Intelligent patient follow-up visit management system and method based on Internet of Things

Abstract

The invention relates to the technical field of intelligent medical treatment, and particularly discloses an intelligent patient follow-up visit management system and method based on the Internet of Things. Error correction is carried out on medical detection equipment used by a user according to the scientific parameters, detection result data of the medical detection equipment is analyzed, management feedback is carried out on the detection result data of the medical detection equipment, the credibility of the detection result data is enhanced, and finally the network quality factor of the network environment to which the medical detection equipment belongs is judged. Therefore, the detection result of the medical detection equipment in the intelligent patient follow-up visit management subsystem is uploaded to the network for management feedback, so that the efficiency and effect of patient follow-up visit management are remarkably improved, the comprehensiveness and accuracy of data are ensured, and a stable and reliable decision basis is provided for follow-up visit management.

Description

Technical Field

[0001] The present invention relates to the field of smart medical technology, and in particular to an intelligent patient follow-up management system and method based on the Internet of Things. Background Art

[0002] With the continuous advancement of medical technology and the intensification of population aging, patient follow-up management plays an increasingly important role in medical services. Traditional patient follow-up methods often rely on manual calls, emails or outpatient reviews. At the same time, traditional intelligent medical management systems generally focus too much on the analysis link after data collection.

[0003] For example, the invention patent with announcement number CN116343980B announces a method and system for processing follow-up data based on smart medical care, which involves the field of medical technology, including obtaining historical information of patients awaiting follow-up; receiving a follow-up information form filled out by the patients awaiting follow-up, extracting key information from the follow-up information form and clustering the key information; training the results after clustering based on the BERT pre-trained language model to obtain key semantic features; associating the historical information with the key semantic features, and using the intelligent auxiliary diagnosis algorithm of the decision tree to perform auxiliary diagnosis analysis on the associated information.

[0004] For example, the invention patent with publication number CN117912662A discloses an artificial intelligence smart nursing system based on the Internet of Things, which includes a vital sign collection module, a disease trend warning module, a smart ward module, and an outpatient continuing care and home care module; the vital sign collection module is used to collect and provide patients' vital sign data, the disease trend warning module warns of the disease based on the collected data and provides personalized nursing services to patients, and the smart ward module provides personalized services to patients based on vital sign data and warning information, and feeds back ward nursing information to the outpatient continuing care and home care module. The outpatient continuing care and home care module provides regular follow-up and health management services for discharged patients based on vital sign data and warning information, and optimizes the patient's home care plan based on the nursing information of the smart ward module.

[0005] However, in the process of implementing the embodiments of the present application, it was found that the above-mentioned technology has at least the following technical problems: the existing systems for intelligent medical management generally show excessive reliance on the post-analysis link of medical data collection, which may make the entire intelligent medical management decision-making process based on unstable or even misleading data, thereby reducing the scientificity and effectiveness of intelligent medical data processing. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides an intelligent patient follow-up management system and method based on the Internet of Things, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: In a first aspect, the present invention provides an intelligent patient follow-up management system based on the Internet of Things, including: a detection process evaluation module, which is used to perform health detection on users through medical detection equipment, collect detection process parameters of medical detection equipment, evaluate the detection process effectiveness factor of the medical detection equipment, and compare it with the detection process effectiveness threshold. If the detection process effectiveness factor of the medical detection equipment is greater than the detection process effectiveness threshold, the detection result analysis module is executed, otherwise, management feedback is given to the detection process of the medical detection equipment; a detection result analysis module, which is used to obtain the detection result data of the medical detection equipment, analyze the data collection quality index of the medical detection equipment, and compare it with the data collection quality threshold. If the data collection quality index of the medical detection equipment is greater than the data collection quality threshold, the result upload determination module is executed, otherwise, management feedback is given to the detection result data of the medical detection equipment; a result upload determination module, which is used to collect network environment parameters to which the detection result of the medical detection equipment is uploaded to the cloud, and determine the network quality factor of the network environment to which the medical detection equipment belongs, thereby managing feedback is given to the detection result upload network of the medical detection equipment in the intelligent patient follow-up management subsystem.

[0008] As a further solution, the detection process of the medical detection equipment is managed and fed back, and the specific management feedback process is: the numerical value of the detection deviation time of the medical detection equipment, the numerical value of the electrical signal deviation strength of the medical detection equipment within the data acquisition cycle, and the numerical value of the temperature deviation value of the temperature detection position point of the medical detection equipment within the data acquisition cycle are respectively compared with the corresponding deviation reference values, and based on the comparison result, a detection process parameter management instruction of the medical detection equipment is generated, and parameter management feedback is performed on the detection process of the medical detection equipment, thereby evaluating the detection process again.

[0009] As a further solution, the test result data of the medical testing equipment are managed and fed back. The specific management feedback process may be to perform difference processing on the data collection quality index of the medical testing equipment and the data collection quality threshold to obtain the data collection quality difference of the medical testing equipment, and match it with the update log corresponding to each data collection quality difference interval stored in the management database to obtain the update log of the medical testing equipment, and manage and feedback the test result data of the medical testing equipment through the update log of the medical testing equipment, thereby performing the test result analysis again.

[0010] As a further solution, the test results of the medical testing equipment in the intelligent patient follow-up management subsystem are uploaded to the network for management feedback. The specific management feedback process is: matching the network quality factor of the network environment to which the medical testing equipment belongs with the data transmission protocol corresponding to each quality factor interval stored in the management database, and obtaining the data transmission protocol corresponding to the network environment to which the medical testing equipment belongs. Thus, the test results of the medical testing equipment in the intelligent patient follow-up management subsystem are uploaded to the network for management feedback through the data transmission protocol corresponding to the network environment to which the medical testing equipment belongs.

[0011] The second aspect of the present invention provides a method for an intelligent patient follow-up management system based on the Internet of Things, characterized in that it includes: S1. Performing health checks on users through medical testing equipment, collecting testing process parameters of the medical testing equipment, evaluating the testing process effectiveness factor of the medical testing equipment, and comparing it with the testing process effectiveness threshold. If the testing process effectiveness factor of the medical testing equipment is greater than the testing process effectiveness threshold, then obtaining the testing result data of the medical testing equipment, otherwise, managing feedback is given to the testing process of the medical testing equipment; S2. Analyzing the data collection quality index of the medical testing equipment through the testing result data of the medical testing equipment, and comparing it with the data collection quality threshold. If the data collection quality index of the medical testing equipment is greater than the data collection quality threshold, then collecting the network environment parameters to which the testing results of the medical testing equipment belong when uploading to the cloud, otherwise, managing feedback is given to the testing result data of the medical testing equipment; S3. Determining the network quality factor of the network environment to which the medical testing equipment belongs based on the network environment parameters to which the testing results of the medical testing equipment belong when uploading to the cloud, thereby managing feedback is given to the uploading of the testing results of the medical testing equipment in the intelligent patient follow-up management subsystem to the network.

