and setting the threshold for warning, the problem of not timely identifying environmental abnormalities and slow reaction is solved. The calculation of abnormality index integrates the differences of each environmental data, making the overall environmental abnormal state more clear. After setting the total abnormality index threshold, the system can automatically determine that when the abnormality index exceeds the set threshold, the warning mechanism is triggered. This method can give an early warning when the environment has potential risks, thus providing sufficient time for subsequent processing and adjustment, ensuring the safety and stability of the storage process of medical waste. By implementing data analysis based processing optimization, the problem of lacking targeted optimization measures after the occurrence of abnormal state is solved. The warning of abnormal state is not only to inform the operator, but also to automatically generate the corresponding adjustment strategy through data analysis and processing optimization, so as to ensure that the storage environment of medical waste can quickly recover to a suitable state. The data analysis method can combine historical data and real-time data to evaluate the environmental state in real time and give the best optimization scheme, which greatly improves the intelligent level of the system and reduces the risk of human intervention and operation errors.

[0075] 5、By calculating the complexity index of medical waste containers, the problem of insufficient complexity and priority evaluation in medical waste container disposal is solved. The complexity index of medical waste containers takes into account multiple influencing factors such as medical waste type, storage environment, chemical composition of medical waste, etc. This can comprehensively evaluate the difficulty and urgency of medical waste container disposal. When calculating the complexity index, different weights are given to different characteristics through a weighted approach, ensuring that more critical characteristics have a full impact on container disposal. Therefore, this step optimizes the medical waste container disposal priority evaluation method, improving the scientificity and accuracy of priority evaluation. By calculating the disposal priority of medical waste containers, the problem of unclear priority judgment in medical waste container resource allocation is solved. The calculation of disposal priority takes into account the complexity index of medical waste containers and other factors such as environmental factors and medical waste risk level, using reasonable weight factors to adjust the influence of these factors, ensuring that the priority calculation is more accurate and meets actual needs. In particular, by introducing a positive exponential decay parameter, the disposal priority is more flexible, allowing dynamic adjustment of the priority to respond to environmental changes and medical waste disposal needs. Through medical waste container priority sorting and resource allocation, the problem of low efficiency and unfairness in resource allocation and processing queue management is solved. After calculating the disposal priority of medical waste containers, the system will automatically sort medical waste containers according to priority, placing high-priority containers at the front of the processing queue to ensure that high-priority medical waste is promptly disposed of. In the resource allocation process, the system can reasonably allocate processing resources according to the disposal priority, avoiding resource waste and unreasonable allocation. This ensures maximum resource utilization in medical waste disposal, while also avoiding delays or resource waste caused by unclear disposal priorities. Through dynamic resource adjustment, the problem of resource surplus or shortage that may occur during medical waste container disposal is solved. Resource allocation in medical waste container disposal is not fixed, but is dynamically adjusted according to the disposal priority of the container, environmental status, and complexity of the container, ensuring that resources are always optimally allocated. Dynamic adjustment not only improves the flexibility of medical waste container disposal, but also allows for unexpected situations, ensuring efficient and stable medical waste disposal.

[0076] 6、By obtaining the total amount of processing resources and allocating priorities, the problem of unfair and unscientific allocation of medical waste container resources is solved. When processing medical waste, it ensures that resources are allocated according to the processing priority of medical waste containers, and medical waste containers with high priority can obtain more resources to ensure timely processing. Specifically, by calculating the priority of medical waste containers when allocating resources, the amount of resources allocated to each container is determined. This priority-driven resource allocation method effectively avoids waste of resources, so that resources can be more targeted to those medical waste that needs emergency treatment, ensuring the efficiency of medical waste treatment. Through the priority-based resource allocation mechanism, the timeliness of the processing task is ensured. The processing priority of medical waste containers takes into account multiple factors, such as the danger of medical waste, the complexity index, the storage environment, etc., so as to ensure the timely processing of high-priority tasks in the medical waste processing process, and avoid low-priority tasks occupying too many resources, causing high-priority tasks to be delayed. This mechanism ensures that the processing of medical waste containers is not delayed, and improves the efficiency and emergency response capability of the overall processing. By dynamically adjusting the processing strategy to respond to changes, the problem of inflexible resource allocation is avoided. In the medical waste processing process, the state of the environment and the characteristics of medical waste may change, causing the original resource allocation scheme to no longer adapt to the new situation. The scheme introduces a mechanism for dynamically adjusting the processing strategy, so that resource allocation strategies can be adjusted in real time during the processing process according to real-time data and environmental changes. This flexibility ensures the adaptability of resources, which can respond to emergencies or environmental changes such as temperature, humidity, gas concentration, etc., optimizing resource utilization in the medical waste processing process. By dynamically adjusting, the long-term optimization of resource allocation is ensured. Resource allocation is not just a one-time completion, but a continuous optimization with various changes in the medical waste container processing process. Dynamic adjustment of the processing strategy not only improves the immediate response capability of the processing process, but also ensures efficient use of resources in the long term. For example, if an abnormal situation occurs in a certain area, the system can adjust the resource allocation in real time to ensure that the abnormal area obtains sufficient processing resources, thereby avoiding potential problems caused by uneven resource distribution.

