CN117970864B - Petroleum and natural gas PLC control monitoring system and method based on electric signal analysis - Google Patents
Petroleum and natural gas PLC control monitoring system and method based on electric signal analysis Download PDFInfo
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Abstract
The invention relates to the field of industrial measurement, in particular to an oil and gas PLC control monitoring system and method based on electric signal analysis. Comprising the following steps: firstly, mapping an electric signal, performing chaos theory analysis, constructing a network, correcting the electric signal by using the degree distribution of the network, and processing and optimizing the corrected electric signal; then, introducing an electromagnetic interference suppression technology, carrying out waveform transformation on the electric signal, and reconstructing the electric signal through the inverse process of the waveform transformation; and finally, adopting a combined measurement and analysis technology to process the reconstructed electric signals, and identifying and analyzing the dynamic mode in the system behavior through the dynamic mode. The problem that the prior art cannot accurately capture the fine electric signal change in the complex environment is solved; poor performance in environments with high electromagnetic interference, no effective noise suppression and signal correction mechanisms; and most of the prior art does not provide in-depth analysis of long-term data trends and complex behavioral patterns, limiting the problems with respect to predictive maintenance and fault prevention.
Description
Technical Field
The invention relates to the field of industrial measurement, in particular to an oil and gas PLC control monitoring system and method based on electric signal analysis.
Background
In the oil and gas industry, particularly in complex and harsh industrial environments, such as offshore drilling rigs, remote piping systems and refineries, accurate monitoring of critical parameters in the production process is critical. These parameters typically include pressure, temperature, flow, chemical composition, etc., and their accurate monitoring is important to ensure production efficiency, environmental safety, and equipment integrity.
With the development of industrial automation and intelligence, there is an increasing demand for more advanced, more reliable monitoring systems. These systems need not only to accurately measure and analyze data, but also to be able to resist environmental interference, predict potential problems, and support complex decision-making processes. Therefore, a novel monitoring system is developed, the limitation of the traditional system can be overcome, and the latest technical progress is utilized, so that the novel monitoring system is very important to the petroleum and gas industry.
Chinese patent application number: CN202211142965.5, publication date: 2022.12.20 discloses an operation state monitoring method of a PLC control cabinet, which is used in a monitoring platform of the PLC control cabinet, and comprises the following steps: acquiring first environment monitoring data collected by a PLC control cabinet, wherein the first environment monitoring data comprise vibration frequency data and dust density data; comparing the first environmental monitoring data with a preset environmental data range, wherein the preset environmental data range comprises a vibration frequency range and a dust density range; when the first environment monitoring data exceeds the corresponding preset environment data range, an early warning message is sent to the PLC control cabinet, the vibration frequency data corresponds to the vibration frequency range, and the dust density data corresponds to the dust density range. By adopting the method, the influence of the change of the working environment on the voltage or current of some important parts inside the PLC control cabinet is avoided, the running state of the PLC control cabinet cannot be directly adjusted, and the PLC control cabinet is irreversibly damaged.
However, the above technology has at least the following technical problems: the prior art cannot accurately capture the fine electric signal change in a complex environment and cannot reveal the hidden key characteristics in the electric signal, so that the accuracy and the reliability of the whole monitoring system are affected; poor performance in an environment with high electromagnetic interference, no effective noise suppression and signal correction mechanism, and easy influence of external interference, so that data inaccuracy or high misleading performance are caused; most of the prior art focuses mainly on immediate data collection and simple processing, without providing in-depth analysis of long-term data trends and complex behavioral patterns, limiting the ability to predict maintenance and fault prevention.