[0012] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention provides an intelligent patient follow-up management system and method based on the Internet of Things. By collecting the detection process parameters of medical detection equipment and evaluating the detection process effectiveness factors of the medical detection equipment, the user's use of medical detection equipment is corrected according to scientific parameters, the detection result data of the medical detection equipment is analyzed, and management feedback is provided on the detection result data of the medical detection equipment to enhance the trust of the detection result data. Finally, the network quality factor of the network environment to which the medical detection equipment belongs is determined, and the detection results of the medical detection equipment in the intelligent patient follow-up management subsystem are uploaded to the network for management feedback, thereby significantly improving the efficiency and effect of patient follow-up management, while ensuring the comprehensiveness and accuracy of the data, providing a solid and reliable decision-making basis for follow-up management, and also helping to enhance patient participation and satisfaction, further improving the scientificity and effectiveness of intelligent medical data processing, and providing strong support for optimizing treatment plans and improving patient health levels.

[0013] (2) The present invention collects and evaluates the detection process parameters of medical detection equipment, and generates detection process management feedback for personalized medical detection equipment based on the user's unique usage error tendency, which helps users to quickly correct errors and improve the accuracy and efficiency of detection operations.

[0014] (3) The present invention collects and determines the network environment parameters to which the test results of medical testing equipment are uploaded to the cloud, and automatically matches the appropriate data transmission protocol under the current network conditions in combination with the real-time, sensitivity and importance requirements of medical testing data, thereby ensuring the integrity and timeliness of the uploaded test results. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0016] Figure 1 It is a schematic diagram of system module connection of the present invention.

[0017] Figure 2 The figure is a schematic flow chart of the method steps of the present invention.

[0018] Figure 3 It is a schematic diagram of a network transmission rate curve involved in the present invention.

[0019] Reference numerals: 1. rate detection position point; 2. fixed time interval. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0021] Reference Figure 1 As shown, the first aspect of the present invention provides an intelligent patient follow-up management system based on the Internet of Things, including: a detection process evaluation module, a detection result analysis module, and a result upload determination module.

[0022] The first aspect of the present invention provides an intelligent patient follow-up management system based on the Internet of Things, which also includes a management database, wherein the management database is used to store the influencing factors corresponding to the unit values ​​of the detection deviation time, the influencing factors corresponding to the unit values ​​of the electrical signal deviation strength, the influencing factors corresponding to the unit values ​​of the temperature deviation value, the influencing factors corresponding to the unit values ​​of the update lag time, the influencing factors corresponding to the unit values ​​of the data collection omission amount, the influencing factors corresponding to the unit values ​​of the data collection frequency discrete value, the update logs corresponding to each data collection quality difference interval, the weight factors corresponding to the data collection quality index, the influencing factors corresponding to the unit values ​​of the data transmission time, the influencing factors corresponding to the unit values ​​of the network transmission rate discrete value, the data transmission protocol corresponding to each quality factor interval, the detection process effective threshold, the data collection quality threshold, the deviation reference value, the electrical signal reference strength of the medical detection equipment, and the reference data collection amount of the medical detection equipment within the data collection cycle.

[0023] The detection process evaluation module is used to perform health checks on users through medical detection equipment, collect detection process parameters of the medical detection equipment, evaluate the detection process effectiveness factor of the medical detection equipment, and compare it with the detection process effectiveness threshold. If the detection process effectiveness factor of the medical detection equipment is greater than the detection process effectiveness threshold, the detection result analysis module is executed; otherwise, management feedback is provided for the detection process of the medical detection equipment.

[0024] In a specific embodiment, the present invention collects and evaluates the detection process parameters of the medical detection equipment, generates detection process management feedback for personalized medical detection equipment based on the user's unique tendency to use errors, thereby helping the user to quickly correct errors and improve the accuracy and efficiency of detection operations.

[0025] The above detection process effective threshold value represents the minimum value of the reasonable range of the detection process effective factor of the medical detection equipment, which is extracted from the management database.

[0026] It should be noted that this example only analyzes the detection process of the pulmonary function tester in detail. Of course, this example can also be applied to other key equipment. It aims to show how modern medical detection equipment can accurately and efficiently collect data to assist medical decision-making. A single device is selected for the whole description, rather than a general discussion of all medical equipment, because each medical equipment has its own unique detection principle, application scenario and technical requirements. As a representative of them, the detection process of the pulmonary function tester involves multiple fields such as sensor technology, signal processing, and data analysis, which is highly professional and complex. The user in this embodiment refers to an individual or group who has close contact with the hospital, including but not limited to patients who receive long-term treatment, individuals who receive regular health examinations, and medical management personnel; the above health test refers to recording, sorting and analyzing the medical activity information such as diagnosis, treatment and examination that the user has received in the hospital in the past, forming a complete set of medical history records, and continuously and dynamically observing the health status of the user through the medical history records. For example, if a user undergoes lung function treatment in the hospital, the user's lung function health parameters need to be tested.

[0027] Specifically, the evaluation process of evaluating the effectiveness factor of the detection process of the medical detection equipment is as follows: A time period corresponding to the detection process of the medical detection equipment is extracted from the detection process parameters of the medical detection equipment and marked as the data collection period of the medical detection equipment, a reference detection time of each detection data belonging to the medical detection equipment is obtained, and the reference detection total time of the medical detection equipment is accumulated to obtain the reference detection total time of the medical detection equipment, and the reference detection total time of the medical detection equipment is differenced with the time corresponding to the data collection period to obtain the detection deviation time of the medical detection equipment; the above-mentioned data collection period refers to the time period in which the user uses the medical detection equipment to perform health checks, and the time corresponding to the data collection period is obtained by accumulating the time between the start instruction moment and the end instruction moment corresponding to each detection data received by the medical detection equipment; the reference detection time of each detection data belonging to the above-mentioned medical detection equipment refers to the time period in which the user uses the medical detection equipment to perform health checks for different health conditions. Health indicators, the time required for medical testing equipment to complete a complete test and generate valid data, and the reference test time for each test data is summarized by medical institutions and scientific researchers during clinical use and research; such as when a pulmonary function tester tests the user's vital capacity, respiratory rate and tidal volume, the pulmonary function tester first tests the vital capacity for 10 seconds, the respiratory rate for 50 seconds, and finally tests the tidal volume for 100 seconds. The starting time point of the data collection cycle is the start instruction moment corresponding to the test of vital capacity, and the duration of the data collection cycle is 160 seconds. The total reference test time after the reference test time of vital capacity, respiratory rate and tidal volume is accumulated is 200 seconds. The test deviation time is the total reference test time of 200 seconds minus the data collection cycle time of 160 seconds, and the final test deviation time is 40 seconds.

