Water body monitoring and early warning system and method based on big data

Abstract

The invention discloses a water body monitoring and early warning system and method based on big data, and relates to the technical field of water body monitoring. Abnormal pollutant parameters in early warning records are collected and analyzed, and specific pollutant parameters are determined; collecting early warning records of the characteristic pollutant parameters, obtaining monitoring time points in the early warning records, dividing a day into a plurality of time periods, and analyzing and determining characteristic time periods of the characteristic pollutant parameters; real-time numerical values of the characteristic pollutant parameters are collected, and the monitoring time interval of the characteristic pollutant parameters is calculated; according to the method, monitoring time intervals of characteristic pollutant parameters are classified and summarized, a monitoring time interval set of a characteristic time period and a monitoring time interval set of a non-characteristic time period are obtained, real-time monitoring time points are obtained through calculation, the condition of a water body is judged at the real-time monitoring time points, the monitoring time points are dynamically adjusted, and resource waste is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of water body monitoring, and specifically to a water body monitoring and early warning system and method based on big data. Background Technique

[0002] With the acceleration of the urbanization process and the development of industrialization, the problem of water resource pollution has become increasingly serious, and water body monitoring has become an important means to protect the water environment; With the rapid development of big data technology, real-time data collection and analysis means have gradually matured. Through the real-time monitoring of water body-related data, more flexible and efficient water body monitoring can be achieved; However, too short a collection time interval will cause a large amount of resource waste, and too long a collection time interval will lead to the inability to detect water pollution in a timely manner. Therefore, developing a water body monitoring and early warning system based on big data, dynamically adjusting the collection time according to the real-time water pollution situation, can not only improve the timeliness and accuracy of water quality monitoring, but also effectively avoid resource waste in traditional monitoring methods, meeting the current demand for sustainable development. It will provide strong support for water resource management and environmental protection, and contribute to the construction of ecological civilization. Summary of the Invention

[0003] The purpose of the present invention is to provide a water body monitoring and early warning system and method based on big data to solve the problems raised in the prior art.

[0004] To solve the above technical problems, the present invention provides the following technical solution: A water body monitoring and early warning method based on big data, the method includes: Step S100: Collect the numerical values of pollutant parameters through a water quality sensor, confirm abnormal pollutant parameters, generate an early warning record, collect and analyze the abnormal pollutant parameters in the historical early warning record, and determine the characteristic pollutant parameters; Step S200: Select a continuous number of days as the training duration. During the training duration, collect the early warning records of the characteristic pollutant parameters, obtain the monitoring time points in the early warning records, divide one day into several time periods, and classify the monitoring time points according to the corresponding time periods, and analyze and determine the characteristic time periods of the characteristic pollutant parameters; Step S300: Monitor the water body in real time, collect the real-time numerical values of the characteristic pollutant parameters, and calculate the monitoring time interval of the characteristic pollutant parameters; Step S400: Obtain the time period corresponding to the monitoring time point, classify and summarize the monitoring time intervals of the characteristic pollutant parameters according to the characteristic time periods of the characteristic pollutant parameters, obtain the set of monitoring time intervals for the characteristic time periods and the set of monitoring time intervals for non-characteristic time periods, calculate the real-time monitoring time point, and judge the water body situation at the real-time monitoring time point.

[0005] Further, step S100 includes: Step S101: By placing a water quality sensor at the sewage discharge outlet, several monitoring time points are set within a day, and the numerical values of several pollutant parameters in the water body at each monitoring time point are collected. The collected numerical values of several pollutant parameters are compared with the standard thresholds of the pollutant parameters. When the numerical value of the pollutant parameter does not meet the standard threshold, the pollutant parameter is set as an abnormal pollutant parameter, and a warning is issued. At the same time, a warning record is generated; Step S102: Collect the abnormal pollutant parameters in the historical warning records, summarize the collected abnormal pollutant parameters to obtain a set of historical warning records of any abnormal pollutant parameter, and count the number of historical warning records in any abnormal pollutant parameter; Step S103: Sort the abnormal pollutant parameters in descending order according to the number of historical warning records, determine the order of the abnormal pollutant parameters, set a quantity threshold for the characteristic pollutant parameters, and compare the order of the abnormal pollutant parameters with the quantity threshold. When the order of the abnormal pollutant parameters is less than the quantity threshold, the abnormal pollutant parameter is set as the characteristic pollutant parameter; The pollutant parameters that can be detected by the water quality sensor include ammonia nitrogen, total phosphorus, chemical oxygen demand, etc. These pollutant parameters are important parameters for evaluating whether the water body is polluted. Therefore, the water quality sensor can be used to judge the water pollution situation at the monitoring time point and issue a warning; Because there are several pollutant parameters, if each pollutant parameter is analyzed, it will consume a large amount of resources and there will be a large error in the calculation result. Therefore, it is necessary to analyze the abnormal pollutant parameters, extract the abnormal pollutant parameters with more occurrences, and set them as characteristic pollutant parameters to ensure the accuracy of subsequent analysis.