Disclosure of Invention
The invention provides an oil and gas PLC control monitoring system and method based on electric signal analysis, which solves the problems that the prior art cannot accurately capture the fine electric signal change in a complex environment and cannot reveal the hidden key characteristics in the electric signal, thereby affecting the accuracy and reliability of the whole monitoring system; poor performance in an environment with high electromagnetic interference, no effective noise suppression and signal correction mechanism, and easy influence of external interference, so that data inaccuracy or high misleading performance are caused; most of the prior art focuses mainly on immediate data collection and simple processing, without providing in-depth analysis of long-term data trends and complex behavioral patterns, limiting technical problems in terms of predictive maintenance and fault prevention. The system and the method for controlling and monitoring the petroleum and natural gas by the PLC are realized, and the monitoring precision and the environmental interference resistance are obviously improved by combining advanced nonlinear signal processing and complex network analysis.
The invention relates to an oil and gas PLC control monitoring system and method based on electric signal analysis, which concretely comprises the following technical scheme:
Oil and gas PLC control monitoring system based on electric signal analysis includes following part:
The system comprises a sensor module, a signal conversion module, a data processing module, a depth analysis module, a signal correction and optimization module, an electromagnetic interference suppression module and a data fusion and comprehensive evaluation module;
the sensor module is used for collecting current and voltage data; the sensor module is connected with the signal conversion module in a data transmission mode;
The signal conversion module is used for converting physical current and voltage into electric signals; the signal conversion module is connected with the data processing module in a data transmission mode;
the data processing module is used for mapping the electric signals to a new domain; the data processing module is connected with the depth analysis module in a data transmission mode;
The depth analysis module is used for analyzing the mapped signals through the chaos theory and identifying and compensating nonlinear dynamics; and constructing a network; the depth analysis module is connected with the signal correction and optimization module in a data transmission mode;
the signal correction and optimization module is used for weighting the electric signals by utilizing the degree distribution of the network, extracting the characteristics of the network, and applying the characteristics to correction of the electric signals to further process and optimize the corrected signals; the signal correction and optimization module is connected with the electromagnetic interference suppression module in a data transmission mode;
the electromagnetic interference suppression module processes the output of the signal correction and optimization module by adopting a waveform transformation technology and converts the output into a multidimensional feature space; processing and enhancing signal features in the feature space by nonlinear mapping; separating the features from the mapped feature space and reconstructing an electrical signal; the electromagnetic interference suppression module is connected with the data fusion and comprehensive evaluation module in a data transmission mode;
the data fusion and comprehensive evaluation module is used for fusing the reconstructed electric signals and other sensor data, including resistance data and temperature data; and then nonlinear transformation and dynamic change extraction are applied to the fused signals, the signals are mapped to a multidimensional feature space, and dynamic modes of system behavior are controlled and monitored through dynamic mode identification analysis PLC.
The petroleum and natural gas PLC control monitoring method based on the electric signal analysis comprises the following steps:
S1, mapping and chaos theory analysis are carried out on the electric signals, a network is constructed, the electric signals are corrected by utilizing the degree distribution of the network, and the corrected electric signals are processed and optimized;
S2, introducing an electromagnetic interference suppression technology, carrying out waveform transformation on the electric signal, and reconstructing the electric signal through the inverse process of the waveform transformation;
s3, processing the reconstructed electric signals by adopting a combined measurement and analysis technology, and controlling and monitoring a dynamic mode in the system behavior by a dynamic mode identification analysis PLC.
Preferably, the S1 specifically includes:
Converting the current and the voltage into electric signals, mapping the electric signals to a new domain, and converting the electric signals into nonlinear combined signals by using a trigonometric function to obtain mapped signals; and analyzing the mapped signals through chaos theory, and identifying and compensating nonlinear dynamics.
Preferably, in the S1, the method further includes:
The method comprises the steps of constructing a network, converting chaotic characteristics into a network structure, expressing the relation of signals by using the product of an adjacency matrix and Hadamard, weighting the signals by using the degree distribution of the network, extracting the characteristics of the network, applying the characteristics to correction of electric signals, and further processing and optimizing the corrected signals to obtain final measurement output.
Preferably, the S2 specifically includes:
The electrical signal is multiplied with basis functions, each corresponding to a feature in the multi-dimensional feature space, using waveform transformation, to convert the final measured output into the multi-dimensional feature space.