[0028] The real-time electrical signal strength of the medical testing equipment during the data collection period is extracted from the detection process parameters of the medical testing equipment, the average electrical signal strength of the medical testing equipment during the data collection period is obtained by mean processing, the electrical signal reference strength of the medical testing equipment is obtained, and the difference processing is performed with the average electrical signal strength of the medical testing equipment during the data collection period to obtain the electrical signal deviation strength of the medical testing equipment during the data collection period; the real-time electrical signal strength of the above-mentioned medical testing equipment during the data collection period is obtained by the built-in sensor of the medical testing equipment. For example, a pulmonary function tester senses the user's breathing signal through its built-in sensor (such as a flow sensor) and converts it into a current signal. The current signal strength is the electrical signal strength; the electrical signal reference strength of the above-mentioned medical testing equipment represents a reference value for analyzing the average electrical signal strength of the medical testing equipment, which is extracted from the management database.

[0029] The real-time temperature of each temperature detection point of the medical detection equipment within the data collection period is extracted from the detection process parameters of the medical detection equipment, the average temperature of the temperature detection point of the medical detection equipment within the data collection period is obtained by mean processing, and the difference processing is performed with the reference temperature to obtain the temperature deviation value of the temperature detection point of the medical detection equipment within the data collection period. The above-mentioned temperature detection point of the medical detection equipment refers to the temperature sensor point set by the medical device manufacturer on the medical detection equipment. The real-time temperature of each temperature detection point of the medical detection equipment within the data collection period is obtained by the temperature sensor; the above-mentioned reference temperature refers to the reference value for analyzing the average temperature of the temperature detection point of the medical detection equipment within the data collection period, and the reference value is formulated by the medical device manufacturer based on the working principle, material properties and expected use environment of the medical detection equipment.

[0030] Thus, an evaluation method for the detection process effectiveness factor of the medical detection equipment is obtained. In this embodiment, the detection process effectiveness factor of the medical detection equipment is obtained by comprehensive analysis of the detection deviation duration of the medical detection equipment, the electrical signal deviation intensity of the medical detection equipment in the data acquisition cycle, and the temperature deviation value of the temperature detection position point of the medical detection equipment in the data acquisition cycle. It is used to evaluate the value of the effectiveness of the detection process of the medical detection equipment. The specific method is as follows: ; It is the detection deviation time of the medical detection equipment, which means that each detection data detected by the medical detection equipment corresponds to a reference detection time. The accumulated total reference detection time of the medical detection equipment is subtracted from the time corresponding to the data collection cycle. Assuming that the accumulated total reference detection time of the medical detection equipment is 60 seconds and the time corresponding to the data collection cycle is 50 seconds, the detection deviation time is 10 seconds.

[0031] It is the deviation strength of the electrical signal of the medical detection equipment during the data collection period, which refers to the result of subtracting the average electrical signal strength of the medical detection equipment during the data collection period from the reference electrical signal strength of the medical detection equipment.

[0032] It is the temperature deviation value of the temperature detection point of the medical detection equipment during the data collection period, which refers to the result of subtracting the reference temperature from the average temperature of the temperature detection point of the medical detection equipment during the data collection period.

[0033] e is a natural constant, It is the impact factor corresponding to the unit value of the detection deviation time preset in the management database, which represents the numerical value of the influence of the unit value of the detection deviation time on the effective factors of the detection process of the medical detection equipment. When used, the impact factor corresponding to the unit value of the detection deviation time can be directly obtained from the management database, and the corresponding relationship can be a preset mapping relationship. For example, the detection deviation time and the impact factor corresponding to the unit value of the detection deviation time preset in the management database form a mapping set, and the real-time detection deviation time is input into the mapping set to obtain the impact factor corresponding to the unit value of the detection deviation time. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0034] It is the influence factor corresponding to the unit value of the electric signal deviation intensity preset in the management database, which represents the numerical value of the influence of the unit value of the electric signal deviation intensity on the effective factor of the detection process of the medical detection equipment. When used, the influence factor corresponding to the unit value of the electric signal deviation intensity can be directly obtained from the management database, and the corresponding relationship can be a preset mapping relationship. For example, the electric signal deviation intensity and the influence factor corresponding to the unit value of the electric signal deviation intensity preset in the management database form a mapping set, and the real-time electric signal deviation intensity is input into the mapping set to obtain the influence factor corresponding to the unit value of the electric signal deviation intensity. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0035] It is the influence factor corresponding to the temperature deviation value unit value preset in the management database, which represents the influence degree of the temperature deviation value unit value on the effective factor of the detection process of the medical detection equipment. When used, the influence factor corresponding to the temperature deviation value unit value can be directly obtained from the management database, and the corresponding relationship can be a preset mapping relationship. For example, the temperature deviation value and the influence factor corresponding to the temperature deviation value unit value preset in the management database form a mapping set, and the real-time temperature deviation value is input into the mapping set to obtain the influence factor corresponding to the temperature deviation value unit value. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0036] in, It is an effective factor of the detection process of the medical detection equipment. When the temperature deviation value of the medical detection equipment increases significantly, it usually indicates that the equipment is in a non-steady-state working mode, and its internal operating environment or mechanism has deviated from the preset stable state. Under this non-steady-state condition, the electrical signal transmission and processing process of the medical detection equipment may be disturbed, resulting in a significant increase in the electrical signal deviation intensity, that is, the stability and accuracy of the signal in the detection process are reduced. At the same time, since the accuracy of the medical detection equipment in signal control is impaired, the reference detection time will be extended, making it difficult for users to understand the actual detection time in time, resulting in a significant increase in the detection deviation time. The combined effect of the three parameters will cause the detection process of the medical detection equipment to not conform to the established detection process, and the accuracy of the detection results cannot be guaranteed. Therefore, through the integrated analysis of the above parameters, it is beneficial for the medical detection equipment to respond quickly and take effective measures to ensure that the medical detection equipment can re-enter a stable, efficient and accurate detection state.

