An intelligent water environment comprehensive management method and system

Abstract

The present invention discloses an intelligent water environment comprehensive management method and system, which relates to the technical field of water environment management, and comprises the following steps: data collection, water quality change detection, calculation of the change rate of water quality parameters by using a water quality change algorithm and combining historical data, dynamic prediction of water environment, after obtaining the change rate by using a water quality change algorithm, prediction of water quality parameters in the future by using a dynamic prediction algorithm, hierarchical management, according to the result of the dynamic prediction algorithm, using an early warning classification algorithm to divide different water quality parameters into early warning levels, and effect evaluation, which has the effect of combining historical water quality changes and change rate calculations, monitoring water quality fluctuations in real time, predicting future water quality changes, achieving early warning effects, preventing pollution from spreading, using dynamic prediction and early warning classification algorithms, automatically adjusting sensitivity, quickly responding to sudden water quality changes, providing accurate early warnings and emergency decision support for management personnel, and improving the efficiency of water environment management.

Description

Technical Field

[0001] The present invention relates to the technical field of water environment management, and in particular to an intelligent water environment comprehensive management method and system. Background Art

[0002] Water environment governance is a process that starts from the thinking logic of pollution reduction, resource reuse and natural replenishment, and builds a complete water environment governance service process that is more in line with regional environmental protection needs. Through comprehensive water environment governance, it can achieve the reduction of pollutant emissions, improve water quality, protect the habitat of aquatic organisms, promote the recovery and development of biodiversity, ensure the sustainable use of water resources, improve the efficiency of water resource utilization, and meet the growing demand for water.

[0003] At present, water environment governance often relies on regular water quality monitoring data, and governance is carried out according to the abnormal data monitored. It can only deal with water environment anomalies that have occurred, and cannot prevent water environment anomalies in advance, which leads to the expansion of water pollution. In addition, since water environment monitoring involves multiple parameters, the standards and influencing factors of each water quality parameter are different. There is currently a lack of grading of different water quality parameters, which reduces the efficiency of water environment governance and makes the effect of water environment governance less than ideal. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent water environment comprehensive management method and system to solve the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent water environment comprehensive management method, comprising the following steps:

[0006] Data collection: installing sensor equipment to monitor the target water area, obtaining water quality parameters, and building a monitoring platform for receiving and displaying the water quality parameters monitored by the sensor equipment;

[0007] Water quality change detection: After obtaining water quality parameters through sensor equipment, the water quality change algorithm is used to calculate the change rate of water quality parameters in combination with historical data to obtain the i-th water quality parameter change rate ΔP i (t), and set ΔP i The change threshold of (t) is Y i , when |ΔP i (t)|>Y i When an alarm occurs, an alarm is sent to the management personnel through the monitoring platform;

[0008] Dynamic prediction of water environment: After obtaining the change rate through the water quality change algorithm, the dynamic prediction algorithm is used to predict the water quality parameters in the future time to obtain the i-th water quality parameter value ΔP at time t+Δt i(t+Δt), the prediction of the target water area in the future, managers can predict the water quality and take countermeasures in advance to avoid the expansion of pollution;

[0009] Grading management: Based on the results of the dynamic prediction algorithm, the early warning classification algorithm is used to divide different water quality parameters into early warning levels, including excellent level, light pollution level, moderate pollution level and high pollution level. According to different early warning levels, corresponding measures are prepared to manage the target waters. When the early warning level is light pollution level, the impact coefficient in the dynamic prediction algorithm is adjusted to improve the sensitivity of the dynamic prediction algorithm;

[0010] Effect evaluation: regularly evaluate the treatment effect, compare the water quality parameters of the target water area before and after treatment, and prepare a water environment treatment report for subsequent use.

[0011] Optionally, the monitoring platform includes an interactive interface. After receiving the water quality parameters monitored by the sensor device, the monitoring platform displays them in the form of a chart on the interactive interface.