[0077] 7、By obtaining the abnormality index of medical waste containers and calculating the priority change amount, the problem of not timely assigning priority due to abnormal state changes in the medical waste treatment process is solved. The scheme can accurately reflect the current treatment urgency and environmental changes of the medical waste containers by obtaining the abnormality index of medical waste containers at different time points in real time. Based on these data, the priority change amount of the medical waste containers is calculated, and the processing priority is adjusted accordingly to ensure that medical waste with higher priority can be processed in time. This adjustment mechanism avoids the problem of fixed medical waste treatment priority and slow response to unexpected situations, improving the adaptability and flexibility of the system to environmental changes. By introducing dynamic adjustment coefficients, the response capability of the system to changes is enhanced. The use of dynamic adjustment coefficients ensures that the adjustment of the priority change amount is flexible and controllable. According to different environments and medical waste conditions, dynamic adjustment coefficients can be adjusted in real time, making the treatment strategy more in line with actual needs. For example, when a medical waste container experiences a sudden abnormality, the dynamic adjustment coefficient can automatically increase the processing priority of the container, and resources are allocated to process it first, avoiding the problem of not processing in time in unexpected situations and reducing the potential threat to the environment and safety. By calculating the priority change amount of the medical waste containers, the accurate allocation of resources is ensured. The calculation of the priority change amount helps the system determine the true processing urgency of each medical waste container at different time points, ensuring that resources can be accurately allocated to the containers that need them most at each moment. By calculating the priority change in real time, the system can optimize the processing queue in real time during the processing of medical waste containers, making the processing queue more in line with actual conditions and avoiding resource waste and inefficient processing caused by unreasonable static priority settings. Through the comprehensive evaluation of priority adjustment and abnormality index, the emergency response capability of medical waste treatment is improved. During the medical waste treatment process, abnormal situations such as increased environmental temperature and increased concentration of harmful gases may occur, which need to be quickly responded to and processed. In the scheme, the processing strategy is dynamically adjusted so that the system can quickly adjust the priority of the medical waste container when it is abnormal, ensuring that processing resources are allocated to these abnormal containers first, reducing potential safety risks and enhancing the system's emergency response capability. BRIEF DESCRIPTION OF DRAWINGS

[0078] Figure 1 The flowchart of the present application is shown. DETAILED DESCRIPTION

[0079] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0080] Embodiments, with reference to Figure 1 A medical waste information management method based on Internet of Things, comprising:

[0081] installing a two-dimensional code on each medical waste container, so that each medical waste container has a unique identifier, denoted as UID, and the first medical waste container is denoted as , the following operations are performed on the medical waste container :

[0082] real-time input the basic information of each medical waste container through a two-dimensional code scanning device, including the type of medical waste, the source hospital, and the generation time;

[0083] store all the basic information in a pre-constructed central database and associate it with the UID of the medical waste container;

[0084] set a medical waste category set, denoted as , where m is the total number of medical waste categories;

[0085] wherein the medical waste categories are classified according to the hazard level of medical waste, for example, the hazard level of medical waste can be classified into high-hazard waste and low-hazard waste, wherein high-hazard waste can be divided into infectious waste, pathological waste, damage waste, drug waste, chemical waste and radioactive waste; low-hazard waste can be divided into general waste;

[0086] for medical waste X, obtain all the feature values of medical waste X, and set the feature vector of medical waste X, denoted as , where n is the total number of feature values of medical waste X, represents the th feature value of medical waste X;

[0087] wherein the feature vector includes specific measurement data of the physical, chemical and biological properties of medical waste, for example, physical properties include weight, volume, density, color and particle size, etc.; chemical properties include pH value, chemical composition and volatile organic compounds, etc., and biological properties include microbial content, pathogen detection and biodegradability, etc.;

[0088] the medical waste is all non-prohibited medical waste;

[0089] denote the th data in the medical waste category set as , set the feature center value set of , and obtain The center value of all features of is added to the feature center value set of to obtain wherein, is the total number of the center values of the features of .

