CN114511233A - Water quality abnormity cause analysis method and device and computer equipment - Google Patents
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Abstract
The application discloses a water quality abnormity cause analysis method, a device and computer equipment, relates to the field of water pollution control, and can solve the problem that pollution source tracing and treatment cannot be timely and effectively carried out on polluted water quality. Extracting abnormal target monitoring factors from the collected three-dimensional geographic data of the target monitoring river reach according to a preset quality standard interval; determining a preset historical time period before the target monitoring factor is abnormal, and extracting historical monitoring data of the target monitoring factor and at least one first monitoring factor collected in the preset historical time period from a database; according to a Pearson correlation coefficient calculation formula, firstly, calculating a first correlation coefficient of a target monitoring factor and a first monitoring factor, and extracting a strong correlation monitoring factor of which the first correlation coefficient is greater than a first preset threshold value from the first monitoring factor; and calculating a second correlation coefficient of the target monitoring factor and the strong correlation monitoring factor, and generating a water quality abnormity analysis result of the target monitoring river reach according to the second correlation coefficient.
Description
Technical Field
The application relates to the field of water pollution control, in particular to a water quality abnormity cause analysis method and device and computer equipment.
Background
Along with the continuous improvement of living standard, people have higher and higher call for improving the ecological environment, and meanwhile, higher requirements are provided for river pollution prevention and control, so that finding a source of water pollution and then pertinently treating the source has important significance for river pollution prevention and control.
At present, when a supervision department carries out water pollution treatment, water quality monitoring is mainly carried out on river flow points of a key monitoring river reach by manpower, the monitoring mode can only judge whether the existing water quality meets the standard according to a monitoring result, specific pollution events causing water quality abnormity can not be accurately judged, and then timely and effective pollution source tracing and treatment can not be carried out on the polluted water quality.
Disclosure of Invention
In view of this, the application provides a water quality abnormality cause analysis method, a device and a computer device, relates to the field of water pollution control, and can solve the problem that the pollution source tracing and treatment of polluted water quality cannot be timely and effectively carried out.
According to an aspect of the present application, there is provided a water quality abnormality cause analysis method including:
extracting abnormal target monitoring factors from a database of the collected target monitoring river reach according to a preset quality standard interval;
determining a preset historical time period before the target monitoring factor is abnormal, and extracting historical monitoring data of the target monitoring factor and at least one first monitoring factor collected in the preset historical time period from the database;
calculating a first correlation coefficient of the target monitoring factor and the first monitoring factor according to a Pearson correlation coefficient calculation formula and the historical monitoring data, and extracting a strong correlation monitoring factor of which the first correlation coefficient is greater than a first preset threshold value from the first monitoring factor;
and calculating a second correlation coefficient of the target monitoring factor and the strong correlation monitoring factor according to a Pearson correlation coefficient calculation formula and the historical monitoring data, and generating a water quality abnormity analysis result of the target monitoring river reach according to the second correlation coefficient.
According to another aspect of the present application, there is provided a water quality abnormality cause analyzing apparatus including:
the first extraction module is used for extracting abnormal target monitoring factors from a database of the acquired target monitoring river reach according to a preset quality standard interval;
the second extraction module is used for determining a preset historical time period before the target monitoring factor is abnormal, and extracting historical monitoring data of the target monitoring factor and at least one first monitoring factor collected in the preset historical time period from the database;
the first calculation module is used for calculating a first correlation coefficient of the target monitoring factor and the first monitoring factor according to a Pearson correlation coefficient calculation formula and the historical monitoring data, and extracting a strong correlation monitoring factor of which the first correlation coefficient is greater than a first preset threshold value from the first monitoring factor;
and the second calculation module is used for calculating a second correlation coefficient of the target monitoring factor and the strong correlation monitoring factor according to a Pearson correlation coefficient calculation formula and the historical monitoring data, and generating a water quality abnormity analysis result of the target monitoring river reach according to the second correlation coefficient.
According to still another aspect of the present application, there is provided a nonvolatile readable storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described water quality abnormality cause analysis method.
According to still another aspect of the present application, there is provided a computer apparatus including a nonvolatile readable storage medium, a processor, and a computer program stored on the nonvolatile readable storage medium and executable on the processor, the processor implementing the above-mentioned water quality abnormality cause analysis method when executing the program.
By means of the technical scheme, the application provides a water quality abnormity cause analysis method, a device and computer equipment, and solves the problem that pollution source tracing and treatment cannot be timely and effectively carried out on polluted water quality. Firstly, extracting abnormal target monitoring factors from a database of an acquired target monitoring river reach according to a preset quality standard interval; determining a preset historical time period before the target monitoring factor is abnormal, and extracting historical monitoring data of the target monitoring factor and at least one first monitoring factor collected in the preset historical time period from a database; calculating a first correlation coefficient of a target monitoring factor and a first monitoring factor according to a Pearson correlation coefficient calculation formula and historical monitoring data, and extracting a strong correlation monitoring factor of which the first correlation coefficient is greater than a first preset threshold value from the first monitoring factor; and calculating a second correlation coefficient of the target monitoring factor and the strong correlation monitoring factor according to a Pearson correlation coefficient calculation formula and historical monitoring data, and generating a water quality abnormity analysis result of the target monitoring river reach according to the second correlation coefficient. Through the technical scheme in this application, when quality of water is unusual, carry out twice screening in a large amount of data and obtain the factor that influences the quality of water anomaly event the biggest to on the one hand need not to carry out the precision processing to whole data and can obtain accurate result, on the other hand can in time carry out the investigation of tracing to the source, carries out the pertinence and administers and avoids polluting the diffusion upgrading.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical solutions of the present application more clearly understood, and the following detailed description of the present application is provided in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application to the disclosed embodiment. In the drawings:
FIG. 1 is a schematic flow chart of a water quality abnormality cause analysis method provided by an embodiment of the present application;
FIG. 2 is a flow chart of another water quality abnormality cause analysis method provided in the embodiment of the present application;
FIG. 3 is a schematic structural diagram of an apparatus for analyzing causes of abnormal water quality according to an embodiment of the present invention;
fig. 4 is a schematic diagram showing a structure of another water quality abnormality cause analysis device according to an embodiment of the present application.
