Method and system for identifying water body odor characteristics

Abstract

The present invention provides a method and system for identifying water body odor characteristics, which relates to the technical field of water body odor identification. The specific steps include: collecting sample characteristic data of odorous water bodies within a historical time period, combining it with data of the current water body to be detected, including chemical components such as volatile organic compounds, humic acid, and sulfide concentrations, as well as environmental parameters such as ambient temperature and water flow rate; extracting an odor characteristic matrix from the historical data; and using an expert-determined odor grade label training model to construct an odor characteristic matrix for the current water body to be detected and obtain its odor grade. Correlation analysis is performed on environmental and chemical parameters to generate environmental impact coefficients and chemical impact coefficients, calculate a comprehensive scoring coefficient, and evaluate changes in the odor grade of the water body to be detected. The present invention can significantly improve the recognition accuracy of water body odor characteristics, effectively predict changes in the odor grade of the water body to be detected, identify potential water quality problems, and take timely intervention measures.

Description

Technical Field

[0001] The present invention relates to the technical field of water body odor recognition, and in particular to a method and system for recognizing water body odor characteristics. Background Art

[0002] Water odor and taste signature identification involves analyzing the chemical composition and physical properties of water samples to determine the odor and potential contamination level of the water. Water odor typically refers to the odor experienced by humans when perceiving water. This odor may originate from components such as volatile organic compounds, humic acid, and sulfides. Water odor and taste signature identification is crucial, not only assisting in water quality monitoring but also providing a scientific basis for water resource management. By identifying water odor and taste signatures, relevant departments can take timely measures to control pollution sources, ensuring drinking water safety and protecting the ecological environment.

[0003] Publication No. CN116502130A provides a method for identifying algae-derived odor characteristics, including the following steps: obtaining algae-derived odor-causing organic compound data and mass spectrum data; obtaining MACCS organic compound molecular fingerprints corresponding to the algae-derived odor-causing organic compound data and the MACCS molecular fingerprints corresponding to the mass spectrum data; training different machine learning models using the MACCS organic compound molecular fingerprints corresponding to the training set of the algae-derived odor-causing organic compound odor classification data to obtain an optimal odor classification model; training different machine learning models using the MACCS organic compound molecular fingerprints corresponding to the training set of the odor threshold data to obtain an optimal odor threshold prediction model; inputting the algae-derived odor-causing organic compound data to the model to output an odor identification result for the algae-derived odor-causing organic compound. This method is low-cost, simple, and rapid, and saves significant manpower, material, and financial resources.

[0004] However, there are still the following deficiencies. As can be seen from the above statements, the existing technology focuses on the identification of algae-derived odor-causing organic matter and its odor characteristics, and does not involve the consideration of environmental parameters (such as temperature, water flow rate, rainfall, etc.) and chemical parameters (such as chemical oxygen demand, biochemical oxygen demand, etc.). The odor of water bodies is not only affected by chemical composition, but also significantly affected by environmental conditions, which reduces the accuracy of the identification results. In addition, it only focuses on the odor characteristics of the current water body and lacks the prediction of the trend of odor changes, resulting in the inability to issue early warnings for pollution incidents.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and system for identifying the odor characteristics of water bodies to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for identifying odor characteristics of water bodies, comprising the following steps:

[0009] S1. Collecting characteristic data of odorous water samples over T historical time periods, as well as characteristic data of the water sample to be tested at the current moment, including volatile organic compound concentration, humic acid concentration, sulfide concentration, water pH value, and dissolved oxygen content. Environmental and chemical parameters that affect the condition of the water sample to be tested at the current moment are also collected. Environmental parameters include ambient temperature, water flow rate, rainfall, light intensity, and wind speed. Chemical parameters include chemical oxygen demand, biochemical oxygen demand, nitrogen concentration, phosphorus concentration, and turbidity.

[0010] S2. Extracting the odor characteristic matrix of the odorous water body from the sample characteristic data of the odorous water body within T historical time periods, including the average volatile organic compound concentration, average humic acid concentration, average sulfide concentration, average water pH value, and average dissolved oxygen content;

[0011] S3. The odor feature matrix of the odorous water body is used as input and the odor level is used as the label for training the model. The odor level is determined by an expert group and the odor level model is trained. The odor level is 1, 2, or 3, where 1 represents a lightly odorous water body, 2 represents a moderately odorous water body, and 3 represents a heavily odorous water body.

[0012] S4. Assign different water system values ​​to water bodies with mild odor, water bodies with moderate odor, and water bodies with severe odor;

[0013] S5. Using the sample characteristic data of the water body to be detected at the current moment, constructing the odor characteristic matrix of the water body to be detected, the odor characteristic matrix of the water body to be detected is input into the odor level model after training, and the odor level of the water body to be detected at the current moment is obtained;

[0014] S6. Processing and correlation analysis of the ambient temperature, water flow rate, rainfall, light intensity, and wind speed that currently affect the water body to be tested to generate an environmental impact coefficient for assessing the deterioration trend of the environmental parameters affecting the water body to be tested; processing and correlation analysis of the chemical oxygen demand, biochemical oxygen demand, nitrogen concentration, phosphorus concentration, and turbidity that currently affect the water body to be tested to generate a chemical impact coefficient for assessing the deterioration trend of the chemical parameters affecting the water body to be tested; processing the environmental impact coefficient and chemical impact coefficient of the water body to be tested to generate a comprehensive scoring coefficient for the water body to be tested at the current moment;

[0015] S7. Evaluate changes in the odor level of the water body to be tested based on the comprehensive scoring coefficient, odor level, and water system coefficient of the water body to be tested at the current moment.

[0016] Furthermore, the average volatile organic compound concentration, average humic acid concentration, average sulfide concentration, average water pH value, and average dissolved oxygen content are obtained according to the following public announcement:

[0017]

[0018]

[0019] Among them, μ hy 、μ fz 、μ lh 、μ ss 、μ ry They are the average concentration of volatile organic compounds, average concentration of humic acid, average concentration of sulfide, average pH value of water body, average dissolved oxygen content, hy i 、fz i 、lh i 、ss i 、ry i are the volatile organic compound concentration, humic acid concentration, sulfide concentration, water pH value, and dissolved oxygen content in the i-th historical time period, respectively. i is the index of the historical time period, and i∈[1,T].

[0020] Furthermore, the odor feature matrix of the odorous water body and the water body to be detected is constructed. The specific process is as follows:

[0021]

[0022] Among them, X is the odor characteristic matrix of the odorous water body;

[0023] Using the sample feature data of the water body to be detected at the current moment, the odor feature matrix of the water body to be detected is constructed:

[0024]

[0025] Among them, d is the odor characteristic matrix of the water body to be detected, μ hy '、μ fz '、μ lh '、μ ss '、μ ry ' are the average volatile organic compound concentration, average humic acid concentration, average sulfide concentration, average water pH value, and average dissolved oxygen content of the water body to be tested.