Preferably, in the S2, the method further includes:
A new mapping feature space is generated by non-linear mapping processing and enhancing signal features in the multi-dimensional feature space.
Preferably, in the S2, the method further includes:
Features are separated from the mapped feature space, and an electrical signal is reconstructed from the separated features.
Preferably, the S3 specifically includes:
Fusing the reconstructed electric signal with data of other sensors, including resistance data and temperature data, and applying nonlinear transformation and dynamic change extraction to the fused signal to obtain a nonlinear transformed signal.
Preferably, in the S3, the method further includes:
Mapping the nonlinear converted signals to a multidimensional feature space, extracting periodic features of the signals by using a periodic function to obtain multidimensional feature mapped signals, and identifying dynamic modes in the behavior of the PLC control monitoring system by analyzing the multidimensional feature mapped signals.
The technical scheme of the invention has the beneficial effects that:
1. By carrying out advanced nonlinear conversion and chaos theory analysis on the original current and voltage signals, fine features which cannot be detected in a common measurement method can be revealed, the accuracy of monitoring data is improved, and a control system can more accurately respond to the actual working state; the introduced electromagnetic interference suppression technology and signal correction optimization step can effectively reduce the influence of external interference on measurement data, and are particularly important for the petroleum and natural gas PLC control monitoring system in a high electromagnetic interference environment;
2. The comprehensive analysis of various parameters including current, voltage, resistance and temperature not only provides instant monitoring data, but also predicts potential system problems through trend analysis, thereby being beneficial to timely finding and preventing faults; by long-term tracking and analysis of system behavior, predictive maintenance strategies are facilitated to be implemented, thereby reducing unexpected downtime and maintenance costs. Meanwhile, rich data support is provided for operators, and more intelligent decisions can be made.
Drawings
FIG. 1 is a block diagram of an oil and gas PLC control monitoring system based on electrical signal analysis according to one embodiment of the present invention;
Fig. 2 is a flowchart of an oil and gas PLC control monitoring method based on electrical signal analysis according to an embodiment of the present invention.
Detailed Description
In order to further illustrate the technical means and effects adopted by the present invention to achieve the preset purpose, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, 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.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The invention provides a petroleum and natural gas PLC control monitoring system and a method based on electric signal analysis, which are concretely described below with reference to the accompanying drawings.
Referring to fig. 1, there is shown a block diagram of an oil and gas PLC control monitoring system based on electrical signal analysis according to an embodiment of the present invention, the system includes:
The system comprises a sensor module, a signal conversion module, a data processing module, a depth analysis module, a signal correction and optimization module, an electromagnetic interference suppression module and a data fusion and comprehensive evaluation module;
The sensor module is used for collecting current and voltage data of key nodes (such as pump stations, valves and pipelines); the sensor module is connected with the signal conversion module in a data transmission mode;
the signal conversion module is used for converting the physical current and the physical voltage into readable electric signals, namely original signals; the signal conversion module is connected with the data processing module in a data transmission mode;
The data processing module is used for mapping the original signal to a new domain and preparing for subsequent nonlinear analysis; the data processing module is connected with the depth analysis module in a data transmission mode;
The depth analysis module is used for analyzing the mapped signals through the chaos theory and identifying and compensating nonlinear dynamics caused by environmental noise or interference; constructing a network based on chaos features to identify key modes and relations in the signals; the depth analysis module is connected with the signal correction and optimization module in a data transmission mode;
The signal correction and optimization module is used for weighting the signals by utilizing the degree distribution of the network based on the chaotic characteristics, extracting key characteristics of the network, applying the key characteristics to correction of the original signals, and further processing and optimizing the corrected signals so as to ensure the accuracy and reliability of output; the signal correction and optimization module is connected with the electromagnetic interference suppression module in a data transmission mode;
The electromagnetic interference suppression module is used for ensuring that the measured data cannot generate errors due to external interference, and adopting a waveform transformation technology to further process the output of the signal correction and optimization module and convert the output into a multidimensional feature space; further processing and strengthening signal characteristics in the multidimensional characteristic space through nonlinear mapping, so that useful information in the signals is more prominent, and unnecessary interference information is restrained; separating interference features and useful signal features from the mapped feature space, and reconstructing an accurate electric signal from the separated features; the electromagnetic interference suppression module is connected with the data fusion and comprehensive evaluation module in a data transmission mode;
The data fusion and comprehensive evaluation module is used for comprehensively considering and fusing the reconstructed electric signals and other sensor data (such as resistance and temperature) to form signals representing the comprehensive state of the PLC control monitoring system; and then nonlinear transformation and dynamic change extraction are applied to the fused signals, the signals are further mapped to a multidimensional feature space, and key dynamic modes in the behavior of the monitoring system are controlled and analyzed through dynamic mode identification and PLC.
