Method and system for monitoring running state of health wisdom platform based on Internet of Things

Abstract

The invention belongs to the technical field of operation state monitoring, and particularly relates to a health wisdom platform operation state monitoring method and system based on the Internet of Things. The representative duration, the representative feature and the data acquisition interval of each air index in each sterilization stage in different states are obtained, and the representative duration, the representative feature and the data acquisition interval of each sterilization stage in the new sterilization period are determined according to the state which is the same as the sterilization mode of the new sterilization period and has the maximum similarity of the air indexes; and according to the difference between the characteristic value of the real-time data of each air index and the representative characteristic of each air index in the target stage, the data acquisition interval of each air index in the target stage is adjusted so as to control equipment connected with the Internet of Things to acquire the real-time data, and then the operation state of the health intelligent platform is monitored. According to the invention, the accuracy and reliability of platform operation state monitoring are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of operation status monitoring, and more specifically, to a method and system for monitoring the operation status of a health intelligence platform based on the Internet of Things. Background Art

[0002] In recent years, with the acceleration of urbanization and the increase in population density, public health security is facing unprecedented challenges; in traditional public health disinfection work, manual disinfection methods dominate, 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, automated control and artificial intelligence, a public health and wellness smart platform is proposed, which realizes the intelligent management and efficient disinfection of the sanitary environment in public places.

[0004] Among them, the Internet of Things technology enables various sensors and disinfection equipment to interconnect and collect data on various air indicators in real time, such as temperature, humidity, and concentration of harmful gases; 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 effectiveness of disinfection; automated control technology ensures the precise execution of disinfection operations, and can automatically adjust the intensity and frequency of disinfection according to preset rules and real-time data; the application of artificial intelligence further enhances the intelligence level of the platform.

[0005] When collecting air index data in real time through Internet of Things technology, a fixed data collection interval is usually adopted. However, the fixed data collection interval method cannot flexibly adapt to the needs of different disinfection stages, resulting in inaccurate and inefficient data collection, which in turn makes it impossible to accurately monitor the operating status of the public health and health smart platform. Summary of the invention

[0006] In order to solve the technical problem that the above-mentioned 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 further resulting in the inability to accurately monitor the operating status of the public health and wellness smart platform, the present invention provides solutions in the following aspects.

[0007] In the first aspect, the present invention provides a method for monitoring the operating status of a health intelligence platform based on the Internet of Things, comprising: obtaining test data of each air index under different states, wherein the disinfection modes and initial values ​​of each air index under different states are different; in any state, the test data of each air index is divided according to the numerical value and the change rate of the test data, and a representative sequence of each air index in multiple disinfection stages is formed; for any disinfection stage: according to the time length corresponding to the representative sequence of each air index, the representative time length of each air index is obtained, the distribution discreteness of each feature in the representative sequence of each air index is calculated, and the representative feature with the smallest distribution discreteness in each air index is selected, and the representative feature of each air index and the representative feature of each air index are selected according to the representative feature and The discrete degree of its distribution is used to set the data collection interval of each air indicator; through the state with the same disinfection mode as the new disinfection cycle and the greatest similarity of air indicators, the representative duration, representative characteristics and data collection interval of each disinfection stage in the new disinfection cycle are determined, which are used 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; through the adjusted data collection interval, the devices connected to the Internet of Things are controlled to collect the real-time data of each air indicator, so as to monitor the operation status of the public health and health smart platform.

[0008] The present invention dynamically sets the data collection interval of each air index in different stages according to the characteristics of different disinfection stages, can flexibly adapt to the needs of different disinfection stages, and improve the accuracy and efficiency of data collection. At the same time, when collecting real-time data of each air index in real time according to the data collection 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 collection interval is dynamically adjusted according to the difference to better capture the changing trend of the data. By continuously optimizing the data collection interval, the data collection is made to more accurately match the actual disinfection process to better adapt to the actual operation of the platform, thereby improving the pertinence and timeliness of data collection, and enhancing the accuracy and reliability of platform operation 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 indicators include: TVOC, PM2.5, PM10, formaldehyde, carbon dioxide, temperature and humidity.