Detailed Description
The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Aiming at the problem that the pollution source tracing and treatment of polluted water quality cannot be timely and effectively carried out at present, the embodiment of the application provides a water quality abnormality cause analysis method, as shown in figure 1, the method comprises the following steps:
101. and extracting abnormal target monitoring factors from the collected database of the target monitoring river reach according to a preset quality standard interval.
The target monitoring river reach is a river reach to be subjected to water quality abnormity monitoring and water quality abnormity cause analysis, and the preset quality standard interval is determined according to the international surface water environment quality standard (GB 3838-2002). Preferably, a plurality of monitoring points can be set for the target monitoring river reach, the monitoring points are used for acquiring various monitoring factor data of the area range where each monitoring point is located, the monitoring factor data are stored in a database in real time, the monitoring factors such as Chemical Oxygen Demand (COD), ammonia nitrogen (NH3-N), five-day Biochemical Oxygen Demand (BOD 5), total nitrogen value and the like can be judged through presetting a quality standard interval and the monitoring factor data, if the water quality of the monitoring points is abnormal, the abnormal monitoring factors are determined as the target monitoring factors, and the points where the target monitoring factors are located are determined as the target monitoring points.
Correspondingly, when the abnormal target monitoring factor is extracted according to the preset quality standard interval, various monitoring factor data of each point location of the target monitoring river reach can be specifically extracted from the database, and the water quality detection grade corresponding to each monitoring factor data of each point location is obtained according to the national surface water environment quality standard detection. Specifically, the worst water quality detection grade of all the monitoring factors of each point location can be compared with the standard water quality grade of the point location, if the worst water quality detection grade of a certain point location is lower than the target water quality grade, the point location is judged to have abnormal water quality, the monitoring factor corresponding to the worst water quality detection grade is determined as the target monitoring factor with the abnormal water quality, wherein the water quality grades are classified into I-type, II-type, III-type, IV-type and V-type water, the standard water quality grade of each point location is determined according to the water quality grade requirement of each point location of the target monitoring river reach of an environmental protection department, for example, the downstream of a certain point location is a residential area, and the requirement of the environmental protection department on the water quality grade of the point location is at least II-type water.
The execution main body can be a cause analysis device or equipment for water quality abnormity, and can be configured at the server side or the client side, so that the polluted water quality can be effectively traced back and treated in time after the abnormity of the water quality of the target monitoring river reach is judged based on the three-dimensional geographic data.
102. And determining a preset historical time period before the target monitoring factor is abnormal, and extracting historical monitoring data of the target monitoring factor and at least one first monitoring factor collected in the preset historical time period from a database.
The preset historical time period can be set according to the actual application scene, such as one week of history; the first monitoring factor is a non-abnormal monitoring factor which may have influence on the target monitoring factor, such as temperature, humidity, wind direction, wind speed, monitoring factors of upstream monitoring points and the like.
According to step 101, after the target monitoring factor and the target monitoring point location are determined, an upstream monitoring point location of the target monitoring point location is determined according to a point location of the target monitoring river reach. The historical monitoring data is stored in a database and represents monitoring data in a preset historical time period before abnormity occurs, the historical monitoring data comprises a target monitoring factor and at least one first monitoring factor, and the first monitoring factor is a monitoring factor to be analyzed, which may have influence on the target monitoring factor, such as temperature, humidity, wind direction, wind speed, monitoring factors of upstream monitoring point positions and the like. For the embodiment, in a specific application scenario, historical monitoring data of the target monitoring factor in a preset historical time period, that is, target monitoring factor data, and historical monitoring data of the first monitoring factor in the preset historical time period, that is, first monitoring factor data, may be specifically extracted, and further, a strong correlation monitoring factor having a first correlation coefficient greater than a first preset threshold may be extracted from the first monitoring factors through calculation of the target monitoring factor data and the first monitoring factor data of any first monitoring factor.
103. According to a Pearson correlation coefficient calculation formula and historical monitoring data, a first correlation coefficient of a target monitoring factor and a first monitoring factor is calculated, and a strong correlation monitoring factor with the first correlation coefficient larger than a first preset threshold value is extracted from the first monitoring factor.
The formula for calculating the first correlation coefficient may adopt a pearson correlation coefficient calculation formula:
formula (1) can be derived from formulas (2), (3) and (4), wherein X and Y represent the target monitoring factor and the first monitoring factor respectively, Cov (X, Y) is the covariance of X and Y, N is the number of data, r (X, Y) is the correlation coefficient of X and Y, and Var [ X ] and Var [ Y ] are the variances of X and Y respectively.
Specifically, the first monitoring factor is a non-abnormal monitoring factor such as temperature, humidity, wind direction, wind speed, and a monitoring factor of an upstream monitoring point, which may have an influence on the target monitoring factor, and when the first correlation coefficient is calculated, the target monitoring factor data of the target monitoring point and the first monitoring factor data of the first monitoring factor may be extracted from the historical monitoring data, and the target monitoring factor data and the first monitoring factor data of any first monitoring factor are respectively substituted into X and Y in the formula, and the first correlation coefficient of the target monitoring factor and any first monitoring factor is further calculated, wherein the sum of all the first monitoring factors corresponding to the first correlation coefficients is 1.
