An early warning and dispatch optimization decision method for water ecological protection and restoration

Abstract

The present application relates to the technical field of water ecological protection and restoration, in particular to a kind of early warning scheduling optimization decision method of water ecological protection and restoration.The present application breaks through the coverage limitation of single fixed monitoring point by fusing the data of fixed water quality monitoring equipment group and mobile water quality monitoring equipment group, realizes the comprehensive perception of water ecological region;At the same time, based on the water quality anomaly total value calculated from the analysis of biological activity, material exchange and pollution form, the accuracy and comprehensiveness of pollution level judgment are greatly improved, and single index misjudgment is avoided;It also accurately determines the traceability priority by quantifying the pollution migration probability of each monitoring direction;Combined with the dynamic monitoring unit control mobile monitoring point verification, the preferred pollution deduction direction and early warning area are quickly locked, the lag of manual investigation is reduced, and efficient closed loop from pollution identification to traceability early warning is realized, which provides accurate decision support for timely blocking pollution and carrying out restoration.

Description

Technical Field

[0001] This invention relates to the field of water ecological protection and restoration technology, specifically to an early warning, scheduling, and optimization decision-making method for water ecological protection and restoration. Background Technology

[0002] Aquatic ecosystems are the core carriers of the Earth's community of life, bearing multiple ecological service functions such as water resource supply, biodiversity conservation, and climate regulation. Their health and stability are directly related to national ecological security and sustainable development. Early warning and dispatching, as a forward-looking management tool for water ecological protection and restoration, through precise perception and scientific regulation of hydrological rhythms, water quality dynamics, and ecological responses, can not only ensure the dynamic balance of watershed ecological flow, providing a stable environment for natural processes such as fish migration and wetland conservation, but also coordinate diverse needs such as flood control and water supply, achieving a virtuous cycle between ecological protection and economic and social development.

[0003] Currently, early warning and scheduling for water ecological protection and restoration is a key means to address the increasing complexity of water pollution and the intensification of ecological degradation. my country's river basin aquatic ecosystems face multiple pressures, including the superposition of point and non-point source pollution, disordered hydrological rhythms, and reduced biodiversity. Frequent sudden water pollution incidents coexist with chronic ecological stress. Existing early warning and scheduling for water ecological protection and restoration relies on a certain number of monitoring points for pollution monitoring. However, the fixed locations of these monitoring points cannot cover pollution migration paths, and pollution plumes may bypass monitoring points during sudden pollution incidents, causing even greater pollution. Furthermore, after issuing an early warning, the reliance on manual investigation of pollution sources results in long feedback times and may lead to missed golden windows for pollution control. Summary of the Invention

[0004] This invention provides an early warning scheduling optimization decision-making method for water ecological protection and restoration, which is used to solve the above-mentioned technical problems.

[0005] The first aspect of this invention provides an early warning scheduling optimization decision-making method for water ecological protection and restoration, comprising the following steps:

[0006] Step 1: Edge computing nodes acquire basic data and online signals from water quality monitoring equipment; identify online signals, and when the corresponding water quality monitoring equipment is online, collect water quality data based on a preset basic acquisition strategy to obtain comprehensive water quality data; and transmit the basic data and comprehensive water quality data to the cloud.

[0007] Step 2: The edge computing nodes input the comprehensive water quality data corresponding to each device into the preset pollution verification mechanism. The pollution verification mechanism performs pollution analysis on the comprehensive water quality data to obtain water pollution information.

[0008] As a further improvement to the present invention, pollution analysis is performed on the comprehensive water quality data, and the specific analysis content is as follows:

[0009] A1: Based on comprehensive water quality data, we obtain the biological activity indicators, material exchange flux data, and pollution form transformation data for the monitoring areas corresponding to each water quality monitoring device;

[0010] A2: Based on bioactivity indicators, we obtained regional benthic animal behavior data and algal chlorophyll data. Based on the improved fuzzy hierarchical analysis method, we conducted a comprehensive analysis of the benthic animal behavior data and algal chlorophyll data to obtain bioanomalies and risk-state bioanomalies.

[0011] A3: Based on the mass exchange flux data, the ammonia nitrogen release flux, suspended solids settling rate and water-mud interface shear stress of the sediment were obtained. Based on the improved coupling coordination degree model, the mass exchange anomaly value and the risk state mass exchange anomaly value were obtained by comprehensively analyzing the ammonia nitrogen release flux, suspended solids settling rate and water-mud interface shear stress of the sediment.

[0012] A4: Based on pollution form transformation data, heavy metal valence state data and persistent organic matter data are obtained. Based on the improved matter-element extension model, the heavy metal valence state data and persistent organic matter data are comprehensively analyzed to obtain pollution form anomalies and risk state pollution form anomalies.

