A Monitoring Method and System for the Operating Status of a Health Smart Platform Based on the Internet of Things

Abstract

The present invention belongs to the technical field of operating state monitoring, and specifically relates to a method and system for monitoring the operating state of a health intelligent platform based on the Internet of Things. The method includes: obtaining the representative duration, representative characteristics, and data acquisition interval of each air index in each disinfection stage under different states according to the test data of each air index under different states, determining the representative duration, representative characteristics, and data acquisition interval of each disinfection stage in the new disinfection cycle by the state with the same disinfection mode as the new disinfection cycle and the greatest similarity of air indexes, and adjusting the data acquisition interval of each air index in the target stage according to the difference between the characteristic value of the real-time data of each air index and the representative characteristics of each air index in the target stage, so as to control the devices connected to the Internet of Things to collect real-time data, and further monitor the operating state of the health intelligent platform. The present invention enhances the accuracy and reliability of the platform operating state monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of operating state monitoring. More specifically, the present invention relates to a method and system for monitoring the operating state of a health intelligent platform based on the Internet of Things. Background Art

[0002] In recent years, with the acceleration of the urbanization process and the increase in population density, public health security has faced unprecedented challenges; in traditional public health disinfection work, the manual disinfection method dominates, but this method has many disadvantages such as low efficiency and incomplete coverage, and it is difficult to monitor and manage in real time.

[0003] Therefore, combining advanced technologies such as the Internet of Things, big data analysis, automation control, and artificial intelligence, a public health intelligent platform is proposed, which realizes the intelligent management and efficient disinfection of the public health environment in public places.

[0004] Among them, the Internet of Things technology enables various sensors and disinfection devices to communicate with each other and collect data on various air indicators in real time, such as temperature, humidity, harmful gas concentration, etc.; big data analysis processes and mines these massive data to provide decision-making support, such as predicting the development trend of the epidemic and evaluating the disinfection effect; automation control technology ensures the accurate execution of disinfection operations and can automatically adjust the disinfection intensity and frequency according to preset rules and real-time data; the application of artificial intelligence further improves the intelligent level of the platform.

[0005] When collecting data on air indicators in real time through the Internet of Things technology, a fixed data collection interval is usually adopted. However, the method of fixed data collection interval cannot flexibly adapt to the needs of different disinfection stages, resulting in inaccurate data collection and low efficiency, and further leading to the inability to accurately monitor the operating state of the public health intelligent platform. Summary of the Invention

[0006] To solve the technical problem that the method of fixed data collection interval cannot flexibly adapt to the needs of different disinfection stages, places, and application scenarios, resulting in inaccurate data collection and low efficiency, and further leading to the inability to accurately monitor the operating state of the public health intelligent platform, the present invention provides solutions in the following aspects.

[0007] In a first aspect, the present invention provides a method for monitoring the operating status of a health intelligent platform based on the Internet of Things, including: obtaining test data of each air index under different states, where the disinfection modes and initial values of each air index are different under different states; in any state, dividing the test data of each air index according to the value and change rate of the test data, and forming representative sequences of each air index in multiple disinfection stages; for any disinfection stage: obtaining the representative duration of each air index according to the duration corresponding to the representative sequence of each air index, calculating the distribution dispersion degree of each feature in the representative sequence of each air index, selecting the representative feature with the smallest distribution dispersion degree among each air index, and setting the data acquisition interval of each air index according to the representative feature of each air index and its distribution dispersion degree; determining the representative duration, representative feature, and data acquisition interval of each disinfection stage in the new disinfection cycle through the state with the same disinfection mode as the new disinfection cycle and the largest similarity of air indexes, for judging the target stage where the real-time data of each air index collected in the new disinfection cycle is located, and adjusting the data acquisition interval of each air index in the target stage according to the difference between the characteristic value of the real-time data of each air index and the representative feature of each air index in the target stage; controlling the devices connected to the Internet of Things to collect the real-time data of each air index through the adjusted data acquisition interval, so as to facilitate the monitoring of the operating status of the public health intelligent platform.

[0008] According to the characteristics of different disinfection stages, the present invention dynamically sets the data acquisition interval of each air index in different stages, which can flexibly adapt to the requirements of different disinfection stages, improve the accuracy and efficiency of data acquisition. At the same time, when collecting the real-time data of each air index in real time according to the data acquisition interval of each air index, by comparing the difference between the characteristics of the real-time data and the representative characteristics of the target stage, the data acquisition interval is dynamically adjusted according to the difference, better capturing the change trend of the data. By continuously optimizing the data acquisition interval, the data acquisition can more accurately match the actual disinfection process, so as to better adapt to the actual operation of the platform, thereby improving the pertinence and timeliness of data acquisition and enhancing the accuracy and reliability of the platform operating status monitoring.