[0090] wherein, the center value is the representative statistical value of the th feature in the category , used to measure the distance between the new sample and the category, for example, the representative statistical value can be the mean or the median;

[0091] The distance between each feature value of the medical waste X and the center value of all features of is calculated , and the weighted distance function is specifically:

[0092] .

[0093] wherein, is the weight of the th feature; is the center value of the th feature of .

[0094] The value of is determined by using the entropy weight method, specifically as follows:

[0095] Each feature is normalized to make its value in the range of [0, 1]:

[0096] .

[0097] wherein, is the th feature value of the medical waste X after normalization, is the minimum value function, which returns the minimum feature value among the th feature values of the medical waste X, is the maximum value function, which returns the maximum feature value among the th feature values of the medical waste X;

[0098] The entropy of each feature is calculated:

[0099] .

[0100] wherein, is the th feature value of the medical waste X in the frequency in the class, is the total number of medical waste samples, and log is the logarithmic function;

[0101] Calculate the weight of each feature:

[0102] ; is the total number of features;

[0103] Select the class with the smallest distance to the medical waste feature vector That is:

[0104] ;

[0105] where, is the predicted class, that is, the classification result predicted according to the medical waste feature vector;

[0106] Real-time monitoring and dynamic updating strategy is adopted for medical waste.

[0107] By installing a two-dimensional code and assigning a unique identifier, the problem of tracing the identity of medical waste containers is solved. The two-dimensional code technology assigns a unique identifier to each medical waste container, allowing the information of each container to be accurately tracked throughout its life cycle. In this way, medical waste containers no longer rely on manual records, avoiding data loss or errors, and improving the accuracy and efficiency of information management. Through the two-dimensional code scanning device, the basic information of the medical waste container is recorded in real time and stored in the central database, solving the problem of low efficiency and error-prone of traditional manual data recording. All basic information of medical waste can be immediately recorded and synchronized to the database when the container is stored, ensuring the real-time and accuracy of the data, providing reliable data support for subsequent medical waste treatment, classification and monitoring. By setting a set of medical waste categories and calculating a medical waste feature vector, combined with a weighted distance function for intelligent classification, the error problem that may occur in the medical waste classification process is solved. The weighted distance calculation of the feature vector of the medical waste and the center value of all categories can accurately classify the medical waste according to its specific characteristics, thereby improving the accuracy and automation level of medical waste classification, reducing human intervention and errors. Through real-time monitoring and dynamic updating strategy, the storage environment and state of medical waste are continuously tracked, solving the problem that the storage environment of medical waste may change but cannot be timely responded. Through real-time collection and dynamic updating of the storage environment of medical waste and the state of medical waste, the strategy can be quickly adjusted when an exception occurs, ensuring that medical waste is always in a safe and compliant storage condition, reducing the risk of environmental risks. Through the optimization and weighted calculation of the feature vector of medical waste, the overall intelligent level of the medical waste management system is improved, realizing the automation and precision of medical waste management. Through the calculation of the complexity index and priority ranking, resources can be reasonably allocated according to the urgency and characteristics of medical waste, implementing precise medical waste treatment scheduling, and optimizing the use efficiency of medical waste resources.

[0108] The real-time monitoring and dynamic updating strategy for medical waste includes:

[0109] The container environment storing medical waste is recorded as a medical waste storage site;

[0110] Sensors are set up in the medical waste storage site, including temperature sensors, humidity sensors, and gas concentration sensors;

[0111] The sensors are used to collect relevant data in the medical waste storage site in real time, and the relevant data is uploaded to the cloud server through the Internet of Things platform, including temperature, humidity, air quality, and harmful gas concentration;

[0112] Obtain historical data on medical waste storage sites;

[0113] By using a weighted average dynamic update method, combining historical and real-time data, the environmental conditions corresponding to medical waste storage sites are adjusted.

[0114] Early warning of abnormal environmental conditions.

