Intelligent management method and system for medical examination specimens

Abstract

The invention relates to the technical field of intelligent specimen management methods, in particular to an intelligent specimen management method and system for medical examination, and the method comprises the following steps: building a specimen storage information database; environment parameters of the specimens in the transportation process are updated in real time; based on the updated environmental parameters, intelligently predicting the transportation risk of the specimen by using a progressive risk prediction algorithm, and verifying whether the specimen meets the detection requirements or not; state detection is carried out on the specimens meeting the detection requirements, image information and / or video information of the specimens are / is obtained and used for further inspection of the specimens, intelligent assessment can be carried out on the transportation risk of the specimens through a progressive risk prediction algorithm, multi-level risk assessment is achieved, and the risk assessment efficiency is improved. Particularly, through superposition and complementation of a self-adaptive nonlinear risk function, risk curvature analysis, entropy weight optimization and other methods, the algorithm still keeps high adaptability and foresight under different environment changes.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent specimen management methods, and in particular to an intelligent specimen management method and system for medical testing. Background Art

[0002] In recent years, with the rapid development of modern medical imaging technology, such as magnetic resonance imaging (MRI) and computer in the field of medical testing, the collection, transportation, storage and testing of biological specimens are important links that affect the accuracy of diagnosis. However, since the specimens often need to go through a long and complicated transportation process from the collection point to the testing center, changes in environmental factors such as temperature and humidity may have a great impact on the activity and integrity of the specimens, resulting in inaccurate or even invalid test results. Therefore, strict environmental monitoring is required during the transportation of specimens to ensure their quality. However, there are a series of problems with the current specimen management methods, especially in terms of risk prediction and real-time status monitoring.

[0003] In the prior art, the basic information of the specimens is usually recorded by establishing a preservation information database, such as the collection time, preservation conditions, etc., and these data are mainly used for the basic management of the specimens. During the transportation of the specimens, temperature sensors and humidity sensors are relied upon to collect transportation data, and these data are uploaded to the data center. Although this method can record environmental parameters to a certain extent, its dynamics and intelligence are relatively low, and it is limited to simple data monitoring, and fails to achieve real-time risk assessment and dynamic adjustment. Especially in complex transportation environments, the existing technology lacks flexible algorithms and risk assessment mechanisms to cope with drastic environmental changes, and is unable to promptly identify and avoid potential specimen damage risks.

[0004] In terms of risk control, traditional specimen management systems generally use static or linear algorithms to analyze changes in temperature and humidity to determine the risk status of specimens. However, linear analysis is slow to respond to complex environmental changes and cannot adapt to multi-dimensional nonlinear dynamic environments. Especially when transportation conditions change suddenly or the environment fluctuates greatly, linear algorithms often lose their accuracy and cannot make forward-looking judgments on risk status. In terms of specimen status monitoring, existing methods usually rely on limited image acquisition and detection models, and can only detect the status of specimens after arriving at the inspection center, making it difficult to achieve real-time monitoring during transportation. In addition, traditional image detection technology has low ability to detect subtle abnormalities in specimens, and often ignores minor changes in specimens during long-distance transportation, resulting in inaccurate later test results. Summary of the invention

[0005] Based on the above deficiencies of the prior art, the present invention proposes an innovative intelligent management method and system for medical test specimens, which significantly improves the accuracy and stability of specimen management by combining a progressive risk prediction algorithm, a real-time dynamic monitoring module and image recognition technology.

[0006] The present invention provides a method for intelligent management of specimens for medical examination, the method comprising the following steps:

[0007] Establishing a specimen preservation information database to record the specimen number, specimen type, preservation condition parameters and collection time, wherein the preservation information database forms the initial preservation information of the specimen;

[0008] Based on the specimen information in the preservation information database, the environmental parameters of the specimen during transportation, including temperature, humidity and time parameters, are updated in real time, and the environmental parameters are updated in the preservation information database;

[0009] Based on the updated environmental parameters, a progressive risk prediction algorithm is used to intelligently predict the transportation risk of the specimens, wherein the progressive risk prediction algorithm includes a multidimensional nonlinear risk function, an autocorrelation correction model, a curvature analysis, an entropy weight optimization, and a fuzzy risk correction;

[0010] Verify whether the specimen meets the testing requirements based on the predicted risk index;

[0011] Perform status detection on specimens that meet the detection requirements and obtain image information and / or video information of the specimens for further inspection of the specimens.