[0012] Optionally, in the water quality change detection step, the water quality change algorithm process is as follows:

[0013]

[0014] where ΔP i (t) is the rate of change of the i-th water quality parameter at the current time;

[0015] P i (t) is the value of the i-th water quality parameter at the current time;

[0016] Δt is the time interval in minutes;

[0017] F climate (t) is the climate value at the current time;

[0018] α is the climate impact coefficient, ranging from 0 to 1;

[0019] F climate (t) is expressed as follows:

[0020] F climate (t) = β1 × D(t) + β2 × A(t)

[0021] Where D(t) is the water temperature at the current time;

[0022] A(t) is the rainfall at the current time;

[0023] β1 is the water temperature influence coefficient, ranging from 0.01 to 0.1;

[0024] β2 is the rainfall influence coefficient; its value range is 0.001 to 0.05;

[0025] The change rate of the i-th water quality parameter at the current time ΔP i (t) represents the change of water quality parameters compared with the past time, so that the staff can understand the current trend of water environment changes and set ΔP i The change threshold of (t) is Y i 1. When |ΔP i (t)|>Y i When 1, it means that the change rate of the i-th water quality parameter exceeds the preset value. At this time, corresponding countermeasures are taken for different water quality parameters.

[0026] Optionally, the dynamic prediction algorithm process in the water environment dynamic prediction step is as follows:

[0027]

[0028] Among them, P i (t+Δt) is the value of the i-th water quality parameter at time t+Δt;

[0029] P i (t) is the value of the i-th water quality parameter at the current time;

[0030] Δt is the time interval in minutes;

[0031] m represents the mth time point, n represents the number of time points;

[0032] W m Represents the weight coefficient at time point m, W1+W2+W3+...W n =1;

[0033] ΔP i (t-m) is the rate of change of water quality parameters during the time t-m;

[0034] γ is the coefficient of influence of the rate of change, which is set to 0.8;

[0035] By predicting water quality parameters in the future, managers can respond in advance to avoid the expansion of water pollution.

[0036] Optionally, the early warning classification algorithm process in the hierarchical management step is as follows:

[0037]

[0038] Among them, Alert i (t) represents the warning level of the i-th water quality parameter;

[0039] Z i1 represents the warning threshold of the i-th water quality parameter;

[0040] Z i 2 represents the warning threshold 2 of the i-th water quality parameter;

[0041] Z i 3 represents the warning threshold three of the i-th water quality parameter;

[0042] When Alert i (t) = 1 is the best grade;

[0043] When Alert i (t) = 2 is a light pollution level;

[0044] When Alert i (t) = 3 is a moderate pollution level;

[0045] When Alert i (t) = 4 is a high pollution level;

[0046] Then, according to the dynamic prediction algorithm, the i-th water quality parameter value P at time t+Δt is obtained. i After (t+Δt), the early warning classification algorithm is used to divide it into different early warning levels, and separate classifications are made for different water quality parameters, so that management personnel can respond quickly when the early warning alarm is issued and set the alarm level. i When (t) = 2, which is a light pollution level, the change rate influence coefficient γ in the dynamic prediction algorithm changes. At this time, the change rate influence coefficient γ is adjusted to 1. When the water quality condition deteriorates, the change speed of the water quality parameters will accelerate. By increasing the change rate influence coefficient γ, the dynamic prediction algorithm will be more sensitive to the change rate of water quality parameters and reduce the system lag.

[0047] Optionally, when the warning level is a light pollution level, a moderate pollution level or a high pollution level, a warning message is sent to the terminal device of the manager through the monitoring platform. The warning message is sent to the terminal device of the manager in the form of a pop-up window, and the content and format of the warning message are set according to different pollution levels.

[0048] Optionally, when the warning level is a light pollution level, the pop-up window color is yellow; when the warning level is a moderate pollution level, the pop-up window color is orange; when the warning level is a high pollution level, the pop-up window color is red.