[0115] By installing temperature, humidity, and gas concentration sensors at medical waste storage sites, real-time environmental data collection solves the problem of the inability to monitor medical waste storage environments in real time. These sensors accurately monitor key data such as temperature, humidity, and harmful gas concentrations, ensuring the safety of the medical waste storage environment. This real-time data collection provides comprehensive data support for subsequent environmental status analysis and dynamic adjustments. Uploading data to a cloud server via an IoT platform solves the problem of centralized management and analysis of medical waste environmental monitoring data. IoT technology enables timely uploading of sensor-collected data to the cloud server, ensuring centralized data storage and management, avoiding data silos and information delays, and improving data accessibility and security. Furthermore, the cloud server can process large amounts of data and provide necessary computing resources for subsequent analysis and optimization. A weighted average dynamic update method, combining historical and real-time data, adjusts the state of the medical waste storage environment, solving the problem of untimely response to changes in the environment. The weighted average method precisely adjusts the storage environment state based on a weighted fusion of historical and real-time data. Historical data provides long-term trends in environmental changes, while real-time data ensures the accuracy of the current environmental status. Dynamic updates allow for more flexible and precise adjustments to the medical waste storage environment, avoiding potential safety risks caused by environmental changes. By combining historical and real-time data using a weighted average method, adjustments to the medical waste storage environment are more scientific and accurate, resolving issues of inconsistent adjustment methods and insignificant effects when the storage environment fluctuates. Assigning different weights to different data sources using the weighted average method allows for more precise control of adjustment strategies based on actual environmental changes, resulting in more stable and controllable medical waste storage conditions and reducing the impact of external environmental factors on medical waste treatment.

[0116] The aforementioned dynamic update method, which uses weighted averaging and combines historical and real-time data, adjusts the environmental conditions of medical waste storage sites, specifically including:

[0117] Set the storage location for the data collected by the sensor as follows: ,in For the number of sensors, For the first Real-time data from each sensor;

[0118] The new state is calculated by weighted average, denoted as , specifically:

[0119] ;

[0120] wherein, is the weight of the th sensor, which is set according to the sensor accuracy or the degree of its influence on the environment.

[0121] By setting the environmental data collected by the sensors and obtaining the current environmental state of the medical waste, the problem that the monitoring data of the storage environment of medical waste cannot reflect the environmental changes in real time is solved. The sensors in different positions monitor various environmental parameters of the medical waste storage site in real time, such as temperature, humidity, and gas concentration, etc. These data can accurately reflect the current storage environment state. Through the real-time collection of environmental data, the system can understand the status of medical waste storage at any time, thereby providing timely and effective data basis for subsequent dynamic adjustment. The new environmental state is calculated by the weighted average method, which solves the problem of how to consider the data of each sensor comprehensively and make reasonable decisions. The data collected by each sensor may have certain errors or biases, and the use of weighted average method can integrate the data of each sensor according to the importance of the sensor, thereby avoiding the bias that may be caused by single sensor data. The weights of different sensors are set according to their influence on the overall environmental monitoring, ensuring that the most critical environmental factors contribute more to the final state calculation, so that the update of the environmental state is more scientific and reasonable. Through the weighted average dynamic updating method, the storage environment state of medical waste is adjusted, which solves the problem of lack of flexibility when the storage environment of medical waste responds to changing conditions. Traditional methods often cannot make corresponding adjustments when the environmental state changes, while the weighted average method can combine historical data and real-time data in real time, dynamically adjust the processing strategy according to the latest environmental state, avoid abnormal fluctuations that may occur in the storage environment at different time periods, and improve the stability and controllability of the storage environment of medical waste. The weighted average method improves the accuracy and timeliness of the adjustment of the storage environment of medical waste, solving the problems of lag and inaccuracy in the adjustment of the storage environment state. After using the weighted average method, the system can more accurately adjust the environmental state, especially in the case of rapid changes in environmental conditions, which can avoid slow response or incorrect adjustment of the system, improve the safety of the medical waste storage process, and reduce the negative impact of adverse environmental conditions on the management process of medical waste.