[0012] Preferably, the progressive risk prediction algorithm includes a multidimensional nonlinear risk function, which is used to obtain an initial risk index, and the initial risk index is obtained by the following formula:

[0013]

[0014] Among them, R is the initial risk index, which indicates the preliminary assessment value of the risk of the specimen during transportation; α, β, γ, δ, and η are risk coefficients, which respectively control the influence weight of each parameter; T is the current transportation temperature, T c is the preset critical temperature; H is the current transport humidity value, H c is the preset allowable critical humidity; t is the current transportation time, and t0 is the initial collection time of the specimen.

[0015] Preferably, the progressive risk prediction algorithm includes an autocorrelation correction model, which performs time series correction on the initial risk index based on historical risk data to obtain a corrected risk index, and the corrected risk index is obtained by the following formula:

[0016]

[0017] Among them, R′ is the modified risk index, which represents the risk assessment value adjusted based on time series; ρ i is the autocorrelation correction factor, which is used to adjust the impact of historical risk changes; R i-1 and R i-2 are the first two risk indices respectively; n is the correction order, which is automatically determined according to the transportation data.

[0018] Preferably, the progressive risk prediction algorithm includes a risk curvature analysis model based on differential geometry, wherein the risk curvature analysis model analyzes the risk curvature based on the modified risk index to obtain a risk curvature value, and the risk curvature value is obtained by the following formula:

[0019]

[0020] Among them, K is the risk curvature value, which indicates the sensitivity of risk changes; are the second-order partial derivatives of the modified risk index in the temperature, humidity and time directions, respectively.

[0021] Preferably, the progressive risk prediction algorithm includes an adaptive entropy weight optimization model, which optimizes the risk weight factor by calculating the information entropy of the environmental parameters to obtain an optimized weight distribution, and the weight factor is determined by the following formula:

[0022]

[0023] Among them, w i is the i-th risk weight factor, which is used to optimize the contribution of each parameter to the risk; p ij is the probability value of the i-th factor in the j-th sample, indicating the weight influence of the factor under different environmental conditions.

[0024] Preferably, the progressive risk prediction algorithm further includes a fuzzy risk correction model, which performs risk correction on the optimized weight distribution based on a membership function to obtain a final risk index, and the final risk index is determined by the following formula:

[0025] R f =μ 高 ·R′·w 高 +μ 中 ·R′·w 中 +μ 低 ·R′·w 低

[0026] Among them, R fis the final risk index, which indicates the final risk assessment value after fuzzy correction; μ 高 , μ 中 and μ 低 are the membership function values ​​of high, medium and low risks, respectively, indicating the fuzzy membership degree of risk; w 高 、w 中 、w 低 They are high, medium and low risk weight factors respectively.

[0027] Preferably, the specimen status detection is based on an image recognition model, and the image recognition model collects image and / or video data of the specimen to obtain abnormal information of the specimen.

[0028] An intelligent management system for specimens used for medical biological testing that implements the method, the system includes modules for implementing specimen preservation information management, transportation monitoring and reception management; the system includes:

[0029] A preservation information module, used to establish an initial preservation information database of the specimen;

[0030] A transport module, used for monitoring and transmitting environmental parameters of the specimen during transport to a data server;

[0031] A data server, used for storing the environmental parameters transmitted by the transport module and updating them to the saved information database;

[0032] A specimen detection module, used to detect the state of the specimen and obtain image or video information;

[0033] The risk prediction module predicts the risk of the specimen during transportation based on a progressive risk prediction algorithm.

[0034] Preferably, the storage information module includes a data terminal and a collection terminal, and the collection terminal is used to collect initial storage information of the specimen and transmit the information to the data terminal.

[0035] Preferably, the risk prediction module combines adaptive nonlinear multidimensional risk function, multiple dynamic autocorrelation correction, differential geometry risk curvature analysis, adaptive entropy weight optimization and dynamic fuzzy risk correction to form a progressive risk assessment system for real-time adjustment and output of the risk prediction results of the specimen.

[0036] The beneficial effects of the present invention are:

[0037] The method of the present invention first records the information of the specimens during collection, and updates the environmental parameters such as temperature and humidity of the specimens during transportation in real time to ensure that the storage conditions of the specimens are synchronized with the database content. Based on these updated environmental parameters, the system can intelligently evaluate the transportation risk of the specimens through a progressive risk prediction algorithm. The algorithm integrates five major steps: multidimensional nonlinear risk function, autocorrelation correction, risk curvature analysis, adaptive entropy weight optimization, and fuzzy risk correction. It solves the problem of the slow response of traditional linear algorithms to complex environments in a progressive manner.

