Pollution source detection method and system for water environment treatment

Abstract

The invention belongs to the technical field of pollution source detection, and discloses a pollution source detection method and system for water environment treatment. Comprising the steps that acoustic disturbance data are collected through an underwater acoustic sensor, real-time edge preprocessing is carried out, water environment data of different depths are synchronously collected, water environment gradient data are calculated, and then data synchronization is achieved through a unified timestamp. Fourier transform is adopted to extract frequency domain characteristics of the sound disturbance signals, the main frequency, the energy difference and the frequency spectrum skewness are calculated, and disturbance sound wave indexes are generated according to the main frequency, the energy difference and the frequency spectrum skewness. In combination with disturbance sound wave indexes and water body environment gradient data, a sliding window is adopted to construct a time sequence sample set, a recurrent neural network model for predicting future pollution release risks is trained, differentiated pollution release threshold values are set according to different water body types, and when model output exceeds the threshold values, sediment pollution early warning instructions of corresponding levels are automatically generated. And efficient and graded pollution event intelligent identification and response are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of pollution source detection, and more specifically, to a pollution source detection method and system for water environment management. Background Art

[0002] With the acceleration of industrialization and urbanization, water pollution is becoming increasingly serious. Although various water environment management projects have achieved certain results in surface water purification in recent years, the sediment is an important "sedimentation reservoir" of pollutants. The heavy metals, organic pollutants, nutrients, etc. accumulated in the sediment may be released again under certain conditions, becoming the key source of the "secondary rebound" of water pollution.

[0003] In actual applications, factors such as ship navigation, hydraulic construction, and water flow disturbance may trigger the transfer and release of pollutants in the sediment to the water body, resulting in sudden changes in water quality and even ecological problems such as "returning to black and smelly".

[0004] The Chinese patent with authorization announcement number CN109060414B discloses a river sediment sampling device, which designs a sampling method that has little disturbance to the sediment, maintains the integrity of the sediment sample, and has good pressure and heat preservation performance, which is convenient for subsequent experimental testing.

[0005] The Chinese patent with the authorization announcement number CN115128229B discloses a water environment governance pollution source detection and management system based on big data. By evenly distributing water quality detection points in the river, the first detection module is used to regularly detect the water quality, and the second detection module locates the detection points and determines their standard water quality. The system compares the actual water quality with the standard water quality and dynamically adjusts the detection cycle to improve the detection efficiency.

[0006] However, current research on sediment disturbance release processes is mostly focused on laboratory conditions or offline detection, lacking real-time, automated monitoring methods suitable for actual water environments. In addition, due to the nonlinear, sudden, and complex coupling characteristics of the pollution release process, existing methods are difficult to accurately predict its development trend and cannot provide timely and effective data support for water quality risk warnings and control strategy adjustments.

[0007] Therefore, there is an urgent need for an intelligent detection method and system that can perceive sediment disturbance and its pollution release behavior in real time and has dynamic prediction capabilities to improve the accuracy and response efficiency of water environment monitoring.

[0008] In view of this, the present invention proposes a pollution source detection method and system for water environment management to solve the above problems. Summary of the invention

[0009] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a pollution source detection method and system for water environment management.

[0010] To achieve the above object, the present invention provides the following technical solutions: A method for detecting pollution sources for water environment management, the method comprising: Collect acoustic disturbance data and perform edge preprocessing in real time; collect water environment data synchronously when collecting acoustic disturbance data, and the water environment data includes four groups of indicators at the same time; each group of indicators includes four categories: water temperature, pH value, dissolved oxygen concentration and flow rate; Calculate water environment gradient data based on water environment data; suppose water environment data is ; is the water environment data group number, =1, 2, 3, 4 are the first, second, third and fourth groups of water environment data respectively; is the water environment data category identifier, =1, 2, 3, 4 represent water temperature, pH value, dissolved oxygen concentration and flow rate respectively; the water environment gradient data are: In the formula, =1; Introduce water environment gradient data and acoustic disturbance data into the time synchronization mechanism and give them a unified time stamp; Obtain frequency domain information using Fourier transform based on acoustic disturbance data; obtain main frequency, energy difference and spectrum skewness based on spectrum information; calculate disturbance sound wave index based on spectrum skewness, main frequency and energy difference; A data set is established through a sliding window method based on disturbance acoustic wave indicators and water environment gradient data; a machine learning model is trained based on the data set to predict whether the sediment disturbance will exceed the pollution standard at the next k moments; Set different levels of pollution release thresholds for different water body types; compare the output of the machine learning model with the preset pollution release thresholds of different levels. When the output value of the machine learning model is greater than the pollution release threshold, generate different levels of sediment pollution warning instructions.