[0122] The abnormal environmental state early warning specifically includes:

[0123] According to the category and storage environment of medical waste, a set of environmental data threshold values is set, denoted as wherein is the threshold value of the th sensor data;

[0124] By comparing the real-time collected environmental data with the environmental data threshold, a weighted difference method is used to determine whether an abnormal state occurs:

[0125] Let the obtained current environmental data be denoted as ;

[0126] The difference calculation formula is: ;

[0127] Calculate the total abnormality index: ;

[0128] wherein, is the weight of the th environmental data; is the absolute value of the difference between the th environmental data and the environmental data threshold; is the total abnormality index;

[0129] wherein the value of can be determined according to the following method:

[0130] According to the relationship between each monitoring indicator and the storage safety and environmental impact of medical waste, the contribution degree to the abnormal state is evaluated, and the experts score the importance of each monitoring indicator according to their experience, usually using a scoring system of 1 to 5 (1 indicating not important, 5 indicating extremely important), the expert scoring is standardized so that the sum of the total weights of all monitoring indicators is 1, assuming that the expert evaluates the score of the th monitoring indicator as , then the calculation of is:

[0131] ;

[0132] wherein is the total number of monitoring indicators;

[0133] Set the total abnormality index threshold, denoted as ;

[0134] If , trigger an early warning and implement data analysis-based processing optimization.

[0135] By setting a set of environmental data thresholds and comparing real-time collected data with these thresholds, the system addresses the problem of untimely detection of anomalies in medical waste storage environments. Different types of medical waste have different storage environment requirements; for example, some medical wastes are highly sensitive to temperature, humidity, or gas concentration. Therefore, setting reasonable thresholds is a crucial step in ensuring that the medical waste storage environment meets standards. By comparing real-time collected environmental data with the set thresholds, the system can promptly identify any anomalies in the environment, avoiding potential safety hazards caused by storing medical waste in unsuitable environments. The system uses a weighted difference method to determine the occurrence of abnormal states, solving the problem of accurately judging environmental data anomalies. The weighted difference method combines the weights of data from various sensors, making the judgment of environmental data more accurate. Different sensor measurements have varying degrees of impact on the overall environment; setting weights highlights the influence of important sensor data, thereby improving the accuracy of the judgment. By calculating the difference between real-time collected environmental data and thresholds, the system further precisely judges whether anomalies have occurred, avoiding false alarms caused by errors from individual sensors. By calculating the total anomaly index and setting a threshold for early warning, the system solves the problems of untimely environmental anomaly identification and slow response. The calculation of the anomaly index integrates the differences in various environmental data, making the overall abnormal environmental state clearer. After setting a total anomaly index threshold, the system can automatically determine when the anomaly index exceeds the set threshold and trigger an early warning mechanism. This method can provide early warning when potential risks arise in the environment, thus providing sufficient time for subsequent processing and adjustments, ensuring the safety and stability of medical waste storage. By implementing data analysis-based processing optimization, the problem of lacking targeted optimization measures after anomalies occur is solved. Anomaly warnings not only notify operators but also automatically generate corresponding adjustment strategies through data analysis and processing optimization, ensuring that the medical waste storage environment can be quickly restored to a suitable state. The data analysis method combines historical and real-time data to conduct real-time assessments of the environmental state and provide optimal optimization solutions, which greatly improves the system's intelligence level and reduces the risk of human intervention and operational errors.

[0136] The implementation of data analysis-based processing optimization specifically includes:

[0137] Calculate medical waste containers The complexity index is as follows:

[0138] ;

[0139] in, Medical waste containers The complexity index; For the first The weights of each feature;

[0140] Computing a processing priority of a medical waste container :

[0141] ;

[0142] wherein, is a processing priority of a medical waste container ; , , is a weight factor; is a positive number, a parameter for controlling exponential decay;

[0143] According to the processing priority of the medical waste container, the medical waste container with high processing priority is arranged at the front end of the processing queue, and the medical waste container with low processing priority is arranged at the rear end of the processing queue;

[0144] Resource allocation and adjustment are performed on medical waste treatment.