[0010] Preferably, the test data of each air index is divided according to the numerical value and the change rate of the test data, and the representative sequences of each air index in multiple disinfection stages are formed, including: for any air index, all the test data of the air index are combined into a test sequence of the air index in chronological order; according to the test sequences of all air indicators, the test sequence of each air index is divided into 4 subsequences by constructing and solving the maximum inter-class variance function, which are used 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.

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

[0012] Preferably, 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.

[0013] Preferably, 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.

[0014] Preferably, the representative time length of each air indicator is obtained according to the time length corresponding to the representative sequence of each air indicator, including: for the first three disinfection stages, the time length corresponding to the representative sequence of each air indicator in each disinfection stage is used as the representative time length of each air indicator in each disinfection stage; for the last disinfection stage, the time length between the end of the third disinfection stage and the start of a new disinfection cycle belongs to the last disinfection stage, that is, the representative time length of the last disinfection stage is not limited.

[0015] The present invention obtains the representative duration of each air indicator in each disinfection stage under different conditions, thereby providing a data basis for setting the data collection interval in a new disinfection cycle. In this way, in the new disinfection cycle, the appropriate data collection strategy can be quickly determined according to the representative duration of each air indicator in each disinfection stage under different conditions.

[0016] Preferably, 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 numerical value, change rate, change acceleration rate; the data collection interval of the air index 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 .

[0017] The present invention sets the data collection interval of each air indicator according to the representative characteristics of each air indicator and the degree of distribution dispersion, which can adapt to the monitoring needs of different stages, optimize the data collection efficiency, and thus improve the accuracy of data collection.

[0018] Preferably, the step of determining 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 the air index 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.

[0019] Preferably, the data collection interval of each air index in the target stage is adjusted, including: 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.

[0020] The present invention dynamically adjusts the data collection interval according to the changes in real-time data, so that the data collection can more accurately match the actual disinfection process, ensuring that the platform always collects data at the most appropriate frequency and realizing real-time monitoring of the operating status.

[0021] In a second aspect, the present invention provides a health intelligence platform operation status monitoring system based on the Internet of Things, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned health intelligence platform operation status monitoring method based on the Internet of Things is implemented.

[0022] By adopting the above technical solution, the above-mentioned method for monitoring the operation status of a health intelligence platform based on the Internet of Things is generated into a computer program and stored in a memory to be loaded and executed by a processor, so that a terminal device is made according to the memory and the processor for easy use.

[0023] The beneficial effects of the present invention are: The present invention divides the entire disinfection process into multiple stages by analyzing the numerical values ​​and change rates of the test data, and then optimizes the data collection and processing strategies according to the characteristics of each stage; and then obtains the representative duration, representative characteristics and data collection interval of each air indicator in each disinfection stage under different states, thereby providing a data basis for setting the data collection interval in the new disinfection cycle in the future, and quickly determining the appropriate data collection strategy; at the same time, when real-time data of each air indicator is collected in real time according to the data collection interval of each air indicator, by comparing the differences 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 differences, so as to better capture the changing trends of the data, and by continuously optimizing the data collection interval, the data collection is more accurately matched to the actual disinfection process, so as to better adapt to the actual operation of the platform, thereby improving the pertinence and timeliness of data collection, and enhancing the accuracy and reliability of platform operation status monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flow chart schematically illustrating a method for monitoring the operating status of a health intelligence platform based on the Internet of Things in the present invention; Figure 2 is a flowchart schematically showing step S2. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0026] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0027] The public health and wellness smart platform consists of two parts: software and disinfection units. The disinfection unit is installed in public places, undertakes automated disinfection work, and performs disinfection operations through a variety of disinfection methods; the software part completes the configuration and control of the disinfection unit, while providing complete management and control functions for different application scenarios, as well as visual processing and output of front-end data to complete data interaction.

