Tap water radiation pollution early warning method and device based on machine learning

Abstract

The invention relates to a tap water radiation pollution early warning method and device based on machine learning, and the method comprises the steps: carrying out the periodic sampling of tap water radiation indexes through setting a plurality of different sampling point positions in a target monitoring region, and obtaining a tap water radiation index value sequence of each sampling point position; traversing and finding out an index fluctuation range in the tap water radiation index value sequence of each sampling point position, finding out a plurality of groups of two sampling point positions with fluctuation approaching based on the index fluctuation range, and forming a plurality of groups of early warning analysis data; and calculating a radiation pollution degree index of each group of early warning analysis data, comparing the radiation pollution degree index with a radiation pollution degree threshold value to judge whether a tap water radiation pollution event exists or not, and giving an alarm when the tap water radiation pollution event exists. According to the method, the influence of radiation monitoring index fluctuation caused by the characteristic of high flowability of the tap water is inhibited through the analysis process, so that the accuracy of an early warning result is ensured, and the method belongs to the related technical field of tap water monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field related to tap water monitoring, and in particular to a method and device for early warning of tap water radiation contamination based on machine learning. Background Art

[0002] The current method of warning for radiation contamination of tap water is often to set a radioactive activity threshold in concentration units for alpha particles and beta particles. Once there is a tap water sample that exceeds the screening level, an early warning alert will be issued.

[0003] In addition, the prior application with application number 201610153624.6 discloses an automatic detection and alarm system for tap water radiation contamination and a method thereof, which proposes to use a radiation sensor to monitor the radioactivity count of tap water in the tap water pipe in real time, and store the relevant data in the local memory, or transmit it to a mobile phone or computer client, so as to realize the uninterrupted acquisition of tap water radioactivity data, timely discover the safety hazards of tap water, and send the hazard information in time through SMS, the Internet and other channels, and realize sound and light alarm.

[0004] Although this solution proposes a relatively mature alarm hardware system, its monitoring through radiation sensors has poor monitoring effect on the one hand, and on the other hand, it only issues an alarm based on the number of times the radiation sensor reading exceeds the preset threshold within the monitoring period, and its accuracy is also low.

[0005] Moreover, the above methods do not take into account the high fluidity of tap water. When tap water flows, especially when the flow rate is particularly large, the radiation particles in it fluctuate with the water flow. Therefore, how to issue an early warning for each sampling data in response to this situation to improve the accuracy of the early warning results is an urgent problem that needs to be solved in this field. Summary of the invention

[0006] The purpose of the present invention is to solve at least one of the deficiencies of the prior art and to provide a method and device for early warning of tap water radiation contamination based on machine learning.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions: Specifically, a tap water radiation contamination early warning method based on machine learning is proposed, including the following: The tap water radiation index values ​​at different sampling points in the target monitoring area are obtained by periodic sampling, and a tap water radiation index value sequence at each sampling point is obtained; Traverse and find out the index fluctuation range in the tap water radiation index value sequence at each sampling point; Based on the index fluctuation range, find the second sampling point position that is close to the first sampling point position, and use the tap water radiation index value sequences of the first sampling point position and the second sampling point position as a set of early warning analysis data, thereby obtaining multiple sets of early warning analysis data, wherein the first sampling point position is an arbitrary sampling point position; Calculate and obtain the radiation contamination level index for each set of early warning analysis data; If at least one set of radiation contamination level indicators of early warning analysis data exceeds the radiation contamination level threshold, an early warning reminder will be issued.

[0008] Further, specifically, the locations of different sampling points in the target monitoring area refer to the locations of different tap water outlets in the target monitoring area of ​​the same tap water source.

[0009] Further, specifically, the method of obtaining the tap water radiation index value includes: The radioactivity of α or β particles in tap water is detected by a Geiger counter as the tap water radiation index value.