Through calculation of the first correlation coefficients, the influence degree of all the first monitoring factors on the target monitoring factors can be determined, the larger the correlation coefficient is, the larger the influence of the first monitoring factors on the target monitoring factors is represented, a first preset threshold value is set according to actual business requirements, the first monitoring factors corresponding to the first correlation coefficients and larger than the first preset threshold value are used as strong correlation monitoring factors, and the strong correlation monitoring factors with strong correlation to the next water quality abnormal event can be preliminarily screened from all the first monitoring factors through the steps of the embodiment. The first preset threshold is a value which is greater than 0 and less than 1, and the specific value can be set according to an actual application scene.
104. And calculating a second correlation coefficient of the target monitoring factor and the strong correlation monitoring factor according to a Pearson correlation coefficient calculation formula and historical monitoring data, and generating a water quality abnormity analysis result of the target monitoring river reach according to the second correlation coefficient.
The formula for calculating the second correlation coefficient may adopt a pearson correlation coefficient calculation formula:
formula (1) can be derived from formulas (2), (3) and (4), wherein X and Y represent a target monitoring factor and a strong correlation monitoring factor respectively, Cov (X, Y) is the covariance of X and Y, N is the number of data, r (X, Y) is the correlation coefficient of X and Y, and Var [ X ] and Var [ Y ] are the variances of X and Y respectively.
Preferably, after the first correlation coefficient is calculated and the strong correlation monitoring factors are selected from the first monitoring factors, the second correlation number is calculated because the correlation coefficients are calculated again, so that the sum of the second correlation numbers corresponding to all the strong correlation monitoring factors is 1, the influence of each strong correlation monitoring factor on the water quality abnormality can be further determined according to the size of the second correlation coefficient, the contribution ranking of each strong correlation monitoring factor is generated according to the second correlation number, the associated event corresponding to the strong correlation monitoring factor with the largest influence is determined based on the big data, and the water quality abnormality analysis result containing the contribution ranking and/or the associated event is generated. For example, the most strongly correlated monitoring factor is the target monitoring factor of the upstream monitoring point location, so that the position of the upstream point location is determined, and it is found that a certain factory near the upstream point location causes the emission standard exceeding due to the fact that the order volume is increased to accelerate production.
The application provides a water quality abnormality cause analysis method, a device and computer equipment, which solve the problem that pollution source tracing and treatment cannot be timely and effectively carried out on polluted water quality. Firstly, extracting abnormal target monitoring factors from a database of an acquired target monitoring river reach according to a preset quality standard interval; determining a preset historical time period before the target monitoring factor is abnormal, and extracting historical monitoring data of the target monitoring factor and at least one first monitoring factor collected in the preset historical time period from a database; calculating a first correlation coefficient of a target monitoring factor and a first monitoring factor according to a Pearson correlation coefficient calculation formula and historical monitoring data, and extracting a strong correlation monitoring factor of which the first correlation coefficient is greater than a first preset threshold value from the first monitoring factor; and calculating a second correlation coefficient of the target monitoring factor and the strong correlation monitoring factor according to a Pearson correlation coefficient calculation formula and historical monitoring data, and generating a water quality abnormity analysis result of the target monitoring river reach according to the second correlation coefficient. Through the technical scheme in this application, when quality of water is unusual, carry out twice screening in a large amount of data and obtain the factor that influences the quality of water anomaly event the biggest to on the one hand need not to carry out the precision to whole data and handle and can obtain accurate result, on the other hand can in time trace to the source investigation, carries out the pertinence and administers and avoids polluting the diffusion upgrading.
Further, as a refinement and an extension of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process in this embodiment, another method for analyzing the cause of the abnormal water quality is provided, as shown in fig. 2, the method includes:
201. and detecting each monitoring factor in the database according to a preset quality standard interval, and determining the water quality detection level corresponding to each monitoring factor.
In a specific application scenario, a database for storing real-time water quality parameter data may be created for a target monitoring river reach first, so as to obtain monitoring data to be subjected to data analysis from the database. Correspondingly, the embodiment steps may specifically include: and extracting real-time water quality parameter data of each monitoring point position in the target monitoring river reach, and updating and storing the real-time water quality parameter data to a database so as to extract abnormal target monitoring factors and historical monitoring data acquired by the target monitoring river reach in a preset historical time period from the database.
The target monitoring river reach is a river reach to be subjected to water quality abnormity monitoring and water quality abnormity cause analysis, the monitoring point locations are used for acquiring real-time water quality parameter data of the area range where each monitoring point location is located, and the real-time water quality parameter data comprises various monitoring factor data, temperature, humidity, wind direction, wind speed and the like, wherein the monitoring factors comprise Chemical Oxygen Demand (COD), ammonia nitrogen (NH3-N), five-day Biochemical Oxygen Demand (BOD 5), total nitrogen value and the like. The real-time water quality parameter data is stored in a point location table corresponding to the database, for example, the real-time water quality parameter data corresponding to the point location 1 is stored in the point location table 1, so that the database comprises the real-time water quality parameter data and the historical monitoring data, and after the real-time water quality parameter data and the historical monitoring data are stored in the database, the real-time water quality parameter data and the historical monitoring data are convenient to extract during subsequent anomaly analysis.
