A tailings wastewater analysis method and system based on artificial intelligence

Abstract

The present application provides a tailings wastewater analysis method and system based on artificial intelligence, which performs important content analysis and tailings composition construction on the tailings wastewater data to be analyzed that covers the target items, and obtains the important analysis content information and original tailings composition corresponding to the target items; the original tailings composition includes the important analysis content and the related content between the important analysis contents; the important analysis content information in the original tailings composition of the target item is optimized to obtain the tailings composition optimization result of the target item; based on the tailings composition optimization result of the target item, the tailings pollutant data of the target item is determined. Under the premise that the target is partially interfered with or the target tailings pollutant data is fuzzy, the present application optimizes the original tailings composition of the target item, analyzes the important content that has been interfered with and not analyzed, obtains the complete tailings composition of the target item, and improves the analysis accuracy of the tailings pollutant data of the target item.

Description

Technical Field

[0001] The present application relates to the technical field of tailings wastewater analysis, and specifically to a tailings wastewater analysis method and system based on artificial intelligence. Background Art

[0002] Tailings wastewater is a wastewater containing a large amount of pollutants or toxic substances, which not only affects the soil, but may also endanger people's lives and safety.

[0003] Therefore, tailings wastewater needs to be treated, but the specific pollutants or types and contents of toxic substances in tailings wastewater are currently unknown, and there may be inadequate treatment during treatment. Therefore, tailings wastewater needs to be analyzed. The current analysis technology requires collecting wastewater and sending it to the laboratory for analysis, and then relevant technical personnel are required to make judgments. This wastes a lot of time and there is also the problem of inaccurate analysis. Therefore, there is an urgent need for a tailings wastewater analysis method based on artificial intelligence to improve the above technical problems. Summary of the Invention

[0004] In order to improve the technical problems existing in the relevant technologies, this application provides an artificial intelligence-based tailings wastewater analysis method and system.

[0005] In a first aspect, a tailings wastewater analysis method based on artificial intelligence is provided, wherein the tailings wastewater analysis method based on artificial intelligence comprises:

[0006] Performing important content analysis and tailings composition construction on the tailings wastewater data to be analyzed that covers the target items, and obtaining the important analysis content information and original tailings composition corresponding to the target items; the original tailings composition includes the important analysis content and the related content between the important analysis contents;

[0007] Optimizing each of the important analytical contents in the original tailings composition of the target item to obtain an optimized tailings composition result of the target item; the tailings composition optimization result includes all designated important contents and associated contents between the designated important contents, and the number of the important analytical contents covered in the original tailings composition is not greater than the number of the designated important contents covered in the tailings composition optimization result;

[0008] Based on the tailings composition optimization result of the target item, the tailings pollutant data of the target item is determined.

[0009] In this application, the tailings composition optimization result of the target item is used to determine the tailings pollutant data of the target item, including:

[0010] Extracting descriptive knowledge of the tailings composition optimization result of the target item to obtain descriptive knowledge of the tailings pollutant composition of the target item;

[0011] Extracting descriptive knowledge of tailings pollutant data covering the target items to obtain descriptive knowledge of tailings pollutant data of the target items;

[0012] Performing description knowledge splicing on the tailings pollutant component description knowledge and the tailings pollutant data description knowledge corresponding to the target item to obtain spliced ​​description knowledge of the target item;

[0013] Based on the spliced ​​description knowledge of the target item, the tailings pollutant data of the target item is determined.

[0014] It can be understood that when the tailings composition optimization result is based on the target item, the problem of inaccurate description knowledge extraction is improved, so that the tailings pollutant data of the target item can be accurately determined.

[0015] In the present application, the optimization of each of the important analysis information in the original tailings composition of the target item to obtain the tailings composition optimization result of the target item includes:

[0016] The original tailings components are sequentially expanded and compressed using compression and expansion threads to generate tailings composition optimization results for the target item.

[0017] It can be understood that when optimizing the important analysis content information in the original tailings composition of the target item, the problem of inaccurate compression and expansion threads is avoided, so that the tailings composition optimization result of the target item can be accurately obtained.

[0018] In this application, the training method for compression and expansion threads includes:

[0019] Obtaining a first training contaminant dataset, the first training contaminant dataset comprising first example tailings contaminant data having a first training target, the first training target being associated with annotated tailings composition and a tailings contaminant data catalog;

[0020] The compression and expansion threads are used to sequentially expand and compress the annotated tailings components of the first example tailings pollutant data to generate a regression analysis tailings component of the first training target; the regression analysis tailings component includes all specified important contents of the first training target and related contents between the specified important contents;

[0021] Obtaining the tailings pollutant data of the first training target based on the regression analysis of the tailings composition of the first training target;

[0022] The compression and expansion threads are trained based on the deviation value between the regression analysis tailings pollutant data and the tailings pollutant data directory corresponding to the first training target.

[0023] It is understandable that the compression and expansion threads can be trained to improve the performance of the compression and expansion threads.

