Automatic control system and method for oil field power distribution network

Abstract

The invention discloses an automatic control system and method for an oil field power distribution network, and relates to the technical field of power system automation, and the method comprises the steps: a monitoring equipment configuration and deployment module is responsible for configuring monitoring equipment, and collecting the state information and environment data of each node of the power distribution network; the feature analysis processing module analyzes the collected data by using the intelligent operation module to generate control parameters and abnormal parameters; the remote connection module establishes remote connection with power distribution network equipment through communication equipment, and performs remote control or management according to control and abnormal parameters; the visual interface display module visually displays the state information, the environment data, the control parameters, the abnormal parameters and other data, and helps operators to monitor the operation of the power grid in real time. The technical problem that in an existing oil field power distribution network control system, the power grid operation state cannot be flexibly adjusted to adapt to oil field electric power characteristic changes is solved, and the technical effect of dynamically adjusting the power grid operation state based on oil field electric power characteristics is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of power system automation, and in particular to an automation control system and method for an oilfield distribution network. Background Art

[0002] Distribution network automation systems are widely used in power distribution systems such as oilfield distribution networks, urban distribution networks, and industrial park distribution networks to improve the reliability and economy of power grids and reduce power outage time and power loss. At the same time, it also provides better means of power grid management for oilfields, improving the operating efficiency and service quality of oilfields. Among them, the distribution network automation system is a system that conducts real-time monitoring, intelligent control, and data management of distribution networks based on modern information technology and communication technology, combined with power system automation control theory and technology. By collecting the status information of each node in the distribution network, the operation of the power grid is monitored in real time, and remote control and management of the power grid are realized. The distribution network automation system is a system that uses advanced information technology and automatic control technology to realize intelligent management and automatic operation of the distribution network. Its main functions include monitoring, controlling, protecting and managing all aspects of the distribution network to improve the reliability, safety and economy of the distribution network. With the continuous expansion of oilfield production scale, the management and control of oilfield distribution networks face increasingly complex challenges. Various large-scale equipment in oilfields have strong volatility and impact, especially when the equipment is started, shut down, or the load changes, the power demand will fluctuate violently, which will bring great pressure to the stability of the distribution network. The existing oilfield distribution network control system usually relies on fixed scheduling and control strategies, and cannot flexibly respond to fluctuations in oilfield power loads, resulting in the inability of the power grid to adjust its operating status in a timely manner when faced with load fluctuations, equipment shocks, or changes in the external environment, thus affecting the reliability and stability of the power grid. Therefore, how to achieve dynamic adjustment and real-time response of the distribution network to adapt to changes in oilfield load and power characteristics has become a key issue in improving power grid stability and optimizing oilfield distribution network management.

[0003] In the current relevant technologies, there is a technical problem that the oilfield distribution network control system cannot flexibly adjust the grid operation status to adapt to the changes in the oilfield power characteristics. Summary of the invention

[0004] The present application solves the technical problem that the existing oilfield distribution network control system cannot flexibly adjust the grid operation status to adapt to changes in oilfield power characteristics by providing an automated control system and method for the oilfield distribution network.

[0005] The present application provides an automatic control system for an oilfield distribution network, comprising:

[0006] A monitoring equipment configuration and deployment module, which is used to configure and deploy monitoring equipment, and collect status information and environmental information of each node in the oilfield distribution network; a feature analysis and processing module, which is used to perform operation status feature and power supply feature analysis and processing through a preset intelligent operation module based on the collected status information and environmental information, and obtain control parameters and / or abnormal parameters; a remote connection module, which is used to establish a remote connection with each device in the oilfield distribution network through a communication device, and perform remote control management or feedback based on the control parameters or abnormal parameters; a visual interface display module, which is used to obtain the status information and environmental information, control parameters and abnormal parameters, and remote control tracking data, and display the visual interface.

[0007] The present application provides an automatic control method for an oilfield distribution network, comprising:

[0008] Configure and deploy monitoring equipment to collect status information and environmental information of each node in the oilfield distribution network; analyze and process the operating status characteristics and power supply characteristics through a preset intelligent computing module based on the collected status information and environmental information, and obtain control parameters and / or abnormal parameters; establish a remote connection with each device in the oilfield distribution network through a communication device, and perform remote control management or feedback based on the control parameters or abnormal parameters; obtain the status information and environmental information, control parameters and abnormal parameters, and remote control tracking data, and display them on a visual interface.

[0009] The invention proposes an automatic control system and method for oilfield distribution network. First, the monitoring equipment configuration deployment module is responsible for configuring the monitoring equipment and collecting the status information and environmental data of each node of the distribution network; the characteristic analysis and processing module uses the intelligent operation module to analyze the collected data and generate control parameters and abnormal parameters; the remote connection module establishes a remote connection with the distribution network equipment through the communication equipment, and performs remote control or management according to the control and abnormal parameters; the visual interface display module visualizes the status information, environmental data, control parameters and abnormal parameters, etc., to help operators monitor the operation of the power grid in real time. The distribution network automation system is a system based on advanced information technology and automatic control technology to realize the intelligent management and automatic operation of the distribution network. Through real-time monitoring, intelligent control and data management, the reliability, safety and economy of the distribution network are improved. It is widely used in power distribution systems such as oilfield distribution network, urban distribution network, industrial park distribution network, etc., providing enterprises with better power grid management means and improving operation efficiency and service quality. The development of the distribution network automation system will further promote the intelligentization and automation process of the power system, and achieve the technical effect of dynamically adjusting the operation status of the power grid based on the characteristics of oilfield power. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0011] Figure 1 A schematic diagram of the structure of an automated control system for an oilfield distribution network provided in an embodiment of the present application;

[0012] Figure 2 A flow chart of an automated control method for an oilfield distribution network provided in an embodiment of the present application.

