CN115905807A - Coarse grating optimization operation method based on deep learning - Google Patents
Coarse grating optimization operation method based on deep learning Download PDFInfo
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Abstract
The invention provides a coarse grating optimal operation method based on deep learning, which belongs to the technical field of communication, and automatically judges impurity amount and abnormal impurities through a Transformer neural network model, adjusts the operation rate of a coarse grating by combining the impurity amount and combines a harmful gas concentration sensor, thereby ensuring the safety of an operation site; the invention solves the problem of automatically identifying impurity quantity and abnormal impurities and provides a data base for automatically processing the impurities.
Description
Technical Field
The invention belongs to the technical field of coarse grating optimization, and particularly relates to a dirty coarse grating optimization operation method based on deep learning.
Background
As a first structure on the process flow of a sewage plant, the coarse grating can effectively block large-particle impurities in sewage, generally more than 15mm, so as to ensure that the large-particle impurities do not enter a pump room and influence a water pump or subsequent structure treatment. At present, the automation degree of the operation of the coarse grating is not high enough, the operation of grating slag is controlled by the water level in front of and behind the grating, the feedback is often lagged, the operation and maintenance environment of the coarse grating is poor, and the coarse grating is foul and has bad mosquitoes and flies. The anaerobic environment of the coarse grating is easy to generate toxic gases such as methane, hydrogen sulfide and the like, and a plurality of toxic and coma poisons caused by overhauling in the coarse grating tank occur in China, so that the workers are drowned. On the other hand, the transportation of the coarse grid slag is still subject to scaling, the grid slag is often pushed by people, the working environment is poor, and the degree of automation and intellectualization is urgently required to be improved.
Disclosure of Invention
Aiming at the defects in the prior art, the deep learning-based coarse grating optimization operation method provided by the invention can be used for automatically identifying impurity amount and abnormal impurities, providing a data base for automatically processing the impurities, detecting the safety degree of the impurity area in real time through a sensor, detecting and alarming harmful gas and effectively solving the problem of life safety.
In order to achieve the above purpose, the invention adopts the technical scheme that:
the scheme provides a coarse grating optimization operation method based on deep learning, which comprises the following steps:
s1, collecting video images above a grid channel and a grid slag barrel, labeling the image collected by a camera above the grid channel to obtain a classification training positive and negative sample for judging whether abnormal impurities exist and a training sample for judging the amount of the impurities, and labeling the video image above the grid slag barrel to obtain a classification training positive and negative sample for judging whether the grid slag barrel exists and a positive and negative sample for judging whether grid slag in the grid slag barrel is full;
s2, identifying the grid slag barrel in the video image above the position where the grid slag barrel is placed, judging whether the grid slag barrel exists according to the identification result, if so, performing the step S4, otherwise, entering the step S3;
s3, acquiring a toxic gas concentration value, performing exception handling by using a toxic gas sensor, and ending the process;
s4, training an improved Transformer neural network model by utilizing classified training positive and negative samples for judging whether a grid slag barrel exists or not;
s5, judging the grid slag condition in the grid slag barrel by using a trained Transformer neural network model according to positive and negative samples of whether the grid slag in the grid slag barrel is full, and finishing the process according to the judgment result;
s6, training the improved Transformer neural network model by using the training sample for judging the impurity amount to obtain a first classification model, identifying impurities by using the first classification model, adjusting the rotation speed rate of the coarse grating by combining a liquid level difference, and ending the process;
and S7, training the improved Transformer neural network model by using the classification training positive and negative samples for judging whether abnormal impurities exist or not to obtain a second classification model, judging whether abnormal impurities exist or not by using the second classification model, and ending the process according to a judgment result.
The beneficial effects of the invention are: the method comprises the steps of automatically judging the impurity amount and abnormal impurities through a Transformer neural network model, and regulating the running speed of a coarse grating by combining the impurity amount; the safety of the operation site is ensured by combining a harmful gas concentration sensor; through the design, the automatic impurity treatment level is higher, the workload of operators can be greatly reduced, the impurity treatment efficiency is improved, the automatic identification of impurity amount and abnormal impurities is solved, and a data basis is provided for the automatic impurity treatment.
