Oil and gas field RTU (remote terminal unit) intelligent control method and system based on localization of swan Mongolia system

Abstract

The invention discloses an oil and gas field RTU intelligent control method and system based on the nationalization of a swan Mongolia system, and the method comprises the steps: carrying out the effective screening of oil and gas field production data, and carrying out the application state recognition of the effective production data of the oil and gas field, thereby determining the application production data of the oil and gas field; performing environmental degree adaptation on the oil and gas field application production data and performing feature learning on the oil and gas field environment evaluation production data to determine qualified oil and gas field feature production data; oil and gas field demand production data meeting application scene requirements are obtained through simulation processing and application scene configuration, and the oil and gas field demand production data can be matched with a plurality of relevant factors in the oil and gas field production process; the oil and gas field real-time operation data is subjected to feedback regulation and control through the oil and gas field demand production data to obtain the oil and gas field control strategy, finally oil and gas field RTU intelligent control is performed based on the oil and gas field control strategy, oil and gas field production control efficiency and precision can be improved, and oil and gas field production control demands are met.

Description

Technical Field

[0001] The present invention relates to the technical field of oilfield production management, and in particular to an oil and gas field RTU intelligent control method and system based on the localization of the Hongmeng system, and a computer-readable storage medium. Background Art

[0002] For oil and gas fields, it is necessary to effectively monitor the production process of the oil and gas fields to ensure that the oil and gas fields always operate in a stable and efficient state. At present, the relevant technologies mainly achieve monitoring and control purposes by collecting the operating data of the oil and gas fields in real time. Although this can achieve oil and gas field operation control to a certain extent, its applicability is relatively limited, especially considering that the working conditions faced by oil wells and pumping units are relatively complex, that is, there may be other related factors that affect the production of oil and gas fields. Therefore, the actual operation of the oil and gas fields cannot be reliably grasped, resulting in reduced efficiency and accuracy of oil and gas field production control, and cannot meet the oil and gas field production control needs on the market. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the present invention proposes an oil and gas field RTU intelligent control method and system based on the localization of the Hongmeng system, which can improve the efficiency and accuracy of oil and gas field production control.

[0004] In the first aspect, an embodiment of the present invention provides an oil and gas field RTU intelligent control method based on the localization of the Hongmeng system, which is applied to an RTU intelligent control device built based on the Hongmeng system, and the RTU intelligent control device matches the oil and gas field; the method comprises the following steps: Step S1, obtaining oil and gas field production data; screening the oil and gas field production data for effectiveness, thereby obtaining oil and gas field effective production data; identifying the application status of the oil and gas field effective production data, thereby obtaining oil and gas field application production data; Step S2: performing environmental adaptation on the oil and gas field application production data, thereby obtaining oil and gas field environmental assessment production data; performing feature learning on the oil and gas field environmental assessment production data, thereby obtaining oil and gas field feature production data; Step S3, simulating the characteristic production data of the oil and gas field to obtain the simulated production data of the oil and gas field; configuring the application scenario of the simulated production data of the oil and gas field to obtain the required production data of the oil and gas field; Step S4, obtaining real-time operation data of the oil and gas field; feedback-regulating the real-time operation data of the oil and gas field according to the production data required by the oil and gas field, thereby obtaining the oil and gas field control strategy, and performing intelligent control of the oil and gas field RTU through the oil and gas field control strategy.

[0005] Optionally, in one embodiment of the present invention, step S1 includes the following steps: Step S11, cleaning, verifying and integrating the oil and gas field production data through a preset data analysis model, thereby obtaining the oil and gas field data to be tested; Step S12, performing PCA dimensionality reduction processing on the oil and gas field data to be tested, thereby obtaining oil and gas field composition data; Step S13, extracting key fields from the oil and gas field composition data, and obtaining data values ​​corresponding to the key fields of the oil and gas field composition data; automatically screening the oil and gas field composition data according to the relative size relationship between the data values ​​and the data threshold determined based on the preset field information, and obtaining effective production data of the oil and gas field; Step S14: construct a corresponding correlation feature matrix based on the effective production data of the oil and gas field, determine a feature vector according to the correlation feature matrix, and obtain a feature value of the feature vector; Step S15, performing time series analysis on the effective production data of the oil and gas field by combining the eigenvalue of the eigenvector with the variational autoencoder, thereby determining at least one oil and gas field time series production behavior corresponding to the effective production data of the oil and gas field; Step S16: Statistically analyze the time-series production behaviors of each oil and gas field based on transfer learning to obtain application production data of the oil and gas field.

[0006] Optionally, in one embodiment of the present invention, step S2 includes the following steps: Step S21, performing environmental risk assessment on the oil and gas field application production data, thereby obtaining the environmental risk level corresponding to the oil and gas field application production data; Step S22: performing correlation inference on the environmental risk levels corresponding to the oil and gas field application production data based on the Bayesian linear dynamic model, thereby obtaining a plurality of environmental impact indicators; Step S23, performing similarity analysis on multiple environmental impact indicators to obtain environmental collaboration indicators; updating oil and gas field application production data according to the environmental collaboration indicators to obtain oil and gas field environmental assessment production data; Step S24, performing data amplification on the oil and gas field environmental assessment production data, thereby obtaining a plurality of oil and gas field environmental enhancement production data; Step S25: Based on the attention learning mechanism and the preset feature structure environment, the feature structure of multiple oil and gas field environmental enhanced production data is verified to obtain the feature parameters corresponding to each oil and gas field environmental enhanced production data; the oil and gas field characteristic production data is obtained according to the feature parameters corresponding to each oil and gas field environmental enhanced production data.

