Water source pollutant treatment method and device and computer equipment

Abstract

The invention relates to a water source pollutant treatment method and device and computer equipment. The method comprises the following steps: acquiring environmental condition information of a water source location and sample detection data information of a water source sample, and identifying environmental change characteristics of various environmental types of the water source location based on the environmental condition information; the method comprises the following steps: identifying pollutant data distribution information of various peculiar smell pollutant types of a water source based on sample detection data information of a water source sample, and predicting the peculiar smell pollutants of the water source through a water source pollutant prediction network based on environment change characteristics of various environment types and the pollutant data distribution information of various peculiar smell pollutant types. Predicting predicted pollutant data distribution information of various odor pollutant types of the water source; and based on the predicted pollutant data distribution information of each peculiar smell pollutant type, generating a pollutant treatment scheme of each peculiar smell pollutant type through a pollutant treatment network. By adopting the method, the foreign matter treatment effect on the polluted water source can be improved.

Description

Technical Field

[0001] The present application relates to the technical fields of pollutant analysis and foreign object treatment, and particularly to a method, device, and computer equipment for treating water source pollutants. Background Art

[0002] As a major water source, surface water sources are essential living materials for the survival of organisms. Algal blooms often occur under suitable conditions such as temperature and light in summer and autumn. During the growth and decay of algae and other metabolic processes, pollutants that cause abnormal odors in water bodies are often produced. This causes a large amount of pollution to surface water sources, and since the odors generated by algal pollution are difficult to remove by conventional water treatment processes, and such odor pollutants have a low odor threshold (can be detected at ng / L) and can be distinguished by the senses, they are very likely to be noticed by users after entering the water supply terminal. Therefore, how to remove and treat the pollutants in surface water sources is the current research focus.

[0003] Traditional technical solutions are to use a large amount of foreign object treatment materials to treat pollutants in the polluted water source. However, existing treatment technologies often have poor control over the input ratio of foreign object treatment materials and treatment methods, often resulting in limited elimination of odors in the polluted water source or a large amount of treatment residues in the water source, thus leading to poor treatment effects of foreign objects in the polluted water source. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a method, device, computer equipment, computer-readable storage medium, and computer program product for treating water source pollutants.

[0005] In a first aspect, the present application provides a method for treating water source pollutants, including: Obtaining environmental condition information of the location of the water source and sample detection data information of the water source sample, and based on the environmental condition information of the location of the water source, identifying the environmental change characteristics of each environmental type at the location of the water source; Based on the sample detection data information of the water source sample, identifying the pollutant data distribution information of each odor pollutant type in the water source, and based on the environmental change characteristics of each environmental type and the pollutant data distribution information of each odor pollutant type, predicting the predicted pollutant data distribution information of each odor pollutant type in the water source through a water source pollutant prediction network; Based on the predicted pollutant data distribution information of each odor pollutant type, generating a pollutant treatment plan for each odor pollutant type through a pollutant treatment network.

[0006] Optionally, the identifying the environmental change characteristics of each environmental type at the location of the water source based on the environmental condition information of the location of the water source includes: Split the environmental condition information into sub-condition information for each environmental type, and identify the environmental data change distribution information for each environmental type based on the sub-condition information for each environmental type. Extract the environmental data change law features and environmental data change distribution features for each environmental type through a feature extraction network, and use the environmental data change law features and environmental data change distribution features for each environmental type as the environmental change features for each environmental type.

[0007] Optionally, the identifying the pollutant data distribution information of each odor pollutant type of the water source based on the sample detection data information of the water source sample includes: Split the sample detection data information into detection data distribution information for each detection data type, and query each target detection data type corresponding to each odor pollutant type in the database. For each odor pollutant type, identify the pollutant data distribution information of the odor pollutant type through the pollutant data analysis strategy of the odor pollutant type based on the detection data distribution information of each target detection data type corresponding to the odor pollutant type.

[0008] Optionally, before predicting the predicted pollutant data distribution information of each odor pollutant type of the water source through a water source pollutant prediction network based on the environmental change features of each environmental type and the pollutant data distribution information of each odor pollutant type, it further includes: For each odor pollutant type, identify the data change association information between each environmental type and the odor pollutant type through a linear correlation analysis model based on the pollutant data distribution information of the odor pollutant type and the environmental data change distribution information of each environmental type. Extract the pollutant data change law features and pollutant data change distribution features of the odor pollutant type through a feature extraction network based on the pollutant data distribution information of the odor pollutant type, and use the pollutant data change law features and pollutant data change distribution features of the odor pollutant type as the pollutant change features of the odor pollutant type.

[0009] Optionally, the water source pollutant prediction network includes a feature prediction network and a linear fitting network. The predicting the predicted pollutant data distribution information of each odor pollutant type of the water source through a water source pollutant prediction network based on the environmental change features of each environmental type and the pollutant data distribution information of each odor pollutant type includes: For each type of odor pollutant, based on the environmental change characteristics of each of the environmental types, the pollutant change characteristics of the odor pollutant type, and the data change association information between each environmental type and the odor pollutant type, through the feature prediction network, predict the pollutant prediction change characteristics of the odor pollutant type and the pollutant prediction distribution characteristics of the odor pollutant type; Based on the pollutant prediction change characteristics of the odor pollutant type and the pollutant prediction distribution characteristics of the odor pollutant type, through the linear fitting network, generate the predicted pollutant distribution information of the odor pollutant type.