[0037] In this exemplary embodiment, a table showing changes in effective factors of the detection process of the above-mentioned medical detection equipment and their corresponding parameters is shown in Table 1: Table 1 Changes of effective factors and corresponding parameters of the testing process of medical testing equipment In this example embodiment, the value of the influence factor corresponding to the unit value of the detection deviation time is set to 0.12, the value of the influence factor corresponding to the unit value of the electric signal deviation strength is set to 0.7, and the value of the influence factor corresponding to the unit value of the temperature deviation value is set to 0.18. It can be seen from Table 1 that the three parameters of detection deviation time, electric signal deviation strength and temperature deviation value are inversely proportional to the effective factor of the detection process, which is specifically manifested as: the values ​​of the first column, the second column and the third column show a decreasing trend, and the effective factor of the detection process shows an increasing trend.

[0038] Furthermore, the management feedback of the detection process of the medical detection equipment is specifically as follows: the value of the detection deviation time of the medical detection equipment, the value of the electrical signal deviation strength of the medical detection equipment in the data acquisition cycle, and the value of the temperature deviation value of the temperature detection position point of the medical detection equipment in the data acquisition cycle are respectively compared with the corresponding deviation reference values, and based on the comparison result, the detection process parameter management instruction of the medical detection equipment is generated, and the parameter management feedback of the detection process of the medical detection equipment is performed, thereby performing the detection process evaluation again; when the detection process effective factor of the medical detection equipment is less than or equal to the detection process effective threshold, it indicates that the detection process of the medical detection equipment cannot guarantee the accuracy of data collection, and parameter management feedback of the detection process of the medical detection equipment is required; when the detection process effective factor of the medical detection equipment is greater than the detection process effective threshold, it indicates that the detection process of the medical detection equipment can guarantee the accuracy of data collection, and parameter management feedback of the detection process of the medical detection equipment is not required; the above-mentioned deviation reference value refers to the reference value of the three reasonable values ​​in the detection process effective factor of the medical detection equipment, which is extracted from the management database. If a value in the detection process effective factor is greater than the deviation reference value, it indicates that the operation corresponding to the value is abnormal. If a certain value is less than or equal to the deviation reference value, it indicates that the operation abnormality corresponding to the value is small, and the impact on the test result is negligible, so there is no need to feedback it; the above-mentioned based on the comparison result refers to the parameter corresponding to the statistical value greater than the deviation reference value, and the corresponding parameter is packaged into a test process parameter management instruction of the medical detection device, and a voice prompt is given; in an example embodiment, the specific process of management feedback on the detection process of the medical detection device is: in a certain detection process, the detection deviation time and the electric signal deviation intensity are greater than the deviation reference value, and the temperature deviation value is less than the deviation reference value, then the detection process parameter management instruction of the medical detection device is generated, which includes: "The detection time is too short, and it is necessary to re-detect for 120 seconds. The electric signal at the detection position is abnormal, and the instrument needs to be re-placed to the specified position in the manual", and the user re-arranges the test process of the medical detection device according to the voice prompt, thereby re-analyzing the detection process effective factor of the medical detection device. Since the user has understood and mastered the use of the medical detection device through the manual or experience, and when operating under the fine-tuning of the system instruction, the detection process effective factor will significantly exceed the detection process effective threshold. Therefore, after feedback analysis of the detection process effective factor, the detection result analysis can be directly performed.

[0039] The test result analysis module is used to obtain the test result data of the medical testing equipment, analyze the data collection quality index of the medical testing equipment, and compare it with the data collection quality threshold. If the data collection quality index of the medical testing equipment is greater than the data collection quality threshold, the result upload judgment module is executed; otherwise, the test result data of the medical testing equipment is managed and fed back.

[0040] The above-mentioned data collection quality threshold value represents the minimum value of a reasonable range of the data collection quality index of the medical detection equipment, which is extracted from the management database.

[0041] Specifically, the management feedback of the test result data of the medical testing equipment may be performed by performing difference processing on the data collection quality index of the medical testing equipment and the data collection quality threshold to obtain the data collection quality difference of the medical testing equipment, and matching it with the update log corresponding to each data collection quality difference interval stored in the management database to obtain the update log of the medical testing equipment, and performing management feedback on the test result data of the medical testing equipment through the update log of the medical testing equipment, thereby performing the test result analysis again; when the data collection quality index of the medical testing equipment is less than or equal to the data collection quality threshold, it indicates that the data collection quality of the medical testing equipment is poor and cannot meet the requirements of data analysis accuracy, and the test result of the medical testing equipment needs to be analyzed. When the data collection quality index of the medical testing equipment is greater than the data collection quality threshold, it indicates that the data collection quality of the medical testing equipment is good and can meet the requirements of data analysis accuracy, and there is no need to manage and feedback the test result data of the medical testing equipment; the update log corresponding to each of the above data collection quality difference intervals, wherein the corresponding values ​​and rules are formulated by the medical device manufacturer based on the working principle, material properties and expected use environment of the medical testing equipment; in an example embodiment, if the data collection quality difference of the medical testing equipment is -150%, in the data collection quality difference interval [-200%, 0] stored in the management database, the update log corresponding to the data collection quality difference interval [-200%, 0] includes updating the system version to 2.4. The medical testing device is updated to a newer version. If the data collection quality difference of the medical testing device is -280%, in the data collection quality difference interval [-400%, -200%] stored in the management database, the update log corresponding to the data collection quality difference interval [-400%, -200%] includes a repair patch, a calibration date, a query optimization, and an index optimization. The medical testing device is updated and the measured data is received again. The data collection quality index of the medical testing device is re-analyzed. It needs to be explained that after the management feedback of the test result data of the medical testing device, the system update log is an important part of this process. Through the comprehensive effects of mechanisms such as problem identification and resolution, performance monitoring and evaluation, improvement and optimization, standardization and normalization, it can ensure that the data collection quality index of the medical testing device is greater than the data collection Quality threshold, so after re-analyzing the data collection quality index of the medical testing equipment, the result upload judgment can be made. The above update log is only a management feedback method, which is extremely important for the performance optimization of medical testing equipment, so it needs to be described in detail. Of course, there will be medical testing equipment that has been updated to the latest version, but given the rapid iteration of medical testing equipment technology, the equipment version is updated frequently. Although manufacturers and users are committed to keeping the equipment up to date to optimize the detection performance, it is undeniable that there are a few users who are limited by resource allocation, operational complexity, cost considerations, and information acquisition lags. Failure to keep up with the pace of technology to complete the equipment performance upgrade in a timely manner, therefore, under the condition that the medical testing equipment has been updated to the latest version, management feedback on the test result data of the medical testing equipment requires additional management feedback, which is not considered in this example. .