[0013] A5: Identify biological anomalies, material exchange anomalies, and pollution form anomalies. When the number of anomalies in a risk state is ≥2, trigger the generation of a preset amplification coefficient. Calculate the total water quality anomaly value using a dynamic weighted fusion and interactive amplification coupling model, considering biological anomalies, material exchange anomalies, pollution form anomalies, and the amplification coefficient. Divide the total water quality anomaly value into three water quality pollution level intervals based on preset anomaly intervals. The three water quality pollution level intervals correspond to no water pollution, slight anomaly, and severe anomaly, respectively. Match the current total water quality anomaly value with the water quality pollution level interval to obtain the corresponding water quality pollution level. When the water quality pollution level corresponds to no water pollution, generate water quality pollution information as "no pollution"; when the water quality pollution level corresponds to slight or severe anomalies, generate water quality pollution information as "polluted".

[0014] Furthermore, a comprehensive analysis of benthic animal behavior data and algal chlorophyll data was conducted based on an improved fuzzy hierarchical analysis method. The specific analysis is as follows:

[0015] Based on benthic animal behavior data, benthic animal characteristics corresponding to each benthic animal are obtained for a preset monitoring period. The benthic animal characteristics are matched with the biological behavior feature database to obtain abnormal behaviors of each benthic animal. The number of abnormal behaviors is counted to obtain the number of abnormal behaviors. The number of abnormal behaviors is calculated with the preset monitoring period to obtain the biological indicator as the frequency of abnormal behaviors. The fluorescence quenching rate of chlorophyll a is obtained through algal chlorophyll data. When the fluorescence quenching rate is less than the preset benign rate lower limit, the difference between the fluorescence quenching rate and the benign rate lower limit is calculated to obtain the biological indicator as the activity abnormal value.

[0016] Based on historical data storage periods, historical biological indicators are obtained for each biological indicator. These historical biological indicators include historical abnormal behavior frequency and historical activity anomaly values. The mean and standard deviation of the abnormal behavior frequency are calculated to obtain the corresponding abnormal frequency health mean and abnormal frequency standard deviation. Similarly, the activity anomaly health mean and activity anomaly standard deviation corresponding to the historical activity anomaly values ​​are obtained. The deviation is then calculated using the indicator deviation formula. The deviation degree Ρi corresponding to each biological indicator was calculated. j Among them, F d and F a These are represented as the frequency of abnormal behavior and the activity anomaly value, respectively. σ represents the mean of abnormal frequency and the mean of abnormal activity for each biological indicator. j These are expressed as the standard deviation of the outlier frequency and the standard deviation of the outlier frequency for each biological indicator.

[0017] Introduce a time decay coefficient, denoted as α. t =0.98 t t represents the monitoring duration; and the time decay coefficient and the deviation of each biological indicator at the current time are substituted into the weight calculation formula. The weights ω of each indicator were calculated. j Based on the improved fuzzy hierarchical analysis method, the time decay coefficient, the index deviation and index weight corresponding to each biological indicator are substituted into... The biological anomaly value BA is calculated; when the biological anomaly value is greater than the preset biological anomaly threshold, the biological anomaly value is marked as a risky biological anomaly value.

[0018] Furthermore, based on an improved coupling coordination model, a comprehensive analysis was conducted on the ammonia nitrogen release flux, suspended solids settling rate, and water-mud interface shear stress in the sediment. The specific analysis is as follows:

[0019] The hydraulically corrected baseline release flux, T, is obtained by correcting the sediment ammonia nitrogen release flux based on the shear stress at the water-sediment interface. N修正 =T N ×(1+0.015τ snAmong them, T N修正 T N τ sn These are represented as baseline release flux, sediment ammonia nitrogen release flux, and water-mud interface shear stress, respectively.

[0020] The flux deviation is calculated by comparing the current sediment ammonia nitrogen release flux with the corresponding baseline release flux. The flux deviation degree is then calculated by rationing the flux deviation difference with the baseline release flux. Similarly, the settling rate difference is calculated by comparing the current suspended solids settling rate with the preset normal settling rate. The settling rate deviation degree is then calculated by rationing the settling rate difference with the normal settling rate. Finally, the flux deviation degree and the settling deviation degree are substituted into the coupling coordination coefficient calculation formula. The coupling coordination coefficient O was calculated. NC Among them, PT N PT C These are represented as flux deviation and settlement deviation, respectively.

[0021] Substituting flux deviation, settlement deviation, and coupling coordination coefficient into the improved coupling coordination model ME=(1-O NC )×max(|PT N |,|PT C |) Calculate the material exchange anomaly value; when the material exchange anomaly value is greater than the preset material exchange threshold, mark the material exchange anomaly value as a risk-state material exchange anomaly value.

[0022] Furthermore, a comprehensive analysis of heavy metal valence state data and persistent organic matter data is conducted based on an improved matter-element extension model. The specific analysis is as follows:

[0023] The conversion ratios of heavy metal valence states are obtained from the heavy metal valence state data; the concentration ratio of low-ring to high-ring polycyclic aromatic hydrocarbons (PAHs) is obtained from persistent organic pollutant (POP) data; toxicity data for each heavy metal and PAH are obtained from pre-stored toxicology data in the database; toxicity sequence information is obtained by arranging various heavy metals and PAHs in ascending order based on the toxicity data; toxicity weights are assigned based on the toxicity sequence information, i.e., the higher the toxicity, the greater the weight; the difference between the conversion ratios of various heavy metal valence states and the corresponding preset valence state health ratios is calculated to obtain the valence state conversion ratio difference for each heavy metal; the ratio of the valence state conversion ratio difference to the corresponding valence state health ratio is calculated to obtain the relative deviation of valence states; similarly, the relative deviation of the concentration corresponding to the low-ring to high-ring concentration ratio of PAHs is calculated.