[0026] Furthermore, the odor level model is constructed using a deep neural network based on a multilayer perceptron, wherein the deep neural network of the multilayer perceptron includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer, and an output layer, wherein the first hidden layer, the second hidden layer, and the third hidden layer each have at least two neurons and each use ReLU as an activation function;

[0027] The process of training the environmental impact model is as follows:

[0028] According to the odor characteristic matrix of odorous water bodies in T historical time periods, the corresponding odor level is determined based on the expert scoring method. The higher the odor level, the higher the degree of water deterioration.

[0029] The odor feature matrix of odorous water bodies in T historical time periods is used as input, and the odor level is used as the output label for training. The mean square error is used as the loss function. When the mean square error is in the range of [0, 0.01], the training of the odor level model is completed.

[0030] Furthermore, different water system coefficients are assigned to water bodies with mild odor, water bodies with moderate odor, and water bodies with severe odor. The specific process is as follows:

[0031] The odor level is 1, 2, or 3, where 1 represents a lightly odorous water body, 2 represents a moderately odorous water body, and 3 represents a heavy odorous water body.

[0032] The water system index of water bodies with mild odor is assigned to 0.3, the water system index of water bodies with moderate odor is assigned to 0.5, and the water system index of water bodies with severe odor is assigned to 0.7.

[0033] Furthermore, the temperature, water flow rate, rainfall, light intensity and wind speed of the water body to be detected at the current moment are processed and correlated to generate an environmental impact coefficient for evaluating the deterioration trend of environmental parameters on the water body to be detected, based on the following formula:

[0034]

[0035] Among them, HJxs is the environmental impact coefficient at the current moment, WD is the ambient temperature, GZ is the light intensity, FS is the wind speed, SL is the water flow velocity, JY is the rainfall, ω1 is the weight coefficient of the ambient temperature, ω2 is the weight coefficient of the light intensity, ω3 is the weight coefficient of the wind speed, and ω4 is the weight coefficient of the combination of water flow velocity and rainfall. On the basis of ω1+ω2+ω3+ω4=1, let 0<ω3<ω2<ω1<ω4<1.

[0036] Furthermore, the chemical oxygen demand, biochemical oxygen demand, nitrogen concentration, phosphorus concentration, and turbidity that affect the water body to be tested at the current moment are processed and correlated to generate a chemical impact coefficient for evaluating the deterioration trend of the chemical parameters affecting the water body to be tested. The formula is as follows:

[0037] HXxs=β1(HY×SY) ZD +β2(NN×LN)

[0038] Among them, HXxs is the chemical influence coefficient at the current moment, HY is the chemical oxygen demand, SY is the biochemical oxygen demand, NN is the nitrogen concentration, LN is the phosphorus concentration, ZD is the turbidity, β1 is the weight coefficient of the combination of chemical oxygen demand and biochemical oxygen demand, β2 is the weight coefficient of the combination of nitrogen concentration and phosphorus concentration, and on the basis of β1+β2=1, it is set to 0<β2<β1<1.

[0039] Furthermore, the environmental impact coefficient and chemical impact coefficient of the water body to be tested at the current moment are processed to generate a comprehensive scoring coefficient of the water body to be tested at the current moment, based on the following formula:

[0040] PFxs=γ1HJxs+γ2HXxs

[0041] Among them, PFxs is the comprehensive scoring coefficient of the water body to be tested at the current moment, γ1 is the weight coefficient of the environmental impact coefficient, and γ2 is the weight coefficient of the chemical impact coefficient. The specific values ​​of γ1 and γ2 are determined by the hierarchical analysis method.

[0042] Furthermore, based on the comprehensive scoring coefficient, odor level and water system coefficient of the water body to be tested at the current moment, the change in the odor level of the water body to be tested is evaluated. The specific process is as follows:

[0043] When the comprehensive scoring coefficient is less than the water system coefficient, that is, PFxs<SZxs, it means that the water quality score of the water body to be tested is lower than the standard associated with the current odor level, indicating that the water body has improved and the odor characteristics have become better;

[0044] If the odor level of the water body to be tested is above moderate at the current moment, reduce the odor level by 1 level;

[0045] The odor level of the water body to be tested is currently mild, so the odor level should be kept at mild.

[0046] When the comprehensive scoring coefficient is equal to the water system coefficient, that is, PFxs=SZxs, it means that the water quality score of the water body to be tested is equal to the standard associated with the current odor level, indicating that the water body condition remains unchanged and the odor characteristics remain unchanged;

[0047] Keep the odor level of the water body to be detected at the current moment unchanged;

[0048] When the comprehensive scoring coefficient is greater than the water system coefficient, that is, PFxs>SZxs, it means that the water quality score of the water body to be tested is higher than the standard associated with the current odor level, indicating that the water body has deteriorated and the odor characteristics have become worse;

[0049] If the odor level of the water body to be tested is below severe at the current moment, increase the odor level by 1 level;

[0050] The odor level of the water body to be tested is severe at the current moment, and the odor level will be maintained at severe.

[0051] A water body odor feature recognition system, the system being configured to execute any of the above-mentioned water body odor feature recognition methods, comprising:

[0052] A data acquisition module is used to collect sample characteristic data of odorous water bodies within T historical time periods, as well as sample characteristic data of the water body to be tested at the current moment. The sample characteristic data include volatile organic compound concentration, humic acid concentration, sulfide concentration, water pH value and dissolved oxygen content, and collect environmental parameters and chemical parameters that affect the condition of the water body to be tested at the current moment. Environmental parameters include ambient temperature, water flow rate, rainfall, light intensity and wind speed. Chemical parameters include chemical oxygen demand, biochemical oxygen demand, nitrogen concentration, phosphorus concentration and turbidity.

[0053] A matrix construction module is used to extract the odor characteristic matrix of the odorous water body from the sample characteristic data of the odorous water body in T historical time periods, including the average volatile organic compound concentration, the average humic acid concentration, the average sulfide concentration, the average water pH value, and the average dissolved oxygen content;

[0054] An odor model construction module is used to take the odor feature matrix of the odorous water body as input and the odor level as a label training model. The odor level is determined by an expert group and the odor level model is trained. The odor level is 1, 2 or 3, where 1 represents a lightly odorous water body, 2 represents a moderately odorous water body, and 3 represents a heavily odorous water body.

