Artificial intelligence-based rainfall chemical observation data quality control method, system and equipment

Abstract

The embodiment of the invention provides a rainfall chemical observation data quality control method, system and device based on artificial intelligence, and is applied to the technical field of meteorological data quality control. The method comprises the following steps: acquiring an area needing rainfall chemical monitoring and marking the area as an observation area; analyzing the distribution rationality of observation stations in the observation area; when the distribution of the observation stations is reasonable, analyzing rainfall chemical observation data of the observation stations, and generating rainfall inspection signals; and analyzing the rainfall inspection signal in the preset early warning time period of the observation area to generate a rainfall hazard signal. The distribution reasonability of the observation stations in the observation area is analyzed, the utilization effect of the observation stations and the accuracy of subsequent analysis results are improved, the rainfall chemical observation data are analyzed to identify abnormal observation stations when the observation station distribution reasonability signals are generated, then the rainfall inspection signals are analyzed, and the accuracy of the abnormal observation stations is improved. The damage to the environment due to the fact that subsequent rainfall chemical observation data cannot be accurately analyzed is reduced, and the intelligent degree is high.

Description

Technical Field

[0001] The present disclosure relates to the technical field of meteorological data quality control, and in particular to a precipitation chemical observation data quality control method, system, and equipment based on artificial intelligence. Background Art

[0002] Precipitation chemistry mainly studies the chemical composition of precipitation. Precipitation chemistry is an important part of atmospheric chemistry. The research content of precipitation chemistry is extensive, including the composition and distribution of ions, trace elements, organic matter, etc. in precipitation. Through research, we can deeply understand the chemical characteristics of precipitation and provide a scientific basis for research in the fields of environmental protection and climate change.

[0003] However, it is currently difficult to reasonably analyze the quality of each precipitation in the corresponding area and accurately judge the degree of precipitation hazard in the corresponding area based on precipitation chemical observation data. This is not conducive to timely investigation of the causes and targeted adoption of corresponding environmental improvement measures. It is also impossible to accurately evaluate the rationality of the distribution of precipitation observation stations in the corresponding area, which is not conducive to the distribution adjustment of observation stations and has a low degree of intelligence. Summary of the invention

[0004] The present invention provides a precipitation chemical observation data quality control method, system, device and storage medium based on artificial intelligence.

[0005] According to a first aspect of the present disclosure, a precipitation chemical observation data quality control method based on artificial intelligence is provided, the method comprising:

[0006] Obtain the areas where precipitation chemistry monitoring is required and mark them as observation areas;

[0007] Analyze the rationality of the distribution of observation stations in the observation area;

[0008] Under the condition that the observation stations are reasonably distributed, the precipitation chemical observation data of the observation stations are analyzed to generate precipitation verification signals;

[0009] The precipitation detection signal within the preset warning time period of the observation area is analyzed to generate a precipitation hazard signal.

[0010] In some implementations of the first aspect, analyzing the rationality of the distribution of observation stations in the observation area includes:

[0011] Delineate multiple sub-areas within the observation area;

[0012] The number of observation stations in each sub-region is collected and marked as the observation station number check value, and the variance calculation and mean calculation are performed on the observation station number check value of the sub-region to obtain the observation station number analysis value and the observation station number table value;

[0013] If the analysis value of the observation station data or the table value of the observation station data does not meet the corresponding preset judgment conditions, an unreasonable distribution signal of the observation station is generated;

[0014] If both the analysis value of the observation station data and the table value of the observation station data meet the corresponding preset judgment conditions, then the corresponding sub-region is analyzed and judged to be an unreasonable region;

[0015] The number of unreasonable areas in the observation area is collected and the ratio is calculated with the total number of sub-areas to obtain the unreasonable area occupancy value. The observation station distribution values ​​of all sub-areas are averaged to obtain the observation station distribution value.

[0016] The distribution detection value is determined based on the unreasonable area occupancy value and the observation station distribution value. If the distribution detection value exceeds the preset distribution detection threshold, an unreasonable observation station distribution signal is generated;

[0017] If the distribution detection value does not exceed the preset distribution detection threshold, a distribution qualified signal of the observation station is generated.

[0018] In some implementations of the first aspect, analyzing and determining whether the corresponding sub-region is an unreasonable region includes:

[0019] The observation station distribution value is determined based on the observation station number deviation value and the observation station near-check value. If the observation station distribution value exceeds the preset observation station distribution threshold, the corresponding sub-area is marked as an unreasonable area;

[0020] The observation station number deviation value is the deviation value of the observation station number check value in the corresponding sub-area relative to the set standard observation station number check value, the observation station distance check value is the distance between any two observation stations in the corresponding sub-area, and the observation station proximity check value is the proportion of the observation station distance check values ​​that do not exceed the preset observation station distance check threshold.

[0021] In some implementations of the first aspect, analyzing precipitation chemistry observation data of an observation station includes:

[0022] The precipitation observation value is calculated based on the sulfur and nitrate detection values, ammonium detection values, chloride ion detection values, conductivity detection values, organic pollutant detection values ​​and pH deviation values ​​in the precipitation chemical observation data of the observation station;

[0023] If the precipitation observation value exceeds the preset precipitation observation threshold, the corresponding observation station will be marked as an abnormal observation station;

[0024] The number of abnormal observation stations in the observation area is obtained and the ratio is calculated with the total number of observation stations to obtain the percentage of abnormal detections. The precipitation observation values ​​of all observation stations are averaged to obtain the precipitation table detection value, and the precipitation observation value with the largest value is marked as the precipitation abnormal amplitude value.

[0025] Determine the precipitation inspection value based on the percentage of abnormal inspection numbers, precipitation table inspection value and precipitation abnormal amplitude value;

[0026] If the precipitation test value exceeds the preset precipitation test threshold, a precipitation test abnormality signal is generated for the observation area;

[0027] If the precipitation test value does not exceed the preset precipitation test threshold, a precipitation test normal signal for the observation area is generated.

