Real-time monitoring system of pathogenic microorganisms based on high-risk area division

Abstract

The present invention discloses a real-time monitoring system of the Internet of Things for pathogenic microorganisms based on high-risk area division, and relates to the technical field of pathogenic microorganism monitoring. It includes a fluorescence detection module, which performs PCR amplification on the sample and detects the fluorescence mean of each PCR amplification cycle; a data transmission module, which transmits the detected fluorescence mean to a data processing module; a data processing module, which dynamically calculates the amplification index of the fluorescence mean in each moving window based on a moving window calculation; a first judgment module, which judges that the test result of the sample is positive when the amplification index meets a preset threshold; an early warning module, which issues an alarm and uploads the test result to a disease control center when the sample is detected to be positive. The present invention combines the detection of pathogenic microorganisms with the Internet of Things, which not only eliminates the tediousness of manually recording data, but also ensures the timeliness of data, and can implement early warning and prevention and control measures in the fastest time.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application is a divisional application based on application number 2021101448941, application date: February 2, 2021, and invention name: A real-time monitoring system for pathogenic microorganisms through the Internet of Things. Technical Field

[0003] The present invention relates to the technical field of pathogenic microorganism monitoring, and in particular to a pathogenic microorganism Internet of Things real-time monitoring system based on high-risk area division. Background Art

[0004] Pathogenic microorganisms are microorganisms that cause infection or even infectious diseases. In the infectious disease prevention and control system, monitoring pathogenic microorganisms plays a vital role.

[0005] The current pathogenic microorganism monitoring system is to test samples through testing equipment, obtain experimental data, and then manually enter the system for reporting. Most of the testing equipment has no network function, and the testing operation is complicated, which requires high professional skills of personnel; after the test is completed, the equipment can only generate test reports locally, and cannot transmit data in real time; the experimenter needs to collect reports one by one, sort them out, and then manually enter them for reporting. The entire monitoring system process is too affected by human factors, and the authenticity and timeliness of the data are difficult to guarantee. The current pathogenic microorganism monitoring system is too complicated, and it is easily affected by human factors from experimental operation to result reporting. It is difficult to ensure that the collected test results are true and valid, which greatly increases the difficulty of timely prevention and control of infectious diseases. Therefore, how to achieve accurate judgment after pathogenic microorganism detection, timely and effectively realize epidemic monitoring and early warning, and facilitate later guidance of epidemic prevention and control is a problem that needs to be solved urgently. Summary of the invention

[0006] The purpose of the present invention is to provide a real-time monitoring system for pathogenic microorganisms based on the Internet of Things, which can realize real-time monitoring of pathogenic microorganisms, achieve timely early warning, and guide the effect of epidemic prevention and control.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A pathogenic microorganism Internet of Things real-time monitoring system, comprising a fluorescence detection module, a data transmission module, a data processing module, a first judgment module, and an early warning module;

[0009] The fluorescence detection module performs PCR amplification on the sample and detects the fluorescence mean of each PCR amplification cycle;

[0010] The data transmission module transmits the detected fluorescence mean to the data processing module;

[0011] The data processing module dynamically calculates the amplification index of the fluorescence mean in each moving window based on the moving window calculation;

[0012] The first judgment module judges that the test result of the sample is positive when the amplification index meets the preset threshold;

[0013] The early warning module will sound an alarm and upload the test results to the CDC when a sample is detected to be positive.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention combines the detection of pathogenic microorganisms with the Internet of Things, which not only avoids the tediousness of manual data recording, but also ensures the timeliness of the data; at the same time, the present invention uses a moving window method to perform positive judgment, so that a judgment can be achieved without outputting a fluorescence mean, and early warning and prevention and control measures can be implemented in the fastest time.