[0055] An assignment module is used to assign different water system coefficients to water bodies with light odor, water bodies with moderate odor, and water bodies with heavy odor;

[0056] The water body identification module is used to construct an odor feature matrix of the water body to be detected using the sample feature data of the water body to be detected at the current moment, input the odor feature matrix of the water body to be detected into the trained odor grade model, and obtain the odor grade of the water body to be detected at the current moment;

[0057] a data processing and analysis module for performing data processing and correlation analysis on the ambient temperature, water flow rate, rainfall, light intensity, and wind speed that affect the water body to be tested at the current moment, to generate an environmental impact coefficient for evaluating the deterioration trend of the environmental parameters affecting the water body to be tested; performing data processing and correlation analysis on the chemical oxygen demand, biochemical oxygen demand, nitrogen concentration, phosphorus concentration, and turbidity that affect the water body to be tested at the current moment, to generate a chemical impact coefficient for evaluating the deterioration trend of the chemical parameters affecting the water body to be tested; and performing data processing on the environmental impact coefficient and chemical impact coefficient of the water body to be tested at the current moment to generate a comprehensive scoring coefficient for the water body to be tested at the current moment;

[0058] The water body assessment module is used to assess the change in the odor level of the water body to be detected based on the comprehensive scoring coefficient, odor level and water system coefficient of the water body to be detected at the current moment.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] By introducing environmental and chemical parameters, the present invention can more comprehensively understand the changes in the odor of water bodies. This method ensures that when identifying odor characteristics, it not only relies on the chemical composition of the water body, but also takes into account the impact of the external environment on the water quality, significantly improving the recognition accuracy of the odor characteristics of water bodies. By constructing an odor characteristic matrix of the water body to be detected, the odor characteristic matrix of the water body to be detected is input into the trained odor grade model to obtain the odor grade of the water body to be detected at the current moment. According to the odor grade, comprehensive scoring coefficient and water system coefficient, the odor grade change of the water body to be detected can be predicted, potential water quality problems can be identified, and timely intervention measures can be taken. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 Schematic diagram of the overall method flow of the present invention;

[0062] Figure 2 This is a block diagram of the module composition of the present invention. DETAILED DESCRIPTION

[0063] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0064] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0065] Example 1:

[0066] See also Figure 1 , the present invention provides a technical solution:

[0067] A method for identifying odor characteristics of water bodies, comprising the following steps:

[0068] S1. Collecting characteristic data of odorous water samples over T historical time periods, as well as characteristic data of the water sample to be tested at the current moment, including volatile organic compound concentration, humic acid concentration, sulfide concentration, water pH value, and dissolved oxygen content. Environmental and chemical parameters that affect the condition of the water sample to be tested at the current moment are also collected. Environmental parameters include ambient temperature, water flow rate, rainfall, light intensity, and wind speed. Chemical parameters include chemical oxygen demand, biochemical oxygen demand, nitrogen concentration, phosphorus concentration, and turbidity.

[0069] S2. Extracting the odor characteristic matrix of the odorous water body from the sample characteristic data of the odorous water body within T historical time periods, including the average volatile organic compound concentration, average humic acid concentration, average sulfide concentration, average water pH value, and average dissolved oxygen content;

[0070] S3. The odor feature matrix of the odorous water body is used as input and the odor level is used as the label for training the model. The odor level is determined by an expert group and the odor level model is trained. The odor level is 1, 2, or 3, where 1 represents a lightly odorous water body, 2 represents a moderately odorous water body, and 3 represents a heavily odorous water body.

[0071] S4. Assign different water system values ​​to water bodies with mild odor, water bodies with moderate odor, and water bodies with severe odor;

[0072] S5. Using the sample characteristic data of the water body to be detected at the current moment, constructing the odor characteristic matrix of the water body to be detected, the odor characteristic matrix of the water body to be detected is input into the odor level model after training, and the odor level of the water body to be detected at the current moment is obtained;

[0073] S6. Processing and correlation analysis of the ambient temperature, water flow rate, rainfall, light intensity, and wind speed that currently affect the water body to be tested to generate an environmental impact coefficient for assessing the deterioration trend of the environmental parameters affecting the water body to be tested; processing and correlation analysis of the chemical oxygen demand, biochemical oxygen demand, nitrogen concentration, phosphorus concentration, and turbidity that currently affect the water body to be tested to generate a chemical impact coefficient for assessing the deterioration trend of the chemical parameters affecting the water body to be tested; processing the environmental impact coefficient and chemical impact coefficient of the water body to be tested to generate a comprehensive scoring coefficient for the water body to be tested at the current moment;

[0074] S7. Evaluate changes in the odor level of the water body to be tested based on the comprehensive scoring coefficient, odor level, and water system coefficient of the water body to be tested at the current moment.

[0075] Based on the above embodiment, the method and device for collecting sample feature data are as follows:

[0076] A certain amount of water sample is extracted from the water body, and the VOCs concentration in the sample is analyzed using a gas chromatograph in the laboratory;

[0077] The sample of the odorous water body is filtered to remove suspended matter, and a specific reagent is added to react the humic acid with the reagent to form a colored compound. The absorbance of the sample is measured at a specific wavelength using a spectrophotometer to calculate the concentration of the humic acid;

[0078] A certain amount of water sample is extracted from the water body and a reagent, usually a lead ion solution, is added. The absorbance of the sample after the reaction is measured by a spectrophotometer, and the concentration of sulfide is calculated according to the standard curve;

[0079] Immerse the pH meter's electrode in the water sample, wait for the reading to stabilize, and then record the water's pH value.

[0080] Immerse the dissolved oxygen probe in the water sample, ensuring the electrode or sensor is completely submerged, and read and record the dissolved oxygen content.

[0081] Based on the above embodiment, the method and equipment for collecting the environmental parameters of the water body to be detected are as follows:

[0082] Immerse the thermometer probe in the water sample, wait for the reading to stabilize, and record the temperature value;

[0083] Place the flow meter in the water sample, ensuring the device is aligned with the direction of the water flow, and read and record the water flow rate;

[0084] Install the rainfall gauge in an open area, away from obstructions, and read and record rainfall regularly;

[0085] Place the light intensity meter on the surface of the water sample or near the water surface, read and record the light intensity value;

[0086] Place the anemometer in an unobstructed area, ensure it is functioning properly, and read and record the wind speed.

[0087] Based on the above embodiment, the method and equipment for collecting the chemical parameters of the water body to be tested are as follows:

[0088] An oxidant (such as potassium dichromate) and an acid are added to a water sample and reacted under heating conditions. After the reaction, the absorbance of the sample is measured by a spectrophotometer to calculate the chemical oxygen demand.