[0028] In some implementations of the first aspect, analyzing a precipitation inspection signal within a preset warning time period of an observation area to generate a precipitation hazard signal includes:

[0029] Obtain the precipitation inspection value of the observation area within the warning period, and establish a rectangular coordinate system with time as the X-axis and the precipitation inspection value as the Y-axis, and mark the precipitation inspection value into the rectangular coordinate system in chronological order to form multiple inspection coordinate points;

[0030] Connect two adjacent groups of test coordinate points and mark the corresponding line segments as transient line segments, mark the transient line segments with an upward trend as growth line segments, and mark the ratio of the number of growth line segments to the total number of transient line segments as the growth number test value;

[0031] The growth value of the growth line segment in the Y-axis direction is collected and marked as the increase table data, and the average of all the increase table data is calculated to obtain the increase detection value;

[0032] The number of precipitation detection anomaly signals generated in the observation area during the warning period is obtained and the ratio is calculated with the number of detection coordinate points to obtain the detection anomaly value, and the vertical coordinates of the detection coordinate points are averaged to obtain the precipitation detection value;

[0033] The precipitation hazard warning value is determined based on the growth number detection value, the increase condition detection value, the inspection abnormal condition value and the precipitation analysis value. If the precipitation hazard warning value exceeds the preset precipitation hazard warning threshold, a precipitation high hazard signal is generated;

[0034] If the precipitation hazard warning value does not exceed the preset precipitation hazard warning threshold, a precipitation low hazard signal is generated.

[0035] In some implementations of the first aspect, the intelligent management and control platform is communicatively connected to the data risk detection module, and the intelligent management and control platform stores the precipitation chemical observation data via a storage device;

[0036] The data risk detection module determines the management risk of precipitation chemical observation data by analyzing the data corresponding to the storage device, and generates a data risk qualification signal or a data risk exception signal. Based on the intelligent management and control platform, the data risk qualification signal or the data risk exception signal is sent to the remote terminal, so that the remote terminal can issue a corresponding warning when receiving the data risk exception signal.

[0037] In some implementations of the first aspect, analyzing data corresponding to the storage device to determine the management risk of precipitation chemical observation data and generating a data risk qualification signal or a data risk abnormality signal includes:

[0038] Performing storage risk analysis on the data corresponding to the storage device to obtain a storage risk table value;

[0039] If the storage risk table value exceeds the preset storage risk table threshold, a data risk abnormality signal is generated;

[0040] If the storage risk table value does not exceed the preset storage risk table threshold, the frequency and duration of unauthorized access to the stored precipitation chemical observation data per unit time are collected;

[0041] If the frequency or duration of unauthorized access exceeds the corresponding preset threshold, a data risk abnormality signal is generated;

[0042] If the frequency of unauthorized access and the duration of unauthorized access do not exceed the corresponding preset thresholds, a data risk qualification signal is generated.

[0043] In some implementations of the first aspect, performing storage risk analysis on data corresponding to the storage device to obtain a storage risk table value includes:

[0044] Obtain the total duration of the storage device being in a storage error state per unit time and mark it as a storage error time value;

[0045] Collect the number of times the storage device is attacked by the network in a unit time and mark it as the network threat value, and mark the total duration of the network attack in a unit time as the network risk time value;

[0046] The storage risk table value is determined based on the storage abnormality value, the network threat value and the network risk value.

[0047] According to a second aspect of the present disclosure, there is provided a precipitation chemical observation data quality control system based on artificial intelligence, the system comprising:

[0048] Intelligent management and control platform, used to obtain areas that need precipitation chemical monitoring and mark them as observation areas;

[0049] Observation station distribution detection module, used to analyze the rationality of the distribution of observation stations in the observation area;

[0050] The precipitation chemistry data quality control module is used to analyze the precipitation chemistry observation data of the observation station and generate precipitation verification signals when the observation station has reasonable distribution signals;

[0051] The precipitation hazard warning module is used to analyze the precipitation test signal within the preset warning time period of the observation area and generate a precipitation hazard signal.

[0052] According to a third aspect of the present disclosure, an electronic device is provided, the electronic device comprising: a memory and a processor, wherein a computer program is stored in the memory, and the above method is implemented when the processor executes the program.

[0053] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the above method is implemented.

[0054] The present invention improves the utilization effect of the observation stations and the accuracy of subsequent analysis results by analyzing the rationality of the distribution of observation stations in the observation area. When generating a reasonable signal of the observation station distribution, the precipitation chemical observation data is analyzed to identify abnormal observation stations, and then the precipitation inspection signal is analyzed to reduce the subsequent damage to the environment caused by the inability to accurately analyze the precipitation chemical observation data. The system has a high degree of intelligence.

[0055] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0057] Figure 1 A schematic flow chart of a precipitation chemical observation data quality control method based on artificial intelligence according to an embodiment of the present disclosure is shown;

[0058] Figure 2 A block diagram of a precipitation chemical observation data quality control system based on artificial intelligence according to an embodiment of the present disclosure is shown;

[0059] Figure 3 A schematic flow chart of another precipitation chemical observation data quality control method based on artificial intelligence according to an embodiment of the present disclosure is shown;

[0060] Figure 4 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0062] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0063] In the present disclosure, in order to solve the above technical problems, the area where precipitation chemistry monitoring is required is obtained and marked as the observation area; the rationality of the distribution of observation stations in the observation area is analyzed; when the distribution of observation stations is reasonable, the precipitation chemistry observation data of the observation station is analyzed to generate a precipitation verification signal; the precipitation verification signal within the preset warning time period of the observation area is analyzed to generate a precipitation hazard signal. The present disclosure analyzes the rationality of the distribution of observation stations in the observation area, thereby improving the utilization effect of the observation station and the accuracy of subsequent analysis results. When generating a reasonable distribution signal of the observation station, the precipitation chemistry data of the observation station is analyzed to identify abnormal observation stations, generate precipitation verification signals, and analyze the precipitation hazard status of the observation area within the warning period, thereby reducing the subsequent damage to the environment caused by the inability to accurately analyze the precipitation chemistry observation data, and has a high degree of intelligence.