[0145] The complexity index of the medical waste container is calculated to solve the problem of insufficient complexity and priority evaluation during medical waste container disposal. The complexity index of the medical waste container considers multiple influencing factors, such as medical waste type, storage environment, chemical composition of medical waste, and other characteristics, which can comprehensively evaluate the difficulty and urgency of medical waste container disposal. When calculating the complexity index, different weights are assigned to different characteristics through a weighted approach, ensuring that more critical characteristics have a full impact on container disposal. Therefore, this step optimizes the medical waste container disposal priority evaluation method, improving the scientificity and accuracy of priority evaluation. By calculating the disposal priority of the medical waste container, the problem of unclear priority judgment during medical waste container resource allocation is solved. The calculation method of the disposal priority considers the complexity index of the medical waste container and other factors such as environmental factors and medical waste risk level, and uses reasonable weight factors to adjust the influence of these factors, ensuring that the priority calculation is more accurate and meets actual needs. In particular, by introducing a positive exponential decay parameter, the disposal priority is more flexible and can dynamically adjust the priority to respond to environmental changes and medical waste disposal needs. Through medical waste container priority sorting and resource allocation, the problems of inefficiency and unfairness in resource allocation and processing queue management are solved. After calculating the disposal priority of the medical waste container, the system automatically sorts the medical waste container according to the priority, placing high-priority containers at the front of the processing queue to ensure that high-priority medical waste is promptly disposed of. During resource allocation, the system can reasonably allocate processing resources according to the disposal priority, avoiding resource waste and unreasonable allocation. This ensures maximum resource utilization during medical waste disposal, while also avoiding delays or resource waste caused by unclear disposal priorities. Through dynamic resource adjustment, the problem of resource surplus or shortage during medical waste container disposal is solved. Resource allocation during medical waste container disposal is not fixed, but is dynamically adjusted according to the disposal priority of the container, environmental conditions, and the complexity of the container, ensuring that resources are always optimally allocated. Dynamic adjustment not only improves the flexibility of medical waste container disposal, but also enables the system to respond to unexpected situations, ensuring efficient and stable medical waste disposal.

[0146] The resource allocation and adjustment for medical waste disposal specifically includes:

[0147] The total amount of processing resources is obtained, denoted as ;

[0148] The resources are allocated according to the disposal priority, specifically:

[0149] ;

[0150] wherein, a processing priority of the medical waste container; a processing resource amount allocated to the medical waste container; a processing priority of the medical waste container; a processing resource amount allocated to the medical waste container;

[0151] According to changes in the medical waste processing process, the processing strategy is dynamically adjusted.

[0152] By obtaining the total processing resource amount and performing priority allocation, the problem of unfair and unscientific allocation of medical waste container resources is solved. When processing medical waste, it is ensured that resources are allocated according to the processing priority of the medical waste container, and medical waste containers with high priority can obtain more resources to ensure that they can be processed in time. Specifically, when allocating resources, the priority of the medical waste container is calculated to determine the amount of resources allocated to each container. This priority-driven resource allocation method effectively avoids waste of resources, so that resources can be more targetedly allocated to medical waste that needs urgent processing, ensuring the efficiency of medical waste processing. Through the priority-based resource allocation mechanism, the timeliness of the processing task is ensured. The processing priority of the medical waste container takes into account multiple factors, such as the danger of medical waste, the complexity index, the storage environment, etc., so as to ensure the timely processing of high-priority tasks in the medical waste processing process, and to avoid low-priority tasks occupying too many resources, causing high-priority tasks to be delayed. This mechanism ensures that the processing of medical waste containers is not delayed, and improves the efficiency and emergency response capability of the overall processing. By dynamically adjusting the processing strategy to respond to changes, the problem of inflexible resource allocation is avoided. In the medical waste processing process, the environmental state and the characteristics of the medical waste may change, causing the original resource allocation scheme to no longer adapt to the new situation. The scheme introduces a mechanism for dynamically adjusting the processing strategy, so that resource allocation strategies can be adjusted in real time during the processing process according to real-time data and environmental changes. This flexibility ensures the adaptability of resources, which can respond to sudden events or environmental changes, such as changes in temperature, humidity, gas concentration, etc., optimizing resource utilization in the medical waste processing process. Through dynamic adjustment, long-term optimization of resource allocation is ensured. Resource allocation is not a one-time completion, but a continuous optimization along with various changes in the medical waste container processing process. Dynamic adjustment of the processing strategy not only improves the immediate response capability of the processing process, but also ensures efficient use of resources in the long term. For example, if an abnormal situation occurs in a certain area, the system can adjust the resource allocation in real time to ensure that the abnormal area obtains sufficient processing resources, thereby avoiding potential problems caused by uneven resource distribution.

