Oil and gas production monitoring method and device

Abstract

The invention discloses an oil and gas production monitoring method and device, relates to the technical field of oil and gas production monitoring, and mainly aims to improve the oil and gas production monitoring efficiency and accuracy. According to the main technical scheme, an oil and gas production monitoring platform is deployed with a large language model and at least one monitoring model, and each monitoring model has applicable data features and oil and gas production monitoring services; under the condition that an oil and gas production monitoring instruction input in a natural language mode is obtained, natural language analysis processing is conducted on the oil and gas production monitoring instruction based on a large language model, and a target oil and gas production monitoring service indicated by the oil and gas production monitoring instruction is obtained; selecting a target monitoring model suitable for the target oil and gas production monitoring service; obtaining target oil and gas production data with target data characteristics suitable for the target monitoring model; and calling the target monitoring model to execute the target oil and gas production monitoring service based on the target oil and gas production data to obtain an oil and gas production monitoring result for the oil and gas production monitoring instruction.

Description

Technical Field

[0001] This application relates to the technical field of oil and gas production monitoring, and particularly to an oil and gas production monitoring method and device. Background Art

[0002] Oil and gas production mainly involves processes such as the extraction, processing, storage, and transportation of oil and gas, which is a key industrial field. During the oil and gas production process, it is necessary to monitor oil and gas production based on oil and gas production data generated during oil and gas production (for example, data such as temperature and pressure generated by equipment used in the extraction or processing process), and perform operations such as adjusting production scheduling and conducting equipment maintenance based on the monitoring results.

[0003] Currently, oil and gas production monitoring is usually manually completed by monitoring personnel. However, the knowledge and experience of monitoring personnel in oil and gas production monitoring are limited, resulting in low efficiency of oil and gas production monitoring and it is difficult to give accurate oil and gas production monitoring results.

[0004] Therefore, how to improve the efficiency and accuracy of oil and gas production monitoring has become an urgent problem to be solved. Summary of the Invention

[0005] This application proposes an oil and gas production monitoring method and device, with the main purpose of improving the efficiency and accuracy of oil and gas production monitoring.

[0006] To achieve the above object, this application mainly provides the following technical solutions:

[0007] In a first aspect, this application provides an oil and gas production monitoring method, which is applied to an oil and gas production monitoring platform. The oil and gas production monitoring platform is deployed with a large language model and at least one monitoring model. Each monitoring model has applicable data characteristics and oil and gas production monitoring services. The oil and gas production monitoring method provided in this embodiment may include: when an oil and gas production monitoring instruction input in natural language is obtained, performing natural language analysis and processing on the oil and gas production monitoring instruction based on the large language model to obtain the target oil and gas production monitoring service indicated by the oil and gas production monitoring instruction; selecting a target monitoring model applicable to the target oil and gas production monitoring service; obtaining target oil and gas production data having the target data characteristics applicable to the target monitoring model; and calling the target monitoring model to execute the target oil and gas production monitoring service based on the target oil and gas production data to obtain an oil and gas production monitoring result for the oil and gas production monitoring instruction.

[0008] In some embodiments of the present application, if there are at least one target monitoring models applicable to the target oil and gas production monitoring service and there is no dependency between the target monitoring models, then call the target monitoring models to execute the target oil and gas production monitoring service based on the target oil and gas production data, and obtain the oil and gas production monitoring result for the oil and gas production monitoring instruction, including: calling each target monitoring model to execute the corresponding target oil and gas production monitoring service based on the corresponding target oil and gas production data respectively; summarizing the oil and gas production monitoring results obtained by each target monitoring model executing the corresponding target oil and gas production monitoring service to obtain the oil and gas production monitoring result for the oil and gas production monitoring instruction.

[0009] In some embodiments of the present application, if there are at least two target monitoring models applicable to the target oil and gas production monitoring service, and the target monitoring models have corresponding first call sequences, and the call of the target monitoring model in the latter position in the first call sequence depends on the target monitoring model in the previous position, then call the target monitoring models to execute the target oil and gas production monitoring service based on the target oil and gas production data, and obtain the oil and gas production monitoring result for the oil and gas production monitoring instruction, including: calling the target monitoring model in the first position in the first call sequence to execute the corresponding target oil and gas production monitoring service based on the corresponding target oil and gas production data; respectively executing for each target monitoring model in a non-first position in accordance with the first call sequence: calling the target monitoring model to execute the corresponding target oil and gas production monitoring service based on the corresponding target oil and gas production data and the oil and gas production monitoring result obtained by the target monitoring model in the previous position; taking the oil and gas production monitoring result obtained by the target monitoring model in the last position in the first call sequence as the oil and gas production monitoring result for the oil and gas production monitoring instruction.

[0010] In some embodiments of the present application, if there are at least three target monitoring models applicable to the target oil and gas production monitoring service, and at least two first monitoring models included in the target monitoring model have corresponding second call orders, the target monitoring model located in the latter position in the second call order depends on the target monitoring model in the previous position for calling, and at least one second monitoring model included in the target monitoring model has no dependency on other monitoring models, then, calling the target monitoring model to execute the target oil and gas production monitoring service based on the target oil and gas production data to obtain an oil and gas production monitoring result for the oil and gas production monitoring instruction includes: calling the first monitoring model located at the first position in the second call order to execute the corresponding target oil and gas production monitoring service based on the corresponding target oil and gas production data; respectively executing for each first monitoring model located in a non-first position in the second call order according to the second call order: calling the first monitoring model to execute the corresponding target oil and gas production monitoring service based on the corresponding target oil and gas production data and the oil and gas production monitoring result obtained by the target monitoring model in the previous position; calling each second monitoring model to execute the corresponding target oil and gas production monitoring service based on the corresponding target oil and gas production data; summarizing the oil and gas production monitoring result obtained by the first monitoring model located at the last position in the second call order and the oil and gas production monitoring results obtained by each second monitoring model to obtain an oil and gas production monitoring result for the oil and gas production monitoring instruction.

[0011] In some embodiments of the present application, each monitoring model is configured with a corresponding monitoring agent service on the oil and gas production monitoring platform, and the monitoring agent service is used to call the corresponding monitoring model. Then, the oil and gas production monitoring method provided in this embodiment further includes: determining the target monitoring agent service corresponding to the target monitoring model; triggering the target monitoring agent service so that the target monitoring agent service executes the step of calling the target monitoring model to execute the target oil and gas production monitoring service based on the target oil and gas production data.

[0012] In some embodiments of the present application, the oil and gas production monitoring method provided in this embodiment further includes: detecting whether the byte length of the oil and gas production monitoring instruction reaches a length threshold; if it reaches, perform any of the following operations: feeding back to the user who submitted the oil and gas production monitoring instruction a prompt to re-enter the oil and gas production monitoring instruction, where the prompt is used to prompt that the byte length of the re-entered oil and gas production monitoring instruction is less than the length threshold; or, splitting the oil and gas production monitoring instruction into at least two instruction texts based on the length threshold, so as to execute the step of performing natural language analysis and processing on the oil and gas production monitoring instruction based on the split instruction texts by using the large language model.

[0013] In some embodiments of the present application, after obtaining the oil and gas production monitoring result for the oil and gas production monitoring instruction, the oil and gas production monitoring method provided in this embodiment further includes: if the oil and gas production monitoring result indicates an abnormal oil and gas production, and there is a target device at the oil and gas production site for eliminating the abnormal oil and gas production, then determine the device operation data required for the target device to eliminate the abnormal oil and gas production based on the oil and gas production monitoring result, and send the device operation data to the target device, so that the target device eliminates the abnormal oil and gas production based on the device operation data;

[0014] In some embodiments of the present application, after obtaining the oil and gas production monitoring result for the oil and gas production monitoring instruction, the oil and gas production monitoring method provided in this embodiment further includes: if the oil and gas production monitoring result indicates an abnormal oil and gas production, then issue an abnormal alarm that matches the type of the abnormal oil and gas production.

[0015] In some embodiments of the present application, after obtaining the oil and gas production monitoring result for the oil and gas production monitoring instruction, the oil and gas production monitoring method provided in this embodiment further includes: if the oil and gas production monitoring result indicates an abnormal oil and gas production, then generate oil and gas production monitoring result data for assisting in eliminating the abnormal oil and gas production based on the oil and gas production monitoring result.

[0016] In some embodiments of the present application, the oil and gas production monitoring method provided in this embodiment further includes: if it is monitored that a natural language text is input in a target input box for collecting oil and gas production monitoring instructions in the interaction interface of the oil and gas production monitoring platform, then obtain the input natural language text as an oil and gas production monitoring instruction input in natural language.

[0017] In some embodiments of the present application, the oil and gas production monitoring method provided in this embodiment further includes: if it is monitored that a user audio is collected by an audio collector for collecting oil and gas production monitoring instructions of the oil and gas production monitoring platform, then convert the user audio into a natural language text, and obtain the converted natural language text as an oil and gas production monitoring instruction input in natural language.

[0018] In some embodiments of the present application, each oil and gas production monitoring service has a corresponding monitoring period and an oil and gas production monitoring instruction expressed in natural language. Then, the oil and gas production monitoring method provided in this embodiment further includes: for each of the oil and gas production monitoring services, respectively execute: if it is monitored that the start time point of the corresponding monitoring period of the oil and gas production monitoring service is reached, then obtain the oil and gas production monitoring instruction expressed in natural language corresponding to the oil and gas production monitoring service as an oil and gas production monitoring instruction input in natural language.