[0024] Substituting the relative deviations of valence states, relative deviations of concentrations, and toxicity weights for various heavy metals into the improved matter-element extension model. The pollution pattern anomaly value PT was calculated; where, Let D represent the toxicity weight and relative deviation of valence state for the i-th type of heavy metal, respectively, where n corresponds to the total number of heavy metal species; dh XP dh These represent the toxicity weight and relative concentration deviation of polycyclic aromatic hydrocarbons, respectively. When the abnormal value of the pollution form is greater than the preset pollution form threshold, the abnormal value of the pollution form is marked as a risky pollution form abnormal value.

[0025] Step 3: Identify water pollution information. When the water pollution information corresponds to pollution, a pollution source tracing signal is generated; otherwise, when the water pollution information corresponds to no pollution, no signal is generated. When the preset pollution source tracing mechanism detects a pollution source tracing signal, the water flow state data corresponding to the pollution source tracing signal is obtained, and the pollution inference direction is obtained by analyzing the water flow state data.

[0026] As a further improvement to the present invention, the water flow state data is analyzed, and the specific analysis steps are as follows:

[0027] The water flow status data includes water flow microenvironmental characteristic data and medium conductivity of each monitoring direction at the monitoring point corresponding to the pollution source tracing signal; the monitoring direction is to divide the detection area corresponding to the monitoring point into multiple monitoring areas according to a preset angular interval, and one monitoring area corresponds to one monitoring direction.

[0028] Based on the microenvironmental characteristics of the water flow, turbulent kinetic energy, water level fluctuation amplitude, and tributary confluence residence time are obtained. An initial probability is set for each monitoring direction, and the turbulent kinetic energy in each monitoring direction is acquired. The turbulent kinetic energy attenuation coefficient is then calculated using the formula. The turbulent kinetic energy coefficients corresponding to each monitoring direction were calculated. TD f The turbulent kinetic energy is expressed as f in the monitoring direction; the water level fluctuation amplitude and tributary confluence residence time corresponding to each monitoring direction are substituted into the spatial attenuation factor calculation formula. The spatial attenuation factor corresponding to each monitoring direction was calculated. Among them, H f and max(H) represent the water level fluctuation amplitude and the maximum water level fluctuation amplitude in the monitoring direction, respectively; t zl t zl0 These represent the tributary confluence residence time and the baseline residence time, respectively; the turbulent kinetic energy attenuation coefficient, spatial attenuation factor, and initial probability corresponding to each monitoring direction are introduced into the spatial Markov chain model. The directional probability value GL corresponding to each monitoring direction was calculated. f Among them, GL 0f Let f be the initial probability of the monitoring direction, and F0 be the total number of monitoring directions.

[0029] The conductivity of the medium at two preset monitoring time points in each monitoring direction is taken. The difference between the conductivity of the medium at each monitoring time point is calculated to obtain the conductivity fluctuation amplitude. When the conductivity fluctuation amplitude exceeds the preset conductivity fluctuation threshold, the difference between the conductivity fluctuation amplitude and the conductivity fluctuation threshold is calculated to obtain the conductivity anomaly value.

[0030] When the probability of the monitored direction is greater than the preset probability baseline, the corresponding monitored direction is marked as the pollution source tracing direction, each pollution source tracing direction is marked as a candidate pollution source tracing direction, the conductivity anomaly value of each pollution source tracing direction is obtained, and the conductivity anomaly values ​​are sorted in descending order to obtain the conductivity anomaly sequence number. Based on the order of the conductivity anomaly sequence number, the source tracing priority order of the corresponding candidate pollution source tracing direction is obtained, and the pollution inference direction is output according to the source tracing priority order.

[0031] Step 4: Based on the pollution projection direction, perform early warning analysis to obtain the preferred pollution projection direction and pollution early warning area. Specifically, this involves: acquiring the pollution projection direction and pushing it to the dynamic monitoring unit; the dynamic monitoring unit controls the mobile monitoring point to move to the pollution projection direction to collect pollution data and obtain the water quality pollution level for each pollution projection direction; based on the pollution level of the polluted water, the corresponding pollution projection direction is marked as the preferred pollution projection direction; each preferred pollution projection direction is matched with the corresponding water area map to obtain the pollution early warning area, and the preferred pollution projection direction and pollution early warning area are sent to the cloud for early warning.

[0032] The beneficial effects of the technical solution provided by this invention compared with the prior art are as follows:

[0033] 1. This invention overcomes the coverage limitations of a single fixed monitoring point by integrating data from fixed and mobile water quality monitoring equipment groups, enabling comprehensive perception of aquatic ecological areas. Simultaneously, based on an improved fuzzy hierarchical analysis method, a coupling coordination degree model, and a matter-element extension model, it analyzes anomalies from three dimensions: biological activity, material exchange, and pollution form. Furthermore, it introduces an amplification coefficient to strengthen the impact of synergistic anomalies and combines a dynamic weighted fusion and interactive amplification coupling model to calculate the total value of water quality anomalies, significantly improving the accuracy and comprehensiveness of pollution level judgment and avoiding misjudgments based on a single indicator.