[0010] Further, specifically, the fluctuation range of the index in the tap water radiation index value sequence at each sampling point is traversed and found, including: For any sampling point location, if the preset sampling period of periodic sampling is Cycle1, and the tap water radiation index value is recorded as Rad_In; From the start time to the end time of periodic sampling, the collection time of Rad_In is recorded as C_exam in the order of sampling to determine whether there is index fluctuation, and all collection times C_exam with index fluctuation are found. From the collection times C_exam with index fluctuation, the two collection times with the smallest time interval are found, and they are recorded as C_begin collection time and C_close collection time in the order of collection; Then the range from the C_begin collection time to the C_close collection time is the indicator fluctuation range; Among them, when the following conditions exist, it is determined that there is an index fluctuation at the collection time C_exam of Rad_In, If the value of Rad_In collected at the collection time C_exam is less than the value of Rad_In collected at the previous collection time, i.e., the collection time C_exam-Cycle1, and the value of Rad_In collected at the collection time C_exam-2*Cycle1 is less than the value of Rad_In collected at the collection time C_exam-Cycle1, and the value of Rad_In collected at the collection time C_exam is greater than the value of C_exam_Rad_In_Avg; Wherein, C_exam_Rad_In_Avg represents the average value of non-zero Rad_In values ​​of all sampling points at the acquisition time C_exam.

[0011] Further, specifically, based on the index fluctuation range, finding the second sampling point position that is close to the first sampling point position fluctuation includes: The maximum tap water radiation index value of the first sampling point in the corresponding index fluctuation range is recorded as range1 as Rad_InMaxLoc1, and the minimum tap water radiation index value is Rad_InMinLoc1; define the variable i as the number of the sampling point position, and make the following judgment: If the position of the i-th sampling point satisfies the following conditions, it is determined that the position of the i-th sampling point and the first sampling point are close to each other in terms of fluctuation, and the position of the i-th sampling point is recorded as the second sampling point position close to the first sampling point position in terms of fluctuation. Rad_InMax(i)≤Rad_InMaxLoc1+recom_Rad_In(i), and Rad_InMax(i)≤Rad_InMaxLoc1+recom_Rad_In(i); Among them, Rad_InMax(i) represents the maximum tap water radiation index value of the i-th sampling point within the index fluctuation range range1; Rad_InMin(i) represents the minimum tap water radiation index value of the i-th sampling point within the index fluctuation range range1; recom_Rad_In(i) represents the index compensation value of the i-th sampling point, and the calculation formula of recom_Rad_In(i) is as follows: ; Where N is the total number of sampling point locations, Indicates the maximum tap water radiation index value at the jth sampling point within the index fluctuation range corresponding to the ith sampling point; It represents the minimum tap water radiation index value within the index fluctuation range corresponding to the i-th sampling point position at the j-th sampling point position.

[0012] Further, specifically, the radiation contamination level index of each set of early warning analysis data is calculated and obtained, including: The calculation formula of the radiation contamination index of the qth group of early warning analysis data, marked as Index_q, is as follows:

[0013] in, It represents the average value of all non-zero Rad_In values ​​in the index fluctuation range range1 of the first sampling point. Indicates the average value of all non-zero Rad_In values ​​at the second sampling point in the indicator fluctuation range range1.

[0014] Further, specifically, the method of providing early warning reminders is: Pre-bind multiple administrator IPs. When an early warning reminder is needed, all periodic sampling data and preset text reminder content are packaged and sent to all administrator IPs for subsequent verification and analysis.

[0015] The present invention also proposes a device for early warning of tap water radiation pollution based on machine learning, comprising the following: The data acquisition module is used to obtain the tap water radiation index values ​​at different sampling points in the target monitoring area by periodic sampling, and obtain the tap water radiation index value sequence at each sampling point; The index fluctuation range calculation module is used to traverse and find the index fluctuation range in the tap water radiation index value sequence at each sampling point; A data generation module is used to find a second sampling point position that is close to the first sampling point position based on the index fluctuation range, and use the tap water radiation index value sequences of the first sampling point position and the second sampling point position as a set of early warning analysis data, thereby obtaining multiple sets of early warning analysis data, wherein the first sampling point position is an arbitrary sampling point position; An index calculation module is used to calculate and obtain the radiation contamination degree index of each set of early warning analysis data; The early warning reminder module is used to issue an early warning reminder when the radiation pollution level index of at least one set of early warning analysis data exceeds the radiation pollution level threshold.