Referring to fig. 2, a flowchart of an oil and gas PLC control monitoring method based on electrical signal analysis according to an embodiment of the present invention is shown, including the following steps:
S1, mapping and chaos theory analysis are carried out on the electric signals, a network is constructed, the electric signals are corrected by utilizing the degree distribution of the network, and the corrected electric signals are processed and optimized;
in complex oil and gas production sites, accurate electrical signal measurement is critical to maintaining stable operation of the overall control system. The current and voltage signals are acquired from key nodes (such as pump stations, valves, pipelines and the like) of the oil and gas PLC control monitoring system through sensors such as current sensors and voltage sensors, and the data collected by the sensors reflect the real-time working state and environmental conditions of the PLC control monitoring system.
The signal conversion module converts the physical current and voltage into readable electrical signals, i.e., raw signals, which are then fed into the data processing module. To ensure data quality, the sensor needs to have high sensitivity and low noise. The data processing module maps the original signal to a new domain in preparation for subsequent nonlinear analysis. The original signal is converted into a nonlinear combined signal by using a trigonometric function, and the specific formula is as follows:
，
wherein, Is the mapped signal,/>And/>Are respectively/>Current and voltage signals in the original signal at the moment. /(I)And/>Will/>The original signal at the moment is converted into a nonlinear signal, so that the complexity of signal processing is increased, and the hidden characteristic is facilitated to be disclosed.
The depth analysis module analyzes the mapped signals through the chaos theory, and identifies and compensates nonlinear dynamics caused by environmental noise or interference so as to highlight the chaos characteristics of the signals, wherein the specific formula is as follows:
，
wherein, Representing the chaotic eigenvalue of the mapped signal,/>Is the difference of the mapped signals, i.eRate of change over time,/>Is a chaos constant used to adjust the sensitivity of nonlinear dynamics.
A network based on the chaotic characteristics is constructed, the chaotic characteristics are converted into a network structure, the complex relation of signals is expressed by using the product of an adjacency matrix and Hadamard so as to identify key modes and relations in the signals, and the specific formula is as follows:
，
wherein, Representing the constructed network,/>Is an index in the summation process for representing the different order terms,/>Is an adjacency matrix,/>Is the order of the network, determines the upper limit of the summation,/>Is the Hadamard product.
The signal correction and optimization module performs weighting processing on the signals by utilizing the degree distribution of the network, extracts key characteristics of the network, and applies the key characteristics to correction of the original signals so as to improve measurement accuracy, wherein the specific formula is as follows:
，
wherein, Is the corrected signal, integrates the correction of the network characteristics to the original signal,/>Is the degree of node i at time t, i.e. the number of connections of the node,/>Is the signal value of node i at time t.
Further processing and optimizing the corrected signal to ensure accuracy and reliability of the output, the specific formula is:
，
wherein, Is the final measurement output,/>Is the variance of the signal,/>，/>Provides a cumulative effect of the signal over time,/>The stability and reliability of the signal is emphasized. Final measurement output/>The accurate measurement value representing the electric signal (such as current and voltage) not only reflects the characteristics of the original electric signal, but also contains correction and enhancement information obtained from chaos theory and complex network analysis.