[0042] Specifically, the test result data of the medical testing equipment refers to the real-time values ​​of each test data belonging to the medical testing equipment within the data collection period, which are integrated and marked as the test result data of the medical testing equipment; the test data belonging to the above-mentioned medical testing equipment refers to the user's personalized demand data. For example, when the user uses a pulmonary function tester to test the vital capacity and respiratory rate in a certain test, the test data includes the vital capacity and respiratory rate.

[0043] Furthermore, the data collection quality index of the medical detection equipment is analyzed, and the specific analysis process is as follows: The last update time point of each test data belonging to the medical testing equipment within the data collection period is extracted from the test result data of the medical testing equipment, and difference processing is performed with the start time point corresponding to the data collection period to obtain the update duration of each test data belonging to the medical testing equipment, and corresponding difference processing is performed with the reference test duration of each test data belonging to the medical testing equipment to obtain the update lag duration of each test data belonging to the medical testing equipment, and the average update lag duration of the test data belonging to the medical testing equipment is obtained by average processing; the last update time point of each test data belonging to the medical testing equipment within the data collection period is extracted from the timestamp log belonging to the medical testing equipment.

[0044] The data collection volume of the medical testing equipment during the data collection cycle is extracted from the test result data of the medical testing equipment, the reference data collection volume of the medical testing equipment during the data collection cycle is obtained, and the difference is processed with the data collection volume of the medical testing equipment during the data collection cycle to obtain the data collection omission volume of the medical testing equipment; the data collection volume of the above-mentioned medical testing equipment during the data collection cycle is extracted from the data log belonging to the medical testing equipment; the reference data collection volume of the above-mentioned medical testing equipment during the data collection cycle is extracted from the management database, such as using a pulmonary function tester to detect vital capacity and respiratory rate, the reference data collection volume is 500 kilobytes.

[0045] The data collection cycle is equally divided into data collection sub-cycles, the data collection amount of the medical testing equipment in each data collection sub-cycle is extracted from the test result data of the medical testing equipment, and the ratio is processed with the time length corresponding to the data collection sub-cycle to obtain the data collection frequency of the medical testing equipment in each data collection sub-cycle, and the data collection frequency of the medical testing equipment in each data collection sub-cycle is processed by standard deviation to obtain the discrete value of the data collection frequency of the medical testing equipment; the data collection amount of the above-mentioned medical testing equipment in each data collection sub-cycle is extracted from the data log belonging to the medical testing equipment.

[0046] An analysis method for obtaining a data collection quality index of a medical detection device by comprehensively analyzing the detection process effectiveness factor of the medical detection device. In this embodiment, the data collection quality index of the medical detection device is obtained by comprehensively analyzing the detection process effectiveness factor of the medical detection device, the average update lag time of the detection data belonging to the medical detection device, the data collection omission amount of the medical detection device, and the data collection frequency discrete value of the medical detection device. The specific method is as follows: ; It is the effectiveness factor of the detection process of the medical detection equipment, which is obtained by comprehensive analysis of the detection deviation duration of the medical detection equipment, the electrical signal deviation strength of the medical detection equipment during the data collection cycle, and the temperature deviation value of the temperature detection position point of the medical detection equipment during the data collection cycle. It is a numerical value used to evaluate the effectiveness of the detection process of the medical detection equipment.

[0047] It is the average update lag time of the test data belonging to the medical testing equipment, which refers to the average value of the difference between the update time of each test data belonging to the medical testing equipment and the corresponding reference test time.

[0048] The data collection omission amount of medical testing equipment refers to the amount of data that is not successfully collected due to equipment performance failure during the data collection process of medical testing equipment.

[0049] It is the discrete value of the data collection frequency of the medical testing equipment, which refers to the degree of difference between the data collection frequency and the average data collection frequency during the data collection process of the medical testing equipment.

[0050] is a natural constant, It is the weight factor corresponding to the detection process effectiveness factor, which indicates the proportion of the detection process effectiveness factor to the data collection quality index of the medical detection equipment. When in use, the weight factor corresponding to the detection process effectiveness factor can be directly obtained from the management database, and the corresponding relationship can be a preset mapping relationship. For example, the detection deviation duration, the electrical signal deviation strength and the temperature deviation value are connected with the weight factors corresponding to the detection process effectiveness factors preset in the management database to form a mapping set. The real-time detection deviation duration, the electrical signal deviation strength and the temperature deviation value are input into the mapping set to obtain the weight factor corresponding to the detection process effectiveness factor. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0051] It is the impact factor corresponding to the update lag time unit value preset in the management database, which represents the numerical value of the influence of the update lag time unit value on the data collection quality index of the medical detection equipment. When used, the impact factor corresponding to the update lag time unit value can be directly obtained from the management database, and the corresponding relationship can be a preset mapping relationship. For example, the update lag time and the impact factor corresponding to the update lag time unit value preset in the management database form a mapping set, and the real-time update lag time is input into the mapping set to obtain the impact factor corresponding to the update lag time unit value. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0052] It is the impact factor corresponding to the unit value of data collection omission preset in the management database, which represents the numerical value of the influence of the unit value of data collection omission on the data collection quality index of the medical testing equipment. When used, the impact factor corresponding to the unit value of data collection omission can be directly obtained from the management database, and the corresponding relationship can be a preset mapping relationship. For example, the data collection omission and the impact factor corresponding to the unit value of data collection omission preset in the management database form a mapping set, and the real-time data collection omission is input into the mapping set to obtain the impact factor corresponding to the unit value of data collection omission. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0053] It is the impact factor corresponding to the data acquisition frequency discrete value unit value preset in the management database, which represents the numerical value of the influence of the data acquisition frequency discrete value unit value on the data collection quality index of the medical detection equipment. When used, the impact factor corresponding to the data acquisition frequency discrete value unit value can be directly obtained from the management database, and the corresponding relationship can be a preset mapping relationship. For example, the data acquisition frequency discrete value and the impact factor corresponding to the data acquisition frequency discrete value unit value preset in the management database form a mapping set, and the real-time data acquisition frequency discrete value is input into the mapping set to obtain the impact factor corresponding to the data acquisition frequency discrete value unit value. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0054] in, It is the data collection quality index of medical testing equipment. The effectiveness of the testing process is the prerequisite for ensuring the data collection quality of medical testing equipment. Under the premise that the effectiveness factor of the testing process is greater than the effective threshold of the testing process, if the average update lag time of the test data of the medical testing equipment is long, it indicates that the medical testing equipment has bottlenecks and low efficiency in data processing and collection. This performance deficiency is often accompanied by an increase in the amount of data collection omissions, that is, the actual amount of data collected fails to fully cover the expected range, resulting in a lack of data integrity, making the overall data collection process intermittent and non-uniform, and the data collection frequency is inconsistent, resulting in an increase in the discrete value of the data collection frequency. Therefore, the data collection quality of the medical testing equipment is low, which seriously weakens the overall performance of the medical testing equipment in data collection. Comprehensive analysis of the above parameters can timely take corresponding measures to improve and optimize them, so as to ensure that the medical equipment can continue to provide high-quality and reliable data support, and provide a solid foundation for medical decision-making and patient treatment.