[0016] The beneficial effects of the present invention are: The present invention proposes a method and device for early warning of tap water radiation pollution based on machine learning. By setting multiple different sampling points in the target monitoring area to perform periodic sampling of tap water radiation indicators, a tap water radiation indicator value sequence at each sampling point is obtained. Then, the indicator fluctuation range in the tap water radiation indicator value sequence at each sampling point is traversed and found. Based on the indicator fluctuation range, multiple groups of two sampling point positions with similar fluctuations are found, and multiple groups of early warning analysis data are formed. Then, the radiation pollution degree index of each group of early warning analysis data is calculated, and compared with the radiation pollution degree threshold to determine whether there is a tap water radiation pollution event, and an alarm is issued when there is a tap water radiation pollution event. The present invention suppresses the influence of the fluctuation of the radiation monitoring index caused by the high fluidity of tap water through the above analysis process, and ensures the accuracy of the early warning result as much as possible. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other features of the present disclosure will become more apparent by describing in detail the embodiments shown in the accompanying drawings. The same reference numerals in the accompanying drawings of the present disclosure represent the same or similar elements. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure. For those skilled in the art, other accompanying drawings can be obtained based on these accompanying drawings without creative work. In the accompanying drawings: Figure 1 Shown is a flow chart of a method for early warning of tap water radiation contamination based on machine learning of the present invention; Figure 2 Shown is a structural principle diagram of the tap water radiation contamination early warning device based on machine learning of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the embodiments and drawings to clearly and completely describe the concept, specific structure and technical effects of the present invention, so as to fully understand the purpose, scheme and effect of the present invention. It should be noted that the embodiments in this application and the features in the embodiments can be combined with each other without conflict. The same reference numerals used throughout the drawings indicate the same or similar parts.

[0019] Example 1, reference Figure 1 The present invention proposes a method for early warning of tap water radiation pollution based on machine learning, comprising the following: Step 110: obtaining tap water radiation index values ​​at different sampling points in the target monitoring area by periodic sampling, and obtaining a tap water radiation index value sequence at each sampling point; Step 120, traverse and find out the index fluctuation range in the tap water radiation index value sequence at each sampling point; Step 130: Find the second sampling point position that is close to the first sampling point position based on the index fluctuation range, and use the tap water radiation index value sequences of the first sampling point position and the second sampling point position as a set of early warning analysis data, thereby obtaining multiple sets of early warning analysis data, wherein the first sampling point position is an arbitrary sampling point position; Step 140, calculating and obtaining the radiation contamination degree index of each set of early warning analysis data; Step 150: If the radiation contamination level index of at least one set of early warning analysis data exceeds the radiation contamination level threshold, an early warning reminder is issued.

[0020] In the present embodiment 1, by setting a plurality of different sampling points in the target monitoring area to perform periodic sampling of tap water radiation index, a tap water radiation index value sequence of each sampling point position is obtained, and then the index fluctuation range in the tap water radiation index value sequence of each sampling point position is traversed and found, and then multiple groups of 2 sampling point positions with fluctuations approaching are found based on the index fluctuation range, and multiple groups of early warning analysis data are formed by this; then the radiation pollution degree index of each group of early warning analysis data is calculated, and compared with the radiation pollution degree threshold to determine whether there is a tap water radiation pollution event, and an alarm is issued when there is a tap water radiation pollution event. The present invention suppresses the influence of the fluctuation of the radiation monitoring index caused by the high fluidity of tap water through the above analysis process, and ensures the accuracy of the early warning result as much as possible.

[0021] As a preferred embodiment of the present invention, specifically, the positions of different sampling points in the target monitoring area refer to the positions of different tap water outlets in the target monitoring area of ​​the same tap water source.

[0022] In this preferred embodiment, taking into account the fluidity of tap water, if data sampling and monitoring of tap water is simply performed from a certain location, the monitoring results will be inaccurate. Therefore, considering the actual application scenarios of tap water use, when it is necessary to monitor the tap water in the target monitoring area (i.e., the same tap water source), the above problem can be solved to a certain extent by sampling and analyzing tap water from multiple pre-selected tap water outlets.

[0023] As a preferred embodiment of the present invention, specifically, the method of obtaining the tap water radiation index value includes: The radioactivity of α or β particles in tap water is detected by a Geiger counter as the tap water radiation index value.