[0049] The present invention also provides an intelligent water environment comprehensive management system, including a data acquisition module, a data transmission module, a data analysis module, and a visualization module;

[0050] The data acquisition module is used to collect water quality data of the target water area, and the data transmission module is used to transmit the data acquired by the data acquisition module to the data analysis module for analysis;

[0051] The data analysis module includes a change rate detection unit, a water quality prediction unit, and a water quality grading unit. The change rate detection unit is used to detect changes in the water quality of the target water area. The water quality prediction unit predicts the water quality at a future time by combining the water quality changes. The water quality grading unit grades the water quality at a future time and takes corresponding measures based on the grading results.

[0052] The visualization module is used to visualize the data and display it to management personnel so as to quickly understand the water environment conditions of the target water area.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] Effect 1. The present invention combines the historical changes of water quality parameters and uses a water quality change algorithm to calculate the change rate of water quality parameters to obtain the change rate of each water quality parameter, so that staff can understand the current water environment change trend, and by setting a change threshold for each water quality parameter change rate, when the water quality parameter change rate is greater than the change threshold, it means that the water quality parameter fluctuates too much. At this time, an alarm is sent to the management personnel to take timely response measures. Afterwards, by combining the water quality parameter change rate and using a dynamic prediction algorithm to predict the water quality parameters in the future, the early warning effect is achieved, and early prevention can be carried out before water environment abnormalities occur, preventing the expansion of water pollution and reducing losses.

[0055] Effect 2. The present invention divides different water quality parameters into warning levels based on the results of the dynamic prediction algorithm and the warning classification algorithm. The warning levels include excellent level, light pollution level, moderate pollution level and high pollution level. Management personnel can set response measures in advance for the warning levels corresponding to different water quality parameters, so that after the warning information is sent, management personnel can respond quickly, and when the warning level is light pollution level, the change rate influence coefficient is automatically adjusted to make the dynamic prediction algorithm more sensitive to the change rate of water quality parameters, so that water quality prediction can be adjusted according to actual conditions, reducing system lag, and providing timely and accurate water quality warnings for management personnel. In the event of sudden pollution sources or drastic changes in water quality, the division of water quality levels can provide a basis for quick decision-making, guide specific emergency response measures, and improve the efficiency of water environment management. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 Flow chart of the method of the present invention;

[0057] Figure 2This is a block diagram of the system modules of the present invention. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0059] Example 1:

[0060] See also Figure 1 and Figure 2 This invention provides an intelligent water environment comprehensive management method and system, which includes the following steps:

[0061] Data collection: installing sensor equipment to monitor the target water area, obtaining water quality parameters, and building a monitoring platform to receive and display the water quality parameters monitored by the sensor equipment;

[0062] Specifically, water quality parameters include color, transparency, total suspended solids, pH value, conductivity, hardness, mineralization, salinity, pH value, and dissolved oxygen;

[0063] Water quality change detection: After obtaining water quality parameters through sensor equipment, the water quality change algorithm is used to calculate the change rate of water quality parameters in combination with historical data to obtain the i-th water quality parameter change rate ΔP i (t), and set ΔP i The change threshold of (t) is Y i , when |ΔP i (t)|>Y i When an error occurs, an alarm is sent to the management personnel through the monitoring platform;

[0064] Dynamic prediction of water environment: After obtaining the change rate through the water quality change algorithm, the dynamic prediction algorithm is used to predict the water quality parameters in the future time to obtain the i-th water quality parameter value ΔP at time t+Δt i (t+Δt), the prediction of the target water area in the future, managers can predict the water quality and take countermeasures in advance to avoid the expansion of pollution;

[0065] Grading management: Based on the results of the dynamic prediction algorithm, the early warning classification algorithm is used to divide different water quality parameters into early warning levels, including excellent level, light pollution level, moderate pollution level and high pollution level. According to different early warning levels, corresponding measures are prepared to manage the target waters. When the early warning level is light pollution level, the influence coefficient in the dynamic prediction algorithm is adjusted to improve the sensitivity of the dynamic prediction algorithm;

[0066] Effect evaluation: regularly evaluate the treatment effect, compare the water quality parameters of the target water area before and after treatment, and prepare a water environment treatment report for subsequent use.