[0007] Optionally, in one embodiment of the present invention, when the characteristic parameters corresponding to the environmental enhanced production data of each oil and gas field include a first characteristic parameter, a second characteristic parameter and a third characteristic parameter, wherein the first characteristic parameter represents the expected oil production of the oil and gas field, the second characteristic parameter represents the expected quality of the sampled oil of the oil and gas field, and the third characteristic parameter represents the expected liquid production of the oil and gas field; the step S3 includes the following steps: Step S31, generating a first simulation network model based on the first characteristic parameter, the second characteristic parameter and the third characteristic parameter, and constructing an error simulation function through the first simulation network model and characteristic production data of the oil and gas field; Step S32, performing simulation operation on the error simulation function to obtain a simulation operation value; optimizing and updating the first simulation network model according to the simulation operation value, thereby obtaining a second simulation network model; Step S33, reverse matching the simulation operation value with the second simulation network model to obtain simulated production data of the oil and gas field; Step S34, performing application scenario analysis on the oil and gas field simulation production data, thereby obtaining application scenario characteristics corresponding to the oil and gas field simulation production data; Step S35, obtaining the degree of compatibility between the application scenario characteristics and the preconfigured oil and gas field application scenario model; and configuring the application scenario for the oil and gas field simulation production data according to the degree of compatibility, thereby obtaining the oil and gas field demand production data.

[0008] Optionally, in one embodiment of the present invention, step S32 includes the following steps: Step S321, performing continuous simulation operation on the error simulation function until the obtained simulation operation value is greater than the minimum simulation operation value and less than or equal to the preset balance simulation operation value, wherein the preset balance simulation operation value is less than the maximum simulation operation value; Step S322: Fit the first simulation network model according to the obtained simulation operation value to obtain a second simulation network model.

[0009] Optionally, in one embodiment of the present invention, step S4 includes the following steps: Step S41, obtaining real-time operation data of the oil and gas field; using a frequent item mining algorithm to obtain a first operation parameter corresponding to the oil and gas field demand production data, and optimizing the performance of the first operation parameter to obtain a second operation parameter; Step S42, determining a first operating performance value corresponding to the oil and gas field required production data according to the second operating parameter; Step S43: Generate an oil and gas field control strategy according to the first operation performance value and the second operation performance value corresponding to the acquired real-time operation data of the oil and gas field, so as to perform intelligent control of the oil and gas field RTU through the oil and gas field control strategy.

[0010] Optionally, in one embodiment of the present invention, when the second operating parameter includes the optimized oil production and the optimized liquid production in the time series corresponding to the oil and gas field production demand data, the step S42 includes the following steps: Step S421: Substitute the optimized oil production and optimized liquid production in the time series corresponding to the oil and gas field demand production data into the performance evaluation formula for calculation, so as to obtain the first operation performance value corresponding to the oil and gas field demand production data, wherein the performance evaluation formula is as follows: ; ; is the first operating performance value corresponding to the oil and gas field demand production data, is the start time of the time series corresponding to the oil and gas field demand production data, The end time of the time series corresponding to the oil and gas field demand production data, is the energy consumption parameter of the oil and gas field, To optimize the oil production within the time series corresponding to the oil and gas field demand production data, Optimize liquid production within the time series corresponding to the production data required by the oil and gas field.

[0011] Optionally, in one embodiment of the present invention, step S43 includes the following steps: Step S431, calculating the absolute value of the difference between the first operating performance value and the second operating performance value corresponding to the acquired real-time operating data of the oil and gas field, thereby obtaining a difference result; Step S432: When the difference result is less than or equal to the preset operating performance difference threshold, the reliable operating data of the oil and gas field is obtained according to the oil and gas field demand production data and the oil and gas field real-time operating data, and the oil and gas field reliable operating data is used as the basic data for intelligent control of the oil and gas field RTU; otherwise, the oil and gas field demand production data is used as the basic data for intelligent control of the oil and gas field RTU.

[0012] In the second aspect, an embodiment of the present invention provides an oil and gas field RTU intelligent control system based on the localization of the Hongmeng system, including: at least one processor; at least one memory for storing at least one program; When at least one of the programs is executed by at least one of the processors, the oil and gas field RTU intelligent control method based on the localization of the Hongmeng system as described in the first aspect is implemented.

[0013] In the third aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a program executable by a processor. When the program executable by the processor is executed by the processor, it is used to implement the oil and gas field RTU intelligent control method based on the localization of the Hongmeng system as described in the first aspect.

[0014] The oil and gas field RTU intelligent control method and system based on the localization of the Hongmeng system proposed in the present invention determines the oil and gas field application production data that is effectively available in the oil and gas field production process by screening the oil and gas field production data for effectiveness and identifying the application status of the oil and gas field effective production data; further, by environmental adaptation of the oil and gas field application production data and feature learning of the oil and gas field environmental assessment production data, the feature splitting of the data in the oil and gas field production process is achieved to determine qualified oil and gas field feature production data; and then, through simulation processing and application scenario configuration operations, the oil and gas field demand production data that meets the requirements of the application scenario is obtained, and the oil and gas field demand production data can match multiple correlation factors in the oil and gas field production process, so that the oil and gas field real-time operation data can be feedback-regulated through the oil and gas field demand production data, so that an effective and reliable oil and gas field control strategy can be obtained, and finally the oil and gas field RTU is intelligently controlled based on the oil and gas field control strategy, which can improve the efficiency and accuracy of oil and gas field production control and meet the oil and gas field production control needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of an oil and gas field RTU intelligent control method based on the localization of Hongmeng system provided by one embodiment of the present invention; Figure 2 yes Figure 1 Flow chart of step S1 in FIG. Figure 3 yes Figure 1 Flow chart of step S2 in FIG. Figure 4 yes Figure 1 Flow chart of step S3 in FIG. Figure 5 yes Figure 4 Flow chart of step S32 in FIG. Figure 6 yes Figure 1 Flow chart of step S4 in FIG. Figure 7 yes Figure 6 Flow chart of step S43 in FIG. Figure 8 It is a structural diagram of an oil and gas field RTU intelligent control system based on the localization of the Hongmeng system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0016] like Figure 1As shown, an embodiment of the present invention provides an oil and gas field RTU intelligent control method based on the localization of the Hongmeng system. The method can be but is not limited to being applied to an RTU intelligent control device built based on the Hongmeng system. The RTU intelligent control device is matched with the oil and gas field. The specific matching determining factors can be but are not limited to including the type, scale and environment of the oil and gas field. The method can specifically include but is not limited to steps S1 to S4; wherein the RTU intelligent control device in this embodiment can be but is not limited to being built based on the localization of the open source Hongmeng system. For example, the hardware architecture and software architecture associated with the RTU intelligent control device can be based on the microkernel of the open source Hongmeng system. It is understood that through the efficient computing environment and stable data logic framework provided by the open source Hongmeng system, the oil and gas field RTU is intelligently controlled, thereby improving the efficiency and accuracy of oil and gas field production control. Since the Hongmeng system is a distributed operating system well known to those skilled in the art, the domestically produced Hongmeng system will not be described in detail here to avoid redundancy. The specific architecture and parameters of the RTU intelligent control device built based on the Hongmeng system can be set as needed. There is no restriction here. It only needs to ensure that it is applied to the oil and gas field RTU intelligent control method provided in this embodiment.