In a specific application scenario, a plurality of monitoring point locations are set for a target monitoring river reach, the latitude of each monitoring point location is set, the position relationship of each monitoring point location can be determined according to latitude information, for example, the point location 1 corresponding to the latitude 1 is an upstream monitoring point location of the point location 2 corresponding to the latitude 2, the point location 2 corresponding to the latitude 2 is a downstream monitoring point location of the point location 1 corresponding to the latitude 1, and the position relationship of each monitoring point location is determined in order that the upstream water quality abnormality is a factor of the downstream water quality abnormality, so that when the water quality abnormality cause analysis is performed, whether the downstream water quality abnormality is caused by the upstream water quality abnormality or not can be judged; if the point location 1 is determined to be closest to the discharge port 1 of the village science and technology industry in the enterprise, the associated discharge port of the point location 1 is the discharge port 1, and the purpose of determining the point location associated discharge port is that the pollution discharge of the point location associated discharge port is a factor of the abnormal water quality of the point location, so that when the cause analysis of the abnormal water quality is carried out, whether the abnormal water quality of the point location is caused by the pollution discharge of the point location associated discharge port or not can be judged.
Correspondingly, for the embodiment, the preset quality standard interval is determined according to the international surface water environment quality standard (GB3838-2002), and the international surface water environment quality standard (GB3838-2002) specifies the normal ranges of the monitoring factors under the class i, class ii, class iii, class iv, and class v water quality grades, and specifically includes: the normal ranges of Chemical Oxygen Demand (COD) under the water quality grades of I, II, III, IV and V are respectively less than or equal to 15, 20, 30 and 40, the normal ranges of ammonia nitrogen value (NH3-N) under the water quality grades of I, II, III, IV and V are respectively less than or equal to 0.15, 0.5, 1.0, 1.5 and 2.0, the normal ranges of Biochemical Oxygen Demand (BOD 5) under the water quality grades of I, II, III, IV and V are respectively less than or equal to 3, 4, 6 and 10, and the normal ranges of total nitrogen value under the water quality grades of I, II, III, IV and V are respectively less than or equal to 0.2, 0.5, 1.0, 1.5 and 2.0. For example, the total nitrogen value data of a certain point location is monitored to be 1.8, less than or equal to 2.0 and greater than 1.5, so that the total nitrogen value of the point location can be determined to correspond to class v water, that is, the water quality detection level corresponding to the monitoring factor of the total nitrogen value is class v, and the water quality detection levels corresponding to the monitoring factors of all the point locations of the target monitoring river reach can be determined in the same way.
202. And acquiring standard water quality grades of various monitoring factors corresponding to the target monitoring river reach, and determining the monitoring factors of which the corresponding water quality detection grades are lower than the standard water quality grades as abnormal target monitoring factors.
For this embodiment, the standard water quality grades corresponding to the respective sites are related to requirements of the environmental protection department, and different standard water quality grades can be set for each site, so as to perform targeted management on each site, specifically, for example, Chemical Oxygen Demand (COD), ammonia nitrogen (NH3-N), five-day Biochemical Oxygen Demand (BOD 5), total nitrogen value data of 19 (corresponding to class iii water), 1.2 (corresponding to class iv water), 3.5 (corresponding to class iii water), and 1.8 (corresponding to class v water) respectively monitored at site 1 of the target monitored river reach, and the worst water quality grade among the water quality detection grades respectively corresponding to all monitoring factors is class v, so that the water quality detection grade actually corresponding to site 1 can be determined to be class v water. According to the requirement of an environmental protection department, the standard water quality grade of the point 1 is IV-class water, the water quality detection grade of the point 1 is V-class water, the requirement of the environmental protection department is not met, the abnormal water quality of the point 1 is judged, an alarm event is generated and sent to service personnel, the alarm event displays the starting time of the abnormal water quality, the abnormal point is generated, the abnormal monitoring factor is recorded as a target monitoring factor, the multiple of the standard exceeding is multiplied, and the abnormal water quality ending time is displayed when the water quality is recovered to be normal.
203. And determining a preset historical time period before the target monitoring factor is abnormal, and extracting historical monitoring data of the target monitoring factor and at least one first monitoring factor collected in the preset historical time period from a database.
The specific implementation process may refer to the related description in step 102 of the embodiment, and is not described herein again.
204. According to a Pearson correlation coefficient calculation formula and historical monitoring data, a first correlation coefficient of a target monitoring factor and a first monitoring factor is calculated, and a strong correlation monitoring factor with the first correlation coefficient larger than a first preset threshold value is extracted from the first monitoring factor.
The specific implementation process may refer to the related description in step 103 of the embodiment, and is not described herein again.
In a specific application scenario, when determining a strong correlation monitoring factor, as an optimal implementation manner, the embodiment steps may further include: judging whether the first moment of abnormality of the target monitoring factor is in the last analysis period; if so, analyzing the data according to the strong correlation monitoring factor of the last analysis period to extract the strong correlation monitoring factor of the target monitoring factor.
For example, it is determined according to the alarm event information that the total nitrogen value exceeds the standard in each monitoring factor of the point location 1, at this time, the target monitoring factor is the total nitrogen value, and the time period of exceeding the standard is 10 days at 9, 3 and 3 of 2020: 00-13: 00, determining that the last analysis period is the first six months of the month in which the last abnormality occurs, when a new alarm event occurs in 18 days 9 and 9 months in 2020 and the date of the last abnormality occurrence is 3 days 9 and 9 months, judging that the abnormality in 18 days 9 and the abnormality in 3 days 9 and 9 months occur in 9 months, and then the new alarm event is in the last analysis period, calculating strong correlation monitoring factors by using monitoring data of 3 months to 8 months in 3 months in 2020 and six months in 9 months in 2020, if the new alarm event occurs in any one day in 10 months and the date of the last abnormality occurrence is 3 days 9 months, judging that the abnormality in any one day in 10 months and the abnormality in 3 days 9 months do not both occur in 9 months, and then the new alarm event is not in the last analysis period, acquiring history monitoring data of 4 months to 9 months in 10 months in 2020 and 4 months to 9 months in 2020, and calculating a first correlation coefficient, a threshold is set, and a strong correlation monitoring factor with a first correlation coefficient larger than the threshold is extracted from the threshold. The mode based on this embodiment can realize the real-time supervision to the water, has improved the accuracy of pollution cause analysis. For example, typhoon appearing in 3 months in 2020 is the root cause of abnormality of a certain monitoring factor of a certain point location, typhoon in 4 months in 2020 ends, but a certain pollution enterprise sets a discharge port on the upstream of the point location, sewage discharge of the discharge port is the root cause of abnormality of the certain monitoring factor of the point location, and at this time, data including 3 months is reused, so that the typhoon factor is included in strongly-related monitoring factors, the discharge port on the upstream of the point location is excluded, and finally, the pollution cause analysis is wrong.