[0006] Further, step S200 includes: Step S201: Set a training duration of several consecutive days for the characteristic pollutant parameters, obtain the warning records with a certain characteristic pollutant parameter on any day, collect and summarize the monitoring time points of the warning records, and obtain a set of monitoring time points of a certain characteristic pollutant parameter on any day; Step S202: Divide one day into several time periods, classify the monitoring time points in the set of monitoring time points of any characteristic pollutant parameter that meet each time period, collect the number of monitoring time points in any time period, and summarize the number of monitoring time points in any time period within the set training duration to obtain the total number of monitoring time points in each time period during the entire training duration; Step S203: Calculate the frequency of any time period according to the following formula: ; wherein, Y mb represents the frequency of the m-th characteristic pollutant parameter in the b-th time period, and X mb represents the total number of monitoring time points of the m-th characteristic pollutant parameter in the b-th time period, and X mi represents the total number of monitoring time points of the m-th characteristic pollutant parameter in the i-th time period, and r represents the number of time periods; Step S204: Sort the time periods in descending order according to the frequency of the time periods, and select the time period with the highest frequency as the characteristic time period of the characteristic pollutant parameter; Although the characteristic pollutant parameters are determined, the time periods during which each characteristic pollutant parameter is abnormal are different. Therefore, it is necessary to calculate the characteristic time periods of each characteristic pollutant parameter to provide data support for subsequent calculation of real-time monitoring time points; To determine the characteristic time period of the characteristic pollutant parameter, it is necessary to train the characteristic pollutant parameter for a period of time, and determine the characteristic time period of the characteristic pollutant parameter according to the total number of monitoring time points of each time period during the entire training duration.

[0007] Further, step S300 includes: Step S301: Conduct real-time monitoring of the water body. At the n-th monitoring time point, collect the real-time value of the m-th characteristic pollutant parameter as H mn , and at the (n + 1)-th monitoring time point, collect the real-time value of the m-th characteristic pollutant parameter as H mn+1 ; Step S302: Calculate the monitoring time interval of the m-th characteristic pollutant parameter according to the following time interval formula: ; wherein, T m represents the monitoring time interval of the m-th characteristic pollutant parameter, T n and T n+1 respectively represent the n-th monitoring time point and the (n + 1)-th monitoring time point, H m represents the standard threshold of the m-th characteristic pollutant parameter, A m represents the first weight of the time interval formula, and B m represents the second weight of the time interval formula; Since the difference between the two real-time values represents the change amount of the characteristic pollutant parameter, and the difference between the real-time value and the standard threshold represents the condition of the characteristic pollutant parameter, calculating the monitoring time interval through these two differences can improve the accuracy of the monitoring time interval.

[0008] Further, step S400 includes: Step S401: Obtain the time period corresponding to the (n + 1)-th monitoring time point. According to the characteristic time period of the characteristic pollutant parameters, classify and summarize the monitoring time intervals of the characteristic pollutant parameters whose characteristic time period is the said time period, and obtain the set of monitoring time intervals for the characteristic time period as P = {P1, P2,..., Pe}, and the set of monitoring time intervals for the non-characteristic time period as Q = {Q1, Q2,..., Qf}, where P1, P2,..., Pe respectively represent the 1st, 2nd,..., e-th monitoring time intervals in the set of monitoring time intervals for the characteristic time period, and Q1, Q2,..., Qf respectively represent the 1st, 2nd,..., f-th monitoring time intervals in the set of monitoring time intervals for the non-characteristic time period; Step S402: Calculate the real-time monitoring time point according to the following formula: ; where T represents the real-time monitoring time point, Pj represents the j-th monitoring time interval in the set of monitoring time intervals for the characteristic time period, Qk represents the k-th monitoring time interval in the set of monitoring time intervals for the non-characteristic time period, α represents the weight value of the characteristic time period, and β represents the weight value of the non-characteristic time period; Step S403: At the real-time monitoring time point, collect the numerical values of several pollutant parameters in the water body and judge the water body situation; Since the time periods corresponding to different monitoring time points are different, it is necessary to first obtain the time period. According to the characteristic time period of the characteristic pollutant parameters, classify the monitoring time intervals of the characteristic pollutant parameters. Through this method, data support can be provided for the subsequent calculation of the real-time monitoring time point.