[0019] In some embodiments of the present application, the monitoring model includes at least one of the following: a gradient boosting model, a linear regression model, a long short-term memory network model, and a memory-augmented neural network model; the data features applicable to the gradient boosting model are used to indicate that the oil and gas production data comes from at least one device at the oil and gas production site, and the oil and gas production data from the at least one device is in a non-linear relationship, and the oil and gas production data from the at least one device is at least one modality; the data features applicable to the linear regression model are used to indicate that the oil and gas production data comes from at least two devices at the oil and gas production site, and the oil and gas production data from the at least two devices is in a linear relationship, and the oil and gas production data from the at least two devices is at least one modality; the data features applicable to the long short-term memory network model are used to indicate that the oil and gas production data comes from at least one device at the oil and gas production site, and the oil and gas production from the at least one device is time-series data, and the oil and gas production data from the at least one device is at least one modality; the data features applicable to the memory-augmented neural network model are used to indicate that the oil and gas production data comes from at least one device at the oil and gas production site, and the oil and gas production from the at least one device is time-series data, and the oil and gas production data from the at least one device is at least one modality.

[0020] In some embodiments of the present application, the oil and gas production monitoring method provided in this embodiment further includes: scoring the oil and gas production monitoring results of each oil and gas production monitoring instruction obtained in the current iteration cycle; if there is a target oil and gas production monitoring result whose score does not reach the score threshold, then perform iterative training on the monitoring model called to obtain the target oil and gas production monitoring result.

[0021] In some embodiments of the present application, the oil and gas production monitoring method provided in this embodiment further includes: determining the user who initiated the oil and gas production monitoring instruction; if it is determined that the user does not have the permission to perform the target oil and gas production monitoring service, then issue an over-authorization warning indicating that the target oil and gas production monitoring service cannot be executed.

[0022] In some embodiments of the present application, the oil and gas production monitoring method provided in this embodiment further includes: during the process of obtaining the target oil and gas production data and / or calling the target monitoring model, monitoring whether a malicious attack is suffered. If so, adopt a disposal operation corresponding to the suffered malicious attack to dispose of the malicious attack.

[0023] In some embodiments of the present application, the oil and gas production monitoring platform adopts any one of the following deployment methods: cloud deployment, local deployment, and edge deployment.

[0024] In a second aspect, the present application provides an oil and gas production monitoring device, which is applied to an oil and gas production monitoring platform. The oil and gas production monitoring platform is deployed with a large language model and at least one monitoring model, and each monitoring model has applicable data characteristics and oil and gas production monitoring services. The oil and gas production monitoring device provided in this embodiment includes:

[0025] A processing module, configured to, when an oil and gas production monitoring instruction input in natural language is obtained, perform natural language analysis and processing on the oil and gas production monitoring instruction based on the large language model to obtain the target oil and gas production monitoring service indicated by the oil and gas production monitoring instruction;

[0026] A selection module, configured to select a target monitoring model applicable to the target oil and gas production monitoring service;

[0027] An acquisition module, configured to acquire target oil and gas production data having target data characteristics applicable to the target monitoring model;

[0028] A monitoring module, configured to call the target monitoring model to execute the target oil and gas production monitoring service based on the target oil and gas production data to obtain an oil and gas production monitoring result for the oil and gas production monitoring instruction.

[0029] In a third aspect, the present application provides a computer-readable storage medium. The storage medium includes a stored program, wherein when the program runs, it controls the device where the storage medium is located to execute the oil and gas production monitoring method described in the first aspect.

[0030] In a fourth aspect, the present application provides an electronic device. The electronic device includes: a memory, configured to store a program; and a processor, coupled to the memory, configured to run the program to execute the oil and gas production monitoring method described in the first aspect.

[0031] The oil and gas production monitoring method and device provided by this application, when obtaining an oil and gas production monitoring instruction input in natural language, first performs natural language analysis and processing on the oil and gas production monitoring instruction based on a large language model to obtain the target oil and gas production monitoring service indicated by the oil and gas production monitoring instruction. Then, a target monitoring model suitable for the target oil and gas production monitoring service is selected, and target oil and gas production data with target data characteristics applicable to the target monitoring model is obtained. Finally, the target monitoring model is called to execute the target oil and gas production monitoring service based on the target oil and gas production data, and an oil and gas production monitoring result for the oil and gas production monitoring instruction is obtained. It can be seen that in the solution provided by the embodiments of this application, when a certain oil and gas production monitoring service needs to be executed, a suitable monitoring model is selected to execute the oil and gas production monitoring service, and the oil and gas production monitoring can be completed. The entire oil and gas production monitoring does not require manual intervention, and the oil and gas production monitoring can be completed through a monitoring model trained based on expert data, which can improve the efficiency and accuracy of oil and gas production monitoring.

[0032] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically given below. Brief Description of the Drawings

[0033] In order to more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0034] Figure 1 Shows the flowchart of an oil and gas production monitoring method provided by an embodiment of this application;

[0035] Figure 2 Shows the structural schematic diagram of an oil and gas production monitoring device provided by an embodiment of this application;

[0036] Figure 3 Shows the structural schematic diagram of an oil and gas production monitoring device provided by another embodiment of this application. Detailed Embodiments

[0037] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0038] Oil and gas production monitoring is usually manually completed by monitoring personnel. The knowledge and experience of oil and gas production monitoring mastered by the monitoring personnel are limited, which not only results in low efficiency of oil and gas production monitoring, but also makes it difficult to give accurate oil and gas production monitoring results. Once there are errors in the oil and gas production monitoring results, it will lead to situations such as formulating poor production scheduling, making wrong production decisions, and difficult timely maintenance of equipment, thus causing potential safety hazards in oil and gas production.

[0039] Through research, it is found that if monitoring models applicable to different oil and gas production monitoring services are trained based on the expert data of oil and gas monitoring experts, then when a certain oil and gas production monitoring service needs to be executed, the applicable monitoring model is selected to execute the oil and gas production monitoring service, and the oil and gas production monitoring can be completed. Based on this, without manual intervention, the oil and gas production monitoring can be completed through the monitoring model trained based on expert data, which can not only improve the efficiency of oil and gas production monitoring, but also improve the accuracy of oil and gas production monitoring.

[0040] Based on the above discovery, this embodiment specifically provides a technical solution for oil and gas production monitoring. This technical solution is applied to an oil and gas production monitoring platform, and a large language model and at least one monitoring model are deployed on the oil and gas production monitoring platform. Each monitoring model has applicable data characteristics and oil and gas production monitoring services. When an oil and gas production monitoring instruction input in natural language is obtained, the large language model is used to perform natural language analysis and processing on the oil and gas production monitoring instruction to obtain the target oil and gas production monitoring service indicated by the oil and gas production monitoring instruction; the target monitoring model applicable to the target oil and gas production monitoring service is selected; the target oil and gas production data with the target data characteristics applicable to the target monitoring model is obtained; the target monitoring model is called to execute the target oil and gas production monitoring service based on the target oil and gas production data, and the oil and gas production monitoring result for the oil and gas production monitoring instruction is obtained.

[0041] The technical solution for oil and gas production monitoring provided in this embodiment can be applied to at least one of the processes such as exploration, processing, storage, and transportation in oil and gas production, etc., to improve the efficiency and accuracy of oil and gas production monitoring in these processes.

[0042] Based on the technical solution for oil and gas production monitoring provided in this embodiment above, this embodiment specifically provides an oil and gas production monitoring method and device. The oil and gas production monitoring method and device provided in this embodiment will be specifically described below.

[0043] An embodiment of the present application provides an oil and gas production monitoring method. The oil and gas production monitoring method provided in this embodiment is applied to an oil and gas production monitoring platform. The oil and gas production monitoring platform provided in this embodiment is deployed with a large language model and at least one monitoring model to implement the automatic execution of oil and gas production monitoring based on the large language model and the monitoring model.

[0044] In some embodiments, the large language model has natural language analysis and processing capabilities. It is used to perform natural language analysis and processing on the oil and gas production monitoring instructions input in natural language to obtain the oil and gas production monitoring operations that need to be executed as expressed by the oil and gas production monitoring instructions. Specifically, the large language model is trained based on data in the vertical field of oil and gas production monitoring to improve the applicability of the large language model to oil and gas production monitoring. Exemplarily, the large language model is trained based on multiple sets of data. Each set of data includes sample oil and gas production monitoring instructions expressed in natural language and the oil and gas production monitoring operations indicated by the sample oil and gas production monitoring instructions. It should be noted that the type of the large language model can be flexibly selected based on business needs, and this embodiment does not make any limitations in this regard. Exemplarily, the large language model based on the Transformers architecture has powerful natural language analysis and processing capabilities. Based on this, the large language model selects the large language model based on the Transformers architecture.

[0045] In some embodiments, each monitoring model deployed on the oil and gas production monitoring platform has applicable data characteristics and oil and gas production monitoring operations. The monitoring model is used to execute corresponding oil and gas production monitoring operations based on the oil and gas production data with corresponding data characteristics.

[0046] The data characteristics are used to indicate the characteristics that the oil and gas production data required for the monitoring model to execute the oil and gas production monitoring operations needs to have. The data characteristics are used to indicate but are not limited to at least one of the following: at least one data source, at least one data modality. Here, the at least one data source is used to indicate the entity that generates the oil and gas production data required for the execution of the oil and gas production monitoring operations in oil and gas production. Here, the at least one data modality is used to indicate the modality of the oil and gas production data required for the execution of the oil and gas production monitoring operations. Based on the definition of the data characteristics, it is possible to implement oil and gas production monitoring based on multi-source and multi-modal oil and gas production data, so as to overcome the problem of difficult integration of multi-source and multi-modal oil and gas production data for oil and gas production monitoring in the oil and gas industry. The at least one modality may include but is not limited to at least one of text modality, image modality, video modality, and audio modality. When the data characteristics indicate two or more modalities, the monitoring model can execute the oil and gas production monitoring operations based on multi-modal oil and gas production data, and the multi-modal data complement each other during the execution of the oil and gas production monitoring operations, thereby improving the accuracy of the oil and gas production monitoring operations.

[0047] Exemplarily, the data feature indicates that the data source includes the temperature sensor and pressure sensor of Device 1, and the modality includes the text modality. Then, the oil and gas production data obtained based on the data feature includes the temperature data in the text modality generated by the temperature sensor and the pressure data in the text modality generated by the pressure sensor.