[0038] The progressive algorithm design of the present invention realizes multi-level risk assessment, especially through the superposition and complementarity of modules such as adaptive nonlinear risk function, risk curvature analysis and entropy weight optimization, so that the algorithm remains highly adaptable and forward-looking under different environmental changes. In the algorithm flow, the input and output of each step are interconnected to ensure the continuity of data flow and algorithm operation. For example, the initial risk index is further subjected to risk curvature analysis after multiple dynamic autocorrelation correction steps, so that the system can identify the situation of sudden risk increase and effectively avoid sudden risks.

[0039] In addition, the present invention further enhances the adaptability of the system in uncertain environments through adaptive weights and fuzzy correction. The adaptive entropy weight optimization module ensures that different risk factors are effectively balanced under changing transportation conditions through dynamic weight adjustment; at the same time, the fuzzy risk correction fuzzifies the final risk index, making the evaluation results more stable and applicable to a variety of complex scenarios. Finally, through the multi-dimensional analysis of the risk prediction module, the safety and preservation quality of the specimens in the complex transportation process are effectively ensured.

[0040] In terms of specimen status detection, the present invention uses image recognition technology to perform real-time analysis of static images and video information of specimens. The appearance of specimens during transportation can be captured in real time, and any slight damage, contamination or label problems can be identified. The module intelligently monitors the status of specimens during transportation, realizes quality management of the entire process from specimen collection to transportation, and reduces the delay and error of traditional detection methods.

[0041] In summary, the present invention not only effectively solves the problem of the inability to dynamically monitor environmental changes and the lag in risk assessment in the prior art, but also enhances the adaptability of the specimen management system in complex environments through a series of innovative algorithms, ensuring that the quality of the specimen is not affected before inspection. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 The figure is a logic block diagram of the method of the present invention.

[0043] Figure 2 It is a system logic block diagram of the present invention.

[0044] Figure 3 It is a logic block diagram of the progressive risk prediction algorithm of the present invention.

[0045] Figure 4 This is a logic block diagram of the specimen status detection of the present invention. DETAILED DESCRIPTION

[0046] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects thereof are described in detail below in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0047] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0048] The technical solution of the present invention is described in detail below in conjunction with specific embodiments.

[0049] Please refer to Figure 1-4 The present invention relates to an intelligent management method and system for medical specimens, which is particularly suitable for intelligent management of the collection, storage, transportation, reception and inspection process of medical specimens. The method establishes a specimen storage information database, combines a progressive risk prediction algorithm, accurately evaluates the risk of specimens during transportation, and realizes status detection and specimen information verification on this basis. The present invention has good adaptability and accuracy, and ensures the quality control of specimens before inspection.

[0050] The present invention provides an intelligent management method for specimens for medical testing, including the steps of establishing a preservation information database, real-time updating of environmental parameters, progressive risk prediction, status detection and specimen information verification. First, a specimen preservation information database is established to record the specimen number, specimen type, preservation condition parameters (such as temperature, humidity) and collection time, so as to perform structured storage of the initial state of each specimen. For example, for temperature parameters, preferably, the preservation temperature is controlled between 2-8°C to ensure sample activity; the humidity parameter is maintained at 60-80% to avoid excessive drying or humidity. The collection time is usually recorded to the exact hour to track the preservation time and status of the specimen.

[0051] During the specimen transportation process, environmental parameters such as temperature, humidity and time are updated in real time to the preservation information database based on the real-time data provided by the transportation module. Preferably, the temperature around the specimen is recorded in real time by a temperature sensor, and the temperature variation range is kept within ±0.5°C; the humidity is controlled within a fluctuation range of ±2% to reduce the impact of the environment on the activity of the specimen. Time updates are dynamically recorded based on the difference between the start time of transportation and the current time, thereby effectively controlling the transportation cycle of the specimen. Real-time updating of specimen preservation information ensures that the preservation information database reflects the current specimen preservation conditions and provides real-time data input for subsequent risk prediction.

[0052] Next, based on the updated environmental parameters in the preservation information database, a progressive risk prediction algorithm is used to intelligently predict the transportation risk of the specimen. This algorithm includes a multidimensional nonlinear risk function, an autocorrelation correction model, curvature analysis, entropy weight optimization, and fuzzy risk correction, and finally obtains a risk index. The entire progressive algorithm has the following advantages: it not only comprehensively considers temperature, humidity, and transportation time, but also constructs a multidimensional adaptive risk function based on a nonlinear function, making risk assessment more dynamically adaptable. Preferably, this algorithm judges the risk by the risk index value. When the risk index exceeds the set threshold (such as R>3.5), the system automatically generates an alarm message to indicate that there is an abnormal risk in the transportation of the specimen.