[0011] Preferably, the acoustic disturbance data refers to the echo spectrum signal generated by the sediment particles after being disturbed; the acquisition frequency of the acoustic disturbance data is 10 times per second, and the edge preprocessing includes: signal conversion and invalid signal elimination; The first group of water environment data is four types of indicators at the bottom mud (water temperature, pH value, dissolved oxygen concentration and flow rate); the second group of water environment data is four types of indicators 5 cm vertically upward from the bottom mud; the third group of water environment data is four types of indicators 15 cm vertically upward from the bottom mud; the fourth group of water environment data is four types of indicators 50 cm vertically upward from the bottom mud; there are four groups of water environment gradient data; each group is a three-dimensional vector; the timestamp information includes the collection date, hour, minute, second and quarterly identification.

[0012] Preferably, the Fourier transform is defined as: ; Where: is the Fourier transform frequency distribution; is the time domain signal; is the frequency after Fourier transform; is the start time of Fourier transform; For time; is an imaginary unit; is the time length of the window; The main frequency is the frequency with the largest amplitude in the spectrum; the energy difference refers to the difference in frequency domain energy between the two time windows before and after the disturbance; the spectrum skewness indicates the symmetry of the spectrum distribution; Spectral skewness ; Where: For the Frequency; for The corresponding amplitude on is the mean frequency of the spectrum; is the spectrum standard deviation; is the total number of frequency points.

[0013] Preferably, disturbance sound wave indicator ; In the formula, is the main frequency; is the energy difference; =10 -6 ; =1.2; The process of establishing the data set includes: the disturbance sound wave index and water environment gradient data with the same timestamp are used as feature data, and the label corresponding to the feature data is the pollution release label at the time k after the timestamp; when the pollutant concentration is detected to exceed the standard at any position 5cm, 15cm, and 50cm above the bottom mud, the pollution release label is set to 1; otherwise, it is set to 0; A set of feature data and labels are constructed as samples, and multiple groups of samples are collected to establish a data set; the data set is divided into a training set, a validation set, and a test set, where the training set accounts for 60% of the data set, and the validation set and the test set each account for 20% of the data set.

[0014] Preferably, the training process of the machine learning model includes: Using the training set as the input of the machine learning model, the machine learning model takes the predicted value of the pollution release label at the future k moments as the output, takes the characteristic data of the real-time disturbance sound wave index and the water environment gradient data as the prediction target, and takes minimizing the machine learning model loss function value as the training target; when the machine learning model loss function value is less than or equal to the preset target loss value, the training is stopped; The machine learning model loss function is the mean square error; the mean square error is calculated by Minimize to train the model; in the loss function is the loss function value, i is the sample group number; is the number of sample groups; is the label of the i-th group of samples, The pollution release label predicted for the i-th group of samples; The machine learning model is a recurrent neural network model, and the recurrent neural network model can be a long short-term memory network or a gated recurrent unit.

[0015] Preferably, the urban river threshold sets the third-level pollution release threshold, the artificial water bodies such as regulating reservoirs set the second-level pollution release threshold; the eutrophic lakes set the first-level pollution release threshold; the third-level pollution release threshold> the second-level pollution release threshold> the first-level pollution release threshold.

[0016] Preferably, the first-level sediment pollution warning instruction includes that the system immediately sends a high-level warning, notifies the environmental emergency response team to respond quickly, and initiates the emergency pollution control plan, including the rapid deployment of pollution interception measures, the blocking of pollution sources and the rapid restoration of water quality, etc., to prevent the spread of pollution; Level 2 sediment pollution warning instructions include: the system automatically generates a medium-level warning, notifies on-site management personnel to conduct key inspections and intensive water quality monitoring of relevant waters, and deploys pollution control resources in advance if necessary to prepare for further response; The third-level sediment pollution warning instructions include: the system issues a low-level reminder, recommending that staff conduct regular inspections of the water area, make preparations for pollution prevention and control, pay attention to changes in pollution trends, and record relevant data for analysis and evaluation.