[0048] The oil and gas production monitoring service may include, but is not limited to: equipment fault prediction, equipment generation trend assessment, equipment remaining life assessment, oil and gas field remaining life assessment, seismic waveform analysis of oil and gas fields, etc. The number of monitoring models and the data features and oil and gas production monitoring services applicable to each monitoring model can all be flexibly selected based on business needs, and no specific limitations are imposed in this embodiment.

[0049] Exemplarily, the monitoring model may include, but is not limited to, at least one of the following: gradient boosting model, linear regression model, long short-term memory network model, memory-augmented neural network model.

[0050] The gradient boosting model is used to perform corresponding oil and gas production monitoring services based on the oil and gas production data with specific non-linear features. The oil and gas production data obtained based on the data features of the gradient feature model usually has non-linear features. For example, the oil and gas production data obtained based on the data features of the gradient feature model is mainly the oil and gas production data (such as temperature data, pressure data, flow data, etc.) generated by the sensors of the equipment. These oil and gas production data usually have a highly non-linear relationship, and the gradient feature model can accurately capture the complex relationships of these oil and gas production data through the gradient boosting mechanism, so as to perform oil and gas production monitoring services such as equipment fault prediction and equipment remaining life assessment based on the captured relationships. The type of the gradient boosting model can be flexibly selected based on business needs, and no limitations are imposed in this embodiment. For example, the gradient boosting model is the XGBoost model.

[0051] Exemplarily, the gradient boosting model is the XGBoost model. The data source indicated by the data features applicable to the gradient boosting model includes the temperature sensor, pressure sensor, and flow sensor of Device 1, and the data modalities include the text modality and the image modality. The oil and gas production monitoring service applicable to the gradient boosting model is equipment fault prediction. Based on this, temperature data, pressure data, and flow data in these two modalities of the text modality and the image modality (such as images when each sensor displays corresponding values) are obtained based on the data features. There is a non-linear relationship among the temperature data, pressure data, and flow data. Then, the gradient boosting model is called to perform equipment fault prediction based on the flow data, temperature data, pressure data, and flow data, which are oil and gas production data with non-linear relationships.

[0052] The linear regression model is used to perform corresponding oil and gas production monitoring services based on oil and gas production data with linear characteristics. The oil and gas production data obtained based on the data characteristics of the linear regression model usually has linear characteristics. For example, the oil and gas production data obtained based on the data characteristics of the linear regression model is mainly the oil and gas production data with a linear relationship generated by the sensors of the equipment (such as temperature data and flow data, pressure data and flow data, etc.). These oil and gas production data usually have a high degree of linear relationship, and the linear regression model can analyze the relationship between these variables with a linear relationship through linear regression, so as to perform oil and gas production monitoring services such as equipment fault prediction and equipment remaining life assessment based on the analysis results. The type of the linear regression model can be flexibly selected based on business needs, and this embodiment does not make any limitations in this regard.

[0053] Exemplarily, the data sources indicated by the data characteristics applicable to the linear regression model include the temperature sensor and flow sensor of Equipment 1, and the data modality includes the text modality. The oil and gas production monitoring service applicable to the linear regression model is equipment fault prediction. Based on this, text-modal temperature data and flow data are obtained based on the data characteristics. There is a non-linear relationship between the temperature data and the flow data. Then, the linear regression model is called to perform equipment fault prediction based on these oil and gas production data with a linear relationship, namely the temperature data and the flow data.

[0054] The long short-term memory network model is used to perform corresponding oil and gas production monitoring services based on oil and gas production data with temporal characteristics. The oil and gas production data obtained based on the data characteristics of the long short-term memory network model usually has temporal characteristics. For example, the oil and gas production data obtained based on the data characteristics of the long short-term memory network model is mainly the oil and gas production data at different time points generated by the sensors of the equipment (such as temperature data at different time points). These oil and gas production data have temporal characteristics. The health status and production trend of the equipment usually show temporal characteristics. Therefore, by processing these temporal oil and gas production data based on the long short-term memory network model, the long-term dependencies in the data can be effectively captured, the future production trend can be predicted, and potential equipment faults can be identified, so as to realize oil and gas production monitoring services such as equipment fault prediction and equipment remaining life assessment. The type of the gradient boosting model can be flexibly selected based on business needs, and this embodiment does not make any limitations in this regard. For example, the gradient boosting model is an LSTM model.

[0055] The memory-augmented neural network model is used to perform corresponding oil and gas production monitoring services based on oil and gas production data with temporal characteristics. The memory-augmented neural network model focuses on the memory and utilization of long-term data. Considering that in oil and gas production, the impacts of many factors (such as equipment aging, oilfield changes, etc.) on the oil and gas production process are long-term, traditional neural networks are difficult to effectively capture these long-term dependencies. However, the memory-augmented neural network model can make more accurate predictions and optimization decisions when facing complex and long-term changing data. Based on this, the corresponding oil and gas production monitoring services can be performed through the memory-augmented neural network model based on oil and gas production data with temporal characteristics.

[0056] Based on the oil and gas production monitoring platform in this embodiment, this embodiment specifically provides an oil and gas production monitoring method. As Figure 1 shown, the oil and gas production monitoring method provided in this embodiment may at least include the following steps 101 to 104:

[0057] 101. When an oil and gas production monitoring instruction input in natural language is obtained, perform natural language analysis and processing on the oil and gas production monitoring instruction based on a large language model to obtain the target oil and gas production monitoring service indicated by the oil and gas production monitoring instruction.

[0058] The oil and gas production monitoring method provided in this embodiment starts to execute when an oil and gas production monitoring instruction input in natural language is obtained. Based on this, the oil and gas production monitoring method provided in this embodiment may further include the step of obtaining an oil and gas production monitoring instruction input in natural language. The implementation method of this step may at least include the following three types:

[0059] The first type is to monitor whether a natural language text is input in the target input box for collecting oil and gas production monitoring instructions in the interaction interface of the oil and gas production monitoring platform; if it is monitored that a natural language text is input in the target input box for collecting oil and gas production monitoring instructions in the interaction interface of the oil and gas production monitoring platform, then obtain the input natural language text as the oil and gas production monitoring instruction input in natural language.

[0060] The oil and gas production monitoring platform has an interaction interface for interacting with users, and a target input box for collecting oil and gas production monitoring instructions is configured in the interaction interface, so that users can flexibly input oil and gas production monitoring instructions in natural language in the target input box according to their own oil and gas production monitoring needs.

[0061] When a user needs to perform a certain oil and gas production monitoring service, they can input natural language text into the target input box. The natural language text is used to express the oil and gas production monitoring service to be performed. Based on this, if it is monitored that natural language text is input into the target input box for collecting oil and gas production monitoring instructions in the interaction interface of the oil and gas production monitoring platform, the input natural language text is obtained as an oil and gas production monitoring instruction input in natural language.

[0062] Second, monitor whether the audio collector for collecting oil and gas production monitoring instructions on the oil and gas production monitoring platform has collected user audio; if it is monitored that the audio collector for collecting oil and gas production monitoring instructions on the oil and gas production monitoring platform has collected user audio, convert the user audio into natural language text, and obtain the converted natural language text as an oil and gas production monitoring instruction input in natural language.

[0063] The oil and gas production monitoring platform is deployed with an audio collector for collecting oil and gas production monitoring instructions, so that users can submit oil and gas production monitoring instructions by voice, enhancing the convenience of submitting oil and gas production monitoring instructions. If it is monitored that the audio collector for collecting oil and gas production monitoring instructions on the oil and gas production monitoring platform has collected user audio, the user audio is converted into natural language text through an audio converter, and then the converted natural language text is obtained as an oil and gas production monitoring instruction input in natural language. The audio converter is used to convert audio into natural language text, and it can be flexibly selected based on business needs, which is not limited in this embodiment.

[0064] Third, in order to improve the automation level of oil and gas production monitoring, the oil and gas production monitoring services that need to be automatically executed can be selected in advance, and a corresponding monitoring cycle and oil and gas production monitoring instructions expressed in natural language are set for each oil and gas production monitoring service, so as to automatically execute the oil and gas production monitoring services based on each oil and gas production monitoring service having a corresponding monitoring cycle and oil and gas production monitoring instructions expressed in natural language. Based on this, the following steps are respectively performed for each oil and gas production monitoring service: monitor whether it reaches the start time point of the corresponding monitoring cycle of the oil and gas production monitoring service; if it is monitored that it reaches the start time point of the monitoring cycle, obtain the oil and gas production monitoring instructions expressed in natural language corresponding to the oil and gas production monitoring service as an oil and gas production monitoring instruction input in natural language.

[0065] For the implementation methods of the above three steps of obtaining oil and gas production monitoring instructions input in natural language, at least one can be flexibly selected for use based on business needs, which is not limited in this embodiment.

[0066] In the case of obtaining an oil and gas production monitoring instruction input in natural language, it is first necessary to understand the oil and gas production monitoring operations that need to be executed as expressed by the oil and gas production monitoring instruction, so as to know which oil and gas production monitoring operations need to be carried out. Based on this, the oil and gas production monitoring instruction expressed in natural language is input into a large language model, and the large language model performs natural language analysis and processing on the oil and gas production monitoring instruction to obtain the target oil and gas production monitoring operations indicated by the oil and gas production monitoring instruction.

[0067] In some embodiments, the accuracy of the natural language analysis and processing of the large language model is limited by the byte length of the oil and gas production monitoring used as the model input. Based on this, before performing natural language analysis and processing on the oil and gas production monitoring instruction using the large language model, the oil and gas production monitoring method provided in this embodiment may further include the following steps: detecting whether the byte length of the oil and gas production monitoring instruction reaches a length threshold; if it reaches, perform any one of the following Operations 1 and 2.

[0068] The length threshold is the upper limit value at which the large language model can accurately perform natural language analysis and processing. When the byte length of the oil and gas production monitoring instruction reaches the length threshold, the accuracy of the natural language analysis and processing of the large language model decreases. Based on this, it is necessary to detect whether the byte length of the oil and gas production monitoring instruction reaches the length threshold.

