Lake water environment quality monitoring method and system based on big data analysis

Abstract

The invention relates to the field of data analysis, in particular to a lake water environment quality monitoring method and system based on big data analysis, and the method comprises the steps: determining a water body pollution region according to remote sensing data, and obtaining monitoring data; the mass center point of the water body pollution area serves as a center monitoring point, a water body updating coefficient is calculated, a monitoring interval is determined according to the pollutant concentration change, and surrounding monitoring points are constructed in the preset angle direction; the synchronous degree of pollutant changes of the surrounding monitoring points and the central monitoring point is evaluated, the angle corresponding to the maximum value and the second maximum value is selected as the angle of the diffusion direction of the pollutants, diffusion points are arranged in the diffusion direction, the emergency degree of the diffusion points is calculated, and when the emergency degree is larger than an emergency threshold value, the system gives an alarm. By analyzing the water body updating coefficient and evaluating the synchronization degree, the lake water environment quality is comprehensively monitored, and a data basis is provided for water body pollution treatment.

Description

Technical Field

[0001] The present invention relates to the field of data analysis, and more specifically, to a lake water environment quality monitoring method and system based on big data analysis. Background Art

[0002] Lake water environmental quality monitoring is an important environmental management activity. Various physical, chemical and biological parameters in lake water are measured and analyzed to assess water quality and detect potential pollution problems in a timely manner. Remote sensing technology is usually used for monitoring. Remote sensing monitoring is a method of observing and monitoring the earth's surface and its environmental elements using remote sensing technology. It uses various sensors mounted on satellites, aircraft, drones and other platforms to obtain electromagnetic wave information of ground objects from a long distance, thereby realizing the analysis of surface characteristics, environmental changes, etc.

[0003] The existing Chinese patent application document with publication number CN117575134A discloses a comprehensive evaluation method for river water environment quality, which belongs to the technical field of river and lake water environment prevention and control. The method includes: determining the actual measured value of the evaluation index of the river and lake water body to be measured; using a pre-constructed decentralized monitoring index quantification calculation model to quantify the actual measured value of the evaluation index to obtain the quantified value of the evaluation index; using a pre-constructed evaluation index standardization calculation model to standardize the quantified value of the evaluation index to obtain the standardized value of the evaluation index; weighted calculation of the standardized value of the evaluation index to obtain the criterion layer score; weighted calculation of the criterion layer score to obtain the comprehensive evaluation index of water environment quality; and using the comprehensive evaluation index of water environment quality to conduct a comprehensive evaluation of the river water environment quality.

[0004] The application document can relatively completely evaluate the water quality of different types of rivers and lakes. At present, remote sensing technology is used to obtain information about the earth's surface and its atmosphere, ocean and other environmental elements. Based on remote sensing data, changes in the lake surface can be seen, but the water quality inside the water body cannot be directly monitored. Although remote sensing technology can quickly obtain macroscopic change information on the lake surface, such as color and temperature, it cannot directly monitor the chemical composition and physical properties inside the water body, such as pH value, dissolved oxygen, chemical oxygen demand and other key water quality indicators. Therefore, it is impossible to fully evaluate the environmental quality of lake water by relying solely on remote sensing data, and there is a monitoring blind spot. Summary of the invention

[0005] In order to solve the problem that although remote sensing technology can quickly obtain macroscopic change information on the surface of lake water, it cannot directly monitor the chemical composition and physical properties inside the water body, and the environmental quality of lake water cannot be fully evaluated relying solely on remote sensing data, and there are monitoring blind spots, the present invention provides solutions in the following multiple aspects.

[0006] In the first aspect, a lake water environmental quality monitoring method based on big data analysis includes: determining the water pollution area according to remote sensing data and obtaining the monitoring data of the lake water; taking the centroid point of the water pollution area as the central monitoring point, calculating the water body renewal coefficient of the central monitoring point, calculating the monitoring interval based on the change of pollutant concentration at the central monitoring point at different times, and constructing surrounding monitoring points based on the monitoring interval and the direction of a preset angle; evaluating the degree of synchronization of pollutant changes between the surrounding monitoring points and the central monitoring point, selecting the angle corresponding to the maximum and second largest values ​​of the synchronization degree as the angle of the diffusion direction of the pollutants, setting a new monitoring point under the monitoring interval in the diffusion direction, marking it as a diffusion point, and calculating the urgency of water pollution at the diffusion point, and issuing an alarm in response to the urgency being greater than the emergency threshold; wherein, the urgency of water pollution Satisfies the following relationship: ; In the formula, Indicates the current number of collections. Indicates the collection time interval. Indicates the degree of synchronization between the diffusion point and the central monitoring point, Indicates the central monitoring point The first monitoring The concentration of pollutants, Indicates the central monitoring point The first monitoring The concentration of pollutants, Indicates the central monitoring point The first monitoring The concentration of pollutants, Indicates the central monitoring point The first monitoring The concentration of pollutants, represents the hyperbolic tangent function, Represents the mean value function.