[0024] In this application, the tailings pollutant component description knowledge and the tailings pollutant data description knowledge corresponding to the target item are spliced ​​to obtain the spliced ​​description knowledge of the target item, including:

[0025] Using a description knowledge splicing thread, the tailings pollutant component description knowledge and the tailings pollutant data description knowledge corresponding to the target item are spliced ​​to obtain the spliced ​​description knowledge of the target item;

[0026] It can be understood that when the tailings pollutant component description knowledge and the tailings pollutant data description knowledge corresponding to the target item are spliced, the problem of inaccurate splicing is improved, so that the spliced ​​description knowledge of the target item can be obtained more accurately.

[0027] The training method for describing the knowledge splicing thread includes:

[0028] Obtaining a second training pollutant dataset, the second training pollutant dataset comprising second example tailings pollutant data having a second training target, the second training target being associated with annotated tailings pollutant data description knowledge, annotated tailings pollutant component description knowledge, and a tailings pollutant data catalog;

[0029] Using the description knowledge splicing thread, the annotated tailings pollutant data description knowledge and the annotated tailings pollutant component description knowledge corresponding to the second training target are spliced ​​to obtain the regression analysis splicing description knowledge of the second training target;

[0030] Using a pollutant attribute recognition unit to perform pollutant attribute recognition on the regression analysis splicing description knowledge to obtain regression analysis tailings pollutant data of the second training target;

[0031] The description knowledge splicing thread is trained based on the deviation value between the regression analysis tailings pollutant data and the tailings pollutant data catalog corresponding to the second training target.

[0032] It can be understood that the performance of the description knowledge splicing thread is improved by the second training pollutant dataset.

[0033] In this application, the descriptive knowledge extraction of the tailings pollutant data covering the target item is performed to obtain the descriptive knowledge of the tailings pollutant data of the target item, including:

[0034] Using a description knowledge extraction thread to extract description knowledge of the tailings pollutant data of the target item to obtain description knowledge of the tailings pollutant data of the target item;

[0035] It can be understood that when extracting descriptive knowledge of tailings pollutant data covering the target items, the problem of inaccurate extraction is improved, so that the descriptive knowledge of tailings pollutant data covering the target items can be accurately obtained.

[0036] The training method for describing the knowledge extraction thread includes:

[0037] obtaining a third training pollutant dataset, the third training pollutant dataset comprising third example tailings pollutant data having a third training target, the third training target being associated with a tailings pollutant data catalog;

[0038] Using the original description knowledge extraction unit in the original neural network to perform special extraction on the tailings pollutant data of the third example to obtain a regression analysis description knowledge network of the tailings pollutant data of the third example;

[0039] Using the recognition unit in the original neural network to perform recognition based on the regression analysis description knowledge network of the third example tailings pollutant data, to obtain the regression analysis tailings pollutant data of the third training target in the third example tailings pollutant data;

[0040] The original neural network is trained based on the deviation value between the regression analysis tailings pollutant data corresponding to the third training target in the third example tailings pollutant data and the tailings pollutant data directory, and the original description knowledge extraction unit that has completed the training is determined as the description knowledge extraction thread.

[0041] It can be understood that by training the description knowledge extraction thread with the third training pollutant dataset, the performance of the description knowledge extraction thread is improved.

[0042] In this application, the tailings wastewater data to be analyzed covering the target items are analyzed for important content and tailings composition is constructed to obtain the important analysis content information corresponding to the target items and the original tailings composition, including:

[0043] obtaining a real-time tailings wastewater dataset, wherein the real-time tailings wastewater dataset includes a plurality of real-time tailings wastewater data continuously covering the target matter;

[0044] Performing an important content analysis on the current real-time tailings wastewater data to obtain information on the important content of the analysis of the target matter; the information on the important content of the analysis includes location information of the important content of the analysis and type information of the important content;

[0045] Generating the original tailings composition of the target item based on the information of the important analysis content using a specified rule; the original tailings composition includes a plurality of component elements and the correlation between the main component elements; the important analysis content is determined as the main component element;

[0046] Based on the sorting information of the real-time tailings wastewater data in the real-time tailings wastewater dataset, the target item is associated with the original tailings components in each of the real-time tailings wastewater data.

[0047] It can be understood that when conducting important content analysis and tailings composition construction on the tailings wastewater data to be analyzed that covers the target items, the problem of inaccurate construction is avoided, so that the important content analysis information and original tailings composition corresponding to the target items can be obtained more accurately.

[0048] In the present application, the optimization of each of the important analysis information in the original tailings composition of the target item to obtain the tailings composition optimization result of the target item also includes:

[0049] Based on the current real-time tailings wastewater data and the important content information of the analysis of the target item in the real-time tailings wastewater data adjacent to the current real-time tailings wastewater data, the original tailings composition of the target item in the current real-time tailings wastewater data is optimized to obtain the tailings composition optimization result of the target item.

[0050] It can be understood that when optimizing the important analysis content information in the original tailings composition of the target item, the problem of inaccurate optimization is improved, so that the tailings composition optimization result of the target item can be obtained more accurately.

[0051] In the present application, after the step of splicing the tailings pollutant component description knowledge and the tailings pollutant data description knowledge corresponding to the target item to obtain the spliced ​​description knowledge of the target item, the method further includes:

[0052] Sort the splicing description knowledge corresponding to the target items in each of the real-time tailings wastewater data according to the sorting information of the real-time tailings wastewater data to obtain a splicing description knowledge sequence of the target items;

[0053] The step of determining tailings pollutant data of the target item based on the spliced ​​description knowledge of the target item includes:

[0054] Based on the spliced ​​description knowledge sequence of the target item, the tailings pollutant data of the target item is determined.