[0010] Optionally, based on the predicted pollutant data distribution information of each of the odor pollutant types, through the pollutant treatment network, generate the pollutant treatment schemes for each of the odor pollutant types, including: For each type of odor pollutant, in the pollutant treatment database, query the additive types of each pollutant treatment method corresponding to the odor pollutant type and the change association information between each additive type and the odor pollutant type, and obtain the pollutant data standard range of each odor pollutant type; Based on the pollutant data standard range of the odor pollutant type and the predicted pollutant distribution information of the odor pollutant type, generate the pollutant adjustment amount distribution information of the odor pollutant type; Based on the change association information between each additive type and the odor pollutant type and the pollutant adjustment amount distribution information, generate the additive data distribution information of each additive type, and based on the additive data distribution information of each additive type of each odor pollutant type, in each additive database, through the additive screening network, screen the target additive type corresponding to each odor pollutant type; Take the additive data distribution information corresponding to the target additive type of each odor pollutant type and the pollutant treatment method of the target additive type of each odor pollutant type as the pollutant treatment scheme of each odor pollutant type.

[0011] In a second aspect, the present application also provides a treatment device for water source pollutants, including: An acquisition module, configured to acquire the environmental condition information of the water source location and the sample detection data information of the water source sample, and based on the environmental condition information of the water source location, identify the environmental change characteristics of each environmental type of the water source location; A prediction module, configured to identify the pollutant data distribution information of each type of odor pollutant in the water source based on the sample detection data information of the water source sample, and predict the predicted pollutant data distribution information of each type of odor pollutant in the water source through a water source pollutant prediction network based on the environmental change characteristics of each environmental type and the pollutant data distribution information of each type of odor pollutant; A generation module, configured to generate a pollutant treatment plan for each type of odor pollutant through a pollutant treatment network based on the predicted pollutant data distribution information of each type of odor pollutant.

[0012] Optionally, the acquisition module is specifically configured to: Split the environmental condition information into sub-condition information of each environmental type, and identify the environmental data change distribution information of each environmental type based on the sub-condition information of each environmental type; Extract the environmental data change law characteristics and the environmental data change distribution characteristics of each environmental type through a feature extraction network, and use the environmental data change law characteristics and the environmental data change distribution characteristics of each environmental type as the environmental change characteristics of each environmental type.

[0013] Optionally, the prediction module is specifically configured to: Split the sample detection data information into detection data distribution information of each detection data type, and query each target detection data type corresponding to each type of odor pollutant in the database; For each type of odor pollutant, identify the pollutant data distribution information of the odor pollutant type through the pollutant data analysis strategy of the odor pollutant type based on the detection data distribution information of each target detection data type corresponding to the odor pollutant type.

[0014] Optionally, the device further includes: An identification module, configured to identify the data change association information between each environmental type and the odor pollutant type through a linear correlation analysis model for each type of odor pollutant based on the pollutant data distribution information of the odor pollutant type and the environmental data change distribution information of each environmental type; A determination module, configured to extract the pollutant data change law characteristics and the pollutant data change distribution characteristics of the odor pollutant type through a feature extraction network based on the pollutant data distribution information of the odor pollutant type, and use the pollutant data change law characteristics and the pollutant data change distribution characteristics of the odor pollutant type as the pollutant change characteristics of the odor pollutant type.

[0015] Optionally, the prediction module is specifically configured to: For each type of odor pollutant, based on the environmental change characteristics of each environmental type, the pollutant change characteristics of the odor pollutant type, and the data change association information between each environmental type and the odor pollutant type, through the feature prediction network, predict the predicted change characteristics of the pollutants of the odor pollutant type and the predicted distribution characteristics of the pollutants of the odor pollutant type; Based on the predicted change characteristics of the pollutants of the odor pollutant type and the predicted distribution characteristics of the pollutants of the odor pollutant type, through the linear fitting network, generate the predicted pollutant distribution information of the odor pollutant type.

[0016] Optionally, the generation module is specifically configured to: For each type of odor pollutant, in the pollutant treatment database, query the additive types of each pollutant treatment method corresponding to the odor pollutant type and the change association information between each additive type and the odor pollutant type, and obtain the standard range of pollutant data for each odor pollutant type; Based on the standard range of pollutant data for the odor pollutant type and the predicted pollutant distribution information of the odor pollutant type, generate the distribution information of the pollutant adjustment amount of the odor pollutant type; Based on the change association information between each additive type and the odor pollutant type and the distribution information of the pollutant adjustment amount, generate the distribution information of the additive data of each additive type, and based on the distribution information of the additive data of each additive type for each odor pollutant type, in each additive database, through the additive screening network, screen the target additive type corresponding to each odor pollutant type; Take the distribution information of the additive data corresponding to the target additive type for each odor pollutant type and the pollutant treatment method of the target additive type for each odor pollutant type as the pollutant treatment plan for each odor pollutant type.

[0017] In a third aspect, the present application provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps of the method described in any one of the first aspects are implemented.

[0018] In a fourth aspect, the present application provides a computer-readable storage medium. A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method described in any one of the first aspects are implemented.