[0013] Explanation of the accompanying drawings: monitoring device configuration deployment module 10, feature analysis and processing module 20, remote connection module 30, visualization interface display module 40. DETAILED DESCRIPTION

[0014] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0015] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0016] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0017] The present application embodiment provides an automatic control system for an oilfield distribution network, such as Figure 1 As shown, the method includes:

[0018] The monitoring equipment configuration and deployment module 10 is used to configure and deploy monitoring equipment to collect the status information and environmental information of each node of the oilfield distribution network. Specifically, the monitoring equipment configuration and deployment module 10 analyzes the monitoring needs in the oilfield distribution network automation control system. In view of the large scale, many nodes, complex equipment and special environment of the oilfield distribution network, in order to accurately collect electrical status information, high-precision voltage transformers, current transformers, power analyzers, etc. are selected. For example, the accuracy of voltage transformers can reach 0.1 or above to monitor small fluctuations. Among them, the data acquisition unit is responsible for collecting the status information of each node of the distribution network, including current, voltage, power and other data; for environmental monitoring, it is equipped with a wide-range high-sensitivity temperature and humidity sensor, a gas sensor with high sensitivity and rapid response to flammable and explosive gases, and a dust monitor, etc. The temperature and humidity sensor range covers -40°C to +80°C and 0% to 100% relative humidity. Determine the deployment location of the equipment at each node, power supply node, and install it at key locations such as the incoming line end of the substation and the busbar connection. For example, the incoming line end equipment monitors the power quality; the comprehensive monitoring equipment is installed at intervals along the transmission line tower to locate faults; the load node is installed at the access end of the pumping unit, water injection pump, etc. to grasp the power consumption characteristics, and establish a distribution network automation system including monitoring, control, communication and database modules. These modules are interconnected to realize data collection, processing and transmission, as well as remote control and status monitoring of equipment. Finally, the installation and commissioning are strictly carried out according to the specifications, and intelligent monitoring equipment is installed. The electrical equipment ensures reliable connection and shielding grounding. Environmental equipment is installed as required and calibrated regularly. After installation, professional equipment software is used to test the function, calibrate the accuracy and debug the data transmission channel. Intelligent monitoring equipment is installed in important locations such as distribution lines, substations and distribution equipment, including sensors, monitoring instruments and intelligent terminals. These devices can monitor parameters such as current, voltage, power, and environmental conditions such as temperature and humidity of the equipment in real time, so as to realize the comprehensive and accurate collection of the status and environmental information of each node of the oilfield distribution network. The distribution network automation system can collect the status information of each node of the distribution network in real time, including current, voltage, power and other data, and realize real-time monitoring of the operation of the power grid. Lay the foundation for the operation of the system.

[0019] The feature analysis and processing module 20 is used to analyze and process the operating state characteristics and power supply characteristics through the preset intelligent operation module according to the collected state information and environmental information, and obtain control parameters and / or abnormal parameters. Specifically, the feature analysis and processing module 20 receives the node status and environmental information of the oil field distribution network collected by the monitoring equipment, filters the state information (such as voltage and current) to remove noise, and verifies and corrects the abnormal values ​​of the environmental information (such as temperature and humidity). The intelligent operation module is used to carry out the analysis of the operating state characteristics, including checking the stability and fluctuation range of the voltage amplitude, calculating its fluctuation rate, analyzing the current load characteristics, phase relationship and power factor, and judging the power grid flow distribution through multi-node voltage and current correlation analysis. Combined with the environmental information, the power supply characteristics are analyzed, the line loss increment is calculated according to the temperature and line resistance relationship formula, the humidity and insulation resistance relationship model is used to warn the leakage risk, and the equipment temperature rise is calculated according to the relationship between sand and dust and equipment heat dissipation efficiency and responded in advance. By establishing a real-time monitoring platform, the data collected by each intelligent monitoring device can be centrally managed and displayed, and the real-time monitoring and analysis of the power grid operation status can be realized. At the same time, abnormal data and alarm information can also be sent to operation and maintenance personnel through SMS, email, etc. for timely processing. Finally, control parameters and / or abnormal parameters are generated based on the above analysis results, wherein the control unit: realizes automatic control of the power grid according to the collected data, including switch operation, fault handling, etc., such as generating reactive compensation control parameters of capacitor banks when the power factor is low, generating abnormal parameters when the line current is large and the ambient temperature is high, and generating corresponding control or abnormal parameters when the transformer oil temperature is abnormal, and connecting the distribution equipment with the distribution network automation system to realize remote control. Through the remote management software, the equipment can be monitored, remotely controlled, and parameters can be adjusted to improve the flexibility and dispatchability of the power grid operation. According to the collected data, the distribution network automation system can realize intelligent control of the power grid, including automatic switch operation, fault handling, etc., to improve the reliability and safety of the power grid. In this way, the stable operation of the power grid is guaranteed.

[0020] In a possible implementation, the feature analysis and processing module 20 further includes: an oilfield distribution network topology construction unit, which is used to obtain the power supply nodes and load nodes of the oilfield distribution network and construct the topology of the oilfield distribution network. Specifically, the core task of the oilfield distribution network topology construction unit is to accurately obtain the information of the power supply nodes and load nodes in the oilfield distribution network. In the complex layout of the oilfield distribution network, the power supply nodes are usually power supply sources such as substations, and the substations transmit electricity to various areas through high-voltage transmission lines. The load nodes are numerous oilfield production equipment, such as giant pumping units, large water injection machines, and various oil and gas processing equipment. Through comprehensive combing and data collection of the entire oilfield distribution network, the information of the power supply nodes and load nodes is integrated and sorted. Then, the topology construction algorithm and drawing tools are used to construct the topology of the oilfield distribution network. The topology structure clearly shows the connection relationship between the power supply node and the load node in a graphical way, including the direction of the transmission line, the branching situation, and the relative position of each node in the power grid. For example, taking the distribution network of a large oil field as an example, substation A, as a power supply node, is connected to load nodes in multiple oil well areas through three different transmission lines. The load nodes may have communication lines between each other, which can be accurately presented in the topology structure, providing a basic framework for subsequent in-depth analysis of the operating characteristics of the distribution network.

[0021] A loss relationship construction unit, the loss relationship construction unit is used to analyze the power fluctuation characteristics of the oilfield load and establish the loss relationship between the power fluctuation characteristics and the distribution network. Specifically, the loss relationship construction unit is based on the fact that the oilfield load has giant and large equipment and has fluctuation and impact characteristics, and the fluctuation will cause damage to the stability of the power grid. The loss relationship construction unit focuses on analyzing the power fluctuation characteristics of the oilfield load. The oilfield load is analyzed in magnitude to determine the power size range of different types of loads under various working conditions such as normal operation, startup, shutdown and load change. For example, a large oil pump may generate an instantaneous impact current several times the rated power when it is started, while the power is relatively stable during stable operation but will also fluctuate with the changes in the working conditions of the oil well. Analyze the operational impact of the load and study the instantaneous impact of the load on the grid voltage and current during the state transition process, such as the amplitude and duration of the impact current and the degree of disturbance to the grid frequency. Quantitatively evaluate the load volatility and calculate the parameters such as the fluctuation variance and peak-to-peak value of the load power over a period of time. According to the operating power characteristics, the fluctuation characteristics analysis is carried out to obtain the power parameter fluctuation characteristics, such as the correlation between the voltage fluctuation amplitude and the load power fluctuation, the relationship between the current harmonic content and the load change, etc. Using a large amount of historical sample data, through data fitting and regression analysis and other methods, the influence relationship of the power parameter fluctuation characteristics on the loss of the distribution network is fitted. For example, when the load power fluctuation amplitude increases, the specific quantitative relationship that will lead to the increase of the distribution network line loss is analyzed, as well as the influence law on the loss of equipment such as transformers, so as to establish the relationship between the power fluctuation characteristics and the loss of the distribution network.