[0017] A pollution source detection system for water environment management, the system comprising: The data collection module collects acoustic disturbance data and performs edge preprocessing in real time; while collecting acoustic disturbance data, water environment data is collected synchronously, and the water environment data includes four groups of indicators at the same time; each group of indicators includes four categories: water temperature, pH value, dissolved oxygen concentration and flow rate; The data processing module calculates the water environment gradient data based on the water environment data; assuming that the water environment data is ; is the water environment data group number, =1, 2, 3, 4 are the first, second, third and fourth groups of water environment data respectively; is the water environment data category identifier, =1, 2, 3, 4 represent water temperature, pH value, dissolved oxygen concentration and flow rate respectively; the water environment gradient data are: In the formula, =1; The data synchronization module introduces the water environment gradient data and acoustic disturbance data into the time synchronization mechanism and adds a unified timestamp; The data enhancement module uses Fourier transform to obtain frequency domain information based on the acoustic disturbance data; obtains the main frequency, energy difference and spectrum skewness based on the spectrum information; and calculates the disturbance sound wave index based on the spectrum skewness, main frequency and energy difference; The model training module establishes a data set through a sliding window method based on the disturbance acoustic wave index and water environment gradient data; based on the data set, a machine learning model is trained to predict whether the sediment disturbance will exceed the pollution standard at the next k moments; The post-processing module sets different levels of pollution release thresholds for different water body types; based on the comparison between the output of the machine learning model and the preset pollution release thresholds of different levels, when the output value of the machine learning model is greater than the pollution release threshold, different levels of sediment pollution warning instructions are generated.

[0018] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-mentioned pollution source detection method for water environment management by calling the computer program stored in the memory.

[0019] A computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned pollution source detection method for water environment treatment.

[0020] The technical effects and advantages of the pollution source detection method and system for water environment management of the present invention are as follows: the present application uses underwater acoustic sensors to collect acoustic disturbance data, extracts the main frequency, energy difference and spectrum skewness through Fourier transform, and calculates the disturbance sound wave index; at the same time, water environment data (water temperature, pH, dissolved oxygen and flow rate) at different heights above the bottom mud are collected to construct water environment gradient characteristics, and align with the disturbance sound wave index by timestamp to form a sliding window sample. Finally, a recurrent neural network model is used for training to predict the risk of bottom mud pollution release at future moments, and the graded warning instructions are triggered according to the comparison between the model output and the differentially set pollution threshold.

[0021] This application integrates multi-source data fusion, time series modeling and hierarchical response mechanism to achieve high sensitivity, real-time and predictable identification of pollution release behavior caused by sediment disturbance. Gradient water body data and acoustic wave characteristics work together to enhance the model's comprehensive judgment ability on disturbance paths and pollution intensity; differentiated threshold settings improve the system's adaptability and practicality in different water body types, reducing false alarms and missed alarms; hierarchical warning instruction design enables the system to have practical operational guidance, realizing refined, intelligent and efficient water environment management. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of a pollution source detection method for water environment management according to the present invention; Figure 2 A schematic diagram of a pollution source detection system for water environment management according to the present invention; Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application; Figure 4 It is a schematic diagram of the structure of a computer-readable storage medium provided by an embodiment of the present application. DETAILED DESCRIPTION

[0023] 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. Example 1

[0024] See also Figure 1 As shown, the pollution source detection method and system for water environment management described in this embodiment include: Collect acoustic disturbance data, which is obtained by highly sensitive underwater acoustic sensors deployed at the bottom of the water body; the acoustic disturbance data refers to the echo spectrum signal generated by the sediment particles after being disturbed. The disturbance method is to artificially simulate the disturbance caused by the rowing of a ship or other water disturbance behaviors.

[0025] The frequency of collecting acoustic disturbance data is 10 times per second, and it is uploaded to the edge computing device in real time through the RS485 communication protocol. The edge computing device is used to perform edge preprocessing in real time, and the edge preprocessing includes: signal conversion and invalid signal removal operations. Signal conversion is to convert the signal collected by the acoustic disturbance data into a high-precision digital waveform on a unified timeline. Invalid signal removal is to identify and remove useless information and interference information from the received signal. The method of invalid signal removal is the existing technology in the field and will not be repeated here.

