A cloud-edge collaborative data management method for oil and gas production multi-scene adaptation

Abstract

The present application belongs to the technical field of cloud-edge collaboration, and specifically relates to a cloud-edge collaborative data management method for oil and gas production in multiple scenes, which comprises the following steps: responding to a data management task and synchronously collecting the network access type and real-time computing load state of an edge computing node; based on the network access type and real-time computing load state, performing collaborative capability evaluation on the edge computing node to obtain a node capability profile; based on the node capability profile, assembling a corresponding cloud-edge collaboration protocol for the edge computing node; synchronizing the cloud-edge collaboration protocol to a central cloud platform, and analyzing the cloud-edge collaboration protocol to obtain a synchronization rule; according to the synchronization rule, performing peer-to-peer interconnection on the edge computing node to construct a data synchronization channel and a resource scheduling channel; and based on the data synchronization channel and the resource scheduling channel, performing global situation aggregation on the edge computing node to obtain a management view. The present application can improve the efficiency of collaborative data management in multiple scenes of oil and gas production.

Description

Technical Field

[0001] This invention belongs to the field of cloud-edge collaboration technology, and in particular relates to a cloud-edge collaborative data management method that is adaptable to multiple scenarios in oil and gas production. Background Technology

[0002] In the field of oil and gas production data management, the application of cloud-edge collaboration technology is becoming increasingly widespread. However, existing technologies generally lack the ability to dynamically perceive and adapt to the operating status of edge computing nodes. Traditional cloud-edge collaboration solutions often adopt fixed collaboration protocols and synchronization rules, failing to fully consider the diversity of network access types for edge nodes and the volatility of real-time computing load in oil and gas production scenarios. This leads to problems such as unstable latency and unbalanced bandwidth allocation during data synchronization, making it difficult to meet the core requirements of real-time and stable data transmission in various oil and gas production scenarios. Consequently, data management lacks specificity and flexibility.

[0003] Furthermore, existing technologies rely on a single dimension for evaluating the collaborative capabilities of edge computing nodes. They fail to comprehensively integrate key indicators such as latency stability, bandwidth capacity, CPU utilization, and memory utilization for quantitative analysis. This lack of scientific basis for node resource scheduling directly leads to unreasonable construction of data interaction and resource scheduling channels between edge nodes, resulting in low collaborative efficiency. Poor protocol compatibility between the cloud and edge ends also easily leads to time conflicts and resource waste, failing to effectively support the efficient aggregation and management of data across the entire oil and gas production domain. Ultimately, this results in delayed data management view construction and low overall management efficiency. Therefore, improving the efficiency of collaborative data management in multiple oil and gas production scenarios has become an urgent industry problem to be solved. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the purpose of this invention is to provide a cloud-edge collaborative data management method that is adaptable to multiple scenarios in oil and gas production, which can improve the efficiency of collaborative data management in multiple scenarios in oil and gas production.

[0005] To achieve the above objectives, this invention provides a cloud-edge collaborative data management method adaptable to multiple oil and gas production scenarios, comprising the following steps: S1. Respond to the data management tasks sent by the central cloud platform and simultaneously collect the network access type and real-time computing load status of edge computing nodes in the target oil and gas production area; S2. Based on network access type and real-time computing load status, evaluate the collaborative capabilities of edge computing nodes to obtain a node capability profile for the target oil and gas production area. S3. Based on the node capability profile, equip the edge computing nodes with the corresponding cloud-edge collaboration protocol; S4. Synchronize the cloud-edge collaboration protocol to the central cloud platform and parse the cloud-edge collaboration protocol to obtain the synchronization rules for the target oil and gas production area. S5. Based on the synchronization rules, perform peer-to-peer interconnection of edge computing nodes to build data synchronization channels and resource scheduling channels between edge computing nodes; S6. On the central cloud platform, based on the data synchronization channel and resource scheduling channel, the edge computing nodes are aggregated to obtain a management view of the target oil and gas production area.

[0006] Preferably, in S1, the data management task sent by the response center cloud platform is performed, and the network access type and real-time computing load status of the edge computing nodes in the target oil and gas production area are collected simultaneously, including: Receive data management tasks issued by the central cloud platform, and perform multi-dimensional analysis of the data management tasks to obtain the task identifier and task parameters of the central cloud platform; Encode the task identifier and task parameters into task execution instructions for the central cloud platform; The application task execution command sends a status acquisition request to the edge computing nodes within the target oil and gas production area. The system receives response messages from edge computing nodes and performs feature decomposition on the response messages to obtain the network access type and real-time computing load status of the target oil and gas production area.