[0022] A mapping relationship determination unit, the mapping relationship determination unit is used to fit the loss relationship as the attribute feature of the load node to the oil field distribution network topology structure, and determine the mapping relationship between the loss relationship and the load node. Specifically, after obtaining the loss relationship, the mapping relationship determination unit processes it as the attribute feature of the load node. For each load node, the corresponding loss relationship information is sorted and marked. According to the oil field distribution network topology structure, the load nodes with loss relationship attributes are accurately fitted into the topology structure. In the fitting process, it is ensured that the loss relationship information of each load node matches the connection relationship in the topology structure, so as to determine the accurate mapping relationship between the loss relationship and the load node. For example, in the topology structure, the corresponding loss relationship of the load node in a certain oil well area indicates that a large line loss will be generated under a specific load fluctuation condition. Then, in the topology structure, the node is associated with this loss relationship information, so that in the subsequent analysis of the power grid operation, the influence of the power fluctuation of each load node on the power grid loss can be quickly queried, which provides an important data basis for optimizing the power grid operation.

[0023] A node relationship identification matrix construction unit, the node relationship identification matrix construction unit is used to construct a node relationship identification matrix based on the oilfield distribution network topology structure. Specifically, the node relationship identification matrix construction unit analyzes the constructed oilfield distribution network topology structure. First, the topology structure is subjected to a relational operation by a relational operation subunit. The connection mode between nodes is analyzed, such as series and parallel relationships, and the tightness of the connection. The structural relationship identification matrix is ​​constructed by calculating parameters such as the connection weights between nodes. The matrix preliminarily reflects the connection structure relationship between nodes. For example, if there is a direct high-voltage transmission line connection between two nodes and the line is short and the impedance is small, the corresponding connection weight in the structural relationship identification matrix is ​​large. The supply flow identification decomposition subunit performs supply flow identification decomposition of the mapping node based on the loss relationship. Starting from the power supply node, along the transmission line, the supply flow direction of electric energy between each node and the diversion at each node are analyzed, and the influence of the loss relationship on the supply flow is analyzed at the same time, and the control influence relationship of the supply flow node is obtained. For example, the power fluctuation of a load node will cause the voltage drop of the upstream supply flow node to change, thereby affecting the power supply of other adjacent load nodes. The influence relationship is clarified through the supply flow identification decomposition. Finally, the node relationship identification matrix acquisition subunit integrates the control influence relationship of the supply flow node into the structural relationship identification matrix, adjusts and optimizes the elements in the matrix, comprehensively analyzes the connection structure of the node and the control influence of the supply flow, and obtains a complete node relationship identification matrix. The node relationship identification matrix reflects the complex relationship between nodes in the oilfield distribution network, including the mutual influence relationship between the structural connection relationship and the power supply flow. By analyzing and mining historical data, a fault diagnosis and prediction model is established, which can be used to warn of power grid faults and abnormal conditions in advance and reduce the power outage time and loss caused by faults. At the same time, by analyzing the power grid operation data, the power grid dispatch can be optimized and the stability and efficiency of the power grid can be improved. It provides a strong mathematical model foundation for subsequent precise power grid control and fault diagnosis, which helps to achieve optimal control of the power grid supply flow and ensure the stability of the power grid.

[0024] In a possible implementation, the node relationship identification matrix construction unit further includes: a relationship operation subunit, which is used to perform relationship operations according to the oilfield distribution network topology structure to construct a structural relationship identification matrix. Specifically, the relationship operation subunit is based on the oilfield distribution network topology structure, classifies and organizes the node connection relationships in the topology structure, clarifies whether each node is directly connected or indirectly connected, and the type of connection line, such as high-voltage line, low-voltage line and other information. Quantify the connection relationship. For example, for two nodes that are directly connected with a short line and strong transmission capacity, a higher connection weight value, such as 0.8, is assigned; while for node connections that are indirectly connected or have a long line and large transmission loss, a relatively low weight value, such as 0.3, is assigned. At the same time, considering the hierarchical relationship of the nodes in the topology structure, the nodes located near the power supply source have a higher level, and their representation in the matrix will be different from the end load nodes. By quantifying the connection relationship, a structural relationship identification matrix is ​​constructed. The elements in the matrix reflect the closeness and relative importance of the connection between nodes, and preliminarily outline the presentation of the physical connection architecture of the oilfield distribution network in the mathematical model, laying the foundation for further analysis of the complex relationship between nodes.

[0025] The supply flow identification and decomposition subunit is used to identify and decompose the supply flow of the mapping node according to the loss relationship, and obtain the control influence relationship of the supply flow node. Specifically, the supply flow identification and decomposition subunit operates according to the loss relationship established previously. Due to the volatility of the oil field load, the loss of the distribution network will change, which will affect the supply flow of electric energy between nodes. The supply flow identification and decomposition subunit starts from the power supply node and gradually analyzes the supply flow situation at each mapping node along the power transmission path. For example, when a large oil pump load node has power fluctuations, the line voltage drop changes and power loss changes caused by the fluctuations are calculated according to the loss relationship, and then the impact of such changes on the power output of the upstream supply flow node is determined. If the power of the load node suddenly increases, the output current of the upstream node may increase and the voltage decrease. The change relationship is part of the control influence relationship of the supply flow node. By analyzing the supply flow changes of each mapping node under different load fluctuations, the control influence relationship of the supply flow node is comprehensively obtained, and it is clear how each node affects the direction and size of the supply flow when facing load fluctuations, which provides a key basis for a comprehensive understanding of the dynamic operation characteristics of the distribution network.

