Intelligent water environment comprehensive treatment method and system

Abstract

The invention discloses an intelligent water environment comprehensive treatment method and system, and relates to the technical field of water environment treatment, and the method 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 the water environment, and calculation of the water environment after the change rate is obtained through the water quality change algorithm. And then a dynamic prediction algorithm is utilized to perform prediction and grading treatment on the water quality parameters in future time, and according to the result of the dynamic prediction algorithm, an early warning grading algorithm is utilized to divide different water quality parameters into early warning grades and effect evaluation, so that the water quality fluctuation is monitored in real time by combining the historical water quality change and change rate calculation; the method has the advantages that future water quality changes are predicted, the early warning effect is achieved, pollution diffusion is prevented, a dynamic prediction and early warning grading algorithm is utilized, sensitivity is automatically adjusted, sudden water quality changes are quickly responded, accurate early warning and emergency decision support are provided for managers, and the water environment treatment efficiency is improved.

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 reduce 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 water resource utilization efficiency, and meet the growing demand for water.

[0003] At present, water environment management often relies on regular water quality monitoring data, and management 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 management and makes the effect of water environment management 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 object, 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, which is used to receive and display 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 to obtain the i-th water quality parameter value ΔP at time t+Δt i(t+Δt), prediction of target water areas in the future, managers can predict water quality and take countermeasures in advance to avoid the expansion of pollution;

[0009] Grading governance: 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, medium pollution level and high pollution level. Corresponding measures are prepared for the target waters according to different early warning levels. 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.

[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, and 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) = β 1 ×D(t)+β 2 ×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] β 1 is the water temperature influence coefficient, ranging from 0.01 to 0.1;

[0024] β 2 is the rainfall influence coefficient; the 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) indicates the change of water quality parameters compared with the past time, so that the staff can understand the current trend of water environment change 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, and 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] Where 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, W 1 +W 2 +W 3 +...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, and its value is 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 i 1 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 warning levels, and different water quality parameters are divided separately, so that management personnel can respond quickly when the early warning alarm is issued and set the alarm 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 rate of 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 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, and 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 classification unit. The change rate detection unit is used to detect the change of water quality in the target water area. The water quality prediction unit predicts the water quality at a future time by combining the water quality change. The water quality classification unit classifies the water quality at a future time and takes corresponding measures according to the classification 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 the 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 the abnormal water environment occurs, preventing the expansion of water pollution and reducing losses.

[0055] Effect 2. The present invention divides different water quality parameters into warning levels according to the results of the dynamic prediction algorithm and through the early warning classification algorithm. The warning levels include excellent level, light pollution level, moderate pollution level and high pollution level, and the management personnel can set response measures in advance for the warning levels corresponding to different water quality parameters, so that after the early warning information is sent, the management personnel can respond quickly, and when the early warning level is the 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 the water quality prediction can be adjusted according to the actual situation, reducing the system lag, and providing timely and accurate water quality warnings for management personnel. In the event of a sudden pollution source or a sharp change 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 is a flow chart of the method of the present invention;

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

[0058] 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 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 creative work are within the scope of protection of the present invention.

[0059] Embodiment 1:

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

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

[0062] Specifically, water quality parameters include color, transparency, total suspended solids, pH, conductivity, hardness, mineralization, salinity, pH, 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 a fault 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 to obtain the i-th water quality parameter value ΔP at time t+Δt i (t+Δt), prediction of target water areas in the future, managers can predict water quality and take countermeasures in advance to avoid the expansion of pollution;

[0065] Grading governance: 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, medium pollution level and high pollution level. Corresponding measures are prepared for the target waters according to different early warning levels. 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 write a water environment treatment report for subsequent use.

[0067] More specifically, in this embodiment: the water environment data is monitored in real time by the sensor device to obtain the water quality parameters, and the water quality parameters are transmitted to the monitoring platform for the management personnel to view. Then, the water quality parameter change rate is calculated by combining the historical change of the water quality parameters and using the water quality change algorithm to obtain the i-th water quality parameter change rate ΔP 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) 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 countermeasures. 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 abnormal water environment occurs, preventing the expansion of water pollution and reducing losses.