[0069] If it is detected that the byte length of the oil and gas production monitoring instruction does not reach the length threshold, it means that the large language model can perform natural language analysis and processing on the oil and gas production monitoring instruction relatively accurately. Therefore, continue to execute the step of performing natural language analysis and processing on the oil and gas production monitoring instruction using the large language model to obtain the target oil and gas production monitoring operations indicated by the oil and gas production monitoring instruction.

[0070] If it is detected that the byte length of the oil and gas production monitoring instruction reaches the length threshold, it means that it is difficult to accurately perform natural language analysis and processing on the oil and gas production monitoring instruction using the large language model. Therefore, it is necessary to perform any one of the following Operations 1 and 2.

[0071] Operation 1: Submit a user feedback of the oil and gas production monitoring instruction to prompt for re-entering the oil and gas production monitoring instruction. The prompt is used to prompt that the byte length of the re-entered oil and gas production monitoring instruction is less than the length threshold, so that the user can re-enter an oil and gas production monitoring instruction with a compliant byte length based on the prompt.

[0072] Operation 2: Split the oil and gas production monitoring instruction into at least two instruction texts based on a length threshold, so as to perform the step of natural language analysis and processing of the oil and gas production monitoring instruction based on the large language model for the split instruction texts. The byte length of each split instruction text is not greater than the length threshold, so that the large language model can first perform natural language analysis and processing on each instruction text, and then synthesize the natural language analysis and processing results of each instruction text to obtain the target oil and gas production monitoring service indicated by the oil and gas production monitoring instruction. It should be noted that the specific process of splitting the oil and gas production monitoring instruction into at least two instruction texts based on the length threshold may include: determining the target positions in the oil and gas production monitoring instruction expressed in natural language that do not affect semantic expression, and splitting the oil and gas production monitoring instruction into at least two instruction texts based on the target positions.

[0073] The above Operation 1 and Operation 2 can be flexibly selected based on business needs, and this embodiment does not make any limitations in this regard.

[0074] 102. Select a target monitoring model applicable to the target oil and gas production monitoring service.

[0075] After determining the target oil and gas production monitoring service that needs to be executed, based on the corresponding relationship between the monitoring models and the oil and gas production monitoring services in the oil and gas production monitoring platform, select a target monitoring model applicable to the target oil and gas production monitoring service.

[0076] If, based on the corresponding relationship between the monitoring model and the oil and gas production monitoring service, it is determined that there is only one monitoring model corresponding to the target oil and gas production monitoring service, then select this monitoring model as the target monitoring model applicable to the target oil and gas production monitoring service.

[0077] If, based on the corresponding relationship between the monitoring model and the oil and gas production monitoring service, it is determined that there are two or more monitoring models corresponding to the target oil and gas production monitoring service, then any of the following disposal operations can be adopted: First, display the monitoring models corresponding to the target oil and gas production monitoring service for the user to select, so as to hand over the right to select the target monitoring model to the user; select the monitoring model selected by the user as the target monitoring model applicable to the target oil and gas production monitoring service. Second, select all the monitoring models corresponding to the target oil and gas production monitoring service as the target monitoring models applicable to the target oil and gas production monitoring service. Third, randomly select one monitoring model from the monitoring models corresponding to the target oil and gas production monitoring service as the target monitoring model applicable to the target oil and gas production monitoring service. The above three disposal operations can be flexibly selected based on business needs, and this embodiment does not make any limitations in this regard.

[0078] 103. Obtain target oil and gas production data with target data characteristics applicable to the target monitoring model.

[0079] After determining the target monitoring model, it is necessary to obtain target oil and gas production data with the target data characteristics applicable to the target monitoring model based on the target data characteristics applicable to the target monitoring model, so that the target monitoring model can execute the target oil and gas production monitoring service based on the target oil and gas production data.

[0080] The specific process of obtaining target oil and gas production data with the target data characteristics applicable to the target monitoring model may include the following steps: determining the target time period; obtaining target oil and gas production data with the target data characteristics applicable to the target monitoring model from the oil and gas production data generated during the target time period.

[0081] The methods for determining the target time period may include the following two: One is that if the oil and gas production monitoring instruction also indicates the acquisition time period of the oil and gas production data, then this time period is determined as the target time period. The other is that if the oil and gas production monitoring instruction does not indicate the acquisition time period, then determine the time point when the oil and gas production monitoring instruction is received, determine the time point before this time point and having a preset time interval with this time point, and determine the time period between these two time points as the target time period.

[0082] After determining the target time period, circle the target oil and gas production data acquisition range through the target time period, and obtain target oil and gas production data with the target data characteristics applicable to the target monitoring model within this circled range. Exemplarily, the data sources indicated by the data characteristics applicable to the linear regression model include the temperature sensor and flow sensor of Equipment 1, and the data modality includes the text modality. Based on this, obtain the temperature data and flow data in text modality generated by the temperature sensor and flow sensor of Equipment 1 during the target time period.

[0083] 104. Invoke the target monitoring model to execute the target oil and gas production monitoring service based on the target oil and gas production data, and obtain the oil and gas production monitoring result for the oil and gas production monitoring instruction.

[0084] The step of invoking the target monitoring model to execute the target oil and gas production monitoring service based on the target oil and gas production data and obtaining the oil and gas production monitoring result for the oil and gas production monitoring instruction is related to the number and dependency relationship of the target monitoring models. Based on this, the implementation method of this step may at least include the following three:

[0085] Method 1, if there is at least one target monitoring model applicable to the target oil and gas production monitoring service, and there is no dependency between the target monitoring models, then the specific process of invoking the target monitoring model to perform the target oil and gas production monitoring service based on the target oil and gas production data and obtaining the oil and gas production monitoring result for the oil and gas production monitoring instruction may include the following steps: Invoke each target monitoring model to perform the corresponding target oil and gas production monitoring service based on the corresponding target oil and gas production data respectively; Summarize the oil and gas production monitoring results obtained by each target monitoring model performing the corresponding target oil and gas production monitoring service to obtain the oil and gas production monitoring result for the oil and gas production monitoring instruction.

[0086] There is no dependency between the target monitoring models, indicating that each target monitoring model can independently perform the target oil and gas production monitoring service. Based on this, invoke each target monitoring model to perform the corresponding target oil and gas production monitoring service based on the corresponding target oil and gas production data respectively, and then summarize the oil and gas production monitoring results obtained by each target monitoring model performing the corresponding target oil and gas production monitoring service to obtain the oil and gas production monitoring result for the oil and gas production monitoring instruction, so as to improve the accuracy and comprehensiveness of the oil and gas production monitoring result of the oil and gas production monitoring instruction.

[0087] Exemplarily, the target monitoring models include a gradient boosting model and a linear regression model. Then, the gradient boosting model performs equipment fault prediction based on the temperature data and pressure data within the target time period, and the linear regression model performs equipment fault prediction based on the temperature data and flow data within the target time period. When summarizing, if the equipment fault prediction results of the two are the same, then determine any one of the equipment fault prediction results as the oil and gas production monitoring result for the oil and gas production monitoring instruction. If the equipment fault prediction results of the two are different, then determine the oil and gas production monitoring result with a relatively heavier predicted fault degree in the fault prediction results as the oil and gas production monitoring result for the oil and gas production monitoring instruction, or determine both of the two fault prediction results as the oil and gas production monitoring result for the oil and gas production monitoring instruction, so as to provide a more comprehensive basis for subsequent equipment maintenance.

[0088] Method 2. If there are at least two target monitoring models applicable to the target oil and gas production monitoring service, and the target monitoring models have corresponding first call orders, and the call of the target monitoring model located in the latter position in the first call order depends on the target monitoring model in the previous position, then the specific process of calling the target monitoring model to execute the target oil and gas production monitoring service based on the target oil and gas production data and obtaining the oil and gas production monitoring result for the oil and gas production monitoring instruction may include the following steps: Call the target monitoring model located in the first position in the first call order to execute the corresponding target oil and gas production monitoring service based on the corresponding target oil and gas production data; For each target monitoring model located in a non-first position, execute in accordance with the first call order: Call the target monitoring model to execute the corresponding target oil and gas production monitoring service based on the corresponding target oil and gas production data and the oil and gas production monitoring result obtained by the previous target monitoring model; Obtain the oil and gas production monitoring result obtained by the target monitoring model located in the last position in the first call order as the oil and gas production monitoring result for the oil and gas production monitoring instruction.

[0089] Exemplarily, the target monitoring models include Monitoring Model 1, Monitoring Model 2, and Monitoring Model 3. In the first call order, Monitoring Model 1 is before Monitoring Model 2, and Monitoring Model 2 is before Monitoring Model 3. Based on this, first call Monitoring Model 1 located in the first position to execute the corresponding target oil and gas production monitoring service based on the corresponding target oil and gas production data. Then, based on the first call order, call Monitoring Model 2 to execute the corresponding target oil and gas production monitoring service based on its corresponding target oil and gas production data and the oil and gas production monitoring result obtained by Monitoring Model 1. Finally, call Monitoring Model 3 to execute the corresponding target oil and gas production monitoring service based on its corresponding target oil and gas production data and the oil and gas production monitoring result obtained by Monitoring Model 2. Obtain the oil and gas production monitoring result obtained by Monitoring Model 3 as the oil and gas production monitoring result for the oil and gas production monitoring instruction. In this way, the execution of the target oil and gas production monitoring service can be achieved by integrating multiple monitoring models, thereby improving the accuracy of the oil and gas production monitoring result for the oil and gas production monitoring instruction.