[0024] In this preferred embodiment, tap water radiation index values ​​are obtained based on a Geiger counter, which specifically includes the following processes and precautions: 1. Understand the working principle of Geiger counter The Geiger counter detects the interaction of radioactive particles (such as alpha and beta particles) with the gas inside the detector, producing ionization effects, thereby generating signals. These signals are recorded by the counter as the number of times the particles pass through in a specific time (count).

[0025] 2. Alpha and beta particles have different penetrating abilities Alpha particles: have poor penetrating power and can only penetrate thin paper or the surface of the skin. Since alpha particles can only penetrate a short distance, you need to make sure the window of the Geiger counter is thin enough to effectively detect them.

[0026] Beta particles: have stronger penetrating power and can penetrate thinner materials. For beta particles, a Geiger counter with a thicker window is usually required to avoid excessive interference from alpha particles.

[0027] 3. Configure a suitable Geiger counter Detection of alpha particles: Alpha particles can be effectively detected using a Geiger counter with a thin metal window. For the detection of alpha particles, it is necessary to ensure that the window of the Geiger counter can transmit alpha particles but at the same time block the heavier beta particles.

[0028] Detection of beta particles: A Geiger counter with a thicker window is required to prevent too many alpha particles from affecting the test results. Beta particles can penetrate thicker windows, so they can be detected using an ordinary Geiger counter.

[0029] 4. Measuring Radioactivity Concentration Calibrating the counter: First, make sure the Geiger counter has been calibrated to ensure that its readings are accurate. This can be done using a known radioactive substance or a standard source.

[0030] To make a measurement: Place the Geiger counter close to the sample or source of radioactive material and record the number of counts per unit time, usually counts per minute (cpm).

[0031] Separation of alpha and beta signals: If you need to distinguish between alpha and beta particles, you can do so by using different detectors (e.g., a dedicated window for alpha and a dedicated window for beta) or by using a Geiger counter with filters. Typically, if the Geiger counter does not have separate windows, the alpha particles will be counted significantly less than the beta particles.

[0032] 5. Calculation of Radioactivity Concentration The concentration of alpha and beta radioactive substances in a sample can be calculated from the known relationship between the activity of the substance and the counting rate. Using a standard calibration curve (generated from a standard source of known activity), the concentration of radioactive substances in the sample can be inferred from the results read by the counter.

[0033] 6. Notes Sample handling: Make sure the sample is properly handled and placed in the measuring area of ​​the counter to avoid errors.

[0034] Influence of background radiation: Background radiation may interfere with the measurement results, so it is necessary to record the background radiation without a sample and subtract this background value from the measurement results.

[0035] Through the above steps, the concentration of α and β radioactive substances can be effectively detected using a Geiger counter.

[0036] As a preferred embodiment of the present invention, specifically, traversing and finding the index fluctuation range in the tap water radiation index value sequence at each sampling point location includes: For any sampling point location, if the preset sampling period of periodic sampling is Cycle1, and the tap water radiation index value is recorded as Rad_In; From the start time to the end time of periodic sampling, the collection time of Rad_In is recorded as C_exam in the order of sampling to determine whether there is index fluctuation, and all collection times C_exam with index fluctuation are found. From the collection times C_exam with index fluctuation, the two collection times with the smallest time interval are found, and they are recorded as C_begin collection time and C_close collection time in the order of collection; Then the range from the C_begin collection time to the C_close collection time is the indicator fluctuation range; Among them, when the following conditions exist, it is determined that there is an index fluctuation at the collection time C_exam of Rad_In, If the value of Rad_In collected at the collection time C_exam is less than the value of Rad_In collected at the previous collection time, i.e., the collection time C_exam-Cycle1, and the value of Rad_In collected at the collection time C_exam-2*Cycle1 is less than the value of Rad_In collected at the collection time C_exam-Cycle1, and the value of Rad_In collected at the collection time C_exam is greater than the value of C_exam_Rad_In_Avg; Among them, C_exam_Rad_In_Avg represents the average value of the non-zero Rad_In values ​​of all sampling points at the acquisition time C_exam.

[0037] In this preferred embodiment, through the above analysis method, the index fluctuation range in the tap water radiation index value sequence at any sampling point location can be accurately found based on the adjacent sampling interval data analysis.