[0009] In order to better implement the above method, a water body monitoring and early warning system based on big data is also proposed. The system includes a characteristic pollutant parameter module, a characteristic time period module, a monitoring time interval module, and a real-time monitoring time point module; Characteristic pollutant parameter module: Through a water quality sensor, collect the numerical values of pollutant parameters, confirm abnormal pollutant parameters, and generate an early warning record. Collect and analyze the abnormal pollutant parameters in the historical early warning records to determine the characteristic pollutant parameters; Characteristic time period module: Select a continuous number of days as the training duration. During the training duration, collect the early warning records of the characteristic pollutant parameters, obtain the monitoring time points in the said early warning records, divide a day into several time periods, and classify the monitoring time points according to the corresponding time periods, and analyze and determine the characteristic time period of the characteristic pollutant parameters; Monitoring time interval module: Monitor the water body in real time, collect the real-time numerical values of the characteristic pollutant parameters, and calculate the monitoring time intervals of the characteristic pollutant parameters; Real-time Monitoring Time Point Module: Obtain the time period corresponding to the monitoring time point. According to the characteristic time period of the characteristic pollutant parameters, classify and summarize the monitoring time intervals of the characteristic pollutant parameters to obtain the set of monitoring time intervals for the characteristic time period and the set of monitoring time intervals for the non-characteristic time period. Calculate the real-time monitoring time point, and at the real-time monitoring time point, judge the water body situation.

[0010] Furthermore, the characteristic pollutant parameter module includes a warning record unit and a characteristic pollutant parameter determination unit: Warning Record Unit: Place a number of water quality sensors at the sewage discharge outlet to collect the numerical values of several pollutant parameters in the water body at each monitoring time point. Compare the collected numerical values of several pollutant parameters with the standard threshold of the pollutant parameters. When the numerical value of the pollutant parameter does not meet the standard threshold, set the pollutant parameter as an abnormal pollutant parameter, issue a warning, and at the same time generate a warning record. Characteristic Pollutant Parameter Determination Unit: Collect the abnormal pollutant parameters in the historical warning records to obtain the set of historical warning records of any abnormal pollutant parameter, and count the number of historical warning records in any abnormal pollutant parameter; Sort the abnormal pollutant parameters in descending order according to the number of historical warning records to determine the order of the abnormal pollutant parameters, set the quantity threshold of the characteristic pollutant parameters, and compare the order of the abnormal pollutant parameters with the quantity threshold. When the order of the abnormal pollutant parameters is less than the quantity threshold, set the abnormal pollutant parameter as the characteristic pollutant parameter.

[0011] Furthermore, the characteristic time period module includes a time period frequency calculation unit and a characteristic time period determination unit: Time Period Frequency Calculation Unit: Set the training duration of several consecutive days. Obtain that on any day, there is a warning record of a certain characteristic pollutant parameter. Collect and summarize the monitoring time points of the warning record, classify the monitoring time points that meet each time period in the set of monitoring time points of a certain characteristic pollutant parameter, collect the number of monitoring time points in any time period, obtain the total number of monitoring time points in each time period during the entire training duration, and calculate the frequency of any time period. Characteristic Time Period Determination Unit: Sort the time periods in descending order according to the frequency of the time periods, and select the time period with the highest frequency as the characteristic time period of the characteristic pollutant parameter.

[0012] Furthermore, the monitoring time interval module includes a real-time value collection unit and a monitoring time interval calculation unit: Real-time Value Collection Unit: Conduct real-time monitoring of the water body. At the nth monitoring time point, collect the real-time value of the mth characteristic pollutant parameter. At the (n + 1)th monitoring time point, collect the real-time value of the mth characteristic pollutant parameter. Calculation and monitoring time interval unit: Calculate the monitoring time interval of characteristic pollutant parameters through the time interval formula.

[0013] Furthermore, the real-time monitoring time point module includes a monitoring time interval classification unit and a calculation of real-time monitoring time point unit: Monitoring time interval classification unit: Obtain the time period corresponding to the (n + 1)-th monitoring time point, and classify and summarize the monitoring time intervals of the characteristic pollutant parameters with the said time period as the characteristic time period of the characteristic pollutant parameters according to the characteristic time period of the characteristic pollutant parameters, to obtain a set of monitoring time intervals for the characteristic time period and a set of monitoring time intervals for non-characteristic time periods; Calculation of real-time monitoring time point unit: Calculate the real-time monitoring time point through a formula.