[0026] A node relationship identification matrix acquisition subunit is used to integrate the control influence relationship of the supply flow node into the structural relationship identification matrix to obtain the node relationship identification matrix. Specifically, the node relationship identification matrix acquisition subunit integrates the control influence relationship of the supply flow node into the constructed structural relationship identification matrix. In the specific operation, for each element in the structural relationship identification matrix, adjustment is made in combination with the control influence relationship of the supply flow node. For example, if it is found in the supply flow control influence relationship that the fluctuation of a certain node has a greater weakening effect on the supply flow of the adjacent node, then the weight value of the corresponding element in the structural relationship identification matrix may be reduced accordingly to reflect the negative impact. Through the fusion method, the structural relationship identification matrix is ​​optimized and improved, so that the final node relationship identification matrix not only contains the physical connection structure information between the nodes, but also covers the control influence relationship of the supply flow between the nodes under the condition of load fluctuation. The node relationship identification matrix reflects the complex interactive relationship between the nodes in the oilfield distribution network. Whether in normal operation or in response to load fluctuations, it can provide powerful mathematical model support for the accurate analysis, control and optimization of the distribution network, which helps to ensure the stable operation of the distribution network and improve its operation efficiency.

[0027] In a possible implementation, the node relationship identification matrix acquisition subunit further includes: a connection relationship extraction microunit, which is used to extract the connection relationship between each node, determine the number of connections of each node, and the distribution of connected nodes according to the oilfield distribution network topology. Specifically, the connection relationship extraction microunit is based on the oilfield distribution network topology. The entire topology is fully scanned to identify each node. Check the connection status of each node with other nodes one by one to determine which nodes are connected to the node. For example, in a simple oilfield distribution network topology, node A may be connected to nodes B, C, and D. The connection relationship between each node can be extracted. Count the number of connections of each node, such as node A has 3 connections. Clarify the distribution of other nodes connected to each node, that is, node A is connected to nodes B, C, and D. This step provides basic data for subsequent matrix construction.

[0028] The adjacency matrix constructs micro-units, and the micro-units constructed by the adjacency matrix are used to construct a degree matrix according to the number of connections of each node, and construct an adjacency matrix according to the distribution of connected nodes. Specifically, the degree matrix is ​​constructed based on the number of connections of each node obtained by extracting micro-units based on the connection relationship. The degree matrix is ​​a diagonal matrix, and the elements on its diagonal represent the number of connections of the corresponding nodes. For example, if node A has 3 connections, then the diagonal element corresponding to node A in the degree matrix is ​​3. For the adjacency matrix, it is constructed according to the distribution of connected nodes. If node A is connected to node B, then the elements of row A and column B and row B and column A in the adjacency matrix (because the adjacency matrix is ​​symmetrical) will be assigned according to the connection situation. Usually, if two nodes are connected, the value of this position may be 1 (simple connection situation) or a value related to the transmission capacity.

[0029] A difference calculation micro-unit, the difference calculation micro-unit is used to perform difference calculation according to the degree matrix and the adjacency matrix to obtain the structural relationship identification matrix. Specifically, after the difference calculation micro-unit receives the degree matrix and the adjacency matrix, it performs difference calculation. Specifically, the structural relationship identification matrix is ​​obtained by subtracting the adjacency matrix from the degree matrix. For example, the element D[i, j] in the degree matrix D is subtracted from the corresponding element A[i, j] in the adjacency matrix A (when i=j) to obtain the corresponding element in the structural relationship identification matrix. Difference calculation helps to reflect the structural relationship characteristics of nodes in the network.

[0030] The fluctuation influence determination micro-unit is used to determine the fluctuation influence of each node according to the control influence relationship of the supply flow nodes. Specifically, the fluctuation influence of each node is determined according to the control influence relationship of the supply flow nodes. Considering that the volatility of the oil field load will affect the operation of the power grid, when the supply flow of a node changes, it will have different degrees of impact on other nodes. For example, the load fluctuation of node A may affect the power supply stability of nodes B and C. By analyzing the control influence relationship of such supply flow nodes, the fluctuation influence of each node can be quantified, that is, the extent of the impact of the fluctuation of node A on other nodes.

[0031] The influence weight identification micro-unit is used to identify the node degree correction factor and the influence weight between nodes according to the fluctuation influence of each node. Specifically, the node degree correction factor and the influence weight between nodes are identified according to the fluctuation influence of each node. According to the volatility correction method of the degree matrix and the adjacency matrix mentioned above, for the degree matrix, if the fluctuation influence of node A is large, then its corresponding node degree correction factor will be determined according to its fluctuation intensity. For the adjacency matrix, if the fluctuation of node A has a significant impact on node B, then the influence weight α between node A and node B A will be determined, where and are the volatility factors of node A and node B respectively.

[0032] The matrix optimization and correction micro-unit is used to optimize and correct the structural relationship identification matrix according to the node degree correction factor and the influence weight between nodes to obtain the node relationship identification matrix. Specifically, finally, the structural relationship identification matrix is ​​optimized and corrected using the node degree correction factor and the influence weight between nodes. In the structural relationship identification matrix, the degree matrix related elements are corrected according to the node degree correction factor, for example, the diagonal element D[A, A] of node A in the degree matrix will be adjusted according to. At the same time, the adjacency matrix related elements are corrected according to the influence weight α between nodes, and these corrections are reflected in the structural relationship identification matrix, and finally the node relationship identification matrix is ​​obtained. This matrix comprehensively considers the connection structure and volatility of the nodes, and can more accurately describe the relationship between nodes in the oilfield distribution network. In addition, the distribution network automation system is integrated with distributed energy, energy storage system and intelligent appliances to form an intelligent distribution network. Through intelligent perception, intelligent management and intelligent scheduling, efficient utilization and optimal distribution of electric energy are achieved, and the reliability and economy of the distribution network are improved.

[0033] In a possible implementation, the loss relationship construction unit further includes: an operating power feature acquisition subunit, which is used to analyze the load level, operation impact, and load volatility of the oil field load to obtain the operating power characteristics of each oil field load. Specifically, the operating power feature acquisition subunit conducts a detailed analysis of the oil field load. In terms of load level analysis, data of various loads in the oil field are collected, including giant and large equipment, such as large oil pumping units, water injection pumps, etc. By monitoring the equipment, the power data of the equipment under different operating conditions is obtained, and the power size during normal operation, the instantaneous power peak value during startup, and the power change during shutdown are determined, so as to fully understand the load level. For the analysis of operation impact, the operating power feature acquisition subunit will focus on analyzing the performance of the load during the state transition process. For example, when a large oil pump is started, the size of its starting current, the length of the startup time, and the instantaneous impact amplitude on the grid voltage will be monitored. By collecting and analyzing these data, the impact characteristics of the load during operation are mastered. In the load volatility analysis, the operating power feature acquisition subunit will monitor the changes in load power for a long time. By statistically analyzing the power data over a period of time, we can calculate the power fluctuation variance, standard deviation and other statistical parameters, as well as the frequency and amplitude of power fluctuation, so as to determine the volatility of the load. Combining the analysis results of the above three aspects, we can obtain the operating power characteristics of each oil field load.