[0009] Preferably, the disinfection modes include: "ozone" mode, "purification" mode, "ultraviolet" mode, "ozone + purification" mode, "ozone + ultraviolet" mode, "purification + ultraviolet" mode, and "ozone + purification + ultraviolet" mode; the air indexes include: TVOC, PM2.5, PM10, formaldehyde, carbon dioxide, temperature, and humidity.

[0010] Preferably, according to the numerical values and change rates of the test data, the test data of each air index are divided, and representative sequences of each air index in multiple disinfection and sterilization stages are formed, including: for any air index, all the test data of this air index are arranged in chronological order to form the test sequence of this air index; according to the test sequences of all air indexes, by constructing and solving the between-class variance function, the test sequence of each air index is divided into 4 sub-sequences, which are respectively used as the representative sequences of each air index in 4 disinfection and sterilization stages, and the lengths of the representative sequences of each air index in the same disinfection and sterilization stage are the same.

[0011] The present invention divides the entire disinfection and sterilization process into multiple stages by analyzing the numerical values and change rates of the test data, and then optimizes the data acquisition and processing strategies according to the characteristics of each stage.

[0012] Preferably, the between-class variance function has the following expression: , is the between-class variance between the th sub-sequence and the th sub-sequence of the th air index, is the between-class variance between the change rate sequence of the th sub-sequence and the change rate sequence of the th sub-sequence of the th air index; the change rate sequence of a sub-sequence refers to the sequence composed of the change rates of all the test data in the sub-sequence.

[0013] Preferably, the expression of the change rate of the test data is: ; in the formula, is the change rate of the th test data, , are the , th test data.

[0014] Preferably, obtaining the representative duration of each air index according to the duration corresponding to the representative sequence of each air index includes: for the first 3 disinfection and sterilization stages, the duration corresponding to the representative sequence of each air index in each disinfection and sterilization stage is used as the representative duration of each air index in each disinfection and sterilization stage; for the last 1 disinfection and sterilization stage, the duration from the end of the 3rd disinfection and sterilization stage to the start of a new disinfection and sterilization cycle belongs to the last 1 disinfection and sterilization stage, that is, the representative duration of the last 1 disinfection and sterilization stage is not limited.

[0015] The present invention provides a data basis for setting the data acquisition interval in a new disinfection cycle by obtaining the representative durations of various air indicators in each disinfection stage under different states, enabling the rapid determination of an appropriate data acquisition strategy in the new disinfection cycle according to the representative durations of various air indicators in each disinfection stage under different states.

[0016] Preferably, setting the data acquisition intervals of various air indicators according to the representative characteristics and the distribution dispersion degree thereof includes: the characteristics include value, change rate, and change acceleration rate; the data acquisition interval of the air indicator has the following calculation formula: ; in the formula, is a parameter, is the natural exponential function, is the distribution dispersion degree of the representative characteristic of the air indicator, is the initial acquisition interval of the air indicator; when the representative characteristic of the air indicator is a value, the parameter ; when the representative characteristic of the air indicator is a change rate, the parameter ; when the representative characteristic of the air indicator is a change acceleration rate, the parameter .

[0017] The present invention sets the data acquisition intervals of various air indicators according to the representative characteristics and the distribution dispersion degree thereof, can adapt to the monitoring requirements of different stages, optimize the data acquisition efficiency, and further improve the accuracy of data acquisition.

[0018] Preferably, judging the target stage where the real-time data of each air indicator collected in the new disinfection cycle is located includes: when the collection time of the real-time data of the air indicator is within , then the target stage where the real-time data of the air indicator is located is the first stage; when the collection time of the real-time data of the air indicator is within , then the target stage where the real-time data of the air indicator is located is the second stage; when the collection time of the real-time data of the air indicator is within , then the target stage where the real-time data of the air indicator is located is the third stage; otherwise, the target stage where the real-time data of the air indicator is located is the fourth stage; , , are respectively the representative durations of the first, second, and third stages in the new disinfection cycle.

[0019] Preferably, adjusting the data acquisition intervals of various air indicators in the target stage includes: for the real-time data of any air indicator in the new disinfection cycle: the characteristic value of the real-time data of this air indicator and the representative characteristic of this air indicator in the target stage Difference equals , indicating taking the absolute value; if the difference is less than , there is no need to adjust the data collection interval of each air index in the target stage. is a preset threshold; if the difference is greater than or equal to , then the adjusted data collection interval of this air index in the target stage , is the data collection interval of this air index in the target stage, where is the inverse proportional normalization function.

[0020] The present invention dynamically adjusts the data collection interval according to the change of real-time data, making the data collection more accurately match the actual disinfection process, ensuring that the platform always collects data at the most appropriate frequency, and realizing the real-time monitoring of the running state.

[0021] In a second aspect, the present invention provides a system for monitoring the running state of a health intelligent platform based on the Internet of Things, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned method for monitoring the running state of a health intelligent platform based on the Internet of Things is realized.