[0017] Step S1, obtaining oil and gas field production data; screening the oil and gas field production data for effectiveness, thereby obtaining oil and gas field effective production data; identifying the application status of the oil and gas field effective production data, thereby obtaining oil and gas field application production data; Step S2: performing environmental adaptation on the oil and gas field application production data, thereby obtaining oil and gas field environmental assessment production data; performing feature learning on the oil and gas field environmental assessment production data, thereby obtaining oil and gas field feature production data; Step S3, simulating the characteristic production data of the oil and gas field to obtain the simulated production data of the oil and gas field; configuring the application scenario of the simulated production data of the oil and gas field to obtain the required production data of the oil and gas field; Step S4, obtaining real-time operation data of the oil and gas field; feedback-regulating the real-time operation data of the oil and gas field according to the production data required by the oil and gas field, thereby obtaining the oil and gas field control strategy, and performing intelligent control of the oil and gas field RTU through the oil and gas field control strategy.

[0018] In this step, the oil and gas field production data is screened for effectiveness and the application status of the effective oil and gas field production data is identified, so as to determine the oil and gas field application production data that is effectively available in the oil and gas field production process; further, the oil and gas field application production data is adapted to the environment and the oil and gas field environmental assessment production data is learned for features, so as to achieve feature splitting of the data in the oil and gas field production process to determine qualified oil and gas field feature production data; and then, through simulation processing and application scenario configuration operations, the oil and gas field demand production data that meets the application scenario requirements is obtained, and the oil and gas field demand production data can match multiple correlation factors in the oil and gas field production process, so that the oil and gas field real-time operation data can be feedback-regulated through the oil and gas field demand production data, so as to obtain an effective and reliable oil and gas field control strategy, and finally, the oil and gas field RTU is intelligently controlled based on the oil and gas field control strategy, so as to improve the efficiency and accuracy of oil and gas field production control and meet the oil and gas field production control needs.

[0019] like Figure 2 As shown, in one embodiment of the present invention, step S1 may include but is not limited to the following steps: Step S11, cleaning, verifying and integrating the oil and gas field production data through a preset data analysis model, thereby obtaining the oil and gas field data to be tested; Step S12, performing PCA dimensionality reduction processing on the oil and gas field data to be tested, so as to obtain oil and gas field component data, wherein the PCA dimensionality reduction processing converts the oil and gas field data to be tested into a new coordinate system so that most of the variance in the oil and gas field data to be tested is concentrated in a few dimensions, thereby achieving the purpose of dimensionality reduction, and thus obtaining the oil and gas field component data after simplified component reconstruction; Step S13, extracting key fields from the oil and gas field composition data, and obtaining data values ​​corresponding to the key fields of the oil and gas field composition data; automatically screening the oil and gas field composition data according to the relative size relationship between the data values ​​and the data threshold determined based on the preset field information, and obtaining effective production data of the oil and gas field; Step S14: construct a corresponding correlation feature matrix based on the effective production data of the oil and gas field, determine a feature vector according to the correlation feature matrix, and obtain a feature value of the feature vector; Step S15, performing time series analysis on the effective production data of the oil and gas field by combining the eigenvalue of the eigenvector with the variational autoencoder, so as to determine at least one oil and gas field time series production behavior corresponding to the effective production data of the oil and gas field, wherein the autoencoder (Autoencoder) is an unsupervised learning neural network, which reconstructs the input data by learning the efficient representation of the input data, and the goal is to encode the input into a lower dimensional representation, and then reconstruct the original input data from this representation, and the variational autoencoder (VariationalAutoencoders, VAEs) combines the probability generation model on the basis of the autoencoder to generate new data points; specifically, the eigenvalue of the eigenvector is input into the variational autoencoder, so as to combine the time series situation with the eigenvalue of the eigenvector through the variational autoencoder for analysis, and the time series situation can be characterized by a linear time series model or a nonlinear time series model in the relevant prior art, so as to output at least one oil and gas field time series production behavior corresponding to the effective production data of the oil and gas field, and it can be understood that the specific number of oil and gas field time series production behaviors needs to be determined accordingly according to the actual scenario, and is not limited here; Step S16: Statistically analyze the time-series production behaviors of each oil and gas field based on transfer learning to obtain oil and gas field application production data, wherein transfer learning is used for similarity query, that is, applying model data of the old field to model data of the new field, thereby realizing data migration; specifically, transfer learning can be divided into, but not limited to, inductive transfer learning (InductiveTL), transductive transfer learning (TransductiveTL), unsupervised transfer learning (UnsupervisedTL) and instance-based migration (InstancebasedTL) according to different scenarios. This embodiment mainly adopts sample-based migration, specifically taking the time-series production behaviors of each oil and gas field as the initial source field, and taking the production data corresponding to the time-series production behaviors of the oil and gas fields as the target field, calling the ready-made pre-trained model (which can be multiple and is not limited here) to realize the migration of data fields through feature vector training and other methods, thereby obtaining oil and gas field application production data.