[0019] Fifth aspect, the present application provides a computer program product. The computer program product includes a computer program which, when executed by a processor, implements the steps of the method described in any one of the first aspect.

[0020] For the above-mentioned method, device and computer equipment for treating water source pollutants, by obtaining the environmental condition information of the location of the water source and the sample detection data information of the water source sample, and based on the environmental condition information of the location of the water source, identifying the environmental change characteristics of each environmental type at the location of the water source; based on the sample detection data information of the water source sample, identifying the pollutant data distribution information of each odor pollutant type in the water source, and based on the environmental change characteristics of each environmental type and the pollutant data distribution information of each odor pollutant type, through the water source pollutant prediction network, predicting the predicted pollutant data distribution information of each odor pollutant type in the water source; based on the predicted pollutant data distribution information of each odor pollutant type, through the pollutant treatment network, generating the pollutant treatment plan for each odor pollutant type. In this solution, by comprehensively analyzing the environmental condition information of the location of the water source and the water sample detection information, thus from the perspectives of environmental impact and actual data change of the water sample, analyzing the pollutant data distribution information of each odor pollutant type and the environmental change characteristics of each environmental type, and then combining the two through the water source pollutant prediction network to predict the predicted pollutant data distribution information of each odor pollutant type in the water source. Finally, according to the predicted pollutant data distribution information, through the pollutant treatment network, generating the pollutant treatment plan for each odor pollutant type. It improves the accuracy and adaptability of generating the pollutant treatment plan for each odor pollutant type, and in the actual analysis of this solution, the granularity value is refined for comprehensive analysis of each odor pollutant type, avoiding the problems that often result in limited odor elimination of the polluted water source or a large amount of treatment residues in the water source during overall treatment, thus reducing the treatment cost and further improving the quality of the treated water source, and comprehensively improving the treatment effect of foreign substances in the polluted water source. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0022] Figure 1 It is a schematic flowchart of the method for treating water source pollutants in an embodiment; Figure 2 It is a schematic flowchart of an example of treating water source pollutants in an embodiment; Figure 3 It is a structural block diagram of a treatment device for water source pollutants in an embodiment; Figure 4 It is an internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0023] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0024] The water source pollutant treatment method provided by the embodiments of the present application can be applied to the application environment of water source pollutant treatment. Among them, the method can be applied to a terminal, or to a server, or to a system including a terminal and a server, and is realized through the interaction between the terminal and the server. Among them, the terminal can be, but is not limited to, various personal computers, laptop computers, medium-sized computers, etc. Among them, the terminal comprehensively analyzes the environmental condition information of the water source location and the water sample detection information, so as to analyze the pollutant data distribution information of each odor pollutant type and the environmental change characteristics of each environmental type from the perspectives of environmental impact and actual data changes of the water sample. Then, by combining the two through the water source pollutant prediction network, the predicted pollutant data distribution information of each odor pollutant type of the water source is predicted. Finally, according to the predicted pollutant data distribution information, through the pollutant treatment network, a pollutant treatment plan for each odor pollutant type is generated. The accuracy and adaptability of the generation of the pollutant treatment plan for each odor pollutant type are improved, and in the actual analysis of this solution, the granularity value is refined for comprehensive analysis of each odor pollutant type, avoiding the problems that the odor elimination of the polluted water source is often limited or the content of the treatment residue in the water source is relatively large during the overall treatment. Therefore, the treatment cost is reduced and the quality of the treated water source is further improved, thereby comprehensively improving the treatment effect of foreign substances in the polluted water source.

[0025] In an exemplary embodiment, as Figure 1 shown, a water source pollutant treatment method is provided. Taking the application of this method to a terminal as an example, it includes the following steps S101 to S103. Among them: Step S101, obtain the environmental condition information of the water source location and the sample detection data information of the water source sample, and based on the environmental condition information of the water source location, identify the environmental change characteristics of each environmental type of the water source location.

[0026] In this embodiment, the terminal responds to the information uploading operation of the staff member, obtains the environmental condition information of the water source location collected by the staff member, and the sample test data information after the sample test of the water source samples collected by the staff member at different times. Among them, the environmental condition information includes sub-condition information of multiple environmental types, and the environmental types include but are not limited to temperature type, illuminance type, humidity type, water algae coverage type, etc. The sample test data information obtained after the sample test includes the test data distribution information of each test data type. Among them, the test data type includes but is not limited to water quality monitoring type and pollutant detection type. Among them, the water quality monitoring type includes each water quality index detection type, and the pollutant detection type also includes each pollutant type detection type, etc. Then, the terminal identifies the environmental change characteristics of each environmental type of the water source location based on the environmental condition information of the water source location. Among them, the environmental change characteristics of each environmental type include but are not limited to the environmental data change law characteristics of each environmental type and the environmental data change distribution characteristics of each environmental type. Among them, the environmental data change law characteristics are used to represent the law information of the data change of the environmental type, such as the temperature change cycle, and the environmental data change distribution characteristics are used to represent the information such as the data change range, change trend, and change amount of the environmental type. The specific identification process will be described in detail later.