[0038] As a preferred embodiment of the present invention, specifically, finding the second sampling point position close to the first sampling point position fluctuation based on the indicator fluctuation range includes: The maximum tap water radiation index value of the first sampling point in the corresponding index fluctuation range is recorded as range1 as Rad_InMaxLoc1, and the minimum tap water radiation index value is Rad_InMinLoc1; define the variable i as the number of the sampling point position, and make the following judgment: If the position of the i-th sampling point satisfies the following conditions, it is determined that the position of the i-th sampling point and the first sampling point are close to each other in terms of fluctuation, and the position of the i-th sampling point is recorded as the second sampling point position close to the first sampling point position in terms of fluctuation. Rad_InMax(i)≤Rad_InMaxLoc1+recom_Rad_In(i), and Rad_InMax(i)≤Rad_InMaxLoc1+recom_Rad_In(i); Among them, Rad_InMax(i) represents the maximum tap water radiation index value of the i-th sampling point within the index fluctuation range range1; Rad_InMin(i) represents the minimum tap water radiation index value of the i-th sampling point within the index fluctuation range range1; recom_Rad_In(i) represents the index compensation value of the i-th sampling point, and the calculation formula of recom_Rad_In(i) is as follows:

[0039]

[0040] Where N is the total number of sampling point locations, Indicates the maximum tap water radiation index value at the jth sampling point within the index fluctuation range corresponding to the ith sampling point; It represents the minimum tap water radiation index value within the index fluctuation range corresponding to the i-th sampling point position at the j-th sampling point position.

[0041] In this preferred embodiment, on the one hand, the proximity relationship of the tap water radiation index values ​​between the two sampling point positions is compared through preset rules, and on the other hand, an index compensation value is added to the compared data, and the proximity relationship of the tap water radiation index values ​​between the two sampling point positions is corrected with the data of all sampling point positions as the correlation; and the fluctuation relationship determination result between the first sampling point position and the second sampling point position is made as accurate as possible.

[0042] As a preferred embodiment of the present invention, specifically, the radiation contamination level index of each set of early warning analysis data is calculated and obtained, specifically, the radiation contamination level index of each set of early warning analysis data is calculated and obtained, including: The calculation formula of the radiation contamination index of the qth group of early warning analysis data, marked as Index_q, is as follows:

[0043] in, It represents the average value of all non-zero Rad_In values ​​in the index fluctuation range range1 of the first sampling point. Indicates the average value of all non-zero Rad_In values ​​at the second sampling point in the indicator fluctuation range range1.

[0044] In this preferred embodiment, each set of early warning analysis data found by the above scheme is equivalent to the ROI data to be analyzed, and then the Index_q obtained by averaging the early warning analysis data within the range 1 in the ROI data can basically accurately characterize the value to be compared for this sampling, and then compare it with the screening level standard value of the radioactive screening index set by the World Health Organization for drinking water quality, and then output an accurate early warning result. In addition, according to the difference between the monitored α and β particles, the corresponding screening index is also adjusted accordingly, that is, the radiation contamination level threshold and the radioactive screening index set by the World Health Organization for drinking water quality, that is, the total α and total β radioactivity, the corresponding screening levels are 0.5Bq / L and 1Bq / L respectively, and change synchronously. In addition, as a preferred embodiment, an adjustment coefficient P can be set according to the differences in different target monitoring areas, and P*radiation contamination level threshold can be used to eliminate the differences in different target monitoring areas, and P is a preset coefficient determined according to preliminary experiments.

[0045] As a preferred embodiment of the present invention, specifically, the way of performing the early warning reminder is: Pre-bind multiple administrator IPs. When an early warning reminder is needed, all periodic sampling data and preset text reminder content are packaged and sent to all administrator IPs for subsequent verification and analysis.