[0153] The dynamic adjustment processing strategy specifically includes:

[0154] Obtaining medical waste container At time The abnormal index is recorded as ;

[0155] Obtaining medical waste container At time The abnormal index is recorded as ;

[0156] Calculating the priority change amount of the medical waste container At time Specifically:

[0157] ;

[0158] Wherein, The priority change amount of the medical waste container At time ; The dynamic adjustment coefficient;

[0159] The priority change amount of all medical waste containers at time t is calculated in sequence;

[0160] The priority change amount of the medical waste container is sorted from large to small to obtain the sorting order;

[0161] According to the sorting order, the medical waste in the medical waste container is processed in sequence.

[0162] By obtaining the abnormal index of the medical waste container and calculating the priority change amount, the problem of not timely assigning priority due to abnormal state changes in the medical waste treatment process is solved. The scheme can accurately reflect the current treatment urgency and environmental changes of the medical waste container by obtaining the abnormal index of the medical waste container at different time points in real time. Based on these data, the priority change amount of the medical waste container is calculated, and the treatment priority is adjusted accordingly to ensure that medical waste with higher priority can be treated in time. This adjustment mechanism avoids the problem of fixed medical waste treatment priority and slow response to unexpected situations, improving the adaptability and flexibility of the system to environmental changes. By introducing dynamic adjustment coefficients, the response capability of the system to changes is enhanced. The use of dynamic adjustment coefficients ensures that the adjustment of the priority change amount is flexible and controllable. According to different environments and medical waste conditions, dynamic adjustment coefficients can be adjusted in real time, making the treatment strategy more in line with actual needs. For example, when a medical waste container experiences a sudden abnormality, the dynamic adjustment coefficient can automatically increase the treatment priority of the container, prioritizing resource allocation for processing, avoiding the problem of not timely processing unexpected situations, and reducing the potential threat to the environment and safety. By calculating the priority change amount of the medical waste container, the accurate allocation of resources is ensured. The calculation of the priority change amount helps the system determine the true treatment urgency of each medical waste container at different time points, ensuring that resources can be accurately allocated to the most needed containers at each moment. By calculating the priority change in real time, the system can optimize the treatment process in real time, making the treatment queue more in line with actual conditions, and avoiding resource waste and inefficient processing caused by unreasonable static priority settings. Through the comprehensive evaluation of priority adjustment and abnormal index, the emergency response capability of medical waste treatment is improved. During the medical waste treatment process, abnormal situations such as increased environmental temperature and increased concentration of harmful gases may occur, which need to be quickly responded and processed. In the scheme, the treatment strategy is dynamically adjusted, so that the system can quickly adjust the priority of the medical waste container when it is abnormal, ensuring that processing resources are allocated to these abnormal containers first, reducing potential safety risks, and enhancing the emergency response capability of the system.

[0163] The embodiment also provides a medical waste information management system based on the Internet of Things, comprising:

[0164] Scanning module: real-time input of basic information of each medical waste container through a two-dimensional code scanning device, including the type of medical waste, the source hospital, and the generation time;

[0165] Information storage module: stores all information in the central database and associates it with the UID of the medical waste container;

[0166] Calculation module: for performing data calculations.