[0055] It can be understood that the tailings pollutant data of the target matter can be accurately obtained through the sorting information of the real-time tailings wastewater data.

[0056] In a second aspect, an artificial intelligence-based tailings wastewater analysis system is provided, comprising a processor and a memory communicating with each other, wherein the processor is configured to read a computer program from the memory and execute the program to implement the above-mentioned method.

[0057] The embodiment of the present application provides a tailings wastewater analysis method and system based on artificial intelligence, which performs important content analysis and tailings composition construction on the tailings wastewater data to be analyzed that covers the target items, and obtains the analysis important content information and original tailings composition corresponding to the target items; the original tailings composition includes the analysis important content and the related content between the analysis important contents; the analysis important content information in the original tailings composition of the target item is optimized to obtain the tailings composition optimization result of the target item; the tailings composition optimization result includes all specified important contents and the related content between the specified important contents, and the number of analysis important contents covered in the original tailings composition is not greater than the number of specified important contents covered in the tailings composition optimization result; based on the tailings composition optimization result of the target item, the tailings pollutant data of the target item is determined. Under the premise that the target is partially interfered with or the target tailings pollutant data is fuzzy, this application optimizes the original tailings composition of the target item, analyzes the important contents that have been interfered with and not analyzed, obtains the complete tailings composition of the target item, and improves the analysis accuracy of the tailings pollutant data of the target item. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0059] Figure 1 A flowchart of an artificial intelligence-based tailings wastewater analysis method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] In order to better understand the above technical solution, the technical solution of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0061] See also Figure 1 , shows a tailings wastewater analysis method based on artificial intelligence, which may include the technical solutions described in the following steps S1-S3.

[0062] S1: Conduct important content analysis and tailings composition construction on the tailings wastewater data to be analyzed that covers the target items, and obtain the important analysis content information and original tailings composition corresponding to the target items; the original tailings composition includes the important analysis content and the related content between the important analysis contents.

[0063] In this application, the "critical analysis information" refers to the significant wastewater volume, high suspended solids content, and the presence of a wide variety of hazardous substances at low concentrations. The raw tailings composition includes the significant analysis information for heavy metal ions (copper, zinc, lead, nickel, iron, barium, arsenic, cadmium, etc.) and mineral processing agents (xanthate, nitroglycerin, cyanide, pine root oil, cresol, copper sulfate, heavy metal salts, sodium sulfate, sulfuric acid, lime, etc.).

[0064] For example, the tailings wastewater data to be analyzed is generated by digitally describing the tailings data after testing. The main harmful substances in tailings wastewater are heavy metal ions and various organic and inorganic flotation reagents used in ore flotation, including highly toxic cyanide and chromium cyanide compounds. The wastewater also contains various insoluble coarse and fine dispersed impurities. Tailings wastewater often contains sulfates, chlorides, or hydroxides of sodium, magnesium, and calcium. The acid in tailings wastewater is mainly formed by the oxidation of sulfur-containing minerals by air and mixing with water.

[0065] S2: Optimize the important analytical content information in the original tailings composition of the target item to obtain the tailings composition optimization result of the target item; the tailings composition optimization result includes all specified important contents and the related contents between the specified important contents, and the number of important analytical contents covered in the original tailings composition is not greater than the number of specified important contents covered in the tailings composition optimization result.

[0066] Exemplarily, all designated important contents are understood to be all important contents in the database. Designated important contents are understood to be important contents in the actual tailings composition optimization results. The purpose of optimization in this application is to remove interference data from the important content information of each analysis in the original tailings composition, for example, to remove information that is not pollutant data. This can reduce interference in the data and improve the accuracy of subsequent data classification.

[0067] S3: Based on the tailings composition optimization results of the target item, determine the tailings pollutant data of the target item.

[0068] For example, the tailings composition optimization result is data without interference, and therefore, the pollutant data can be accurately determined by comparing it with the pollutant composition in the database.

[0069] The tailings wastewater analysis method based on artificial intelligence provided in this embodiment optimizes the original tailings composition of the target item, analyzes the important contents that have been interfered with and not analyzed, and obtains the complete tailings composition of the target item, thereby improving the analysis accuracy of the tailings pollutant data of the target item.

[0070] In one possible implementation, the training method for compression and expansion threads specifically includes the following steps.

[0071] S101: Obtain a first training pollutant dataset, where the first training pollutant dataset includes first example tailings pollutant data with a first training target, where the first training target is associated with annotated tailings components and a tailings pollutant data directory.

[0072] Specifically, first example tailings contaminant data is obtained, where the first example tailings contaminant data specifically covers a first training target. The first training target in the first example tailings contaminant data may be partially disturbed. The first training target may be a movable target such as an animal. The first example tailings contaminant data may cover at least one first training target. Important content analysis is performed on the first training target via an important content analysis thread to obtain analyzed important content information for the first training target. The analyzed important content information includes location information of the analyzed important content and the type of the analyzed important content.