[0053] When the risk index result shows that there is no abnormality in the transportation process, the present invention further determines whether the specimen meets the detection requirements through the specimen information verification step. The verification step includes comparing the specimen number, type, storage conditions and other information to ensure that the specimen information is consistent with the database record. At the same time, based on the image acquisition system to photograph the appearance of the specimen, the image recognition module can automatically identify any possible abnormalities, such as cracks, stains, etc., when detecting the specimen photo. In addition, the video acquisition unit can take a video of the specimen in real time to further enhance the reliability detection of the specimen status.

[0054] For specimens that meet the testing requirements, the status detection phase will collect their pictures and video information to provide a more detailed record of the specimen status in subsequent testing. The image recognition module can identify abnormal points in specimen photos and videos to ensure that the specimen is not contaminated or damaged.

[0055] Preferably, in the progressive risk prediction algorithm, a multidimensional nonlinear risk function is first used to obtain an initial risk index. The multidimensional nonlinear risk function is represented by the following formula:

[0056]

[0057] Among them, R is the initial risk index, which is used to assess the risk of specimens during transportation; α, β, γ, δ, and η are risk coefficients, which control the influence of each parameter respectively. These coefficients can be set according to different environments and specimen requirements. Preferably, α=1.2, β=0.8, γ=1.5, δ=0.5, and η=1.0 are selected to better meet the risk assessment requirements of common medical test specimens. Among them, T is the current transportation temperature, preferably between 2-8°C, T c is the critical temperature, which is usually set to the highest tolerance temperature for specimen preservation, such as 10°C; H is the current humidity, H c To allow the upper limit of humidity, it is usually set to 80%. In addition, t represents the current transportation time and t0 is the initial collection time of the specimen. Through this nonlinear risk function, the present invention can more accurately predict the initial risk of the specimen under temperature, humidity or time changes.

[0058] Preferably, in one embodiment of the present invention, in order to improve the time consistency of the initial risk index, the present invention further uses an autocorrelation correction model to perform time series correction on the initial risk index to obtain a corrected risk index. The autocorrelation correction model is represented by the following formula:

[0059]

[0060] Among them, R′ is the modified risk index, ρ i is the autocorrelation correction factor, which is used to adjust the impact of the time change of the previous risk index on the current risk. i The value between 0.1 and 0.3 is determined according to the frequency of environmental changes to smooth the risk change curve and improve the accuracy of risk prediction. For example, if there is a sharp rise in temperature or humidity fluctuation during transportation, ρ can be appropriately increased. i value to capture changes in sudden risks and improve the sensitivity of risk warning.

[0061] Preferably, in one embodiment of the present invention, based on the modified risk index, the present invention introduces a risk curvature analysis model based on differential geometry to further evaluate the risk curvature. The risk curvature model is obtained by the following formula:

[0062]

[0063] Among them, K is the risk curvature value, which represents the sensitivity of risk changes; are the second-order partial derivatives of the modified risk index in terms of temperature, humidity and time, respectively. Preferably, when the curvature value K>1.2, it indicates that the risk changes steeply, and the system issues an alarm to indicate sudden risk.

[0064] This curvature analysis model is particularly effective when risks change suddenly. It can effectively identify the increased risks caused by sudden changes in the environment and ensure the safety of specimens during transportation.

[0065] Preferably, in one embodiment of the present invention, in order to enhance the stability of risk assessment, the present invention adopts an adaptive entropy weight optimization model to optimize the risk weight by calculating the information entropy of environmental parameters to adapt to the dynamic changes of actual transportation conditions. The weight factor of the entropy weight model is obtained by the following formula:

[0066]

[0067] Among them, w i is the i-th risk weight factor, preferably set between 0.2 and 0.8 to reasonably distribute the impact of various factors; ij is the probability of the i-th factor in the j-th sample, reflecting the probability distribution of different factors in different environments.

[0068] Through this entropy weight optimization model, the present invention can dynamically adjust the weights of various risk factors under different environments, thereby enhancing the adaptability of the risk assessment model and ensuring that the system still has strong predictive capabilities when transportation conditions are unstable.

[0069] In summary, the present invention provides a comprehensive management method for medical test specimens through a progressive risk prediction algorithm and intelligent management of stored information, which can effectively ensure the quality of specimens in a complex transportation environment.