[0034] The fluctuation characteristic analysis subunit is used to perform fluctuation characteristic analysis according to the operating power characteristics of each oilfield load to obtain power parameter fluctuation characteristics. Specifically, based on the operating power characteristics of each oilfield load obtained by the operating power characteristic acquisition subunit, the fluctuation characteristic analysis subunit analyzes the fluctuation pattern in the operating power characteristics. For example, for the fluctuation of load power, it is analyzed whether its fluctuation is periodic, or whether it is associated with a specific production operation link. From the perspective of voltage and current, the fluctuation characteristic analysis subunit studies the relationship between the voltage fluctuation amplitude and the load power fluctuation, as well as the correlation between the harmonic content of the current and the load change. Through a detailed analysis of the relationship, power parameter fluctuation characteristics are obtained, such as parameters such as voltage fluctuation frequency and current harmonic distortion rate, which can more accurately describe the characteristics of power fluctuations.

[0035] The loss impact relationship fitting subunit is used to fit the loss impact relationship of the power parameter fluctuation characteristics on the distribution network based on the historical sample data. Specifically, the loss impact relationship fitting subunit works based on the historical sample data. A large amount of past power grid operation data will be collected, and the data includes various parameters of the power grid under different load operating conditions, such as line loss, transformer loss, etc., and the corresponding power parameter fluctuation characteristic data. The power parameter fluctuation characteristic data is fitted with the power grid loss data using data fitting techniques, such as the least squares method. For example, when the load power fluctuation amplitude increases, the increase in line loss is analyzed, and by fitting a large number of data points, a loss impact relationship model of the power parameter fluctuation characteristics on the distribution network is established. The model can quantitatively describe the degree of influence of different power parameter fluctuation characteristics on the distribution network loss.

[0036] The loss coupling subunit is used to perform loss coupling using the power parameter fluctuation characteristics and the loss influence relationship, and obtain the loss relationship between the power fluctuation characteristics and the distribution network. Specifically, the loss coupling subunit operates using the power parameter fluctuation characteristics obtained by the fluctuation characteristic analysis subunit and the loss influence relationship established by the loss influence relationship fitting subunit. Substitute the power parameter fluctuation characteristics into the loss influence relationship model. For example, the voltage fluctuation characteristics and current harmonic characteristics of a certain load are known, and the characteristics are substituted into the fitted loss influence relationship model. The loss situation caused by the load to the distribution network under the specific fluctuation characteristics is calculated, thereby obtaining the loss relationship between the power fluctuation characteristics and the distribution network. The loss relationship is of great significance for evaluating the operating efficiency and stability of the power grid, and formulating the optimization control strategy.

[0037] In a possible implementation, the node relationship identification matrix construction unit further includes: a relationship identification extraction module construction unit, the relationship identification extraction module construction unit is used to construct a relationship identification extraction module based on the node relationship identification matrix. Specifically, the relationship identification extraction module construction unit is based on the node relationship identification matrix. The node relationship identification matrix integrates the connection relationship of each node in the oil field distribution network, the characteristics of power fluctuations and its relationship with the loss of the distribution network. The relationship identification extraction module construction unit analyzes and processes the matrix. First, the various relationships contained in the matrix are identified, including the direct connection relationship between nodes, the relationship of indirect connection through other nodes, and the relationship of the node fluctuation on the operation of the entire distribution network. Then, based on the identified relationship, the relationship identification extraction module is constructed using programming technology. The relationship identification extraction module extracts useful distribution network relationship features based on the input related data. For example, if it is found that the connection relationship of several nodes in the matrix will change significantly when the power grid fluctuates, then a corresponding algorithm will be designed to capture this change feature when constructing the module.

[0038] A module connection construction unit, the module connection construction unit is used to establish a connection between the relationship identification and extraction module and the pre-trained fault identification module and the pre-trained adaptive control module, wherein the input data of the relationship identification and extraction module is the state information collected in real time, and the output is the distribution network impact feature, and the distribution network impact feature is used as the input data of the pre-trained fault identification module and the pre-trained adaptive control module. Specifically, the module connection construction unit establishes a relationship and connects the relationship identification and extraction module with the pre-trained fault identification module and the pre-trained adaptive control module. The input data of the relationship identification and extraction module is the state information collected in real time, and the state information includes electrical parameters such as voltage, current, power, etc. of each node of the oilfield distribution network, as well as external environmental parameters such as ambient temperature and humidity that may affect the operation of the power grid. When the data is input into the relationship identification and extraction module, it is processed by the algorithm inside the module and output as the distribution network impact feature. The distribution network impact feature reflects the impact of each node and its mutual relationship on the entire distribution network under the current power grid operation state. For example, it may be a feature such as the degree of influence of voltage fluctuations at nodes in a certain area on the power supply stability of surrounding nodes. Then, the module connection construction unit transmits the distribution network impact characteristics as input data to the pre-trained fault identification module and the pre-trained adaptive control module respectively, thereby establishing a data transmission and processing relationship between the three.

[0039] A parameter acquisition unit, the parameter acquisition unit is used for the pre-trained fault identification module and the pre-trained adaptive control module to perform fault identification and adaptive control analysis according to the output result of the relationship identification and extraction module, and obtain the control parameters and / or abnormal parameters. Specifically, the pre-trained fault identification module and the pre-trained adaptive control module process the distribution network impact characteristics after receiving the output of the relationship identification and extraction module. The pre-trained fault identification module analyzes the distribution network impact characteristics to determine whether there is a fault in the current power grid operation. For example, if the input distribution network impact characteristics show that the voltage of a certain line node drops sharply and the current increases abnormally, the fault identification module will identify that the line may have a short circuit fault according to its internal fault judgment criteria. At the same time, the pre-trained adaptive control module also performs adaptive control analysis based on the received distribution network impact characteristics. According to the current operating status and potential problems of the power grid, the corresponding control parameters are calculated, such as the reactive compensation amount that needs to be adjusted to stabilize the voltage, or the power dispatching amount that needs to be performed to balance the load. In the event of a fault, abnormal parameters may also be generated, such as the number of the fault line, the type of fault and other information. These control parameters and / or abnormal parameters are the results finally obtained by the parameter acquisition unit, which are of great significance for ensuring the stable operation of the oilfield distribution network and timely handling of faults.