Further, the step S3 includes the steps of:
s301, acquiring a toxic gas concentration value;
s302, judging whether the toxic gas concentration value is larger than a set safety value or not by using the toxic gas sensor, if so, alarming and ending the process, otherwise, judging that the toxic gas concentration value is lower than the set safety value and ending the process.
The beneficial effects of the further scheme are as follows: the invention detects the safety degree of the impurity area in real time through the sensor, solves the detection and alarm of harmful gas and effectively solves the problem of life safety.
Still further, the step S5 includes the steps of:
s501, judging whether the grid slag in the grid slag barrel is full by utilizing a trained Transformer neural network model according to positive and negative samples of whether the grid slag in the grid slag barrel is full, if so, entering the step S502, and otherwise, ending the flow;
and S502, sending an instruction for lifting and replacing the grid slag barrel, and ending the process.
The beneficial effects of the further scheme are as follows: the method utilizes the improved Transformer neural network model to judge whether the grid slag in the grid slag barrel is full, can stably and accurately judge whether the grid slag barrel is full, and provides judgment basis for automatically replacing the grid slag barrel by a system.
Furthermore, the Transformer neural network model comprises a full connection layer, an encoder, a multi-layer perception classifier and an output layer which are connected in sequence;
the full connection layer is used for obtaining an image feature block with the same size as the video image block according to the exception processing result, and flattening and compressing the image feature block;
the encoder is used for extracting the abstract image characteristics of the image characteristic block and the position of the encoding image block according to the processing result;
the multi-layer perception classifier is used for classifying the transform neural network model coding result according to the extracted result to obtain image classification;
and the output layer is used for outputting the grid slag condition in the grid slag barrel according to image classification.
The beneficial effects of the above further scheme are: after the Transformer neural network model is used for sample training, whether the grid slag barrel is filled with slag or not can be stably and accurately judged, and judgment basis is provided for the system to automatically replace the grid slag barrel.
Still further, the step S7 includes the steps of:
s701, training the improved Transformer neural network model by using classification training positive and negative samples for judging whether abnormal impurities exist or not to obtain a second classification model;
and S702, judging whether abnormal impurities exist or not by using the second classification model, if so, executing the step S3, and otherwise, ending the process.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of a Transformer model structure.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Examples
As shown in fig. 1, the invention provides a coarse grid optimization operation method based on deep learning, which is implemented as follows:
s1, collecting video images above a grid channel and a grid slag barrel, labeling the image collected by a camera above the grid channel to obtain a classification training positive and negative sample for judging whether abnormal impurities exist and a training sample for judging the amount of the impurities, and labeling the video image above the grid slag barrel to obtain a classification training positive and negative sample for judging whether the grid slag barrel exists and a positive and negative sample for judging whether grid slag in the grid slag barrel is full;
in this embodiment, videos above the grid channel and the grid slag barrel are collected and marked to obtain two classification training samples (normal impurities and abnormal impurities) for judging the existence of abnormal impurities and training samples for judging impurity amount (five grades: trace, small, medium, large and large), about 2 million images of image data of the two types of samples are used as positive samples, a certain amount of negative samples are collected at the same time, and the samples are marked manually. Then step S2 and step S5 are performed simultaneously in parallel.
S2, identifying the grid slag barrel in the video image above the position where the grid slag barrel is placed, judging whether the grid slag barrel exists according to the identification result, if so, performing the step S4, otherwise, entering the step S3;
in this embodiment, whether there is a grid slag bucket is determined: and identifying the grid slag barrel in the video image above the position where the grid slag barrel is placed, judging whether the grid slag barrel exists according to the identification result, executing the step S4 if the grid slag barrel does not exist, and executing the step S3 if the grid slag barrel exists.