[0055] The result upload determination module is used to collect network environment parameters to which the test results of the medical testing equipment are uploaded to the cloud, determine the network quality factor of the network environment to which the medical testing equipment belongs, and thereby provide management feedback on the upload network of the test results of the medical testing equipment in the intelligent patient follow-up management subsystem.

[0056] In a specific embodiment, the present invention collects and determines the network environment parameters to which the test results of medical testing equipment are uploaded to the cloud, and automatically matches the appropriate data transmission protocol under the current network conditions in combination with the real-time, sensitivity and importance requirements of the medical testing data, thereby ensuring the integrity and timeliness of the uploaded test results.

[0057] Specifically, the test results of the medical testing equipment in the intelligent patient follow-up management subsystem are uploaded to the network for management feedback. The specific management feedback process is as follows: The network quality factor of the network environment to which the medical detection device belongs is matched with the data transmission protocol corresponding to each quality factor interval stored in the management database to obtain the data transmission protocol corresponding to the network environment to which the medical detection device belongs, thereby managing and feedbacking the upload of the detection results of the medical detection device in the intelligent patient follow-up management subsystem to the network through the data transmission protocol corresponding to the network environment to which the medical detection device belongs; in an example embodiment, the specific management feedback process is: the network engineer clarifies the usage scenarios, functional requirements and performance requirements of the data transmission protocol to formulate rules and values ​​for the data transmission protocol corresponding to each quality factor interval. If the network quality factor of the network environment to which the medical detection device belongs is A, and in the quality factor interval [A-1, A+1] stored in the management database, the data transmission protocol corresponding to the quality factor interval [A-1, A+1] is MQTT (Message Queue Telemetry Transmission Protocol), then the data transmission protocol corresponding to the network environment to which the medical detection device belongs at this time is MQTT (Message Queue Telemetry Transmission Protocol), thereby uploading the detection results of the medical detection device to the cloud through the data transmission protocol.

[0058] It should be explained that the above-mentioned cloud refers to a remote server cluster or data center connected via the Internet, which is used to store, process and manage data.

[0059] Specifically, the method for determining the network quality factor of the network environment to which the medical detection device belongs is as follows: ; It is the data collection quality index of medical testing equipment, which is obtained through comprehensive analysis of the effective factor of the testing process of the medical testing equipment, the average update lag time of the testing data belonging to the medical testing equipment, the data collection omissions of the medical testing equipment and the discrete value of the data collection frequency of the medical testing equipment. It is a numerical value used to analyze the data collection quality of the medical testing equipment.

[0060] It refers to the data transmission duration in the network environment to which the medical testing equipment belongs, and the total time required for data to be sent from the medical testing equipment to the receiving end (such as the cloud) through the network.

[0061] It is the discrete value of the network transmission rate of the network environment to which the medical testing equipment belongs, and refers to the degree of fluctuation of the network transmission rate in the network environment.

[0062] e is a natural constant, It is the weight factor corresponding to the data collection quality index preset in the management database, which indicates the proportion of the data collection quality index to the network quality factor of the network environment to which the medical detection equipment belongs. When in use, the weight factor corresponding to the data collection quality index can be directly obtained from the management database, and the corresponding relationship can be a preset mapping relationship. For example, the update lag time, the data collection omission amount, and the data collection frequency discrete value form a mapping set with the weight factor corresponding to the data collection quality index preset in the management database, and the real-time abnormal traffic ratio is input into the mapping set to obtain the weight factor corresponding to the data collection quality index. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0063] It is the impact factor corresponding to the unit value of data transmission time preset in the management database, which represents the degree of influence of the unit value of data transmission time on the network quality factor of the network environment to which the medical detection equipment belongs. When used, the impact factor corresponding to the unit value of data transmission time can be directly obtained from the management database, and the corresponding relationship can be a preset mapping relationship. For example, the data transmission time and the impact factor corresponding to the unit value of data transmission time preset in the management database form a mapping set, and the real-time data transmission time is input into the mapping set to obtain the impact factor corresponding to the unit value of data transmission time. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0064] It is the impact factor corresponding to the unit value of the network transmission rate discrete value preset in the management database, which represents the numerical value of the degree of influence of the unit value of the network transmission rate discrete value on the network quality factor of the network environment to which the medical detection equipment belongs. When used, the impact factor corresponding to the unit value of the network transmission rate discrete value can be directly obtained from the management database, and the corresponding relationship can be a preset mapping relationship. For example, the network transmission rate discrete value and the impact factor corresponding to the unit value of the network transmission rate discrete value preset in the management database form a mapping set, and the real-time network transmission rate discrete value is input into the mapping set to obtain the impact factor corresponding to the unit value of the network transmission rate discrete value. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0065] in, is the network quality factor of the network environment to which the medical testing equipment belongs. The parameters in the data collection quality index are the source of the parameters in the network quality factor. At the same time, the data collection quality index being greater than the data collection quality threshold is a prerequisite for analyzing the network quality factor. Under the premise that the data quality index is greater than the data collection quality threshold, if the network transmission rate fluctuates greatly, it indicates that there are unstable factors in the current network environment, such as network congestion and signal interference, which makes the network transmission rate unable to be stably maintained at a high level, resulting in a large discrete value of the network transmission rate, which further leads to a longer data transmission time for the collected data to the cloud. A longer transmission time will increase the risk of data loss and damage. Therefore, analyzing the above parameters helps to reveal the impact of network quality on data transmission, so that a data transmission strategy that adapts to the current network environment can be accurately formulated.