[0028] Among them, the disinfection unit includes automated disinfection equipment and sensor components. The automated disinfection equipment is installed in different areas of public places and adopts a variety of disinfection methods, such as ultraviolet irradiation, spray disinfection, plasma disinfection, etc. These devices can automatically perform disinfection tasks according to the instructions of the software platform to ensure the comprehensiveness and uniformity of the disinfection operation; the sensor component cooperates with the data acquisition module of the software part to monitor the air indicators in the disinfection process in real time, such as temperature, humidity, etc. These data are fed back to the software platform for evaluating the disinfection effect and adjusting the operation parameters to achieve closed-loop control.

[0029] Among them, the software part has the following functional modules: 1. Data collection and integration module: Through the Internet of Things technology, various sensors and disinfection equipment can be interconnected, and 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. These data are cleaned and pre-processed to ensure accuracy, and then integrated into the platform's database.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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: 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.

[0036] 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.

[0037] Specifically, for the test site, different states are set, and the disinfection modes and initial values ​​of each air indicator are different in different states; the test site is disinfected in different states, and a complete disinfection cycle includes the start of one disinfection to the start of the next disinfection; during the disinfection cycle, according to the maximum value in the initial collection interval of all air indicators, the devices connected to the Internet of Things are controlled to collect the test data of each air indicator in real time.

[0038] 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.

[0039] There are 7 air indicators, 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 , the unit of PM2.5, PM10, formaldehyde and carbon dioxide is ppm, the unit of temperature is °C, and the unit of humidity is %.

[0040] 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 interval of TVOC, PM2.5 and PM10 is set to 1 minute; the concentration of carbon dioxide is mainly affected by the population density and 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 there will be no drastic fluctuations, while the release rate of formaldehyde is affected by temperature, humidity and ventilation conditions, and changes relatively slowly. Therefore, the initial collection interval of temperature, humidity and formaldehyde is set to 8 minutes.

[0041] It should be noted that by collecting test data under different disinfection modes and initial values, we can fully understand the performance of the platform under various operating conditions, provide specific data support for data analysis and collection interval adjustment in subsequent steps, and enable the platform to adapt to different disinfection needs and environmental conditions.

[0042] S2. In any state, the test data of each air indicator is divided according to the value and change rate of the test data, and a representative sequence of each air indicator in multiple disinfection stages is formed; the representative duration, representative characteristics and data collection interval of each air indicator in each disinfection stage are obtained.

[0043] See the flowchart of step S2. Figure 2 , including steps S201 to S204, specifically: S201. 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.

[0044] It should be noted that the values ​​and change rates of air indicators can reflect the 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 to facilitate subsequent targeted data collection and management. This can more accurately monitor and manage the disinfection process and improve the scientificity and effectiveness of the platform operation.

[0045] Specifically, through The difference between a test data and its multiple adjacent test data is obtained The specific calculation formula for the change rate of test data is: ; In the formula, For the The changing rate of the test data, , For the , Test data, test data , , , It is Test data Multiple adjacent test data.

[0046] It should be noted that the complete disinfection cycle from the start of one disinfection to the start of the next disinfection can be divided into the following stages according to different test data: 1. The first stage is the disinfection stage: the disinfection unit is performing disinfection operations, and the concentration of harmful gases in the air will gradually decrease.

[0047] 2. The second stage is the short-term stage after the disinfection is completed: the disinfection operation has just ended and the air quality begins to gradually stabilize, but it is still at a high level and requires continuous monitoring.

[0048] 3. The third stage is the mid-term stage after the disinfection is completed: After the short-term stage, the air quality is further improved, but monitoring is still required to ensure the continued safety of the environment.

[0049] 4. The fourth stage is the long-term stage after the disinfection is completed: after a period of time, the air quality reaches a stable and good state, and the environmental quality is guaranteed.