[0064] Figure 1 A flow chart of a precipitation chemical observation data quality control method based on artificial intelligence provided according to an embodiment of the present disclosure is shown, such as Figure 1 As shown, the precipitation chemical observation data quality control method 100 based on artificial intelligence may include:

[0065] S101, obtaining the area where precipitation chemistry monitoring is required and marking it as an observation area;

[0066] S102, analyzing the rationality of the distribution of observation stations in the observation area;

[0067] In order to accurately analyze the rationality of the distribution of observation stations in the observation area, in some embodiments, analyzing the rationality of the distribution of observation stations in the observation area may specifically include:

[0068] Delineate multiple sub-areas within the observation area;

[0069] The number of observation stations in each sub-region is collected and marked as the observation station number check value, and the variance and mean calculations are performed on the observation station number check values ​​of all sub-regions to obtain the observation station number analysis value and the observation station number table value;

[0070] If the analysis value of the observation station data or the table value of the observation station data does not meet the corresponding preset judgment conditions, an unreasonable distribution signal of the observation station is generated;

[0071] If both the observation station number analysis value and the observation station number table value meet the corresponding preset judgment conditions, then analyze and judge whether the corresponding sub-region is an unreasonable region, wherein the observation station number analysis value meets the corresponding preset judgment condition that the observation station number analysis value is greater than the preset observation station number analysis threshold, and the observation station number table value meets the corresponding preset judgment condition that the observation station number table value is within the preset observation station number table value range, and the preset judgment condition can also be adjusted according to the actual situation, which is not limited here;

[0072] The number of unreasonable areas in the observation area is collected and the ratio is calculated with the total number of sub-areas to obtain the unreasonable area occupancy value. The observation station distribution values ​​of all sub-areas are averaged to obtain the observation station distribution value.

[0073] The distribution detection value is determined based on the unreasonable area occupancy value and the observation station distribution value. If the distribution detection value exceeds the preset distribution detection threshold, an unreasonable observation station distribution signal is generated;

[0074] If the distribution detection value does not exceed the preset distribution detection threshold, a distribution qualified signal of the observation station is generated.

[0075] Specifically, in the process of determining the distribution detection value based on the unreasonable area occupancy value and the observation station distribution value, the unreasonable area occupancy value FS and the observation station distribution value FW can be numerically calculated to obtain the distribution detection value FX through the formula FX=kp1*FS+kp2*FW / kp1, wherein kp1 and kp2 are preset proportional coefficients, kp1>kp2>0; moreover, the larger the numerical value of the distribution detection value FX, the worse the distribution performance of the observation stations in the observation area; the distribution detection value FX is numerically compared with the preset distribution detection threshold, if the distribution detection value FX exceeds the preset distribution detection threshold, indicating that the distribution performance of the observation stations in the observation area is poor, then an unreasonable observation station distribution signal is generated; if the distribution detection value FX does not exceed the preset distribution detection threshold, indicating that the distribution performance of the observation stations in the observation area is good, then a qualified observation station distribution signal is generated.

[0076] In the above embodiment, the observation area is divided into sub-areas, and the variance and mean calculations are performed on the observation station data of the sub-areas respectively, and different judgment conditions are combined to accurately analyze the rationality of the distribution of the observation stations.

[0077] In order to accurately analyze whether a sub-region is an unreasonable region, in some embodiments, analyzing and determining whether a corresponding sub-region is an unreasonable region may specifically include:

[0078] The observation station distribution value is determined based on the observation station number deviation value and the observation station near-check value. If the observation station distribution value exceeds the preset observation station distribution threshold, the corresponding sub-area is marked as an unreasonable area;

[0079] The observation station number deviation value is the deviation value of the observation station number check value in the corresponding sub-area relative to the set standard observation station number check value, the observation station distance check value is the distance between any two observation stations in the corresponding sub-area, and the observation station proximity check value is the proportion of the observation station distance check values ​​that do not exceed the preset observation station distance check threshold.

[0080] Specifically, the observation station condition value can be determined based on the observation station number deviation and the observation station close inspection value. The observation station condition value GF can be obtained by numerically calculating the observation station number deviation GN and the observation station close inspection value GM based on the formula GF=hy1*GN+hy2*GM, wherein hy1 and hy2 are preset proportional coefficients, and the values ​​of hy1 and hy2 are both positive numbers.

[0081] In addition, it should be noted that the larger the value of the observation station condition value GF, the more abnormal the distribution of observation stations in the corresponding target area; the observation station condition value GF is numerically compared with the preset observation station condition threshold. If the observation station condition value GF exceeds the preset observation station condition threshold, it indicates that the distribution of observation stations in the corresponding target area is relatively abnormal, and the corresponding target area is marked as an unreasonable area.

[0082] In the above embodiment, by determining the observation station number deviation value, the observation station distance check value and the observation station proximity check value, whether the corresponding sub-area is an unreasonable area is accurately analyzed and judged based on the observation station number deviation value and the observation station proximity check value.

[0083] S103, when the observation stations are reasonably distributed, analyzing the precipitation chemical observation data of the observation stations to generate a precipitation verification signal;

[0084] After generating the qualified signal of observation station distribution, when it rains, the observation stations in the observation area are used to take precipitation samples to collect precipitation chemical observation data. The precipitation chemical data quality control module analyzes the precipitation chemical data of all observation stations, identifies abnormal observation stations, and generates precipitation inspection abnormal signals or precipitation inspection normal signals. The precipitation inspection abnormal signals or precipitation inspection normal signals are sent to the precipitation hazard warning module and the remote terminal via the intelligent management and control platform, which enables management personnel to grasp the quality status of each precipitation in the observation area in detail, and conduct cause investigations in a timely manner as needed, so as to facilitate subsequent targeted environmental improvement measures.