[0073] Based on the designated rules and the types of the important analysis contents corresponding to the first training target, associated contents are established between the important analysis contents to form the annotated tailings component of the first training target.

[0074] S102: Using compression and expansion threads, the annotated tailings components of the first example tailings pollutant data are sequentially expanded and compressed to generate a regression analysis tailings component of the first training target; the regression analysis tailings component includes all designated important contents of the first training target and related contents between the designated important contents.

[0075] Exemplarily, the expansion processing is to perform local processing on the annotated tailings components of the first example tailings pollutant data, so as to display the annotated tailings component information of the first example tailings pollutant data more comprehensively, and the compression processing is to extract the key information of the annotated tailings components of the first example tailings pollutant data.

[0076] S103: Using a pollutant attribute identification unit to perform pollutant attribute identification on the regression analysis tailings components of the first training target, to obtain the regression analysis tailings pollutant data of the first training target.

[0077] For example, the pollutant attribute can understand why a substance has what kind of pollution.

[0078] Specifically, the state recognition thread performs a regression analysis based on the material content of each component element in the specified tailings composition of the first training target to obtain the regression analysis tailings pollutant data of the first training target. The regression analysis tailings pollutant data can be the probability of each specified tailings pollutant data.

[0079] S104: Training the compression and expansion threads based on the deviation value between the regression analysis tailings pollutant data and the tailings pollutant data directory corresponding to the first training target.

[0080] Among them, it is to improve the performance of compression and expansion threads.

[0081] Specifically, a deviation value is calculated by regression analysis of tailings pollutant data and the operating status catalog corresponding to the first training objective. The parameters of the compression and expansion threads are optimized based on the deviation value, thereby achieving training for the compression and expansion threads. When the deviation value of the regression analysis of the compression and expansion threads is less than a specified error, the optimization of the compression and expansion threads can be stopped. Alternatively, the optimization of the compression and expansion threads can be stopped after a specified number of iterations has been reached.

[0082] In one possible implementation, the compression and expansion thread can also be trained based on a training real-time tailings wastewater dataset. Specifically, a training real-time tailings wastewater dataset covering a first training target is input into the compression and expansion thread. The first training target has an annotated tailings pollutant composition map in each training real-time tailings wastewater dataset, and the first training target also has a tailings pollutant data directory. The first training target has a corresponding sequence of annotated tailings pollutant composition maps in the training real-time tailings wastewater dataset. The compression and expansion thread performs expansion and compression processing on the tailings pollutant composition maps in the sequence of annotated tailings pollutant composition maps for the first training target, respectively, to obtain regression analysis tailings compositions for each tailings pollutant composition map. Based on the sorting information of the sequence of annotated tailings pollutant composition maps, each regression analysis tailings composition is sorted to generate a regression analysis tailings composition sequence for the first training target. A pollutant attribute recognition unit is used to identify pollutant attributes in the regression analysis tailings composition sequence for the first training target, obtaining regression analysis tailings pollutant data for the first training target. The compression and expansion thread is trained based on the deviation between the regression analysis tailings pollutant data corresponding to the first training target and the tailings pollutant data directory.

[0083] Specifically, when the training example of the training compression and expansion thread is to train a real-time tailings wastewater data set, the compression and expansion thread determines the regression analysis tailings composition of the first training target in the current real-time tailings wastewater data based on the original tailings composition of the first training target in the current real-time tailings wastewater data and the original tailings composition of the first training target in the real-time tailings wastewater data adjacent to the current real-time tailings wastewater data set to generate the regression analysis tailings composition of the first training target in the current real-time tailings wastewater data.

[0084] In one possible implementation, a training method for describing a knowledge splicing thread specifically includes the following steps.

[0085] S201: Obtain a second training pollutant dataset, the second training pollutant dataset including second example tailings pollutant data with a second training target, the second training target being associated with annotated tailings pollutant data description knowledge, annotated tailings pollutant component description knowledge, and a tailings pollutant data catalog.

[0086] Among them, descriptive knowledge is understood as features.

[0087] Specifically, a second example tailings pollutant data is obtained, and the second example tailings pollutant data specifically covers the second training target. The second training target in the second example tailings pollutant data can be partially interfered with. The second example tailings pollutant data can cover at least one second training target. The second training target has annotated tailings pollutant data description knowledge and annotated tailings pollutant component description knowledge. The annotated tailings pollutant data description knowledge of the second training target is obtained by extracting the description knowledge of the tailings pollutant data covering the second training target using the target description knowledge extraction thread. The annotated tailings pollutant component description knowledge of the second training target is obtained by optimizing and extracting the description knowledge of the original tailings composition of the second training target using the compression and expansion thread trained in the above steps. The original tailings composition of the second training target is generated according to specified rules based on the important analysis content of the second training target. The target description knowledge extraction thread can be a convolutional neural network (CNN). In this embodiment, the second example tailings pollutant data can be the same as or different from the first example tailings pollutant data; the second training target can be the same as or different from the first training target.

[0088] S202: Using a description knowledge splicing thread, the description knowledge of the annotated tailings pollutant data and the description knowledge of the annotated tailings pollutant components corresponding to the second training target are spliced ​​to obtain the regression analysis splicing description knowledge of the second training target.