[0034] 2. This invention integrates the microenvironmental characteristics of water flow and the fluctuation data of medium conductivity through a spatial Markov chain model, quantifies the probability of pollution migration in each monitoring direction, and accurately determines the priority order of source tracing. Combined with the verification of the moving monitoring point controlled by the dynamic monitoring unit, it can quickly lock the preferred pollution projection direction and early warning area, reduce the lag of manual investigation, and realize an efficient closed loop from pollution identification to source tracing and early warning, providing accurate decision support for timely pollution blocking and remediation. Attached Figure Description

[0035] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. The following drawings are not deliberately drawn to scale according to the actual size, but are intended to show the main idea of ​​this application.

[0036] Figure 1 This is a flowchart of the method of the present invention;

[0037] Figure 2 This is a schematic diagram illustrating the principle of the contamination verification mechanism of the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1-2 In one embodiment of the present invention, an early warning scheduling optimization decision-making method for water ecological protection and restoration includes:

[0040] Step 1, Multi-source monitoring data fusion processing, specifically involves: edge computing nodes acquiring basic data and online signals from water quality monitoring equipment; identifying online signals; when a corresponding water quality monitoring equipment is online, collecting water quality data based on a preset basic acquisition strategy to obtain comprehensive water quality data; and transmitting the basic data and comprehensive water quality data to the cloud.

[0041] Comprehensive water quality data includes biological activity indicators, material exchange flux data, and pollution form transformation data; water quality monitoring equipment includes fixed water quality monitoring equipment groups and mobile water quality monitoring equipment groups that are pre-designed and deployed in the aquatic ecological area.

[0042] Step 2, pollution monitoring and verification, specifically involves the edge computing nodes inputting the comprehensive water quality data corresponding to each device into a preset pollution verification mechanism. The pollution verification mechanism then performs pollution analysis on the comprehensive water quality data to obtain water pollution information.

[0043] Pollution analysis was performed on the comprehensive water quality data, and the specific analysis content is as follows:

[0044] A1: Based on comprehensive water quality data, we obtain the biological activity indicators, material exchange flux data, and pollution form transformation data for the monitoring areas corresponding to each water quality monitoring device;

[0045] A2: Based on bioactivity indicators, obtain benthic animal behavior data and algal chlorophyll data for the region. Based on the benthic animal behavior data, obtain the benthic animal characteristics corresponding to each benthic animal for a preset monitoring period. Match the benthic animal characteristics with the biological behavior feature library in the database to obtain the abnormal behavior of each benthic animal. Count the number of abnormal behaviors to obtain the number of abnormal behaviors. Calculate the number of abnormal behaviors with the preset monitoring period to obtain the bioindicator as the frequency of abnormal behavior. Obtain the fluorescence quenching rate of chlorophyll a through algal chlorophyll data. When the fluorescence quenching rate is less than the preset benign rate lower limit, calculate the difference between the fluorescence quenching rate and the benign rate lower limit to obtain the bioindicator as the abnormal activity value.

[0046] Abnormal behaviors include, but are not limited to, the escape or aggregation behaviors of tubifex worms and snails; the fluorescence quenching rate directly reflects the effective activity of photosynthesis. When the water body is unpolluted, the fluorescence quenching rate of algae and other phytoplankton is at a high level and the photosynthetic activity is good. When the water body is under pollution stress from heavy metals, pesticides, eutrophication or organic pollution, the structure of chlorophyll a in the photosystem II reaction center will be destroyed, resulting in a sharp drop in the fluorescence quenching rate.

[0047] A21: Based on the preset historical data storage period, obtain the historical biological indicators corresponding to each biological indicator. The historical biological indicators include the historical abnormal behavior frequency and the historical activity abnormal value. Calculate the mean and standard deviation based on the historical abnormal behavior frequency to obtain the corresponding abnormal frequency health mean and abnormal frequency standard deviation. Similarly, obtain the activity abnormal health mean and activity abnormal standard deviation corresponding to the historical activity abnormal value.

[0048] A22: Calculation formula for indicator deviation The deviation degree Ρi corresponding to each biological indicator was calculated. j Among them, F d and F a These are represented as the frequency of abnormal behavior and the activity anomaly value, respectively. σ represents the mean of abnormal frequency and the mean of abnormal activity for each biological indicator. j These are expressed as the standard deviation of the outlier frequency and the standard deviation of the outlier frequency for each biological indicator.

[0049] A23: Introduce a time decay coefficient, denoted as α. t =0.98 t t represents the monitoring duration; and the time decay coefficient and the deviation of each biological indicator at the current time are substituted into the weight calculation formula. The weights ω of each indicator were calculated. j ;

[0050] A24: Based on the improved fuzzy hierarchical analysis method, the time decay coefficient, the index deviation and index weight of each biological indicator are substituted into... The biological anomaly value BA is calculated; when the biological anomaly value is greater than the preset biological anomaly threshold, the biological anomaly value is marked as a risky biological anomaly value.