[0007] The effect is: by preliminarily determining the water pollution area through remote sensing data, the monitoring scope can be quickly narrowed, blind sampling in large areas of water can be avoided, and the pertinence and efficiency of monitoring can be improved. The centroid of the water pollution area is used as the central monitoring point, and the monitoring interval is dynamically calculated according to the water renewal coefficient and the change of pollutant concentration. New monitoring points are set in the diffusion direction, which can capture the diffusion dynamics of pollutants in real time, ensure the scientificity and rationality of the layout of monitoring points, and improve the accuracy of monitoring data; by collecting lake water monitoring data in real time and calculating the urgency of water pollution, when it is greater than the preset threshold, the system will automatically issue an alarm. Through real-time monitoring and early warning mechanisms, pollution incidents can be discovered in a timely manner, providing relevant departments with a time window for rapid response to prevent the spread of pollution and the expansion of harm.

[0008] Preferably, the calculation of the water body renewal coefficient of the central monitoring point includes: Take any central monitoring point as the marked monitoring point, monitor the marked monitoring point multiple times, calculate the average value of the sum of the absolute differences between the ratio of the concentrations of the pollutants in two adjacent monitorings and the average concentration ratio, and obtain the water renewal coefficient of the marked monitoring point.

[0009] The effect is that by calculating the ratio between two consecutive monitoring pollutant concentrations, the concentration changes of pollutants at different time points can be quantified, thereby evaluating the rate of change of pollutants, which helps to understand the dynamic change process of pollutants in water bodies; the water body renewal coefficient combines multiple monitoring data and reflects the average renewal rate of pollutants within a certain period of time. If the water body renewal coefficient is large, it means that the pollutant concentration changes quickly and the water body is renewed slowly; conversely, if the water body renewal coefficient is small, it means that the pollutant concentration changes slowly and the water body is renewed quickly.

[0010] Preferably, the calculation of the water body renewal coefficient of the central monitoring point also includes: Take any central monitoring point as the marked monitoring point, conduct multiple monitoring of the marked monitoring point, where one monitoring includes multiple pollutant concentrations, and calculate the concentration of the first The second monitoring and The concentration changes of the same pollutant monitored in the second monitoring are similar to those in the third monitoring. The absolute value of the ratio of the pollutant concentrations monitored in two consecutive monitoring times is used to obtain the concentration change rate, and the average value of the concentration change rates of all two adjacent monitoring times is used to obtain the water body renewal coefficient of the marked monitoring point.

[0011] The effect is that the sampling frequency of the monitoring point can be dynamically adjusted according to the size of the water renewal coefficient. For monitoring points with higher water renewal coefficients, the sampling frequency should be increased and the sampling interval should be shortened to more accurately capture the changing trends and peaks of pollutants; for monitoring points with lower water renewal coefficients, the sampling frequency can be appropriately reduced to reduce monitoring costs.

[0012] Preferably, the calculating the monitoring interval includes: Take any central monitoring point as the marked monitoring point, monitor the marked monitoring point multiple times, obtain the concentration sequence of different pollutants in each monitoring of the marked monitoring point, calculate the concentration ratio of the same pollutant in two adjacent monitorings in the concentration sequence, obtain the sequence ratio, obtain the minimum value in the sequence ratio, and take the product of the minimum value, the monitoring time interval and the water flow velocity of the marked monitoring point as the monitoring interval.

[0013] The effect is that by dynamically adjusting the layout of monitoring points, the number of monitoring points in areas with small changes in pollutants can be reduced, unnecessary monitoring can be avoided, and monitoring costs can be reduced and monitoring efficiency can be improved. If the monitoring interval of a certain monitoring point is continuously small, it may indicate that there is a pollution source that continuously emits pollutants in the area. Combined with other monitoring data and geographic information, potential pollution sources can be further identified, providing clues for pollution control.