[0050] Furthermore, according to the numerical value and change rate of the test data, the test data of each air index is divided to form a representative sequence of each air index in multiple disinfection stages, including: for any air index, all the test data of the air index are combined into a test sequence of the air index in chronological order; according to the test sequences of all air indicators, the test sequence of each air index is divided into 4 subsequences by constructing and solving the maximum inter-class variance function, which are used as the representative sequences of each air index in the 4 disinfection stages, and the lengths of the representative sequences of each air indicator in the same disinfection stage are the same.

[0051] Among them, the air quality in different disinfection stages is different, and the corresponding test data values ​​and change rates are different. Therefore, the maximum inter-class variance function is constructed through the inter-class variance of different subsequences. The specific expression is as follows: ; In the formula, is the maximum inter-class difference function, 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 rate of change sequence of subsequences; since the test sequence is divided into 4 subsequences, the sequence number of the subsequence The value range is ; Since there are 7 air quality indicators, the order of the air quality indicators is The value range is .

[0052] The subsequence change rate sequence refers to a sequence composed of the change rates of all test data in the subsequence.

[0053] when hour, It is The first air index The subsequence and its left adjacent The between-class variance of subsequences, It is The first air index The subsequence and its left adjacent The inter-class variance of the rate of change sequence of subsequences, so when When The first air index The subsequence and The between-class variance of subsequences and The first air index The subsequence and The inter-class variance of the rate of change sequence of subsequences , there is no The first air index The subsequence and The between-class variance of subsequences and The first air index The subsequence and The inter-class variance of the rate of change sequence of subsequences ;when hour, It is The first air index The subsequence and its right adjacent The between-class variance of subsequences, It is The first air index The subsequence and its right adjacent The inter-class variance of the rate of change sequence of subsequences, so when When The first air index The subsequence and The between-class variance of subsequences and The first air index The subsequence and The inter-class variance of the rate of change sequence of subsequences , there is no The first air index The subsequence and The between-class variance of subsequences and The first air index The subsequence and The inter-class variance of the rate of change sequence of subsequences .

[0054] It should be noted that dividing the stages based on the changing patterns of 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 data collection and processing strategies according to the characteristics of each stage.

[0055] On the basis of dividing the disinfection stages, it is necessary to determine the representative duration and representative characteristics of each stage. The representative duration reflects the proportion of time of this stage in the overall disinfection process, and the representative characteristics (such as numerical value, change rate, and change acceleration rate) describe the main change law of air indicators in this stage; by calculating the distribution discreteness of these characteristics, the most stable characteristics are selected as representatives, so as to set a reasonable data collection interval.

[0056] S202. Obtain the representative duration of each air indicator in each disinfection stage according to the duration corresponding to the representative sequence of each air indicator in each disinfection stage.

[0057] Specifically, 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, and therefore, the representative duration of the last disinfection stage is not restricted.

[0058] S203, calculating the distribution dispersion of each feature in the representative sequence of each air index, and selecting the representative feature with the smallest distribution dispersion in each air index.

[0059] Specifically, for any disinfection stage, the distribution dispersion degree of each feature in the representative sequence of each air index in the disinfection stage is calculated; wherein the features include numerical values, change rates, and change acceleration rates; the distribution dispersion degree is equal to the ratio of the variance to the mean of each feature in the representative sequence, and the larger the variance and the smaller the mean of the feature in the representative sequence, the greater the distribution dispersion degree of the feature in the representative sequence; the feature with the smallest distribution dispersion degree is selected as the representative feature of each air index in the disinfection stage.

[0060] Among them, the acceleration rate of change of test data is equal to , is the change rate of the previous test data of the test data, It is the rate of change of the test data after this test data.

[0061] It should be noted that by selecting the most stable feature as the representative feature, the changes in air indicators at that stage can be reflected more accurately.

[0062] S204. According to the representative characteristics of each air indicator in each disinfection stage and the degree of distribution dispersion, the data collection interval of each air indicator in each disinfection stage is set.