[0089] Specifically, the annotated tailings pollutant data description knowledge and the annotated tailings pollutant component description knowledge corresponding to the second training target are simultaneously input into the description knowledge splicing thread. The description knowledge splicing thread performs description knowledge splicing on the annotated tailings pollutant data description knowledge and the annotated tailings pollutant component description knowledge to obtain the regression analysis spliced ​​description knowledge of the second training target. The description knowledge splicing thread can be a fully connected layer.

[0090] In this embodiment, the description knowledge splicing thread has at least two inputs to facilitate splicing the tailings pollutant composition description knowledge and the tailings pollutant data description knowledge of the second training target. Furthermore, by splicing the tailings pollutant composition description knowledge and the tailings pollutant data description knowledge of the second training target through the description knowledge splicing thread, richer semantic information is obtained, thereby facilitating improved accuracy of pollutant attribute analysis for the second training target.

[0091] S203: Using a pollutant attribute recognition unit to perform pollutant attribute recognition on the regression analysis splicing description knowledge, and obtaining the regression analysis tailings pollutant data of the second training target.

[0092] Specifically, a state recognition thread is used to perform state recognition on the regression analysis splicing description knowledge of the second training target to generate the regression analysis tailings pollutant data of the second training target. The regression analysis tailings pollutant data can be the probability of each specified tailings pollutant data.

[0093] S204: Training the description knowledge splicing thread based on the deviation value between the regression analysis tailings pollutant data and the tailings pollutant data catalog corresponding to the second training target.

[0094] Here, the deviation value is understood as the error value.

[0095] The loss value between the tailings pollutant data and the tailings pollutant data catalog based on the regression analysis of the second training objective is used to optimize the parameters in the description knowledge splicing thread to achieve the training of the description knowledge splicing thread.

[0096] When the loss value of the descriptive knowledge stitching thread falls below a specified value, optimization of the thread can be stopped. Alternatively, optimization can be stopped after a specified number of iterations. Alternatively, a specified validation dataset can be input into the thread to evaluate its training performance. This allows adjustments to the tailings contaminant composition and hyperparameters of the thread to ensure it can effectively stitch together different types of descriptive knowledge and improve the accuracy of target state classification.

[0097] In the above embodiment, the description knowledge splicing thread can also be trained based on the training real-time tailings wastewater data set. Specifically, the annotated tailings pollutant data description knowledge sequence and the annotated tailings pollutant component description knowledge sequence corresponding to the training real-time tailings wastewater data set covering the second training target are input into the description knowledge splicing thread. Among them, the annotated tailings pollutant data description knowledge sequence is the tailings pollutant data description knowledge sequence composed of the tailings pollutant data description knowledge corresponding to the second training target in each real-time tailings wastewater data according to the time series; the annotated tailings pollutant component description knowledge sequence is the tailings pollutant component description knowledge sequence composed of the tailings pollutant component description knowledge corresponding to the second training target in each real-time tailings wastewater data according to the time series, and the second training target has a tailings pollutant data directory. The description knowledge splicing thread performs description knowledge splicing on the tailings pollutant component description knowledge and the tailings pollutant data description knowledge of the second training target in the same real-time tailings wastewater data to generate regression analysis splicing description knowledge in the real-time tailings wastewater data. The regression analysis splicing description knowledge corresponding to each real-time tailings wastewater data can be obtained through the above steps. The regression analysis splicing description knowledge is used to generate a regression analysis splicing description knowledge sequence for the second training target based on the time series of real-time tailings wastewater data. A pollutant attribute recognition unit is used to identify pollutant attributes in the regression analysis splicing description knowledge sequence for the second training target, generating the regression analysis tailings pollutant data for the second training target. The description knowledge splicing thread is trained based on the deviation between the regression analysis tailings pollutant data corresponding to the second training target and the tailings pollutant data catalog.

[0098] The above steps can realize the training of compression and expansion threads and description knowledge splicing threads.

[0099] In a possible implementation embodiment, a training method for describing a knowledge extraction thread specifically includes the following steps.

[0100] A third training pollutant data set is obtained, the third training pollutant data set including third example tailings pollutant data having a third training target, the third training target being associated with a tailings pollutant data directory; the original description knowledge extraction unit in the original neural network is used to perform special extraction on the third example tailings pollutant data to obtain a regression analysis description knowledge network of the third example tailings pollutant data; the recognition unit in the original neural network is used to perform recognition based on the regression analysis description knowledge network of the third example tailings pollutant data to obtain regression analysis tailings pollutant data of the third training target in the third example tailings pollutant data; the original neural network is trained based on the deviation value between the regression analysis tailings pollutant data corresponding to the third training target in the third example tailings pollutant data and the tailings pollutant data directory, and the original description knowledge extraction unit that has completed the training is determined as a description knowledge extraction thread. The description knowledge extraction thread in this embodiment can be the CNN thread in the above embodiment.

[0101] Specifically, in step S1, important content analysis and tailings composition construction are performed on the tailings wastewater data to be analyzed that covers the target items, and the specific implementation method of obtaining the important content information of the analysis corresponding to the target items and the original tailings composition is as follows.