[0007] Preferably, in S2, a node capability profile of the target oil and gas production area is obtained, including: By analyzing the network access type, the latency stability index and bandwidth index of the target oil and gas production area can be obtained. The real-time computing load status is decomposed in a structured manner to obtain the central processing unit utilization rate and memory utilization rate of the target oil and gas production area. The collaborative capability quantification value of edge computing nodes is determined based on latency stability indicators, bandwidth indicators, CPU utilization indicators, and memory utilization indicators. Based on the quantified value of collaborative capability, edge computing nodes are classified into different levels to obtain the collaborative capability level of edge computing nodes; By comprehensively outlining the characteristics of latency stability indicators, bandwidth indicators, CPU utilization indicators, memory utilization indicators, and collaborative capability levels, a node capability profile of the target oil and gas production area is obtained.

[0008] Preferably, the formula for calculating the quantification value of collaborative capability is: ; Where C represents the quantified value of collaborative capability, and B represents the bandwidth indicator. This indicates the preset reference bandwidth specification. This indicates the CPU utilization rate. D represents the memory utilization metric, and D represents the latency stability metric. This represents the preset reference delay stability index. This represents an exponential function.

[0009] Preferably, in S3, based on the node capability profile, the edge computing nodes are equipped with corresponding cloud-edge collaboration protocols, including: Based on the node capability profile, edge computing nodes are categorized into the corresponding protocol adaptation groups; Logically encapsulate the protocol adaptation group to obtain the basic protocol framework for edge computing nodes; A quantitative assessment of node capabilities is conducted to obtain the network comprehensive utility index and computing resource surplus of edge computing nodes. Based on the network comprehensive utility index and the abundance of computing resources, the configurable parameters in the basic protocol framework are assigned values ​​to obtain the parameter set of the edge computing node; By binding the parameter set and the basic protocol framework, the cloud-edge collaboration protocol of the edge computing node is obtained.

[0010] Preferably, by binding the parameter set and the basic protocol framework, a cloud-edge collaboration protocol for edge computing nodes is obtained, specifically including: Extract the heartbeat interval, data fragment size, and retransmission mechanism from the parameter set. The heartbeat interval is the time interval between the cloud platform and the edge computing node sending heartbeat signals. Configure the corresponding triggering period in the basic protocol framework based on the heartbeat interval; The data fragment size is used as the largest transmission unit in the basic protocol framework; The retransmission mechanism is optimized to obtain the response strategy of the basic protocol framework; The triggering period, maximum transmission unit, and response strategy are integrated into a cloud-edge collaboration protocol for edge computing nodes.

[0011] Preferably, in S4, the synchronization rules for the target oil and gas production area are obtained, including: Receive the cloud-edge collaboration protocol uploaded by the edge computing node, and decapsulate the cloud-edge collaboration protocol to obtain the protocol configuration parameter set and protocol framework identifier of the target oil and gas production area; In the central cloud platform, based on the protocol framework identifier, the protocol configuration parameter set is structured and parsed to obtain the synchronization triggering conditions, synchronization direction and data format requirements of the target oil and gas production area; The synchronization triggering conditions, synchronization direction, data format requirements and node capability profiles are correlated and matched to obtain the preliminary synchronization strategy for the target oil and gas production area. The initial synchronization strategies are merged, and time and resource conflicts in the merged synchronization strategies are eliminated to obtain the synchronization rules for the target oil and gas production area.

[0012] Preferably, in S5, the construction of data synchronization channels and resource scheduling channels between edge computing nodes includes: According to the synchronization rules, a unique session identifier is assigned to each edge computing node; Based on the session identifier, a secure handshake is performed on the edge computing nodes to obtain the initial peer connection of the edge computing nodes; The initial peer-to-peer connection is tunneled to obtain the logical channel of the edge computing node; According to the synchronization rules, the logical channel is divided into a data synchronization sub-channel and a resource scheduling sub-channel for edge computing nodes; Based on synchronization rules, the data synchronization sub-channel and resource scheduling sub-channel are format-encapsulated to obtain the data synchronization channel and resource scheduling channel of the edge computing node.