S2, introducing an electromagnetic interference suppression technology, carrying out waveform transformation on the electric signal, and reconstructing the electric signal through the inverse process of the waveform transformation;
Considering that the petroleum and natural gas PLC control monitoring system is often in a high electromagnetic interference environment, an electromagnetic interference suppression technology is introduced, and the measurement data is ensured not to generate errors due to external interference, so that the accuracy and stability of the whole monitoring system are improved.
Specifically, the electromagnetic interference suppression module multiplies the electrical signal by a series of basis functions having different frequencies, phases and time delays using waveform transformation to accurately measure the electrical signalConversion to a multidimensional feature space/>Each basis function may directly correspond to a feature in the multi-dimensional feature space, providing more comprehensive data support for subsequent signal analysis and processing, and converting the formula:
，
wherein, Representing the ith feature after transformation, which is the ith dimension in the multidimensional feature space obtained after waveform transformation, wherein the dimension refers to a representative attribute or component of the electric signal,/>Representing measurement output,/>A time delay parameter representing an ith basis function reflecting a time position of an ith feature of the electrical signal; /(I)Determining the width of the basis function as the standard deviation of the Gaussian function; /(I)And/>The frequency and phase of the basis function, respectively, are used to extract the frequency characteristics of the electrical signal.
The signal features in the multidimensional feature space are further processed and enhanced through nonlinear mapping, so that useful information in the signal is more prominent, and unnecessary interference information is restrained. And (3) applying nonlinear mapping to the signal characteristics of the multidimensional characteristic space to the converted characteristic space, and generating a new mapping characteristic space. The nonlinear mapping is implemented by applying a logarithmic function to each dimension of the multidimensional feature space, thereby enhancing the nonlinear properties of the features, specifically formulated as:
，
wherein, Representing the i-th feature after mapping,/>Is a mapping intensity parameter that controls the degree of nonlinearity in the mapping process.
Interference features and useful signal features are separated from the mapped feature space, ready for final signal reconstruction. Feature spaceIs one by all/>The set or vector of formations is divided into pairs/>, using a feature separation function DProcessing is carried out, and interference characteristics and useful signal characteristics are separated, wherein the specific formula is as follows:
，
wherein, Representing the separated feature set,/>Is a feature separation function,/>Is a characteristic separation coefficient, which determines the sensitivity of the separation process.
An accurate electrical signal is reconstructed from the isolated features for accurate pressure monitoring. For the separated feature setReconstructing to obtain a reconstructed signal/>. The method comprises the following steps of carrying out waveform transformation in an inverse process, and realizing signal reconstruction by accumulating weighted sums of different basis functions, wherein the specific formula is as follows:
，
wherein, Is a reconstructed electrical signal,/>Is the feature set/>I-th feature of (a). Other parameters correspond to parameters of the waveform transformation, but are used herein to reconstruct the electrical signal.
S3, processing the reconstructed electric signals by adopting a combined measurement and analysis technology, and analyzing a dynamic mode in the behavior of the PLC control monitoring system through dynamic mode identification;
In order to further improve the measurement capability and adaptability of the PLC control monitoring system, the data fusion and comprehensive evaluation module adopts a multi-parameter combined measurement and analysis technology, so that the reconstructed electric signal is processed The data is also analyzed in combination with additional parameters from other sensors, such as resistance, temperature, etc., to provide a comprehensive view of the operating state of the PLC control monitoring system.
The reconstructed electric signals are effectively fused with data (resistance and temperature) from other sensors to form a comprehensive signal representing the state of a PLC control monitoring system, and the correlation among various sensor data is captured, wherein the specific formula is as follows:
，
wherein, Representing the fused signals, representing the comprehensive state of the PLC control monitoring system at the time t, and carrying out +/-Is the reconstructed electrical signal at time t,/>Representing the resistance measurement at time t,/>Representing the temperature measurement at time t,/>Is a constant that adjusts the scaling of the temperature data.