[0022] By adopting the above technical solution, the above-mentioned method for monitoring the running state of a health intelligent platform based on the Internet of Things is generated into a computer program and stored in the memory, so as to be loaded and executed by the processor, and thus a terminal device is manufactured according to the memory and the processor, which is convenient to use.

[0023] The beneficial effects of the present invention are as follows:

[0024] By analyzing the numerical value and change rate of the test data, the present invention divides the entire disinfection process into multiple stages, and then optimizes the data collection and processing strategies for the characteristics of each stage; furthermore, by obtaining the representative duration, representative characteristics and data collection intervals of each air index in each disinfection stage under different states, it provides a data basis for setting the data collection interval in a new disinfection cycle, and quickly determines a suitable data collection strategy; at the same time, when collecting the real-time data of each air index according to the data collection interval of each air index in real time, by comparing the difference between the characteristics of the real-time data and the representative characteristics of the target stage, the data collection interval is dynamically adjusted according to the difference, better capturing the change trend of the data. By continuously optimizing the data collection interval, the data collection more accurately matches the actual disinfection process, so as to better adapt to the actual operation situation of the platform, thereby improving the pertinence and timeliness of data collection, and enhancing the accuracy and reliability of the platform running state monitoring. Brief Description of the Drawings

[0025] Figure 1 is a flowchart schematically showing the operation status monitoring method of a health intelligent platform based on the Internet of Things in the present invention;

[0026] Figure 2 is a flowchart schematically showing step S2. Detailed Description of the Invention

[0027] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0028] Next, the detailed implementation manners of the present invention will be described in detail in conjunction with the accompanying drawings.

[0029] The public health intelligent platform includes two parts: software and a disinfection unit. Among them, the disinfection unit is installed in public places, undertakes automated disinfection work, and performs disinfection operations through various disinfection means; the software part completes the configuration and control of the disinfection unit, and at the same time provides perfect management and control functions for different application scenarios, as well as visualizes and outputs the front-end data to complete data interaction.

[0030] Among them, the disinfection unit includes automated disinfection equipment and a sensor component. Among them, the automated disinfection equipment is installed in different areas of public places and uses various disinfection means, such as ultraviolet irradiation, spray disinfection, plasma disinfection, etc. These devices can automatically execute disinfection tasks according to the instructions of the software platform to ensure the comprehensiveness and uniformity of disinfection operations; the sensor component cooperates with the data acquisition module of the software part to real-time monitor air indicators during the disinfection process, such as temperature, humidity, etc. These data are fed back to the software platform for evaluating the disinfection effect and adjusting operation parameters to achieve closed-loop control.

[0031] Among them, the software part has the following functional modules:

[0032] 1. Data acquisition and integration module: Through Internet of Things technology, various sensors and disinfection equipment can be interconnected. The data of various air indicators are collected in real time through various sensors, including air quality sensors (such as TVOC, PM2.5, PM10, formaldehyde, carbon dioxide, etc.), temperature and humidity sensors, etc. After these data are cleaned and preprocessed to ensure accuracy, they are integrated into the platform's database.

[0033] 2. Intelligent analysis and decision-making module: Use big data analysis and artificial intelligence algorithms to conduct in-depth analysis of the collected data; for example, combine personnel flow and activity patterns to formulate the optimal disinfection plan.

[0034] 3. Visual display and interaction module: The platform's various data and operating functions are presented in an intuitive graphical interface; users can view the sanitation conditions, disinfection progress and historical records of public places anytime and anywhere through computers or mobile terminals, and can remotely control the disinfection unit to adjust parameters and perform other operations.

[0035] 4. Batch management and control module: Batch management and unified control can be achieved for multiple disinfection units in different application scenarios; each disinfection unit can be grouped and managed according to factors such as the scale, function and personnel distribution of the site, and different working modes and task plans can be set to improve management efficiency.

[0036] Through the close collaboration between software and disinfection units, the public health and wellness smart platform has achieved real-time monitoring, intelligent analysis and precise disinfection of the sanitary environment of public places, effectively improving the efficiency and quality of public health management and creating a safer and healthier living and working environment for people.

[0037] Among them, when collecting air index data in real time through Internet of Things technology, the method of fixed data collection interval cannot flexibly adapt to the needs of different disinfection stages, places and application scenarios, resulting in inaccurate and inefficient data collection, and thus the inability to accurately monitor the operating status of the public health and health smart platform; therefore, it is necessary to adopt a method of dynamically adjusting the data collection interval to improve the monitoring accuracy of the platform's operating status and ensure the efficient operation and precise management of the public health and health smart platform.

[0038] The embodiment of the present invention discloses a method for monitoring the operation status of a health intelligence platform based on the Internet of Things, referring to Figure 1 , comprising steps S1 to S5:

[0039] S1. Obtain test data of various air indicators under different states. The disinfection modes and initial values ​​of various air indicators under different states are different.