[0063] It should be noted that when setting the data collection interval, the representative characteristics of the air index are the key; the representative characteristics reflect the main change characteristics of the index in a specific stage: if the representative characteristic is a numerical value, it means that the index is relatively stable at this stage and changes little, so a larger data collection interval can be set; if the representative characteristic is the rate of change, it means that the index is changing at a certain speed, and a medium collection interval is required to capture its change trend; if the representative characteristic is the acceleration rate of change, it means that the change speed of the indicator itself is changing, and it may be in a rapid change stage, and a smaller collection interval is required to record its change process in detail.

[0064] Specifically, for any disinfection stage, the data collection interval of each air indicator is set according to the representative characteristics of each air indicator and the degree of distribution dispersion: different representative characteristics of the air indicators have different parameters, and accordingly, the data collection interval of the air indicators is different, and the degree of distribution dispersion of the representative characteristics of the air indicators is different, so the data collection interval of the air indicators is different, and the greater the degree of distribution dispersion of the representative characteristics of the air indicators, the smaller the data collection interval of the air indicators.

[0065] Therefore, the calculation formula for the data collection interval of air indicators is as follows: ; In the formula, is the data collection interval for air indicators, 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 .

[0066] It should be noted that when the representative feature of the air index is a numerical value, it means that the data is relatively stable. A larger data collection interval can reduce data redundancy and avoid storing and processing a large amount of duplicate data, thereby improving the efficiency of data collection and processing. When the representative feature of the air index is the rate of change, a medium-sized data collection interval can capture the main change trend of the indicator while avoiding excessive data collection and balancing the frequency and amount of information collected. When the representative feature of the air index is the acceleration rate of change, a smaller collection interval can ensure that when the indicator changes rapidly, key data points can be captured in time to avoid missing important change information.

[0067] It should be noted that setting the data collection interval according to the representative characteristics of each stage can ensure that sufficient data is obtained in the critical stage, while avoiding the collection of too much redundant data in the non-critical stage, which not only improves the efficiency of data collection, but also reduces the burden of data processing.

[0068] S3. When starting a new disinfection cycle, set the disinfection mode and collect the initial values ​​of each air indicator; 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.

[0069] It should be noted that the pattern and initial value of the new disinfection cycle are similar to the previous state. The existing data can be used to set up a new disinfection cycle. By matching similar disinfection patterns and air indicators, the representative duration, characteristics and data collection intervals of each stage in the new disinfection cycle can be determined to achieve rapid adaptation and precise management of the new cycle.

[0070] Specifically, when the user independently selects a disinfection mode in the "operation interface", it means that a new disinfection cycle has been started. At the same time, the disinfection mode selected by the user is the disinfection mode of the new disinfection cycle. When a new disinfection cycle is started, the devices connected to the Internet of Things will collect data on various air indicators in real time as the initial values ​​of each air indicator in the new disinfection cycle.

[0071] Furthermore, a target state is obtained that has the same disinfection mode as the new disinfection cycle and the greatest similarity in air indicators, and the representative duration, representative characteristics, and data collection interval of each disinfection stage in the target state are used as the representative duration, representative characteristics, and data collection interval of each disinfection stage in the new disinfection cycle.

[0072] In one embodiment, the similarity of the air indicators is equal to the cosine similarity between the initial values ​​of the air indicators in the new disinfection cycle and the initial values ​​of the air indicators in different states; in other embodiments, other indicators can be selected to calculate the similarity of the air indicators, such as the Euclidean distance. In this case, the Euclidean distance of the air indicators is inversely proportional to the similarity of the air indicators.

[0073] It should be noted that in the new disinfection cycle, the appropriate data collection strategy should be quickly determined based on representative characteristics and duration.

[0074] S4. According to the representative duration of each disinfection stage in the new disinfection cycle, determine 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.