[0089] Place the water sample in a biochemical oxygen demand bottle, seal it, and incubate it in a constant temperature incubator for 5 days. Then measure the dissolved oxygen content again and calculate the biochemical oxygen demand based on the difference between the initial and post-incubation DO values.

[0090] Specific reagents are added to water samples to form measurable colored compounds with nitrogen and phosphorus. The absorbance is measured using a spectrophotometer to calculate the concentrations of nitrogen and phosphorus.

[0091] A sample of the water is placed in the turbidimeter and the turbidity measurement is read.

[0092] Among them, the data acquisition module includes a gas chromatograph, a pH meter, a dissolved oxygen detector, a thermometer, a flow meter, a rainfall meter, a light intensity meter, an anemometer, an optical dissolved oxygen meter, and a turbidity meter, which are respectively used to collect VOCs concentration, pH value, dissolved oxygen content, temperature, water flow rate, rainfall, light intensity, wind speed, biochemical oxygen demand and turbidity, while the spectrophotometer can be used to collect humic acid concentration, chemical oxygen demand, nitrogen concentration and phosphorus concentration. The above-mentioned collection equipment can all adopt models of existing equipment and are not limited here.

[0093] Gas chromatographs, pH meters, dissolved oxygen detectors, spectrophotometers, thermometers, flow meters, rainfall meters, light intensity meters, anemometers, optical dissolved oxygen meters, and turbidity meters are all in multiple sets (e.g., 3 sets) and placed in water samples to measure VOCs concentration, humic acid concentration, sulfide concentration, water pH value, dissolved oxygen content, temperature, water flow rate, rainfall, light intensity, wind speed, chemical oxygen demand, biochemical oxygen demand, nitrogen concentration, phosphorus concentration, and turbidity. The same data collected multiple times are then averaged, and the final average is used as the corresponding data to avoid accidental errors from individual points.

[0094] Based on the above examples, the average volatile organic compound concentration, average humic acid concentration, average sulfide concentration, average water pH value, and average dissolved oxygen content are obtained according to the following public statement:

[0095]

[0096] Among them, μ hy 、μ fz 、μ lh 、μ ss 、μ ry They are the average concentration of volatile organic compounds, average concentration of humic acid, average concentration of sulfide, average pH value of water body, average dissolved oxygen content, hy i 、fz i 、lh i 、ss i 、ry i are the volatile organic compound concentration, humic acid concentration, sulfide concentration, water pH value, and dissolved oxygen content in the i-th historical time period, respectively. i is the index of the historical time period, and i∈[1,T].

[0097] On the basis of the above embodiment, the odor characteristic matrix of the odorous water body is constructed, and the specific process is as follows:

[0098]

[0099] Where X is the odor characteristic matrix of the odorous water body.

[0100] Based on the above embodiment, the odor level model is constructed using a deep neural network based on a multilayer perceptron, wherein the deep neural network of the multilayer perceptron includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer, and an output layer, wherein the first hidden layer, the second hidden layer, and the third hidden layer each have at least two neurons and each use ReLU as an activation function;

[0101] In this embodiment, the input features of the deep learning network of the multilayer perceptron include five features: average volatile organic compound concentration, average humic acid concentration, average sulfide concentration, average water pH value, and average dissolved oxygen content;

[0102] The structure of the deep learning network of multilayer perceptron is:

[0103] Input layer: receives input of 5 features;

[0104] The first hidden layer has 64 neurons and uses ReLU as the activation function.

[0105] The second hidden layer has 32 neurons and also uses the ReLU activation function.

[0106] The third hidden layer has 16 neurons and uses the ReLU activation function.

[0107] Output layer: has a single neuron, odor level.

[0108] The process of training the environmental impact model is as follows:

[0109] Based on the odor characteristic matrix of odorous water bodies in T historical time periods, the corresponding odor level is determined by expert scoring method between 1 and 9. The higher the odor level, the higher the degree of water deterioration;

[0110] The odor feature matrix of odorous water bodies in T historical time periods is used as input, and the odor level is used as the output label for training. The mean square error is used as the loss function. When the mean square error is in the range of [0, 0.01], the training of the odor level model is completed.

[0111] On the basis of the above embodiment, different water system coefficients are assigned to water bodies with light odor, water bodies with moderate odor, and water bodies with heavy odor. The specific process is as follows:

[0112] The odor level is 1, 2, or 3, where 1 represents a lightly odorous water body, 2 represents a moderately odorous water body, and 3 represents a heavy odorous water body.

[0113] The water system index of water bodies with mild odor is assigned to 0.3, the water system index of water bodies with moderate odor is assigned to 0.5, and the water system index of water bodies with severe odor is assigned to 0.7.

[0114] The Water System Parameter is a preset standard value associated with a specific odor level and represents the minimum acceptable water quality requirement for that specific odor level.

[0115] On the basis of the above embodiment, the odor feature matrix of the water body to be detected is constructed using the sample feature data of the water body to be detected at the current moment. The specific process is as follows:

[0116]

[0117] Among them, d is the odor characteristic matrix of the water body to be detected, μ hy '、μ fz '、μ lh '、μ ss '、μ ry ' are the average volatile organic compound concentration, average humic acid concentration, average sulfide concentration, average water pH value, and average dissolved oxygen content of the water body to be tested;

[0118] The odor feature matrix X of the water body to be detectedd The data is input into the trained odor grade model to obtain the odor grade of the water body to be detected. The odor grade of the water body to be detected is a light odor water body, a moderate odor water body, or a heavy odor water body.

[0119] Based on the above embodiment, the temperature, water flow rate, rainfall, light intensity and wind speed of the water body to be detected at the current moment are processed and correlated to generate an environmental impact coefficient for evaluating the deterioration trend of the environmental parameters on the water body to be detected, based on the following formula:

[0120]

[0121] Among them, HJxs is the environmental impact coefficient at the current moment, WD is the ambient temperature, GZ is the light intensity, FS is the wind speed, SL is the water flow velocity, JY is the rainfall, ω1 is the weight coefficient of the ambient temperature, ω2 is the weight coefficient of the light intensity, ω3 is the weight coefficient of the wind speed, and ω4 is the weight coefficient of the combination of water flow velocity and rainfall.