[0085] In order to accurately analyze the precipitation chemical observation data of the observation station, in some embodiments, analyzing the precipitation chemical observation data of the observation station may specifically include:

[0086] The precipitation observation value is calculated based on the sulfur and nitrate detection value (i.e. the data value of the sum of the sulfate ion concentration and the nitrate ion concentration in the precipitation), the ammonium detection value (i.e. the data value of the ammonium ion concentration in the precipitation), the chloride ion detection value (i.e. the data value of the chloride ion concentration in the precipitation), the conductivity detection value (i.e. the data value of the current conduction ability of the ions in the precipitation, which is used to evaluate the dissolved ions and impurities content in the precipitation), the organic pollutant detection value (i.e. the data value of the concentration and value of the organic pollutants that need to be monitored in the precipitation) and the pH deviation value in the precipitation chemical observation data of the observation station;

[0087] If the precipitation observation value exceeds the preset precipitation observation threshold, the corresponding observation station will be marked as an abnormal observation station;

[0088] The number of abnormal observation stations in the observation area is obtained and the ratio is calculated with the total number of observation stations to obtain the percentage of abnormal detections. The precipitation observation values ​​of all observation stations are averaged to obtain the precipitation table detection value, and the precipitation observation value with the largest value is marked as the precipitation abnormal amplitude value.

[0089] Determine the precipitation inspection value based on the percentage of abnormal inspection numbers, precipitation table inspection value and precipitation abnormal amplitude value;

[0090] If the precipitation test value exceeds the preset precipitation test threshold, a precipitation test abnormality signal is generated in the observation area, that is, it indicates that the precipitation quality detected by the corresponding observation station is poor;

[0091] If the precipitation test value does not exceed the preset precipitation test threshold, a precipitation test normal signal for the observation area is generated.

[0092] Specifically, in the process of calculating the precipitation observation value, the sulfur and nitrate detection value QR, the ammonium detection value QT, the chloride ion detection value QS, the conductivity detection value QD, the organic pollutant detection value QF and the pH deviation value QK can be weighted and summed to obtain the precipitation observation value QY based on the formula QY=(c1*QR+c2*QT+c3*QS+c4*QD+c5*QF+c6*QK) / 6; wherein c1, c2, c3, c4, c5, and c6 are preset proportional coefficients, and the values ​​of c1, c2, c3, c4, c5, and c6 are all positive numbers; and the larger the value of the precipitation observation value QY is, the worse the precipitation quality detected by the corresponding observation station is, and the greater the harm caused to the corresponding area;

[0093] In the above process of determining the precipitation inspection value based on the percentage of abnormal inspection numbers, precipitation table inspection value and precipitation abnormal amplitude value, it can be specifically determined by the formula The abnormal detection number YG, the precipitation table detection value YK and the precipitation abnormal amplitude value YP are numerically calculated to obtain the precipitation inspection value YW, wherein rg1, rg2, and rg3 are preset proportional coefficients, and rg1>rg2>rg3>0; and the larger the numerical value of the precipitation inspection value YW is, the worse the quality performance of the corresponding precipitation in the observation area is in general; the precipitation inspection value YW is numerically compared with the preset precipitation inspection threshold value, if the precipitation inspection value YW exceeds the preset precipitation inspection threshold value, it indicates that the quality performance of the corresponding precipitation in the observation area is poor in general, and then a precipitation inspection abnormal signal of the observation area is generated; if the precipitation inspection value YW does not exceed the preset precipitation inspection threshold value, it indicates that the quality performance of the corresponding precipitation in the observation area is good in general, and then a precipitation inspection normal signal of the observation area is generated.

[0094] In the above embodiment, the precipitation observation value is calculated according to the sulfur and nitrate detection value, ammonium detection value, chloride ion detection value, conductivity detection value, organic pollutant detection value and pH detection value in the precipitation chemical observation data of the observation station, and specific calculations and judgments are performed based on the obtained precipitation observation value and the preset precipitation observation threshold, so as to accurately analyze the precipitation chemical observation data of the observation station.

[0095] S104, analyzing the precipitation detection signal within the preset warning time period of the observation area to generate a precipitation hazard signal.

[0096] In order to accurately and comprehensively analyze the precipitation test signal within the preset warning time period of the observation area and then generate the precipitation hazard signal, in some embodiments, analyzing the precipitation test signal within the preset warning time period of the observation area and generating the precipitation hazard signal may specifically include:

[0097] Obtain the precipitation inspection value of the observation area within the warning period, and establish a rectangular coordinate system with time as the X-axis and the precipitation inspection value as the Y-axis, and mark the precipitation inspection value into the rectangular coordinate system in chronological order to form multiple inspection coordinate points, wherein the warning period can be T1, T1 can be 60 days, and can also be adjusted according to actual conditions;

[0098] Connect two adjacent groups of test coordinate points and mark the corresponding line segments as transient line segments, mark the transient line segments with an upward trend as growth line segments, and mark the ratio of the number of growth line segments to the total number of transient line segments as the growth number test value;

[0099] The growth value of the growth line segment in the Y-axis direction is collected and marked as the increase table data, and the average of all the increase table data is calculated to obtain the increase detection value;

[0100] The number of precipitation detection anomaly signals generated in the observation area during the warning period is obtained and the ratio is calculated with the number of detection coordinate points to obtain the detection anomaly value, and the vertical coordinates of the detection coordinate points are averaged to obtain the precipitation detection value;

[0101] The precipitation hazard warning value is determined based on the growth number detection value, the increase condition detection value, the inspection abnormal condition value and the precipitation analysis value. If the precipitation hazard warning value exceeds the preset precipitation hazard warning threshold, a precipitation high hazard signal is generated;

[0102] If the precipitation hazard warning value does not exceed the preset precipitation hazard warning threshold, a precipitation low hazard signal is generated.

[0103] In addition, the generated high-hazard precipitation signal or low-hazard precipitation signal can be sent to the remote terminal via the intelligent management and control platform to accurately feedback the precipitation hazard in the observation area, remind and urge management personnel to take targeted environmental improvement measures in a timely manner, thereby reducing the degree of damage to the environment caused by subsequent precipitation.

[0104] Specifically, the above process of determining the precipitation hazard warning value based on the growth number detection value, the growth condition detection value, the inspection abnormal condition value and the precipitation analysis value can be based on the formula The growth number check value TY, the increase condition detection value TN, the inspection abnormality value TF and the precipitation analysis value TQ are numerically calculated to obtain the precipitation hazard warning value TX, among which sq1, sq2, sq3, and sq4 are preset proportional coefficients, and the values ​​of sq1, sq2, sq3, and sq4 are all positive numbers; and the larger the value of the precipitation hazard warning value TX, the greater the precipitation hazard in the observation area during the warning period.