[0046] In this preferred embodiment, by presetting the administrator IP, when an early warning reminder is needed, the relevant administrator is informed through the preset text reminder content to conduct further verification and analysis, and all periodic sampling data are attached for the administrator to screen and use, and then make the final result judgment. Reference Figure 2 , Embodiment 2, the present invention also proposes a device for early warning of tap water radiation pollution based on machine learning, comprising the following: The data acquisition module 100 is used to acquire the tap water radiation index values ​​at different sampling points in the target monitoring area by periodic sampling, and obtain the tap water radiation index value sequence at each sampling point; The index fluctuation range calculation module 200 is used to traverse and find the index fluctuation range in the tap water radiation index value sequence at each sampling point; The data generation module 300 is used to find the second sampling point position that is close to the first sampling point position based on the index fluctuation range, and use the tap water radiation index value sequences of the first sampling point position and the second sampling point position as a set of early warning analysis data, thereby obtaining multiple sets of early warning analysis data, wherein the first sampling point position is an arbitrary sampling point position; An index calculation module 400 is used to calculate and obtain the radiation contamination degree index of each set of early warning analysis data; The early warning reminder module 500 is used to issue an early warning reminder when the radiation pollution level index of at least one set of early warning analysis data exceeds the radiation pollution level threshold.

[0047] In this embodiment 2, consistent with the method embodiment proposed in the present invention, by setting multiple different sampling point positions in the target monitoring area to perform periodic sampling of tap water radiation indicators, a tap water radiation indicator value sequence at each sampling point position is obtained, and then the indicator fluctuation range in the tap water radiation indicator value sequence at each sampling point position is traversed and found, and then multiple groups of 2 sampling point positions with fluctuations approaching are found based on the indicator fluctuation range, and multiple groups of early warning analysis data are formed by this; then the radiation pollution degree index of each group of early warning analysis data is calculated, and compared with the radiation pollution degree threshold to determine whether there is a tap water radiation pollution event, and an alarm is issued when there is a tap water radiation pollution event. The present invention suppresses the influence of the fluctuation of the radiation monitoring index caused by the high fluidity of tap water through the above analysis process, and ensures the accuracy of the early warning result as much as possible.

[0048] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of software functional modules.

[0049] If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or system that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0050] Although the description of the present invention has been quite detailed and specifically describes several described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, but should be regarded as providing a broad possible interpretation of these claims in view of the prior art by reference to the appended claims, thereby effectively covering the intended scope of the present invention. In addition, the above description of the present invention is based on the embodiments foreseeable by the inventor, and its purpose is to provide a useful description, and those non-substantial changes to the present invention that have not yet been foreseen may still represent equivalent changes to the present invention.

[0051] The above is only a preferred embodiment of the present invention. The present invention is not limited to the above implementation. As long as the technical effect of the present invention is achieved by the same means, it should belong to the protection scope of the present invention. Within the protection scope of the present invention, its technical scheme and / or implementation method can have various modifications and changes.

Claims

1. A tap water radiation pollution early warning method based on machine learning, characterized in that: Includes the following: The tap water radiation index values ​​at different sampling points in the target monitoring area are obtained by periodic sampling, and a tap water radiation index value sequence at each sampling point is obtained; Traverse and find out the index fluctuation range in the tap water radiation index value sequence at each sampling point; Based on the index fluctuation range, find the second sampling point position that is close to the first sampling point position, and use the tap water radiation index value sequences of the first sampling point position and the second sampling point position as a set of early warning analysis data, thereby obtaining multiple sets of early warning analysis data, wherein the first sampling point position is an arbitrary sampling point position; Calculate and obtain the radiation contamination level index for each set of early warning analysis data; If at least one set of radiation contamination level indicators of early warning analysis data exceeds the radiation contamination level threshold, an early warning reminder will be issued.

2. The method for early warning of tap water radiation pollution based on machine learning according to claim 1 is characterized in that: Specifically, the locations of different sampling points in the target monitoring area refer to the locations of different tap water outlets in the target monitoring area of ​​the same tap water source.

3. The method for early warning of tap water radiation pollution based on machine learning according to claim 1 is characterized in that: Specifically, the method of obtaining the tap water radiation index value includes: The radioactivity of α or β particles in tap water is detected by a Geiger counter as the tap water radiation index value.