[0122] It should be noted that the higher the ambient temperature WD, the larger the environmental impact coefficient HJxs, and the faster the water body deterioration trend; the stronger the light intensity GZ, the larger the environmental impact coefficient HJxs, and the faster the water body deterioration trend; the faster the wind speed FS, the larger the environmental impact coefficient HJxs, and the faster the water body deterioration trend; the faster the water flow rate SL, the smaller the environmental impact coefficient HJxs, and the slower the water body deterioration trend; the larger the rainfall JY, the smaller the environmental impact coefficient HJxs, and the slower the water body deterioration trend; therefore, the environmental impact coefficient HJxs is positively correlated with the ambient temperature WD, light intensity GZ, and wind speed FS, and negatively correlated with the water flow rate SL and rainfall JY;

[0123] An increase in rainfall JY will not only increase the water level and flow of the water body, but also change the water flow velocity SL. During rainfall, the fluidity of the water body increases, leading to the dispersion of pollutants, and high flow velocity can dilute the pollutants brought by rainfall. When the rainfall JY is large, the change in flow velocity will also affect the concentration of pollutants. The interaction between the water flow velocity SL and rainfall JY will jointly determine the water body. Therefore, the water flow velocity SL and rainfall JY are set to be multiplied.

[0124] Since different environmental parameters have different degrees of influence on water bodies, it is necessary to set weight coefficients of environmental parameters. By setting weight coefficients ω1, ω2, ω3 and ω4, the degree of influence of environmental parameters on water bodies can be more accurately reflected, emphasizing the interaction between various factors.

[0125] In summary, the calculation formula for the environmental impact coefficient HJxs in the above form is set.

[0126] The value range of the environmental impact coefficient H]xs is [0, 1];

[0127] When HJxs is close to 0, it means that the environmental parameters have little impact on the deterioration trend of the water body;

[0128] When HJxs is close to 1, it means that the environmental parameters have a great influence on the deterioration trend of the water body;

[0129] Therefore, the larger the environmental impact coefficient HJxs is, the greater the impact of environmental conditions on the deterioration trend of the water body to be tested, and the faster the deterioration trend of the water body is.

[0130] Water flow rate (SL) is a key factor influencing the self-purification capacity of water bodies. A higher water flow rate (SL) can effectively dilute pollutants in the water and promote their diffusion, thereby slowing water degradation. Faster water flow enhances the fluidity of the water, helping to dilute pollutants to lower concentrations and reduce their negative impacts. Rainfall can cause changes in water fluidity, which in turn affects the concentration and distribution of pollutants. Especially during heavy rainfall, the interaction between the dilution effect and the introduction of pollutants can accelerate changes in the water. Therefore, the combined effect of water flow rate (SL) and rainfall (JY) has a significant impact on water bodies. This combination of water flow rate (SL) and rainfall (JY) has the highest correlation with the environmental impact coefficient (H]xs), so the highest weight coefficient (ω4) is set.

[0131] Temperature WD has a more comprehensive impact on water bodies, involving the promotion of biological metabolism and chemical reaction rates, so its impact is greater. Although light intensity GZ has a direct impact on algae growth, its influence is relatively limited to the promotion of photosynthesis. Its overall impact on water bodies is not as significant as that of temperature WD. Due to the significant and wide-ranging impact of temperature WD, the temperature weight coefficient ωω1 is higher and the light intensity weight coefficient ωω2 is lower.

[0132] Light intensity GZ promotes algae photosynthesis. Under strong light conditions, algae will reproduce in large numbers, causing water hypoxia. Wind speed FS mainly affects gas exchange and improves the dissolved oxygen level in the water, but its effect on algae growth is small, and its effect will be diluted under the influence of water flow and rainfall. Therefore, the weight of light intensity is higher than that of wind speed, ωω2>ωω3.

[0133] To sum up, on the basis of ωω1+ωω2+ωω3+ωω4=1, let 0<ωω3<ωω2<ωω1<ωω4<1.

[0134] As an implementation mode, the value range of ωω1 is 0.35-0.4, the value range of ωω2 is 0.25-0.3, the value range of ωω3 is 0.1-0.2, and the value range of ωω4 is 0.45-0.5. The specific values ​​are set by technical personnel according to actual conditions and are not limited here.

[0135] Based on the above embodiment, the chemical oxygen demand, biochemical oxygen demand, nitrogen concentration, phosphorus concentration, and turbidity that affect the water body to be tested at the current moment are processed and correlated to generate a chemical influence coefficient for evaluating the deterioration trend of the chemical parameters affecting the water body to be tested, according to the following formula:

[0136] HXxs=β1(HY×SY) ZD +β2(NN×LN)

[0137] Where HXxs is the chemical influence coefficient at the current moment, HY is the chemical oxygen demand, SY is the biochemical oxygen demand, NN is the nitrogen concentration, LN is the phosphorus concentration, ZD is the turbidity, β1 is the weight coefficient of the combination of chemical oxygen demand and biochemical oxygen demand, and β2 is the weight coefficient of the combination of nitrogen concentration and phosphorus concentration.

[0138] It should be noted that the higher the chemical oxygen demand HY, the larger the chemical influence coefficient HXxs, and the faster the water body deterioration trend; the higher the biochemical oxygen demand SY, the larger the chemical influence coefficient HXxs, and the faster the water body deterioration trend; the higher the turbidity ZD, the larger the chemical influence coefficient HXxs, and the faster the water body deterioration trend; the higher the nitrogen concentration NN, the larger the chemical influence coefficient HXxs, and the faster the water body deterioration trend; the higher the phosphorus concentration LN, the larger the chemical influence coefficient HXxs, and the faster the water body deterioration trend; therefore, the chemical influence coefficient HXxs is positively correlated with chemical oxygen demand HY, biochemical oxygen demand SY, turbidity ZD, nitrogen concentration NN, and phosphorus concentration LN;

[0139] Chemical oxygen demand (HY) and biochemical oxygen demand (SY) work together. The higher the chemical oxygen demand (HY), the more serious the organic pollution in the water body. The higher the biochemical oxygen demand (SY), the greater the biodegradation capacity. However, if the organic content is too high, it may still lead to water deterioration. Therefore, the product of the two can comprehensively express the severity of water pollution.

[0140] When the turbidity ZD increases, the impact of organic matter (HY×SY) in the water body on the water body will be aggravated, by increasing (HY×SY) to the power of turbidity (HY×SY) ZD , this impact can be emphasized. For example, when turbidity increases, it means there are more suspended matter in the water. At this time, the photosynthesis of aquatic plants is inhibited and the decomposition rate of organic matter is slowed down, resulting in more serious water deterioration:

[0141] Nitrogen and phosphorus are essential nutrients for the growth of aquatic plants. Excessive nitrogen and phosphorus can lead to algae blooms. When nitrogen and phosphorus exist at high concentrations, their combined effect, unlike their individual effects, will accelerate the eutrophication process of water bodies, leading to further deterioration of water bodies.