[0105] In the above embodiment, after obtaining the precipitation inspection value of the observation area within the warning period, multiple inspection coordinate points in the rectangular coordinate system are determined based on the precipitation inspection value, and then a specific analysis is performed based on the multiple inspection coordinate points in the rectangular coordinate system to obtain the growth number inspection value, the growth condition detection value, the inspection abnormality value and the precipitation analysis value, and a judgment is made in combination with the precipitation hazard warning value to accurately analyze the precipitation inspection signal within the preset warning time period of the observation area, thereby generating a precipitation hazard signal.

[0106] In some embodiments, the intelligent management and control platform is in communication connection with the data risk detection module, and the intelligent management and control platform stores the precipitation chemical observation data via a storage device;

[0107] The data risk detection module determines the management risk of precipitation chemical observation data by analyzing the data corresponding to the storage device, and generates a data risk qualification signal or a data risk exception signal. Based on the intelligent management and control platform, the data risk qualification signal or the data risk exception signal is sent to the remote terminal, so that the remote terminal can issue a corresponding early warning when receiving the data risk exception signal, so that the management personnel can conduct a timely investigation of the cause and make corresponding improvement and optimization measures, thereby reducing the subsequent damage to the environment caused by the inability to accurately analyze the precipitation chemical observation data. It has a high degree of intelligence.

[0108] In some embodiments, analyzing the data corresponding to the storage device to determine the management risk of the precipitation chemical observation data and generating a data risk qualification signal or a data risk abnormality signal may include:

[0109] Performing storage risk analysis on the data corresponding to the storage device to obtain a storage risk table value;

[0110] If the storage risk table value exceeds the preset storage risk table threshold, a data risk abnormality signal is generated;

[0111] If the storage risk table value does not exceed the preset storage risk table threshold, the frequency and duration of unauthorized access to the stored precipitation chemical observation data per unit time are collected;

[0112] If the frequency or duration of unauthorized access exceeds the corresponding preset threshold, a data risk abnormality signal is generated;

[0113] If the frequency of unauthorized access and the duration of unauthorized access do not exceed the corresponding preset thresholds, a data risk qualification signal is generated.

[0114] In the above embodiment, the storage risk table value is obtained by analyzing the data corresponding to the storage device, and the risk of the data is accurately judged based on the comparison between the obtained storage risk table value and the preset storage risk table threshold.

[0115] In some embodiments, performing storage risk analysis on data corresponding to the storage device to obtain a storage risk table value may specifically include:

[0116] Obtain the total duration of the storage device being in a storage error state per unit time and mark it as a storage error time value;

[0117] Collect the number of times the storage device is attacked by the network in a unit time and mark it as the network threat value, and mark the total duration of the network attack in a unit time as the network risk time value;

[0118] The storage risk table value is determined based on the storage abnormality value, the network threat value and the network risk value.

[0119] In a specific example, the above-mentioned process of performing storage risk condition analysis on the data corresponding to the storage device to obtain the storage risk table value can specifically be to collect real-time temperature data and real-time vibration data of the storage device, and compare the real-time temperature data and the real-time vibration data with a preset real-time temperature data range and a preset real-time vibration data threshold, respectively; if the real-time temperature data exceeds the preset real-time temperature data threshold or the real-time vibration data is not within the preset real-time vibration data range, indicating that the operating performance of the storage device is poor, then the storage device is judged to be in an abnormal state; the total time the storage device is in the abnormal state per unit time is obtained and marked as the abnormal time value, and the number of times the storage device is attacked by the network per unit time is collected and marked as the network threat value, and the total duration of the network attack per unit time is marked as the network risk time value;

[0120] Then, the storage abnormality value WS, the network threat value WN and the network risk value WR are numerically calculated using the formula WP=a2*WN+(a1*WS+a3*WR) / 2 to obtain the storage risk table value WP; wherein a1, a2, and a3 are preset proportional coefficients, a2>a3>a1>0.

[0121] In addition, it should be noted that the larger the value of the storage risk table value WP is, the greater the storage risk of the precipitation chemical observation data is; the storage risk table value WP is numerically compared with the preset storage risk table threshold. If the storage risk table value WP exceeds the preset storage risk table threshold, it indicates that the storage risk of the precipitation chemical observation data is relatively large, and a data risk abnormality signal is generated;

[0122] If the storage risk table value WP does not exceed the preset storage risk table threshold, indicating that the storage risk of the precipitation chemical observation data is relatively low, then the unauthorized access frequency (i.e., the data volume value of the number of unauthorized accesses per unit time) and the unauthorized access duration (i.e., the data volume value of the length of unauthorized accesses per unit time) for the stored precipitation chemical observation data are collected;

[0123] The unauthorized access frequency and the unauthorized access duration are numerically compared with the preset unauthorized access frequency threshold and the preset unauthorized access duration threshold respectively. If the unauthorized access frequency or the unauthorized access duration exceeds the corresponding preset threshold, it indicates that the access risk to the stored precipitation chemical observation data is relatively high, and a data risk abnormality signal is generated; if the unauthorized access frequency and the unauthorized access duration do not exceed the corresponding preset threshold, it indicates that the management risk to the stored precipitation chemical observation data is relatively low, and a data risk qualified signal is generated.

[0124] In the process of S101-S104, the rationality of the distribution of observation stations in the observation area is analyzed to improve the utilization effect of the observation stations and the accuracy of subsequent analysis results. When the reasonable distribution signal of the observation station is generated, the precipitation chemical observation data of the observation station is analyzed to identify the abnormal observation station, and a precipitation inspection signal is generated to enable the management personnel to have a detailed grasp of the quality status of each precipitation in the observation area, and analyze the precipitation hazard status of the observation area within the early warning period. When the high-hazard precipitation signal is generated, the management personnel are reminded and urged to take targeted environmental improvement measures in a timely manner, thereby reducing the subsequent damage to the environment caused by the inability to accurately analyze the precipitation data. The degree of intelligence is high.