[0014] Preferably, the calculating the monitoring distance further includes: Take any central monitoring point as the marked monitoring point, monitor the marked monitoring point multiple times, obtain the concentration sequence of different pollutants in each monitoring of the marked monitoring point, calculate the concentration ratio of the same pollutant in two adjacent monitorings in the concentration sequence, get the sequence ratio, get the mean of the sequence ratio, and take the product of the mean, the monitoring time interval and the water flow velocity of the marked monitoring point as the monitoring interval.

[0015] Preferably, the synchronization degree includes: According to the types of pollutants, the Pearson correlation coefficient between the pollutant concentration series of the central monitoring point and the pollutant concentration series of the surrounding monitoring points is calculated, and the average value of the sum of the Pearson correlation coefficients of all pollutants is used as the synchronization coefficient of other surrounding monitoring points except the central monitoring point.

[0016] The effect is that by evaluating the degree of synchronization between the surrounding monitoring points and the central monitoring point, the main direction of pollutant diffusion can be identified. Monitoring points with a high degree of synchronization are usually located on the path of pollutant diffusion, and their corresponding angles can be used as an indication of the direction of pollutant diffusion. This helps to understand the propagation path and impact range of pollutants, so as to formulate more targeted monitoring and governance strategies.

[0017] Preferably, the synchronization degree further includes: According to the concentration of each pollutant, the absolute difference between the average concentration of the surrounding monitoring points and the average concentration of the central monitoring point is calculated, and the sum of the average values ​​of the surrounding monitoring points and the average concentration of the central monitoring point is calculated. The complement of the ratio of the absolute difference to the sum of the average concentrations is taken as the synchronization index, and the average value of the synchronization indices of all pollutants is taken as the degree of synchronization of the surrounding monitoring points.

[0018] Preferably, the diffusion angle of the pollutants includes: Obtain the maximum value, second maximum value and angle corresponding to the second maximum value of the synchronization degree, calculate the ratio of the second maximum value to the sum of the maximum value and the second maximum value, multiply by the preset angle to obtain the angle offset, and take the angle offset plus the angle corresponding to the second maximum value as the angle of the pollutant diffusion direction.

[0019] In the second aspect, a lake water environment quality monitoring system based on big data analysis includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned lake water environment quality monitoring method based on big data analysis is implemented.

[0020] The present invention has the following effects: 1. The present invention determines the water pollution area through remote sensing data, and takes the centroid point of the water pollution area as the central monitoring point, which can accurately locate the pollution area, carry out monitoring and control work in a targeted manner, avoid blind monitoring and waste of resources, and analyze the changes of pollutants from multiple dimensions, including the renewal speed, diffusion direction and degree of pollutants, by calculating the water renewal coefficient of the central monitoring point, evaluating the synchronization degree of pollutant changes between the surrounding monitoring points and the central monitoring point, etc., to provide more accurate data support for pollution control.

[0021] 2. The present invention constructs surrounding monitoring points based on the monitoring spacing and the direction of the preset angle, and dynamically adjusts the layout of the monitoring points according to the urgency and diffusion direction of water pollution, so as to reasonably allocate monitoring resources, ensure that sufficient monitoring points are set in the key areas and directions of pollutant diffusion, and improve monitoring efficiency and effectiveness; by calculating the water renewal coefficient, the renewal speed of pollutants in the water body can be understood, so as to reasonably adjust the monitoring frequency. For areas with low water renewal coefficients, the monitoring frequency can be appropriately reduced to reduce monitoring costs and resource consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 It is a method flow chart of steps S1 to S3 in the lake water environment quality monitoring method based on big data analysis in an embodiment of the present invention.

[0023] Figure 2 It is a schematic diagram of setting a central monitoring point in a water pollution area in a lake water environment quality monitoring method based on big data analysis in an embodiment of the present invention.

[0024] Figure 3 It is a structural block diagram of a lake water environment quality monitoring system based on big data analysis according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0026] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0027] Reference Figure 1 The lake water environment quality monitoring method based on big data analysis includes steps S1 to S3, which are as follows: S1: Determine the water pollution area based on remote sensing data and obtain lake water monitoring data.

[0028] In this embodiment, the monitoring data include but are not limited to: pH value, conductivity, dissolved oxygen, chemical oxygen demand (COD), ammonia nitrogen, total phosphorus and total nitrogen.