205. And calculating a second correlation coefficient of the target monitoring factor and the strong correlation monitoring factor according to the Pearson correlation coefficient calculation formula and the historical monitoring data.
The formula for calculating the second correlation coefficient may adopt a pearson correlation coefficient calculation formula:
formula (1) can be derived from formulas (2), (3) and (4), wherein X and Y represent a target monitoring factor and a strong correlation monitoring factor respectively, Cov (X, Y) is the covariance of X and Y, N is the number of data, r (X, Y) is the correlation coefficient of X and Y, and Var [ X ] and Var [ Y ] are the variances of X and Y respectively.
206a, sequencing the strong correlation monitoring factors in a descending order according to the second correlation number to obtain a contribution degree ranking of the strong correlation monitoring factors, and generating a water quality abnormity analysis result containing the contribution degree ranking.
Through the calculation in step 205 of the embodiment, the second correlation coefficients corresponding to the strong correlation monitoring factors are obtained, and the second correlation coefficients are sorted from large to small to serve as the contribution rank of the strong correlation monitoring factors, that is, the contribution rank of the strong correlation monitoring factors to the current water quality abnormal event, so that the contribution rank can be used as a water quality abnormal analysis result for the current water quality abnormal event, so that a user can visually determine the factors causing the water quality abnormal event according to the contribution rank. For example, the strong relevant monitoring factor ranked first in the contribution degree ranking is determined as the wind speed in the meteorological data of the nearby area, so that the condition that the water quality is abnormal due to the existence of the wind speed can be judged, and if the wind speed is small, the pollutant discharge is not smooth enough.
In an embodiment step 206b parallel to the embodiment step 206a, a target strong correlation monitoring factor with a second correlation number greater than a second preset threshold is extracted from the strong correlation monitoring factors, a correlation event of the target strong correlation monitoring factor is determined, and a water quality abnormality analysis result including the correlation event is generated.
The second preset threshold is a value which is larger than 0 and smaller than 1, and is the same as or different from the first preset threshold, and the specific value can be set according to the actual application scene.
In the embodiment, the calculation in step 205 is performed to obtain a second correlation coefficient corresponding to the strong correlation monitoring factor, set a second preset threshold, take the strong correlation monitoring factor with the second number of phase relationships greater than the second preset threshold as the target strong correlation monitoring factor, locate the correlation event corresponding to the target strong correlation monitoring factor, and generate a water quality abnormality analysis result including the correlation event, for example, the target strong correlation monitoring factor is the monitoring factor of the upstream monitoring point, further, the position of the upstream monitoring point can be determined according to the latitude information of the upstream monitoring point, and the correlation event of the upstream monitoring point can be obtained, for example, it is found that a certain factory near the upstream monitoring point has an excessive discharge due to order quantity increase, so that the production speed is increased, and the water quality abnormality analysis result with the excessive discharge of the upstream monitoring point can be generated.
As yet another alternative, in generating the water quality abnormality analysis result, the embodiment steps 206a and 206b may be combined to further generate the water quality abnormality analysis result including the contribution degree ranking and the associated event. Correspondingly, the embodiment steps may specifically include: sequencing the strong correlation monitoring factors according to the sequence of the second correlation coefficients from large to small to obtain the contribution ranking of the strong correlation monitoring factors; extracting target strong correlation monitoring factors of which the second correlation number is greater than a second preset threshold value from the strong correlation monitoring factors, and determining correlation events of the target strong correlation monitoring factors; and generating a water quality abnormity analysis result containing the contribution degree ranking and the associated event.
In a specific application scenario, in order to better analyze the cause of the abnormal water quality, as a preferred method, the embodiment may further include: determining monitoring point positions of a target monitoring river reach; counting multiple accumulated contribution degree ranks of the monitoring points in the investigation period about the strong correlation monitoring factors, and marking the strong correlation monitoring factor with the highest multiple accumulated contribution degree ranks as a susceptible monitoring factor of the monitoring points; and establishing a point location file of the target monitoring river reach according to the susceptible monitoring factors, wherein the susceptible monitoring factors of the monitoring point location are stored in the point location file.
For the embodiment, each time a water quality abnormality occurs, the water quality abnormality correspondingly occurs at a certain monitoring point, each monitoring point corresponds to a plurality of water quality abnormality events in an examination period, strong correlation monitoring factor contribution ranking corresponding to a plurality of water quality abnormality events of each monitoring point is counted, then each monitoring point has a plurality of strong correlation monitoring factor contribution ranking, a susceptibility monitoring factor with the highest accumulated strong correlation monitoring factor contribution ranking for a plurality of times of each monitoring point is obtained through calculation by a statistical method, the susceptibility monitoring factor of each monitoring point is stored in a point file corresponding to a target monitoring river reach, a pertinent department is facilitated to provide a pertinent solution for point management by establishing a point file, and the point file is updated in real time when one or more water quality abnormality events occur, the method is beneficial to the related departments to master the latest condition, thereby providing an accurate point location management solution.