[0014] Compared with the prior art, the beneficial effects of the present invention are: Through a water quality sensor, collect the numerical values of pollutant parameters, confirm abnormal pollutant parameters, and generate a warning record, collect and analyze the abnormal pollutant parameters in the warning record, determine the characteristic pollutant parameters. By this method, it is possible to avoid artificially setting the characteristic pollutant parameters and ensure the accuracy of the characteristic pollutant parameters; Select a continuous number of days as the training duration. During the training duration, collect the warning records of the characteristic pollutant parameters, obtain the monitoring time points in the said warning records, divide a day into several time periods, and classify the said monitoring time points according to the corresponding time periods, and analyze and determine the characteristic time period of the characteristic pollutant parameters. By this method, the characteristic time period of the characteristic pollutant parameters is obtained, which can ensure the accuracy of the characteristic time period; Monitor the water body in real time, collect the real-time numerical values of the characteristic pollutant parameters, calculate the monitoring time interval of the characteristic pollutant parameters; obtain the time period corresponding to the monitoring time point, and classify and summarize the monitoring time intervals of the characteristic pollutant parameters according to the characteristic time period of the characteristic pollutant parameters, to obtain a set of monitoring time intervals for the characteristic time period and a set of monitoring time intervals for non-characteristic time periods, calculate the real-time monitoring time point, and judge the water body situation at the real-time monitoring time point. By this method, it is possible to dynamically adjust the real-time monitoring time point, improve the real-time performance and accuracy of water quality monitoring, and effectively avoid resource waste in traditional monitoring methods. Description of the Drawings

[0015] Figure 1 It is a schematic flow chart of a water body monitoring and warning method based on big data according to the present invention. Detailed Embodiments

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0017] Please refer to Figure 1 , the present invention provides a technical solution: a water body monitoring and early warning method based on big data, and the method includes: Step S100: Collect the numerical values of pollutant parameters through a water quality sensor, confirm the abnormal pollutant parameters, generate a warning record, collect and analyze the abnormal pollutant parameters in the historical warning records, and determine the characteristic pollutant parameters; Among them, step S100 includes: Step S101: Place a water quality sensor at the sewage discharge outlet, set several monitoring time points within a day, collect the numerical values of several pollutant parameters in the water body at each monitoring time point, compare the collected numerical values of several pollutant parameters with the standard threshold of the pollutant parameters. When the numerical value of the pollutant parameter does not meet the standard threshold, set the pollutant parameter as an abnormal pollutant parameter, issue a warning, and generate a warning record at the same time; Step S102: Collect the abnormal pollutant parameters in the historical warning records, summarize the collected abnormal pollutant parameters to obtain a set of historical warning records of any abnormal pollutant parameter, and count the number of historical warning records in any abnormal pollutant parameter; Step S103: Sort the abnormal pollutant parameters in descending order according to the number of historical warning records, determine the order of the abnormal pollutant parameters, set the quantity threshold of the characteristic pollutant parameters, compare the order of the abnormal pollutant parameters with the quantity threshold. When the order of the abnormal pollutant parameter is less than the quantity threshold, set the abnormal pollutant parameter as the characteristic pollutant parameter; For example, the number of historical warning records of the first abnormal pollutant parameter is 15, the number of historical warning records of the second abnormal pollutant parameter is 10, the number of historical warning records of the third abnormal pollutant parameter is 18, and the number of historical warning records of the fourth abnormal pollutant parameter is 5; The sorted set is {the third abnormal pollutant parameter, the first abnormal pollutant parameter, the second abnormal pollutant parameter, the fourth abnormal pollutant parameter}. Set the quantity threshold of the characteristic pollutant parameter to 3, then the third abnormal pollutant parameter, the first abnormal pollutant parameter, and the second abnormal pollutant parameter are the characteristic pollutant parameters.