[0020] In this step, the oil and gas field production data is preprocessed by a preset data analysis model to obtain compliant oil and gas field test data to improve the processing accuracy of the oil and gas field data; then the oil and gas field test data is subjected to PCA dimensionality reduction processing to determine the comprehensive index data of the oil and gas field data, and then the data values ​​corresponding to the key fields of the oil and gas field component data are obtained in the next step to determine the key field characteristics in the oil and gas field component data, and then the oil and gas field component data can be automatically screened according to the relative size relationship between the data value and the data threshold determined based on the preset field information, so that the oil and gas field component data can be determined. The redundant data part in the data is filtered out to obtain the effective production data of the oil and gas fields; on this basis, the corresponding correlation feature matrix is ​​constructed through the effective production data of the oil and gas fields and the eigenvalues ​​of its eigenvector are obtained to determine the characteristic attributes of the correlation feature matrix, so as to facilitate the time series analysis of the effective production data of the oil and gas fields through the variational autoencoder combined with the eigenvalues ​​of the eigenvector, so as to determine the production time series situation of the effective production data of the oil and gas fields, that is, the time series data situation of the oil and gas fields in the production process, so as to perform statistical analysis on the time series production behavior of each oil and gas field to obtain effective and available oil and gas field application production data.

[0021] In one embodiment, the data cleaning in step S11 is used to correct errors in the oil and gas field production data, the data verification is used to re-verify and confirm the cleaned data, and the data integration is used to integrate the cleaned data and the verified data to obtain the oil and gas field data to be tested; there can be multiple data analysis models, which can be set accordingly according to the actual application scenario. For example, it can be but not limited to the first type of data analysis model, which is mainly constructed in Python language, and the Pandas library can be used to clean, verify and integrate the oil and gas field production data. Compared with other means, it provides rich functions and application methods, or it is constructed based on R language, and uses packages such as dplyr and tidyr to process and clean the oil and gas field production data, such as a clustering cleaning model, a genetic network cleaning model, etc.; or it is a second type of data analysis model, which is constructed based on SQL language, and can be but not limited to being used for cleaning and processing oil and gas field production data, especially when processing large-scale oil and gas field production data, the effect is better, such as a fuzzy data matching cleaning model, a knowledge base data cleaning model, etc., which is not limited here.

[0022] In one embodiment, the specific method of extracting key fields from the oil and gas field component data in step S13 can be multiple, which is not limited here, for example, predetermining or setting certain key characters in the oil and gas field component data, and then finding which fields contain the key characters shown above, so as to determine these fields as key fields, wherein the character length of the key fields can be set by oneself; in addition, the calculation method of the data value corresponding to the key field of the oil and gas field component data can be set according to actual conditions, for example, at least one field type rule is preset, and the data value corresponding to the key field is calculated according to the field type rule, and the field type rule can be but not limited to setting the basic data value according to the field length, such as if the number of fields is 5, the data value is 1, and so on, which is not limited here; the step in step S13, according to the relative size relationship between the data value and the data threshold determined based on the preset field information, the oil and gas field component data is automatically screened, which can be but not limited to: automatically deleting all key fields corresponding to the data threshold determined based on the preset field information, so as to obtain effective production data of the oil and gas field.

[0023] In one embodiment, the step in step S14, constructing a corresponding correlation feature matrix based on the effective production data of the oil and gas field, can be implemented in sequence through but not limited to the steps of collecting data, preprocessing data, converting data, filling data into a matrix, etc., wherein collecting data and preprocessing data have been performed in the aforementioned steps and can be ignored here, and data conversion involves standardizing or normalizing the data, and finally filling the processed data into the matrix according to the dimension and data of the matrix, so as to construct the corresponding correlation feature matrix, as for determining the eigenvector according to the correlation feature matrix and obtaining the eigenvalue of the eigenvector, that is, obtaining its eigenvector and eigenvalue through the matrix, this part of the technology is well known to those skilled in the art and will not be elaborated here.

[0024] like Figure 3 As shown, in one embodiment of the present invention, step S2 may include but is not limited to the following steps: Step S21, performing environmental risk assessment on the oil and gas field application production data, thereby obtaining the environmental risk level corresponding to the oil and gas field application production data; Step S22: Based on the Bayesian linear dynamic model, the environmental risk level corresponding to the oil and gas field application production data is inferred for correlation, so as to obtain a plurality of environmental impact indicators. Specifically, the Bayesian linear dynamic model is used to perform an internally refined sensitivity analysis on the determined environmental risk level and the environmental risk level is differentiated according to the determined sensitivity, so as to determine the refined environmental risk level of different degrees, and then obtain the corresponding environmental impact indicators according to the refined content, so as to obtain a plurality of environmental impact indicators; Step S23, performing similarity analysis on multiple environmental impact indicators to obtain environmental collaboration indicators; updating the oil and gas field application production data according to the environmental collaboration indicators to obtain oil and gas field environmental assessment production data, wherein similarity analysis is well known to those skilled in the art, for example, it can be but not limited to using a SimRank-based similarity algorithm to perform similarity analysis on each environmental impact indicator, and the obtained environmental collaboration indicators can reflect the collaboration information between the various environmental impact indicators; updating the oil and gas field application production data can be but not limited to solving the optimal solution set of the oil and gas field application production data under the influence of the environmental collaboration indicators through a multi-objective evolutionary algorithm, and then selecting one of the optimal solutions from the optimal solution set as the oil and gas field environmental assessment production data, the optimal solution set considers the single-objective influences of different environmental collaboration indicators respectively, which is equivalent to a solution set after all single-objective influences are optimized, so the result is more accurate; Step S24: performing data amplification on the oil and gas field environmental assessment production data to obtain a plurality of oil and gas field environmental enhanced production data. Specifically, the oil and gas field environmental assessment production data is expanded by using a small amount of data prior knowledge to generate more similar generated data, thereby obtaining a plurality of oil and gas field environmental enhanced production data. For example, it can be but not limited to refitting, reinforcement learning, organic replacement or style transfer of the oil and gas field environmental assessment production data. The above operations basically belong to the relevant prior art in this field and are not described in detail here. Step S25: Based on the attention learning mechanism and the preset feature structure environment, the feature structure of multiple oil and gas field environmental enhanced production data is verified to obtain the feature parameters corresponding to each oil and gas field environmental enhanced production data; the oil and gas field characteristic production data is obtained according to the feature parameters corresponding to each oil and gas field environmental enhanced production data.