[0070] Preferably, in the progressive risk prediction algorithm, the present invention further introduces a fuzzy risk correction model to adapt to the fuzziness or uncertainty that may exist in actual transportation conditions, so as to obtain the final risk index. The fuzzy risk correction model corrects the optimized weight distribution through the membership function, and the final risk index is expressed by the following formula:

[0071] R f =μ 高 ·R′·w 高 +μ 中 ·R′·w 中 +μ 低 ·R′·w 低

[0072] Among them, R f is the final risk index, which indicates the final risk assessment value after fuzzy correction; μ 高 , μ 中 and μ 低 are the membership function values ​​of high, medium and low risks, respectively, indicating the fuzzy membership degree of risk.

[0073] Preferably, the value range of the membership degree is between 0 and 1, and is adjusted according to the actual risk level. For example, under high risk conditions, μ 高 The value of may be set to 0.8 or more, while μ 中 and μ 低 They are set below 0.2 and 0.1 respectively to ensure sensitivity under high-risk conditions.

[0074] w 高 、w 中 、w 低 are weight factors for high, medium, and low risk conditions, respectively. Preferably, they can be dynamically adjusted based on historical data and environmental parameters to be closer to the actual risk. For example, under transportation conditions with drastic humidity fluctuations, w 中 and w 高 The proportion makes the risk index more sensitive to the influence of humidity.

[0075] The fuzzy risk correction model can effectively enhance the prediction applicability of the system by fuzzifying the uncertainty parameters. Especially under complex and changeable transportation conditions, fuzzy risk correction can make risk assessment more robust.

[0076] Preferably, in one embodiment of the present invention, in the specimen state detection module of the present invention, an image recognition model is further used to perform state detection on the specimen, and image and / or video information of the specimen is collected to obtain the state information of the specimen. Preferably, the state detection module is composed of an image acquisition unit 1 and a video acquisition unit 2, wherein the image acquisition unit 1 is used to take a static photo of the specimen, and the video acquisition unit 2 is used to collect video information of the specimen during transportation. The image recognition model analyzes the collected image and video data to detect whether there are abnormal points in the specimen.

[0077] For example, when collecting specimen images, the image recognition model can capture problems such as surface contamination, cracks or label damage. The video acquisition unit 2 is used to record the movement of the specimen during transportation, thereby identifying possible collisions or tilts. This state detection process not only increases the quality control of the specimen, but also provides more detailed data for subsequent inspections.

[0078] The present invention realizes intelligent monitoring of specimen status through image and video acquisition, greatly improves the reliability and accuracy of specimen detection, and effectively reduces the risk of misdetection due to abnormal specimen status.

[0079] The present invention provides an intelligent management system for specimens used for medical biological testing, which is used to implement specimen preservation information management, transportation monitoring and reception management, and ensure quality control of the entire process from specimen collection to testing. The system includes the following modules:

[0080] The storage information module 3 is used to establish and maintain the initial storage information database of the specimen, record the specimen number, type, storage condition parameters and collection time. Preferably, this module can quickly query the basic information of the specimen and track the changes in its storage conditions.

[0081] The transport module 4 is used to monitor the environmental parameters of the specimen during transportation, especially the temperature and humidity. The module is equipped with sensors that can obtain information such as temperature and humidity in real time and transmit the data to the data server 5. Preferably, the temperature sensor and humidity sensor are set within the range of -20°C to 40°C and 10%-90% humidity to ensure that the monitoring covers different transportation conditions.

[0082] The data server 5 is used to store the environmental parameters transmitted by the transport module 4 and update the saved information database according to the parameter changes. Through the data server 5, the content of the saved information database can be synchronized in real time to ensure the timeliness and accuracy of the data.

[0083] The specimen detection module 6 is equipped with an image acquisition unit 1 and a video acquisition unit 2, and is used to detect the status of the specimen. During the specimen receiving and management process, the specimen detection module 6 uploads the collected image and video information to the system for further analysis to identify potential problems during transportation.

[0084] The risk prediction module 7 evaluates the transportation risk of the specimen based on a progressive risk prediction algorithm and generates an early warning message when the risk exceeds a set threshold. Preferably, the risk threshold can be set to R_f>4.0 to ensure that the system can respond in time and issue a warning under high-risk conditions.

[0085] The system has a clear structure, and the modules are interconnected through data flows, ensuring that specimen information is accurately and completely preserved and updated throughout the entire management process, ultimately achieving intelligent management of the entire process from specimen collection to testing.

[0086] Preferably, in one embodiment of the present invention, the information storage module 3 includes a data terminal and a collection terminal. The data terminal is used to record and store the basic information of the specimen, and the collection terminal is used to collect the storage condition parameters of the specimen during collection and transmit the data to the data terminal. In practical applications, the data terminal can use a structured storage tool such as an SQL database to ensure the integrity of the specimen information.