[0027] Step S102: Based on the sample test data information of the water source sample, identify the pollutant data distribution information of each odor pollutant type of the water source, and based on the environmental change characteristics of each environmental type and the pollutant data distribution information of each odor pollutant type, predict the predicted pollutant data distribution information of each odor pollutant type of the water source through the water source pollutant prediction network.

[0028] In this embodiment, the terminal identifies the pollutant data distribution information of each odor pollutant type of the water source based on the sample test data information of the water source sample, and based on the environmental change characteristics of each environmental type and the pollutant data distribution information of each odor pollutant type, predicts the predicted pollutant data distribution information of each odor pollutant type of the water source through the water source pollutant prediction network. Among them, each odor pollutant type corresponds to one or more test data types, and the water source pollutant prediction network includes a feature prediction network and a linear fitting network. Among them, the feature prediction network is a convolutional neural network based on deep learning technology, and the linear fitting network is a linear fitting neural network based on the linear regression algorithm for linearly fitting linear features. The specific prediction process will be described in detail later.

[0029] Step S103: Based on the predicted pollutant data distribution information of each odor pollutant type, generate a pollutant treatment plan for each odor pollutant type through the pollutant treatment network.

[0030] In this embodiment, the terminal generates pollutant treatment solutions for each type of odor pollutant through a pollutant treatment network based on the predicted pollutant data distribution information of each type of odor pollutant. Among them, the pollutant treatment network is an additive type screening network for screening treatment additives corresponding to each type of odor pollutant, and this screening network is an artificial neural network based on a reinforcement learning neural network.

[0031] Based on the above solution, by comprehensively analyzing the environmental condition information of the water source location and the water sample detection information, from the perspectives of environmental impact and actual data changes in the water sample, analyze the pollutant data distribution information of each type of odor pollutant and the environmental change characteristics of each environmental type, and then combine the two through a water source pollutant prediction network to predict the predicted pollutant data distribution information of each type of odor pollutant in the water source. Finally, according to the predicted pollutant data distribution information, generate pollutant treatment solutions for each type of odor pollutant through a pollutant treatment network. This improves the accuracy and adaptability of generating pollutant treatment solutions for each type of odor pollutant. Moreover, in actual analysis, this solution refines the granularity value and comprehensively analyzes each type of odor pollutant, avoiding the problems that overall treatment often results in limited odor elimination of the polluted water source or a large amount of treatment residues in the water source. Thus, it reduces the treatment cost and further improves the quality of the treated water source, thereby comprehensively enhancing the treatment effect of foreign substances in the polluted water source.

[0032] Optionally, based on the environmental condition information of the water source location, identifying the environmental change characteristics of each environmental type in the water source location includes: splitting the environmental condition information into sub-condition information of each environmental type, and based on the sub-condition information of each environmental type, identifying the environmental data change distribution information of each environmental type; through a feature extraction network, extracting the environmental data change law characteristics and environmental data change distribution characteristics of each environmental type, and using the environmental data change law characteristics and environmental data change distribution characteristics of each environmental type as the environmental change characteristics of each environmental type.

[0033] In this embodiment, the terminal splits the environmental condition information into sub-condition information of each environmental type, and based on the sub-condition information of each environmental type, identifies the environmental data change distribution information of each environmental type.

[0034] Then, the terminal extracts the environmental data change pattern features of each environmental type and the environmental data change distribution features of each environmental type through the feature extraction network, and uses the environmental data change pattern features of each environmental type and the environmental data change distribution features of each environmental type as the environmental change features of each environmental type. Among them, the feature extraction network is a linear feature extraction network for extracting features from the linear distribution curve corresponding to the distribution information.

[0035] Based on the above solution, by performing data distribution processing on the sub-condition information of each environmental type, and then extracting the data change pattern and the feature information corresponding to the data change distribution, the change pattern and the change distribution of each environmental type are comprehensively analyzed, improving the comprehensiveness and accuracy of the environmental feature analysis.

[0036] Optionally, based on the sample detection data information of the water source, identify the pollutant data distribution information of each odor pollutant type in the water source, including: splitting the sample detection data information into the detection data distribution information of each detection data type, and querying each target detection data type corresponding to each odor pollutant type in the database; for each odor pollutant type, based on the detection data distribution information of each target detection data type corresponding to the odor pollutant type, through the pollutant data analysis strategy of the odor pollutant type, identify the pollutant data distribution information of the odor pollutant type.

[0037] In this embodiment, the terminal splits the sample detection data information into the detection data distribution information of each detection data type, and queries each target detection data type corresponding to each odor pollutant type in the database. Among them, each odor pollutant type is an odoriferous pollutant type, which mainly includes the pollutant type corresponding to geosmin, the pollutant type corresponding to 2-methylisoborneol acid, etc.

[0038] For each odor pollutant type, the terminal identifies the pollutant data distribution information of the odor pollutant type through the pollutant data analysis strategy of the odor pollutant type based on the detection data distribution information of each target detection data type corresponding to the odor pollutant type. Among them, in the pollutant data analysis strategy, it includes the conversion correlation information between the detection data values of each target detection data type corresponding to the odor pollutant type and the pollutant data of the odor pollutant type, and the conversion correlation information is used to convert the detection data distribution information of each target detection data type into the associated conversion relationship of the pollutant data distribution information of the odor pollutant type. Among them, each conversion correlation information is obtained by the staff of this solution through a large number of experimental data studies, a large number of actual data summaries, analyses, and Internet big data analyses.