[0018] Step S200: Select a continuous number of days as the training duration. During the training duration, collect the warning records of the characteristic pollutant parameters, obtain the monitoring time points in the warning records, divide one day into several time periods, classify the monitoring time points according to the corresponding time periods, and analyze and determine the characteristic time periods of the characteristic pollutant parameters. Among them, step S200 includes: Step S201: Set a continuous number of days as the training duration for the characteristic pollutant parameters. Obtain the warning records of a certain characteristic pollutant parameter on any day, collect and summarize the monitoring time points of the warning records, and obtain the set of monitoring time points of a certain characteristic pollutant parameter on any day. Step S202: Divide one day into several time periods, classify the monitoring time points in the set of monitoring time points of any characteristic pollutant parameter that meet each time period, collect the number of monitoring time points in any time period, and summarize the number of monitoring time points in any time period during the set training duration to obtain the total number of monitoring time points of each time period during the entire training duration. Step S203: Calculate the frequency of any time period according to the following formula: ; Among them, Y mb represents the frequency of the m-th characteristic pollutant parameter in the b-th time period, X mb represents the total number of monitoring time points of the m-th characteristic pollutant parameter in the b-th time period, X mi represents the total number of monitoring time points of the m-th characteristic pollutant parameter in the i-th time period, and r represents the number of time periods. Step S204: Sort the time periods in descending order according to the frequency of the time periods, and select the time period with the highest frequency as the characteristic time period of the characteristic pollutant parameter. For example, on the first day, the set of monitoring time points of the first characteristic pollutant parameter is {8:00, 9:00, 13:00}, and on the second day, the set of monitoring time points of the first characteristic pollutant parameter is {7:00, 14:00, 16:00, 17:00}. Divide one day into 4 time periods, then the first time period is [0, 6), the second time period is [6, 12), the third time period is [12, 18), and the fourth time period is [18, 24); The number of the first characteristic pollutant parameter in the first time period is 0, the number of the first characteristic pollutant parameter in the second time period is 3, the number of the first characteristic pollutant parameter in the third time period is 4, and the number of the first characteristic pollutant parameter in the fourth time period is 0.

[0019] Step S300: Monitor the water body in real time, collect the real-time values of the characteristic pollutant parameters, and calculate the monitoring time interval of the characteristic pollutant parameters. Among them, step S300 includes: Step S301: Conduct real-time monitoring of the water body. At the nth monitoring time point, collect the real-time value of the mth characteristic pollutant parameter as H mn , and at the (n + 1)th monitoring time point, collect the real-time value of the mth characteristic pollutant parameter as H mn+1 ; Step S302: Calculate the monitoring time interval of the mth characteristic pollutant parameter according to the following time interval formula: ; Where, T m represents the monitoring time interval of the mth characteristic pollutant parameter, T n and T n+1 respectively represent the nth monitoring time point and the (n + 1)th monitoring time point, H m represents the standard threshold of the mth characteristic pollutant parameter, A m represents the first weight of the time interval formula, and B m represents the second weight of the time interval formula.

[0020] Step S400: Obtain the time period corresponding to the monitoring time point. According to the characteristic time period of the characteristic pollutant parameter, classify and summarize the monitoring time intervals of the characteristic pollutant parameter to obtain the monitoring time interval set of the characteristic time period and the monitoring time interval set of the non-characteristic time period, calculate the real-time monitoring time point, and judge the water body situation at the real-time monitoring time point; Among them, step S400 includes: Step S401: Obtain the time period corresponding to the (n + 1)th monitoring time point. According to the characteristic time period of the characteristic pollutant parameter, classify and summarize the monitoring time intervals of the characteristic pollutant parameter whose characteristic time period is the time period to obtain the monitoring time interval set of the characteristic time period as P = {P1, P2,..., Pe}, and the monitoring time interval set of the non-characteristic time period as Q = {Q1, Q2,..., Qf}, where P1, P2,..., Pe respectively represent the 1st, 2nd,..., e-th monitoring time intervals in the monitoring time interval set of the characteristic time period, and Q1, Q2,..., Qf respectively represent the 1st, 2nd,..., f-th monitoring time intervals in the monitoring time interval set of the non-characteristic time period; Step S402: Calculate the real-time monitoring time point according to the following formula: ; Wherein, T represents the real-time monitoring time point, Pj represents the j-th monitoring time interval in the set of monitoring time intervals for the characteristic time period, Qk represents the k-th monitoring time interval in the set of monitoring time intervals for the non-characteristic time period, α represents the weight of the characteristic time period, and β represents the weight of the non-characteristic time period; Step S403: At the real-time monitoring time point, collect the numerical values of several pollutant parameters in the water body and judge the water body condition.

[0021] In order to better implement the above method, a water body monitoring and early warning system based on big data is also proposed. The system includes a characteristic pollutant parameter module, a characteristic time period module, a monitoring time interval module, and a real-time monitoring time point module; Characteristic pollutant parameter module: Through water quality sensors, collect the numerical values of pollutant parameters, confirm abnormal pollutant parameters, and generate warning records. Collect and analyze the abnormal pollutant parameters in the historical warning records to determine the characteristic pollutant parameters; Among them, the characteristic pollutant parameter module includes a warning record unit and a unit for determining characteristic pollutant parameters: Warning record unit: By placing several water quality sensors at the sewage discharge outlet, collect the numerical values of several pollutant parameters in the water body at each monitoring time point, compare the collected numerical values of several pollutant parameters with the standard threshold of the pollutant parameters. When the numerical value of the pollutant parameter does not meet the standard threshold, set the pollutant parameter as an abnormal pollutant parameter, issue a warning, and generate a warning record at the same time; Unit for determining characteristic pollutant parameters: Collect the abnormal pollutant parameters in the historical warning records to obtain the set of historical warning records of any abnormal pollutant parameter, and count the number of historical warning records in any abnormal pollutant parameter; Sort the abnormal pollutant parameters in descending order according to the number of historical warning records to determine the order of the abnormal pollutant parameters, set the quantity threshold of the characteristic pollutant parameters, and compare the order of the abnormal pollutant parameters with the quantity threshold. When the order of the abnormal pollutant parameter is less than the quantity threshold, set the abnormal pollutant parameter as the characteristic pollutant parameter.