[0029] It should be noted that remote sensing data refers to information about the earth's surface and its atmosphere, ocean and other environmental elements obtained through remote sensing technology. Remote sensing data can cover a wide geographical area and is particularly suitable for monitoring large areas of lakes, forests, farmland, etc. Satellite remote sensing can obtain lake water information around the world in a short period of time, providing data support for global water environment monitoring. The polluted area of ​​lake water is located through remote sensing data. The scenario of this embodiment is: using a drone equipped with a sensor to collect lake water samples and obtain monitoring data of lake water samples at different locations.

[0030] By using the information provided by remote sensing data, the centroid of the water pollution area is selected as the central monitoring point. Remote sensing data can cover a large area and can quickly identify abnormal changes in the lake surface or suspected pollution areas. Therefore, determining the central monitoring point based on this can ensure the pertinence and effectiveness of the monitoring network.

[0031] To further explain, water pollutants in the lake water flow with the water flow, but the water flow in some areas is poor, which makes pollutants easily accumulate. Therefore, when monitoring points with different pollutants, the water renewal coefficient of the water location is calculated. For sampling points with high water renewal coefficients, the collection frequency of the sampling point needs to be increased (shorten the collection interval time), otherwise, the collection frequency of the sampling point can be reduced.

[0032] S2: Take the centroid of the water pollution area as the central monitoring point, calculate the water renewal coefficient of the central monitoring point, calculate the monitoring interval based on the change of pollutant concentration at the central monitoring point at different times, and construct surrounding monitoring points based on the monitoring interval and the direction of the preset angle.

[0033] Reference Figure 2 ,In the figure, A represents the central monitoring point and B represents the ,surrounding monitoring points.

[0034] The water renewal coefficient of the central monitoring point includes the following steps: Taking any central monitoring point as the marked monitoring point, the marked monitoring point is monitored multiple times, and the average value of the sum of the absolute differences between the ratio of the concentrations of the pollutants in two adjacent monitorings and the average concentration ratio is calculated to obtain the water renewal coefficient of the marked monitoring point.

[0035] Specifically, the water renewal coefficient satisfies the following relationship: ; In the formula, represents the water renewal coefficient of the monitoring point, Indicates the total number of monitoring times. Indicates The concentration of pollutants, Indicates The first monitoring The concentration of pollutants, Indicates The first monitoring The concentration of pollutants, Indicates The first monitoring The concentration of pollutants, Indicates The first monitoring The concentration of pollutants, Represents the mean value function.

[0036] It should be noted that if the water renewal coefficient of a certain monitoring point is high, it means that the pollutants in the area are renewed quickly and the monitoring frequency should be increased. It can also be understood as shortening the monitoring interval. If the water renewal coefficient is low, it means that the pollutants are renewed slowly and the monitoring frequency can be reduced.

[0037] By calculating the average of the historical concentration ratios, the long-term trend of pollutant concentration changes can be identified. For example, if the average concentration ratio is greater than 1, it means that the pollutant concentration has generally increased during the monitoring period; if the average concentration ratio is less than 1, it means that the pollutant concentration has generally decreased. Short-term concentration fluctuations may have a greater impact on a single monitoring result, but by considering the historical average concentration change rate, these short-term fluctuations can be smoothed, so that the water renewal coefficient can better reflect the actual situation of pollutant concentration changes.

[0038] In addition, another embodiment further includes: Take any central monitoring point as the marked monitoring point, conduct multiple monitoring of the marked monitoring point, where one monitoring includes multiple pollutant concentrations, and calculate the concentration of the first The second monitoring and The concentration changes of the same pollutant monitored in the second monitoring are similar to those in the third monitoring. The absolute value of the ratio of the pollutant concentrations monitored in two consecutive monitoring times is used to obtain the concentration change rate, and the average value of the concentration change rates of all two adjacent monitoring times is used to obtain the water body renewal coefficient of the marked monitoring point.

[0039] Specifically, the water renewal coefficient satisfies the following relationship: ; In the formula, represents the water renewal coefficient of the monitoring point, Indicates the total number of monitoring times. Indicates The concentration of pollutants, Indicates The first monitoring The concentration of pollutants, Indicates The first monitoring The concentration of pollutants.