[0026] When collecting acoustic disturbance data, water environment data is collected simultaneously, including four types of indicators: water temperature, pH value, dissolved oxygen concentration and flow rate. It should be noted that the water environment data includes four groups of indicators at the same time; each group of indicators includes four types: water temperature, pH value, dissolved oxygen concentration and flow rate; The first group of water environment data is four types of indicators at the bottom mud (water temperature, pH value, dissolved oxygen concentration and flow rate); the second group of water environment data is four types of indicators 5 cm vertically upward from the bottom mud; the third group of water environment data is four types of indicators 15 cm vertically upward from the bottom mud; the fourth group of water environment data is four types of indicators 50 cm vertically upward from the bottom mud.

[0027] This setting is because when the sediment is disturbed, only the composition of the sediment is often considered, which cannot timely reflect the diffusion of the sediment and the actual nonlinear pollution behavior. By setting the water environment data of the gradient height, the changes in the close range after the sediment disturbance can be reflected first, and as the diffusion range increases, the set gradient gradually increases, which effectively prevents the collection of invalid or unclear data and facilitates the accuracy of subsequent analysis.

[0028] The water temperature was obtained by a high-precision digital thermometer in degrees Celsius (°C); the pH value was determined by a water quality ion electrode; the dissolved oxygen concentration was measured using a fluorescence sensor with a range of 0~20 mg / L; and the flow velocity was measured by a Doppler flowmeter.

[0029] Environmental data reflects changes in hydrodynamic and biochemical conditions and is an important driver of sediment pollution release behavior. The higher the flow rate, the easier it is for the sediment to be disturbed and resuspended; changes in water temperature affect microbial activity and indirectly regulate the release rate of pollutants; pH and dissolved oxygen levels determine the release form and migration path of heavy metals and nutrients.

[0030] Calculate water environment gradient data based on water environment data; suppose water environment data is ; is the water environment data group number, =1, 2, 3, 4 are the first, second, third and fourth groups of water environment data respectively; is the water environment data category identifier, =1, 2, 3, 4 represent water temperature, pH value, dissolved oxygen concentration and flow rate respectively; the water environment gradient data are: In the formula, =1; The water environment gradient data also consists of four groups; each group is a three-dimensional vector; the collection of water environment gradient data is to improve the sensitivity to abnormal pollution release events; traditional sediment collection and analysis only exist in the laboratory, which is far from enough if the timely response capability of the environmental protection department needs to be improved. By obtaining water environment gradient data and amplifying the gradient impact of sediment disturbance, it is conducive to timely and real-time detection and early warning of water pollution. The vector method can better reflect the gradient trend than single data.

[0031] The water environment gradient data and acoustic disturbance data are introduced into the time synchronization mechanism, which gives the water environment gradient data and acoustic disturbance data a unified timestamp, accurate to seconds. The timestamp information includes the acquisition date, hour, minute, second and quarterly identification, which is used to subsequently construct the time series input feature matrix. The quarterly information is automatically determined by the system's built-in calendar module. The introduction of time features can enhance the model's ability to identify periodic disturbance events and seasonal pollution release trends.

[0032] Calculating the disturbance sound wave index based on the sound disturbance data; the calculation process includes: obtaining frequency domain information using Fourier transform based on the sound disturbance data; Fourier transform is defined as: ; Where: is the Fourier transform frequency distribution; is the time domain signal; is the frequency after Fourier transform; is the start time of Fourier transform; For time; is an imaginary unit; is the time length of the window.

[0033] Based on the spectrum information, the main frequency, energy difference and spectrum skewness are obtained. The main frequency is the frequency with the largest amplitude in the spectrum, which means that in the current disturbance signal, the strongest energy is concentrated on a specific frequency, which helps to identify the key frequency of the pollution disturbance; the energy difference refers to the frequency domain energy difference between the two time windows before and after the disturbance; the spectrum skewness indicates the symmetry of the spectrum distribution; if the skewness is large, it means that the spectrum is asymmetric and may be interfered or abnormal; if the skewness is small, it means that the energy distribution of this disturbance is uniform or concentrated, and the structure is more stable and reliable. Spectrum skewness ; Where: For the Frequency; for The corresponding amplitude on is the mean frequency of the spectrum; is the spectrum standard deviation; is the total number of frequency points; Calculate disturbance sound wave indicators based on spectrum skewness, main frequency, and energy difference: ; In the formula, is the main frequency; is the energy difference; =10 -6 ; =1.2; The higher the main frequency, the more subtle and intense the disturbance may be. The greater the energy difference, the greater the natural disturbance and the larger the disturbance sound wave index. The greater the spectrum skewness, the more asymmetric and dispersed the spectrum is, and the corresponding signal quality is lower. Therefore, the greater the spectrum skewness, the smaller the disturbance sound wave index. The disturbance sound wave index is an important indicator for judging whether the disturbance is significant and stable, and avoiding irrelevant disturbances. The more concentrated the disturbance, the more drastic the energy change. The smaller the skewness, the more significant the disturbance is. At this time, the sediment disturbance has a clear directionality, which is convenient for identifying disturbances.