[0040] The remote connection module 30 is used to establish a remote connection with each device in the oilfield distribution network through a communication device, and to perform remote control management or feedback according to the control parameters or abnormal parameters. Specifically, the remote connection module 30 first identifies various types of equipment in the oilfield distribution network, adapts to its different communication interfaces such as RS-232, RS-485, Ethernet, etc., selects and configures equipment such as wireless LAN, 4G / 5G or power line carrier communication according to the oilfield scope and communication requirements, and sends a connection request to each device according to the corresponding communication protocol through the configured communication device to establish a two-way remote connection, wherein the communication unit is responsible for data communication with each node of the distribution network to realize data transmission and interaction. When the control parameters are received, they are parsed, and the transmission path and method are determined according to the parameter type and the target device, and the parameters are accurately sent to the target devices such as transformers and circuit breakers according to the communication protocol of the target device to drive them to perform corresponding operations. After receiving the abnormal parameters, they are processed immediately, the abnormal equipment is located according to the equipment identification and the connection status is checked, the abnormal parameters are sorted and converted into alarm information, and sent to the monitoring center via Ethernet or to the maintenance personnel's mobile terminal via the 4G / 5G network. Through the communication unit, the distribution network automation system can realize remote management of the distribution network without manual on-site operation, saving manpower and time costs, and facilitating timely processing.

[0041] In a possible implementation, the remote connection module 30 further includes: a communication influence relationship establishment unit, the communication influence relationship establishment unit is used to establish the communication influence relationship between the environmental information and the node communication signal, and the communication influence relationship includes the influence relationship of multiple communication dimension parameters. Specifically, the communication influence relationship establishment unit constructs the communication influence relationship between the environmental information and the node communication signal. Due to the remote and complex environment of the oil field, there are many factors that affect the communication signal, such as strong wind and sand, extreme temperature, high humidity, etc. For the wind and sand factor, it may cause wear of the communication line, signal attenuation, and dust erosion of the communication equipment, affecting the signal transmission quality. In a high temperature environment, the performance of the electronic components of the communication equipment may change, such as increased resistance, capacitance drift, etc., thereby interfering with the normal sending and receiving of the signal. A high humidity environment may cause problems such as line short circuit and decreased insulation performance of the equipment, which have an adverse effect on the communication signal. The communication influence relationship establishment unit analyzes the change law of the communication signal under different environmental parameters through a large number of field tests and data collection. For example, in a windy and sandy environment with different wind speeds and dust concentrations, measure the intensity attenuation of communication signals, bit error rate and other indicators; under different temperature and humidity conditions, monitor the signal transmission delay, frequency offset and other parameters of communication equipment. Organize and analyze the environmental information and the corresponding communication signal change data, and establish an influence relationship including multi-dimensional communication parameters. For example, establish a relationship model between wind speed and signal attenuation coefficient, a functional relationship between temperature and signal transmission delay, and an equation for the correlation between humidity and bit error rate, etc., so as to comprehensively cover the impact of environmental factors on various aspects of communication signals and provide a basis for subsequent calculations and corrections.

[0042] The influence operation unit is used to perform influence operation based on the collected environmental information using the communication influence relationship to obtain the communication influence dimension characteristics and their influence degree. Specifically, the influence operation unit uses the established communication influence relationship to carry out influence operation based on the collected environmental information. When receiving environmental information from the monitoring device, such as the current wind speed, temperature, humidity and other data, the data is substituted into the corresponding communication influence relationship model. For example, if the current wind speed is measured to be 10 meters per second, the attenuation degree of the signal at this wind speed is calculated according to the relationship model between the wind speed and the signal attenuation coefficient; if the ambient temperature is 40 degrees Celsius, the signal transmission delay increase value is obtained according to the functional relationship between the temperature and the signal transmission delay. By calculating the influence of each communication dimension parameter, the communication influence dimension characteristics, such as signal attenuation characteristics, transmission delay characteristics, bit error rate characteristics, etc., are obtained, and the characteristics comprehensively reflect the overall influence of the current environment on the communication signal. At the same time, the influence degree corresponding to each feature is calculated, that is, the quantitative degree of influence of each environmental factor on the communication signal. For example, the signal attenuation impact caused by wind and sand is calculated to be 0.3 (indicating a 30% attenuation of the signal strength), and the transmission delay impact caused by temperature is calculated to be 0.2 (indicating a 20% increase in transmission delay), etc., so that the priority and magnitude of corrections to the communication network parameters can be determined later according to the magnitude of the impact.

[0043] A communication network parameter positioning unit, the communication network parameter positioning unit is used to locate the communication network parameters according to the communication impact dimension characteristics, and to compensate the parameters according to the impact degree. Specifically, the communication network parameter positioning unit locates the communication network parameters according to the obtained communication impact dimension characteristics. For example, if the communication impact dimension characteristics show that the signal attenuation is more serious, then the network parameters related to the signal power are located, such as the power setting of the transmitter, the gain of the amplifier, etc.; if the transmission delay characteristics are obvious, the parameters related to the signal transmission time are determined, such as the clock frequency of the signal processing chip, the bandwidth allocation of the transmission line, etc. After locating the relevant communication network parameters, the parameters are compensated according to the impact degree. For situations where the signal attenuation impact is high, the power of the transmitter is appropriately increased or the gain of the amplifier is increased to compensate for the signal strength loss caused by environmental factors; for problems with large transmission delay impact, the clock frequency of the signal processing chip is adjusted or the bandwidth allocation of the transmission line is optimized to reduce the signal transmission time. Through targeted parameter compensation, the problem of poor communication signals caused by environmental interference can be effectively corrected to avoid untimely data, inaccurate transmission and control delays, etc., to ensure the accuracy and timeliness of data transmission in the oilfield distribution network automation control system, and reduce the losses that may be caused by communication problems. In addition, a network security protection mechanism should be established. In the process of implementing distribution network automation, network security issues must be considered. Establish a network security protection mechanism, including firewalls, intrusion detection systems and security access control, to ensure the safe and reliable operation of the distribution network automation system. The distribution network automation implementation plan includes the steps of establishing a distribution network automation system, installing intelligent monitoring equipment, establishing a real-time monitoring platform, implementing remote control, establishing fault diagnosis and prediction models, implementing intelligent distribution networks and establishing network security protection mechanisms. Through these measures, the reliability, safety and economy of the power grid can be improved.