[0040] That is, the concentration change rate Reflects the The relative size of the change in concentration of a pollutant between two adjacent monitorings. If the rate of change is large, it means that the pollutant concentration has changed significantly; if the rate of change is small, it means that the pollutant concentration has not changed significantly. The water renewal coefficient reflects the average relative size of the change in pollutant concentration. If the water renewal coefficient is small, it means that the pollutant concentration changes stably and the water body renews quickly; if the water renewal coefficient is large, it means that the pollutant concentration changes unstably and the water body renews slowly.

[0041] Monitoring spacing, including: Take any central monitoring point as the marked monitoring point, monitor the marked monitoring point multiple times, obtain the concentration sequence of different pollutants in each monitoring of the marked monitoring point, calculate the concentration ratio of the same pollutant in two adjacent monitorings in the concentration sequence, obtain the sequence ratio, obtain the minimum value in the sequence ratio, and take the product of the minimum value, the monitoring time interval and the water flow velocity of the marked monitoring point as the monitoring interval.

[0042] Specifically, the monitoring interval satisfies the following relationship: ; In the formula, Indicates the monitoring distance. Indicates the monitoring time interval, Indicates The first monitoring The concentration of pollutants, Indicates The first monitoring The concentration of pollutants, Indicates The first monitoring The concentration of pollutants, Indicates The first monitoring The concentration of pollutants, Indicates The water flow rate monitored at the time, Represents the minimum function.

[0043] That is to say, in this embodiment, the monitoring time interval is once per hour, which can be adjusted according to the specific situation. Reflects the The concentration change of a pollutant between two consecutive monitorings. If the ratio is greater than 1, it means that the pollutant concentration has increased; if the ratio is less than 1, it means that the pollutant concentration has decreased; the minimum concentration ratio It means the time when the pollutant concentration changes most significantly in all monitoring, which means the diffusion or reduction rate of pollutants is the fastest in this monitoring.

[0044] If the minimum concentration ratio is small, it means that the pollutant concentration has changed significantly, and it is necessary to set up monitoring points more densely to capture the diffusion of pollutants; if the minimum concentration ratio is large, it means that the pollutant concentration has not changed significantly, and the monitoring interval can be appropriately increased.

[0045] In addition, another embodiment further includes: Take any central monitoring point as the marked monitoring point, monitor the marked monitoring point multiple times, obtain the concentration sequence of different pollutants in each monitoring of the marked monitoring point, calculate the concentration ratio of the same pollutant in two adjacent monitorings in the concentration sequence, get the sequence ratio, get the mean of the sequence ratio, and take the product of the mean, the monitoring time interval and the water flow velocity of the marked monitoring point as the monitoring interval.

[0046] Specifically, the monitoring interval satisfies the following relationship: ; In the formula, Indicates the monitoring distance. Indicates the monitoring time interval, Indicates The first monitoring The concentration of pollutants, Indicates The first monitoring The concentration of pollutants, Indicates The first monitoring The concentration of pollutants, Indicates The first monitoring The concentration of pollutants, Indicates The water flow rate monitored at that time, represents the mean function.

[0047] That is to say, in this embodiment, the monitoring time interval is once per hour, which can be adjusted according to specific circumstances. It represents the overall trend of pollutant concentration changes. If the average concentration ratio is greater than 1, it means that the pollutant concentration is generally increasing during the monitoring period; if the average concentration ratio is less than 1, it means that the pollutant concentration is generally decreasing. The average concentration ratio can be used to dynamically adjust the monitoring interval between the central monitoring point and the surrounding monitoring points.

[0048] If the average concentration ratio is small, it means that the pollutant concentration has changed significantly, and it is necessary to set up monitoring points more densely to capture the diffusion of pollutants; if the average concentration ratio is large, it means that the pollutant concentration has not changed significantly, and the monitoring interval can be appropriately increased.

[0049] It should be noted that the preset angles are: monitoring points constructed in eight directions: 0 degrees, 45 degrees, 90 degrees, 135 degrees, 180 degrees, 225 degrees, 270 degrees, and 315 degrees. Implementers can adjust them according to specific circumstances. They can be monitoring points in four directions: 0 degrees, 90 degrees, 180 degrees, and 270 degrees.

[0050] It is also necessary to explain that since the direction of movement of the water in the lake is seriously affected by wind, it is necessary to determine the direction of movement of pollutants in the lake water. The more consistent the changes in pollutants between the surrounding points and the central point, the higher the degree to which pollutants flow from the center to the surrounding points. Further analysis is needed to determine the degree of synchronization of pollutant changes between the surrounding monitoring points and the central monitoring point.