[0022] Characteristic time period module: Select several consecutive days as the training duration. During the training duration, collect the warning records of the characteristic pollutant parameters, obtain the monitoring time points in the warning records, divide a day into several time periods, and classify the monitoring time points according to the corresponding time periods, and analyze and determine the characteristic time period of the characteristic pollutant parameters; Among them, the characteristic time period module includes a calculation time period frequency unit and a unit for determining the characteristic time period: Calculation time period frequency unit: Set the training duration for several consecutive days. Obtain the warning records of a certain characteristic pollutant parameter on any day. Collect and summarize the monitoring time points of the warning records. Classify the monitoring time points in the monitoring time point set of a certain characteristic pollutant parameter that conform to each time period. Collect the number of monitoring time points in any time period to obtain the total number of monitoring time points in each time period during the entire training duration, and calculate the frequency of any time period; Characteristic time period determination unit: Sort the time periods in descending order according to the frequency of the time periods, and select the time period with the highest frequency as the characteristic time period of the characteristic pollutant parameter.

[0023] Monitoring time interval module: Monitor the water body in real time, collect the real-time values of the characteristic pollutant parameters, and calculate the monitoring time interval of the characteristic pollutant parameters; Among them, the monitoring time interval module includes a real-time value collection unit and a monitoring time interval calculation unit: Real-time value collection unit: Monitor the water body in real time. At the nth monitoring time point, collect the real-time value of the mth characteristic pollutant parameter. At the (n + 1)th monitoring time point, collect the real-time value of the mth characteristic pollutant parameter; Monitoring time interval calculation unit: Calculate the monitoring time interval of the characteristic pollutant parameter through the time interval formula.

[0024] Real-time monitoring time point module: Obtain the time period corresponding to the monitoring time point. According to the characteristic time period of the characteristic pollutant parameter, classify and summarize the monitoring time intervals of the characteristic pollutant parameter to obtain the monitoring time interval set of the characteristic time period and the monitoring time interval set of the non-characteristic time period, calculate the real-time monitoring time point, and judge the water body condition at the real-time monitoring time point; Among them, the real-time monitoring time point module includes a monitoring time interval classification unit and a real-time monitoring time point calculation unit: Monitoring time interval classification unit: Obtain the time period corresponding to the (n + 1)th monitoring time point. According to the characteristic time period of the characteristic pollutant parameter, classify and summarize the monitoring time intervals of the characteristic pollutant parameter whose characteristic time period is the time period to obtain the monitoring time interval set of the characteristic time period and the monitoring time interval set of the non-characteristic time period; Real-time monitoring time point calculation unit: Calculate the real-time monitoring time point through the formula.

[0025] It is obvious to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A water body monitoring and early warning method based on big data, characterized in that: Methods include: Step S100: Collect the values ​​of pollutant parameters through water quality sensors, confirm abnormal pollutant parameters, generate warning records, collect and analyze abnormal pollutant parameters in historical warning records, and determine characteristic pollutant parameters; Step S200: Selecting a number of consecutive days as a training duration, collecting warning records of characteristic pollutant parameters during the training duration, obtaining monitoring time points in the warning records, dividing a day into a number of time periods, and classifying the monitoring time points according to the corresponding time periods, and analyzing and determining characteristic time periods of characteristic pollutant parameters; Step S300: real-time monitoring of the water body, collecting real-time values ​​of characteristic pollutant parameters, and calculating monitoring time intervals of characteristic pollutant parameters; Step S400: Obtain the time period corresponding to the monitoring time point, classify and summarize the monitoring time intervals of the characteristic pollutant parameters according to the characteristic time period of the characteristic pollutant parameters, obtain a set of monitoring time intervals for the characteristic time period and a set of monitoring time intervals for the non-characteristic time period, calculate the real-time monitoring time point, and judge the water condition at the real-time monitoring time point.