[0167] It is to be noted that, in the present document, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0168] The above description is only the preferred embodiment of the present application, it should be pointed out that, for those skilled in the art, without departing from the technical principles of the present application, a number of improvements and refinements can be made, these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A medical waste information management method based on Internet of Things, characterized by, The application comprises: A two-dimensional code is installed on each medical waste container so that each medical waste container has a unique identifier, which is denoted as UID, and the first medical waste container is denoted as The second medical waste container is denoted as The following operations are performed for the medical waste container ​ Real-time entry of basic information of each medical waste container through a two-dimensional code scanning device, including the type of medical waste, the source hospital, and the generation time; Storing all basic information in a pre-constructed central database and associating it with the UID of the medical waste container; A medical waste category set is set, denoted as wherein m is the total number of medical waste categories; For medical waste X, obtain all feature values ​​of medical waste X, and set the feature vector of medical waste X, denoted as . Where n is the total number of characteristic values ​​of medical waste X, The first medical waste X represents the One eigenvalue; The medical waste is all non-prohibited medical waste; The first category in the medical waste category set Each data point is denoted as ,set up The set of feature center values, to obtain The center value of all features will Add the center values ​​of all features to The set of feature center values ​​is obtained. ,in, for The total number of central values ​​of the features; Using the weighted distance function, the distance of each feature value of the medical waste X from the center value of all features of ; and ; Real-time monitoring and dynamic updating strategy is adopted for medical waste; The distance between each feature value of the medical waste X and the center value of all features of the medical waste X is calculated using a weighted distance function , specifically comprising:​ The weighted distance function is specifically: ； wherein, is the weight of the th feature; is the center value of the th feature; selecting the class with the smallest distance to the medical waste feature vector i.e.: ； wherein, is the predicted class, i.e. the classification result predicted from the medical waste feature vector. 2.The medical waste information management method based on the Internet of Things according to claim 1, characterized in that, The real-time monitoring and dynamic updating strategy for medical waste specifically includes: The container environment storing medical waste is recorded as a medical waste storage site; Sensors are arranged in the medical waste storage site, wherein the sensors include temperature sensors, humidity sensors, and gas concentration sensors; Real-time collection of relevant data in the medical waste storage site by sensors and uploading of the relevant data to a cloud server through an Internet of Things platform, wherein the relevant data includes temperature, humidity, air quality, and harmful gas concentration; Historical data of the medical waste storage site are obtained; The environmental state corresponding to the medical waste storage site is adjusted by a dynamic updating method of weighted average, combining historical data and real-time data; Abnormal environmental state is warned. 3.The medical waste information management method based on the Internet of Things according to claim 2, characterized in that, The dynamic updating method of weighted average, combining historical data and real-time data, adjusts the environmental state corresponding to the medical waste storage site, specifically including: Set the storage location for the data collected by the sensor as follows: Where k is the number of sensors, For the first Real-time data from each sensor; The new environmental state is calculated by a weighted average, denoted by , and is given by: ； wherein, is the weight of the th sensor. 4.The medical waste information management method based on the Internet of Things according to claim 2, characterized in that, The warning of abnormal environmental state specifically includes: A set of environmental data threshold values, denoted as is set according to the category of medical waste and the storage environment, wherein is the threshold value of the th sensor data. Comparison of real-time collected environmental data with environmental data threshold value to determine whether an abnormal state occurs using a weighted difference method: The acquired current environment data is denoted as ; The difference calculation formula is: ; The total anomaly index is calculated: ; wherein, is the first item of environmental data; is the first absolute value of the difference between the item of environmental data and the environmental data threshold value; is the total anomaly index; A total anomaly index threshold is set, denoted as ; If then a warning is triggered and a data analysis based process optimization is implemented. 5.The medical waste information management method based on the Internet of Things according to claim 4, characterized in that, The processing optimization based on data analysis is implemented, specifically including: Computing medical waste container a complexity index, in particular: ； wherein, a medical waste container a complexity index; a weight for the th feature; Computing medical waste container processing priority ； wherein is a medical waste container of a treatment priority; 、 、 is a weight factor; is a positive number, a parameter for controlling the exponential decay; According to the processing priority of the medical waste container, the processing priority is sorted, with high processing priority at the front end of the processing queue and low processing priority at the back end of the processing queue; Resource allocation and adjustment are performed for medical waste treatment. 6.The medical waste information management method based on the Internet of Things according to claim 5, characterized in that, The resource allocation and adjustment for medical waste treatment specifically include: The total processing resource amount is acquired, denoted as ; According to the processing priority, resources are allocated, specifically: ； wherein, a medical waste container a processing priority; a medical waste container an amount of processing resources assigned thereto; According to the changes in the medical waste treatment process, dynamic adjustment of the treatment strategy is implemented. 7.The medical waste information management method based on the Internet of Things according to claim 6, characterized in that, The dynamic adjustment of the treatment strategy specifically includes: Acquiring medical waste container The anomaly index at time t, denoted as ; Acquiring medical waste container The abnormality index at time t-1, denoted as ; Computing medical waste container The priority change amount at time t is specifically: ； wherein, Medical waste container The priority change amount at time t; is a dynamic adjustment coefficient; The priority change amount of all medical waste containers at time t is calculated in sequence; The priority change amount of the medical waste container is sorted from large to small to obtain an order sequence; According to the order sequence, the medical waste in the medical waste container is processed in sequence.

8. A medical waste information management system based on the Internet of Things, applied to the medical waste information management method based on the Internet of Things in any one of claims 1-7, characterized in that, The system comprises: A scanning module: real-time entry of basic information of each medical waste container through a two-dimensional code scanning device, including the type of medical waste, the source hospital, and the generation time; An information storage module: storing all information in a central database and associating it with the UID of the medical waste container; A calculation module: for performing data calculation.

Images