[0040] It should be noted that different disinfection modes and different initial values ​​of air indicators will affect the changing pattern of air indicators during the disinfection cycle. In order to fully understand the operating status of the platform under various conditions, it is necessary to collect test data under different disinfection modes and initial conditions to provide a basis for subsequent analysis.

[0041] Specifically, for the test site, different states are set, and the disinfection modes and the initial values of each air index are different for different states; the test site is disinfected under different states, and a complete disinfection cycle includes the start of one disinfection to the start of the next disinfection; within the disinfection cycle, according to the maximum value in the initial collection intervals of all air indexes, the devices connected to the Internet of Things are controlled to collect the test data of each air index in real time.

[0042] Among them, there are 7 disinfection modes, namely: "ozone" mode, "purification" mode, "ultraviolet" mode, "ozone + purification" mode, "ozone + ultraviolet" mode, "purification + ultraviolet" mode, and "ozone + purification + ultraviolet" mode.

[0043] Among them, there are 7 air indexes, namely: TVOC (total volatile organic compounds), PM2.5 (fine particulate matter), PM10 (inhalable particulate matter), formaldehyde, carbon dioxide, temperature, and humidity; the unit of TVOC is mg / m 3 , and the units of PM2.5, PM10, formaldehyde, and carbon dioxide are ppm, the unit of temperature is °C, and the unit of humidity is %.

[0044] Among them, the concentrations of TVOC, PM2.5, and PM10 will change rapidly due to factors such as indoor activities and indoor ventilation conditions. Therefore, the initial collection intervals of TVOC, PM2.5, and PM10 are set to 1 minute; the concentration of carbon dioxide is mainly affected by the personnel density and the ventilation system, and the change rate is usually relatively stable. Therefore, the initial collection interval of carbon dioxide is set to 5 minutes; the changes in temperature and humidity are relatively slow and usually do not show violent fluctuations, and the release rate of formaldehyde is affected by temperature, humidity, and ventilation conditions and changes relatively slowly. Therefore, the initial collection intervals of temperature, humidity, and formaldehyde are set to 8 minutes.

[0045] It should be noted that by collecting the test data under different disinfection modes and initial values, the performance of the platform under various operating conditions can be comprehensively understood, providing specific data support for data analysis and adjustment of collection intervals in the subsequent steps, enabling the platform to adapt to different disinfection requirements and environmental conditions.

[0046] S2. Under any state, according to the values and change rates of the test data, divide the test data of each air index and form representative sequences of each air index in multiple disinfection stages; obtain the representative durations, representative characteristics, and data collection intervals of each air index in each disinfection stage.

[0047] The flowchart of step S2 refers to Figure 2 , including steps S201 to S204, specifically as follows:

[0048] S201. Divide the test data of each air index according to the value and change rate of the test data, and form representative sequences of each air index in multiple disinfection stages.

[0049] It should be noted that the value and change rate of the air index can reflect different stages of the disinfection process. Therefore, by analyzing the test data, the disinfection process is divided into multiple stages, and a representative sequence is determined for each stage, which is convenient for subsequent targeted data collection and management, can more accurately monitor and manage the disinfection process, and improve the scientificity and effectiveness of the platform operation.

[0050] Specifically, through the th test data and the differences between it and multiple adjacent test data, obtain the change rate of the th test data. The specific calculation formula is:

[0051] ;

[0052] In the formula, is the change rate of the th test data, , are the , th test data. The test data , , , are multiple adjacent test data of the th test data .

[0053] It should be noted that for a complete disinfection cycle from the start of one disinfection to the start of the next disinfection, according to different test data, it can be divided into the following stages:

[0054] 1. The first stage, i.e., the disinfection in-progress stage: The disinfection unit is performing disinfection operations, and at this time, the concentration of harmful gases in the air will gradually decrease.

[0055] 2. The second stage, i.e., the short-term stage after disinfection: The disinfection operation has just ended, and the air quality begins to gradually stabilize but is still at a relatively high level, and continuous monitoring is required.

[0056] 3. The third stage, i.e., the medium-term stage after disinfection: After the short-term stage, the air quality is further improved, but continuous monitoring is still required to ensure the continuous safety of the environment.

[0057] 4. The fourth stage, i.e., the long-term stage after disinfection: After a period of time, the air quality reaches a stable and good state, and the environmental quality is guaranteed.

[0058] Further, according to the values and change rates of the test data, the test data of each air index are divided and representative sequences of each air index in multiple disinfection stages are formed, including: for any air index, all the test data of this air index are arranged in chronological order to form the test sequence of this air index; according to the test sequences of all air indexes, by constructing and solving the maximum between-class variance function, the test sequence of each air index is divided into 4 sub-sequences, which are respectively used as the representative sequences of each air index in 4 disinfection stages, and the lengths of the representative sequences of each air index in the same disinfection stage are the same.