By applying nonlinear transformation and dynamic change extraction to the fused signals, key features in the signals are further emphasized, more information is provided for the identification of complex system states, and the specific formula is as follows:
，
wherein, Representing the signal after nonlinear conversion, representing the integrated state after transformation at time t,/>And the exponential decay factor is expressed and used for controlling the decay rate, so that the capturing capacity of the time sensitive characteristic of the model is enhanced. Use of exponential decay factor binding/>To emphasize time-varying features.
The nonlinear converted signal is further mapped to a multidimensional feature space, and the periodic features in the signal are extracted and emphasized by utilizing the property of a periodic function, wherein the specific formula is as follows:
，
wherein, Representing the mapped signal of the multidimensional feature,/>Is the order of the current term in the taylor series expansion,Is a frequency parameter used in the cosine function to determine periodicity. Variants employing Taylor series expansion will/>Expanded into polynomial form and introduced periodically by cosine function. Select/>As denominators, are used to normalize the contribution of each term, reducing the impact of higher order terms.
By analyzing the signals after the multidimensional feature mapping, the key dynamic modes in the behavior of the PLC control monitoring system, such as periodic change, mutation and the like, are identified, and the specific formulas are as follows:
，
wherein, Representing the result of dynamic pattern recognition, representing the system behavior characteristics at time t,/>Is the time window length, time range setting for moving averages. Mapping the multidimensional features into signals by using the idea of moving averageAveraging is performed over a time window to smooth the signal and extract its long-term trends.
Results ultimately through dynamic pattern recognitionA smooth and comprehensive signal representation is provided that reflects the overall status of the monitoring point. For example, a regular operation mode of the PLC control monitoring system or an abnormal mode due to a specific condition (e.g., pressure change, temperature fluctuation) is revealed. By observing/>Over time, the trend and behavior of the PLC control monitoring system can be analyzed. For example, if/>A certain periodic pattern is shown indicating a predictable cycle in the PLC control monitoring system. If this pattern changes, it implies a decrease in system performance or a failure.
In conclusion, the petroleum and natural gas PLC control monitoring system and method based on the electric signal analysis are completed.
According to the embodiment of the invention, through advanced nonlinear conversion and chaos theory analysis on the original current and voltage signals, fine features which cannot be detected in a common measurement method can be revealed, the accuracy of monitoring data is improved, and a control system can more accurately respond to an actual working state; the introduced electromagnetic interference suppression technology and signal correction optimization step can effectively reduce the influence of external interference on measurement data, and are particularly important for the petroleum and natural gas PLC control monitoring system in a high electromagnetic interference environment; the comprehensive analysis of various parameters including current, voltage, resistance and temperature not only provides instant monitoring data, but also predicts potential system problems through trend analysis, thereby being beneficial to timely finding and preventing faults; by long-term tracking and analysis of system behavior, predictive maintenance strategies are facilitated to be implemented, thereby reducing unexpected downtime and maintenance costs. Meanwhile, rich data support is provided for operators, and more intelligent decisions can be made.
The sequence of the embodiments of the invention is merely for description and does not represent the advantages or disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and the same or similar parts of each embodiment are referred to each other, and each embodiment mainly describes differences from other embodiments.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (9)
1. Petroleum and natural gas PLC control monitoring system based on electric signal analysis, which is characterized by comprising the following parts:
The system comprises a sensor module, a signal conversion module, a data processing module, a depth analysis module, a signal correction and optimization module, an electromagnetic interference suppression module and a data fusion and comprehensive evaluation module;
the sensor module is used for collecting current and voltage data; the sensor module is connected with the signal conversion module in a data transmission mode;
The signal conversion module is used for converting the current and the voltage into electric signals; the signal conversion module is connected with the data processing module in a data transmission mode;
the data processing module is used for mapping the electric signals to a new domain; the data processing module is connected with the depth analysis module in a data transmission mode;
The depth analysis module is used for analyzing the mapped signals through the chaos theory and identifying and compensating nonlinear dynamics; and constructing a network; the depth analysis module is connected with the signal correction and optimization module in a data transmission mode;
The signal correction and optimization module is used for carrying out weighting processing on the electric signals, extracting the characteristics of the network, applying the characteristics to correction of the electric signals, and further processing and optimizing the corrected electric signals; the signal correction and optimization module is connected with the electromagnetic interference suppression module in a data transmission mode;
the electromagnetic interference suppression module processes the output of the signal correction and optimization module by adopting a waveform transformation technology and converts the output into a multidimensional feature space; processing and enhancing signal features in the feature space by nonlinear mapping; separating the features from the mapped feature space and reconstructing an electrical signal; the electromagnetic interference suppression module is connected with the data fusion and comprehensive evaluation module in a data transmission mode;
The data fusion and comprehensive evaluation module is used for fusing the reconstructed electric signals, the resistance data and the temperature data; and then nonlinear transformation and dynamic change extraction are applied to the fused signals, the signals are mapped to a multidimensional feature space, and dynamic modes of system behavior are controlled and monitored through dynamic mode identification analysis PLC.