[0044] Visual interface display module 40, the visual interface display module 40 is used to obtain the state information and environmental information, control parameters and abnormal parameters, remote control tracking data, and perform visual interface display. Specifically, the visual interface display module 40 first establishes a data connection with the monitoring equipment configuration deployment module, etc., collects the state information of each node such as voltage, current, power and the equipment operation state information, obtains environmental information such as temperature, humidity, dust, gas from the environmental monitoring sensor, receives the control parameters generated by the feature analysis and processing module, the abnormal parameters fed back by the remote connection module, and the remote control tracking data. Classify and organize the data, process the state information according to electrical parameters, equipment type, etc., classify the environmental information according to environmental factors, distinguish the control parameters according to the control object and type, divide the abnormal parameters according to the fault type and location, sort the remote control tracking data according to the time sequence and operation process, and pre-process some data. Among them, the data management unit is responsible for storing, processing and analyzing the collected data, generating reports and statistical analysis results. Then, the interface layout is designed according to the data classification. The power grid operation status display area uses dashboards and charts to display status information. The environmental information display area marks environmental parameters in the form of maps. The control parameter display area lists control operations and parameters. The abnormal alarm area prominently displays abnormal parameters. The remote control recording area records and tracks data. Among them, the user interface: an interface provided to users for system operation and monitoring, including PC and mobile applications. Data is displayed in real time according to the layout and display method, and the latest data update interface is obtained at a certain time interval according to the dynamic update mechanism, so that operation and maintenance personnel can grasp the overall operation of the oilfield distribution network and make timely decisions. The distribution network automation system can store, process and analyze the collected data, generate reports and statistical analysis results, and provide decision-making basis for power grid operation and management. The distribution network automation system provides a user interface, and users can operate and monitor the system through PC and mobile applications. The operation is simple and convenient.

[0045] The embodiment of the present application adopts a monitoring device configuration deployment module to configure the monitoring equipment and collect the status information and environmental data of each node in the distribution network; the feature analysis and processing module uses the intelligent computing module to analyze the collected data and generate control parameters and abnormal parameters; the remote connection module establishes a remote connection with the distribution network equipment through the communication equipment, and performs remote control or management according to the control and abnormal parameters; the visual interface display module visualizes data such as status information, environmental data, control parameters and abnormal parameters to help operators monitor the operation of the power grid in real time, thereby achieving the technical effect of dynamically adjusting the operation status of the power grid based on the power characteristics of the oil field.

[0046] In the above, refer to Figure 1 The present invention describes in detail an automatic control system for an oilfield distribution network according to an embodiment of the present invention. Figure 2An automatic control method for an oilfield distribution network according to an embodiment of the present invention is described.

[0047] An automatic control method for oil field distribution network, such as Figure 2 As shown, the method includes:

[0048] Configure and deploy monitoring equipment to collect status information and environmental information of each node in the oilfield distribution network; analyze and process the operating status characteristics and power supply characteristics through a preset intelligent computing module based on the collected status information and environmental information, and obtain control parameters and / or abnormal parameters; establish a remote connection with each device in the oilfield distribution network through a communication device, and perform remote control management or feedback based on the control parameters or abnormal parameters; obtain the status information and environmental information, control parameters and abnormal parameters, and remote control tracking data, and display them on a visual interface.

[0049] In a possible implementation, the automated control method for an oilfield distribution network also includes: acquiring power supply nodes and load nodes of the oilfield distribution network, and constructing a topological structure of the oilfield distribution network; analyzing the power fluctuation characteristics of the oilfield load, and establishing a loss relationship between the power fluctuation characteristics and the distribution network; using the loss relationship as an attribute feature of the load node, fitting it to the topological structure of the oilfield distribution network, and determining a mapping relationship between the loss relationship and the load node; and constructing a node relationship identification matrix based on the topological structure of the oilfield distribution network.

[0050] In a possible implementation, the automated control method for an oilfield distribution network also includes: performing relational operations based on the topological structure of the oilfield distribution network to construct a structural relationship identification matrix; performing supply flow identification and decomposition of mapping nodes based on the loss relationship to obtain control influence relationships of supply flow nodes; and integrating the control influence relationships of the supply flow nodes into the structural relationship identification matrix to obtain the node relationship identification matrix.

[0051] In a possible implementation, the automated control method for an oilfield distribution network also includes: extracting the connection relationship between each node according to the topological structure of the oilfield distribution network, determining the number of connections of each node, and the distribution of connected nodes; constructing a degree matrix according to the number of connections of each node, and constructing an adjacency matrix according to the distribution of connected nodes; performing difference calculation between the degree matrix and the adjacency matrix to obtain the structural relationship identification matrix; determining the fluctuation influence of each node according to the control influence relationship of the supply flow nodes; identifying the node degree correction factor and the influence weight between nodes according to the fluctuation influence of each node; optimizing and correcting the structural relationship identification matrix according to the node degree correction factor and the influence weight between nodes to obtain the node relationship identification matrix.

[0052] In one possible implementation, the automated control method for an oilfield distribution network also includes: performing load level, operating impact, and load fluctuation analysis on the oilfield load to obtain the operating power characteristics of each oilfield load; performing fluctuation characteristic analysis based on the operating power characteristics of each oilfield load to obtain the power parameter fluctuation characteristics; fitting the loss influence relationship of the power parameter fluctuation characteristics on the distribution network based on historical sample data; and performing loss coupling using the power parameter fluctuation characteristics and the loss influence relationship to obtain the loss relationship between the power fluctuation characteristics and the distribution network.

[0053] In a possible implementation, the automated control method for an oilfield distribution network also includes: constructing a relationship identification and extraction module based on the node relationship identification matrix; establishing a connection between the relationship identification and extraction module and a pre-trained fault identification module and a pre-trained adaptive control module, wherein the input data of the relationship identification and extraction module is real-time collected status information, and the output is distribution network impact characteristics, and the distribution network impact characteristics are used as input data of the pre-trained fault identification module and the pre-trained adaptive control module; the pre-trained fault identification module and the pre-trained adaptive control module perform fault identification and adaptive control analysis according to the output results of the relationship identification and extraction module to obtain the control parameters and / or abnormal parameters.

[0054] In one possible implementation, the automated control method for an oilfield distribution network also includes: establishing a communication influence relationship between the environmental information and the node communication signal, the communication influence relationship including the influence relationship of multiple communication dimension parameters; performing influence operations using the communication influence relationship based on the collected environmental information to obtain communication influence dimension characteristics and their influence degrees; locating communication network parameters based on the communication influence dimension characteristics, and performing parameter compensation based on the influence degrees.