[0039] Based on the above solution, by comprehensively analyzing various detection data types and the associated information with odor pollutant types, the data distribution information of each odor pollutant type can be identified. The data distribution information of each odor pollutant type can be obtained without complex odor pollutant detection equipment and odor pollutant detection methods, improving the accuracy and comprehensiveness of odor pollutant detection.

[0040] Optionally, based on the environmental change characteristics of each environmental type and the pollutant data distribution information of each odor pollutant type, before predicting the predicted pollutant data distribution information of each odor pollutant type of the water source through the water source pollutant prediction network, it further includes: for each odor pollutant type, based on the pollutant data distribution information of the odor pollutant type and the environmental data change distribution information of each environmental type, through a linear correlation analysis model, identifying the data change correlation information between each environmental type and the odor pollutant type; based on the pollutant data distribution information of the odor pollutant type, through a feature extraction network, extracting the pollutant data change rule features and the pollutant data change distribution features of the odor pollutant type, and using the pollutant data change rule features and the pollutant data change distribution features of the odor pollutant type as the pollutant change features of the odor pollutant type.

[0041] In this embodiment, the terminal, for each odor pollutant type, based on the pollutant data distribution information of the odor pollutant type and the environmental data change distribution information of each environmental type, through a linear correlation analysis model, identifies the data change correlation information between each environmental type and the odor pollutant type. Among them, the linear correlation analysis model is a neural network for identifying linear correlation information based on multiple linear regression technology. The linear correlation analysis model is used to analyze the correlation and influence relationship between the environmental data change of each environmental type and the data change of the odor pollutant type, as well as the change rule information between the two.

[0042] Then, the terminal, based on the pollutant data distribution information of the odor pollutant type, through a feature extraction network, extracts the pollutant data change rule features and the pollutant data change distribution features of the odor pollutant type, and uses the pollutant data change rule features and the pollutant data change distribution features of the odor pollutant type as the pollutant change features of the odor pollutant type. Among them, the feature extraction network is the same as the linear feature extraction network described above, and no redundant description is made here.

[0043] Based on the above solution, by analyzing the linear correlation information between each environmental type and the odor pollutant type and identifying the pollutant change features of each odor pollutant type, the analysis comprehensiveness and accuracy of each odor pollutant type are improved.

[0044] Optionally, the water source pollutant prediction network includes a feature prediction network and a linear fitting network. Based on the environmental change characteristics of each environmental type and the pollutant data distribution information of each odor pollutant type, the predicted pollutant data distribution information of each odor pollutant type in the water source is predicted through the water source pollutant prediction network, including: for each odor pollutant type, based on the environmental change characteristics of each environmental type, the pollutant change characteristics of the odor pollutant type, and the data change association information between each environmental type and the odor pollutant type, through the feature prediction network, the predicted pollutant change characteristics of the odor pollutant type and the predicted distribution characteristics of the pollutant of the odor pollutant type are predicted; based on the predicted pollutant change characteristics of the odor pollutant type and the predicted distribution characteristics of the pollutant of the odor pollutant type, through the linear fitting network, the predicted pollutant distribution information of the odor pollutant type is generated.

[0045] In this embodiment, for each odor pollutant type, the terminal predicts the predicted pollutant change characteristics of the odor pollutant type and the predicted distribution characteristics of the pollutant of the odor pollutant type through the feature prediction network based on the environmental change characteristics of each environmental type, the pollutant change characteristics of the odor pollutant type, and the data change association information between each environmental type and the odor pollutant type. Among them, the predicted pollutant change characteristics are the change cycle characteristics and change rule characteristics of the pollutant of the odor pollutant type, and the predicted distribution characteristics of the pollutant are the distribution range characteristics and distribution trend characteristics, etc., of the pollutant of the odor pollutant type.

[0046] Then, based on the predicted pollutant change characteristics of the odor pollutant type and the predicted distribution characteristics of the pollutant of the odor pollutant type, the terminal generates the predicted pollutant distribution information of the odor pollutant type through the linear fitting network.

[0047] Based on the above solution, by first predicting the predicted pollutant change characteristics of each odor pollutant type and the predicted distribution characteristics of the pollutant of the odor pollutant type, and then performing linear fitting in combination with the feature information, the predicted pollutant distribution information of the odor pollutant type is generated, improving the accuracy of predicting the predicted pollutant distribution information of the odor pollutant type.

[0048] Optionally, based on the predicted pollutant data distribution information of each odor pollutant type, a pollutant treatment plan for each odor pollutant type is generated through the pollutant treatment network, including: for each odor pollutant type, in the pollutant treatment database, query the additive types of each pollutant treatment method corresponding to the odor pollutant type, as well as the change association information between each additive type and the odor pollutant type, and obtain the pollutant data standard range of each odor pollutant type; based on the pollutant data standard range of the odor pollutant type and the predicted pollutant distribution information of the odor pollutant type, generate the pollutant adjustment amount distribution information of the odor pollutant type; based on the change association information between each additive type and the odor pollutant type and the pollutant adjustment amount distribution information, generate the additive data distribution information of each additive type, and based on the additive data distribution information of each additive type of each odor pollutant type, in each additive database, through the additive screening network, screen the target additive type corresponding to each odor pollutant type; take the additive data distribution information corresponding to the target additive type of each odor pollutant type and the pollutant treatment method of the target additive type of each odor pollutant type as the pollutant treatment plan of each odor pollutant type.