[0051] S3: Evaluate the degree of synchronization of pollutant changes between the surrounding monitoring points and the central monitoring point, select the angles corresponding to the maximum and second largest values ​​of the synchronization degree as the angles of the diffusion direction of the pollutants, set new monitoring points under the monitoring interval in the diffusion direction, mark them as diffusion points, and calculate the urgency of water pollution at the diffusion points. When the urgency is greater than the emergency threshold, an alarm is issued.

[0052] For example, the emergency threshold in this embodiment is 0.6, and the implementers can adjust it according to the specific situation. When the emergency level is greater than the emergency threshold, the pollutant concentration, pollutant diffusion direction and range can be obtained, and the diffusion direction and impact range of the pollutants can be determined by analyzing the spatial distribution and change trend of the pollutant concentration. This helps to identify the location of the pollution source and the propagation path of the pollutants, and provide directional guidance for pollution control.

[0053] The levels of synchronization include: According to the types of pollutants, the Pearson correlation coefficient between the pollutant concentration series of the central monitoring point and the pollutant concentration series of the surrounding monitoring points is calculated, and the average value of the sum of the Pearson correlation coefficients of all pollutants is used as the synchronization coefficient of other surrounding monitoring points except the central monitoring point.

[0054] Specifically, the degree of synchronization satisfies the following relationship: ; In the formula, Indicates the degree of synchronization of surrounding monitoring points, Indicates the total number of monitored pollutant types, Indicates The first monitoring The concentration of pollutants, Indicates The first monitoring The concentration of pollutants, Indicates the central monitoring point The first monitoring The concentration of pollutants, Indicates the central monitoring point The first monitoring The concentration of pollutants, represents the Pearson correlation coefficient.

[0055] That is to say, It can express the changes of pollutants between the surrounding monitoring points and the central monitoring point over time. When the value is closer to 1, it means that the surrounding monitoring points are closer to the trajectory of pollutant diffusion; a larger degree of synchronization indicates that the pollutant concentration change trends of the surrounding monitoring points and the central monitoring point are generally consistent, that is, the possibility of pollutants diffusing from the central monitoring point to the surrounding monitoring points is higher; if the degree of synchronization is smaller, it means that the pollutant concentration change trends of the surrounding monitoring points and the central monitoring point are inconsistent, that is, the diffusion direction of pollutants may not be from the central monitoring point to the surrounding monitoring points.

[0056] In addition, another embodiment further includes: According to the concentration of each pollutant, the absolute difference between the average concentration of the surrounding monitoring points and the average concentration of the central monitoring point is calculated, and the sum of the average values ​​of the surrounding monitoring points and the average concentration of the central monitoring point is calculated. The complement of the ratio of the absolute difference to the sum of the average concentrations is taken as the synchronization index, and the average value of the synchronization indices of all pollutants is taken as the degree of synchronization of the surrounding monitoring points.

[0057] Specifically, the degree of synchronization satisfies the following relationship: ; In the formula, Indicates the degree of synchronization of surrounding monitoring points, Indicates the total number of monitored pollutant types, Indicates the number of surrounding monitoring points The average concentration of pollutants, Indicates the central monitoring point The average concentration of a pollutant.

[0058] That is to say, , Represents the surrounding monitoring points and the central monitoring point in a period of time. Average levels of pollutants; Indicates the difference between the surrounding monitoring points and the central monitoring point in Differences in the concentrations of pollutants; Used to evaluate the relationship between surrounding monitoring points and central monitoring points in The degree of synchronization in the concentration of pollutants. and If they are equal, the synchronization index is 1, indicating complete synchronization; and The difference is large and the synchronization index is close to 0, indicating a lack of synchronization.

[0059] The angle of the pollutant's diffusion direction, including: Get the maximum value, second maximum value and the angle corresponding to the second maximum value of the synchronization degree, calculate the ratio of the second maximum value to the sum of the maximum value and the second maximum value, multiply by the preset angle to get the angle offset, and take the angle offset plus the angle corresponding to the second maximum value as the angle of the pollutant diffusion direction.

[0060] Exemplarily, in this embodiment, the preset angle is 45 degrees.