[0125] Moreover, the present invention also analyzes and determines the management risk of precipitation chemical observation data, and generates a data risk qualification signal or a data risk abnormality signal. When a data risk abnormality signal is generated, the remote terminal issues a corresponding warning so that the management personnel can promptly conduct a cause investigation and make corresponding improvement and optimization measures, thereby reducing the management risk of precipitation chemical observation data and significantly reducing the difficulty of data information management.

[0126] The above is an introduction to the method embodiment. The following is a further explanation of the disclosed solution through a system embodiment.

[0127] Figure 2 A block diagram of a precipitation chemistry observation data quality control system based on artificial intelligence according to an embodiment of the present disclosure is shown.

[0128] like Figure 2 As shown, the precipitation chemical observation data quality control system 200 based on artificial intelligence may include:

[0129] The intelligent management and control platform 201 is used to obtain the area where precipitation chemistry monitoring is required and mark it as an observation area;

[0130] An observation station distribution detection module 202 is used to analyze the rationality of the distribution of observation stations in the observation area;

[0131] The precipitation chemical data quality control module 203 is used to analyze the precipitation chemical observation data of the observation station and generate a precipitation verification signal when the observation station has reasonable distribution signals;

[0132] The precipitation hazard warning module 204 is used to analyze the precipitation detection signal within the preset warning time period of the observation area, generate a precipitation hazard signal, and send the precipitation hazard signal to the remote terminal 205 based on the intelligent management and control platform 201.

[0133] In some embodiments, analyzing the rationality of the distribution of observation stations in the observation area includes:

[0134] Delineate multiple sub-areas within the observation area;

[0135] The number of observation stations in each sub-region is collected and marked as the observation station number check value, and the variance calculation and mean calculation are performed on the observation station number check value of the sub-region to obtain the observation station number analysis value and the observation station number table value;

[0136] If the analysis value of the observation station data or the table value of the observation station data does not meet the corresponding preset judgment conditions, an unreasonable distribution signal of the observation station is generated;

[0137] If both the analysis value of the observation station data and the table value of the observation station data meet the corresponding preset judgment conditions, then the corresponding sub-region is analyzed and judged to be an unreasonable region;

[0138] The number of unreasonable areas in the observation area is collected and the ratio is calculated with the total number of sub-areas to obtain the unreasonable area occupancy value. The observation station distribution values ​​of all sub-areas are averaged to obtain the observation station distribution value.

[0139] The distribution detection value is determined based on the unreasonable area occupancy value and the observation station distribution value. If the distribution detection value exceeds the preset distribution detection threshold, an unreasonable observation station distribution signal is generated;

[0140] If the distribution detection value does not exceed the preset distribution detection threshold, a distribution qualified signal of the observation station is generated.

[0141] In some embodiments, analyzing and determining whether the corresponding sub-region is an unreasonable region may include:

[0142] The observation station distribution value is determined based on the observation station number deviation value and the observation station near-check value. If the observation station distribution value exceeds the preset observation station distribution threshold, the corresponding sub-area is marked as an unreasonable area;

[0143] The observation station number deviation value is the deviation value of the observation station number check value in the corresponding sub-area relative to the set standard observation station number check value, the observation station distance check value is the distance between any two observation stations in the corresponding sub-area, and the observation station proximity check value is the proportion of the observation station distance check values ​​that do not exceed the preset observation station distance check threshold.

[0144] In some embodiments, analyzing precipitation chemistry observation data from an observation station may include:

[0145] The precipitation observation value is calculated based on the sulfur and nitrate detection values, ammonium detection values, chloride ion detection values, conductivity detection values, organic pollutant detection values ​​and pH deviation values ​​in the precipitation chemical observation data of the observation station;

[0146] If the precipitation observation value exceeds the preset precipitation observation threshold, the corresponding observation station will be marked as an abnormal observation station;

[0147] The number of abnormal observation stations in the observation area is obtained and the ratio is calculated with the total number of observation stations to obtain the percentage of abnormal detections. The precipitation observation values ​​of all observation stations are averaged to obtain the precipitation table detection value, and the precipitation observation value with the largest value is marked as the precipitation abnormal amplitude value.

[0148] Determine the precipitation inspection value based on the percentage of abnormal inspection numbers, precipitation table inspection value and precipitation abnormal amplitude value;

[0149] If the precipitation test value exceeds the preset precipitation test threshold, a precipitation test abnormality signal is generated for the observation area;

[0150] If the precipitation test value does not exceed the preset precipitation test threshold, a precipitation test normal signal for the observation area is generated.

[0151] In some embodiments, analyzing the precipitation detection signal within the preset warning time period of the observation area to generate the precipitation hazard signal may include:

[0152] Obtain the precipitation inspection value of the observation area within the warning period, and establish a rectangular coordinate system with time as the X-axis and the precipitation inspection value as the Y-axis, and mark the precipitation inspection value into the rectangular coordinate system in chronological order to form multiple inspection coordinate points;

[0153] Connect two adjacent groups of test coordinate points and mark the corresponding line segments as transient line segments, mark the transient line segments with an upward trend as growth line segments, and mark the ratio of the number of growth line segments to the total number of transient line segments as the growth number test value;

[0154] The growth value of the growth line segment in the Y-axis direction is collected and marked as the increase table data, and the average of all the increase table data is calculated to obtain the increase detection value;

[0155] The number of precipitation detection anomaly signals generated in the observation area during the warning period is obtained and the ratio is calculated with the number of detection coordinate points to obtain the detection anomaly value, and the vertical coordinates of the detection coordinate points are averaged to obtain the precipitation detection value;

[0156] The precipitation hazard warning value is determined based on the growth number detection value, the increase condition detection value, the inspection abnormal condition value and the precipitation analysis value. If the precipitation hazard warning value exceeds the preset precipitation hazard warning threshold, a precipitation high hazard signal is generated;

[0157] If the precipitation hazard warning value does not exceed the preset precipitation hazard warning threshold, a precipitation low hazard signal is generated.

[0158] In some embodiments, the intelligent management and control platform 201 is in communication connection with the data risk detection module, and the intelligent management and control platform 201 stores the precipitation chemical observation data through a storage device;

[0159] The data risk detection module determines the management risk of precipitation chemical observation data by analyzing the data corresponding to the storage device, and generates a data risk qualification signal or a data risk abnormality signal, and sends the data risk qualification signal or the data risk abnormality signal to the remote terminal 205 based on the intelligent management and control platform 201, so that the remote terminal 205 can issue a corresponding warning when receiving the data risk abnormality signal.