[0051] A3: Based on mass exchange flux data, we obtained the ammonia nitrogen release flux in the sediment, the settling rate of suspended solids, and the shear stress at the water-sludge interface.

[0052] A31: The hydraulically corrected baseline release flux, T, is obtained by correcting the sediment ammonia nitrogen release flux based on the shear stress at the water-sediment interface. N修正 =T N ×(1+0.015τ sn Among them, T N修正 T N τ sn These are represented as baseline release flux, sediment ammonia nitrogen release flux, and water-sediment interface shear stress, respectively, reflecting the enhancing effect of hydraulic disturbance on sediment release.

[0053] A32: Calculate the flux deviation difference by comparing the current corresponding sediment ammonia nitrogen release flux with the corresponding baseline release flux, and calculate the flux deviation degree by comparing the flux deviation difference with the baseline release flux; calculate the settling rate difference by comparing the current corresponding suspended solids settling rate with the preset normal settling rate, and calculate the settling deviation degree by comparing the settling rate difference with the normal settling rate.

[0054] Substitute flux deviation and settlement deviation into the formula for calculating the coupling compatibility coefficient. The coupling coordination coefficient O was calculated. NC Among them, PT N PT C These are represented as flux deviation and settlement deviation, respectively; the coupling coordination coefficient is used to measure the synergy between the deviations of the two indicators, and the smaller the value, the more synergistic the deviations.

[0055] A33: Substituting flux deviation, settlement deviation, and coupling coordination coefficient into the improved coupling coordination coefficient model ME=(1-O NC )×max(|PT N |,|PT C |) Calculate the material exchange anomaly value; when the material exchange anomaly value is greater than the preset material exchange threshold, mark the material exchange anomaly value as a risky material exchange anomaly value.

[0056] A4: Heavy metal valence state data and persistent organic compound (POC) data are obtained based on pollution speciation data; the conversion ratio of heavy metal valence states is obtained from the detected data; the concentration ratio of low-ring to high-ring polycyclic aromatic hydrocarbons (PAHs) is obtained based on POC data; the conversion ratio of heavy metal valence states includes, but is not limited to, the valence state ratios of mercury, chromium, arsenic, lead, and cadmium, for example, Cr... 6+ / Cr 3+ ratio;

[0057] A41: Toxicity data of various heavy metals and polycyclic aromatic hydrocarbons are obtained based on pre-stored toxicological data in the database. Based on the toxicity data, the various heavy metals and polycyclic aromatic hydrocarbons are sorted in ascending order to obtain toxicity sequence information. Based on the toxicity sequence information, toxicity weights are assigned to obtain the corresponding toxicity weights, that is, the higher the toxicity, the greater the weight. For example, if chromium and phosphorus are detected as heavy metals, and polycyclic aromatic hydrocarbons are also detected, the toxicity weights of chromium, phosphorus and polycyclic aromatic hydrocarbons are sorted by toxicity to obtain corresponding toxicity weights of 0.4, 0.3 and 0.3, respectively.

[0058] A42: The difference between the conversion ratio of various heavy metal valence states and the corresponding preset valence state health ratio is calculated to obtain the valence state conversion ratio difference for each heavy metal. The ratio of the valence state conversion ratio difference to the corresponding valence state health ratio is then calculated to obtain the relative valence state deviation. Similarly, the relative concentration deviation corresponding to the low-ring to high-ring concentration ratio of polycyclic aromatic hydrocarbons is calculated.

[0059] A43: Substitute the relative deviations of valence states, relative deviations of concentrations, and toxicity weights for various heavy metals into the improved matter-element extension model. The pollution pattern anomaly value PT was calculated; where, Let D represent the toxicity weight and relative deviation of valence state for the i-th type of heavy metal, respectively, where n corresponds to the total number of heavy metal species; dh XP dh These represent the toxicity weight and relative concentration deviation of polycyclic aromatic hydrocarbons, respectively. When the abnormal value of the pollution form is greater than the preset pollution form threshold, the abnormal value of the pollution form is marked as a risky pollution form abnormal value.

[0060] A5: The total water quality anomaly value is calculated by using a dynamic weighted fusion and interactive amplification coupling model to analyze biological anomalies, material exchange anomalies, and pollution pattern anomalies. This is achieved using the formula SY=η1·BA+η2·ME+η3·PT+λ. 放大 The function `max(BA,ME,PT)` calculates and outputs the total water quality anomaly value `SY`; where `η1`, `η2`, and `η3` are set dynamic weights with values ​​of [values ​​to be filled in]. λ 放大 This represents the preset magnification factor, with a value of λ. 放大=0.3, and its triggering condition is to identify biological anomalies, material exchange anomalies and pollution form anomalies. It is triggered when the number of anomalies in the risk state is ≥2, which is used to strengthen the synergistic anomaly effect.