[0034] A data set is established through a sliding window method based on the disturbance acoustic wave index and water environment gradient data; the disturbance acoustic wave index and water environment gradient data with the same timestamp are used as feature data, and the label corresponding to the feature data is the pollution release label at the time k after the timestamp; when the pollutant concentration is detected to exceed the standard at any position of 5cm, 15cm, and 50cm above the bottom mud, the pollution release label is set to 1; otherwise, it is set to 0; The pollutant concentration is obtained in real time by online water quality monitoring instruments, including but not limited to online ammonia nitrogen analyzers and online heavy metal monitors. The standards for pollutant concentration exceeding the standard are formulated by relevant departments.

[0035] A set of feature data and labels are constructed as samples, and multiple sets of samples are collected to establish a data set; the data set is divided into a training set, a validation set, and a test set, where the training set accounts for 60% of the data set, and the validation set and the test set each account for 20% of the data set; The present invention provides the following example of the sliding window method: Assuming that there are 5 minutes of characteristic data of disturbance sound wave index and water environment gradient data, the 5 minutes of characteristic data are marked as a characteristic data set , { , , , , }, ={ }, Indicates the characteristic data of the first minute, and so on; Release the label for the pollution in the 1+kth minute; and Construct a set of samples; the next set of samples is { , , , ,}and{ }; Use sliding windows to build multiple samples and labels; It should be noted that constructing the data set in this way can effectively extract the potential impact of disturbance signals on pollution release at different time stages, and the multi-layer water environment gradient data can characterize the path after the disturbance; introducing acoustic disturbance indicators as input variables can improve the model's sensitivity and discrimination for instantaneous disturbance identification.

[0036] Based on the data set, a machine learning model is trained to predict whether the sediment disturbance will exceed the pollution standard at the next k moments; Using the training set as the input of the machine learning model, the machine learning model takes the predicted value of the pollution release label at the future k moments as the output, takes the characteristic data of the real-time disturbance sound wave index and the water environment gradient data as the prediction target, and takes minimizing the machine learning model loss function value as the training target; when the machine learning model loss function value is less than or equal to the preset target loss value, the training is stopped; The machine learning model loss function is the mean square error; the mean square error is calculated by Minimize to train the model; in the loss function is the loss function value, i is the sample group number; is the number of sample groups; is the label of the i-th group of samples, The pollution release label predicted for the i-th group of samples; Preferably, the machine learning model is a recurrent neural network model, and the recurrent neural network model can be a long short-term memory network (LSTM) or a gated recurrent unit (GRU).

[0037] Other model parameters of the machine learning model, such as the depth of the network model, the number of neurons in each layer, the activation function used by the network model, the convergence condition, the ratio of the training set, the test set, and the validation set, and the loss function, are all obtained through actual engineering implementation and continuous experimental tuning.

[0038] Different levels of pollution release thresholds are set for different water body types. Different pollution release thresholds are set by qualified water environment management personnel based on factors such as the functional positioning, pollution carrying capacity and management standards of specific water bodies; Based on the comparison between the output of the machine learning model and the preset pollution release thresholds of different levels, when the output value of the machine learning model is greater than the pollution release threshold, different levels of sediment pollution warning instructions are generated; Since different water bodies have significantly different sensitivities to pollutant concentrations, targeted threshold designs are required. Taking urban rivers as an example, they are mostly responsible for drainage, landscaping and certain ecological functions, and have a certain tolerance for pollution. In order to avoid frequent triggering of false alarms and affect urban management efficiency, the pollution release threshold can be appropriately increased as the third-level pollution release threshold to ensure that the early warning system is more stable and practical. For artificial water bodies such as regulating reservoirs, their original design intention is to receive rainwater and sewage and reduce non-point source pollution. A certain degree of pollution accumulation is allowed during the operation process, but strict requirements are placed on outflow control. Therefore, a second-level pollution release threshold is set; eutrophic lakes are extremely sensitive to pollution inputs and are prone to endogenous pollution due to sediment disturbance, leading to ecological problems such as algal blooms or black and smelly water. Such water bodies should set lower threshold standards, identify potential risks caused by minor disturbances in advance, and set a first-level pollution release threshold. The third-level pollution release threshold > the second-level pollution release threshold > the first-level pollution release threshold; The advantage of differentiated threshold design is that it not only enhances the practical guiding significance of the model prediction results and makes the output results closer to the response needs of actual water management, but also avoids the false alarm or missed alarm problems caused by unified thresholds, thereby improving the stability and reliability of the early warning system.