[0090] Method 3: If there are at least three target monitoring models applicable to the target oil and gas production monitoring service, and at least two first monitoring models included in the target monitoring models have corresponding second call sequences, the target monitoring models in the second call sequence that are located later depend on the target monitoring models in the previous position, and at least one second monitoring model included in the target monitoring models, and the second monitoring model has no dependence on other monitoring models. Then, the specific process of calling the target monitoring model to perform the target oil and gas production monitoring service based on the target oil and gas production data and obtaining the oil and gas production monitoring result for the oil and gas production monitoring instruction may include: calling the first monitoring model in the first position of the second call sequence to perform the corresponding oil and gas production monitoring service based on the corresponding target oil and gas production data; sequentially performing for each non-first-position first monitoring model in the second call sequence: calling the first monitoring model to perform the corresponding target oil and gas production monitoring service based on the corresponding target oil and gas production data and the oil and gas production monitoring result obtained by the target monitoring model in the previous position; calling each second monitoring model to perform the corresponding target oil and gas production monitoring service based on the corresponding target oil and gas production data; summarizing the oil and gas production monitoring result obtained by the first monitoring model in the last position of the second call sequence and the oil and gas production monitoring results obtained by each second monitoring model to obtain the oil and gas production monitoring result for the oil and gas production monitoring instruction.

[0091] In this method, there are both target monitoring models that can independently perform the target oil and gas production monitoring service and target monitoring models that are interdependent. Based on this, when performing the target oil and gas production monitoring service, the target oil and gas production monitoring service needs to be performed separately for these two types of target monitoring models. The specific process can refer to Method 1 and Method 2 above respectively, and will not be elaborated here.

[0092] When summarizing the oil and gas production monitoring result obtained by the first monitoring model in the last position of the second call sequence and the oil and gas production monitoring results obtained by each second monitoring model, if the oil and gas production monitoring result obtained by the first monitoring model in the last position of the second call sequence and the oil and gas production monitoring results obtained by each second monitoring model are all the same, then any one of the oil and gas production monitoring results is determined as the oil and gas production monitoring result for the oil and gas production monitoring instruction. If there are differences between the oil and gas production monitoring result obtained by the first monitoring model in the last position of the second call sequence and the oil and gas production monitoring results obtained by each second monitoring model, then the oil and gas production monitoring result with the most serious relative harm in the fault prediction results is determined as the oil and gas production monitoring result for the oil and gas production monitoring instruction, or both fault prediction results are determined as the oil and gas production monitoring result for the oil and gas production monitoring instruction, so as to provide a more comprehensive basis for subsequent equipment maintenance and other operations.

[0093] The above three methods of calling the target monitoring model to execute the target oil and gas production monitoring business based on the target oil and gas production data to obtain the oil and gas production monitoring results for the oil and gas production monitoring instructions can be flexibly used based on the specific circumstances of the target monitoring model, and this embodiment does not limit this.

[0094] After obtaining the oil and gas production monitoring results for the oil and gas production monitoring instructions, a corresponding oil and gas production monitoring report can be generated based on the oil and gas production monitoring results, so that oil and gas production business personnel can perform corresponding production scheduling adjustments, equipment maintenance and other operations based on the oil and gas production monitoring, so as to improve the safety and efficiency of oil and gas production.

[0095] The oil and gas production monitoring method provided in the embodiment of the present application, when obtaining the oil and gas production monitoring instruction input in natural language, first performs natural language analysis and processing on the oil and gas production monitoring instruction based on the large language model to obtain the target oil and gas production monitoring business indicated by the oil and gas production monitoring instruction. Then, a target monitoring model suitable for the target oil and gas production monitoring business is selected, and target oil and gas production data with target data features suitable for the target monitoring model is obtained. Finally, the target monitoring model is called to execute the target oil and gas production monitoring business based on the target oil and gas production data to obtain the oil and gas production monitoring result for the oil and gas production monitoring instruction. It can be seen that in the solution provided in the embodiment of the present application, when it is necessary to execute a certain oil and gas production monitoring business, an applicable monitoring model is selected to execute the oil and gas production monitoring production business, so that oil and gas production monitoring can be completed. The entire oil and gas production monitoring does not require manual intervention, and the oil and gas production monitoring can be completed by using the monitoring model obtained by training based on expert data, which can improve the efficiency and accuracy of oil and gas production monitoring.

[0096] In some embodiments of the present application, in order to ensure the safety of oil and gas production, the oil and gas production monitoring method provided in this embodiment may also include at least the following two safety protection schemes:

[0097] Solution 1. In some embodiments, in order to prevent the oil and gas production monitoring service from being maliciously executed, after the oil and gas production monitoring instruction input in natural language is obtained in the above step 101, the oil and gas production monitoring method provided in this embodiment may also include the following steps: determining the user who initiates the oil and gas production monitoring instruction; and determining whether the user has the authority to perform the target oil and gas production monitoring service.

[0098] If it is determined that the user does not have the authority to perform the target oil and gas production monitoring business, it means that the user has exceeded the authority, so an unauthorized alarm is issued to indicate that the target oil and gas production monitoring business cannot be executed, and the execution of subsequent steps is terminated.

[0099] If it is determined that the user has the permission to perform the target oil and gas production monitoring service, it indicates that the user has the qualification to execute the target oil and gas production monitoring service. Therefore, in order to ensure the timely execution of the target oil and gas production monitoring service, continue to execute the step of performing natural language analysis and processing on the oil and gas production monitoring instruction based on the large language model in the above step 101 to obtain the target oil and gas production monitoring service indicated by the oil and gas production monitoring instruction.

[0100] Solution 2. In some embodiments, considering that the leakage of oil and gas production data and the attack during the execution of the oil and gas production monitoring service will both cause harm to oil and gas production. Based on this, the oil and gas production monitoring method provided in this embodiment may further include the following steps: During the process of obtaining the target oil and gas production data in the above step 103 and / or during the process of calling the target monitoring model in the above step 103, monitor whether it is under malicious attack. If so, adopt the corresponding disposal operation for the malicious attack received to dispose of the malicious attack.

[0101] If attacked during the process of obtaining the target oil and gas production data, the target oil and gas production data may be leaked or may be maliciously tampered with. Based on this, when a malicious attack is monitored, adopt the corresponding disposal operation for the malicious attack received to dispose of the malicious attack to reduce the harm caused by the attack. Further, the target oil and gas production data may be pre-encrypted, so that even if attacked, the possibility of leakage and malicious tampering of the target oil and gas production data is relatively low.

[0102] If attacked during the process of calling the target monitoring model, the execution of the target oil and gas production monitoring service by calling the target monitoring model may be tampered with. Based on this, in order to avoid these situations, when a malicious attack is monitored, adopt the corresponding disposal operation for the malicious attack received to dispose of the malicious attack to reduce the harm caused by the attack.

[0103] At least one of the above two solutions can be flexibly selected based on business needs, and this embodiment does not make any limitations in this regard. The above two solutions adopt multi-level security protection measures such as data encryption, behavior monitoring, and permission control to ensure the security and privacy protection of oil and gas production data acquisition and the execution of oil and gas production monitoring services. At the same time, establish a strict security management system and emergency response mechanism to be able to respond and handle quickly in the face of security threats such as malicious attacks. By strengthening security measures, the response ability in the face of security threats is guaranteed, and the reliability and stability of the oil and gas production monitoring platform are improved.

[0104] In some embodiments of the present application, each monitoring model is configured with a corresponding monitoring agent service in the oil and gas production monitoring platform. The monitoring agent service is used to call the corresponding monitoring model. Then, the oil and gas production monitoring method provided in this embodiment may further include the following steps: determining the target monitoring agent service corresponding to the target monitoring model; triggering the target monitoring agent service so that the target monitoring agent service executes step 104 above to call the target monitoring model to perform the target oil and gas production monitoring service based on the target oil and gas production data.

[0105] The monitoring model can be deployed in the monitoring agent service. Adopting a microservices architecture, the monitoring agent service is encapsulated as an independent microservice, which is not only convenient for the deployment of the monitoring model but also for the invocation of the monitoring model. After determining the target monitoring agent service corresponding to the target monitoring model, the target monitoring agent service can be triggered so that the target monitoring agent service executes the step of calling the target monitoring model to perform the target oil and gas production monitoring service based on the target oil and gas production data, thereby realizing the rapid execution of the target oil and gas production monitoring service.

[0106] In some embodiments of the present application, after obtaining the oil and gas production monitoring result for the oil and gas production monitoring instruction in step 104 above, if the oil and gas production monitoring result indicates that the oil and gas production is abnormal, then the oil and gas production abnormality needs to be disposed of. Based on this, the oil and gas production monitoring method provided in this embodiment may at least include the following three disposal steps:

[0107] First, if the oil and gas production monitoring result indicates that the oil and gas production is abnormal and there is a target device for eliminating the oil and gas production abnormality at the oil and gas production site, then determine the device operation data required for the target device to eliminate the oil and gas production abnormality based on the oil and gas production monitoring result, and send the device operation data to the target device so that the target device eliminates the oil and gas production abnormality based on the device operation data.

[0108] If the oil and gas production monitoring result indicates that the oil and gas production is abnormal and there is a target device for eliminating the oil and gas production abnormality at the oil and gas production site, it means that the oil and gas production abnormality can be eliminated through the target device. Based on this, in order to improve the automation degree of eliminating the oil and gas production abnormality, obtain the data in the oil and gas production monitoring result for indicating the oil and gas production abnormality, and then determine the device operation data required for eliminating the oil and gas production abnormality based on this data. Send the device operation data to the target device so that the target device eliminates the oil and gas production abnormality based on the device operation data.

[0109] Exemplarily, if the oil and gas production monitoring result indicates that the abnormal oil and gas production is excessive equipment pressure, and there is a target device "pressure relief valve" at the oil and gas production site for eliminating the abnormal oil and gas production, then based on the pressure value indicating excessive pressure in the oil and gas production monitoring result, determine the valve opening degree required for the pressure relief valve to reduce the pressure to the normal operating pressure of the equipment based on the pressure value, and send the valve opening degree to the pressure relief valve, so that the pressure relief valve is opened based on the valve opening degree, and the equipment pressure is reduced to the normal operating pressure after opening.

[0110] Second, if the oil and gas production monitoring result indicates abnormal oil and gas production, an abnormal alarm matching the abnormal type of the abnormal oil and gas production is issued, so as to prompt the user of what type of abnormal oil and gas production exists through the abnormal alarm, so that the user can perform targeted abnormal elimination operations on the abnormal oil and gas production based on the abnormal alarm.