2. The water body monitoring and early warning method based on big data according to claim 1 is characterized in that: The step S100 includes the following steps: Step S101: by placing a water quality sensor at the sewage outlet, setting several monitoring time points in one day, collecting the values ​​of several pollutant parameters in the water body at each monitoring time point, and comparing the collected values ​​of the several pollutant parameters with the standard threshold value of the pollutant parameters. When the value of the pollutant parameter does not meet the standard threshold value, the pollutant parameter is set as an abnormal pollutant parameter, and an early warning is issued, and an early warning record is generated at the same time; Step S102: Collect abnormal pollutant parameters in historical warning records, summarize the collected abnormal pollutant parameters, obtain a historical warning record set of any abnormal pollutant parameters, and count the number of historical warning records in any abnormal pollutant parameters; Step S103: Sort the abnormal pollutant parameters from high to low according to the number of historical warning records, determine the order of the abnormal pollutant parameters, set the quantity threshold of the characteristic pollutant parameters, compare the order of the abnormal pollutant parameters with the quantity threshold, and when the order of the abnormal pollutant parameters is less than the quantity threshold, set the abnormal pollutant parameters as the characteristic pollutant parameters.

3. The water body monitoring and early warning method based on big data according to claim 2 is characterized in that: The step S200 includes the following steps: Step S201: setting a training duration of several consecutive days for a characteristic pollutant parameter, obtaining an early warning record of a characteristic pollutant parameter on any day, collecting and summarizing the monitoring time points of the early warning record, and obtaining a set of monitoring time points of a characteristic pollutant parameter on any day; Step S202: Divide a day into several time periods, classify the monitoring time points in the monitoring time point set of any characteristic pollutant parameter that meet each time period, collect the number of monitoring time points in any time period, and summarize the number of monitoring time points in any time period within the set training duration to obtain the total number of monitoring time points in each time period during the entire training duration; Step S203: Calculate the frequency of any time period according to the following formula: ； Among them, Y mb It is expressed as the frequency of the mth characteristic pollutant parameter in the bth time period, X mb It is expressed as the total number of monitoring time points of the mth characteristic pollutant parameter in the bth time period, X mi It is expressed as the total number of monitoring time points of the mth characteristic pollutant parameter in the i-th time period, and r is expressed as the number of time periods; Step S204: sort the time periods from high to low according to the frequency of the time periods, and select the time period with the highest frequency as the characteristic time period of the characteristic pollutant parameter.

4. The water body monitoring and early warning method based on big data according to claim 3 is characterized in that: The step S300 includes the following steps: Step S301: Real-time monitoring of the water body is performed. At the nth monitoring time point, the real-time value of the mth characteristic pollutant parameter is collected as H mn At the n+1th monitoring time point, the real-time value of the mth characteristic pollutant parameter is collected as H mn+1 ; Step S302: Calculate the monitoring time interval of the mth characteristic pollutant parameter according to the following time interval formula: ； Among them, T m Expressed as the monitoring time interval of the mth characteristic pollutant parameter, T n and T n+1 They are respectively represented as the nth monitoring time point and the n+1th monitoring time point, H m It is expressed as the standard threshold value of the mth characteristic pollutant parameter, A m Expressed as the first weight of the time interval formula, B m Expressed as the second weight of the time interval formula.

5. The water body monitoring and early warning method based on big data according to claim 1 is characterized in that: The step S400 includes the following steps: Step S401: Obtain the time period corresponding to the n+1th monitoring time point, and classify and summarize the monitoring time intervals of the characteristic pollutant parameters in the characteristic time period according to the characteristic time period of the characteristic pollutant parameters, so as to obtain a monitoring time interval set of the characteristic time period as P={P1, P2, ..., Pe}, and a monitoring time interval set of the non-characteristic time period as Q={Q1, Q2, ..., Qf}, wherein P1, P2, ..., Pe respectively represent the 1st, 2nd, ..., eth monitoring time intervals in the monitoring time interval set of the characteristic time period, and Q1, Q2, ..., Qf respectively represent the 1st, 2nd, ..., fth monitoring time intervals in the monitoring time interval set of the non-characteristic time period; Step S402: Calculate the real-time monitoring time point according to the following formula: ； Wherein, T represents the real-time monitoring time point, Pj represents the jth monitoring time interval in the monitoring time interval set of the characteristic time period, Qk represents the kth monitoring time interval in the monitoring time interval set of the non-characteristic time period, α represents the weight of the characteristic time period, and β represents the weight of the non-characteristic time period; Step S403: At the real-time monitoring time point, the values ​​of several pollutant parameters in the water body are collected to determine the water body condition.