[0039] The first-level sediment pollution warning instructions include the system immediately sending a high-level warning, notifying the environmental emergency team to respond quickly, and launching an emergency pollution control plan, including quickly deploying pollution interception measures, sealing off pollution sources, and quickly repairing water quality to prevent the spread of pollution.

[0040] The second-level sediment pollution warning instructions include: the system automatically generates a medium-level warning, notifies on-site management personnel to conduct key inspections and intensive water quality monitoring of relevant waters, and deploys pollution control resources in advance when necessary to prepare for further response.

[0041] The third-level sediment pollution warning instructions include: the system issues a low-level reminder, recommending that staff conduct regular inspections of the water area, make preparations for pollution prevention and control, pay attention to changes in pollution trends, and record relevant data for analysis and evaluation.

[0042] This application uses underwater acoustic sensors to collect acoustic disturbance data, extracts the main frequency, energy difference and spectrum skewness through Fourier transform, and calculates the disturbance sound wave index; at the same time, water environment data (water temperature, pH, dissolved oxygen and flow rate) at different heights above the sediment are collected to construct water environment gradient characteristics, and align them with the disturbance sound wave index by timestamp to form a sliding window sample. Finally, a recurrent neural network model is used for training to predict the risk of sediment pollution release at future moments, and the graded warning instructions are triggered based on the comparison of the model output with the differentially set pollution threshold.

[0043] This application integrates multi-source data fusion, time series modeling and hierarchical response mechanism to achieve high sensitivity, real-time and predictable identification of pollution release behavior caused by sediment disturbance. Gradient water body data and acoustic wave characteristics work together to enhance the model's comprehensive judgment ability on disturbance paths and pollution intensity; differentiated threshold settings improve the system's adaptability and practicality in different water body types, reducing false alarms and missed alarms; hierarchical warning instruction design enables the system to have practical operational guidance, realizing refined, intelligent and efficient water environment management. Example 2

[0044] See also Figure 2 As shown, the part not described in detail in this embodiment is described in Example 1, and a pollution source detection system for water environment management is provided, the system comprising: The data collection module collects acoustic disturbance data and performs edge preprocessing in real time; while collecting acoustic disturbance data, water environment data is collected synchronously, and the water environment data includes four groups of indicators at the same time; each group of indicators includes four categories: water temperature, pH value, dissolved oxygen concentration and flow rate; The data processing module calculates the water environment gradient data based on the water environment data; assuming that the water environment data is ; is the water environment data group number, =1, 2, 3, 4 are the first, second, third and fourth groups of water environment data respectively; is the water environment data category identifier, =1, 2, 3, 4 represent water temperature, pH value, dissolved oxygen concentration and flow rate respectively; the water environment gradient data are: In the formula, =1; The data synchronization module introduces the water environment gradient data and acoustic disturbance data into the time synchronization mechanism and adds a unified timestamp; The data enhancement module uses Fourier transform to obtain frequency domain information based on the acoustic disturbance data; obtains the main frequency, energy difference and spectrum skewness based on the spectrum information; and calculates the disturbance sound wave index based on the spectrum skewness, main frequency and energy difference; The model training module establishes a data set through a sliding window method based on the disturbance acoustic wave index and water environment gradient data; based on the data set, a machine learning model is trained to predict whether the sediment disturbance will exceed the pollution standard at the next k moments; The post-processing module sets different levels of pollution release thresholds for different water body types; based on the comparison between the output of the machine learning model and the preset pollution release thresholds of different levels, when the output value of the machine learning model is greater than the pollution release threshold, different levels of sediment pollution warning instructions are generated.

[0045] The modules are connected via wired and / or wireless networks. Example 3

[0046] See also Figure 3 As shown, according to another aspect of the present application, an electronic device 500 is also provided. The electronic device 500 may include one or more processors and one or more memories. The memories store computer readable codes, and when the computer readable codes are executed by one or more processors, a pollution source detection method and system for water environment treatment as described above may be executed.