[0111] Third, if the oil and gas production monitoring result indicates abnormal oil and gas production, oil and gas production monitoring result data for assisting in eliminating the abnormal oil and gas production is generated based on the oil and gas production monitoring result. The oil and gas production monitoring result data may include, but is not limited to: the oil and gas production data used to obtain the oil and gas production monitoring result, the monitoring process data based on the oil and gas production monitoring result, and the oil and gas production monitoring result. So that the user can refer to the oil and gas production monitoring result data to eliminate the abnormal oil and gas production.

[0112] At least one of the above three disposal steps can be flexibly selected based on business needs for use, and this embodiment does not limit this.

[0113] In some embodiments of the present application, in order to continuously improve the effect of the monitoring model in performing the oil and gas production monitoring service, the oil and gas production monitoring method provided in this embodiment may further include the following steps: score the oil and gas production monitoring results of each oil and gas production monitoring instruction obtained in the current iteration cycle; if there is a target oil and gas production monitoring result whose score does not reach the score threshold, perform iterative training on the monitoring model called to obtain the target oil and gas production monitoring result.

[0114] Considering that the production conditions, equipment status, and environmental changes in the oil and gas production process are highly dynamic, a reward feedback mechanism is introduced in this embodiment to continuously optimize each monitoring model. Through the reward feedback mechanism, continuously obtain feedback information from the environment, that is, the score of the oil and gas production monitoring result, and when there is a target oil and gas production monitoring result whose score does not reach the score threshold, perform iterative training on the monitoring model called to obtain the target oil and gas production monitoring result, thereby forming a closed-loop optimization process. This mechanism can respond to the changes in the production site in real time and achieve dynamic adjustment and continuous optimization of the monitoring model.

[0115] In this embodiment, a reward feedback model is used to score the oil and gas production monitoring results of each oil and gas production monitoring instruction obtained in the current iteration cycle. If there is a target oil and gas production monitoring result whose score does not reach the score threshold, it indicates that the monitoring effect of the monitoring model called to obtain the target oil and gas production monitoring result is not good. Therefore, these monitoring models need to be iteratively trained. During iterative training, an iteratively trained data set annotated by experts in the oil and gas production field is obtained, and the monitoring model is iteratively trained.

[0116] In some embodiments of the present application, the oil and gas production monitoring platform provided in this embodiment adopts any one of the following deployment methods: cloud deployment, local deployment, and edge deployment, so that the oil and gas production monitoring platform can meet the usage requirements. Exemplarily, considering that in the complex and changeable oil and gas field operation environment, edge deployment can achieve local processing and real-time analysis of data, ensuring the stable operation of the oil and gas production monitoring platform in a harsh environment. Therefore, the edge deployment method can be used to deploy the oil and gas production monitoring platform.

[0117] In some embodiments, regardless of the deployment method adopted, the large language model and at least one monitoring model in the oil and gas production monitoring platform provided in this embodiment can both adopt a microservices architecture, and the large language model and each monitoring model are respectively encapsulated as corresponding microservices. In this way, not only can the deployment and invocation of microservices be realized through the middleware architecture, but also the scalability and flexibility of the oil and gas production monitoring platform are improved (for example, expanding new monitoring models), which is convenient for the subsequent maintenance and upgrade of the oil and gas production monitoring platform.

[0118] In some embodiments of the present application, the large language model and the monitoring model are respectively trained using data in the vertical field of oil and gas production. When training the large language model and the monitoring model, first, data in each link of oil and gas production is collected. This data can include, but is not limited to, sensor data, historical operation data, geological data, optical fiber monitoring data, seismic data, etc. Considering that these data sources are diverse, the formats are not unified, and the quality is uneven. Based on this, data cleaning and preprocessing technologies are adopted to ensure the high quality and consistency of the data. Specifically, a data standardization process is introduced to uniformly format and standardize data from different sources to ensure the quality, security, and compliance of the data. The data after cleaning and standardization is imported into the training data set. The training data set can adopt a distributed storage architecture to support the storage and management of massive data, so as to facilitate subsequent data acquisition for model training. In practical applications, data can be classified, annotated, and indexed through a metadata management tool to facilitate subsequent retrieval and use of the data in the data set.

[0119] In some embodiments, when training a large language model, multiple sets of data are obtained from a training dataset. Each set of data includes sample oil and gas production monitoring instructions expressed in natural language and the oil and gas production monitoring operations indicated by the sample oil and gas production monitoring instructions. The large language model is trained based on this data. The large language model thus trained can perform natural language analysis and processing on the oil and gas production monitoring instructions to obtain the target oil and gas production monitoring operations indicated by the oil and gas production monitoring instructions.

[0120] In some embodiments, when training a monitoring model, first, applicable data features and oil and gas production monitoring operations are determined for the monitoring model. Then, multiple sets of data are obtained from the training dataset. Each set of data includes oil and gas production data corresponding to the data features and the oil and gas production monitoring results after performing the oil and gas production monitoring operations based on the oil and gas production monitoring operations. In this way, the trained monitoring model can perform the oil and gas production monitoring operations based on the oil and gas production monitoring operations with the data features and accurately obtain the corresponding oil and gas production monitoring results.

[0121] In some embodiments of the present application, the oil and gas production monitoring method provided in this embodiment has at least the following effects: First, through the efficient application of the large language model and the monitoring model, oil and gas production monitoring can be automated without manual intervention. This not only reduces labor costs and improves monitoring efficiency but also avoids errors in manual monitoring and improves monitoring accuracy. Second, both the large language model and the monitoring model can be designed in a modular manner such as microservices. This reduces the complexity of maintaining and upgrading the subsequent oil and gas production monitoring platform and can save operating costs. Third, due to the improvement of monitoring efficiency, real-time monitoring and early warning functions can be realized to a certain extent, enabling potential risks in the oil and gas production process to be discovered and processed in a timely manner, thus greatly enhancing the safety of the oil and gas production process and providing double protection for equipment operation and personnel safety. Fourth, both the large language model and the monitoring model can be designed in a modular manner such as microservices, and the oil and gas production monitoring platform can select a deployment method that meets business requirements. In this way, the oil and gas production monitoring platform has extremely high flexibility and scalability and can flexibly adjust functions according to the enterprise scale and scenario requirements to adapt to different application environments and business needs. Fifth, by inputting oil and gas production monitoring instructions in natural language by monitoring personnel to the oil and gas production monitoring platform, oil and gas production monitoring can be achieved, which simplifies the oil and gas production monitoring operation process and reduces the requirements for the professional skills of monitoring personnel. Sixth, the large language model used in this embodiment, as the core of the oil and gas production monitoring platform, can process structured and unstructured data (such as text data, equipment logs, etc.) and interact with users through natural language oil and gas production monitoring instructions, thereby enhancing the intelligence and user experience of the oil and gas production monitoring platform.

[0122] Furthermore, an embodiment of the present application also provides an oil and gas production monitoring device, which is applied to an oil and gas production monitoring platform. The oil and gas production monitoring platform is deployed with a large language model and at least one monitoring model, and each monitoring model has applicable data characteristics and oil and gas production monitoring services, such as Figure 2 As shown, the oil and gas production monitoring device provided in this embodiment may at least include:

[0123] A processing module 21, configured to, when an oil and gas production monitoring instruction input in natural language is obtained, perform natural language analysis and processing on the oil and gas production monitoring instruction based on the large language model to obtain the target oil and gas production monitoring service indicated by the oil and gas production monitoring instruction;

[0124] A selection module 22, configured to select a target monitoring model applicable to the target oil and gas production monitoring service;

[0125] An acquisition module 23, configured to acquire target oil and gas production data having target data characteristics applicable to the target monitoring model;

[0126] A monitoring module 24, configured to call the target monitoring model to execute the target oil and gas production monitoring service based on the target oil and gas production data, and obtain an oil and gas production monitoring result for the oil and gas production monitoring instruction.

[0127] For the oil and gas production monitoring device provided in the embodiment of the present application, when an oil and gas production monitoring instruction input in natural language is obtained, first, natural language analysis and processing are performed on the oil and gas production monitoring instruction based on the large language model to obtain the target oil and gas production monitoring service indicated by the oil and gas production monitoring instruction. Then, a target monitoring model applicable to the target oil and gas production monitoring service is selected, and target oil and gas production data having target data characteristics applicable to the target monitoring model is acquired. Finally, the target monitoring model is called to execute the target oil and gas production monitoring service based on the target oil and gas production data, and an oil and gas production monitoring result for the oil and gas production monitoring instruction is obtained. It can be seen that in the solution provided by the embodiment of the present application, when a certain oil and gas production monitoring service needs to be executed, a suitable monitoring model is selected to execute the oil and gas production monitoring service, and the oil and gas production monitoring can be completed. The entire oil and gas production monitoring does not require manual intervention, and the oil and gas production monitoring can be completed through the monitoring model trained based on expert data, which can improve the efficiency and accuracy of oil and gas production monitoring.

[0128] In some embodiments of the present application, such as Figure 3 As shown, if the target monitoring model applicable to the target oil and gas production monitoring service is at least one and there is no dependency between the target monitoring models, then the monitoring module 24 includes:

[0129] The first calling unit 241 is configured to call each target monitoring model to perform corresponding target oil and gas production monitoring services based on corresponding target oil and gas production data respectively;

[0130] The first determining unit 242 is configured to summarize the oil and gas production monitoring results obtained by each target monitoring model performing corresponding oil and gas production monitoring services, so as to obtain the oil and gas production monitoring result for the oil and gas production monitoring instruction.