[0102] In one possible implementation, tailings wastewater data to be analyzed in a target area is obtained in real time using tailings pollutant data collection equipment. The tailings wastewater data to be analyzed covers target items. Target analysis is performed on the tailings wastewater data to obtain analysis boxes for each target item. Each analysis box only covers one target item. Key content analysis is performed on the analysis box covering the target item to obtain key content information for the analysis of the target item.

[0103] In one possible embodiment, an offline real-time tailings wastewater dataset is obtained, where the real-time tailings wastewater dataset includes multiple real-time tailings wastewater data that continuously cover target items. All real-time tailings wastewater data in the real-time tailings wastewater dataset are traversed, and each real-time tailings wastewater data is sequentially determined as the current real-time tailings wastewater data. A target analysis is performed on the current real-time tailings wastewater data to obtain an analysis frame covering the target item, where the analysis frame only covers one target item. An important content analysis is performed on the analysis frame covering the target item to obtain information on the important content of the analysis of the target item. Through the above steps, information on the important content of the analysis of the target item in each real-time tailings wastewater data is obtained.

[0104] When the information obtained is a real-time tailings wastewater data set, based on the sorting information of the real-time tailings wastewater data in the real-time tailings wastewater data set, the original tailings components of the target item in each real-time tailings wastewater data are associated to generate the original tailings component sequence of the target item.

[0105] Specifically, in step S2, the original tailings composition is optimized based on the important content information of the analysis of the target item, and the specific implementation method of obtaining the tailings composition optimization result of the target item is as follows.

[0106] In one possible implementation, the original tailings composition corresponding to the target item is input into the compression and expansion thread trained in the above steps S101 to S104. The compression and expansion thread performs expansion and compression processing on the original tailings composition in turn to generate an optimized tailings composition result for the target item.

[0107] In a possible implementation, if the category information of the important content to be analyzed does not belong to an external component element, the original content of the important content to be analyzed is set to 0.

[0108] The original material content of each core component in the specific original tailings composition.

[0109] In a specific embodiment, the original tailings composition of the target item in the current real-time tailings wastewater data is optimized based on the important content information of the analysis of the target item in the current real-time tailings wastewater data and the real-time tailings wastewater data adjacent to the current real-time tailings wastewater data to obtain the tailings composition optimization result of the target item.

[0110] Specifically, the substance content of each designated important content in the tailings composition optimization result of the target item in the current real-time tailings wastewater data can be determined based on the following formula.

[0111] Specifically, the specific implementation method of determining the tailings pollutant data of the target item based on the tailings composition optimization result of the target item in step S3 is as follows.

[0112] The process of step S3 of a specific embodiment of the tailings wastewater analysis method based on artificial intelligence is provided.

[0113] S31: Splicing the tailings composition optimization results of the target item with the tailings wastewater data to be analyzed that covers the target item to obtain splicing description knowledge of the target item.

[0114] The process of step S31 of a specific embodiment of the tailings wastewater analysis method based on artificial intelligence is provided.

[0115] S311: Extract descriptive knowledge of the tailings composition optimization results of the target item to obtain descriptive knowledge of the tailings pollutant components of the target item.

[0116] In one possible implementation, each designated key element in the tailings composition optimization results for the target item has a corresponding material content. The material content of each designated key element reflects the probability that the target item falls within the designated tailings pollutant data. By extracting the material content of all designated key elements in the tailings composition optimization results for the target item, descriptive knowledge of the tailings pollutant composition for the target item is generated.

[0117] S312: Extract descriptive knowledge of the tailings pollutant data covering the target items to obtain descriptive knowledge of the tailings pollutant data of the target items.

[0118] In a possible implementation example, descriptive knowledge is extracted from the analysis frame covering the target item to obtain tailings pollutant data descriptive knowledge of the target item.

[0119] In a specific embodiment, a CNN thread is used to extract descriptive knowledge from an analysis box covering a target item, thereby obtaining descriptive knowledge of tailings pollutant data of the target item.

[0120] S313: performing description knowledge splicing on the tailings pollutant composition description knowledge and the tailings pollutant data description knowledge corresponding to the target item to obtain the spliced ​​description knowledge of the target item.

[0121] In a possible implementation example, the tailings pollutant composition description knowledge and the tailings pollutant data description knowledge corresponding to the target item are input into the description knowledge splicing thread trained in the above steps S201 to S204 to obtain the spliced ​​description knowledge of the target item.

[0122] S32: Determine the tailings pollutant data of the target item based on the splicing description knowledge of the target item.

[0123] Specifically, a description knowledge splicing thread is used to splice the tailings pollutant composition description knowledge and tailings pollutant data description knowledge corresponding to the target item to obtain the spliced ​​description knowledge of the target item. A pre-trained tailings pollutant data identification thread is used to determine the tailings pollutant data of the target item based on the spliced ​​description knowledge of the target item.

[0124] In one specific embodiment, the corresponding concatenated description knowledge of the target item in each real-time tailings wastewater data is sorted based on the sorting information of the real-time tailings wastewater data to obtain a concatenated description knowledge sequence for the target item. A pre-trained tailings pollutant data identification thread is then used to determine the tailings pollutant data for the target item based on the concatenated description knowledge sequence for the target item.