[0015] Furthermore, the specific processing steps of the data processing module are as follows:

[0016] CL1, take the fluorescence mean from the i-th time to the i+k-th time as the judgment data of the i-th group;

[0017] CL2, calculate the increment ΔR of the jth fluorescence mean relative to the i-th fluorescence mean ij :

[0018] ΔR ij =R i+j -R i

[0019] Among them, R i is the i-th fluorescence mean, 1≤j≤k;

[0020] CL3, K = [1, k] as the horizontal coordinate, ln(ΔR ij ) is the ordinate;

[0021] CL4, solve the amplification index of the i-th group of judgment data, including the variation index CV i , slope i and the correlation index ρ i :

[0022]

[0023]

[0024]

[0025] Among them, Cov(K,ln(ΔR ij )) is K and ln(ΔR ij), Var[K] is the variance of K, Var[ln(ΔR ij )] is ln(ΔR ij )’s variance.

[0026] Furthermore, the judgment standard of the first judgment module is: when the variation index CV i , slope i , correlation index ρ i When both are greater than the corresponding thresholds, the sample is judged to be positive.

[0027] Furthermore, the variation index CV i The threshold is 0.044 and the slope is i The threshold value is 0.235, and the correlation index ρ i The threshold value is 0.963.

[0028] Furthermore, it also includes a positioning module, which regularly detects the geographical location and uploads the geographical location together with the detection result to achieve positioning statistics of positive samples.

[0029] Furthermore, the CDC is provided with an administrative area division module, a map marking module, a second judgment module, and a third judgment module, so as to facilitate early warning prompts for administrative areas at different levels.

[0030] The administrative region division module divides the electronic map into administrative regions of different levels;

[0031] The map marking module extracts the geographical location corresponding to the detection result and marks it in the corresponding administrative area on the electronic map;

[0032] The second judgment module counts the number of positive samples in the lowest-level administrative area, and if the number is greater than the set lowest-level threshold, marks the lowest-level administrative area as a high-risk area;

[0033] The third judgment module counts administrative regions of other levels and the number of positive samples in the administrative region. If the number is greater than the set threshold of the corresponding level, the evenness of the distribution of high-risk areas in the subordinate next-level administrative regions is calculated. If the evenness is greater than the set evenness threshold, the administrative region of other levels is marked as a high-risk area.

[0034] Furthermore, the calculation method of the evenness is as follows:

[0035] TP1: Count the administrative areas at the next level marked as high-risk areas under the current level of administrative areas;

[0036] TP2: If there are two or more adjacent secondary administrative regions that are high-risk areas, draw their common circumscribed circle;

[0037] TP3: Calculate the uniformity ρ:

[0038]

[0039] Among them, S0 is the administrative area of ​​the current level; S single is the area of ​​a single sub-level administrative region marked as a high-risk area; S common is the area of ​​the common circumscribed circle; ∑ is the summation operator, which calculates the union of multiple areas.

[0040] Furthermore, it also includes a reagent bottle, a QR code bound to the reagent bottle, and a QR code recognition module, which effectively avoids misoperation of non-professional operators in setting test parameters.

[0041] The reagent bottle contains a reagent for fluorescence detection;

[0042] The two-dimensional code, as the unique identification code of the reagent, records the experimental steps, parameters, detected pathogen type and production date of the reagent;

[0043] The two-dimensional code recognition module is used to recognize the two-dimensional code and display the recorded content of the two-dimensional code.

[0044] Furthermore, it also includes an analog-to-digital conversion module, which converts the analog signal of the fluorescence mean value detected by the fluorescence detection module into a digital signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the overall structure of an embodiment of the present invention.

[0046] Figure 2 FIG. 4 is a schematic diagram of uniformity calculation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] See also Figure 1The present invention provides a real-time monitoring system for pathogenic microorganisms in the Internet of Things, including a local monitoring terminal and a disease control center; the local monitoring terminal realizes local pathogenic microorganism detection and transmits the detection results to the disease control center, and the disease control center summarizes and analyzes the detection results transmitted by each local monitoring terminal to realize timely regional early warning.

[0049] The local monitoring terminal includes a fluorescence detection module, an analog-to-digital conversion module, a data transmission module, a data processing module, a first judgment module, an early warning module and a positioning module.