[0067] More specifically, in this embodiment, water environment data is monitored in real time by sensor equipment to obtain water quality parameters, which are then transmitted to the monitoring platform for management personnel to view. Then, by combining the historical changes in water quality parameters and using a water quality change algorithm to calculate the change rate of water quality parameters, the i-th water quality parameter change rate ΔP is obtained. i (t) represents the variation range of water quality parameters. The lower the variation range of water quality parameters, the more stable the water quality of the current target water area is. On the contrary, the more unstable it is. By setting the i-th water quality parameter variation rate ΔP i The change threshold of (t) is Y i , when ΔP i (t) is less than Y i When ΔP i (t) is greater than Y i When it is detected, it means that the water quality parameters fluctuate too much. At this time, an alarm is sent to the management personnel, who take timely response measures. Then, by combining the water quality parameter change rate and using a dynamic prediction algorithm to predict the water quality parameters in the future, the early warning effect is achieved, and prevention can be carried out in advance before the water environment anomaly occurs, preventing the expansion of water pollution and reducing losses.

[0068] Finally, based on the results of the dynamic prediction algorithm, different water quality parameters are divided into warning levels through the early warning classification algorithm. The warning levels include excellent level, light pollution level, moderate pollution level and high pollution level. Managers can set response measures for the warning levels corresponding to different water quality parameters in advance, so that managers can respond quickly after the early warning information is sent. By providing managers with timely and accurate water quality warnings, the division of water quality levels can provide a basis for quick decision-making in the event of sudden pollution sources or drastic changes in water quality, guide specific emergency response measures, and improve the efficiency of water environment management.

[0069] Furthermore, the monitoring platform includes an interactive interface. After receiving the water quality parameters monitored by the sensor equipment, the monitoring platform displays them in the form of a chart on the interactive interface.

[0070] Specifically, the forms of charts include line charts and bar charts. In actual applications, the rate of change of water quality parameters can be displayed through line charts, so that managers can quickly and clearly understand the changes in water quality parameters of the current target water area for subsequent decision-making.

[0071] Furthermore, in the water quality change detection step, the water quality change algorithm process is as follows:

[0072]

[0073] where ΔP i (t) is the rate of change of the i-th water quality parameter at the current time;

[0074] P i (t) is the value of the i-th water quality parameter at the current time;

[0075] Δt is the time interval in minutes;

[0076] F climate (t) is the climate value at the current time;

[0077] α is the climate impact coefficient, ranging from 0 to 1;

[0078] F climate (t) is expressed as follows:

[0079] F climate (t) = β1 × D(t) + β2 × A(t)

[0080] Where D(t) is the water temperature at the current time;

[0081] A(t) is the rainfall at the current time;

[0082] β1 is the water temperature influence coefficient, ranging from 0.01 to 0.1;

[0083] β2 is the rainfall influence coefficient; its value range is 0.001 to 0.05;

[0084] Specifically, the change rate of the i-th water quality parameter at the current time ΔP i (t) represents the change of water quality parameters compared with the past time, ΔP i The smaller (t) is, the more stable the water quality of the current target water area is. i The larger the (t) is, the more unstable it is. When the water quality parameters change dramatically, the staff can understand the current trend of water environment changes and set ΔP i The change threshold of (t) is Y i 1. When |ΔP i (t)|<Y i When |ΔP i (t)|>Y i When 1, it means that the change rate of the i-th water quality parameter exceeds the preset value. At this time, corresponding countermeasures are taken for different water quality parameters. For example, when the i-th water quality parameter is dissolved oxygen, Y i1 is specifically 5%, and the normal range of dissolved oxygen is 5-8 mg / L. When |ΔP i (t)| When it is greater than 5%, it indicates abnormal fluctuation of dissolved oxygen. Management personnel should check the sensor equipment and the actual water environment in time to prevent the expansion of pollution.

