CN116359285A - Oil gas concentration intelligent detection system and method based on big data - Google Patents
Oil gas concentration intelligent detection system and method based on big data Download PDFInfo
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Abstract
The invention provides an oil gas concentration intelligent detection system and method based on big data, and relates to the technical field of oil gas concentration intelligent detection. The system comprises a big data oil gas concentration intelligent detection system, a data acquisition module, a data receiving module, a big data analysis module and a data visualization module. The invention provides an oil gas concentration intelligent detection system and method based on big data, when an oil gas sensor detects that the oil gas concentration exists in the environment, the system uploads the collected data to a cloud storage platform through an optical fiber transmission mode for data processing and analysis, and then the collected data is processed to monitor the change trend and the exceeding condition of the oil gas concentration, and timely send out early warning, so that the monitoring and management of environmental pollution are realized, the purpose of intelligent detection is achieved, the participation of manpower is greatly reduced, and the detection efficiency of the oil gas concentration is improved.
Description
Technical Field
The invention relates to the technical field of intelligent detection of oil gas concentration, in particular to an intelligent detection system and method of oil gas concentration based on big data.
Background
The large data refer to a large amount of diversified data which are emerging at high speed, the characteristics of the large data can be simply summarized into 3V, namely Velocity, volume, variety, the large scale of the large data can make the traditional data processing software be unprecedented, the novel large data technology can rapidly and efficiently process the data, in the detection process of the oil gas concentration, great convenience can be brought to people by calculating the large data, and the intelligent detection of the oil gas concentration based on the large data can be realized by utilizing the large data technology and related algorithms to monitor, analyze and predict the oil gas concentration in an oil field in real time so as to improve the oil gas exploitation efficiency, ensure the ecological environment and public safety and timely early warn and respond to abnormal conditions.
However, the traditional oil gas concentration detection mainly depends on methods such as manual sampling, laboratory analysis and the like, and has the problems of complex operation, long time loss, poor real-time performance, large human factors and the like, so that technical improvement is urgently needed.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides an oil gas concentration intelligent detection system and method based on big data, which can solve the problem that the traditional oil gas concentration detection mainly depends on manual sampling, and simultaneously solve the problems of complicated operation, long time loss and poor instantaneity of the traditional oil gas concentration detection.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: the system comprises a big data oil gas concentration intelligent detection system, a data acquisition module, a data receiving module, a big data analysis module and a data visualization module;
when the oil gas sensor detects that the oil gas concentration exists in the environment, the system uploads the collected data to the cloud storage platform in an optical fiber transmission mode for data processing and analysis, and the collected data is processed to monitor the change trend and the exceeding condition of the oil gas concentration and timely send out early warning, so that the monitoring and management of the environmental pollution are realized, and the purpose of intelligent detection is achieved;
the data acquisition module is used for acquiring the detected oil gas concentration data;
the data receiving module is used for processing and cleaning the collected oil gas concentration data, and extracting concentration-related characteristics such as geological geographic conditions, temperature, humidity, time and the like from the data;
the big data analysis module analyzes the oil gas concentration data to obtain the distribution condition of various indexes, detects the occurrence condition of toxin and waste gas pollutants and determines the influence of the toxin and waste gas pollutants on human bodies, ecology and environment;
a data visualization module that presents predictions, such as line graphs and thermodynamic diagrams, to better understand concentration changes.
Preferably, the data acquisition module comprises an oil gas concentration sensor and an air quality monitoring instrument, the purpose of monitoring the oil gas concentration in the environment in real time can be achieved through the oil gas concentration sensor, meanwhile, the oil gas sensor detects by utilizing an electrical principle, and the air quality monitoring instrument detects and analyzes the oil gas component in the air, so that the oil gas concentration is rapidly and accurately detected and monitored, and the problem of environmental pollution is effectively avoided.
Preferably, the data receiving module comprises a data preprocessing module, and the data preprocessing module processes and cleans the collected original data, and mainly comprises the steps of selecting a data column to be analyzed, renaming a column name, deleting a repeated value, processing a missing value, conforming, sequencing, processing an abnormal value and the like, wherein the operations can be realized by using related tools and libraries of programming languages such as Python and the like.
Preferably, the data receiving module comprises a data feature extraction module, features related to the analysis target are extracted from the data through the data feature extraction module, feature selection is required to be frequently carried out in the early modeling process, and only the features related to the target are guaranteed to be used for subsequent calculation.
Preferably, the data receiving module comprises a data storage module and a cloud storage platform.