According to the water quality abnormity cause analysis method, abnormal target monitoring factors can be extracted from a database of an acquired target monitoring river reach according to a preset quality standard interval; determining a preset historical time period before the target monitoring factor is abnormal, and extracting historical monitoring data of the target monitoring factor and at least one first monitoring factor collected in the preset historical time period from a database; calculating a first correlation coefficient of a target monitoring factor and a first monitoring factor according to a Pearson correlation coefficient calculation formula and historical monitoring data, and extracting a strong correlation monitoring factor of which the first correlation coefficient is greater than a first preset threshold value from the first monitoring factor; and calculating a second correlation coefficient of the target monitoring factor and the strong correlation monitoring factor according to the Pearson correlation coefficient calculation formula and the historical monitoring data, and generating a water quality abnormity analysis result of the target monitoring river reach according to the second correlation coefficient. Through the technical scheme in this application, when quality of water is unusual, carry out twice screening in a large amount of data and obtain the factor that influences the quality of water anomaly event the biggest to on the one hand need not to carry out the precision to whole data and handle and can obtain accurate result, on the other hand can go on in time to trace to the source investigation, carry out the pertinence and administer and avoid pollution diffusion upgrading. The point location files are established, so that the point location management can be provided with a targeted solution by related departments, and the point location files are updated in real time when one or more abnormal water quality events occur, so that the related departments can master the latest condition, and an accurate point location management solution is provided.
Further, as a specific implementation of the method shown in fig. 1 and fig. 2, an embodiment of the present application provides a water quality abnormality cause analysis apparatus, as shown in fig. 3, the apparatus includes: a first extraction module 31, a second extraction module 32, a first calculation module 33, a second calculation module 34;
the first extraction module 31 is configured to extract an abnormal target monitoring factor from a database of the collected target monitoring river reach according to a preset quality standard interval;
the second extraction module 32 is configured to determine a preset historical time period before the target monitoring factor is abnormal, and extract historical monitoring data of the target monitoring factor and at least one first monitoring factor collected in the preset historical time period from the database;
the first calculating module 33 is configured to calculate a first correlation coefficient between the target monitoring factor and the first monitoring factor according to a pearson correlation coefficient calculation formula and historical monitoring data, and extract a strong correlation monitoring factor with the first correlation coefficient being greater than a first preset threshold from the first monitoring factor;
and the second calculating module 34 is configured to calculate a second correlation coefficient between the target monitoring factor and the strong correlation monitoring factor according to the pearson correlation coefficient calculation formula and the historical monitoring data, and generate a water quality abnormality analysis result of the target monitoring river reach according to the second correlation coefficient.
In a specific application scenario, in order to extract an abnormal target monitoring factor from a database of the collected target monitoring river reach according to a preset quality standard interval, the first extraction module 31 may be specifically configured to: detecting each monitoring factor in the database according to a preset quality standard interval, and determining the water quality detection level corresponding to each monitoring factor; and acquiring the standard water quality grade of each monitoring factor corresponding to the target monitoring river reach, and determining the monitoring factor of which the corresponding water quality detection grade is lower than the standard water quality grade as an abnormal target monitoring factor.
In a specific application scenario, when a water quality anomaly analysis result of the target monitoring river reach is generated according to the second correlation coefficient, the second calculation module 34 may be specifically configured to: sequencing the strong correlation monitoring factors according to the sequence of the second correlation coefficients from large to small to obtain the contribution ranking of the strong correlation monitoring factors; and generating a water quality abnormity analysis result containing the contribution degree ranking.
In a specific application scenario, a water quality abnormality analysis result of the target monitoring river reach is generated according to the second correlation coefficient, and the second calculation module 34 may be further configured to: extracting a target strong correlation monitoring factor of which the second correlation coefficient is greater than a second preset threshold value from the strong correlation monitoring factors; and determining a correlation event of the target strong correlation monitoring factor, and generating a water quality abnormity analysis result containing the correlation event.
In a specific application scenario, as shown in fig. 4, the apparatus further includes: a judgment module 35 and a third extraction module 36;
the judging module 35 is configured to judge whether the first time point of the abnormality of the target monitoring factor is within a previous analysis period;
a third extracting module 36, configured to extract a strong correlation monitoring factor of the target monitoring factor according to analysis data of the strong correlation monitoring factor in a last analysis period if the target monitoring factor is abnormal at a first time in the last analysis period;
if the first time point of the abnormality of the target monitoring factor is not in the last analysis period, determining a preset historical time period before the abnormality of the target monitoring factor by using a second extraction module 32, and extracting historical monitoring data collected by the target monitoring river reach in the preset historical time period from a database, wherein the historical monitoring data comprises the target monitoring factor and at least one first monitoring factor; and calculating a first correlation coefficient of the target monitoring factor and the first monitoring factor by using a second extraction module 32 according to a Pearson correlation coefficient calculation formula and historical monitoring data, and extracting a strong correlation monitoring factor of which the first correlation coefficient is greater than a preset threshold value from the first monitoring factor.
In a specific application scenario, as shown in fig. 4, the apparatus further includes: an update module 37;
the updating module 37 may be configured to extract real-time water quality parameter data of each monitoring point location in the target monitoring river reach, and update and store the real-time water quality parameter data in the database, so as to extract abnormal target monitoring factors and historical monitoring data acquired by the target monitoring river reach within a preset historical time period from the database.