[0131] In some embodiments of the present application, as Figure 3 shown, if there are at least two target monitoring models applicable to the target oil and gas production monitoring service, and the target monitoring models have corresponding first calling orders, and the calling of the target monitoring model in the latter position in the first calling order depends on the target monitoring model in the previous position, then, the monitoring module 24 includes:

[0132] The second calling unit 243 is configured to call the target monitoring model in the first position in the first calling order to perform corresponding target oil and gas production monitoring services based on corresponding target oil and gas production data;

[0133] The third calling unit 244 is configured to perform the following operations on each non-first target monitoring model in accordance with the first calling order: call the target monitoring model to perform corresponding target oil and gas production monitoring services based on corresponding target oil and gas production data and the oil and gas production monitoring results obtained by the previous target monitoring model;

[0134] The second determining unit 245 is configured to obtain the oil and gas production monitoring result obtained by the target monitoring model in the last position in the first calling order as the oil and gas production monitoring result for the oil and gas production monitoring instruction.

[0135] In some embodiments of the present application, as Figure 3 shown, if there are at least three target monitoring models applicable to the target oil and gas production monitoring service, and at least two first monitoring models included in the target monitoring models have corresponding second calling orders, the calling of the target monitoring model in the latter position in the second calling order depends on the target monitoring model in the previous position, and at least one second monitoring model included in the target monitoring models, and the second monitoring model has no dependence on other monitoring models, then, the monitoring module 24 includes:

[0136] The fourth calling unit 246 is configured to call the first monitoring model in the first position in the second calling order to perform corresponding target oil and gas production monitoring services based on corresponding target oil and gas production data;

[0137] The fifth calling unit 247 is configured to respectively execute, for each first monitoring model that is not in the first position, according to the second calling order: calling the first monitoring model to execute the corresponding target oil and gas production monitoring service based on the corresponding target oil and gas production data and the target monitoring model of the previous position; calling each second monitoring model to execute the corresponding target oil and gas production monitoring service based on the corresponding target oil and gas production data;

[0138] The third determination unit 248 is configured to summarize the oil and gas production monitoring results obtained by the first monitoring model at the last position in the second calling order and the oil and gas production monitoring results obtained by each second monitoring model, so as to obtain the oil and gas production monitoring result for the oil and gas production monitoring instruction.

[0139] In some embodiments of the present application, as Figure 3 shown, each monitoring model is configured with a corresponding monitoring agent service in the oil and gas production monitoring platform, and the monitoring agent service is used to call the corresponding monitoring model. Then, the oil and gas production monitoring device provided in this embodiment may further include:

[0140] The trigger module 25 is configured to determine the target monitoring agent service corresponding to the target monitoring model; trigger the target monitoring agent service, so that the target monitoring agent service controls the monitoring module 24 to execute the step of calling the target monitoring model to execute the target oil and gas production monitoring service based on the target oil and gas production data.

[0141] In some embodiments of the present application, as Figure 3 shown, the oil and gas production monitoring device provided in this embodiment may further include:

[0142] The first detection module 26 is configured to detect whether the byte length of the oil and gas production monitoring instruction reaches a length threshold; if it reaches, perform any of the following operations: trigger the prompt module 27 to feedback a prompt for re-entering the oil and gas production monitoring instruction to the user who submits the oil and gas production monitoring instruction, and the prompt is used to prompt that the byte length of the re-entered oil and gas production monitoring instruction is less than the length threshold; or, trigger the splitting module 28 to split the oil and gas production monitoring instruction into at least two instruction texts based on the length threshold, so as to perform the step of natural language analysis and processing of the oil and gas production monitoring instruction based on the large language model by the processing module 21 based on the split instruction texts.

[0143] In some embodiments of the present application, as Figure 3 shown, the oil and gas production monitoring device provided in this embodiment may further include:

[0144] A disposal module 29, configured to, after a monitoring module 24 obtains an oil and gas production monitoring result for the oil and gas production monitoring instruction, if the oil and gas production monitoring result indicates an abnormal oil and gas production and there is a target device at the oil and gas production site for eliminating the abnormal oil and gas production, determine device operation data required for the target device to eliminate the abnormal oil and gas production based on the oil and gas production monitoring result, and send the device operation data to the target device, so that the target device eliminates the abnormal oil and gas production based on the device operation data.

[0145] In some embodiments of the present application, as Figure 3 shown, the oil and gas production monitoring device provided in this embodiment may further include:

[0146] An alarm module 30, configured to, after the monitoring module 24 obtains an oil and gas production monitoring result for the oil and gas production monitoring instruction, if the oil and gas production monitoring result indicates an abnormal oil and gas production, issue an abnormal alarm matching the abnormal type of the abnormal oil and gas production.

[0147] In some embodiments of the present application, as Figure 3 shown, the oil and gas production monitoring device provided in this embodiment may further include:

[0148] A generation module 31, configured to, after the monitoring module 24 obtains an oil and gas production monitoring result for the oil and gas production monitoring instruction, if the oil and gas production monitoring result indicates an abnormal oil and gas production, generate oil and gas production monitoring result data for assisting in eliminating the abnormal oil and gas production based on the oil and gas production monitoring result.

[0149] In some embodiments of the present application, as Figure 3 shown, the oil and gas production monitoring device provided in this embodiment may further include:

[0150] A first monitoring module 32, configured to, if it monitors that a natural language text is input in a target input box for collecting an oil and gas production monitoring instruction in an interaction interface of the oil and gas production monitoring platform, obtain the input natural language text as an oil and gas production monitoring instruction input in natural language.

[0151] In some embodiments of the present application, as Figure 3 shown, the oil and gas production monitoring device provided in this embodiment may further include:

[0152] A second monitoring module 33, configured to, if it monitors that a user audio is collected by an audio collector for collecting an oil and gas production monitoring instruction of the oil and gas production monitoring platform, convert the user audio into a natural language text, and obtain the converted natural language text as an oil and gas production monitoring instruction input in natural language.

[0153] In some embodiments of the present application, as Figure 3 shown, each oil and gas production monitoring service has a corresponding monitoring cycle and an oil and gas production monitoring instruction expressed in natural language. Then, the oil and gas production monitoring device provided in this embodiment may further include:

[0154] A third monitoring module 34, configured to perform, for each of the oil and gas production monitoring services: if it is monitored that the start time point of the monitoring cycle corresponding to the oil and gas production monitoring service is reached, then obtain the oil and gas production monitoring instruction expressed in natural language corresponding to the oil and gas production monitoring service as an oil and gas production monitoring instruction input in natural language.

[0155] In some embodiments of the present application, the data feature is used to indicate at least one of the following: at least one data source, at least one data modality. The at least one data source is used to indicate the entity that generates the oil and gas production data required for the execution of the oil and gas production monitoring service during oil and gas production, and the at least one data modality is used to indicate the modality of the oil and gas production data required for the execution of the oil and gas production monitoring service.

[0156] In some embodiments of the present application, the monitoring model includes at least one of the following: a gradient boosting model, a linear regression model, a long short-term memory network model, and a memory-augmented neural network model; the gradient boosting model is used to perform corresponding oil and gas production monitoring services based on the oil and gas production data with specific non-linear features; the linear regression model is used to perform corresponding oil and gas production monitoring services based on the oil and gas production data with linear features; the long short-term memory network model is used to perform corresponding oil and gas production monitoring services based on the oil and gas production data with temporal features; the memory-augmented neural network model is used to perform corresponding oil and gas production monitoring services based on the oil and gas production data with temporal features.

[0157] In some embodiments of the present application, as Figure 3 shown, the oil and gas production monitoring device provided in this embodiment may further include:

[0158] An iteration module 35, configured to score the oil and gas production monitoring results of each oil and gas production monitoring instruction obtained within the current iteration cycle; if there is a target oil and gas production monitoring result whose score does not reach the score threshold, then perform iterative training on the monitoring model called to obtain the target oil and gas production monitoring result;

[0159] In some embodiments of the present application, as Figure 3 shown, the oil and gas production monitoring device provided in this embodiment may further include:

[0160] A judgment module 36 is configured to determine the user who initiates the oil and gas production monitoring instruction; if it is determined that the user does not have the permission to perform the target oil and gas production monitoring service, an over-authorization warning for indicating that the target oil and gas production monitoring service cannot be executed is issued.

[0161] In some embodiments of the present application, as Figure 3 shown, the oil and gas production monitoring device provided in this embodiment may further include:

[0162] A second detection module 37 is configured to monitor whether a malicious attack occurs during the process that the acquisition module 23 acquires the target oil and gas production data and / or the monitoring module 24 invokes the target monitoring model. If a malicious attack occurs, a disposal operation corresponding to the malicious attack is adopted to dispose of the malicious attack.

[0163] In some embodiments of the present application, the oil and gas production monitoring platform in this embodiment adopts any one of the following deployment methods: cloud deployment, local deployment, and edge deployment.

[0164] In the oil and gas production monitoring device provided in the embodiments of the present application, the detailed explanations adopted during the operation of each functional module can refer to the corresponding explanations in the above embodiments of the oil and gas production monitoring method, which will not be elaborated here.

[0165] Further, an embodiment of the present application further provides a computer-readable storage medium. The storage medium includes a stored program. When the program runs, it controls the device where the storage medium is located to execute the above-mentioned oil and gas production monitoring method.

[0166] Further, an embodiment of the present application further provides an electronic device. The electronic device includes: a memory for storing a program; a processor coupled to the memory for running the program to execute the above-mentioned oil and gas production monitoring method.

[0167] In the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0168] It can be understood that the relevant features in the above methods and devices can be referred to each other. In addition, the "first", "second", etc. in the above embodiments are used to distinguish each embodiment, and do not represent the advantages and disadvantages of each embodiment.

[0169] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, which will not be elaborated here.

[0170] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. A variety of general-purpose systems can also be used in conjunction with the teachings based herein. The structure required to construct such systems will be apparent from the above description. Additionally, the present application is not directed to any particular programming language. It should be understood that the content of the present application described herein can be implemented using a variety of programming languages, and the description of a particular language above is for the purpose of disclosing the preferred embodiments of the present application.