[0061] The total value of water quality anomalies is divided into three water pollution level intervals based on preset anomaly intervals. The water pollution levels corresponding to the three water pollution level intervals are no water pollution, slight anomaly, and severe anomaly, respectively. The current total value of water quality anomalies is matched with the water pollution level interval to obtain the corresponding water pollution level. When the water pollution level corresponds to no water pollution, the generated water pollution information is no pollution; when the water pollution level corresponds to slight anomaly or severe anomaly, the generated water pollution information is polluted.

[0062] Step 3, pollution path tracing and deduction, specifically involves: identifying water pollution information; generating a pollution source tracing signal when the water pollution information corresponds to pollution; conversely, no signal is generated when the water pollution information corresponds to no pollution; when the preset pollution source tracing mechanism detects a pollution source tracing signal, acquiring the water flow state data corresponding to the pollution source tracing signal, and analyzing the water flow state data to obtain the pollution deduction direction.

[0063] The specific steps for analyzing water flow state data are as follows:

[0064] The water flow status data includes water flow microenvironmental characteristic data and medium conductivity of each monitoring direction at the monitoring point corresponding to the pollution source tracing signal; the monitoring direction is to divide the detection area corresponding to the monitoring point into multiple monitoring areas according to a preset angular interval, and one monitoring area corresponds to one monitoring direction.

[0065] Based on the microenvironmental characteristics of the water flow, turbulent kinetic energy, water level fluctuation amplitude, and tributary confluence residence time are obtained. An initial probability is set for each monitoring direction, and the turbulent kinetic energy in each monitoring direction is acquired. The turbulent kinetic energy attenuation coefficient is then calculated using the formula. The turbulent kinetic energy coefficients corresponding to each monitoring direction were calculated. TD f The turbulent kinetic energy in the monitoring direction is expressed as f; the water level fluctuation amplitude and tributary confluence residence time corresponding to each monitoring direction are substituted into the spatial attenuation factor calculation formula. The spatial attenuation factor corresponding to each monitoring direction was calculated. Among them, H f and max(H) represent the water level fluctuation amplitude and the maximum water level fluctuation amplitude in the monitoring direction, respectively; t zl t zl0 These represent the tributary confluence residence time and the baseline residence time, respectively; the turbulent kinetic energy attenuation coefficient, spatial attenuation factor, and initial probability corresponding to each monitoring direction are introduced into the spatial Markov chain model. The directional probability value GL corresponding to each monitoring direction was calculated. f Among them, GL 0f Let f be the initial probability of the monitoring direction, and F0 be the total number of monitoring directions.

[0066] The conductivity of the medium at two preset monitoring time points in each monitoring direction is taken. The difference between the conductivity of the medium at each monitoring time point is calculated to obtain the conductivity fluctuation amplitude. When the conductivity fluctuation amplitude exceeds the preset conductivity fluctuation threshold, the difference between the conductivity fluctuation amplitude and the conductivity fluctuation threshold is calculated to obtain the conductivity anomaly value.

[0067] When the probability of the monitored direction is greater than the preset probability baseline, the corresponding monitored direction is marked as the pollution source tracing direction, each pollution source tracing direction is marked as a candidate pollution source tracing direction, the conductivity anomaly value of each pollution source tracing direction is obtained, and the conductivity anomaly values ​​are sorted in descending order to obtain the conductivity anomaly sequence number. Based on the order of the conductivity anomaly sequence number, the source tracing priority order of the corresponding candidate pollution source tracing direction is obtained, and the pollution inference direction is output according to the source tracing priority order.

[0068] Step four, pollution monitoring point optimization feedback, specifically involves: obtaining the pollution projection direction and pushing it to the dynamic monitoring unit; the dynamic monitoring unit controls the mobile monitoring point to move to the pollution projection direction to collect pollution data and obtain the water pollution level of each pollution projection direction; based on the pollution level of the polluted water, the corresponding pollution projection direction is marked as the preferred pollution projection direction; each preferred pollution projection direction is matched with the corresponding water area map to obtain the pollution warning area, and the preferred pollution projection direction and pollution warning area are sent to the cloud for warning; the dynamic monitoring unit includes numerical monitoring equipment that can move autonomously.