[0142] In summary, the calculation formula for the chemical influence coefficient HXxs in the above form is set.

[0143] The value range of the chemical influence coefficient HXxs is [0, 1];

[0144] When HJxs is close to 0, it means that the chemical parameters have little influence on the deterioration trend of the water body;

[0145] When HJxs is close to 1, it means that the chemical parameters have a great influence on the deterioration trend of the water body;

[0146] Therefore, the larger the chemical influence coefficient HXxs is, the greater the impact of chemical conditions on the deterioration trend of the water body to be tested, and the faster the deterioration trend of the water body is.

[0147] Since the increase of chemical oxygen demand HY and biochemical oxygen demand SY is directly related to the pollution level of water bodies, giving β1 a larger weight can emphasize the impact of organic matter on water bodies. For example, in the case of severe pollution, even if the biochemical oxygen demand SY is high, the self-purification capacity of the water body will not be enough to offset the impact of organic matter.

[0148] Although increases in nitrogen concentration NN and phosphorus concentration LN can cause water problems, their impacts usually occur based on the presence of organic matter. In the case of low organic matter pollution, the impacts of nitrogen and phosphorus are relatively small. Assigning a smaller weight to β2 can reflect the relatively minor impacts of nitrogen and phosphorus on water bodies.

[0149] To sum up, on the basis of β1+β2=1, let 0<β2<β1<1.

[0150] As an implementation manner, the value range of β1 is 0.5-0.7, and the value range of β2 is 0.3-0.4. The specific values ​​are set by technicians according to actual conditions and are not limited here.

[0151] On the basis of the above embodiment, the environmental impact coefficient and chemical impact coefficient of the water body to be detected at the current moment are processed to generate a comprehensive scoring coefficient of the water body to be detected at the current moment, according to the following formula:

[0152] PFxs=γ1HJxs+γ2HXxs

[0153] Among them, PFxs is the comprehensive scoring coefficient of the water body to be tested at the current moment. The comprehensive scoring coefficient is used to combine the environmental impact coefficient and the chemical impact coefficient to comprehensively evaluate the water body to be tested. The larger the comprehensive scoring coefficient PFxs is, the faster the deterioration trend of the water body to be tested is.

[0154] It should be noted that the larger the environmental impact coefficient, the faster the water body deteriorates, and the larger the chemical impact coefficient, the faster the water body deteriorates. Therefore, the comprehensive scoring coefficient PFxs is positively correlated with the environmental impact coefficient HJxs and the chemical impact coefficient HXxs. Therefore, the calculation formula of the comprehensive scoring coefficient PFxs in the above weighted sum form is set;

[0155] In the formula, γ1 is the weight coefficient of the environmental impact coefficient, γ2 is the weight coefficient of the chemical impact coefficient, and the specific values ​​of γ1 and γ2 are determined by the hierarchical analysis method. The specific logic is as follows:

[0156] The environmental impact coefficient HJxs and the chemical impact coefficient HXxs are marked, and the relative importance between the two indicators is determined by the nine-scale method to construct a judgment matrix, in which the index of the environmental impact coefficient is marked as 1 and the index of the chemical impact coefficient is marked as 2. The constructed judgment matrix [q uv ] 3×3 for:

[0157]

[0158] Among them, u and v are the indexes of the coefficients, and u∈[1,2], v∈[1,2], which means the importance of the coefficient with index u to the comprehensive score coefficient relative to the coefficient with index v. uv The specific value of q is determined by relevant experts using a 1-9 scoring method. uv =9 means that the coefficient with index u is more important to the comprehensive score coefficient than the coefficient with index v. uv =1 means that the coefficient indexed as u is extremely unimportant to the overall score coefficient compared to the coefficient indexed as v;

[0159] Divide each element value in the judgment matrix by the sum of its columns to obtain a normalized judgment matrix. Calculate the mean of the element values ​​in each row of the normalized judgment matrix, and use the mean of the element values ​​in the first row as the proportional coefficient of the environmental impact coefficient HJxs, and the mean of the element values ​​in the second row as the proportional coefficient of the chemical impact coefficient HXxs. With the constraint that the sum of the scaled values ​​is equal to 1, scale the two proportional coefficients equally, and use the scaled values ​​as the weights of the corresponding coefficients.

[0160] On the basis of the above embodiment, the change of the odor level of the water body to be detected is evaluated according to the comprehensive scoring coefficient, odor level and water system coefficient of the water body to be detected at the current moment. The specific process is as follows:

[0161] When the comprehensive scoring coefficient is less than the water system coefficient, that is, PFxs<SZxs, it means that the water quality score of the water body to be tested is lower than the standard associated with the current odor level, indicating that the water body has improved and the odor characteristics have become better;

[0162] If the odor level of the water body to be tested is above moderate at the current moment, reduce the odor level by 1 level;

[0163] The odor level of the water body to be tested is currently mild, so the odor level should be kept at mild.

[0164] When the comprehensive scoring coefficient is equal to the water system coefficient, that is, PFxs=SZxs, it means that the water quality score of the water body to be tested is equal to the standard associated with the current odor level, indicating that the water body condition remains unchanged and the odor characteristics remain unchanged;

[0165] Keep the odor level of the water body to be detected at the current moment unchanged;

[0166] When the comprehensive scoring coefficient is greater than the water system coefficient, that is, PFxs>SZxs, it means that the water quality score of the water body to be tested is higher than the standard associated with the current odor level, indicating that the water body has deteriorated and the odor characteristics have become worse;

[0167] If the odor level of the water body to be tested is below severe at the current moment, increase the odor level by 1 level;

[0168] The odor level of the water body to be tested is severe at the current moment, and the odor level will be maintained at severe.

[0169] See also Figure 2 , the present invention also provides a technical solution:

[0170] A water body odor feature recognition system, the system being configured to execute any of the above-mentioned water body odor feature recognition methods, comprising:

[0171] A data acquisition module is used to collect sample characteristic data of odorous water bodies within T historical time periods, as well as sample characteristic data of the water body to be tested at the current moment. The sample characteristic data include volatile organic compound concentration, humic acid concentration, sulfide concentration, water pH value and dissolved oxygen content, and collect environmental parameters and chemical parameters that affect the condition of the water body to be tested at the current moment. Environmental parameters include ambient temperature, water flow rate, rainfall, light intensity and wind speed. Chemical parameters include chemical oxygen demand, biochemical oxygen demand, nitrogen concentration, phosphorus concentration and turbidity.