[0050] The fluorescence detection module performs PCR amplification on the sample and detects the fluorescence mean of each PCR amplification cycle. The optical transmission and detection method adopted by the fluorescence detection module are transmitted through an optical fiber system, which has good light conductivity, so that the fluorescence value detected after PCR amplification is more accurate. And in the fluorescence detection link, a silicon photodiode from Hamamatsu, Japan is used. This sensor has the characteristics of good reliability and high stability, which can provide a guarantee for the performance of the present invention.

[0051] In addition, the hardware solution mainly uses the temperature control algorithm of the main control chip and the conversion algorithm of fluorescence collection in terms of performance control, which makes the performance stable, which is specifically reflected in the control of the inter-hole difference within 3% and the stability CV within 3%. The temperature control accuracy is as high as 1 degree deviation range, mainly because of the use of mature sensor PT100 (A-level deviation), which can provide the system with better temperature control. The operating system is Android 4.4, which has the advantages of stable performance and mature technology, and can provide users with superior operating experience.

[0052] All modules of this hardware solution have their own control chips, and the modules communicate with each other through the UART serial port, which makes the system stable and has the advantages of less connecting wires. There are 3 control modules in total, one is the temperature control module, one is the LED light excitation light control module, and one is the central control board module, which includes the fluorescence collection module and the hot cover temperature control module.

[0053] During the experiment, the fluorescence values ​​were read 6 times in each PCR amplification cycle, and the average of these 6 fluorescence values ​​was taken to reduce data errors.

[0054] It is worth mentioning that in the detection of pathogenic microorganisms, there are strict requirements for the reagents used, and it is usually necessary for staff who have been trained for a long time to identify and operate. In the case of a large-scale epidemic, it is inevitable that there will be a shortage of professionals. In another embodiment of the present invention, it also includes a reagent bottle, a QR code bound to the reagent bottle, and a QR code recognition module; effectively avoid the misoperation of non-professional operators to set test parameters. The reagent bottle is equipped with a reagent for fluorescence detection; the QR code is affixed to the reagent bottle as the unique identification code of the reagent, and records the experimental steps, parameters, detection pathogen type and production date of the reagent; the QR code recognition module is used to identify the QR code and display the recorded content of the QR code. So that non-professionals can also use the pathogenic microorganism detection instrument through simple training, and can also filter out the reagents that have not been used for a long time and invalidate them according to the production date of the reagent, so as to avoid experimental errors caused by the use of invalid reagents.

[0055] The analog-to-digital conversion module converts the analog signal of the fluorescence mean value detected by the fluorescence detection module into a digital signal.

[0056] The data transmission module transmits the detected fluorescence mean to the data processing module.

[0057] The data processing module dynamically calculates the amplification index of the fluorescence mean in each moving window based on the moving window calculation;

[0058] Specifically, the specific processing steps of the data processing module are as follows:

[0059] CL1, take the fluorescence mean from the i-th time to the i+k-th time as the i-th group of judgment data; for example, it takes about 40-50 minutes to complete an experiment, and each experiment will transmit 40 fluorescence mean values ​​through the serial port, and k is 4. Then from the 1st fluorescence mean to the 5th fluorescence mean as the first group of judgment data, and so on, the fluorescence mean values ​​of 2-6 times are used as the second group of judgment data, the fluorescence mean values ​​of 3-7 times are used as the third group of judgment data, and so on, the fluorescence mean values ​​of 36-40 times are used as the 36th group of judgment data, and there are a total of 36 groups of judgment data. For each group of judgment data, the following processing steps from CL2 to CL4 are performed in sequence:

[0060] CL2, calculate the increment ΔR of the jth fluorescence mean relative to the i-th fluorescence mean ij :

[0061] ΔR ij =R i+j -R i

[0062] Among them, R i is the i-th fluorescence mean, 1≤j≤k;

[0063] CL3, K = [1, k] as the horizontal coordinate, ln(ΔR ij ) is the ordinate;

[0064] CL4, solve the amplification index of the i-th group of judgment data, including the variation index CV i , slope i and the correlation index ρ i :

[0065]

[0066]

[0067]

[0068] Among them, Cov(K,ln(ΔR ij )) is K and ln(ΔR ij ), Var[K] is the variance of K, Var[ln(ΔR ij )] is ln(ΔR ij )’s variance.