Preferably, the big data analysis module comprises a model training module and a data model selection module, wherein the model training module is used for selecting a proper learning model, such as linear regression, a decision tree, a support vector machine and a neural network, according to actual conditions, and the model training module is used for training the selected model through historical data to obtain a model support vector machine and the neural network for predicting future concentration change.
Preferably, the big data analysis module comprises a concentration prediction module, and the trained model is used for predicting the concentration change of the oil field in a future period of time according to the current geographic conditions, weather conditions and the like through the concentration prediction module.
Preferably, the intelligent detection method for the oil gas concentration based on big data comprises the following steps:
s1, data acquisition module
Firstly, detecting the oil gas concentration data of the geographic position of oil gas storage in real time through a data acquisition module;
s2, data receiving module
The method mainly comprises the steps of selecting a data column to be analyzed, renaming the column name, deleting the repeated value, processing the missing value, unifying, sequencing, processing the abnormal value and the like, and simultaneously, the operations can be realized by using related tools and libraries of programming languages such as Python and the like;
s3, data feature extraction module
Extracting concentration-related characteristics from the data, such as geological geographic conditions, temperature, humidity, time and the like;
s4, data storage module
Secondly, transmitting the data to a cloud storage platform through a data storage module;
s5, big data analysis module
Analyzing the oil gas concentration data through a big data analysis module to obtain the distribution condition of various indexes, detecting the occurrence condition of toxins and waste gas pollutants, and determining the influence of the toxins and waste gas pollutants on human bodies, ecology and environment;
s6, data model selection module
Selecting a proper learning model, such as linear regression, a decision tree, a support vector machine and a neural network, according to actual conditions by a data model selection module;
s7, model training module
Training the selected model through historical data by a model training module to obtain a model support vector machine and a neural network for predicting future concentration change;
s8, concentration prediction module
The trained model is made to predict the oilfield concentration change in a period of time in the future according to the current geographic, meteorological and other conditions by a concentration prediction module;
s9, data visualization module
And then, the data visualization module displays the prediction results, such as a line graph and a thermodynamic diagram, so that the concentration change and the oilfield concentration change in a future trend period are better understood.
(III) beneficial effects
The invention provides an oil gas concentration intelligent detection system and method based on big data. The beneficial effects are as follows:
1. the invention provides an oil gas concentration intelligent detection system and method based on big data, when an oil gas sensor detects that the oil gas concentration exists in the environment, the system uploads the collected data to a cloud storage platform through an optical fiber transmission mode for data processing and analysis, and then the collected data is processed to monitor the change trend and the exceeding condition of the oil gas concentration and timely send out early warning, so that the monitoring and management of environmental pollution are realized, the purpose of intelligent detection is achieved, the participation of manpower is greatly reduced, and the detection efficiency of the oil gas concentration is improved.
2. The invention provides an oil gas concentration intelligent detection system and method based on big data, the method analyzes the oil gas concentration data through a big data analysis module to obtain the distribution situation of various indexes, detects the occurrence situation of toxins and waste gas pollutants, determines the influence of the toxins and waste gas pollutants on human bodies, ecology and environment, and simultaneously displays the prediction results, such as a line graph and a thermodynamic diagram, through a data visualization module, thereby better understanding the concentration change and the oilfield concentration change in a future trend in a period of time.
Drawings
FIG. 1 is a block diagram of a method of the present invention;
fig. 2 is a block diagram of the system architecture of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples:
1-2, the embodiment of the invention provides an oil gas concentration intelligent detection system and method based on big data, wherein the system comprises a big data oil gas concentration intelligent detection system, a data acquisition module, a data receiving module, a big data analysis module and a data visualization module;
when the oil gas sensor detects that the oil gas concentration exists in the environment, the system uploads the collected data to the cloud storage platform in an optical fiber transmission mode for data processing and analysis, and the collected data is processed to monitor the change trend and the exceeding condition of the oil gas concentration and timely send out early warning, so that the monitoring and management of the environmental pollution are realized, and the purpose of intelligent detection is achieved;
the data acquisition module is used for acquiring the detected oil gas concentration data;
the data receiving module is used for processing and cleaning the collected oil gas concentration data, and extracting concentration-related characteristics such as geological geographic conditions, temperature, humidity, time and the like from the data;
the big data analysis module analyzes the oil gas concentration data to obtain the distribution condition of various indexes, detects the occurrence condition of toxin and waste gas pollutants and determines the influence of the toxin and waste gas pollutants on human bodies, ecology and environment;
a data visualization module that presents predictions, such as line graphs and thermodynamic diagrams, to better understand concentration changes.