[0172] A matrix construction module is used to extract the odor characteristic matrix of the odorous water body from the sample characteristic data of the odorous water body in T historical time periods, including the average volatile organic compound concentration, the average humic acid concentration, the average sulfide concentration, the average water pH value, and the average dissolved oxygen content;

[0173] An odor model construction module is used to take the odor feature matrix of the odorous water body as input and the odor level as a label training model. The odor level is determined by an expert group and the odor level model is trained. The odor level is 1, 2 or 3, where 1 represents a lightly odorous water body, 2 represents a moderately odorous water body, and 3 represents a heavily odorous water body.

[0174] An assignment module is used to assign different water system coefficients to water bodies with light odor, water bodies with moderate odor, and water bodies with heavy odor;

[0175] The water body identification module is used to construct an odor feature matrix of the water body to be detected using the sample feature data of the water body to be detected at the current moment, input the odor feature matrix of the water body to be detected into the trained odor grade model, and obtain the odor grade of the water body to be detected at the current moment;

[0176] a data processing and analysis module for performing data processing and correlation analysis on the ambient temperature, water flow rate, rainfall, light intensity, and wind speed that affect the water body to be tested at the current moment, to generate an environmental impact coefficient for evaluating the deterioration trend of the environmental parameters affecting the water body to be tested; performing data processing and correlation analysis on the chemical oxygen demand, biochemical oxygen demand, nitrogen concentration, phosphorus concentration, and turbidity that affect the water body to be tested at the current moment, to generate a chemical impact coefficient for evaluating the deterioration trend of the chemical parameters affecting the water body to be tested; and performing data processing on the environmental impact coefficient and chemical impact coefficient of the water body to be tested at the current moment to generate a comprehensive scoring coefficient for the water body to be tested at the current moment;

[0177] The water body assessment module is used to assess the change in the odor level of the water body to be detected based on the comprehensive scoring coefficient, odor level and water system coefficient of the water body to be detected at the current moment.

[0178] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0179] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by computer software, electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0180] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0181] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A method for identifying odor characteristics of water, characterized in that: The specific steps include: S1. Collecting characteristic data of odorous water samples over T historical time periods, as well as characteristic data of the water sample to be tested at the current moment, including volatile organic compound concentration, humic acid concentration, sulfide concentration, water pH value, and dissolved oxygen content. Environmental and chemical parameters that affect the condition of the water sample to be tested at the current moment are also collected. Environmental parameters include ambient temperature, water flow rate, rainfall, light intensity, and wind speed. Chemical parameters include chemical oxygen demand, biochemical oxygen demand, nitrogen concentration, phosphorus concentration, and turbidity. S2. Extracting the odor characteristic matrix of the odorous water body from the sample characteristic data of the odorous water body within T historical time periods, including the average volatile organic compound concentration, average humic acid concentration, average sulfide concentration, average water pH value, and average dissolved oxygen content; S3. The odor feature matrix of the odorous water body is used as input and the odor level is used as the label for training the model. The odor level is determined by an expert group and the odor level model is trained. The odor level is 1, 2, or 3, where 1 represents a lightly odorous water body, 2 represents a moderately odorous water body, and 3 represents a heavily odorous water body. S4. Assign different water system values ​​to water bodies with mild odor, water bodies with moderate odor, and water bodies with severe odor; S5. Using the sample characteristic data of the water body to be detected at the current moment, constructing the odor characteristic matrix of the water body to be detected, the odor characteristic matrix of the water body to be detected is input into the odor level model after training, and the odor level of the water body to be detected at the current moment is obtained; S6. Processing and correlation analysis of the ambient temperature, water flow rate, rainfall, light intensity, and wind speed that currently affect the water body to be tested to generate an environmental impact coefficient for assessing the deterioration trend of the environmental parameters affecting the water body to be tested; processing and correlation analysis of the chemical oxygen demand, biochemical oxygen demand, nitrogen concentration, phosphorus concentration, and turbidity that currently affect the water body to be tested to generate a chemical impact coefficient for assessing the deterioration trend of the chemical parameters affecting the water body to be tested; processing the environmental impact coefficient and chemical impact coefficient of the water body to be tested to generate a comprehensive scoring coefficient for the water body to be tested at the current moment; S7. Evaluate changes in the odor level of the water body to be tested based on the comprehensive scoring coefficient, odor level, and water system coefficient of the water body to be tested at the current moment.

2. The method for identifying water body odor characteristics according to claim 1, wherein: The average volatile organic compound concentration, average humic acid concentration, average sulfide concentration, average water pH value, and average dissolved oxygen content are obtained based on the following public announcement: Among them, μ hy 、μ fz 、μ lh 、μ ss 、μ ry They are the average concentration of volatile organic compounds, average concentration of humic acid, average concentration of sulfide, average pH value of water body, average dissolved oxygen content, hy i 、fz i 、lh i 、ss i 、ry i are the volatile organic compound concentration, humic acid concentration, sulfide concentration, water pH value, and dissolved oxygen content in the i-th historical time period, respectively. i is the index of the historical time period, and i∈[1,T].

3. The method for identifying water body odor characteristics according to claim 2, characterized in that: Construct the odor feature matrix of the odorous water body and the water body to be detected. The specific process is as follows: Among them, X is the odor characteristic matrix of the odorous water body; Using the sample feature data of the water body to be detected at the current moment, the odor feature matrix of the water body to be detected is constructed: Among them, d is the odor characteristic matrix of the water body to be detected, μ hy’ 、μ fz’ 、μ lh’ 、μ ss’ 、μ ry’ They are the average volatile organic compound concentration, average humic acid concentration, average sulfide concentration, average water pH value, and average dissolved oxygen content of the water body to be tested.

4. The method for identifying water body odor characteristics according to claim 3, characterized in that: The odor level model is constructed using a deep neural network based on a multilayer perceptron, wherein the deep neural network of the multilayer perceptron includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer, and an output layer, wherein the first hidden layer, the second hidden layer, and the third hidden layer each have at least two neurons and each use ReLU as an activation function; The process of training the environmental impact model is as follows: According to the odor characteristic matrix of odorous water bodies in T historical time periods, the corresponding odor level is determined based on the expert scoring method. The higher the odor level, the higher the degree of water deterioration. The odor feature matrix of odorous water bodies in T historical time periods is used as input, and the odor level is used as the output label for training. The mean square error is used as the loss function. When the mean square error is in the range of [0, 0.01], the training of the odor level model is completed.

5. The method for identifying water body odor characteristics according to claim 4, characterized in that: Different water system coefficients are assigned to water bodies with mild odor, water bodies with moderate odor, and water bodies with severe odor. The specific process is as follows: The odor level is 1, 2, or 3, where 1 represents a lightly odorous water body, 2 represents a moderately odorous water body, and 3 represents a heavy odorous water body. The water system index of water bodies with mild odor is assigned to 0.3, the water system index of water bodies with moderate odor is assigned to 0.5, and the water system index of water bodies with severe odor is assigned to 0.