[0085] And by adding climate value influencing factors to the water quality change algorithm, climate values ​​include water temperature and rainfall. Since an increase usually leads to a decrease in dissolved oxygen and accelerates the decomposition of organic matter, changes in precipitation will cause water dilution or pollutants to be washed into the water body, thereby changing water quality parameters, making the calculation results of the water quality parameter change rate more accurate, so that staff can have a more comprehensive understanding of the reasons for water quality changes. In actual applications, influencing factors can also be added or deleted according to the specific water quality parameters, such as water ecological factors. The growth of aquatic plants may absorb nitrogen and phosphorus in the water and reduce eutrophication of water bodies, making the water quality parameter change rate more in line with actual conditions and improving the quality of subsequent water environment management.

[0086] Furthermore, the dynamic prediction algorithm process in the water environment dynamic prediction step is as follows:

[0087]

[0088] Among them, P i (t+Δt) is the value of the i-th water quality parameter at time t+Δt;

[0089] P i (t) is the value of the i-th water quality parameter at the current time;

[0090] Δt is the time interval in minutes;

[0091] m represents the mth time point, n represents the number of time points;

[0092] W m Represents the weight coefficient at time point m, W1+W2+W3+...W n =1;

[0093] ΔP i (t-m) is the rate of change of water quality parameters during the time t-m;

[0094] γ is the coefficient of influence of the rate of change, which is set to 0.8;

[0095] Specifically, by predicting water quality parameters in the future, managers can respond in advance, avoid serious pollution incidents, and improve the effectiveness of water environment management. By adding weights for different time points in the algorithm, water quality parameter predictions can be adjusted according to the specific circumstances of historical data. Specifically, recent data can have a greater impact on the prediction, while the impact of long-term data gradually weakens, thereby adapting to the rapidity of water quality changes and improving the accuracy and reliability of the prediction.

[0096] Furthermore, the early warning classification algorithm process in the hierarchical governance step is as follows:

[0097]

[0098] Among them, Alert i (t) represents the warning level of the i-th water quality parameter;

[0099] Z i 1 represents the warning threshold of the i-th water quality parameter;

[0100] Z i 2 represents the warning threshold 2 of the i-th water quality parameter;

[0101] Z i 3 represents the warning threshold three of the i-th water quality parameter;

[0102] When Alert i (t) = 1 is the best grade;

[0103] When Alert i (t) = 2 is a light pollution level;

[0104] When Alert i (t) = 3 is a moderate pollution level;

[0105] When Alert i (t) = 4 is a high pollution level;

[0106] The specific responses to different water quality parameters and levels are as follows:

[0107] When the water quality parameter is dissolved oxygen, no action is required if the water quality parameter is excellent;

[0108] In the case of mild pollution, water aeration should be strengthened to promote the oxidation and decomposition of organic matter in the water;

[0109] In the case of moderate pollution levels, add oxygen supplement equipment to increase the dissolved oxygen level in the water and increase the inspection frequency;

[0110] In the case of high pollution levels, artificial oxygenation is carried out and water mobility is enhanced to quickly restore water quality;

[0111] When the water quality parameter is ammonia nitrogen, no operation is required if the water quality parameter is excellent;

[0112] In the case of mild pollution, denitrification measures are initiated, such as adding denitrification pools to reduce ammonia nitrogen concentrations;

[0113] In the case of moderate pollution level, add ammonia nitrogen removal process, such as using ammonia nitrogen adsorbent or selective catalytic reaction treatment;

[0114] In the case of high pollution levels, the pollution source should be shut down urgently and biological denitrification processes such as ammonia oxidation tanks and nitrification-denitrification processes should be implemented for removal.

[0115] Specifically, the water quality parameter value P at time t+Δt is obtained according to the dynamic prediction algorithm. i After (t+Δt), the early warning classification algorithm is used to divide it into different warning levels, and separate classifications are made for different water quality parameters. Through clear level division, managers can quickly understand the specific abnormal conditions of the current water environment when viewing the alarm information, and operate according to the preset response plan without excessive technical analysis, reducing resource usage, enhancing response capabilities, and improving the efficiency and quality of handling abnormal situations.