[0160] In some embodiments, analyzing the data corresponding to the storage device to determine the management risk of the precipitation chemical observation data and generating a data risk qualification signal or a data risk abnormality signal may include:

[0161] Performing storage risk analysis on the data corresponding to the storage device to obtain a storage risk table value;

[0162] If the storage risk table value exceeds the preset storage risk table threshold, a data risk abnormality signal is generated;

[0163] If the storage risk table value does not exceed the preset storage risk table threshold, the frequency and duration of unauthorized access to the stored precipitation chemical observation data per unit time are collected;

[0164] If the frequency or duration of unauthorized access exceeds the corresponding preset threshold, a data risk abnormality signal is generated;

[0165] If the frequency of unauthorized access and the duration of unauthorized access do not exceed the corresponding preset thresholds, a data risk qualification signal is generated.

[0166] In some embodiments, performing storage risk analysis on data corresponding to a storage device to obtain a storage risk table value includes:

[0167] Obtain the total duration of the storage device being in a storage error state per unit time and mark it as a storage error time value;

[0168] Collect the number of times the storage device is attacked by the network in a unit time and mark it as the network threat value, and mark the total duration of the network attack in a unit time as the network risk time value;

[0169] The storage risk table value is determined based on the storage abnormality value, the network threat value and the network risk value.

[0170] Figure 3 A flow chart of another precipitation chemical observation data quality control method based on artificial intelligence provided according to an embodiment of the present disclosure is shown. Based on the description of the above data quality control system, the precipitation chemical observation data quality control method based on artificial intelligence may specifically include:

[0171] Step 1: The observation station distribution detection module analyzes the rationality of the observation station distribution in the observation area, and generates a qualified observation station distribution signal or an unreasonable observation station distribution signal through analysis;

[0172] Step 2: After generating the qualified signal of observation station distribution, use the observation stations in the observation area to sample precipitation to collect precipitation chemical observation data;

[0173] Step 3: The precipitation chemical data quality control module analyzes the precipitation chemical data of all observation stations to identify abnormal observation stations and generates a precipitation inspection abnormal signal or a precipitation inspection normal signal;

[0174] Step 4: The precipitation hazard warning module analyzes the precipitation hazard status of the observation area within the warning period, and generates a high precipitation hazard signal or a low precipitation hazard signal through analysis;

[0175] Step 5: When an unreasonable observation station distribution signal, an abnormal precipitation inspection signal, or a high precipitation hazard signal is generated, the remote terminal issues a corresponding warning.

[0176] The artificial intelligence-based precipitation chemical observation data quality control system proposed in the present disclosure includes an intelligent management and control platform, an observation station distribution detection module, a precipitation chemical data quality control module, a precipitation hazard warning module and a remote terminal; wherein the intelligent management and control platform obtains the area where precipitation chemical monitoring is required and marks it as the observation area, and the observation station distribution detection module analyzes the rationality of the distribution of observation stations in the observation area, and generates a qualified observation station distribution signal or an unreasonable observation station distribution signal through the analysis, and the qualified observation station distribution signal or the unreasonable observation station distribution signal is sent to the remote terminal via the intelligent management and control platform. When the unreasonable observation station distribution signal is generated, the management personnel promptly optimize and adjust the distribution of the observation stations to ensure the rationality of the distribution of the observation stations, and improve the utilization effect of the observation stations and the accuracy of the subsequent analysis results.

[0177] Moreover, in specific use, the rationality of the distribution of observation stations in the observation area is analyzed through the observation station distribution detection module, and the distribution of observation stations is optimized and adjusted when an unreasonable observation station distribution signal is generated to ensure the rationality of the distribution of observation stations, improve the utilization effect of observation stations and the accuracy of subsequent analysis results, and analyze the precipitation chemical data of all observation stations through the precipitation chemical data quality control module to identify abnormal observation stations when a qualified observation station distribution signal is generated, and generate precipitation inspection abnormal signals or precipitation inspection normal signals, so that management personnel can grasp the quality status of each precipitation in the observation area in detail, and analyze the precipitation hazard status of the observation area within the warning period through the precipitation hazard warning module, and remind and urge management personnel to take targeted environmental improvement measures in a timely manner when a high precipitation hazard signal is generated, thereby reducing the subsequent damage to the environment caused by the inability to accurately analyze precipitation chemical observation data, and the degree of intelligence is high.

[0178] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0179] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0180] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0181] Figure 4 A block diagram of an exemplary electronic device capable of implementing an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0182] The device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0183] A number of components in the device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0184] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 401 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the method 100 described above may be performed.

[0185] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0186] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0187] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0188] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0189] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communications network). Examples of communications networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0190] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0191] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of this disclosure can be achieved, and this document is not limited here.

[0192] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A precipitation chemical observation data quality control method based on artificial intelligence, characterized in that: The method comprises: Obtain the areas where precipitation chemistry monitoring is required and mark them as observation areas; Analyze the rationality of the distribution of observation stations in the observation area; Under the condition that the observation stations are reasonably distributed, the precipitation chemical observation data of the observation stations are analyzed to generate precipitation verification signals; The precipitation detection signal within the preset warning time period of the observation area is analyzed to generate a precipitation hazard signal.

2. The method according to claim 1, characterized in that The analysis of the rationality of the distribution of observation stations in the observation area includes: Delineate multiple sub-areas within the observation area; The number of observation stations in each sub-region is collected and marked as the observation station number check value, and the variance calculation and mean calculation are performed on the observation station number check value of the sub-region to obtain the observation station number analysis value and the observation station number table value; If the analysis value of the observation station data or the table value of the observation station data does not meet the corresponding preset judgment conditions, an unreasonable distribution signal of the observation station is generated; If both the analysis value of the observation station data and the table value of the observation station data meet the corresponding preset judgment conditions, then the corresponding sub-region is analyzed and judged to be an unreasonable region; The number of unreasonable areas in the observation area is collected and the ratio is calculated with the total number of sub-areas to obtain the unreasonable area occupancy value. The observation station distribution values ​​of all sub-areas are averaged to obtain the observation station distribution value. The distribution detection value is determined based on the unreasonable area occupancy value and the observation station distribution value. If the distribution detection value exceeds the preset distribution detection threshold, an unreasonable observation station distribution signal is generated; If the distribution detection value does not exceed the preset distribution detection threshold, a distribution qualified signal of the observation station is generated.