The data acquisition module comprises an oil gas concentration sensor and an air quality monitoring instrument, the purpose of monitoring the oil gas concentration in the environment in real time can be achieved through the oil gas concentration sensor, meanwhile, the oil gas sensor detects and analyzes the oil gas component in the air by utilizing an electrical principle, further, the oil gas concentration is rapidly and accurately detected and monitored by the air quality monitoring instrument, the occurrence of the environmental pollution problem is effectively avoided, the data receiving module comprises a data preprocessing module, the data preprocessing module is used for processing and cleaning the acquired original data, the data preprocessing module mainly comprises the steps of selecting data columns needing to be analyzed, repeatedly naming, deleting repeated values, processing missing values, conforming, sequencing, processing abnormal values and the like, and meanwhile, the operations can be realized by using related tools and libraries of programming languages such as Python, the data receiving module comprises a data feature extraction module, the data feature extraction module is used for extracting features related to an analysis target from data, feature selection is needed to be carried out frequently in the early modeling process, only the features related to the target are guaranteed to be used for subsequent calculation, the data receiving module comprises a data storage module and a cloud storage platform, the big data analysis module comprises a model training module and a data model selection module, a proper learning model such as linear regression, a decision tree, a support vector machine and a neural network is selected according to actual conditions through the model training module, the selected model is trained through historical data through the model training module, a model support vector machine and a neural network for predicting future concentration change are obtained, the big data analysis module comprises a concentration prediction module, the trained model is enabled through the concentration prediction module, and predicting the change of the oilfield concentration in a future period of time according to the current geographical conditions, weather conditions and the like.
An oil gas concentration intelligent detection method based on big data comprises the following steps:
s1, data acquisition module
Firstly, detecting the oil gas concentration data of the geographic position of oil gas storage in real time through a data acquisition module;
s2, data receiving module
The method mainly comprises the steps of selecting a data column to be analyzed, renaming the column name, deleting the repeated value, processing the missing value, unifying, sequencing, processing the abnormal value and the like, and simultaneously, the operations can be realized by using related tools and libraries of programming languages such as Python and the like;
s3, data feature extraction module
Extracting concentration-related characteristics from the data, such as geological geographic conditions, temperature, humidity, time and the like;
s4, data storage module
Secondly, transmitting the data to a cloud storage platform through a data storage module;
s5, big data analysis module
Analyzing the oil gas concentration data through a big data analysis module to obtain the distribution condition of various indexes, detecting the occurrence condition of toxins and waste gas pollutants, and determining the influence of the toxins and waste gas pollutants on human bodies, ecology and environment;
s6, data model selection module
Selecting a proper learning model, such as linear regression, a decision tree, a support vector machine and a neural network, according to actual conditions by a data model selection module;
s7, model training module
Training the selected model through historical data by a model training module to obtain a model support vector machine and a neural network for predicting future concentration change;
s8, concentration prediction module
The trained model is made to predict the oilfield concentration change in a period of time in the future according to the current geographic, meteorological and other conditions by a concentration prediction module;
s9, data visualization module
And then, the data visualization module displays the prediction results, such as a line graph and a thermodynamic diagram, so that the concentration change and the oilfield concentration change in a future trend period are better understood.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. Oil gas concentration intelligent detection system based on big data, its characterized in that: the system comprises a big data oil gas concentration intelligent detection system, a data acquisition module, a data receiving module, a big data analysis module and a data visualization module;
when the oil gas sensor detects that the oil gas concentration exists in the environment, the system uploads the collected data to the cloud storage platform in an optical fiber transmission mode for data processing and analysis, and the collected data is processed to monitor the change trend and the exceeding condition of the oil gas concentration and timely send out early warning, so that the monitoring and management of the environmental pollution are realized, and the purpose of intelligent detection is achieved;
the data acquisition module is used for acquiring the detected oil gas concentration data;
the data receiving module is used for processing and cleaning the collected oil gas concentration data, and extracting concentration-related characteristics such as geological geographic conditions, temperature, humidity, time and the like from the data;
the big data analysis module analyzes the oil gas concentration data to obtain the distribution condition of various indexes, detects the occurrence condition of toxin and waste gas pollutants and determines the influence of the toxin and waste gas pollutants on human bodies, ecology and environment;
a data visualization module that presents predictions, such as line graphs and thermodynamic diagrams, to better understand concentration changes.