[0059] Among them, the air quality in different disinfection stages is different, and correspondingly, the values and change rates of the test data are different. Therefore, the maximum between-class variance function constructed through the between-class variances of different sub-sequences has the following specific expression:

[0060] ;

[0061] In the formula, is the maximum between-class difference function, is the between-class variance between the th sub-sequence and the th sub-sequence of the th air index, is the between-class variance between the change rate sequences of the th sub-sequence and the th sub-sequence of the th air index; since the test sequence is divided into 4 sub-sequences, the serial number of the sub-sequence has a value range of ; since there are 7 air indexes in total, the serial number of the air index has a value range of .

[0062] Among them, the change rate sequence of the sub-sequence refers to the sequence composed of the change rates of all the test data in the sub-sequence.

[0063] When , is the between-class variance between the th sub-sequence of the th air index and its adjacent left th sub-sequence, is the between-class variance between the change rate sequences of the th sub-sequence of the th air index and its adjacent left th sub-sequence. Therefore, when , there is only the th sub-sequence of the The between-class variance of the subsequence and the th subsequence, and the between-class variance of the rate-of-change sequence of the th subsequence of the th air index and the th subsequence, there is no between-class variance of the th subsequence of the th air index and the th subsequence; When is the between-class variance of the th subsequence of the th air index and the th subsequence adjacent to it on the right, ; is the between-class variance of the rate-of-change sequence of the th subsequence of the th air index and the th subsequence adjacent to it on the right. Therefore, when is the between-class variance of the th subsequence of the th air index and the th subsequence adjacent to it on the right, there is only the between-class variance of the th subsequence of the th air index and the th subsequence, and the between-class variance of the rate-of-change sequence of the th subsequence of the th air index and the th subsequence, and there is no between-class variance of the th subsequence of the th air index and the th subsequence, nor the between-class variance of the rate-of-change sequence of the th subsequence of the th air index and the th subsequence. .

[0064] It should be noted that dividing the stages based on the changing patterns of the actual data makes the management and control of the disinfection process more scientific and reasonable, and thus enables more accurate monitoring and management of the disinfection process, which is conducive to further optimizing the data collection and processing strategies according to the characteristics of each stage.

[0065] On the basis of dividing the disinfection and sterilization stages, it is necessary to determine the representative duration and representative characteristics of each stage. Among them, the representative duration reflects the time proportion of this stage in the overall disinfection and sterilization process, and the representative characteristics (such as numerical value, change rate, change acceleration rate) describe the main change rules of the air indicators in this stage; by calculating the distribution dispersion degree of these characteristics, select the most stable characteristic as the representative, so as to set a reasonable data acquisition interval.

[0066] S202. Obtain the representative duration of each air indicator in each disinfection and sterilization stage according to the duration corresponding to the representative sequence of each air indicator in each disinfection and sterilization stage.

[0067] Specifically, for the first 3 disinfection and sterilization stages, take the duration corresponding to the representative sequence of each air indicator in each disinfection and sterilization stage as the representative duration of each air indicator in each disinfection and sterilization stage; for the last 1 disinfection and sterilization stage, the duration from the end of the 3rd disinfection and sterilization stage to the start of a new disinfection and sterilization cycle belongs to the last 1 disinfection and sterilization stage. Therefore, there is no limit to the representative duration of the last 1 disinfection and sterilization stage.

[0068] S203. Calculate the distribution dispersion degree of each characteristic in the representative sequence of each air indicator, and select the representative characteristic with the smallest distribution dispersion degree among each air indicator.

[0069] Specifically, for any disinfection and sterilization stage, calculate the distribution dispersion degree of each characteristic in the representative sequence of each air indicator in this stage; among them, the characteristics include numerical value, change rate, change acceleration rate; the distribution dispersion degree is equal to the ratio of the variance to the mean of each characteristic in the representative sequence. The larger the variance and the smaller the mean of the characteristics in the representative sequence, the greater the distribution dispersion degree of the characteristics in the representative sequence; select the characteristic with the smallest distribution dispersion degree as the representative characteristic of each air indicator in this stage.

[0070] Among them, the change acceleration rate of the test data is equal to , is the change rate of the previous test data of this test data, is the change rate of the next test data of this test data.

[0071] It should be noted that by selecting the most stable characteristic as the representative characteristic, the change situation of the air indicators in this stage can be reflected more accurately.

[0072] S204. Set the data acquisition interval of each air indicator in each disinfection and sterilization stage according to the representative characteristic and its distribution dispersion degree of each air indicator in each disinfection and sterilization stage.

[0073] It should be noted that when setting the data acquisition interval, the representative characteristics of air indicators are crucial; the representative characteristics reflect the main change characteristics of the indicator at a specific stage: if the representative characteristic is a numerical value, it indicates that the indicator is relatively stable and has small changes at this stage, so a larger data acquisition interval can be set; if the representative characteristic is the change rate, it means that the indicator is changing at a certain speed, and a medium acquisition interval is required to capture its change trend; if the representative characteristic is the change acceleration rate, it means that the change speed of the indicator itself is changing, and it may be in a stage of rapid change, and a smaller acquisition interval is needed to record its change process in detail.