6. A water body monitoring and early warning system based on big data, used to implement a water body monitoring and early warning method based on big data as described in any one of claims 1 to 5, characterized in that: The system includes a characteristic pollutant parameter module, a characteristic time period module, a monitoring time interval module and a real-time monitoring time point module; The characteristic pollutant parameter module: collects the values ​​of pollutant parameters through water quality sensors, confirms abnormal pollutant parameters, and generates warning records, collects and analyzes abnormal pollutant parameters in historical warning records, and determines characteristic pollutant parameters; The characteristic time period module: selects a number of consecutive days as the training time period, collects warning records of characteristic pollutant parameters during the training time period, obtains monitoring time points in the warning records, divides a day into several time periods, and classifies the monitoring time points according to the corresponding time periods, and analyzes and determines the characteristic time periods of characteristic pollutant parameters; The monitoring time interval module: monitors the water body in real time, collects the real-time values ​​of characteristic pollutant parameters, and calculates the monitoring time interval of the characteristic pollutant parameters; The real-time monitoring time point module obtains the time period corresponding to the monitoring time point, classifies and summarizes the monitoring time intervals of the characteristic pollutant parameters according to the characteristic time period of the characteristic pollutant parameters, obtains the monitoring time interval set of the characteristic time period and the monitoring time interval set of the non-characteristic time period, calculates the real-time monitoring time point, and judges the water condition at the real-time monitoring time point.

7. The water body monitoring and early warning system based on big data according to claim 6 is characterized in that: The characteristic pollutant parameter module includes an early warning recording unit and a characteristic pollutant parameter determination unit: The early warning recording unit: by placing a plurality of water quality sensors at the sewage discharge port, collecting the values ​​of a plurality of pollutant parameters in the water body at each monitoring time point, comparing the collected values ​​of the plurality of pollutant parameters with the standard threshold values ​​of the pollutant parameters, and when the values ​​of the pollutant parameters do not meet the standard threshold values, setting the pollutant parameters as abnormal pollutant parameters, issuing an early warning, and generating an early warning record; The characteristic pollutant parameter determination unit: collects abnormal pollutant parameters in historical warning records, obtains a historical warning record set of any abnormal pollutant parameters, and counts the number of historical warning records in any abnormal pollutant parameters; sorts the abnormal pollutant parameters from high to low according to the number of historical warning records, determines the bit sequence of the abnormal pollutant parameters, sets a quantity threshold of the characteristic pollutant parameters, compares the bit sequence of the abnormal pollutant parameters with the quantity threshold, and when the bit sequence of the abnormal pollutant parameters is less than the quantity threshold, sets the abnormal pollutant parameters as the characteristic pollutant parameters.

8. The water body monitoring and early warning system based on big data according to claim 6 is characterized in that: The characteristic time period module includes a time period frequency calculation unit and a characteristic time period determination unit: The calculation time period frequency unit: set the training duration for several consecutive days, obtain the early warning record of a certain characteristic pollutant parameter on any day, collect and summarize the monitoring time points of the early warning record, classify the monitoring time points in the monitoring time point set of a certain characteristic pollutant parameter that meet the requirements of each time period, collect the number of monitoring time points in any time period, obtain the total number of monitoring time points in each time period during the entire training period, and calculate the frequency of any time period; The characteristic time period determination unit is: sorting the time periods from high to low according to the frequency of the time periods, and selecting the time period with the highest frequency as the characteristic time period of the characteristic pollutant parameter.

9. The water body monitoring and early warning system based on big data according to claim 6 is characterized in that: The monitoring time interval module includes a real-time value acquisition unit and a monitoring time interval calculation unit: The real-time value collecting unit: performs real-time monitoring on the water body, collects the real-time value of the m-th characteristic pollutant parameter at the n-th monitoring time point, and collects the real-time value of the m-th characteristic pollutant parameter at the n+1-th monitoring time point; The monitoring time interval calculation unit calculates the monitoring time interval of the characteristic pollutant parameters through a time interval formula.

10. The water body monitoring and early warning system based on big data according to claim 6 is characterized in that: The real-time monitoring time point module includes a monitoring time interval classification unit and a real-time monitoring time point calculation unit: The monitoring time interval classification unit: obtains the time period corresponding to the n+1th monitoring time point, and classifies and summarizes the monitoring time intervals of the characteristic pollutant parameters whose characteristic time period is the characteristic pollutant parameters of the time period according to the characteristic time period, to obtain a monitoring time interval set of the characteristic time period and a monitoring time interval set of the non-characteristic time period; The real-time monitoring time point calculation unit is used to calculate the real-time monitoring time point through a formula.

Images