In a specific application scenario, as shown in fig. 4, the apparatus further includes: a determination module 38, a marking module 39, a creation module 310;
a determining module 38, configured to determine a monitoring point location of the target monitoring river reach;
the marking module 39 is configured to count multiple accumulated contribution ranks of the monitoring point in the investigation period with respect to the strong correlation monitoring factor, and mark the strong correlation monitoring factor with the highest multiple accumulated contribution ranks as a susceptible monitoring factor of the monitoring point;
the creating module 310 may be configured to create a point location archive of the target monitoring river reach according to the susceptibility monitoring factor, where the susceptibility monitoring factor of the monitored point location is stored in the point location archive.
It should be noted that other corresponding descriptions of the functional units related to the water quality abnormality cause analysis apparatus provided in this embodiment may refer to the corresponding descriptions in fig. 1 to fig. 2, and are not repeated herein.
Based on the method shown in fig. 1 to fig. 2, correspondingly, the present embodiment further provides a storage medium, which may be volatile or nonvolatile, and has computer readable instructions stored thereon, and when the computer readable instructions are executed by a processor, the method for analyzing the cause of the water quality abnormality shown in fig. 1 to fig. 2 is implemented.
Based on such understanding, the technical solution of the present application may be embodied in the form of a software product, which may be stored in a storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, or the like), and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device, or the like) to execute the method of the embodiments of the present application.
Based on the method shown in fig. 1 to fig. 2 and the virtual device embodiments shown in fig. 3 and fig. 4, in order to achieve the above object, the present embodiment further provides a computer device, where the computer device includes a storage medium and a processor; a storage medium for storing a computer program; a processor for executing a computer program to implement the water quality abnormality cause analysis method as shown in fig. 1 to 2.
Optionally, the computer device may further include a user interface, a network interface, a camera, Radio Frequency (RF) circuitry, a sensor, audio circuitry, a WI-FI module, and so forth. The user interface may include a Display screen (Display), an input unit such as a keypad (Keyboard), etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), etc.
It will be understood by those skilled in the art that the present embodiment provides a computer device structure that does not constitute a limitation of the physical device, and may include more or less components, or some components in combination, or a different arrangement of components.
The storage medium may further include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the computer device described above, supporting the execution of information handling programs and other software and/or programs. The network communication module is used for realizing communication among components in the storage medium and communication with other hardware and software in the information processing entity device.
Through the above description of the embodiments, those skilled in the art will clearly understand that the present application can be implemented by software plus a necessary general hardware platform, and can also be implemented by hardware.
Through the technical scheme, compared with the prior art, the method, the device and the computer equipment for analyzing the water quality abnormal cause solve the problems that the tracing ability of the causes of water pollution is insufficient, a discharge main body is difficult to position in time with specific pollution events, the supervision is not timely, and the pertinence is not strong. Firstly, extracting abnormal target monitoring factors from a database of an acquired target monitoring river reach according to a preset quality standard interval to determine a preset historical time period before the target monitoring factors are abnormal, and extracting the target monitoring factors acquired in the preset historical time period and historical monitoring data of at least one first monitoring factor from the database; calculating a first correlation coefficient of a target monitoring factor and a first monitoring factor according to a Pearson correlation coefficient calculation formula and historical monitoring data, and extracting a strong correlation monitoring factor of which the first correlation coefficient is greater than a first preset threshold value from the first monitoring factor; and calculating a second correlation coefficient of the target monitoring factor and the strong correlation monitoring factor according to the Pearson's correlation coefficient calculation formula and the historical monitoring data, and generating a water quality abnormity analysis result of the target monitoring river reach according to the second correlation coefficient. Through the technical scheme in this application, when quality of water is unusual, carry out twice screening in a large amount of data and obtain the factor that influences this time quality abnormal event the most, thereby on the one hand need not to carry out the precision processing to whole data and can obtain accurate result, on the other hand can in time trace to the source investigation, confirm the pollution main part, and carry out the pertinence and administer and avoid polluting the diffusion upgrading, and simultaneously, establish the archives for every position, to the biggest factor of quality of water abnormal event influence in the statistics historical period, be favorable to the pertinence to administer the position like this, provide the prevention scheme of pertinence for environmental management personnel, in time supervise, improve environmental management efficiency.
Those skilled in the art will appreciate that the figures are merely schematic representations of one preferred implementation scenario and that the blocks or processes in the figures are not necessarily required to practice the present application. Those skilled in the art will appreciate that the modules in the devices in the implementation scenario may be distributed in the devices in the implementation scenario according to the description of the implementation scenario, or may be located in one or more devices different from the present implementation scenario with corresponding changes. The modules of the implementation scenario may be combined into one module, or may be further split into a plurality of sub-modules.
The above application serial numbers are for description purposes only and do not represent the superiority or inferiority of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of the present application, but the present application is not limited thereto, and any variations that can be made by those skilled in the art are intended to fall within the scope of the present application.
Claims (10)
1. A method for analyzing causes of water quality abnormality is characterized by comprising the following steps:
extracting abnormal target monitoring factors from a database of the collected target monitoring river reach according to a preset quality standard interval;
determining a preset historical time period before the target monitoring factor is abnormal, and extracting historical monitoring data of the target monitoring factor and at least one first monitoring factor collected in the preset historical time period from the database;
calculating a first correlation coefficient of the target monitoring factor and the first monitoring factor according to a Pearson correlation coefficient calculation formula and the historical monitoring data, and extracting a strong correlation monitoring factor of which the first correlation coefficient is greater than a first preset threshold value from the first monitoring factor;
and calculating a second correlation coefficient of the target monitoring factor and the strong correlation monitoring factor according to a Pearson correlation coefficient calculation formula and the historical monitoring data, and generating a water quality abnormity analysis result of the target monitoring river reach according to the second correlation coefficient.