[0061] Specifically, the angle of the pollutant diffusion direction satisfies the following relationship: ; In the formula, The angle representing the direction of pollutant diffusion, represents the angle corresponding to the second largest value, Indicates the preset angle, Indicates the second largest value of synchronization degree, Indicates the maximum degree of synchronization.

[0062] That is to say, reflects the proportion of the second largest value in the sum of the maximum and the second largest value. If is relatively large, indicating that the pollutant diffusion direction corresponding to the second largest value is close to the pollutant diffusion direction corresponding to the maximum value; if is smaller, indicating that the pollutant diffusion direction corresponding to the second largest value is quite different from that corresponding to the maximum value; Represents the offset from the angle corresponding to the second largest value to the angle of the pollutant diffusion direction. If The larger the value, the smaller the angle deviation, indicating that the pollutant diffusion direction is closer to the angle corresponding to the second largest value; if The smaller the value, the larger the angle deviation, which means that the pollutant diffusion direction is closer to the angle corresponding to the maximum value.

[0063] In addition, another embodiment further includes: Specifically, the angle of the pollutant diffusion direction satisfies the following relationship: ; In the formula, The angle representing the direction of pollutant diffusion, represents the angle corresponding to the maximum value, Indicates the preset angle, Indicates the second largest value of synchronization. Indicates the maximum degree of synchronization.

[0064] It should be noted that the two embodiments of calculating the angle of the pollutant diffusion direction mainly differ in the method of calculating the angle, but their physical meanings and effects are similar, both of which are to more accurately determine the diffusion direction of pollutants so as to more effectively conduct environmental monitoring and pollution control.

[0065] Specifically, the urgency of water pollution satisfies the following relationship: ; In the formula, Indicates the current number of collections. Indicates the collection time interval. Indicates the degree of synchronization between the diffusion point and the central monitoring point, Indicates the central monitoring point The first monitoring The concentration of pollutants, Indicates the central monitoring point The first monitoring The concentration of pollutants, Indicates the central monitoring point The first monitoring The concentration of pollutants, Indicates the central monitoring point The first monitoring The concentration of pollutants, represents the hyperbolic tangent function, Represents the mean value function.

[0066] That is to say, This is the fastest growth rate of pollutants, indicating that the concentration of pollutants increases rapidly in a short period of time, which may indicate that the emission intensity of the pollution source is large or the diffusion speed of pollutants is fast. In other words, the fastest growth rate of pollutants is when the lake flow rate is large or the pollution source expands quickly. is the density of pollutant diffusion, If it is large, it means that the changes in pollutant concentrations at the diffusion point and the central monitoring point are highly consistent, the pollutants diffuse closely between the two, the diffusion path is clear, and the speed and range of pollutant transmission may be large, which requires special attention and monitoring; If the value is small, it means that the pollutant concentration changes at the diffusion point and the central monitoring point are inconsistent, and the pollutants are not diffused closely between the two. It may be that the diffusion is hindered or the diffusion path is complex. The propagation speed and range of pollutants are relatively small, but we still need to pay attention to its potential diffusion risk. As time urgency increases, gradually increases with the increase of .

[0067] The present invention also provides a lake water environment quality monitoring system based on big data analysis. Figure 3 As shown, the system includes a processor and a memory, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the lake water environment quality monitoring method based on big data analysis according to the first aspect of the present invention is implemented.

[0068] The system also includes other components familiar to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0069] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented by computer-readable / executable instructions stored or otherwise maintained by such a computer-readable medium.

[0070] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0071] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A lake water environment quality monitoring method based on big data analysis, characterized in that: include: Determine water pollution areas based on remote sensing data and obtain lake water monitoring data; Taking the centroid of the water pollution area as the central monitoring point, the water renewal coefficient of the central monitoring point is calculated, the monitoring interval is calculated based on the change of pollutant concentration at the central monitoring point at different times, and the surrounding monitoring points are constructed based on the monitoring interval and the direction of the preset angle; Evaluate the synchronization degree of pollutant changes between the surrounding monitoring points and the central monitoring point, select the maximum and second maximum values ​​of the synchronization degree corresponding to the angle as the diffusion direction of the pollutants, set a new monitoring point under the monitoring interval in the diffusion direction, mark it as a diffusion point, and calculate the urgency of water pollution at the diffusion point, and issue an alarm in response to the urgency being greater than the emergency threshold; The urgency of the water pollution Satisfies the following relationship: ; In the formula, Indicates the current number of collections. Indicates the collection time interval. Indicates the degree of synchronization between the diffusion point and the central monitoring point, Indicates the central monitoring point The first monitoring The concentration of pollutants, Indicates the central monitoring point The first monitoring The concentration of pollutants, Indicates the central monitoring point The first monitoring The concentration of pollutants, Indicates the central monitoring point The first monitoring The concentration of pollutants, represents the hyperbolic tangent function, Represents the mean value function.