4. The method for early warning of tap water radiation pollution based on machine learning according to claim 1 is characterized in that: Specifically, traverse and find the fluctuation range of the index in the tap water radiation index value sequence at each sampling point, including: For any sampling point location, if the preset sampling period of periodic sampling is Cycle1, and the tap water radiation index value is recorded as Rad_In; From the start time to the end time of periodic sampling, the collection time of Rad_In is recorded as C_exam in the order of sampling to determine whether there is index fluctuation, and all collection times C_exam with index fluctuation are found. From the collection times C_exam with index fluctuation, the two collection times with the smallest time interval are found, and they are recorded as C_begin collection time and C_close collection time in the order of collection; Then the range from the C_begin collection time to the C_close collection time is the indicator fluctuation range; Among them, when the following conditions exist, it is determined that there is an index fluctuation at the collection time C_exam of Rad_In, If the value of Rad_In collected at the collection time C_exam is less than the value of Rad_In collected at the previous collection time, i.e., the collection time C_exam-Cycle1, and the value of Rad_In collected at the collection time C_exam-2*Cycle1 is less than the value of Rad_In collected at the collection time C_exam-Cycle1, and the value of Rad_In collected at the collection time C_exam is greater than the value of C_exam_Rad_In_Avg; Among them, C_exam_Rad_In_Avg represents the average value of the non-zero Rad_In values ​​of all sampling points at the acquisition time C_exam.

5. The method for early warning of tap water radiation pollution based on machine learning according to claim 4 is characterized in that: Specifically, based on the indicator fluctuation range, finding the second sampling point position that is close to the first sampling point position fluctuation includes: The maximum tap water radiation index value of the first sampling point in the corresponding index fluctuation range is recorded as range1 as Rad_InMaxLoc1, and the minimum tap water radiation index value is Rad_InMinLoc1; define the variable i as the number of the sampling point position, and make the following judgment: If the position of the i-th sampling point satisfies the following conditions, it is determined that the position of the i-th sampling point and the first sampling point are close to each other in terms of fluctuation, and the position of the i-th sampling point is recorded as the second sampling point position close to the first sampling point position in terms of fluctuation. Rad_InMax(i)≤Rad_InMaxLoc1+recom_Rad_In(i), and Rad_InMax(i)≤Rad_InMaxLoc1+recom_Rad_In(i); Among them, Rad_InMax(i) represents the maximum tap water radiation index value of the i-th sampling point within the index fluctuation range range1; Rad_InMin(i) represents the minimum tap water radiation index value of the i-th sampling point within the index fluctuation range range1; recom_Rad_In(i) represents the index compensation value of the i-th sampling point, and the calculation formula of recom_Rad_In(i) is as follows: ； Where N is the total number of sampling point locations, Indicates the maximum tap water radiation index value at the jth sampling point within the index fluctuation range corresponding to the ith sampling point; It represents the minimum tap water radiation index value within the index fluctuation range corresponding to the i-th sampling point position at the j-th sampling point position.

6. The method for early warning of tap water radiation pollution based on machine learning according to claim 5 is characterized in that: Specifically, the radiation contamination index of each set of early warning analysis data is calculated and obtained, including: The calculation formula of the radiation contamination index of the qth group of early warning analysis data, marked as Index_q, is as follows: ; in, It represents the average value of all non-zero Rad_In values ​​in the index fluctuation range range1 of the first sampling point. Indicates the average value of all non-zero Rad_In values ​​at the second sampling point in the indicator fluctuation range range1.

7. The method for early warning of tap water radiation pollution based on machine learning according to claim 1, characterized in that: Specifically, the method of providing early warning reminder is: Pre-bind multiple administrator IPs. When an early warning reminder is needed, all periodic sampling data and preset text reminder content are packaged and sent to all administrator IPs for subsequent verification and analysis.

8. A device for early warning of tap water radiation contamination based on machine learning, characterized in that: Includes the following: The data acquisition module is used to obtain the tap water radiation index values ​​at different sampling points in the target monitoring area by periodic sampling, and obtain the tap water radiation index value sequence at each sampling point; The index fluctuation range calculation module is used to traverse and find the index fluctuation range in the tap water radiation index value sequence at each sampling point; A data generation module is used to find a second sampling point position that is close to the first sampling point position based on the index fluctuation range, and use the tap water radiation index value sequences of the first sampling point position and the second sampling point position as a set of early warning analysis data, thereby obtaining multiple sets of early warning analysis data, wherein the first sampling point position is an arbitrary sampling point position; An index calculation module is used to calculate and obtain the radiation contamination degree index of each set of early warning analysis data; The early warning reminder module is used to issue an early warning reminder when the radiation pollution level index of at least one set of early warning analysis data exceeds the radiation pollution level threshold.

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