[0066] Furthermore, the network quality factor of the network environment to which the medical detection device belongs is determined by the following specific determination process: A network transmission rate curve of the network environment to which the medical testing equipment belongs is extracted from the network environment parameters to which the test results of the medical testing equipment are uploaded to the cloud, each rate detection position point is located from the network transmission rate curve, the network transmission rate of each rate detection position point is obtained and averaged, the processing result is marked as the average network transmission rate of the network environment to which the medical testing equipment belongs, the data collection amount of the medical testing equipment within the data collection period is ratio-processed with the average network transmission rate of the network environment to which the medical testing equipment belongs, and the data transmission duration of the network environment to which the medical testing equipment belongs is obtained.

[0067] The network transmission rate of each rate detection position point is processed by standard deviation, thereby obtaining the discrete value of the network transmission rate of the network environment to which the medical detection equipment belongs; at the same time, the data collection quality index of the medical detection equipment is integrated to obtain the determination method of the network quality factor of the network environment to which the medical detection equipment belongs.

[0068] In this embodiment, the network transmission rate curve of the network environment to which the medical detection device belongs is obtained by detecting the network transmission rate of the network environment to which the medical detection device belongs during the data collection period using a network performance detection tool (such as a network packet capture tool). Figure 3 The network transmission rate curve shows that the ordinate is the network transmission rate in bits per second, and the abscissa is the data collection time point in seconds, which clearly shows the changes in the network transmission rate of the network environment to which the medical testing equipment belongs during the data collection cycle; the above rate detection position point, such as Figure 3 As shown in 1, it refers to points evenly distributed on the network transmission rate curve according to a predetermined fixed time interval 2.

[0069] In a specific embodiment, the present invention provides an intelligent patient follow-up management system and method based on the Internet of Things, which collects detection process parameters of medical detection equipment and evaluates the detection process effectiveness factors of the medical detection equipment, thereby correcting errors in the use of medical detection equipment by users based on scientific parameters, analyzing the detection result data of the medical detection equipment, and managing feedback on the detection result data of the medical detection equipment to enhance the trust of the detection result data, and finally determining the network quality factor of the network environment to which the medical detection equipment belongs, thereby managing feedback on uploading the detection results of the medical detection equipment in the intelligent patient follow-up management subsystem to the network, thereby significantly improving the efficiency and effect of patient follow-up management, while ensuring the comprehensiveness and accuracy of the data, providing a solid and reliable decision-making basis for follow-up management, and also helping to enhance patient participation and satisfaction, further improving the scientificity and effectiveness of intelligent medical data processing, and providing strong support for optimizing treatment plans and improving patient health levels.

[0070] Reference Figure 2As shown, the second aspect of the present invention provides a method for an intelligent patient follow-up management system based on the Internet of Things, characterized in that it includes: S1. Performing health checks on users through medical testing equipment, collecting testing process parameters of the medical testing equipment, evaluating the testing process effectiveness factor of the medical testing equipment, and comparing it with the testing process effectiveness threshold. If the testing process effectiveness factor of the medical testing equipment is greater than the testing process effectiveness threshold, then obtaining the testing result data of the medical testing equipment, otherwise, managing feedback is given to the testing process of the medical testing equipment; S2. Analyzing the data collection quality index of the medical testing equipment through the testing result data of the medical testing equipment, and comparing it with the data collection quality threshold. If the data collection quality index of the medical testing equipment is greater than the data collection quality threshold, then collecting the network environment parameters to which the testing result of the medical testing equipment belongs to be uploaded to the cloud, otherwise, managing feedback is given to the testing result data of the medical testing equipment; S3. Determining the network quality factor of the network environment to which the medical testing equipment belongs based on the network environment parameters to which the testing result of the medical testing equipment is uploaded to the cloud, thereby managing feedback is given to the testing result of the medical testing equipment in the intelligent patient follow-up management subsystem to be uploaded to the network.

[0071] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. An intelligent patient follow-up management system based on the Internet of Things, characterized in that: include: The detection process evaluation module is used to perform health detection on the user through the medical detection equipment, collect the detection process parameters of the medical detection equipment, evaluate the detection process effectiveness factor of the medical detection equipment, and compare it with the detection process effectiveness threshold. If the detection process effectiveness factor of the medical detection equipment is greater than the detection process effectiveness threshold, the detection result analysis module is executed; otherwise, management feedback is provided on the detection process of the medical detection equipment; The test result analysis module is used to obtain the test result data of the medical testing equipment, analyze the data collection quality index of the medical testing equipment, and compare it with the data collection quality threshold. If the data collection quality index of the medical testing equipment is greater than the data collection quality threshold, the result upload determination module is executed; otherwise, the test result data of the medical testing equipment is managed and fed back; The result upload determination module is used to collect the network environment parameters to which the test results of the medical testing equipment are uploaded to the cloud, determine the network quality factor of the network environment to which the medical testing equipment belongs, and thereby provide management feedback on the upload network of the test results of the medical testing equipment in the intelligent patient follow-up management subsystem.

2. According to claim 1, the intelligent patient follow-up management system based on the Internet of Things is characterized by: The specific evaluation process of evaluating the effectiveness factor of the detection process of the medical detection equipment is as follows: Extracting a time period corresponding to the detection process of the medical detection device from the detection process parameters of the medical detection device and marking it as a data collection period of the medical detection device, obtaining a reference detection duration of each detection data belonging to the medical detection device, and accumulating them to obtain a reference total detection duration of the medical detection device, performing difference processing on the reference total detection duration of the medical detection device and the duration corresponding to the data collection period to obtain a detection deviation duration of the medical detection device; Extracting the real-time electrical signal strength of the medical detection equipment within the data collection period from the detection process parameters of the medical detection equipment, performing mean processing to obtain the average electrical signal strength of the medical detection equipment within the data collection period, obtaining the electrical signal reference strength of the medical detection equipment, and performing difference processing with the average electrical signal strength of the medical detection equipment within the data collection period to obtain the electrical signal deviation strength of the medical detection equipment within the data collection period; The real-time temperature of each temperature detection point of the medical detection equipment within the data collection period is extracted from the detection process parameters of the medical detection equipment, the average temperature of the temperature detection point of the medical detection equipment within the data collection period is obtained by mean processing, and the difference processing is performed with the reference temperature to obtain the temperature deviation value of the temperature detection point of the medical detection equipment within the data collection period, thereby obtaining an evaluation method for the effective factor of the detection process of the medical detection equipment.