[0069] At this point, a set of amplification indices is obtained by transferring the fluorescence mean each time.

[0070] The first judgment module, when the variation index CV i , slope i , correlation index ρ i When both are greater than the corresponding threshold, the test result of the sample is judged to be positive. i The threshold is 0.044 and the slope is i The threshold value is 0.235, and the correlation index ρ i The threshold value is 0.963. Therefore, when a fluorescence mean is output in each PCR amplification cycle, a test result can be output, and the sample data with positive test results can be screened out at the first time, and timely early warning prevention and control measures can be taken.

[0071] The early warning module will sound an alarm and upload the test results to the CDC when a sample is detected to be positive.

[0072] The positioning module detects the geographic location at regular intervals and uploads the geographic location along with the detection results to achieve positioning statistics of positive samples, facilitating subsequent regional early warning.

[0073] The disease control center is equipped with an electronic map, an administrative area division module, a map marking module, a second judgment module, and a third judgment module, so as to facilitate early warning prompts for administrative areas of different levels.

[0074] The administrative region division module divides the electronic map into administrative regions of different levels. The specific administrative region division content depends on different regions. The following is an example of the division of provinces, cities, counties, towns, townships and villages.

[0075] The map marking module extracts the geographical location corresponding to the detection result and marks it in the corresponding administrative area on the electronic map.

[0076] The second judgment module counts the number of positive samples in the lowest-level administrative area (village), and if it is greater than the set minimum level threshold, the lowest-level administrative area is marked as a high-risk area. For example, if the set minimum level threshold is 5, when the number of samples with positive pathogenic microorganism test results in a village reaches 6, the village is marked as a high-risk area.

[0077] The third judgment module counts administrative regions of other levels, counts the number of positive samples in the administrative region, and if it is greater than the set corresponding level threshold, calculates the average distribution of high-risk areas in the subordinate next-level administrative regions, and if the average is greater than the set average threshold, the administrative region of other levels is marked as a high-risk area. For example, the township-level threshold of a township is set to 20 people, and the average threshold is set to 0.5. Then, when the total number of positive samples in all villages under the township reaches 21 people, and the average distribution of high-risk areas in these villages reaches 0.5 or more, the township is marked as a high-risk area; the high-risk marking of towns, counties, cities, and provinces is the same.

[0078] In this embodiment, the average degree is used to characterize the uniformity of high-risk areas in the subordinate administrative regions of the administrative region. The smaller the average degree, the more concentrated the positive samples of pathogenic microorganisms are, and they only occur in a few specific villages. At this time, it is not appropriate to mark the township as a high-risk area, otherwise it will cause improper allocation of prevention and control personnel and materials. On the contrary, the larger the average degree, the wider the distribution of positive samples of pathogenic microorganisms is. At this time, it is obviously not enough to focus on prevention and control in a few villages, but the entire township should be controlled.

[0079] Specifically, the calculation method of the evenness is as follows:

[0080] TP1: Count the administrative areas of the current level that are marked as high-risk areas. Figure 2 As shown, a certain township includes 12 villages from A1 to A12, among which A3, A6, A7 and A12 are high-risk areas.

[0081] TP2: If there are two or more adjacent secondary administrative regions that are high-risk areas, draw their common circumscribed circle; Figure 2A3, A6, and A7 are geographically adjacent to each other, so their common circumscribed circle is drawn.

[0082] TP3: Calculate the uniformity ρ:

[0083]

[0084] Among them, S0 is the administrative area of ​​the current level; S single is the area of ​​a single sub-level administrative region marked as a high-risk area; S common is the area of ​​the common circumscribed circle; ∑ is a summation operator, which calculates the union of multiple areas. In this embodiment, the first union of the areas of all single sub-level administrative regions marked as high-risk areas is first independently calculated, and then the second union of the areas of all common circumscribed circles is independently calculated, and then the first union and the second union are used to obtain the third union. Finally, the intersection of the third union and the total area of ​​the township is used as the numerator, and the total area of ​​the township is used as the denominator to solve for the uniformity ρ. The reason why the area of ​​the circumscribed circle is used here to characterize the risk level of two or more adjacent sub-level administrative regions as high-risk areas is based on the fact that two or more adjacent high-risk areas are adjacent to each other, and there is a potential risk of large-scale infection. Therefore, the area of ​​the circumscribed circle is used instead of the sum of the areas of its high-risk areas to increase its risk level.