[0125] The tailings wastewater analysis method based on artificial intelligence provided in this embodiment optimizes the original tailings composition of the target item, analyzes the important contents that have been interfered with and not analyzed, and obtains the complete tailings composition of the target item, thereby improving the analysis accuracy of the tailings pollutant data of the target item.

[0126] Based on the above, a tailings wastewater analysis device based on artificial intelligence is provided, which includes:

[0127] A knowledge fragment extraction module is used to extract knowledge fragments from the example collapse hidden danger data to obtain minor knowledge fragments and important knowledge fragments of the example collapse hidden danger data;

[0128] a label building module, configured to build secondary description labels based on secondary knowledge fragments corresponding to a plurality of said example collapse hazard data, and to build important description labels based on important knowledge fragments corresponding to a plurality of said example collapse hazard data;

[0129] A component acquisition module is used to perform important content analysis and tailings composition construction on the tailings wastewater data to be analyzed that covers the target items, and obtain the important analysis content information corresponding to the target items and the original tailings composition; the original tailings composition includes the important analysis content and the related content between the important analysis contents;

[0130] a result optimization module, configured to optimize the information of each of the analyzed important contents in the original tailings composition of the target item to obtain an optimized result of the tailings composition of the target item; the optimized result of the tailings composition includes all designated important contents and associated contents between the designated important contents, and the number of the analyzed important contents covered in the original tailings composition is not greater than the number of the designated important contents covered in the optimized result of the tailings composition;

[0131] The data determination module is used to determine the tailings pollutant data of the target item based on the tailings composition optimization result of the target item.

[0132] Based on the above, an artificial intelligence-based tailings wastewater analysis system is shown, which includes a processor and a memory that communicate with each other, and the processor is used to read a computer program from the memory and execute it to implement the above method.

[0133] Based on the above, a computer-readable storage medium is also provided, on which a computer program stored implements the above method when running.

[0134] In summary, based on the above scheme, the tailings wastewater data to be analyzed that covers the target items are analyzed for important content and the tailings composition is constructed to obtain the analysis important content information and the original tailings composition corresponding to the target items; the original tailings composition includes the analysis important content and the related content between the analysis important contents; the analysis important content information in the original tailings composition of the target item is optimized to obtain the tailings composition optimization result of the target item; the tailings composition optimization result includes all specified important contents and the related content between the specified important contents, and the number of analysis important contents covered in the original tailings composition is not greater than the number of specified important contents covered in the tailings composition optimization result; based on the tailings composition optimization result of the target item, the tailings pollutant data of the target item is determined. Under the premise that the target is partially interfered with or the target tailings pollutant data is fuzzy, this application optimizes the original tailings composition of the target item, analyzes the important contents that have been interfered with and not analyzed, obtains the complete tailings composition of the target item, and improves the analysis accuracy of the tailings pollutant data of the target item.

[0135] It should be understood that the system and its modules shown above can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated hardware. Those skilled in the art will understand that the above-mentioned methods and systems can be implemented using computer-executable instructions and / or contained in processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the system and its modules of the present application. Not only can hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc. be implemented, they can also be implemented using software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software (for example, firmware).

[0136] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or any other possible beneficial effects.

Claims

1. A tailings wastewater analysis method based on artificial intelligence, characterized in that: The tailings wastewater analysis method based on artificial intelligence includes: Performing important content analysis and tailings composition construction on the tailings wastewater data to be analyzed that covers the target items, and obtaining the important analysis content information and original tailings composition corresponding to the target items; the original tailings composition includes the important analysis content and the related content between the important analysis contents; Optimizing each of the important analytical contents in the original tailings composition of the target item to obtain an optimized tailings composition result of the target item; the tailings composition optimization result includes all designated important contents and associated contents between the designated important contents, and the number of the important analytical contents covered in the original tailings composition is not greater than the number of the designated important contents covered in the tailings composition optimization result; Determining tailings pollutant data for the target item based on the tailings composition optimization result for the target item; Wherein, the tailings pollutant data of the target item is determined based on the tailings composition optimization result of the target item, including: Extracting descriptive knowledge of the tailings composition optimization result of the target item to obtain descriptive knowledge of the tailings pollutant composition of the target item; Extracting descriptive knowledge of tailings pollutant data covering the target items to obtain descriptive knowledge of tailings pollutant data of the target items; Performing description knowledge splicing on the tailings pollutant component description knowledge and the tailings pollutant data description knowledge corresponding to the target item to obtain spliced ​​description knowledge of the target item; Based on the spliced ​​description knowledge of the target item, the tailings pollutant data of the target item is determined.

2. The tailings wastewater analysis method based on artificial intelligence according to claim 1, characterized in that: The optimizing of each of the important analysis information in the original tailings composition of the target item to obtain the tailings composition optimization result of the target item includes: The original tailings components are sequentially expanded and compressed using compression and expansion threads to generate tailings composition optimization results for the target item.

3. The tailings wastewater analysis method based on artificial intelligence according to claim 2, characterized in that: The training method of the compression and expansion thread includes: Obtaining a first training contaminant dataset, the first training contaminant dataset comprising first example tailings contaminant data having a first training target, the first training target being associated with annotated tailings composition and a tailings contaminant data catalog; The compression and expansion threads are used to sequentially expand and compress the annotated tailings components of the first example tailings pollutant data to generate a regression analysis tailings component of the first training target; the regression analysis tailings component includes all specified important contents of the first training target and related contents between the specified important contents; Obtaining the tailings pollutant data of the first training target based on the regression analysis of the tailings composition of the first training target; The compression and expansion threads are trained based on the deviation value between the regression analysis tailings pollutant data and the tailings pollutant data directory corresponding to the first training target.