[0025] In this step, an environmental risk assessment is performed on the oil and gas field application production data to determine the environmental risk level corresponding to the oil and gas field application production data. The environmental risk level reflects the production environment of the oil and gas field application production data, and then the environmental risk level is inferred based on the Bayesian linear dynamic model, that is, the environmental risk level is characterized by specific indicators, thereby obtaining multiple environmental impact indicators; the oil and gas field application production data is further updated and enhanced through similarity analysis, thereby obtaining multiple oil and gas field environmental enhanced production data under optimized conditions; finally, the characteristic parameters corresponding to each oil and gas field environmental enhanced production data can be accurately and reliably obtained through feature structure verification, and the characteristic parameters can characterize the comprehensive production situation of the oil and gas field environmental enhanced production data combined with the corresponding environmental factors, so that qualified and effective oil and gas field characteristic production data can be obtained according to the characteristic parameters corresponding to each oil and gas field environmental enhanced production data.

[0026] In one embodiment, in step S21, there may be multiple ways to conduct environmental risk assessment on oil and gas field application production data, which are not limited here. For example, first, some data related to preset environmental factors in the oil and gas field application production data are counted, and the environmental factors here may be oil well pressure, oil well operating temperature, and oil well depth, etc., and then these data are assigned environmental risk levels according to weights, so as to obtain the environmental risk level corresponding to the oil and gas field application production data. The weight calculation method here is the proportion of the determined current environmental factors relative to the overall environmental factors. For example, the weight of oil well pressure is 23%, and the environmental risk level can be assigned accordingly.

[0027] In one embodiment, in step S25, the attention learning mechanism may specifically be a node attention learning mechanism or a semantic attention learning mechanism, and the feature structure environment is used to reflect the environment structure that may be configured for each oil and gas field environment enhanced production data, which may be pre-configured. For example, in this embodiment, when processing multiple oil and gas field environment enhanced production data based on the semantic attention learning mechanism, the attention weight of each oil and gas field environment enhanced production data may be dynamically adjusted to highlight the important oil and gas field environment enhanced production data, and then the corresponding structure verification of this part of the oil and gas field environment enhanced production data is performed based on the preset feature structure environment, so as to determine the feature parameters corresponding to this part of the oil and gas field environment enhanced production data; on this basis, the oil and gas field characteristic production data is obtained according to the obtained feature parameters, which belongs to the inverse process of feature extraction in machine learning, and the corresponding training data set can be obtained by reversely training the feature parameters. The training data set is the oil and gas field characteristic production data. Since this part of the data is well known to those skilled in the art, it will not be elaborated here.

[0028] like Figure 4 As shown, in one embodiment of the present invention, when the characteristic parameters corresponding to the environmental enhanced production data of each oil and gas field include a first characteristic parameter, a second characteristic parameter and a third characteristic parameter, wherein the first characteristic parameter represents the expected oil production of the oil and gas field, the second characteristic parameter represents the expected quality of the sampled oil of the oil and gas field, and the third characteristic parameter represents the expected liquid production of the oil and gas field, step S3 may include but is not limited to the following steps: Step S31, generating a first simulation network model based on the first characteristic parameter, the second characteristic parameter and the third characteristic parameter, and constructing an error simulation function through the first simulation network model and the characteristic production data of the oil and gas field, wherein, taking the first simulation network model as a neural network model as an example, firstly introducing the first characteristic parameter, the second characteristic parameter and the third characteristic parameter through the torch.nn namespace in PyTorch, and since the torch.nn namespace provides various types of modules required for the neural network model, the modules required for the neural network model can be defined according to the preset training equipment and the introduced characteristic parameters, thereby realizing the construction of the first simulation network model, and then the simulation data corresponding to the characteristic production data of the oil and gas field can be output through the first simulation network model, and the error simulation function can be constructed by comparing the simulation data with the characteristic production data of the oil and gas field; Step S32, performing simulation operation on the error simulation function to obtain a simulation operation value; optimizing and updating the first simulation network model according to the simulation operation value, thereby obtaining a second simulation network model; Step S33, reverse matching the simulation operation value with the second simulation network model, so as to obtain the simulated production data of the oil and gas field; it can be understood that, since the error simulation function can be constructed by the first simulation network model and the characteristic production data of the oil and gas field, the first simulation network model and the characteristic production data of the oil and gas field can correspond to the simulation operation value of the error simulation function, and the second simulation network model is obtained based on the optimization and update of the first simulation network model, so in essence the second simulation network model and the characteristic production data of the oil and gas field can also correspond to the simulation operation value of the error simulation function, and the simulated production data of the oil and gas field can be obtained by performing reverse operation based on the functional relationship; Step S34, performing application scenario analysis on the oil and gas field simulation production data, thereby obtaining application scenario characteristics corresponding to the oil and gas field simulation production data; Step S35, obtaining the degree of compatibility between the application scenario characteristics and the preconfigured oil and gas field application scenario model; and configuring the application scenario for the oil and gas field simulation production data according to the degree of compatibility, thereby obtaining the oil and gas field demand production data.

[0029] In this step, a first simulation network model is generated through the first characteristic parameter, the second characteristic parameter and the third characteristic parameter, and then an error simulation function is constructed through the first simulation network model and the characteristic production data of the oil and gas field. The error simulation function can be used to reflect the preliminary error situation of the simulation of the characteristic production data of the oil and gas field, and then the first simulation network model is optimized and updated according to the error simulation function to obtain a second simulation network model with a higher simulation degree, and the second simulation network model can be used to determine the simulated production data of the oil and gas field; finally, by performing application scenario analysis on the simulated production data of the oil and gas field, the application scenario characteristics corresponding to the simulated production data of the oil and gas field are obtained, and the adaptability of the application scenario characteristics can be considered to characterize the application production situation of the oil and gas field data. Therefore, by configuring the application scenario of the simulated production data of the oil and gas field according to the adaptability, the oil and gas field demand production data that meets the requirements can be obtained.