3. The method according to claim 2, characterized in that The analyzing and judging whether the corresponding sub-region is an unreasonable region includes: The observation station distribution value is determined based on the observation station number deviation value and the observation station near-check value. If the observation station distribution value exceeds the preset observation station distribution threshold, the corresponding sub-area is marked as an unreasonable area; The observation station number deviation value is the deviation value of the observation station number check value in the corresponding sub-area relative to the set standard observation station number check value, the observation station distance check value is the distance between any two observation stations in the corresponding sub-area, and the observation station proximity check value is the proportion of the observation station distance check values ​​that do not exceed the preset observation station distance check threshold.

4. The method according to claim 1, characterized in that: The analysis of precipitation chemical observation data from the observation station includes: The precipitation observation value is calculated based on the sulfur and nitrate detection values, ammonium detection values, chloride ion detection values, conductivity detection values, organic pollutant detection values ​​and pH deviation values ​​in the precipitation chemical observation data of the observation station; If the precipitation observation value exceeds the preset precipitation observation threshold, the corresponding observation station will be marked as an abnormal observation station; The number of abnormal observation stations in the observation area is obtained and the ratio is calculated with the total number of observation stations to obtain the percentage of abnormal detections. The precipitation observation values ​​of all observation stations are averaged to obtain the precipitation table detection value, and the precipitation observation value with the largest value is marked as the precipitation abnormal amplitude value. Determine the precipitation inspection value based on the percentage of abnormal inspection numbers, precipitation table inspection value and precipitation abnormal amplitude value; If the precipitation test value exceeds the preset precipitation test threshold, a precipitation test abnormality signal is generated for the observation area; If the precipitation test value does not exceed the preset precipitation test threshold, a precipitation test normal signal for the observation area is generated.

5. The method according to claim 1, characterized in that The step of analyzing the precipitation detection signal within the preset warning time period of the observation area to generate a precipitation hazard signal includes: Obtain the precipitation inspection value of the observation area within the warning period, and establish a rectangular coordinate system with time as the X-axis and the precipitation inspection value as the Y-axis, and mark the precipitation inspection value into the rectangular coordinate system in chronological order to form multiple inspection coordinate points; Connect two adjacent groups of test coordinate points and mark the corresponding line segments as transient line segments, mark the transient line segments with an upward trend as growth line segments, and mark the ratio of the number of growth line segments to the total number of transient line segments as the growth number test value; The growth value of the growth line segment in the Y-axis direction is collected and marked as the increase table data, and the average of all the increase table data is calculated to obtain the increase detection value; The number of precipitation detection anomaly signals generated in the observation area during the warning period is obtained and the ratio is calculated with the number of detection coordinate points to obtain the detection anomaly value, and the vertical coordinates of the detection coordinate points are averaged to obtain the precipitation detection value; The precipitation hazard warning value is determined based on the growth number detection value, the increase condition detection value, the inspection abnormal condition value and the precipitation analysis value. If the precipitation hazard warning value exceeds the preset precipitation hazard warning threshold, a precipitation high hazard signal is generated; If the precipitation hazard warning value does not exceed the preset precipitation hazard warning threshold, a precipitation low hazard signal is generated.

6. The method according to claim 1, characterized in that The intelligent management and control platform is connected to the data risk detection module in communication, and the intelligent management and control platform stores the precipitation chemical observation data through the storage device; The data risk detection module determines the management risk of precipitation chemical observation data by analyzing the data corresponding to the storage device, and generates a data risk qualification signal or a data risk abnormality signal, and sends the data risk qualification signal or the data risk abnormality signal to the remote terminal based on the intelligent management and control platform, so that the remote terminal can issue a corresponding warning when receiving the data risk abnormality signal.

7. The method according to claim 6, characterized in that The analyzing the data corresponding to the storage device to determine the management risk of the precipitation chemical observation data and generating a data risk qualified signal or a data risk abnormal signal includes: Performing storage risk analysis on the data corresponding to the storage device to obtain a storage risk table value; If the storage risk table value exceeds the preset storage risk table threshold, a data risk abnormality signal is generated; If the storage risk table value does not exceed the preset storage risk table threshold, the frequency and duration of unauthorized access to the stored precipitation chemical observation data per unit time are collected; If the frequency or duration of unauthorized access exceeds the corresponding preset threshold, a data risk abnormality signal is generated; If the frequency of unauthorized access and the duration of unauthorized access do not exceed the corresponding preset thresholds, a data risk qualification signal is generated.

8. The method according to claim 7, characterized in that The performing storage risk analysis on the data corresponding to the storage device to obtain a storage risk table value includes: Obtain the total duration of the storage device being in a storage error state per unit time and mark it as a storage error time value; Collect the number of times the storage device is attacked by the network in a unit time and mark it as the network threat value, and mark the total duration of the network attack in a unit time as the network risk time value; A storage risk table value is determined based on the abnormal condition value, the network threat value and the network risk value.

9. An artificial intelligence-based precipitation chemical observation data quality control system, used to execute the method according to any one of claims 1 to 8, characterized in that: The system comprises: Intelligent management and control platform, used to obtain areas that need precipitation chemical monitoring and mark them as observation areas; Observation station distribution detection module, used to analyze the rationality of the distribution of observation stations in the observation area; The precipitation chemistry data quality control module is used to analyze the precipitation chemistry observation data of the observation station and generate precipitation verification signals when the observation station has reasonable distribution signals; The precipitation hazard warning module is used to analyze the precipitation test signal within the preset warning time period of the observation area and generate a precipitation hazard signal.

10. An electronic device, characterized in that: include: at least one processor; and a memory communicatively coupled to the at least one processor; It is characterized in that the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to any one of claims 1 to 8.

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