[0171] In addition, the memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0172] Those skilled in the art will appreciate that the embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0173] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in Figure 1 one or more of the flows or multiple flows and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0174] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in Figure 1 one or more of the flows or multiple flows and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0175] These computer program instructions can also be loaded onto a computer or other programmable data splicing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 steps of the functions specified in one block or multiple blocks. In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0176] The memory may include non-permanent memory in the computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

[0177] Computer-readable media includes permanent and non-permanent, removable and non-removable media and can be implemented by any method or technology for information storage. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0178] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising the element.

[0179] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0180] The above are only the embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for monitoring oil and gas production, characterized in that: Applied to an oil and gas production monitoring platform, the oil and gas production monitoring platform is deployed with a large language model and at least one monitoring model, each monitoring model has applicable data features and oil and gas production monitoring services, the method includes: When an oil and gas production monitoring instruction input in natural language is obtained, natural language analysis and processing is performed on the oil and gas production monitoring instruction based on the large language model to obtain a target oil and gas production monitoring business indicated by the oil and gas production monitoring instruction; Selecting a target monitoring model suitable for the target oil and gas production monitoring business; Acquire target oil and gas production data having target data features applicable to the target monitoring model; The target monitoring model is called to execute the target oil and gas production monitoring service based on the target oil and gas production data, and the oil and gas production monitoring result for the oil and gas production monitoring instruction is obtained.

2. The method according to claim 1, characterized in that If there is at least one target monitoring model applicable to the target oil and gas production monitoring service, and there is no dependency between the target monitoring models, then the target monitoring model is called to execute the target oil and gas production monitoring service based on the target oil and gas production data to obtain the oil and gas production monitoring result for the oil and gas production monitoring instruction, including: calling each target monitoring model to execute the corresponding target oil and gas production monitoring service based on the corresponding target oil and gas production data; summarizing the oil and gas production monitoring results obtained by each target monitoring model executing the corresponding target oil and gas production monitoring service to obtain the oil and gas production monitoring result for the oil and gas production monitoring instruction; or, If there are at least two target monitoring models applicable to the target oil and gas production monitoring service, and the target monitoring models have a corresponding first calling order, and the calling of the target monitoring model at the latter position in the first calling order depends on the calling of the target monitoring model at the previous position, then, calling the target monitoring model to execute the target oil and gas production monitoring service based on the target oil and gas production data to obtain the oil and gas production monitoring result for the oil and gas production monitoring instruction includes: calling the target monitoring model at the first position in the first calling order to execute the corresponding target oil and gas production monitoring service based on the corresponding target oil and gas production data; executing for each target monitoring model not at the first position respectively according to the first calling order: calling the target monitoring model to execute the corresponding target oil and gas production monitoring service based on the corresponding target oil and gas production data and the oil and gas production monitoring result obtained by the previous target monitoring model; obtaining the oil and gas production monitoring result obtained by the target monitoring model at the last position in the first calling order as the oil and gas production monitoring result for the oil and gas production monitoring instruction; or, If there are at least three target monitoring models applicable to the target oil and gas production monitoring business, and at least two first monitoring models included in the target monitoring models have a corresponding second calling order, the calling of the target monitoring model at the latter position in the second calling order depends on the calling of the target monitoring model at the former position, and at least one second monitoring model included in the target monitoring model, and the second monitoring model has no dependency on other monitoring models, then, calling the target monitoring model to execute the target oil and gas production monitoring business based on the target oil and gas production data to obtain the oil and gas production monitoring result for the oil and gas production monitoring instruction includes: calling the first monitoring model at the first position in the second calling order based on the corresponding target oil and gas The target oil and gas production monitoring service is executed according to the production data; each first monitoring model that is not in the first position is executed separately according to the second calling order: the first monitoring model is called to execute the corresponding target oil and gas production monitoring service based on the oil and gas production monitoring result obtained based on the corresponding target oil and gas production data and the previous target monitoring model; each second monitoring model is called to execute the corresponding target oil and gas production monitoring service based on the corresponding target oil and gas production data; the oil and gas production monitoring result obtained by the last first monitoring model in the second calling order and the oil and gas production monitoring result obtained by each second monitoring model are summarized to obtain the oil and gas production monitoring result for the oil and gas production monitoring instruction.

3. The method according to claim 1, characterized in that Each monitoring model is configured with a corresponding monitoring proxy service on the oil and gas production monitoring platform, and the monitoring proxy service is used to call the corresponding monitoring model. Then, the method further includes: determining the target monitoring proxy service corresponding to the target monitoring model; triggering the target monitoring proxy service so that the target monitoring proxy service executes the step of calling the target monitoring model to execute the target oil and gas production monitoring service based on the target oil and gas production data; and / or, The method also includes: detecting whether the byte length of the oil and gas production monitoring instruction reaches a length threshold; if reached, performing any of the following operations: feeding back a prompt to the user who submitted the oil and gas production monitoring instruction to re-enter the oil and gas production monitoring instruction, wherein the prompt is used to prompt that the byte length of the re-entered oil and gas production monitoring instruction is less than the length threshold; or, based on the length threshold, splitting the oil and gas production monitoring instruction into at least two instruction texts, and executing the step of performing natural language analysis and processing on the oil and gas production monitoring instruction based on the large language model based on the instruction texts obtained by the splitting.

4. The method according to claim 1, characterized in that: After obtaining the oil and gas production monitoring result for the oil and gas production monitoring instruction, the method further includes: If the oil and gas production monitoring result indicates that the oil and gas production is abnormal, and there is a target device for eliminating the oil and gas production abnormality at the oil and gas production site, then determine the device operation data required for the target device to eliminate the oil and gas production abnormality based on the oil and gas production monitoring result, and send the device operation data to the target device, so that the target device eliminates the oil and gas production abnormality based on the device operation data; and / or, if the oil and gas production monitoring result indicates that the oil and gas production is abnormal, an abnormality alarm matching the abnormality type of the oil and gas production abnormality is issued; And / or, if the oil and gas production monitoring result indicates that the oil and gas production is abnormal, oil and gas production monitoring result data for assisting in eliminating the oil and gas production abnormality is generated based on the oil and gas production monitoring result.

5. The method according to claim 1, characterized in that The method further includes: if it is monitored that a natural language text is input into a target input box for collecting oil and gas production monitoring instructions in an interactive interface of the oil and gas production monitoring platform, acquiring the input natural language text as an oil and gas production monitoring instruction input in a natural language; and / or, The method further includes: if the audio collector for collecting oil and gas production monitoring instructions of the oil and gas production monitoring platform collects user audio, converting the user audio into natural language text, and acquiring the converted natural language text as the oil and gas production monitoring instruction input in natural language; and / or, Each oil and gas production monitoring service has a corresponding monitoring cycle and oil and gas production monitoring instructions expressed in natural language. The method further includes: executing for each of the oil and gas production monitoring services separately: if the monitoring reaches the start time point of the monitoring cycle corresponding to the oil and gas production monitoring service, then the oil and gas production monitoring instructions expressed in natural language corresponding to the oil and gas production monitoring service are obtained as oil and gas production monitoring instructions input in natural language.

6. The method according to claim 1, characterized in that The data feature is used to indicate at least one of the following: at least one data source and at least one data modality, wherein the at least one data source is used to indicate a subject that generates oil and gas production data required for the execution of oil and gas production monitoring services in oil and gas production, and the at least one data modality is used to indicate a modality of oil and gas production data required for the execution of oil and gas production monitoring services; and / or, The monitoring model includes at least one of the following: a gradient boosting model, a linear regression model, a long short-term memory network model, and a memory-enhanced neural network model; the gradient boosting model is used to perform corresponding oil and gas production monitoring services based on oil and gas production data with specific nonlinear characteristics; the linear regression model is used to perform corresponding oil and gas production monitoring services based on oil and gas production data with linear characteristics; the long short-term memory network model is used to perform corresponding oil and gas production monitoring services based on oil and gas production data with time series characteristics; the memory-enhanced neural network model is used to perform corresponding oil and gas production monitoring services based on oil and gas production data with time series characteristics.

7. The method according to any one of claims 1 to 6, characterized in that The method further includes: scoring the oil and gas production monitoring result of each oil and gas production monitoring instruction obtained in the current iteration cycle; if there is a target oil and gas production monitoring result whose score does not reach the scoring threshold, iteratively training the monitoring model called to obtain the target oil and gas production monitoring result; and / or, The method further includes: determining a user who initiated the oil and gas production monitoring instruction; if it is determined that the user does not have the authority to perform the target oil and gas production monitoring service, issuing an unauthorized warning indicating that the target oil and gas production monitoring service cannot be performed; and / or, The method further comprises: in the process of acquiring the target oil and gas production data and / or calling the target monitoring model, monitoring whether a malicious attack is received, and if received, using a disposal operation corresponding to the malicious attack to dispose of the malicious attack; and / or, The oil and gas production monitoring platform adopts any of the following deployment methods: cloud deployment, local deployment and edge deployment.

8. An oil and gas production monitoring device, characterized in that: Applied to an oil and gas production monitoring platform, the oil and gas production monitoring platform is deployed with a large language model and at least one monitoring model, each monitoring model has applicable data features and oil and gas production monitoring services, the device includes: a processing module, configured to, when obtaining an oil and gas production monitoring instruction input in natural language, perform natural language analysis and processing on the oil and gas production monitoring instruction based on the large language model to obtain a target oil and gas production monitoring business indicated by the oil and gas production monitoring instruction; A selection module, used to select a target monitoring model suitable for the target oil and gas production monitoring business; An acquisition module, used for acquiring target oil and gas production data having target data features applicable to the target monitoring model; The monitoring module is used to call the target monitoring model to execute the target oil and gas production monitoring business based on the target oil and gas production data, and obtain the oil and gas production monitoring result for the oil and gas production monitoring instruction.

9. A computer-readable storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the oil and gas production monitoring method described in any one of claims 1 to claim 7.

10. An electronic device, characterized in that: The electronic device comprises: Memory, used to store programs; A processor, coupled to the memory, is used to run the program to execute the oil and gas production monitoring method as described in any one of claims 1 to claim 7.

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