[0047] The method or system according to the embodiment of the present application can also be used by Figure 3 The electronic device architecture shown in FIG. Figure 3 As shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to a network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, may store a pollution source detection method for water environment treatment provided by the present application. Furthermore, the electronic device 500 may also include a user interface 508. Of course, Figure 3 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 3 One or more components of an electronic device are shown. Example 4

[0048] See also Figure 4 As shown, a computer-readable storage medium 600 according to an embodiment of the present application is shown. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are executed by the processor, a pollution source detection method for water environment management according to an embodiment of the present application described with reference to the above figures can be executed. The storage medium 600 includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0049] In addition, according to the embodiments of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the present application provides a non-transitory machine-readable storage medium, which stores machine-readable instructions, and the machine-readable instructions can be executed by a processor to execute instructions corresponding to the method steps provided in the present application. When the computer program is executed by a central processing unit (CPU), the above functions defined in the method of the present application are executed.

[0050] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through a wired network or a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0051] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0052] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0053] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a method and system for detecting pollution sources for water environment management. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0054] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0055] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0056] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0057] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A pollution source detection method for water environment management, characterized in that: include: Collect acoustic disturbance data and perform edge preprocessing in real time; When collecting the acoustic disturbance data, water environment data is collected simultaneously, and the water environment data includes four groups of indicators at the same time; each group of indicators includes four categories: water temperature, pH value, dissolved oxygen concentration and flow rate; Calculate water environment gradient data based on water environment data; suppose water environment data is ; is the water environment data group number, =1, 2, 3, 4 are the first, second, third and fourth groups of water environment data respectively; is the water environment data category identifier, =1, 2, 3, 4 represent water temperature, pH value, dissolved oxygen concentration and flow rate respectively; the water environment gradient data are: In the formula, =1; Introduce water environment gradient data and acoustic disturbance data into the time synchronization mechanism and give them a unified time stamp; Obtain frequency domain information using Fourier transform based on acoustic disturbance data; obtain main frequency, energy difference and spectrum skewness based on spectrum information; calculate disturbance sound wave index based on spectrum skewness, main frequency and energy difference; A data set is established through a sliding window method based on disturbance acoustic wave indicators and water environment gradient data; a machine learning model is trained based on the data set to predict whether the sediment disturbance will exceed the pollution standard at the next k moments; Setting different levels of pollution release thresholds for different water body types; The output of the machine learning model is compared with the preset pollution release thresholds of different levels. When the output value of the machine learning model is greater than the pollution release threshold, different levels of sediment pollution warning instructions are generated.

2. A method for detecting pollution sources for water environment management according to claim 1, characterized in that: The acoustic disturbance data refers to the echo spectrum signal generated by the sediment particles after being disturbed; The frequency of collecting acoustic disturbance data is 10 times per second, and the edge preprocessing includes: signal conversion and invalid signal elimination; The first group of water environment data includes four types of indicators at the bottom mud, including water temperature, pH value, dissolved oxygen concentration and flow rate; the second group of water environment data includes four types of indicators at 5 cm vertically upward from the bottom mud; the third group of water environment data includes four types of indicators at 15 cm vertically upward from the bottom mud; the fourth group of water environment data includes four types of indicators at 50 cm vertically upward from the bottom mud; there are four groups of water environment gradient data; each group is a three-dimensional vector; the timestamp information includes the collection date, hour, minute, second and quarter mark.

3. A method for detecting pollution sources for water environment management according to claim 2, characterized in that: The Fourier transform is defined as: ; Where: is the Fourier transform frequency distribution; is the time domain signal; is the frequency after Fourier transform; is the start time of Fourier transform; For time; is an imaginary unit; is the time length of the window; The main frequency is the frequency with the largest amplitude in the spectrum; the energy difference refers to the difference in frequency domain energy between the two time windows before and after the disturbance; the spectrum skewness indicates the symmetry of the spectrum distribution; Spectral skewness ; Where: For the Frequency; for The corresponding amplitude on is the mean frequency of the spectrum; is the spectrum standard deviation; is the total number of frequency points.