4. The tailings wastewater analysis method based on artificial intelligence according to claim 1, characterized in that: The step of performing description knowledge splicing on the tailings pollutant component description knowledge and the tailings pollutant data description knowledge corresponding to the target item to obtain the spliced ​​description knowledge of the target item includes: Using a description knowledge splicing thread, the tailings pollutant component description knowledge and the tailings pollutant data description knowledge corresponding to the target item are spliced ​​to obtain the spliced ​​description knowledge of the target item; The training method for describing the knowledge splicing thread includes: Obtaining a second training pollutant dataset, the second training pollutant dataset comprising second example tailings pollutant data having a second training target, the second training target being associated with annotated tailings pollutant data description knowledge, annotated tailings pollutant component description knowledge, and a tailings pollutant data catalog; Using the description knowledge splicing thread, the annotated tailings pollutant data description knowledge and the annotated tailings pollutant component description knowledge corresponding to the second training target are spliced ​​to obtain the regression analysis splicing description knowledge of the second training target; Using a pollutant attribute recognition unit to perform pollutant attribute recognition on the regression analysis splicing description knowledge to obtain regression analysis tailings pollutant data of the second training target; The description knowledge splicing thread is trained based on the deviation value between the regression analysis tailings pollutant data and the tailings pollutant data catalog corresponding to the second training target.

5. The tailings wastewater analysis method based on artificial intelligence according to claim 1, characterized in that: The extracting of descriptive knowledge of the tailings pollutant data covering the target item to obtain descriptive knowledge of the tailings pollutant data of the target item includes: Using a description knowledge extraction thread to extract description knowledge of the tailings pollutant data of the target item to obtain description knowledge of the tailings pollutant data of the target item; The training method for describing the knowledge extraction thread includes: obtaining a third training pollutant dataset, the third training pollutant dataset comprising third example tailings pollutant data having a third training target, the third training target being associated with a tailings pollutant data catalog; Using the original description knowledge extraction unit in the original neural network to perform special extraction on the tailings pollutant data of the third example to obtain a regression analysis description knowledge network of the tailings pollutant data of the third example; Using the recognition unit in the original neural network to perform recognition based on the regression analysis description knowledge network of the third example tailings pollutant data, to obtain the regression analysis tailings pollutant data of the third training target in the third example tailings pollutant data; The original neural network is trained based on the deviation value between the regression analysis tailings pollutant data corresponding to the third training target in the third example tailings pollutant data and the tailings pollutant data directory, and the original description knowledge extraction unit that has completed the training is determined as the description knowledge extraction thread.

6. The tailings wastewater analysis method based on artificial intelligence according to any one of claims 1 to 5, characterized in that: The important content analysis and tailings composition construction of the tailings wastewater data to be analyzed covering the target items are performed to obtain the important analysis content information corresponding to the target items and the original tailings composition, including: obtaining a real-time tailings wastewater dataset, wherein the real-time tailings wastewater dataset includes a plurality of real-time tailings wastewater data continuously covering the target matter; Performing an important content analysis on the current real-time tailings wastewater data to obtain information on the important content of the analysis of the target matter; the information on the important content of the analysis includes location information of the important content of the analysis and type information of the important content; Generating the original tailings composition of the target item based on the information of the important analysis content using a specified rule; the original tailings composition includes a plurality of component elements and the correlation between the main component elements; the important analysis content is determined as the main component element; Based on the sorting information of the real-time tailings wastewater data in the real-time tailings wastewater dataset, the target item is associated with the original tailings components in each of the real-time tailings wastewater data.

7. The tailings wastewater analysis method based on artificial intelligence according to claim 6 is characterized in that: The step of optimizing the important analysis information in the original tailings composition of the target item to obtain the tailings composition optimization result of the target item further includes: Based on the current real-time tailings wastewater data and the important content information of the analysis of the target item in the real-time tailings wastewater data adjacent to the current real-time tailings wastewater data, the original tailings composition of the target item in the current real-time tailings wastewater data is optimized to obtain the tailings composition optimization result of the target item.

8. The tailings wastewater analysis method based on artificial intelligence according to claim 7, characterized in that: After the step of performing description knowledge splicing on the tailings pollutant component description knowledge and the tailings pollutant data description knowledge corresponding to the target item to obtain the spliced ​​description knowledge of the target item, the method further includes: Sort the splicing description knowledge corresponding to the target items in each of the real-time tailings wastewater data according to the sorting information of the real-time tailings wastewater data to obtain a splicing description knowledge sequence of the target items; The step of determining tailings pollutant data of the target item based on the spliced ​​description knowledge of the target item includes: Based on the spliced ​​description knowledge sequence of the target item, the tailings pollutant data of the target item is determined.

9. An artificial intelligence-based tailings wastewater analysis system, characterized in that: The method comprises a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute the computer program to implement the method according to any one of claims 1 to 8.

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