[0069] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for early warning, scheduling, and optimization decision-making in water ecological protection and restoration, characterized in that, Includes the following steps: Step 1: Edge computing nodes acquire basic data and online signals from water quality monitoring equipment; identify online signals, and when the corresponding water quality monitoring equipment is online, collect water quality data based on a preset basic acquisition strategy to obtain comprehensive water quality data; and transmit the basic data and comprehensive water quality data to the cloud. Step 2: The edge computing nodes input the comprehensive water quality data corresponding to each device into the preset pollution verification mechanism. The pollution verification mechanism performs pollution analysis on the comprehensive water quality data to obtain water pollution information. Pollution analysis was performed on the comprehensive water quality data, and the specific analysis content is as follows: A1: Based on comprehensive water quality data, we obtain the biological activity indicators, material exchange flux data, and pollution form transformation data for the monitoring areas corresponding to each water quality monitoring device; A2: Based on bioactivity indicators, we obtained regional benthic animal behavior data and algal chlorophyll data. Based on the improved fuzzy hierarchical analysis method, we conducted a comprehensive analysis of the benthic animal behavior data and algal chlorophyll data to obtain bioanomalies and risk-state bioanomalies. A3: Based on the mass exchange flux data, the ammonia nitrogen release flux, suspended solids settling rate and water-mud interface shear stress of the sediment were obtained. Based on the improved coupling coordination degree model, the mass exchange anomaly value and the risk state mass exchange anomaly value were obtained by comprehensively analyzing the ammonia nitrogen release flux, suspended solids settling rate and water-mud interface shear stress of the sediment. A4: Based on pollution form transformation data, heavy metal valence state data and persistent organic matter data are obtained. Based on the improved matter-element extension model, the heavy metal valence state data and persistent organic matter data are comprehensively analyzed to obtain pollution form anomalies and risk state pollution form anomalies. A5: Identify biological anomalies, material exchange anomalies, and pollution form anomalies. When the number of anomalies in the risk state is ≥2, trigger the generation of a preset amplification factor. Calculate the total water quality anomaly value by using a dynamic weighted fusion and interactive amplification coupling model for biological anomalies, material exchange anomalies, pollution form anomalies, and amplification factors. Water pollution information is obtained by analyzing the pollution level based on the total value of water quality anomalies; Step 3: Identify water pollution information. When the water pollution information corresponds to pollution, a pollution source tracing signal is generated; otherwise, when the water pollution information corresponds to no pollution, no signal is generated. When the preset pollution source tracing mechanism detects a pollution source tracing signal, the water flow state data corresponding to the pollution source tracing signal is obtained, and the pollution inference direction is obtained by analyzing the water flow state data. The specific analysis steps for the water flow state data are as follows: The water flow status data includes water flow microenvironmental characteristic data and medium conductivity of each monitoring direction at the monitoring point corresponding to the pollution source tracing signal; the monitoring direction is to divide the detection area corresponding to the monitoring point into multiple monitoring areas according to a preset angular interval, and one monitoring area corresponds to one monitoring direction. Based on the microenvironmental characteristics of the water flow, turbulent kinetic energy, water level fluctuation amplitude, and tributary confluence residence time are obtained. An initial probability is set for each monitoring direction, and the turbulent kinetic energy of each monitoring direction is obtained. The turbulent kinetic energy coefficient corresponding to each monitoring direction is calculated using the formula for calculating the turbulent kinetic energy attenuation coefficient. The water level fluctuation amplitude and tributary confluence residence time corresponding to each monitoring direction are substituted into the formula for calculating the spatial attenuation factor to obtain the spatial attenuation factor corresponding to each monitoring direction. The turbulent kinetic energy attenuation coefficient, spatial attenuation factor, and initial probability corresponding to each monitoring direction are introduced into a spatial Markov chain model to calculate the directional probability value corresponding to each monitoring direction. The dielectric conductivity at two preset monitoring time points in each monitoring direction is taken, and the difference between the dielectric conductivity at each monitoring time point is calculated to obtain the conductivity fluctuation amplitude. When the conductivity fluctuation amplitude exceeds the preset conductivity fluctuation threshold, the difference between the conductivity fluctuation amplitude and the conductivity fluctuation threshold is calculated to obtain the conductivity anomaly value. When the probability of the monitoring direction is greater than the preset probability baseline, the corresponding monitoring direction is marked as the pollution source tracing direction, each pollution source tracing direction is marked as a candidate pollution source tracing direction, the conductivity anomaly value of each pollution source tracing direction is obtained, and the conductivity anomaly values ​​are sorted in descending order to obtain the conductivity anomaly sequence number. Based on the order of the conductivity anomaly sequence number, the source tracing priority order of the corresponding candidate pollution source tracing direction is obtained, and the pollution inference direction is output according to the source tracing priority order. Step 4: Based on the pollution projection direction, conduct early warning analysis to obtain the optimal pollution projection direction and pollution early warning area.

2. The early warning, scheduling, and optimization decision-making method for water ecological protection and restoration according to claim 1, characterized in that, A comprehensive analysis of benthic animal behavior data and algal chlorophyll data was conducted based on an improved fuzzy hierarchical analysis method. The specific analysis is as follows: By using benthic animal behavior data, the characteristics of each benthic animal corresponding to a preset monitoring period are obtained. The benthic animal characteristics are matched with the biological behavior feature database to obtain the abnormal behaviors of each benthic animal. The number of abnormal behaviors is counted to obtain the number of abnormal behaviors. The number of abnormal behaviors is calculated with the preset monitoring period to obtain biological indicator one, which is the frequency of abnormal behaviors. The fluorescence quenching rate of chlorophyll a is obtained by using algal chlorophyll data. When the fluorescence quenching rate is less than the preset benign rate lower limit, the difference between the fluorescence quenching rate and the benign rate lower limit is calculated to obtain biological indicator two, which is the abnormal activity value. Based on the preset historical data storage period, the historical biological indicators corresponding to each biological indicator are obtained. The historical biological indicators include the historical abnormal behavior frequency and the historical activity abnormal value. The mean and standard deviation of the abnormal frequency are calculated based on the historical abnormal behavior frequency to obtain the corresponding abnormal frequency health mean and abnormal frequency standard deviation. Similarly, the mean value and standard deviation of the activity abnormality corresponding to the historical activity abnormality values ​​are obtained; The deviation of each biological indicator is calculated using the indicator deviation calculation formula. A time decay coefficient is introduced, and the time decay coefficient and the deviation of each biological indicator are substituted into the weight calculation formula to calculate the weight of each indicator. Based on the improved fuzzy hierarchical analysis method, the time decay coefficient, the index deviation degree and index weight corresponding to each biological index are calculated to obtain the biological anomaly value; when the biological anomaly value is greater than the preset biological anomaly threshold, the biological anomaly value is marked as a risk state biological anomaly value.