S3, acquiring a toxic gas concentration value, performing exception handling by using a toxic gas sensor, and ending the process, wherein the implementation method comprises the following steps:
s301, acquiring a toxic gas concentration value;
s302, judging whether the toxic gas concentration value is larger than a set safety value or not by using the toxic gas sensor, if so, alarming and ending the process, otherwise, judging that the toxic gas concentration value is lower than the set safety value and ending the process.
In this embodiment, first, toxic gas sensors, such as a methane concentration sensor and a hydrogen sulfide concentration sensor, are called, if one or more toxic gas concentrations are greater than a set safety value, an alarm is given to complete the process, and if all monitored toxic gas concentrations are lower than the safety value, the process is completed by manual processing.
S4, training an improved Transformer neural network model by using the positive and negative samples of the classified training with or without the grid slag bucket;
s5, judging the grid slag condition in the grid slag barrel by using the trained Transformer neural network model according to positive and negative samples whether the grid slag in the grid slag barrel is full, and ending the process according to the judgment result, wherein the realization method comprises the following steps:
s501, judging whether the grid slag in the grid slag barrel is full by utilizing a trained Transformer neural network model according to positive and negative samples of whether the grid slag in the grid slag barrel is full, if so, entering the step S502, and otherwise, ending the flow;
and S502, sending an instruction for lifting and replacing the grid slag barrel, and ending the process.
The Transformer neural network model comprises a full connection layer, an encoder, a multi-layer perception classifier and an output layer which are connected in sequence;
the full connection layer is used for obtaining an image feature block with the same size as the video image block according to the exception processing result, and flattening and compressing the image feature block;
the encoder is used for extracting the abstract image characteristics of the image characteristic block and the position of the encoding image block according to the processing result;
the multilayer perception classifier is used for classifying the transform neural network model coding result according to the extracted result to obtain image classification;
and the output layer is used for outputting the grid slag condition in the grid slag barrel according to image classification.
In this embodiment, a Transformer model is invoked to determine whether the grid slag bucket is full: and calling an improved Transformer model to judge whether the grid slag barrel is full, if the grid slag barrel is full, sending an instruction for lifting and replacing the grid slag barrel to complete the flow, and if the grid slag barrel is not full, ending the flow.
As shown in fig. 2, in order to improve information of each image block, full connection is merged into the transform, the image block first passes through a full connection layer to obtain a feature block with the same size as the image block, and the image feature block is subjected to image block flattening and compression (Linear Projection of Flattened Patches) and a subsequent encoding flow of the transform. Extracting abstract image features of the image feature block and the positions of the coding image blocks by using a coder; classifying the transform neural network model coding result by using a multilayer perception classifier to obtain image classification; and finally, outputting the grid slag condition in the grid slag barrel through an output layer.
S6, training the improved Transformer neural network model by using the training sample for judging the impurity amount to obtain a first classification model, identifying impurities by using the first classification model, adjusting the rotation speed rate of the coarse grating by combining a liquid level difference, and ending the process;
in this embodiment, a trained improved Transformer model is called to identify the impurity condition types (trace, small, medium, large and abnormal impurities), and the operation rate of the coarse grating is adjusted by combining the liquid level difference to complete the process. The trained improved Transformer model is a first classification model obtained by training the Transformer model in the step S4 by using the impurity amount training sample data in the step S1.
S7, training the improved Transformer neural network model by utilizing the classification training positive and negative samples for judging whether abnormal impurities exist, obtaining a second classification model, judging whether abnormal impurities exist or not by utilizing the second classification model, and ending the process according to a judgment result, wherein the implementation method comprises the following steps:
s701, training the improved Transformer neural network model by utilizing the classification training positive and negative samples for judging whether abnormal impurities exist or not to obtain a second classification model;
and S702, judging whether abnormal impurities exist or not by using the second classification model, if so, executing the step S3, and otherwise, ending the process.
In this embodiment, the trained improved Transformer model is called to determine whether there is an abnormal impurity, if there is an abnormal impurity, step S3 is executed, and if there is no abnormal impurity, the process is ended. The trained improved Transformer model is a second classification model obtained by training the Transformer model in the step S4 by using the two-classification sample data for judging whether abnormal impurities exist or not in the step S1.