[0075] It should be noted that in the new disinfection cycle, the real-time collected air index data will be continuously transmitted back to the platform. By comparing these real-time data with the representative characteristics of each stage, it can be determined whether the current data collection interval is applicable: if the difference between the characteristic value of the real-time data and the representative characteristics of the target stage is large, it means that the current data collection interval is no longer applicable and needs to be adjusted.

[0076] Specifically, for the real-time data of any air index in the new disinfection cycle: when the real-time data of the air index 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 When the air index real-time data is within the range, the target stage is the third stage; when the collection time of the air index real-time data is greater than When , the target stage of the real-time data of air indicators is the fourth stage; , , They are the representative durations of the first, second and third stages in the new disinfection cycle.

[0077] Furthermore, for the real-time data of any air indicator in the new disinfection cycle: if the representative feature of the air indicator in the target stage is a numerical value, the real-time data of the air indicator is directly used as the characteristic value of the real-time data of the air indicator; if the representative feature of the air indicator in the target stage is the change rate, the change rate of the real-time data of the air indicator is calculated, and the calculated change rate is used as the characteristic value of the real-time data of the air indicator; if the representative feature of the air indicator in the target stage is the change acceleration rate, the change acceleration rate of the real-time data of the air indicator is calculated, and the calculated change acceleration rate is used as the characteristic value of the real-time data of the air indicator.

[0078] Furthermore, 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; 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, the data collection interval of the air indicator in the target stage is adjusted, including: 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.

[0079] It should be noted that the difference between the characteristics of real-time data and the representative characteristics of the target stage can reflect the degree of abnormality 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 real-time data and the representative characteristics of the target stage, the greater the degree of abnormality of the real-time data, and more frequent data collection is required.

[0080] In one embodiment, the inverse normalization function , is a natural exponential function; in other embodiments, the inverse normalized function , is the Sigmoid function.

[0081] The preset threshold The specific value can be set according to the actual application scenario and requirements, and the preset threshold The value range of is [0.3, 0.4], and the present invention sets the preset threshold to 0.36.

[0082] It should be noted that by comparing the characteristics of real-time data with the representative characteristics of the target stage, and then dynamically adjusting the data collection interval based on the differences, we can better capture the changing trends of the data. By continuously optimizing the data collection interval, data collection can be more accurately matched to the actual disinfection process to better adapt to the actual operation of the platform, further improving the pertinence and timeliness of data collection, and enhancing the accuracy and reliability of platform operation status monitoring.

[0083] S5. Through the adjusted 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 health intelligence platform.

[0084] It should be noted that the adjusted data collection interval can better meet the needs of platform operation status monitoring. By precisely controlling the data collection of IoT devices, resource utilization can be optimized and the monitoring effect can be improved.

[0085] Specifically, according to the adjusted data collection interval, control instructions are sent to the IoT devices to ensure that the devices connected to the IoT collect real-time data of various air indicators according to the set data collection interval; through the visual interface of the health intelligence platform, the real-time data of various air indicators are displayed in a visual way, so as to facilitate real-time monitoring of the operating status of the health intelligence platform.

[0086] It should be noted that by reasonably setting the data collection interval, it is possible to achieve efficient monitoring of the platform's operating status while ensuring data validity and monitoring purposes, providing strong support for timely discovery and handling of problems, and improving the overall operating efficiency and management level of the platform.

[0087] An embodiment of the present invention also discloses a health intelligence platform operation status monitoring system based on the Internet of Things, including a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, a health intelligence platform operation status monitoring method based on the Internet of Things according to the present invention is implemented.

[0088] 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 configuration 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 and change rate of the test data, and the representative sequences of each air index in multiple disinfection stages are 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 of each feature in the representative sequence of each air index is calculated, and the representative feature with the smallest distribution dispersion in each air index is selected; according to the representative feature of each air index and its distribution dispersion, 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 operation 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 of the air index real-time data is the first stage; when the air index real-time data collection time is 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 this 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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