[0087] The collection terminal automatically records environmental parameters, especially temperature and humidity, during the specimen collection process. For example, when the storage conditions are set at 2-8°C, the collection terminal will record the temperature fluctuations and transmit them to the data terminal in real time. This information is transmitted to the risk prediction module as initial data during the transportation phase for further risk analysis.

[0088] The setting of this module simplifies the collection and storage process of specimen information, improves data transmission efficiency, and ensures that specimen information remains consistent and accurate throughout the entire process.

[0089] Preferably, in one embodiment of the present invention, the risk prediction module 7 constructs a progressive risk assessment system through a progressive risk prediction algorithm, combined with an adaptive nonlinear multidimensional risk function, multiple dynamic autocorrelation correction, differential geometry risk curvature analysis, adaptive entropy weight optimization, and dynamic fuzzy risk correction, so as to achieve real-time adjustment and output of the risk prediction results of the specimen. Based on the data provided by the transportation module 4 and the storage information module 3, the risk prediction module 7 gradually improves the accuracy and sensitivity of the risk assessment through a progressive algorithm.

[0090] In the progressive risk prediction, first, the risk prediction module 7 obtains the initial risk index through an adaptive nonlinear multidimensional risk function, which is calculated based on environmental parameters such as temperature, humidity and time. Next, through multiple dynamic autocorrelation corrections, time series corrections are introduced into the risk index to capture risk fluctuations during transportation. After that, the risk curvature is further adjusted based on the curvature analysis of differential geometry to identify sudden risk changes. Subsequently, entropy weight optimization allows the system to dynamically assign weights according to actual environmental conditions, and finally adjusts the risk through fuzzy risk correction, so that the final risk prediction result R f Closer to the actual situation.

[0091] For example, in a transportation environment with unstable temperature control, the risk prediction module 7 can dynamically increase the temperature-related weights when the temperature fluctuation increases, so that the system can respond to the risks brought by temperature changes more promptly. The progressive evaluation structure of this module makes risk prediction more forward-looking and can provide timely decision support for specimen management.

[0092] In summary, the intelligent management method and system for medical test specimens of the present invention realizes the intelligent management of the whole process from specimen collection to detection through progressive risk prediction algorithm and multi-module collaborative work. In practical applications, the risk assessment and specimen status detection of the present invention not only improve the quality control level of medical test specimens, but also significantly improve the management efficiency of specimens, providing a solid guarantee for medical tests.

[0093] In order to verify the superiority of the present invention in the intelligent management of medical test specimens, we designed an embodiment and a comparative example to ensure that the risk of specimens can be accurately assessed and predicted in the actual transportation environment, and ultimately effectively guarantee the integrity and quality of the specimens. The main indicators of the test include the temperature and humidity fluctuations of the specimens during transportation, the final risk index, and the integrity of the specimen status detection. All indicators are based on the innovation of the present invention and are tested using authoritative medical standards to reflect the advantages of the present invention in risk prediction, environmental parameter control, and specimen quality management.

[0094] The embodiment adopts the progressive risk prediction algorithm of the present invention, including five progressive steps, namely, adaptive nonlinear multidimensional risk function, multiple dynamic autocorrelation correction, differential geometry risk curvature analysis, adaptive entropy weight optimization and dynamic fuzzy risk correction, to enhance the accuracy and adaptability of risk prediction. The embodiment monitors the temperature, humidity and time of the specimen environment in real time during the actual transportation process, and makes dynamic adjustments according to the changes in environmental parameters to ensure the stability of the specimen during transportation.

[0095] The comparison ratio uses the traditional linear risk assessment method, which does not have a progressive risk prediction structure. The comparison ratio only performs a simple linear weighted assessment of changes in temperature and humidity, and lacks processing in multi-dimensional nonlinear risk prediction and adaptive tuning. This traditional method often shows low prediction accuracy and environmental adaptability in environments with complex transportation conditions and large parameter fluctuations.

[0096] In order to evaluate the performance of the present invention in practical applications, we tested the embodiments and comparative examples according to the following indicators:

[0097] 1. Temperature fluctuation: Use high-precision temperature sensors to monitor the temperature fluctuation of specimens during transportation to ensure that the temperature is maintained within the set range (2-8°C) in a complex transportation environment. The smaller the temperature fluctuation, the more stable the risk prediction and control.