[0030] In one embodiment, the steps in step S34 can be performed in a variety of ways to perform application scenario analysis on the oil and gas field simulation production data, which are not limited here. For example, scenario modeling of the oil and gas field simulation production data is performed through a Gaussian mixture model, and then the dynamic background features in the modeling scenario are read as application scenario features, or the scenario features in the oil and gas field simulation production data are directly read through feature point extraction network matching, and used as application scenario features, etc.

[0031] In one embodiment, the step in step S35, obtaining the degree of fit between the application scenario characteristics and the preconfigured oil and gas field application scenario model can be, but is not limited to, a comparative adaptation method, that is, the oil and gas field application scenario model is compared with the application scenario characteristics to check whether the application scenario characteristics appear in any oil and gas field application scenario model as a first factor, and if it appears in a certain oil and gas field application scenario model, the proportion of the application scenario characteristics in the oil and gas field application scenario model as a second factor, combined with the specific values ​​determined by the above two factors, the degree of fit between the application scenario characteristics and the preconfigured oil and gas field application scenario model can be determined by multiplying the two values, wherein the oil and gas field application scenario model can be preconfigured and pre-stored through an existing storage unit, and can be directly called when needed; further, since the corresponding oil and gas field application scenario model has been determined according to the degree of fit in the aforementioned steps, the application scenario involved in the oil and gas field application scenario model can be added to the oil and gas field simulation production data, thereby enriching the application scenario of the oil and gas field simulation production data, and based on the application scenario combined with the original oil and gas field simulation production data, the oil and gas field demand production data is obtained.

[0032] like Figure 5 As shown, in one embodiment of the present invention, step S32 may include but is not limited to the following steps: Step S321, performing continuous simulation operation on the error simulation function until the obtained simulation operation value is greater than the minimum simulation operation value and less than or equal to the preset balance simulation operation value, wherein the preset balance simulation operation value is less than the maximum simulation operation value; Step S322: Fit the first simulation network model according to the obtained simulation operation value to obtain a second simulation network model.

[0033] In this step, the error degree of the simulation operation can be reduced by performing continuous simulation operation on the error simulation function, especially until the obtained simulation operation value is greater than the minimum simulation operation value and less than or equal to the preset equilibrium simulation operation value, the continuous simulation operation will not be stopped, thus ensuring that the required simulation operation value can be found, and then the first simulation network model is fitted according to the obtained simulation operation value to obtain an optimized second simulation network model, wherein there can be multiple fitting methods, such as graphical fitting, least squares fitting, etc., which are not limited here, and this part of the content is well known to those skilled in the art and will not be elaborated on.

[0034] Preferably, the simulation operation value is taken as the minimum simulation operation value, at which time the simulation loss is the smallest. In this case, the accuracy of the fitted second simulation network model is higher.

[0035] like Figure 6 As shown, in one embodiment of the present invention, step S4 may include but is not limited to the following steps: Step S41, obtaining real-time operation data of the oil and gas field; using a frequent item mining algorithm to obtain a first operation parameter corresponding to the oil and gas field demand production data, and optimizing the performance of the first operation parameter to obtain a second operation parameter; Step S42, determining a first operating performance value corresponding to the oil and gas field required production data according to the second operating parameter; Step S43: Generate an oil and gas field control strategy according to the first operation performance value and the second operation performance value corresponding to the acquired real-time operation data of the oil and gas field, so as to perform intelligent control of the oil and gas field RTU through the oil and gas field control strategy.

[0036] In this step, by determining the first operating performance value corresponding to the oil and gas field demand production data, the first operating performance value is compared with the second operating performance value corresponding to the acquired oil and gas field real-time operating data, thereby generating an effective and reliable oil and gas field control strategy. Finally, based on the oil and gas field control strategy, the oil and gas field RTU intelligent control is performed, which can improve the efficiency and accuracy of oil and gas field production control and meet the oil and gas field production control needs.

[0037] In one embodiment, the first operating parameter and the second operating parameter in step S41 are both used to characterize the real-time operating parameter status corresponding to the oil and gas field demand production data; this embodiment uses a frequent item mining algorithm based on fp-growth for calculation, specifically, firstly find frequent items from the oil and gas field demand production data, then find other frequent items from the subset containing the frequent items, and finally determine the first operating parameter corresponding to the oil and gas field demand production data based on all the aggregated frequent items. Similarly, an apriori-based frequent item mining algorithm can also be used for calculation, and its basic principle is similar and will not be elaborated on; the performance optimization of the first operating parameter may include but is not limited to operations such as denoising, compensation and reconstruction.

[0038] In one embodiment, when the second operating parameter includes the optimized oil production and the optimized liquid production in the time series corresponding to the oil and gas field production demand data, step S42 may include but is not limited to the following steps: Step S421: Substitute the optimized oil production and optimized liquid production in the time series corresponding to the oil and gas field demand production data into the performance evaluation formula for calculation, so as to obtain the first operation performance value corresponding to the oil and gas field demand production data, wherein the performance evaluation formula is as follows: ; ; is the first operating performance value corresponding to the oil and gas field demand production data, is the start time of the time series corresponding to the oil and gas field demand production data, The end time of the time series corresponding to the oil and gas field demand production data, is the energy consumption parameter of the oil and gas field, To optimize the oil production within the time series corresponding to the oil and gas field demand production data, Optimize liquid production within the time series corresponding to the production data required by the oil and gas field.

[0039] like Figure 7 As shown, in one embodiment of the present invention, step S43 may include but is not limited to the following steps: Step S431, calculating the absolute value of the difference between the first operating performance value and the second operating performance value corresponding to the acquired real-time operating data of the oil and gas field, thereby obtaining a difference result; Step S432: When the difference result is less than or equal to the preset operating performance difference threshold, the reliable operating data of the oil and gas field is obtained according to the oil and gas field demand production data and the oil and gas field real-time operating data, and the oil and gas field reliable operating data is used as the basic data for intelligent control of the oil and gas field RTU; otherwise, the oil and gas field demand production data is used as the basic data for intelligent control of the oil and gas field RTU.