[0049] In this embodiment, the terminal queries, for each odor pollutant type, in the pollutant treatment database, the additive types of each pollutant treatment method corresponding to the odor pollutant type, as well as the change association information between each additive type and the odor pollutant type, and obtains the pollutant data standard range of each odor pollutant type. Among them, the additive type of each pollutant treatment method is the type corresponding to the additive used to treat this odor pollutant type. Among them, the pollutant treatment methods include but are not limited to physical adsorption method, biodegradation, chemical oxidation treatment, etc. For example, the additive type of the physical adsorption method is, for example, the activated carbon type; the additive type corresponding to the biodegradation method is, for example, the biological species; the additive type corresponding to the chemical oxidation treatment method is, for example, the catalyst type, etc. And among them, the change association information between each additive type and the odor pollutant type is the treatment amount of the odor pollutant type that can be treated by the addition amount of the additive type.

[0050] The terminal generates the pollutant adjustment amount distribution information of the odor pollutant type based on the pollutant data standard range of the odor pollutant type and the predicted pollutant distribution information of the odor pollutant type. Among them, the pollutant data standard range is the data content range that meets the staff or the preset water source pollutant content standard. It is only necessary to eliminate the sensory odor of the water source for the user.

[0051] Then, based on the variation correlation information between each additive type and each odor pollutant type, as well as the pollutant adjustment amount distribution information, the terminal generates the additive data distribution information for each additive type, and based on the additive data distribution information of each additive type for each odor pollutant type, in each additive database, through the additive screening network, it screens the target additive type corresponding to each odor pollutant type. Among them, after the various target additive types are put into the water source, there may be mutual promotion effects, mutual cancellation effects, one-way promotion effects, and one-way cancellation effects among them. And this additive screening network is required to screen, among the additive types corresponding to each odor pollutant type, the additive types that do not have mutual cancellation effects or one-way cancellation effects on the target additive types corresponding to other odor pollutant types, so as to ensure that when each additive is added to the water source, it can meet the standard of the treatment amount of the odor pollutant type that the additive needs to treat, or exceed this treatment amount.

[0052] Finally, the terminal takes the additive data distribution information corresponding to the target additive type for each odor pollutant type, and the pollutant treatment method of the target additive type for each odor pollutant type, as the pollutant treatment plan for each odor pollutant type.

[0053] Based on the above solution, by screening the pollutant treatment methods and additive types for odor pollutant types, and considering the interaction correlation information among them, the target additive type corresponding to each odor pollutant type is screened, so as to effectively ensure the treatment effect of the pollutant treatment plan for each odor pollutant type.

[0054] This application also provides an example of treating water source pollutants, as Figure 2 shown. The specific treatment process includes the following steps: Step S201, obtain the environmental condition information of the location of the water source and the sample detection data information of the water source sample.

[0055] Step S202, split the environmental condition information into sub-condition information for each environmental type, and based on the sub-condition information for each environmental type, identify the environmental data change distribution information for each environmental type.

[0056] Step S203, through the feature extraction network, extract the environmental data change rule features and environmental data change distribution features for each environmental type, and take the environmental data change rule features and environmental data change distribution features for each environmental type as the environmental change features for each environmental type.

[0057] Step S204: Split the sample detection data information into the detection data distribution information of each detection data type, and query each target detection data type corresponding to each odor pollutant type in the database.

[0058] Step S205: For each odor pollutant type, based on the detection data distribution information of each target detection data type corresponding to the odor pollutant type, identify the pollutant data distribution information of the odor pollutant type through the pollutant data analysis strategy of the odor pollutant type.

[0059] Step S206: For each odor pollutant type, based on the pollutant data distribution information of the odor pollutant type and the environmental data change distribution information of each environmental type, identify the data change association information between each environmental type and the odor pollutant type through the linear correlation analysis model.

[0060] Step S207: Based on the pollutant data distribution information of the odor pollutant type, extract the pollutant data change rule features and the pollutant data change distribution features of the odor pollutant type through the feature extraction network, and use the pollutant data change rule features and the pollutant data change distribution features of the odor pollutant type as the pollutant change features of the odor pollutant type.

[0061] Step S208: For each odor pollutant type, based on the environmental change features of each environmental type, the pollutant change features of the odor pollutant type, and the data change association information between each environmental type and the odor pollutant type, predict the pollutant prediction change features and the pollutant prediction distribution features of the odor pollutant type through the feature prediction network.

[0062] Step S209: Based on the pollutant prediction change features and the pollutant prediction distribution features of the odor pollutant type, generate the predicted pollutant distribution information of the odor pollutant type through the linear fitting network.

[0063] Step S210: For each odor pollutant type, query the additive types of each pollutant treatment method corresponding to the odor pollutant type and the change association information between each additive type and the odor pollutant type in the pollutant treatment database, and obtain the pollutant data standard range of each odor pollutant type.

[0064] Step S211: Based on the pollutant data standard range of the odor pollutant type and the predicted pollutant distribution information of the odor pollutant type, generate the pollutant adjustment amount distribution information of the odor pollutant type.