[0098] 2. Humidity fluctuations: Use precision humidity sensors to record humidity changes and control humidity between 60-80%. Stable humidity values ​​can avoid the risk of specimen inactivation or contamination.

[0099] 3. Risk Index R f :Evaluate the transportation risk based on the final risk index. The lower the index, the more controllable the risk. The risk index is calculated by the formula R f =μ 高 ·R′·w 高 +μ 中 ·R′·w 中 +μ 低 ·R′·w 低 Calculated.

[0100] 4. Specimen status detection: The integrity and surface condition of the specimens after transportation are detected through the image recognition model to evaluate whether the specimens meet the requirements of subsequent inspections. The integrity score is based on a 10-point scale, and the higher the score, the better the specimen status.

[0101] The test results and analysis are as follows:

[0102] index Example) Comparative Example Temperature fluctuation range (℃) ±0.3 ±1.2 Humidity fluctuation range (%) ±1.5 ±5.0 Risk Index R 2.1 5.3 Specimen integrity scoring 9.5 6.8

[0103] In terms of temperature fluctuation, the progressive risk prediction algorithm in the embodiment effectively controls the temperature fluctuation range through multi-level tuning, and always maintains it within ±0.3°C, while the traditional linear risk prediction method used in the comparative example cannot accurately predict the complex changes in environmental parameters, resulting in large temperature fluctuations of ±1.2°C. The significant difference in temperature fluctuations illustrates the advantages of the nonlinear multidimensional risk function of the present invention in controlling environmental parameter fluctuations, especially when the temperature parameters fluctuate violently, the present invention can better maintain temperature stability and reduce the impact on the activity of the specimen.

[0104] In terms of humidity control, the present invention optimizes environmental changes in real time through adaptive entropy weight optimization, so that the humidity fluctuation is only ±1.5%, while the humidity fluctuation of the control ratio is as high as ±5.0%. This is because the entropy weight optimization model in the present invention can dynamically allocate the weight factor of humidity according to the changes in the actual transportation environment, so that the humidity can be effectively adjusted in different transportation stages. The stability of humidity is crucial to the preservation of specimens. Large fluctuations in humidity will affect the integrity of the specimens. The superiority of the present invention in humidity control allows the activity of the specimens to be better maintained.

[0105] In terms of risk index, the final risk index R generated by the progressive risk prediction algorithm used in the embodiment is f The risk index of the control group is 2.1, while the risk index of the control group is 5.3. The low risk index value reflects the advantages of the present invention in multi-level risk optimization and dynamic fuzzy correction. Especially when the environmental conditions are complex and changeable, the embodiment can dynamically adjust the risk weight through fuzzy risk correction, and finally achieve accurate control of the risk. The control group adopts a simple linear weighted model, and the risk assessment is relatively rough, which fails to effectively respond to the fluctuations in transportation conditions, resulting in a significantly higher risk index.

[0106] In terms of specimen status detection, the integrity score of the embodiment reached 9.5 points, while the control score was only 6.8 points. This shows that the image recognition module of the present invention is effective in specimen quality control, and can analyze the surface conditions of the specimens through image and video data to ensure that the specimens remain in good condition after transportation. The specimens of the control group were obviously damaged and contaminated during transportation, indicating that the traditional linear method failed to effectively adjust the changes in the specimen environment in real time, resulting in damage to the specimen state.

[0107] It can be seen from the above test results that the present invention has significant advantages in the process of intelligent specimen management. The progressive risk prediction algorithm is significantly better than the traditional method in controlling temperature and humidity fluctuations, ensuring the stability of specimens during transportation. In addition, the risk index in the embodiment is low and the specimen status score is high, indicating the excellent performance of the present invention in dynamic tuning, risk prediction and quality control. These results further verify the practicality and innovation of the present invention in the intelligent management of medical test specimens, effectively reducing the quality loss of specimens due to fluctuations in the transportation environment, and ensuring the integrity of specimens from collection to inspection.

[0108] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for intelligent management of specimens for medical examination, characterized in that: The method comprises the following steps: Establishing a specimen preservation information database to record the specimen number, specimen type, preservation condition parameters and collection time, wherein the preservation information database forms the initial preservation information of the specimen; Based on the specimen information in the preservation information database, the environmental parameters of the specimen during transportation, including temperature, humidity and time parameters, are updated in real time, and the environmental parameters are updated in the preservation information database; Based on the updated environmental parameters, a progressive risk prediction algorithm is used to intelligently predict the transportation risk of the specimens, wherein the progressive risk prediction algorithm includes a multidimensional nonlinear risk function, an autocorrelation correction model, a curvature analysis, an entropy weight optimization, and a fuzzy risk correction; Verify whether the specimen meets the testing requirements based on the predicted risk index; Perform status detection on specimens that meet the detection requirements and obtain image information and / or video information of the specimens for further inspection of the specimens.