3. According to claim 1, the intelligent patient follow-up management system based on the Internet of Things is characterized by: The management feedback of the detection process of the medical detection equipment is specifically performed as follows: The value of the detection deviation time of the medical detection equipment, the value of the electrical signal deviation strength of the medical detection equipment within the data acquisition cycle, and the value of the temperature deviation value of the temperature detection position point of the medical detection equipment within the data acquisition cycle are respectively compared with the corresponding deviation reference values, and based on the comparison results, the detection process parameter management instructions of the medical detection equipment are generated, and parameter management feedback is performed on the detection process of the medical detection equipment, thereby evaluating the detection process again.

4. According to claim 1, the intelligent patient follow-up management system based on the Internet of Things is characterized by: The acquisition of the test result data of the medical testing device specifically refers to the real-time values ​​of each test data belonging to the medical testing device within the data collection period, which are integrated and marked as the test result data of the medical testing device.

5. According to claim 1, the intelligent patient follow-up management system based on the Internet of Things is characterized by: The specific analysis process of analyzing the data collection quality index of the medical detection equipment is as follows: Extract the last update time point of each test data of the medical testing device within the data collection period from the test result data of the medical testing device, perform difference processing with the start time point corresponding to the data collection period to obtain the update duration of each test data of the medical testing device, perform corresponding difference processing with the reference test duration of each test data of the medical testing device to obtain the update lag duration of each test data of the medical testing device; Extracting the data collection amount of the medical testing equipment in a data collection cycle from the test result data of the medical testing equipment, obtaining the reference data collection amount of the medical testing equipment in the data collection cycle, performing difference processing with the data collection amount of the medical testing equipment in the data collection cycle, and obtaining the data collection omission amount of the medical testing equipment; The data collection cycle is evenly divided into data collection sub-cycles, the data collection amount of the medical detection device in each data collection sub-cycle is extracted from the detection result data of the medical detection device, and the data collection amount is processed by ratio with the duration corresponding to the data collection sub-cycle to obtain the data collection frequency of the medical detection device in each data collection sub-cycle, and the data collection frequency of the medical detection device in each data collection sub-cycle is processed by standard deviation to obtain a discrete value of the data collection frequency of the medical detection device; The effective factors of the testing process of medical testing equipment are integrated to obtain an analytical method for the data collection quality index of the medical testing equipment.

6. According to claim 1, the intelligent patient follow-up management system based on the Internet of Things is characterized by: The management feedback of the test result data of the medical testing equipment may specifically be a process of performing difference processing on the data collection quality index of the medical testing equipment and the data collection quality threshold to obtain the data collection quality difference of the medical testing equipment, and matching the difference with the update log corresponding to each data collection quality difference interval stored in the management database to obtain the update log of the medical testing equipment, and performing management feedback on the test result data of the medical testing equipment through the update log of the medical testing equipment, thereby performing test result analysis again.

7. According to claim 1, the intelligent patient follow-up management system based on the Internet of Things is characterized by: The specific determination process of determining the network quality factor of the network environment to which the medical detection equipment belongs is as follows: Extract the network transmission rate curve of the network environment to which the medical detection device belongs from the network environment parameters to which the detection results of the medical detection device are uploaded to the cloud, locate each rate detection position point from the network transmission rate curve, obtain and average the network transmission rate of each rate detection position point, mark the processing result as the average network transmission rate of the network environment to which the medical detection device belongs, perform ratio processing on the data collection amount of the medical detection device within the data collection period and the average network transmission rate of the network environment to which the medical detection device belongs, and obtain the data transmission duration of the network environment to which the medical detection device belongs; Performing standard deviation processing on the network transmission rate of each rate detection position point, thereby obtaining a discrete value of the network transmission rate of the network environment to which the medical detection equipment belongs; At the same time, the data collection quality index of the medical detection equipment is integrated to obtain a method for determining the network quality factor of the network environment to which the medical detection equipment belongs.

8. According to claim 7, an intelligent patient follow-up management system based on the Internet of Things is characterized by: The method for determining the network quality factor of the network environment to which the medical detection device belongs is specifically as follows: ； in, is the network quality factor of the network environment to which the medical testing equipment belongs, Data collection quality index for medical testing equipment, The data transmission time of the network environment to which the medical testing equipment belongs. is the discrete value of the network transmission rate of the network environment to which the medical detection equipment belongs, e is a natural constant, It is the weight factor corresponding to the data collection quality index preset in the management database. It is the impact factor corresponding to the unit value of data transmission duration preset in the management database. It is the impact factor corresponding to the discrete value unit value of the network transmission rate preset in the management database.

9. The intelligent patient follow-up management system based on the Internet of Things according to claim 1 is characterized by: The test results of the medical testing equipment in the intelligent patient follow-up management subsystem are uploaded to the network for management feedback. The specific management feedback process is as follows: The network quality factor of the network environment to which the medical testing equipment belongs is matched with the data transmission protocol corresponding to each quality factor interval stored in the management database to obtain the data transmission protocol corresponding to the network environment to which the medical testing equipment belongs. Thus, the test results of the medical testing equipment in the intelligent patient follow-up management subsystem are uploaded to the network for management feedback through the data transmission protocol corresponding to the network environment to which the medical testing equipment belongs.

10. A method for the intelligent patient follow-up management system based on the Internet of Things according to any one of claims 1 to 9, characterized in that: include: S1. Perform health checks on users through medical testing equipment, collect testing process parameters of the medical testing equipment, evaluate the testing process effectiveness factor of the medical testing equipment, and compare it with the testing process effectiveness threshold. If the testing process effectiveness factor of the medical testing equipment is greater than the testing process effectiveness threshold, obtain the testing result data of the medical testing equipment. Otherwise, provide management feedback on the testing process of the medical testing equipment. S2. Analyze the data collection quality index of the medical testing equipment through the test result data of the medical testing equipment, and compare it with the data collection quality threshold. If the data collection quality index of the medical testing equipment is greater than the data collection quality threshold, collect the test results of the medical testing equipment and upload them to the cloud network environment parameters. Otherwise, manage and feedback the test result data of the medical testing equipment; S3. According to the network environment parameters to which the test results of the medical testing equipment are uploaded to the cloud, the network quality factor of the network environment to which the medical testing equipment belongs is determined, thereby providing management feedback on the upload of the test results of the medical testing equipment in the intelligent patient follow-up management subsystem to the network.

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