[0040] In this step, the difference between the first operating performance value and the second operating performance value corresponding to the acquired real-time operating data of the oil and gas field is calculated to determine the degree of differentiation between the first operating performance value and the second operating performance value, so that the oil and gas field control strategy can be constructed according to the specific degree of differentiation. That is, if the difference result is less than or equal to the preset operating performance difference threshold, it means that the oil and gas field demand production data and the oil and gas field real-time operating data are both available oil and gas field data, so that the corresponding oil and gas field reliable operating data is obtained according to the oil and gas field demand production data and the oil and gas field real-time operating data. In this way, both the impact of the real-time operation of the oil and gas field and the demand analysis results are taken into account. , so that more accurate reliable operation data of the oil and gas field can be obtained. Otherwise, it means that the real-time operation data of the oil and gas field does not meet the requirements. In this case, it is only necessary to use the oil and gas field demand production data as the basic data for the oil and gas field RTU intelligent control. Among them, the method of obtaining the reliable operation data of the oil and gas field according to the oil and gas field demand production data and the oil and gas field real-time operation data can be but not limited to: obtaining the mean of the oil and gas field demand production data and the oil and gas field real-time operation data to obtain the reliable operation data of the oil and gas field; or, respectively allocating and weighting the oil and gas field demand production data and the oil and gas field real-time operation data according to preset proportions to obtain the reliable operation data of the oil and gas field, etc.

[0041] It should be noted that there is no restriction on the specific method of using the reliable operation data of the oil and gas field as the basic data for the intelligent control of the oil and gas field RTU or using the demand production data of the oil and gas field as the basic data for the intelligent control of the oil and gas field RTU. It is only necessary to ensure that the real-time operation data of the oil and gas field is adjusted to the reliable operation data of the oil and gas field or the demand production data of the oil and gas field. Specific feedback adjustment measures that can achieve the above purpose (such as adjusting the operation cycle of the oil pump, changing the position of the anchor reservoir depth, etc.) can be set by yourself.

[0042] Figure 8 This is a structural diagram of an oil and gas field RTU intelligent control system 1000 based on the localization of the Hongmeng system provided by an embodiment of the present invention. Figure 8 As shown, the oil and gas field RTU intelligent control system 1000 based on the localization of the Hongmeng system includes a memory 1100 and a processor 1200. The number of the memory 1100 and the processor 1200 can be one or more. Figure 8 In the embodiment, a memory 1100 and a processor 1200 are taken as an example; the memory 1100 and the processor 1200 in the device may be connected via a bus or other means. Figure 8 The example of connecting through bus is taken in the following.

[0043] The memory 1100, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as the program instructions / modules corresponding to the oil and gas field RTU intelligent control method based on the localization of the Hongmeng system provided in any embodiment of the present invention. The processor 1200 implements the above-mentioned oil and gas field RTU intelligent control method based on the localization of the Hongmeng system by running the software programs, instructions and modules stored in the memory 1100.

[0044] The memory 1100 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function. In addition, the memory 1100 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 1100 may further include a memory remotely arranged relative to the processor 1200, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0045] An embodiment of the present invention also provides a computer-readable storage medium storing computer-executable instructions, which are used to execute the oil and gas field RTU intelligent control method based on the localization of the Hongmeng system as provided in any embodiment of the present invention.

[0046] An embodiment of the present invention also provides a computer program product, including a computer program or computer instructions, which are stored in a computer-readable storage medium. The processor of a computer device reads the computer program or computer instructions from the computer-readable storage medium, and the processor executes the computer program or computer instructions, so that the computer device executes the localized oil and gas field RTU intelligent control method based on the Hongmeng system as provided in any embodiment of the present invention.

[0047] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0048] In hardware implementation, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or transient medium). As is well known to those of ordinary skill in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0049] The terms "component", "module", "system", etc. used in this specification are used to represent computer-related entities, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, or a computer. By way of illustration, both applications running on a computing device and a computing device can be components. One or more components may reside in a process or an execution thread, and a component may be located on a computer or distributed between two or more computers. In addition, these components may be executed from various computer-readable media having various data structures stored thereon. Components may communicate, for example, through local or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system, or a network, such as the Internet interacting with other systems through signals).

Claims

1. The intelligent control method of RTU in oil and gas fields based on the localization of Hongmeng system is characterized by: Applied to an RTU intelligent control device built based on the Hongmeng system, the RTU intelligent control device matches the oil and gas field; the method comprises the following steps: Step S1, obtaining oil and gas field production data; screening the oil and gas field production data for effectiveness, thereby obtaining oil and gas field effective production data; identifying the application status of the oil and gas field effective production data, thereby obtaining oil and gas field application production data; Step S2, performing environmental adaptation on the oil and gas field application production data, thereby obtaining oil and gas field environmental assessment production data; Perform feature learning on oil and gas field environmental assessment production data to obtain oil and gas field characteristic production data; Step S3, simulating the characteristic production data of the oil and gas field to obtain the simulated production data of the oil and gas field; configuring the application scenario of the simulated production data of the oil and gas field to obtain the required production data of the oil and gas field; Step S4, obtaining real-time operation data of the oil and gas field; feedback-regulating the real-time operation data of the oil and gas field according to the production data required by the oil and gas field, thereby obtaining the oil and gas field control strategy, and performing intelligent control of the oil and gas field RTU through the oil and gas field control strategy.

2. According to the localization method of Hongmeng system for oil and gas field RTU according to claim 1, it is characterized in that: The step S1 comprises the following steps: Step S11, cleaning, verifying and integrating the oil and gas field production data through a preset data analysis model, thereby obtaining the oil and gas field data to be tested; Step S12, performing PCA dimensionality reduction processing on the oil and gas field data to be tested, thereby obtaining oil and gas field composition data; Step S13, extracting key fields from the oil and gas field composition data, and obtaining data values ​​corresponding to the key fields of the oil and gas field composition data; automatically screening the oil and gas field composition data according to the relative size relationship between the data values ​​and the data threshold determined based on the preset field information, and obtaining effective production data of the oil and gas field; Step S14: construct a corresponding correlation feature matrix based on the effective production data of the oil and gas field, determine a feature vector according to the correlation feature matrix, and obtain a feature value of the feature vector; Step S15, performing time series analysis on the effective production data of the oil and gas field by combining the eigenvalue of the eigenvector with the variational autoencoder, thereby determining at least one oil and gas field time series production behavior corresponding to the effective production data of the oil and gas field; Step S16: Statistically analyze the time-series production behaviors of each oil and gas field based on transfer learning to obtain application production data of the oil and gas field.