[0055] The adaptive variable frequency speed regulation system based on the scraper conveyor provided by the embodiment of the present invention can execute the adaptive variable frequency speed regulation control method based on the scraper conveyor provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0056] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0057] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. An automatic control system for oil field distribution network, characterized in that: include: A monitoring equipment configuration and deployment module, which is used to configure and deploy monitoring equipment to collect status information and environmental information of each node in the oilfield distribution network; A feature analysis and processing module, which is used to analyze and process the operating state characteristics and power supply characteristics through a preset intelligent operation module according to the collected state information and environmental information, and obtain control parameters and / or abnormal parameters; A remote connection module, which is used to establish a remote connection with each device in the oilfield distribution network through a communication device, and perform remote control management or feedback according to the control parameters or abnormal parameters; A visual interface display module is used to obtain the status information and environmental information, control parameters and abnormal parameters, and remote control tracking data, and perform visual interface display.

2. The automatic control system for oilfield distribution network according to claim 1, characterized in that: The feature analysis processing module comprises: An oilfield distribution network topology structure construction unit, the oilfield distribution network topology structure construction unit is used to obtain power supply nodes and load nodes of the oilfield distribution network and construct the oilfield distribution network topology structure; A loss relationship building unit, the loss relationship building unit is used to analyze the power fluctuation characteristics of the oil field load and establish a loss relationship between the power fluctuation characteristics and the distribution network; A mapping relationship determination unit, the mapping relationship determination unit is used to fit the loss relationship as an attribute feature of the load node to the oil field distribution network topology structure, and determine the mapping relationship between the loss relationship and the load node; A node relationship identification matrix construction unit is used to construct a node relationship identification matrix based on the oilfield distribution network topology structure.

3. The automatic control system for oil field distribution network according to claim 2, characterized in that: The node relationship identification matrix construction unit comprises: A relational operation subunit, the relational operation subunit is used to perform relational operations according to the topological structure of the oilfield distribution network and construct a structural relationship identification matrix; A supply flow identification and decomposition subunit, the supply flow identification and decomposition subunit is used to perform supply flow identification and decomposition of mapping nodes according to the loss relationship to obtain a control influence relationship of the supply flow node; A node relationship identification matrix acquisition subunit is used to merge the control influence relationship of the supply flow node into the structural relationship identification matrix to obtain the node relationship identification matrix.

4. The automatic control system for oilfield distribution network according to claim 3, characterized in that: The node relationship identification matrix acquisition subunit includes: A connection relationship extraction micro-unit, wherein the connection relationship extraction micro-unit is used to extract the connection relationship between nodes according to the topological structure of the oil field distribution network, determine the number of connections of each node, and the distribution of connection nodes; The adjacency matrix constructs a micro-unit, wherein the adjacency matrix constructs a micro-unit for constructing a degree matrix according to the number of connections of each node, and constructs an adjacency matrix according to the distribution of the connected nodes; A difference calculation micro-unit, the difference calculation micro-unit is used to perform difference calculation according to the degree matrix and the adjacency matrix to obtain the structural relationship identification matrix; A fluctuation influence determination micro unit, the fluctuation influence determination micro unit is used to determine the fluctuation influence of each node according to the control influence relationship of the supply flow node; An influence weight identification micro-unit, wherein the influence weight identification micro-unit is used to identify a node degree correction factor and an influence weight between nodes according to the fluctuation influence of each node; The matrix optimization and correction micro-unit is used to optimize and correct the structural relationship identification matrix according to the node degree correction factor and the influence weight between nodes to obtain the node relationship identification matrix.

5. The automatic control system for oil field distribution network according to claim 2, characterized in that: The loss relationship building unit comprises: An operating power characteristic acquisition subunit, the operating power characteristic acquisition subunit is used to analyze the load magnitude, operating impact, and load fluctuation of the oil field load, and obtain the operating power characteristics of each oil field load; A fluctuation characteristic analysis subunit, the fluctuation characteristic analysis subunit is used to perform fluctuation characteristic analysis according to the operating power characteristics of each oil field load to obtain power parameter fluctuation characteristics; A loss impact relationship fitting subunit, wherein the loss impact relationship fitting subunit is used to fit the loss impact relationship of power parameter fluctuation characteristics on the distribution network based on historical sample data; A loss coupling subunit is used to perform loss coupling using the power parameter fluctuation characteristics and the loss influence relationship to obtain the power fluctuation characteristics and the loss relationship of the distribution network.

6. The automatic control system for oil field distribution network according to claim 2, characterized in that: The feature analysis processing module comprises: A relationship identification and extraction module construction unit, wherein the relationship identification and extraction module construction unit is used to construct a relationship identification and extraction module based on the node relationship identification matrix; A module connection construction unit, the module connection construction unit is used to establish a connection between the relationship identification and extraction module and the pre-trained fault identification module and the pre-trained adaptive control module, wherein the input data of the relationship identification and extraction module is the state information collected in real time, and the output is the distribution network impact characteristics, and the distribution network impact characteristics are used as the input data of the pre-trained fault identification module and the pre-trained adaptive control module; A parameter acquisition unit is used for the pre-trained fault identification module and the pre-trained adaptive control module to perform fault identification and adaptive control analysis according to the output results of the relationship identification and extraction module to obtain the control parameters and / or abnormal parameters.

7. The automatic control system for oilfield distribution network according to claim 1, characterized in that: The remote connection module also includes: A communication influence relationship establishing unit, the communication influence relationship establishing unit is used to establish a communication influence relationship between the environmental information and the node communication signal, the communication influence relationship includes an influence relationship of multiple communication dimension parameters; An influence calculation unit, the influence calculation unit is used to perform influence calculation based on the collected environmental information and the communication influence relationship to obtain communication influence dimension characteristics and their influence degree; A communication network parameter positioning unit is used to position communication network parameters according to the communication impact dimension characteristics and to perform parameter compensation according to the impact degree.

8. An automatic control method for an oilfield distribution network, characterized in that: The method is applied to the automatic control system for oilfield distribution network according to any one of claims 1 to 7, and the method comprises: Configure and deploy monitoring equipment to collect status information and environmental information of each node in the oilfield distribution network; According to the collected status information and environmental information, the preset intelligent computing module is used to analyze and process the operating status characteristics and power supply characteristics to obtain control parameters and / or abnormal parameters; Establishing a remote connection with each device in the oilfield distribution network through a communication device, and performing remote control management or feedback according to the control parameters or abnormal parameters; The status information and environmental information, control parameters and abnormal parameters, and remote control tracking data are obtained and displayed on a visual interface.

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