4. A method for detecting pollution sources for water environment management according to claim 3, characterized in that: Disturbance Sonic Wave Indicator ; In the formula, is the main frequency; is the energy difference; =10 -6 ; =1.2; The process of establishing the data set includes: the disturbance sound wave index and water environment gradient data with the same timestamp are used as feature data, and the label corresponding to the feature data is the pollution release label at the time k after the timestamp; when the pollutant concentration is detected to exceed the standard at any position 5cm, 15cm, and 50cm above the bottom mud, the pollution release label is set to 1; otherwise, it is set to 0; A set of feature data and labels are constructed as samples, and multiple groups of samples are collected to establish a data set; the data set is divided into a training set, a validation set, and a test set, where the training set accounts for 60% of the data set, and the validation set and the test set each account for 20% of the data set.

5. A method for detecting pollution sources for water environment management according to claim 4, characterized in that: The training process of the machine learning model includes: Using the training set as the input of the machine learning model, the machine learning model takes the predicted value of the pollution release label at the future k moments as the output, takes the characteristic data of the real-time disturbance sound wave index and the water environment gradient data as the prediction target, and takes minimizing the machine learning model loss function value as the training target; when the machine learning model loss function value is less than or equal to the preset target loss value, the training is stopped; The machine learning model loss function is the mean square error; the mean square error is calculated by Minimize to train the model; in the loss function is the loss function value, i is the sample group number; is the number of sample groups; is the label of the i-th group of samples, The pollution release label predicted for the i-th group of samples; The machine learning model is a recurrent neural network model, and the recurrent neural network model can be a long short-term memory network or a gated recurrent unit.

6. A method for detecting pollution sources for water environment management according to claim 5, characterized in that: The third-level pollution release threshold is set for urban river thresholds, the second-level pollution release threshold is set for artificial water bodies such as regulating reservoirs, and the first-level pollution release threshold is set for eutrophic lakes; the third-level pollution release threshold > the second-level pollution release threshold > the first-level pollution release threshold.

7. A method for detecting pollution sources for water environment management according to claim 6, characterized in that: The first-level sediment pollution warning instructions include the system immediately sending a high-level warning, notifying the environmental emergency team to respond quickly, and launching the emergency pollution control plan, including the rapid deployment of pollution interception measures, the blocking of pollution sources and the rapid restoration of water quality, etc., to prevent the spread of pollution; Level 2 sediment pollution warning instructions include: the system automatically generates a medium-level warning, notifies on-site management personnel to conduct key inspections and intensive water quality monitoring of relevant waters, and deploys pollution control resources in advance if necessary to prepare for further response; The third-level sediment pollution warning instructions include: the system issues a low-level reminder, recommending that staff conduct regular inspections of the water area, make preparations for pollution prevention and control, pay attention to changes in pollution trends, and record relevant data for analysis and evaluation.

8. A pollution source detection system for water environment management, the system comprising: The data collection module collects acoustic disturbance data and performs edge preprocessing in real time; while collecting acoustic disturbance data, water environment data is collected synchronously, and the water environment data includes four groups of indicators at the same time; each group of indicators includes four categories: water temperature, pH value, dissolved oxygen concentration and flow rate; The data processing module calculates the water environment gradient data based on the water environment data; assuming that the water environment data is ; is the water environment data group number, =1, 2, 3, 4 are the first, second, third and fourth groups of water environment data respectively; is the water environment data category identifier, =1, 2, 3, 4 represent water temperature, pH value, dissolved oxygen concentration and flow rate respectively; the water environment gradient data are: In the formula, =1; The data synchronization module introduces the water environment gradient data and acoustic disturbance data into the time synchronization mechanism and adds a unified timestamp; The data enhancement module uses Fourier transform to obtain frequency domain information based on the acoustic disturbance data; obtains the main frequency, energy difference and spectrum skewness based on the spectrum information; and calculates the disturbance sound wave index based on the spectrum skewness, main frequency and energy difference; The model training module establishes a data set through a sliding window method based on the disturbance acoustic wave index and water environment gradient data; based on the data set, a machine learning model is trained to predict whether the sediment disturbance will exceed the pollution standard at the next k moments; Post-processing module, setting different levels of pollution release thresholds for different water types; The output of the machine learning model is compared with the preset pollution release thresholds of different levels. When the output value of the machine learning model is greater than the pollution release threshold, different levels of sediment pollution warning instructions are generated.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the pollution source detection method for water environment management according to any one of claims 1 to 7 by calling the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute a pollution source detection method for water environment treatment as described in any one of claims 1 to 7.

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