[0116] And set the Alert i When (t)=2 is a light pollution level, the change rate influence coefficient γ in the dynamic prediction algorithm changes. At this time, the change rate influence coefficient γ is adjusted to 1. When the water quality condition deteriorates, the change speed of the water quality parameters will accelerate. By increasing the change rate influence coefficient γ, the dynamic prediction algorithm will be more sensitive to the change rate of water quality parameters, so that the water quality prediction can be adjusted according to the actual situation, reducing the system lag and improving the intelligence level.

[0117] Furthermore, when the warning level is a light pollution level, a moderate pollution level or a high pollution level, a warning message is sent to the terminal device of the manager through the monitoring platform. The warning message is sent to the terminal device of the manager in the form of a pop-up window, and the warning message content and format are set according to the pollution level. When the warning level is an excellent level, the color is green. When the warning level is a light pollution level, the pop-up window color is yellow. When the warning level is a moderate pollution level, the pop-up window color is orange. When the warning level is a high pollution level, the pop-up window color is red.

[0118] Specifically, by setting colors to distinguish pollution levels, managers can be reminded in a striking manner, which increases their attention and enables them to quickly understand the current water pollution situation.

[0119] Based on the above examples, please refer to Figure 2,The present invention provides an intelligent water environment comprehensive management system, including a water environment data acquisition module, a data transmission module, a water environment analysis module, and a visualization module;

[0120] The water environment data acquisition module is used to collect water quality data of the target water area, and the data transmission module is used to transmit the data acquired by the data acquisition module to the data analysis module for analysis;

[0121] The water environment analysis module includes a change rate detection unit, a water quality prediction unit, and a water quality classification unit. The change rate detection unit is used to detect changes in water quality in the target water area. The water quality prediction unit predicts the water quality in the future by combining the water quality changes. The water quality classification unit classifies the water quality in the future and takes corresponding measures based on the classification results.

[0122] The visualization module is used to visualize the data and display it to managers so that they can quickly understand the water environment conditions of the target waters.

[0123] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent water environment comprehensive management method, characterized in that: The following steps are involved: Step S1: Data collection: installing sensor equipment to monitor the target water area, obtaining water quality parameters, and building a monitoring platform for receiving and displaying the water quality parameters monitored by the sensor equipment; Step S2: Water quality change detection. After obtaining the water quality parameters through the sensor equipment, the water quality change algorithm is used to calculate the change rate of the water quality parameters in combination with historical data to obtain the i-th water quality parameter change rate ΔP i (t), and set ΔP i The change threshold of (t) is Y i , when |ΔP i (t)|>Y i When an alarm occurs, an alarm is sent to the management personnel through the monitoring platform; Step S3: Dynamic prediction of water environment. After obtaining the change rate through the water quality change algorithm, the dynamic prediction algorithm is used to predict the water quality parameters in the future time to obtain the i-th water quality parameter value ΔP at time t+Δt. i (t+Δt), the prediction of the target water area in the future, managers can predict the water quality and take countermeasures in advance to avoid the expansion of pollution; Step S4: hierarchical management, based on the results of the dynamic prediction algorithm, using the early warning classification algorithm to divide different water quality parameters into early warning levels; Step S5: Effect evaluation: regularly evaluate the treatment effect, compare the water quality parameters of the target water area before and after treatment, and prepare a water environment treatment report for subsequent use; In the water quality change detection step, the water quality change algorithm process is as follows: where ΔP i (t) is the rate of change of the i-th water quality parameter at the current time; P i (t) is the value of the i-th water quality parameter at the current time; Δt is the time interval in minutes; F climate (t) is the climate value at the current time; α is the climate impact coefficient, ranging from 0 to 1; F climate (t) is expressed as follows: F climate (t)=β1×D(t)+β2×A(t) Where D(t) is the water temperature at the current time; A(t) is the rainfall at the current time; β1 is the water temperature influence coefficient, ranging from 0.01 to 0.1; β2 is the rainfall influence coefficient; its value range is 0.001 to 0.05; The change rate of the i-th water quality parameter at the current time ΔP i (t) represents the change of water quality parameters compared with the past time, so that the staff can understand the current trend of water environment changes and set ΔP i The change threshold of (t) is Y i 1. When |ΔP i (t)|>Y i When 1, it means that the change rate of the i-th water quality parameter exceeds the preset value. At this time, corresponding countermeasures are taken for different water quality parameters.