[0074] Specifically, for any disinfection stage, according to the representative characteristics and their distribution dispersion degrees of each air indicator, set the data acquisition intervals of each air indicator: if the representative characteristics of the air indicators are different, the parameters are different, and correspondingly, the data acquisition intervals of the air indicators are different; if the distribution dispersion degrees of the representative characteristics of the air indicators are different, the data acquisition intervals of the air indicators are different, and the greater the distribution dispersion degree of the representative characteristics of the air indicator, the smaller the data acquisition interval of the air indicator.

[0075] Therefore, the calculation formula for the data acquisition interval of the air indicator is as follows:

[0076] ;

[0077] In the formula, is the data acquisition interval of the air indicator, is the parameter, is the natural exponential function, is the distribution dispersion degree of the representative characteristics of the air indicator, is the initial acquisition interval of the air indicator; when the representative characteristic of the air indicator is a numerical value, the parameter ; when the representative characteristic of the air indicator is the change rate, the parameter ; when the representative characteristic of the air indicator is the change acceleration rate, the parameter .

[0078] It should be noted that when the representative characteristic of the air indicator is a numerical value, it indicates that the data is relatively stable, and a larger data acquisition interval can reduce data redundancy and avoid storing and processing a large amount of duplicate data, thereby improving the efficiency of data acquisition and processing; when the representative characteristic of the air indicator is the change rate, a medium-sized data acquisition interval can capture the main change trend of the indicator while avoiding over-acquiring data and balancing the frequency and amount of information of data acquisition; when the representative characteristic of the air indicator is the change acceleration rate, a smaller acquisition interval can ensure that key data points can be captured in a timely manner when the indicator changes rapidly, avoiding missing important change information.

[0079] It should be noted that setting the data acquisition interval according to the representative characteristics of each stage can ensure sufficient data acquisition in critical stages while avoiding excessive redundant data acquisition in non-critical stages, which not only improves the efficiency of data acquisition but also reduces the burden of data processing.

[0080] S3. When starting a new disinfection cycle, set the disinfection mode and collect the initial values of each air index; determine the representative duration, representative characteristics, and data acquisition interval of each disinfection stage in the new disinfection cycle through the state with the same disinfection mode as the new disinfection cycle and the greatest similarity of air indexes.

[0081] It should be noted that there is a similarity between the mode and initial values of the new disinfection cycle and the previous state. Existing data can be used for reference to set the new disinfection cycle. By matching similar disinfection modes and air indexes, determine the representative duration, characteristics, and data acquisition interval of each stage in the new disinfection cycle to achieve rapid adaptation and precise management of the new cycle.

[0082] Specifically, when the user independently selects a disinfection mode on the "operation interface", it represents the start of a new disinfection cycle. At the same time, the disinfection mode selected by the user is the disinfection mode of the new disinfection cycle; when starting a new disinfection cycle, the data of each air index collected in real time by the Internet of Things-connected devices is used as the initial value of each air index in the new disinfection cycle.

[0083] Furthermore, obtain the target state with the same disinfection mode as the new disinfection cycle and the greatest similarity of air indexes, and use the representative duration, representative characteristics, and data acquisition interval of each disinfection stage in the target state as the representative duration, representative characteristics, and data acquisition interval of each disinfection stage in the new disinfection cycle.

[0084] Among them, in one embodiment, the similarity of the air indexes is equal to the cosine similarity between the initial values of each air index in the new disinfection cycle and the initial values of each air index in different states; in other embodiments, other indexes can be selected to calculate the similarity of the air indexes, such as the Euclidean distance. At this time, the Euclidean distance of the air indexes is inversely proportional to the similarity of the air indexes.

[0085] It should be noted that in the new disinfection cycle, a suitable data acquisition strategy can be quickly determined according to the representative characteristics and duration.

[0086] S4. According to the representative duration of each disinfection stage in the new disinfection cycle, judge the target stage where the real-time data of each air index collected in the new disinfection cycle is located, and adjust the data acquisition interval of each air index in the target stage according to the difference between the characteristic value of the real-time data of each air index and the representative characteristics of each air index in the target stage.

[0087] It should be noted that in the new disinfection and sterilization cycle, the data of the air indicators collected in real time will be continuously transmitted back to the platform. By comparing these real-time data with the representative characteristics of each stage, it can be judged whether the current data collection interval is applicable: if the difference between the characteristic value of the real-time data and the representative characteristic of the target stage is large, it means that the current data collection interval is no longer applicable and needs to be adjusted.

[0088] Specifically, for the real-time data of any air indicator in the new disinfection and sterilization cycle: when the collection time of the real-time data of the air indicator is within the range, the target stage where the real-time data of the air indicator is located is the first stage; when the collection time of the real-time data of the air indicator is within the range, the target stage where the real-time data of the air indicator is located is the second stage; when the collection time of the real-time data of the air indicator is within the range, the target stage where the real-time data of the air indicator is located is the third stage; when the collection time of the real-time data of the air indicator is greater than , the target stage where the real-time data of the air indicator is located is the fourth stage; , , are the representative durations of the first, second, and third stages in the new disinfection and sterilization cycle respectively.