2. The lake water environment quality monitoring method based on big data analysis according to claim 1 is characterized in that: The water body update coefficient of the computing center monitoring point includes: Take any central monitoring point as the marked monitoring point, monitor the marked monitoring point multiple times, calculate the average value of the sum of the absolute differences between the ratio of the concentrations of the pollutants in two adjacent monitorings and the average concentration ratio, and obtain the water renewal coefficient of the marked monitoring point.

3. The lake water environment quality monitoring method based on big data analysis according to claim 1 is characterized in that: The calculation of the water body update coefficient of the central monitoring point also includes: Take any central monitoring point as the marked monitoring point, conduct multiple monitoring of the marked monitoring point, where one monitoring includes multiple pollutant concentrations, and calculate the The second monitoring and The concentration changes of the same pollutant monitored in the second monitoring are similar to those in the third monitoring. The absolute value of the ratio of the pollutant concentrations monitored in two consecutive monitoring times is used to obtain the concentration change rate, and the average value of the concentration change rates of all two adjacent monitoring times is used to obtain the water body renewal coefficient of the marked monitoring point.

4. The lake water environment quality monitoring method based on big data analysis according to claim 1 is characterized in that: The calculating of the monitoring interval comprises: Take any central monitoring point as the marked monitoring point, monitor the marked monitoring point multiple times, obtain the concentration sequence of different pollutants in each monitoring of the marked monitoring point, calculate the concentration ratio of the same pollutant in two adjacent monitorings in the concentration sequence, obtain the sequence ratio, obtain the minimum value in the sequence ratio, and take the product of the minimum value, the monitoring time interval and the water flow velocity of the marked monitoring point as the monitoring interval.

5. The lake water environment quality monitoring method based on big data analysis according to claim 1 is characterized in that: The calculating monitoring distance further includes: Take any central monitoring point as the marked monitoring point, monitor the marked monitoring point multiple times, obtain the concentration sequence of different pollutants in each monitoring of the marked monitoring point, calculate the concentration ratio of the same pollutant in two adjacent monitorings in the concentration sequence, get the sequence ratio, get the mean of the sequence ratio, and take the product of the mean, the monitoring time interval and the water flow velocity of the marked monitoring point as the monitoring interval.

6. The lake water environment quality monitoring method based on big data analysis according to claim 1 is characterized in that: The degree of synchronization includes: According to the types of pollutants, the Pearson correlation coefficient between the pollutant concentration series of the central monitoring point and the pollutant concentration series of the surrounding monitoring points is calculated, and the average value of the sum of the Pearson correlation coefficients of all pollutants is used as the synchronization coefficient of other surrounding monitoring points except the central monitoring point.

7. The lake water environment quality monitoring method based on big data analysis according to claim 1 is characterized in that: The synchronization level also includes: According to the concentration of each pollutant, the absolute difference between the average concentration of the surrounding monitoring points and the average concentration of the central monitoring point is calculated, and the sum of the average values ​​of the surrounding monitoring points and the average concentration of the central monitoring point is calculated. The complement of the ratio of the absolute difference to the sum of the average concentrations is taken as the synchronization index, and the average value of the synchronization indices of all pollutants is taken as the degree of synchronization of the surrounding monitoring points.

8. The lake water environment quality monitoring method based on big data analysis according to claim 1 is characterized in that: The angle of the diffusion direction of the pollutant includes: Obtain the maximum value, second maximum value and angle corresponding to the second maximum value of the synchronization degree, calculate the ratio of the second maximum value to the sum of the maximum value and the second maximum value, multiply by the preset angle to obtain the angle offset, and take the angle offset plus the angle corresponding to the second maximum value as the angle of the pollutant diffusion direction.

9. The lake water environment quality monitoring system based on big data analysis is characterized by: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a lake water environment quality monitoring method based on big data analysis according to any one of claims 1 to 8 is implemented.

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