2. The intelligent detection system for oil gas concentration based on big data according to claim 1, wherein: the data acquisition module comprises an oil gas concentration sensor and an air quality monitoring instrument, the purpose of monitoring the oil gas concentration in the environment in real time can be achieved through the oil gas concentration sensor, meanwhile, the oil gas sensor detects by utilizing an electrical principle, and the air quality monitoring instrument detects and analyzes the oil gas component in the air, so that the oil gas concentration is rapidly and accurately detected and monitored, and the problem of environmental pollution is effectively avoided.
3. The intelligent detection system for oil gas concentration based on big data according to claim 1, wherein: the data receiving module comprises a data preprocessing module, processes and cleans the collected original data through the data preprocessing module, and mainly comprises the steps of selecting a data column needing to be analyzed, renaming the column name, deleting the repeated value, processing the missing value, conforming, sequencing, processing the abnormal value and the like, wherein the operations can be realized by using related tools and libraries of programming languages such as Python and the like.
4. The intelligent detection system for oil gas concentration based on big data according to claim 1, wherein: the data receiving module comprises a data feature extraction module, features related to the analysis target are extracted from the data through the data feature extraction module, feature selection is required to be carried out frequently in the early modeling process, and only the features related to the target are guaranteed to be used for subsequent calculation.
5. The intelligent detection system for oil gas concentration based on big data according to claim 1, wherein: the data receiving module comprises a data storage module and a cloud storage platform.
6. The intelligent detection system for oil gas concentration based on big data according to claim 1, wherein: the big data analysis module comprises a model training module and a data model selection module, wherein the model training module is used for selecting a proper learning model such as linear regression, a decision tree, a support vector machine and a neural network according to actual conditions, and the model training module is used for training the selected model through historical data to obtain a model support vector machine and a neural network for predicting future concentration change.
7. The intelligent detection system for oil gas concentration based on big data according to claim 1, wherein: the big data analysis module comprises a concentration prediction module, and the trained model is used for predicting the concentration change of the oil field in a future period of time according to the current geographic conditions, weather conditions and the like through the concentration prediction module.
8. An oil gas concentration intelligent detection method based on big data is characterized in that: the method comprises the following steps:
s1, data acquisition module
Firstly, detecting the oil gas concentration data of the geographic position of oil gas storage in real time through a data acquisition module;
s2, data receiving module
The method mainly comprises the steps of selecting a data column to be analyzed, renaming the column name, deleting the repeated value, processing the missing value, unifying, sequencing, processing the abnormal value and the like, and simultaneously, the operations can be realized by using related tools and libraries of programming languages such as Python and the like;
s3, data feature extraction module
Extracting concentration-related characteristics from the data, such as geological geographic conditions, temperature, humidity, time and the like;
s4, data storage module
Secondly, transmitting the data to a cloud storage platform through a data storage module;
s5, big data analysis module
Analyzing the oil gas concentration data through a big data analysis module to obtain the distribution condition of various indexes, detecting the occurrence condition of toxins and waste gas pollutants, and determining the influence of the toxins and waste gas pollutants on human bodies, ecology and environment;
s6, data model selection module
Selecting a proper learning model, such as linear regression, a decision tree, a support vector machine and a neural network, according to actual conditions by a data model selection module;
s7, model training module
Training the selected model through historical data by a model training module to obtain a model support vector machine and a neural network for predicting future concentration change;
s8, concentration prediction module
The trained model is made to predict the oilfield concentration change in a period of time in the future according to the current geographic, meteorological and other conditions by a concentration prediction module;
s9, data visualization module
And then, the data visualization module displays the prediction results, such as a line graph and a thermodynamic diagram, so that the concentration change and the oilfield concentration change in a future trend period are better understood.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116776104A (en) * | 2023-08-21 | 2023-09-19 | 华南理工大学 | Method and system for analyzing change rule of atmospheric components based on machine learning |
| CN117434227A (en) * | 2023-12-20 | 2024-01-23 | 河北金隅鼎鑫水泥有限公司 | Method and system for monitoring waste gas components of cement manufacturing plant |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116776104A (en) * | 2023-08-21 | 2023-09-19 | 华南理工大学 | Method and system for analyzing change rule of atmospheric components based on machine learning |
| CN116776104B (en) * | 2023-08-21 | 2023-11-10 | 华南理工大学 | Method and system for analyzing change rule of atmospheric components based on machine learning |
| CN117434227A (en) * | 2023-12-20 | 2024-01-23 | 河北金隅鼎鑫水泥有限公司 | Method and system for monitoring waste gas components of cement manufacturing plant |
| CN117434227B (en) * | 2023-12-20 | 2024-04-30 | 河北金隅鼎鑫水泥有限公司 | Method and system for monitoring waste gas components of cement manufacturing plant |
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