3. According to the localization method of Hongmeng system for oil and gas field RTU according to claim 1, it is characterized in that: The step S2 comprises the following steps: Step S21, performing environmental risk assessment on the oil and gas field application production data, thereby obtaining the environmental risk level corresponding to the oil and gas field application production data; Step S22: performing correlation inference on the environmental risk levels corresponding to the oil and gas field application production data based on the Bayesian linear dynamic model, thereby obtaining a plurality of environmental impact indicators; Step S23, performing similarity analysis on multiple environmental impact indicators to obtain environmental collaboration indicators; updating oil and gas field application production data according to the environmental collaboration indicators to obtain oil and gas field environmental assessment production data; Step S24, performing data amplification on the oil and gas field environmental assessment production data, thereby obtaining a plurality of oil and gas field environmental enhancement production data; Step S25: Based on the attention learning mechanism and the preset feature structure environment, the feature structure of multiple oil and gas field environmental enhanced production data is verified to obtain the feature parameters corresponding to each oil and gas field environmental enhanced production data; the oil and gas field characteristic production data is obtained according to the feature parameters corresponding to each oil and gas field environmental enhanced production data.

4. According to claim 3, the oil and gas field RTU intelligent control method based on the localization of the Hongmeng system is characterized in that: When the characteristic parameters corresponding to the environmental enhanced production data of each oil and gas field include a first characteristic parameter, a second characteristic parameter and a third characteristic parameter, wherein the first characteristic parameter represents the expected oil production of the oil and gas field, the second characteristic parameter represents the expected quality of the sampled oil of the oil and gas field, and the third characteristic parameter represents the expected liquid production of the oil and gas field; the step S3 includes the following steps: Step S31, generating a first simulation network model based on the first characteristic parameter, the second characteristic parameter and the third characteristic parameter, and constructing an error simulation function through the first simulation network model and characteristic production data of the oil and gas field; Step S32, performing simulation operation on the error simulation function to obtain a simulation operation value; optimizing and updating the first simulation network model according to the simulation operation value, thereby obtaining a second simulation network model; Step S33, reverse matching the simulation operation value with the second simulation network model to obtain simulated production data of the oil and gas field; Step S34, performing application scenario analysis on the oil and gas field simulation production data, thereby obtaining application scenario characteristics corresponding to the oil and gas field simulation production data; Step S35, obtaining the degree of compatibility between the application scenario characteristics and the preconfigured oil and gas field application scenario model; and configuring the application scenario for the oil and gas field simulation production data according to the degree of compatibility, thereby obtaining the oil and gas field demand production data.

5. According to claim 4, the oil and gas field RTU intelligent control method based on the localization of the Hongmeng system is characterized in that: The step S32 comprises the following steps: Step S321, performing continuous simulation operation on the error simulation function until the obtained simulation operation value is greater than the minimum simulation operation value and less than or equal to the preset balance simulation operation value, wherein the preset balance simulation operation value is less than the maximum simulation operation value; Step S322: Fit the first simulation network model according to the obtained simulation operation value to obtain a second simulation network model.

6. According to the localization method of Hongmeng system for oil and gas field RTU intelligent control method according to claim 1, it is characterized in that: The step S4 comprises the following steps: Step S41, obtaining real-time operation data of the oil and gas field; using a frequent item mining algorithm to obtain a first operation parameter corresponding to the oil and gas field demand production data, and optimizing the performance of the first operation parameter to obtain a second operation parameter; Step S42, determining a first operating performance value corresponding to the oil and gas field required production data according to the second operating parameter; Step S43: Generate an oil and gas field control strategy according to the first operation performance value and the second operation performance value corresponding to the acquired real-time operation data of the oil and gas field, so as to perform intelligent control of the oil and gas field RTU through the oil and gas field control strategy.

7. The oil and gas field RTU intelligent control method based on the localization of Hongmeng system according to claim 6 is characterized in that: When the second operating parameter includes the optimized oil production and the optimized liquid production in the time series corresponding to the oil and gas field production demand data, the step S42 includes the following steps: Step S421: Substitute the optimized oil production and optimized liquid production in the time series corresponding to the oil and gas field demand production data into the performance evaluation formula for calculation, so as to obtain the first operation performance value corresponding to the oil and gas field demand production data, wherein the performance evaluation formula is as follows: ； ; is the first operating performance value corresponding to the oil and gas field demand production data, is the start time of the time series corresponding to the oil and gas field demand production data, The end time of the time series corresponding to the oil and gas field demand production data, is the energy consumption parameter of the oil and gas field, To optimize the oil production within the time series corresponding to the oil and gas field demand production data, Optimize liquid production within the time series corresponding to the production data required by the oil and gas field.

8. The oil and gas field RTU intelligent control method based on the localization of Hongmeng system according to claim 6 is characterized in that: The step S43 comprises the following steps: Step S431, calculating the absolute value of the difference between the first operating performance value and the second operating performance value corresponding to the acquired real-time operating data of the oil and gas field, thereby obtaining a difference result; Step S432: When the difference result is less than or equal to the preset operating performance difference threshold, the reliable operating data of the oil and gas field is obtained according to the oil and gas field demand production data and the oil and gas field real-time operating data, and the oil and gas field reliable operating data is used as the basic data for intelligent control of the oil and gas field RTU; otherwise, the oil and gas field demand production data is used as the basic data for intelligent control of the oil and gas field RTU.

9. An oil and gas field RTU intelligent control system based on the localization of Hongmeng system, characterized in that: include: at least one processor; at least one memory for storing at least one program; When at least one of the programs is executed by at least one of the processors, the oil and gas field RTU intelligent control method based on the localization of the Hongmeng system as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium, characterized in that: A processor-executable program is stored therein, and when the processor-executable program is executed by the processor, it is used to implement the oil and gas field RTU intelligent control method based on the localization of the Hongmeng system as described in any one of claims 1 to 8.

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