[0085] The CDC can make reasonable allocation of prevention and control materials and personnel according to the actual situation in the high-risk areas marked on the electronic map to avoid the further spread of the epidemic.

[0086] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be considered exemplary and non-restrictive in all respects, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present invention.

Claims

1. A real-time monitoring system of pathogenic microorganisms based on high-risk area division, characterized in that: include The fluorescence detection module performs PCR amplification on the sample and detects the fluorescence mean of each PCR amplification cycle; A data transmission module, transmitting the detected fluorescence mean to a data processing module; The data processing module dynamically calculates the amplification index of the fluorescence mean in each moving window based on the moving window calculation; The first judgment module judges that the test result of the sample is positive when the amplification index meets the preset threshold; The early warning module, when a sample is detected as positive, issues an alarm and uploads the test results to the CDC; A positioning module that periodically detects the geographic location and uploads the geographic location along with the detection results; The disease control center is provided with an administrative area division module, a map marking module, a second judgment module, and a third judgment module; The administrative area division module divides the electronic map into administrative areas of different levels; A map marking module extracts the geographical location corresponding to the test result and marks it in the corresponding administrative area on the electronic map; The second judgment module counts the number of positive samples in the lowest-level administrative area. If the number is greater than the set lowest-level threshold, the lowest-level administrative area is marked as a high-risk area. The third judgment module counts administrative regions of other levels, and counts the number of positive samples in the administrative region. If it is greater than the set corresponding level threshold, the average distribution of high-risk areas in the subordinate next-level administrative regions is calculated. If the average is greater than the set average threshold, the administrative region of other levels is marked as a high-risk area; The specific processing steps of the data processing module are as follows: CL1, take The fluorescence mean value is The mean fluorescence value was taken as Group judgment data; CL2, calculate the The fluorescence mean value of the second The increment of the fluorescence mean : in, For the The mean fluorescence value, ; CL3, order is the horizontal axis, Make a graph for the ordinate; CL4, solve The amplification index of the group judgment data, including the variation index , slope and correlation index : in, for and The covariance of for The variance of for The variance of The judgment standard of the first judgment module is: when the variation index , slope , correlation index When both are greater than the corresponding threshold, the sample is judged to be positive; The calculation method of the evenness is as follows: TP1: Count the administrative areas at the next level marked as high-risk areas under the current level of administrative areas; TP2: If there are two or more adjacent secondary administrative regions that are high-risk areas, draw their common circumscribed circle; TP3: Calculate the mean : in, is the administrative area at the current level; The area of ​​a single sub-administrative region marked as a high-risk area; is the area of ​​the common circumcircle; For the sum operator, calculate the union of multiple areas.

2. The real-time monitoring system of pathogenic microorganisms based on high-risk area division according to claim 1 is characterized in that: Variation Index The threshold is 0.044, and the slope The threshold is 0.235, and the correlation index The threshold value is 0.

963.

3. The real-time monitoring system of pathogenic microorganisms based on high-risk area division according to claim 1 is characterized in that: It also includes a reagent bottle, a QR code bound to the reagent bottle, and a QR code recognition module; The reagent bottle contains a reagent for fluorescence detection; The two-dimensional code, as the unique identification code of the reagent, records the experimental steps, parameters, detected pathogen type and production date of the reagent; The two-dimensional code recognition module is used to recognize the two-dimensional code and display the recorded content of the two-dimensional code.

4. The real-time monitoring system of pathogenic microorganisms based on high-risk area division according to claim 1 is characterized in that: It also includes an analog-to-digital conversion module, which converts the analog signal of the fluorescence mean value detected by the fluorescence detection module into a digital signal.

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