[0089] Furthermore, for the real-time data of any air indicator in the new disinfection and sterilization cycle: if the representative characteristic of the air indicator in the target stage is a numerical value, directly use the real-time data of the air indicator as the characteristic value of the real-time data of the air indicator; if the representative characteristic of the air indicator in the target stage is a change rate, calculate the change rate of the real-time data of the air indicator, and use the calculated change rate as the characteristic value of the real-time data of the air indicator; if the representative characteristic of the air indicator in the target stage is a change acceleration rate, calculate the change acceleration rate of the real-time data of the air indicator, and use the calculated change acceleration rate as the characteristic value of the real-time data of the air indicator.

[0090] Furthermore, for the real-time data of any air indicator in the new disinfection and sterilization cycle: the characteristic value of the real-time data of the air indicator and the difference from the representative characteristic of the air indicator in the target stage , where || represents taking the absolute value; according to the difference between the characteristic value of the real-time data of the air indicator and the representative characteristic of the air indicator in the target stage, adjust the data collection interval of the air indicator in the target stage, including: if the difference is less than , there is no need to adjust the data collection interval of each air index in the target stage, is a preset threshold; if the difference is greater than or equal to , then the adjusted data collection interval of this air index in the target stage , is the data collection interval of this air index in the target stage, is an inverse proportional normalization function.

[0091] It should be noted that the difference between the characteristics of the real-time data and the representative characteristics of the target stage can reflect the abnormality degree of the real-time data, and further reflect whether the data collection interval needs to be adjusted; the greater the difference between the characteristics of the real-time data and the representative characteristics of the target stage, the greater the abnormality degree of the real-time data, and the data needs to be collected more frequently.

[0092] Among them, in one embodiment, the inverse proportional normalization function , is the natural exponential function; in other embodiments, the inverse proportional normalization function , is the Sigmoid function.

[0093] Among them, the specific value of the preset threshold can be set according to the actual application scenario and requirements, and the value range of the preset threshold is [0.3, 0.4], and the present invention sets the preset threshold to 0.36.

[0094] It should be noted that by comparing the difference between the characteristics of the real-time data and the representative characteristics of the target stage, and then dynamically adjusting the data collection interval through the difference, the change trend of the data can be better captured. By continuously optimizing the data collection interval, the data collection can more accurately match the actual disinfection process, so as to better adapt to the actual operation situation of the platform, further improve the pertinence and timeliness of the data collection, and enhance the accuracy and reliability of the platform operation status monitoring.

[0095] S5. Control the devices connected to the Internet of Things to collect the real-time data of each air index through the adjusted data collection interval, so as to monitor the operation status of the health intelligent platform.

[0096] It should be noted that the adjusted data collection interval can better meet the needs of the platform operation status monitoring. By precisely controlling the data collection of the Internet of Things devices, the resource utilization can be optimized and the monitoring effect can be improved.

[0097] Specifically, according to the adjusted data collection interval, control instructions are sent to the Internet of Things devices to ensure that the devices connected to the Internet of Things collect real-time data of each air index according to the set data collection interval; through the visualization interface of the health intelligent platform, the real-time data of each air index is displayed in a visual way, so as to monitor the running state of the health intelligent platform in real time.

[0098] It should be noted that by reasonably setting the data collection interval, while ensuring data validity and monitoring purposes, efficient monitoring of the platform running state can be achieved, providing strong support for timely discovery and handling of problems, and improving the overall running efficiency and management level of the platform.

[0099] The embodiment of the present invention also discloses a monitoring system for the running state of a health intelligent platform based on the Internet of Things, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for monitoring the running state of a health intelligent platform based on the Internet of Things according to the present invention is implemented.

[0100] The above system also includes other components well-known to those skilled in the art such as a communication bus and a communication interface, and their settings and functions are known in the art, so they will not be described in detail here.

Claims

1. A method for monitoring the operating status of a health intelligence platform based on the Internet of Things, characterized in that: include: The test data of each air index under different conditions are obtained. The disinfection mode and initial value of each air index under different conditions are different. In any state, the test data of each air index is divided according to the value, change rate and change acceleration rate of the test data, and the representative sequence of each air index in multiple disinfection stages is formed; for any disinfection stage: according to the duration corresponding to the representative sequence of each air index, the representative duration of each air index is obtained, the distribution dispersion degree of each feature in the representative sequence of each air index is calculated, and the representative feature with the smallest distribution dispersion degree in each air index is selected; according to the representative feature of each air index and its distribution dispersion degree, the data collection interval of each air index is set; Determine the representative duration, representative characteristics, and data collection interval of each disinfection stage in the new disinfection cycle through the state with the same disinfection mode as the new disinfection cycle and the greatest similarity of air indicators, so as to judge the target stage of the real-time data of each air indicator collected in the new disinfection cycle, and adjust the data collection interval of each air indicator in the target stage according to the difference between the characteristic value of the real-time data of each air indicator and the representative characteristics of each air indicator in the target stage; By adjusting the data collection interval, the devices connected to the Internet of Things are controlled to collect real-time data of various air indicators, so as to monitor the operating status of the public health and wellness smart platform.