2. The intelligent water environment comprehensive management method according to claim 1 is characterized by: The dynamic prediction algorithm process in the water environment dynamic prediction step is as follows: Among them, P i (t+Δt) is the value of the i-th water quality parameter at time t+Δt; P i (t) is the value of the i-th water quality parameter at the current time; Δt is the time interval in minutes; m represents the mth time point, n represents the number of time points; W m Represents the weight coefficient at time point m, W1+W2+W3+...W n =1; ΔP i (t-m) is the rate of change of water quality parameters during the time t-m; γ is the coefficient of influence of the rate of change, which is set to 0.8; By predicting water quality parameters in the future, managers can respond in advance to avoid the expansion of water pollution.

3. The intelligent water environment comprehensive management method according to claim 2 is characterized by: The early warning classification algorithm process in the hierarchical management step is as follows: Among them, Alert i (t) represents the warning level of the i-th water quality parameter; Z i 1 represents the warning threshold of the i-th water quality parameter; Z i 2 represents the warning threshold 2 of the i-th water quality parameter; Z i 3 represents the warning threshold three of the i-th water quality parameter; When Alert i (t) = 1 is the best grade; When Alert i (t) = 2 is a light pollution level; When Alert i (t) = 3 is a moderate pollution level; When Alert i (t) = 4 is a high pollution level; Then, according to the dynamic prediction algorithm, the i-th water quality parameter value P at time t+Δt is obtained. i After (t+Δt), the early warning classification algorithm is used to divide it into different early warning levels, and separate classifications are made for different water quality parameters, so that management personnel can respond quickly when the early warning alarm is issued and set the alarm level. i When (t) = 2, which is a light pollution level, the change rate influence coefficient γ in the dynamic prediction algorithm changes. At this time, the change rate influence coefficient γ is adjusted to 1. When the water quality condition deteriorates, the change speed of the water quality parameters will accelerate. By increasing the change rate influence coefficient γ, the dynamic prediction algorithm will be more sensitive to the change rate of water quality parameters and reduce the system lag.

4. The intelligent water environment comprehensive treatment method according to claim 3 is characterized by: The warning levels include excellent, light pollution, moderate pollution and high pollution. Different warning levels are used to prepare corresponding measures for the treatment of target waters. When the warning level is light pollution, the influence coefficient in the dynamic prediction algorithm is adjusted to improve the sensitivity of the dynamic prediction algorithm. The monitoring platform includes an interactive interface. After receiving the water quality parameters monitored by the sensor equipment, the monitoring platform displays them in the form of a chart on the interactive interface.

5. The intelligent water environment comprehensive management method according to claim 4 is characterized in that: When the warning level is light pollution level, moderate pollution level or high pollution level, warning information will be sent to the manager's terminal device through the monitoring platform. The warning information will be sent to the manager's terminal device in the form of a pop-up window, and the warning information content and format will be set according to the pollution level.

6. The intelligent water environment comprehensive treatment method according to claim 5, characterized in that: When the warning level is a light pollution level, the pop-up window color is yellow; when the warning level is a moderate pollution level, the pop-up window color is orange; when the warning level is a high pollution level, the pop-up window color is red.

7. The comprehensive management system used in the intelligent water environment comprehensive management method according to claim 1 is characterized by: Including data acquisition module, data transmission module, data analysis module and visualization module; The data acquisition module is used to collect water quality data of the target water area, and the data transmission module is used to transmit the data acquired by the data acquisition module to the data analysis module for analysis; The data analysis module includes a change rate detection unit, a water quality prediction unit, and a water quality grading unit. The change rate detection unit is used to detect changes in the water quality of the target water area. The water quality prediction unit predicts the water quality at a future time by combining the water quality changes. The water quality grading unit grades the water quality at a future time and takes corresponding measures based on the grading results. The visualization module is used to visualize the data and present it to management personnel.

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