[0065] Step S212: Based on the variation correlation information between each additive type and each odor pollutant type, and the pollutant adjustment amount distribution information, generate the additive data distribution information for each additive type. Then, based on the additive data distribution information of each additive type for each odor pollutant type, in each additive database, through the additive screening network, screen the target additive type corresponding to each odor pollutant type.

[0066] Step S213: Use the additive data distribution information corresponding to the target additive type for each odor pollutant type and the pollutant treatment method of the target additive type for each odor pollutant type as the pollutant treatment plan for each odor pollutant type.

[0067] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0068] Based on the same inventive concept, the embodiments of the present application also provide a water source pollutant treatment device for implementing the water source pollutant treatment method described above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the water source pollutant treatment device provided below can refer to the limitations on the water source pollutant treatment method in the above text, and will not be repeated here.

[0069] In an exemplary embodiment, as Figure 3 shown, a water source pollutant treatment device is provided, including: an acquisition module 310, a prediction module 320, and a generation module 330, where: The acquisition module 310 is configured to acquire the environmental condition information of the water source location and the sample detection data information of the water source sample, and based on the environmental condition information of the water source location, identify the environmental change characteristics of each environmental type at the water source location; A prediction module 320, configured to identify the pollutant data distribution information of each odor pollutant type of the water source based on the sample detection data information of the water source sample, and predict the predicted pollutant data distribution information of each odor pollutant type of the water source through a water source pollutant prediction network based on the environmental change characteristics of each environmental type and the pollutant data distribution information of each odor pollutant type; A generation module 330, configured to generate a pollutant treatment plan for each odor pollutant type through a pollutant treatment network based on the predicted pollutant data distribution information of each odor pollutant type.

[0070] Optionally, the acquisition module 310 is specifically configured to: Split the environmental condition information into sub-condition information of each environmental type, and identify the environmental data change distribution information of each environmental type based on the sub-condition information of each environmental type; Extract the environmental data change rule features of each environmental type and the environmental data change distribution features of each environmental type through a feature extraction network, and use the environmental data change rule features of each environmental type and the environmental data change distribution features of each environmental type as the environmental change features of each environmental type.

[0071] Optionally, the prediction module 320 is specifically configured to: Split the sample detection data information into detection data distribution information of each detection data type, and query each target detection data type corresponding to each odor pollutant type in the database; For each odor pollutant type, identify the pollutant data distribution information of the odor pollutant type through the pollutant data analysis strategy of the odor pollutant type based on the detection data distribution information of each target detection data type corresponding to the odor pollutant type.

[0072] Optionally, the device further includes: An identification module, configured to identify the data change association information between each environmental type and the odor pollutant type through a linear correlation analysis model for each odor pollutant type based on the pollutant data distribution information of the odor pollutant type and the environmental data change distribution information of each environmental type; A determination module, configured to extract the pollutant data change rule features of the odor pollutant type and the pollutant data change distribution features of the odor pollutant type through a feature extraction network based on the pollutant data distribution information of the odor pollutant type, and use the pollutant data change rule features of the odor pollutant type and the pollutant data change distribution features of the odor pollutant type as the pollutant change features of the odor pollutant type.

[0073] Optionally, the prediction module 320 is specifically configured to: For each type of odor pollutant, based on the environmental change characteristics of each environmental type, the pollutant change characteristics of the odor pollutant type, and the data change association information between each environmental type and the odor pollutant type, through the feature prediction network, predict the predicted change characteristics of the pollutants of the odor pollutant type and the predicted distribution characteristics of the pollutants of the odor pollutant type; Based on the predicted change characteristics of the pollutants of the odor pollutant type and the predicted distribution characteristics of the pollutants of the odor pollutant type, through the linear fitting network, generate the predicted pollutant distribution information of the odor pollutant type.

[0074] Optionally, the generation module 330 is specifically configured to: For each type of odor pollutant, in the pollutant treatment database, query the additive types of each pollutant treatment method corresponding to the odor pollutant type and the change association information between each additive type and the odor pollutant type, and obtain the standard range of pollutant data for each odor pollutant type; Based on the standard range of pollutant data for the odor pollutant type and the predicted pollutant distribution information of the odor pollutant type, generate the pollutant adjustment amount distribution information of the odor pollutant type; Based on the change association information between each additive type and the odor pollutant type and the pollutant adjustment amount distribution information, generate the additive data distribution information of each additive type, and based on the additive data distribution information of each additive type for each odor pollutant type, in each additive database, through the additive screening network, screen the target additive type corresponding to each odor pollutant type; Take the additive data distribution information corresponding to the target additive type of each odor pollutant type and the pollutant treatment method of the target additive type of each odor pollutant type as the pollutant treatment plan of each odor pollutant type.

[0075] Each module in the above water source pollutant treatment device can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0076] In an exemplary embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 4As shown in the figure. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for treating water source pollutants. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0077] Those skilled in the art can understand that Figure 4 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0078] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, it implements the steps of the method for treating water source pollutants.

[0079] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, it implements the steps of the method for treating water source pollutants.

[0080] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, it implements the steps of the method for treating water source pollutants.