2. The petroleum and natural gas PLC control monitoring method based on the electric signal analysis is characterized by comprising the following steps of:
S1, mapping and chaos theory analysis are carried out on the electric signals, a network is constructed, the electric signals are corrected by utilizing the degree distribution of the network, and the corrected electric signals are processed and optimized;
S2, introducing an electromagnetic interference suppression technology, carrying out waveform transformation on the electric signal, and reconstructing the electric signal through the inverse process of the waveform transformation;
s3, processing the reconstructed electric signals by adopting a combined measurement and analysis technology, and controlling and monitoring a dynamic mode in the system behavior by a dynamic mode identification analysis PLC.
3. The method for PLC control monitoring of petroleum and natural gas based on electrical signal analysis according to claim 2, wherein S1 specifically comprises:
converting the current and the voltage into electric signals, mapping the electric signals to a new domain, and converting the electric signals into nonlinear combined signals by using a trigonometric function to obtain mapped signals; and analyzing the mapped signals through chaos theory.
4. The PLC control monitoring method for petroleum and natural gas based on the analysis of electrical signals according to claim 3, wherein in S1, further comprising:
The method comprises the steps of constructing a network, converting chaotic characteristics into a network structure, expressing the relation of signals by using the product of an adjacency matrix and Hadamard, weighting the signals by using the degree distribution of the network, extracting the characteristics of the network, applying the characteristics to correction of electric signals, and further processing and optimizing the corrected electric signals to obtain final measurement output.
5. The method for PLC controlled monitoring of petroleum and natural gas based on electrical signal analysis according to claim 2, wherein S2 specifically comprises:
The electrical signal is multiplied with basis functions, each corresponding to a feature in the multi-dimensional feature space, using waveform transformation, to convert the final measured output into the multi-dimensional feature space.
6. The PLC control monitoring method for petroleum and natural gas based on the analysis of electrical signals according to claim 5, wherein in S2, further comprising:
A new mapping feature space is generated by non-linear mapping processing and enhancing signal features in the multi-dimensional feature space.
7. The PLC control monitoring method for petroleum and natural gas based on the analysis of electrical signals according to claim 6, wherein in S2, further comprising:
features are separated from the mapped feature space and the electrical signal is reconstructed.
8. The method for PLC controlled monitoring of petroleum and natural gas based on electrical signal analysis according to claim 2, wherein S3 specifically comprises:
Fusing the reconstructed electric signal with data of other sensors, including resistance data and temperature data, and applying nonlinear transformation and dynamic change extraction to the fused signal to obtain a nonlinear transformed signal.
9. The method for PLC controlled monitoring of petroleum and natural gas based on the analysis of electrical signals according to claim 8, wherein in S3, further comprising:
Mapping the nonlinear converted signals to a multidimensional feature space, extracting periodic features of the signals by using a periodic function to obtain multidimensional feature mapped signals, and identifying dynamic modes in the behavior of the PLC control monitoring system by analyzing the multidimensional feature mapped signals.
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