3. The early warning, scheduling, and optimization decision-making method for water ecological protection and restoration according to claim 2, characterized in that, A comprehensive analysis of ammonia nitrogen release flux, suspended solids settling rate, and water-mud interface shear stress in sediment was conducted based on an improved coupling coordination model. The specific analysis is as follows: The hydraulically corrected baseline release flux is obtained by correcting the ammonia nitrogen release flux in the sediment based on the shear stress at the water-sludge interface. The flux deviation difference is calculated by comparing the current sediment ammonia nitrogen release flux with the baseline release flux, and the flux deviation degree is calculated by comparing the flux deviation difference with the baseline release flux. The settling rate difference is calculated by comparing the current suspended solids settling rate with the preset normal settling rate, and the settling rate deviation degree is calculated by comparing the settling rate difference with the normal settling rate. The coupling coordination coefficient is obtained by substituting the flux deviation and settlement deviation into the coupling coordination coefficient calculation formula; Substituting flux deviation, sedimentation deviation, and coupling coordination coefficient into the improved coupling coordination model, the material exchange anomaly value was calculated. When an abnormal value of material exchange exceeds a preset material exchange threshold, the abnormal value of material exchange is marked as a risky material exchange abnormal value.

4. The early warning, scheduling, and optimization decision-making method for water ecological protection and restoration according to claim 3, characterized in that, A comprehensive analysis of heavy metal valence state data and persistent organic matter data was conducted based on an improved matter-element extension model. The specific analysis is as follows: The conversion ratios of heavy metal valence states are obtained from the heavy metal valence state data; the concentration ratio of low-ring to high-ring polycyclic aromatic hydrocarbons (PAHs) is obtained from the persistent organic compounds (POCs) data; toxicity data of each heavy metal and PAH are obtained from the pre-stored toxicology data in the database; toxicity sequence information is obtained by sorting the various heavy metals and PAHs in ascending order based on the toxicity data; toxicity weights are assigned based on the toxicity sequence information, i.e., the higher the toxicity, the greater the weight; the difference between the conversion ratios of various heavy metal valence states and the corresponding preset valence state health ratio is calculated to obtain the valence state conversion ratio difference for each heavy metal; the ratio of the valence state conversion ratio difference to the corresponding valence state health ratio is calculated to obtain the relative deviation of the valence state. Similarly, the relative concentration deviation corresponding to the concentration ratio of low-ring to high-ring polycyclic aromatic hydrocarbons was calculated. The relative deviation of valence state, relative deviation of concentration, and toxicity weight of various heavy metals are substituted into the improved matter-element extension model to calculate the pollution form anomaly value; when the pollution form anomaly value is greater than the preset pollution form threshold, the pollution form anomaly value is marked as a risk state pollution form anomaly value.

5. The early warning, scheduling, and optimization decision-making method for water ecological protection and restoration according to claim 4, characterized in that, Water pollution information is obtained through pollution level analysis based on the total value of water quality anomalies, specifically as follows: The total value of water quality anomalies is divided into three water pollution level intervals based on preset anomaly intervals. The water pollution levels corresponding to the three water pollution level intervals are no water pollution, slight anomaly, and severe anomaly, respectively. The current total value of water quality anomalies is matched with the water pollution level interval to obtain the corresponding water pollution level. When the water pollution level corresponds to no water pollution, the generated water pollution information is no pollution; when the water pollution level corresponds to slight anomaly or severe anomaly, the generated water pollution information is polluted.

6. The early warning, scheduling, and optimization decision-making method for water ecological protection and restoration according to claim 1, characterized in that, The comprehensive water quality data includes biological activity indicators, material exchange flux data, and pollution form transformation data; the water quality monitoring equipment includes fixed water quality monitoring equipment groups and mobile water quality monitoring equipment groups pre-designed and deployed in the aquatic ecological area.

7. The early warning, scheduling, and optimization decision-making method for water ecological protection and restoration according to claim 1, characterized in that, The early warning analysis based on pollution projection directions yields preferred pollution projection directions and pollution warning areas. Specifically, the pollution projection directions are obtained and pushed to the dynamic monitoring unit. The dynamic monitoring unit controls mobile monitoring points to move to the pollution projection directions to collect pollution data and obtain the water quality pollution level for each pollution projection direction. Based on the pollution level of the polluted water, the corresponding pollution projection direction is marked as the preferred pollution projection direction. Each preferred pollution projection direction is matched with the corresponding water area map to obtain the pollution warning area. The preferred pollution projection directions and pollution warning areas are then sent to the cloud for early warning.

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