7.

6. The method for identifying water body odor characteristics according to claim 5, characterized in that: The temperature, water flow rate, rainfall, light intensity and wind speed of the water body to be tested at the current moment are processed and correlated to generate an environmental impact coefficient for evaluating the deterioration trend of environmental parameters on the water body to be tested. The formula is as follows: Among them, HJxs is the environmental impact coefficient at the current moment, WD is the ambient temperature, GZ is the light intensity, FS is the wind speed, SL is the water flow velocity, JY is the rainfall, ω1 is the weight coefficient of the ambient temperature, ω2 is the weight coefficient of the light intensity, ω3 is the weight coefficient of the wind speed, and ω4 is the weight coefficient of the combination of water flow velocity and rainfall. On the basis of ω1+ω2+ω3+ω4=1, let 0<ω3<ω2<ω1<ω4<1.

7. The method for identifying water body odor characteristics according to claim 6, characterized in that: The chemical oxygen demand, biochemical oxygen demand, nitrogen concentration, phosphorus concentration and turbidity that affect the water body to be tested at the current moment are processed and correlated to generate a chemical impact coefficient for evaluating the deterioration trend of the chemical parameters affecting the water body to be tested. The formula is as follows: HXxs=β1(HY×SY) ZD +β2(NN×LN) Where HXxs is the chemical influence coefficient at the current moment, HY is the chemical oxygen demand, SY is the biochemical oxygen demand, NN is the nitrogen concentration, LN is the phosphorus concentration, ZD is the turbidity, β1 is the weight coefficient of the combination of chemical oxygen demand and biochemical oxygen demand, β2 is the weight coefficient of the combination of nitrogen concentration and phosphorus concentration. On the basis of β1+β2=1, let 0<β2<β1<1.

8. The method for identifying water body odor characteristics according to claim 7, characterized in that: The environmental impact coefficient and chemical impact coefficient of the water body to be tested at the current moment are processed to generate a comprehensive scoring coefficient of the water body to be tested at the current moment, based on the following formula: PFxs=γ1HJxs+γ2HXxs Among them, PFxs is the comprehensive scoring coefficient of the water body to be tested at the current moment, γ1 is the weight coefficient of the environmental impact coefficient, and γ2 is the weight coefficient of the chemical impact coefficient. The specific values ​​of γ1 and γ2 are determined by the hierarchical analysis method.

9. The method for identifying water body odor characteristics according to claim 8, characterized in that: According to the comprehensive scoring coefficient, odor level and water system coefficient of the water body to be tested at the current moment, the change in the odor level of the water body to be tested is evaluated. The specific process is as follows: When the comprehensive scoring coefficient is less than the water system coefficient, i.e., PFxs < SZxs, it means that the water quality score of the water body to be detected is lower than the standard associated with the current odor level, indicating that the water body has improved and the odor characteristics have become better; When the odor level of the water body to be detected at the current moment is above moderate, reduce the odor level by 1 level; When the odor level of the water body to be detected at the current moment is mild, keep the odor level at mild; When the comprehensive scoring coefficient is equal to the water system coefficient, i.e., PFxs = SZxs, it means that the water quality score of the water body to be detected is equal to the standard associated with the current odor level, indicating that the water body condition remains unchanged and the odor characteristics remain the same; Keep the odor level of the water body to be detected at the current moment unchanged; When the comprehensive scoring coefficient is greater than the water system coefficient, i.e., PFxs > SZxs, it means that the water quality score of the water body to be detected is higher than the standard associated with the current odor level, indicating that the water body has deteriorated and the odor characteristics have become worse; When the odor level of the water body to be detected at the current moment is below severe, increase the odor level by 1 level; When the odor level of the water body to be detected at the current moment is severe, keep the odor level at severe.

10. A water body odor feature recognition system, the system being configured to execute the water body odor feature recognition method according to any one of claims 1 to 9, characterized in that: It includes: A data acquisition module, which is used to collect the sample characteristic data of the odor water body in T historical time periods, and collect the sample characteristic data of the water body to be detected at the current moment. The sample characteristic data includes the concentration of volatile organic compounds, the concentration of humic acid, the concentration of sulfide, the water body pH value, and the dissolved oxygen content, and collect the environmental parameters and chemical parameters that affect the condition of the water body to be detected at the current moment. The environmental parameters include environmental temperature, water flow rate, rainfall, light intensity, and wind speed. The chemical parameters include chemical oxygen demand, biochemical oxygen demand, nitrogen concentration, phosphorus concentration, and turbidity; A matrix construction module, which is used to extract the odor characteristic matrix of the odor water body from the sample characteristic data of the odor water body in T historical time periods, including the average concentration of volatile organic compounds, the average concentration of humic acid, the average concentration of sulfide, the average water body pH value, and the average dissolved oxygen content; An odor model construction module, which is used to take the odor characteristic matrix of the odor water body as the input and the odor level as the label to train the model. The odor level is determined by the expert group, and train the odor level model. The odor level is 1 or 2 or 3. 1 represents a mild odor water body, 2 represents a moderate odor water body, and 3 represents a severe odor water body; An assignment module, which is used to assign different water system coefficients to mild odor water bodies, moderate odor water bodies, and severe odor water bodies respectively; A water body to be detected identification module, which is used to use the sample characteristic data of the water body to be detected at the current moment to construct the odor characteristic matrix of the water body to be detected, and input the odor characteristic matrix of the water body to be detected into the trained odor level model to obtain the odor level of the water body to be detected at the current moment; a data processing and analysis module for performing data processing and correlation analysis on the ambient temperature, water flow rate, rainfall, light intensity, and wind speed that affect the water body to be tested at the current moment, to generate an environmental impact coefficient for evaluating the deterioration trend of the environmental parameters affecting the water body to be tested; performing data processing and correlation analysis on the chemical oxygen demand, biochemical oxygen demand, nitrogen concentration, phosphorus concentration, and turbidity that affect the water body to be tested at the current moment, to generate a chemical impact coefficient for evaluating the deterioration trend of the chemical parameters affecting the water body to be tested; and performing data processing on the environmental impact coefficient and chemical impact coefficient of the water body to be tested at the current moment to generate a comprehensive scoring coefficient for the water body to be tested at the current moment; The water body assessment module is used to assess the change in the odor level of the water body to be detected based on the comprehensive scoring coefficient, odor level and water system coefficient of the water body to be detected at the current moment.

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