2. According to the method for monitoring the operation status of a health intelligence platform based on the Internet of Things in claim 1, it is characterized in that: The disinfection modes include: "ozone" mode, "purification" mode, "ultraviolet" mode, "ozone + purification" mode, "ozone + ultraviolet" mode, "purification + ultraviolet" mode and "ozone + purification + ultraviolet" mode; the air indicators include: TVOC, PM2.5, PM10, formaldehyde, carbon dioxide, temperature and humidity.

3. According to the method for monitoring the operation status of a health intelligence platform based on the Internet of Things in claim 1, it is characterized in that: According to the numerical value and change rate of the test data, the test data of each air index is divided, and a representative sequence of each air index in multiple disinfection stages is formed, including: For any air index, all the test data of the air index are organized into a test sequence of the air index in chronological order; according to the test sequences of all air indexes, the test sequence of each air index is divided into 4 subsequences by constructing and solving the maximum inter-class variance function, which serve as the representative sequences of each air index in the 4 disinfection stages, and the lengths of the representative sequences of each air index in the same disinfection stage are the same.

4. The method for monitoring the operating status of a health intelligence platform based on the Internet of Things according to claim 3 is characterized in that: The maximum between-class variance function The expression is: , For the The first air index The subsequence and The between-class variance of subsequences, For the The first air index The rate of change sequence of the subsequence is The inter-class variance of the change rate sequence of a subsequence; the change rate sequence of a subsequence refers to the sequence composed of the change rates of all test data in the subsequence.

5. The method for monitoring the operating status of a health intelligence platform based on the Internet of Things according to claim 1 or 4, characterized in that: The expression of the change rate of the test data is: ； In the formula, For the The changing rate of the test data, , For the , Test data.

6. The method for monitoring the operation status of a health intelligence platform based on the Internet of Things according to claim 1 is characterized in that: The step of obtaining the representative duration of each air index according to the duration corresponding to the representative sequence of each air index includes: For the first three disinfection stages, the duration corresponding to the representative sequence of each air indicator in each disinfection stage is used as the representative duration of each air indicator in each disinfection stage; for the last disinfection stage, the duration between the end of the third disinfection stage and the start of a new disinfection cycle belongs to the last disinfection stage, that is, there is no limit on the representative duration of the last disinfection stage.

7. The method for monitoring the operation status of a health intelligence platform based on the Internet of Things according to claim 1 is characterized in that: The data collection interval of each air index is set according to the representative characteristics of each air index and its distribution dispersion, including: The characteristics include value, rate of change, and acceleration rate of change; Data collection interval for air indicators The calculation formula is: ; In the formula, As parameters, is the natural exponential function, is the distribution dispersion of the representative characteristics of air indicators, is the initial collection interval of the air index; when the representative feature of the air index is a numerical value, the parameter ; When the characteristic of the air index is the rate of change, the parameter ; When the representative characteristic of the air index is the acceleration rate of change, the parameter .

8. The method for monitoring the operating status of a health intelligence platform based on the Internet of Things according to claim 1 is characterized in that: The determination of the target stage of the real-time data of each air index collected in the new disinfection cycle includes: When the real-time data of air indicators is collected at When the air index real-time data is within the range, the target stage is the first stage; when the air index real-time data is collected at When the air index real-time data is within the range, the target stage is the second stage; when the air index real-time data is collected at If the air quality index is within the target range, the target stage of the real-time data of the air index is the third stage; otherwise, the target stage of the real-time data of the air index is the fourth stage; , , They are the representative durations of the first, second and third stages in the new disinfection cycle.

9. The method for monitoring the operation status of a health intelligence platform based on the Internet of Things according to claim 1, characterized in that: The adjustment of the data collection interval of each air index in the target stage includes: For the real-time data of any air index in the new disinfection cycle: the characteristic value of the real-time data of the air index Representative characteristics of the air index in the target stage The Difference equal , Indicates taking the absolute value; If the difference Less than , then there is no need to adjust the data collection interval of each air index in the target stage. is the preset threshold; if the difference Greater than or equal to , then the data collection interval of the air index after adjustment in the target stage is , is the data collection interval for the air index in the target phase, is the inverse normalization function.

10. A health intelligence platform operation status monitoring system based on the Internet of Things, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for monitoring the operating status of a health intelligence platform based on the Internet of Things according to any one of claims 1 to 9 is implemented.

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