The method automatically judges the impurity amount and abnormal impurities through a Transformer neural network model, and adjusts the operation rate of the coarse grating by combining the impurity amount; the safety of the operation site is ensured by combining a harmful gas concentration sensor; through the design, the automatic impurity treatment level is higher, the workload of operators can be greatly reduced, the impurity treatment efficiency is improved, the automatic identification of impurity amount and abnormal impurities is solved, and a data basis is provided for the automatic impurity treatment.
Claims (5)
1. A coarse grid optimization operation method based on deep learning is characterized by comprising the following steps:
s1, collecting video images above a grid channel and a grid slag barrel, labeling the image collected by a camera above the grid channel to obtain a classification training positive and negative sample for judging whether abnormal impurities exist and a training sample for judging the amount of the impurities, and labeling the video image above the grid slag barrel to obtain a classification training positive and negative sample for judging whether the grid slag barrel exists and a positive and negative sample for judging whether grid slag in the grid slag barrel is full;
s2, identifying the grid slag barrel in the video image above the position where the grid slag barrel is placed, judging whether the grid slag barrel exists according to the identification result, if so, performing the step S4, otherwise, entering the step S3;
s3, acquiring a toxic gas concentration value, performing exception handling by using a toxic gas sensor, and ending the process;
s4, training an improved Transformer neural network model by using the positive and negative samples of the classified training with or without the grid slag bucket;
s5, judging the grid slag condition in the grid slag barrel by using a trained Transformer neural network model according to positive and negative samples of whether grid slag in the grid slag barrel is full, and finishing the process according to a judgment result;
s6, training the improved Transformer neural network model by using the training sample for judging the impurity amount to obtain a first classification model, identifying impurities by using the first classification model, adjusting the rotation speed rate of the coarse grating by combining a liquid level difference, and ending the process;
and S7, training the improved Transformer neural network model by using the classification training positive and negative samples for judging whether abnormal impurities exist or not to obtain a second classification model, judging whether abnormal impurities exist or not by using the second classification model, and ending the process according to a judgment result.
2. The deep learning based coarse grid optimization operation method according to claim 1, wherein the step S3 comprises the steps of:
s301, acquiring a toxic gas concentration value;
s302, judging whether the toxic gas concentration value is larger than a set safety value or not by using the toxic gas sensor, if so, alarming and ending the process, otherwise, judging that the toxic gas concentration value is lower than the set safety value and ending the process.
3. The deep learning based coarse grid optimization operation method according to claim 2, wherein the step S5 comprises the steps of:
s501, judging whether the grid slag in the grid slag barrel is full by using a trained Transformer neural network model according to positive and negative samples of whether the grid slag in the grid slag barrel is full, if so, entering the step S502, and otherwise, ending the process;
and S502, sending an instruction for lifting and replacing the grid slag barrel, and ending the process.
4. The deep learning-based coarse grid optimization operation method according to claim 3, wherein the Transformer neural network model comprises a fully-connected layer, an encoder, a multi-layer perceptual classifier and an output layer which are connected in sequence;
the full connection layer is used for obtaining an image feature block with the same size as the video image block according to the exception processing result, and flattening and compressing the image feature block;
the encoder is used for extracting the abstract image characteristics of the image characteristic block and the position of the encoding image block according to the processing result;
the multi-layer perception classifier is used for classifying the transform neural network model coding result according to the extracted result to obtain image classification;
and the output layer is used for outputting the grid slag condition in the grid slag barrel according to image classification.
5. The deep learning based coarse grid optimization operation method according to claim 4, wherein the step S7 comprises the steps of:
s701, training the improved Transformer neural network model by utilizing the classification training positive and negative samples for judging whether abnormal impurities exist or not to obtain a second classification model;
and S702, judging whether abnormal impurities exist or not by using the second classification model, if so, executing the step S3, and otherwise, ending the process.
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