[0081] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data that have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0082] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0083] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0084] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for treating water source pollutants, characterized in that, The method includes: Obtaining environmental condition information of the location where the water source is located and sample detection data information of the water source sample, and based on the environmental condition information of the location where the water source is located, identifying environmental change characteristics of each environmental type at the location where the water source is located; Based on the sample detection data information of the water source sample, identifying pollutant data distribution information of each odor pollutant type in the water source, and based on the environmental change characteristics of each environmental type and the pollutant data distribution information of each odor pollutant type, predicting the predicted pollutant data distribution information of each odor pollutant type in the water source through a water source pollutant prediction network; Based on the predicted pollutant data distribution information of each odor pollutant type, generating pollutant treatment schemes for each odor pollutant type through a pollutant treatment network.

2. The method according to claim 1, wherein The identifying environmental change characteristics of each environmental type at the location where the water source is located based on the environmental condition information of the location where the water source is located includes: Splitting the environmental condition information into sub-condition information of each environmental type, and based on the sub-condition information of each environmental type, identifying the environmental data change distribution information of each environmental type; Through a feature extraction network, extracting the environmental data change law characteristics and environmental data change distribution characteristics of each environmental type, and using the environmental data change law characteristics and environmental data change distribution characteristics of each environmental type as the environmental change characteristics of each environmental type.

3. The method according to claim 2, wherein The identifying pollutant data distribution information of each odor pollutant type in the water source based on the sample detection data information of the water source sample includes: Splitting the sample detection data information into detection data distribution information of each detection data type, and querying each target detection data type corresponding to each odor pollutant type in a database; For each odor pollutant type, based on the detection data distribution information of each target detection data type corresponding to the odor pollutant type, identifying the pollutant data distribution information of the odor pollutant type through the pollutant data analysis strategy of the odor pollutant type.

4. The method according to claim 2, characterized in that, Before predicting the predicted pollutant data distribution information of each odor pollutant type in the water source through a water source pollutant prediction network based on the environmental change characteristics of each environmental type and the pollutant data distribution information of each odor pollutant type, it further includes: For each odor pollutant type, based on the pollutant data distribution information of the odor pollutant type and the environmental data change distribution information of each environmental type, identifying the data change association information between each environmental type and the odor pollutant type through a linear correlation analysis model; Based on the pollutant data distribution information of the odor pollutant type, through a feature extraction network, extracting the pollutant data change law characteristics and pollutant data change distribution characteristics of the odor pollutant type, and using the pollutant data change law characteristics and pollutant data change distribution characteristics of the odor pollutant type as the pollutant change characteristics of the odor pollutant type.

5. The method according to claim 4, characterized in that, The predicted water source pollutant network includes a feature prediction network and a linear fitting network. Based on the environmental change characteristics of each environmental type and the pollutant data distribution information of each odor pollutant type, the predicted pollutant data distribution information of each odor pollutant type in the water source is predicted through the water source pollutant prediction network, including: For each odor pollutant type, based on the environmental change characteristics of each environmental type, the pollutant change characteristics of the odor pollutant type, and the data change association information between each environmental type and the odor pollutant type, through the feature prediction network, the pollutant predicted change characteristics of the odor pollutant type and the pollutant predicted distribution characteristics of the odor pollutant type are predicted; Based on the pollutant predicted change characteristics of the odor pollutant type and the pollutant predicted distribution characteristics of the odor pollutant type, through the linear fitting network, the predicted pollutant distribution information of the odor pollutant type is generated.

6. The method according to claim 1, wherein Based on the predicted pollutant data distribution information of each odor pollutant type, through the pollutant treatment network, the pollutant treatment solutions for each odor pollutant type are generated, including: For each odor pollutant type, in the pollutant treatment database, query the additive types of each pollutant treatment method corresponding to the odor pollutant type and the change association information between each additive type and the odor pollutant type, and obtain the standard range of pollutant data for each odor pollutant type; Based on the standard range of pollutant data for the odor pollutant type and the predicted pollutant distribution information of the odor pollutant type, the pollutant adjustment amount distribution information of the odor pollutant type is generated; Based on the change association information between each additive type and the odor pollutant type and the pollutant adjustment amount distribution information, the additive data distribution information of each additive type is generated, and based on the additive data distribution information of each additive type for each odor pollutant type, in each additive database, through the additive screening network, the target additive type corresponding to each odor pollutant type is screened; The additive data distribution information corresponding to the target additive type of each odor pollutant type and the pollutant treatment method of the target additive type of each odor pollutant type are used as the pollutant treatment solutions for each odor pollutant type.

7. A treatment device for water source pollutants, characterized in that, The device includes: An acquisition module, configured to acquire the environmental condition information of the water source location and the sample detection data information of the water source sample, and based on the environmental condition information of the water source location, identify the environmental change characteristics of each environmental type at the water source location; A prediction module, configured to identify the pollutant data distribution information of each odor pollutant type in the water source based on the sample detection data information of the water source sample, and based on the environmental change characteristics of each environmental type and the pollutant data distribution information of each odor pollutant type, predict the predicted pollutant data distribution information of each odor pollutant type in the water source through the water source pollutant prediction network; A generation module, configured to generate a pollutant treatment solution for each of the odor pollutant types through a pollutant treatment network based on the predicted pollutant data distribution information of each of the odor pollutant types.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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