2. The method according to claim 1, wherein the extracting of the abnormal target monitoring factor from the database of the collected target monitoring river reach according to the preset quality standard interval comprises:
detecting each monitoring factor in the database according to a preset quality standard interval, and determining a water quality detection level corresponding to each monitoring factor;
and acquiring standard water quality grades of the target monitoring river reach corresponding to the monitoring factors, and determining the monitoring factors corresponding to the water quality detection grade lower than the standard water quality grade as abnormal target monitoring factors.
3. The method according to claim 1, wherein the generating of the water quality abnormality analysis result of the target monitoring river reach according to the second correlation coefficient comprises:
sequencing the strong correlation monitoring factors in the order of the second correlation numbers from large to small to obtain the contribution ranking of the strong correlation monitoring factors;
and generating a water quality abnormity analysis result containing the contribution degree ranking.
4. The method according to claim 1, wherein the generating of the water quality abnormality analysis result of the target monitoring river reach according to the strongly correlated monitoring factor further comprises:
extracting the target strong correlation monitoring factors of which the second correlation number is greater than a second preset threshold value from the strong correlation monitoring factors;
and determining a correlation event of the target strong correlation monitoring factor, and generating a water quality abnormity analysis result containing the correlation event.
5. The method according to any one of claims 1 to 4, further comprising:
judging whether the first moment of abnormity of the target monitoring factor is in the last analysis period or not;
if so, extracting the strong correlation monitoring factor of the target monitoring factor according to the analysis data of the strong correlation monitoring factor of the last analysis period;
if not, determining a preset historical time period before the target monitoring factor is abnormal, and extracting historical monitoring data collected by the target monitoring river reach in the preset historical time period from the database, wherein the historical monitoring data comprises the target monitoring factor and at least one first monitoring factor; and calculating a first correlation coefficient of the target monitoring factor and the first monitoring factor according to a Pearson correlation coefficient calculation formula and the historical monitoring data, and extracting a strong correlation monitoring factor of which the first correlation coefficient is greater than a preset threshold value from the first monitoring factor.
6. The method of claim 1, further comprising:
extracting real-time water quality parameter data of each monitoring point position in a target monitoring river reach, and updating and storing the real-time water quality parameter data to a database so as to extract abnormal target monitoring factors and historical monitoring data collected by the target monitoring river reach in the preset historical time period from the database.
7. The method of claim 1, further comprising:
determining monitoring point positions of the target monitoring river reach;
counting multiple accumulated contribution degree ranks of the monitoring point in an investigation period about the strong correlation monitoring factors, and marking the strong correlation monitoring factor with the highest multiple accumulated contribution degree ranks as a susceptible monitoring factor of the monitoring point;
and establishing a point location file of the target monitoring river reach according to the susceptibility monitoring factors, wherein the susceptibility monitoring factors of the monitored point locations are stored in the point location file.
8. A water quality abnormality cause analyzing apparatus, comprising:
the first extraction module is used for extracting abnormal target monitoring factors from a database of the acquired target monitoring river reach according to a preset quality standard interval;
the second extraction module is used for determining a preset historical time period before the target monitoring factor is abnormal, and extracting historical monitoring data collected by the target monitoring river reach in the preset historical time period from the database, wherein the historical monitoring data comprises the target monitoring factor and at least one first monitoring factor;
the first calculation module is used for calculating a first correlation coefficient of the target monitoring factor and the first monitoring factor according to a Pearson correlation coefficient calculation formula and the historical monitoring data, and extracting a strong correlation monitoring factor of which the first correlation coefficient is larger than a first preset threshold value from the first monitoring factor;
and the second calculation module is used for calculating a second correlation coefficient of the target monitoring factor and the strong correlation monitoring factor according to a Pearson correlation coefficient calculation formula and the historical monitoring data, and generating a water quality abnormity analysis result of the target monitoring river reach according to the second correlation coefficient.
9. A storage medium on which a computer program is stored, wherein the program, when executed by a processor, implements the water quality abnormality cause analysis method according to any one of claims 1 to 7.
10. A computer device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the method for analyzing a cause of a water abnormality according to any one of claims 1 to 7 when executing the program.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115952700A (en) * | 2023-03-15 | 2023-04-11 | 江西飞尚科技有限公司 | Temperature-associated data compensation method, system, computer and storage medium |
| CN116308952A (en) * | 2023-03-08 | 2023-06-23 | 浪潮智慧科技有限公司 | Water quality monitoring method and device based on unmanned ship |
| CN116990479A (en) * | 2023-09-27 | 2023-11-03 | 上海科泽智慧环境科技有限公司 | Water quality monitoring method, system, equipment and medium based on Zigbee technology |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116308952A (en) * | 2023-03-08 | 2023-06-23 | 浪潮智慧科技有限公司 | Water quality monitoring method and device based on unmanned ship |
| CN116308952B (en) * | 2023-03-08 | 2023-09-22 | 浪潮智慧科技有限公司 | Water quality monitoring method and device based on unmanned ship |
| CN115952700A (en) * | 2023-03-15 | 2023-04-11 | 江西飞尚科技有限公司 | Temperature-associated data compensation method, system, computer and storage medium |
| CN116990479A (en) * | 2023-09-27 | 2023-11-03 | 上海科泽智慧环境科技有限公司 | Water quality monitoring method, system, equipment and medium based on Zigbee technology |
| CN116990479B (en) * | 2023-09-27 | 2023-12-15 | 上海科泽智慧环境科技有限公司 | Water quality monitoring method, system, equipment and medium based on Zigbee technology |
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