2. The intelligent management method for medical biological testing specimens according to claim 1, characterized in that: The progressive risk prediction algorithm includes a multidimensional nonlinear risk function, which is used to obtain an initial risk index, and the initial risk index is obtained by the following formula: Among them, R is the initial risk index, which indicates the preliminary assessment value of the risk of the specimen during transportation; α, β, γ, δ, and η are risk coefficients, which respectively control the influence weight of each parameter; T is the current transportation temperature, T c is the preset critical temperature; H is the current transport humidity value, H c is the preset allowable critical humidity; t is the current transportation time, and t0 is the initial collection time of the specimen.

3. The intelligent management method for medical biological testing specimens according to claim 2, characterized in that: The progressive risk prediction algorithm includes an autocorrelation correction model, which performs time series correction on the initial risk index based on historical risk data to obtain a corrected risk index, and the corrected risk index is obtained by the following formula: Among them, R′ is the modified risk index, which represents the risk assessment value adjusted based on time series; ρ i is the autocorrelation correction factor, which is used to adjust the impact of historical risk changes; R i-1 and R i-2 are the first two risk indices respectively; n is the correction order, which is automatically determined according to the transportation data.

4. The intelligent management method for medical biological testing specimens according to claim 3, characterized in that: The progressive risk prediction algorithm includes a risk curvature analysis model based on differential geometry, wherein the risk curvature analysis model analyzes the risk curvature based on the modified risk index to obtain a risk curvature value, and the risk curvature value is obtained by the following formula: Among them, K is the risk curvature value, which indicates the sensitivity of risk changes; are the second-order partial derivatives of the modified risk index in the temperature, humidity and time directions, respectively.

5. The intelligent management method for medical biological testing specimens according to claim 4, characterized in that: The progressive risk prediction algorithm includes an adaptive entropy weight optimization model, which optimizes the risk weight factor by calculating the information entropy of the environmental parameters to obtain an optimized weight distribution, and the weight factor is determined by the following formula: Among them, w i is the i-th risk weight factor, which is used to optimize the contribution of each parameter to the risk; p ij is the probability value of the i-th factor in the j-th sample, indicating the weight influence of the factor under different environmental conditions.

6. The intelligent management method for specimens for medical biological testing according to claim 5, characterized in that: The progressive risk prediction algorithm includes a fuzzy risk correction model, which performs risk correction on the optimized weight distribution based on a membership function to obtain a final risk index, which is determined by the following formula: R f =μ 高 ·R′·w 高 +m 中 ·R′·w 中 +m 低 ·R′·w 低 Among them, R f is the final risk index, which indicates the final risk assessment value after fuzzy correction; μ 高 , μ 中 and μ 低 are the membership function values ​​of high, medium and low risks, respectively, indicating the fuzzy membership degree of risk; w 高 、w 中 、w 低 They are high, medium and low risk weight factors respectively.

7. The intelligent management method for medical biological testing specimens according to claim 1, characterized in that: The specimen status detection is based on an image recognition model, and the image recognition model collects image and / or video data of the specimen to obtain abnormal information of the specimen.

8. An intelligent management system for medical biological testing specimens implementing the method according to any one of claims 1 to 7, characterized in that: The system includes modules for implementing specimen preservation information management, transportation monitoring and reception management; the system includes: A preservation information module, used to establish an initial preservation information database of the specimen; A transport module, used for monitoring and transmitting environmental parameters of the specimen during transport to a data server; A data server, used for storing the environmental parameters transmitted by the transport module and updating them to the saved information database; A specimen detection module, used to detect the state of the specimen and obtain image or video information; The risk prediction module predicts the risk of the specimen during transportation based on a progressive risk prediction algorithm.

9. The intelligent management system for medical biological testing specimens according to claim 8, characterized in that: The storage information module includes a data terminal and a collection terminal, and the collection terminal is used to collect initial storage information of the specimen and transmit the information to the data terminal.

10. The intelligent management system for medical biological testing specimens according to claim 8, characterized in that: The risk prediction module combines adaptive nonlinear multidimensional risk function, multiple dynamic autocorrelation correction, differential geometry risk curvature analysis, adaptive entropy weight optimization and dynamic fuzzy risk correction to form a progressive risk assessment system for real-time adjustment and output of the risk prediction results of the specimen.

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