[0068] Finally, according to 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 charts include line charts and bar charts. In practical 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 waters 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) = β 1 ×D(t)+β 2 ×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] β 1 is the water temperature influence coefficient, ranging from 0.01 to 0.1;

[0083] β 2 is the rainfall influence coefficient; the 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 (t) The larger the value, the more unstable it is. When 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 i1. 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 i 1 is 5%, and the normal range of dissolved oxygen is 5-8 mg / L. i (t)|When it is greater than 5%, it indicates abnormal fluctuation of dissolved oxygen. The 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, the climate value includes water temperature and rainfall. Since an increase in temperature usually leads to a decrease in dissolved oxygen and accelerates the decomposition of organic matter, changes in precipitation can cause water dilution or pollutants to be washed into the water body, thereby changing the water quality parameters. This makes the calculation results of the water quality parameter change rate more accurate, allowing staff to 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 different 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] Where 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, W 1 +W 2 +W 3 +...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, and its value is 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 will gradually weaken, 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 operation is required in the case of excellent level;

[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 level, add oxygen supplement equipment, improve 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 under the excellent level;

[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 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 different water quality parameters are divided separately. Through clear level division, management personnel can quickly understand the specific abnormal situation 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 indicates 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 rate 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 the water quality parameters, so that the water quality prediction can be adjusted according to the actual situation, reducing the system lag and improving the level of intelligence.

[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 content and format of the warning message 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 color of the pop-up window is yellow. When the warning level is a moderate pollution level, the color of the pop-up window is orange. When the warning level is a high pollution level, the color of the pop-up window is red.

[0118] Specifically, by setting colors to distinguish pollution levels, managers can be reminded in a striking way, which increases their attention and enables them to quickly understand the current water environment 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 the change of water quality in the target water area. The water quality prediction unit predicts the water quality in the future by combining the water quality change. The water quality classification unit classifies the water quality in the future and takes corresponding measures according to 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] Although 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present 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, which is used to receive and display 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 to obtain the i-th water quality parameter value ΔP at time t+Δt. i (t+Δt), prediction of target water areas in the future, managers can predict water quality and take countermeasures in advance to avoid the expansion of pollution; Step S4: hierarchical management, according to the results of the dynamic prediction algorithm, use 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 before and after the treatment of the target water area, and prepare a water environment treatment report for subsequent use.

2. The intelligent water environment comprehensive management method according to claim 1 is characterized by: The warning levels include excellent level, light pollution level, medium pollution level and high pollution level. Corresponding measures are prepared for the target waters according to different warning levels. When the 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. 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.

3. The intelligent water environment comprehensive management method according to claim 2 is characterized by: 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) indicates the change of water quality parameters compared with the past time, so that the staff can understand the current trend of water environment change 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, and corresponding countermeasures are taken for different water quality parameters.

4. The intelligent water environment comprehensive management method according to claim 3 is characterized by: The dynamic prediction algorithm process in the water environment dynamic prediction step is as follows: Where 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, and its value is 0.8; By predicting water quality parameters in the future, managers can respond in advance to avoid the expansion of water pollution.

5. The intelligent water environment comprehensive management method according to claim 4 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 warning levels, and different water quality parameters are divided separately, so that management personnel can respond quickly when the early warning alarm is issued and set the alarm 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 rate of 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 system lag.

6. The intelligent water environment comprehensive management method according to claim 5 is characterized by: 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.

7. The intelligent water environment comprehensive management method according to claim 6 is characterized by: 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.

8. The comprehensive management system used in the intelligent water environment comprehensive management method according to claim 2 is characterized by: It includes 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 classification unit. The change rate detection unit is used to detect the change of water quality in the target water area. The water quality prediction unit predicts the water quality at a future time by combining the water quality change. The water quality classification unit classifies the water quality at a future time and takes corresponding measures according to the classification results. The visualization module is used to visualize the data and present it to management personnel.

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