[0013] Preferably, in S6, a management view of the target oil and gas production area is obtained, including: Through the data synchronization channel, real-time production status data and node health data of edge computing nodes are received; Receive snapshots of task execution status and resource utilization of edge computing nodes through the resource scheduling channel; The real-time production status data, node health data, task execution status and resource utilization snapshots are formatted and normalized to obtain a standard aggregated data stream of edge computing nodes. Multidimensional correlation analysis was performed on the standard aggregated data stream to obtain the correlation relationships of the target oil and gas production areas. Based on the relationships, generate comprehensive management information for the target oil and gas production area; Visual mapping of integrated management information yields a management view of the target oil and gas production area.

[0014] Preferably, multidimensional correlation analysis is performed on the standard aggregated data stream to obtain the correlation relationships of the target oil and gas production areas, specifically including: Multi-dimensional slicing of the standard aggregated data stream yields a data cube of the target oil and gas production area. High-order association mining is performed on the data cube to obtain strongly associated data pairs within the data cube; By analyzing the sequence of changes and the consistency of trends between strongly correlated data pairs, the potential causal relationship between the strongly correlated data pairs can be obtained. Cross-validation of potential causal relationships yields correlations among target oil and gas production areas.

[0015] The present invention has the following beneficial effects: This invention constructs a comprehensive node capability profile and matches it with an appropriate cloud-edge collaboration protocol by accurately collecting the network access type and real-time computing load status of edge computing nodes. This significantly improves the fit between data management tasks and edge node capabilities, making the cloud-edge collaboration process more targeted and efficient, and significantly improving the overall operational efficiency of data management in various oil and gas production scenarios.

[0016] This invention constructs dedicated data synchronization and resource scheduling channels, and combines them with global situational awareness aggregation to form a complete management view. This ensures smooth data transmission and resource scheduling between edge nodes, while also achieving comprehensive control over data in the target oil and gas production area. This improves the integrity and real-time performance of data management, and provides stable and reliable data support for oil and gas production-related operations and decisions. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the method of the present invention. Detailed Implementation

[0018] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0019] Example 1: As Figure 1 As shown, a cloud-edge collaborative data management method adaptable to multiple oil and gas production scenarios includes the following steps: S1. Respond to the data management tasks sent by the central cloud platform and simultaneously collect the network access type and real-time computing load status of edge computing nodes in the target oil and gas production area; S2. Based on network access type and real-time computing load status, evaluate the collaborative capabilities of edge computing nodes to obtain a node capability profile for the target oil and gas production area. S3. Based on the node capability profile, equip the edge computing nodes with the corresponding cloud-edge collaboration protocol; S4. Synchronize the cloud-edge collaboration protocol to the central cloud platform and parse the cloud-edge collaboration protocol to obtain the synchronization rules for the target oil and gas production area. S5. Based on the synchronization rules, perform peer-to-peer interconnection of edge computing nodes to build data synchronization channels and resource scheduling channels between edge computing nodes; S6. On the central cloud platform, based on the data synchronization channel and resource scheduling channel, the edge computing nodes are aggregated to obtain a management view of the target oil and gas production area.

[0020] In S1, the system responds to data management tasks sent by the central cloud platform and simultaneously collects the network access type and real-time computing load status of edge computing nodes in the target oil and gas production area, including: Receive data management tasks issued by the central cloud platform, and perform multi-dimensional analysis of the data management tasks to obtain the task identifier and task parameters of the central cloud platform; Encode the task identifier and task parameters into task execution instructions for the central cloud platform; The application task execution command sends a status acquisition request to the edge computing nodes within the target oil and gas production area. The system receives response messages from edge computing nodes and performs feature decomposition on the response messages to obtain the network access type and real-time computing load status of the target oil and gas production area.

[0021] The system receives data management tasks from the central cloud platform via a pre-defined communication interface. This interface adheres to the pre-defined cloud-edge collaboration transmission specifications to ensure that data transmission is not lost or damaged during the task data transfer process. The received data stream is structurally decomposed and analyzed from multiple fixed dimensions, including the core instruction field, target scope field, and execution requirement field. The core instruction field directly corresponds to the task identifier, which is a unique identification information assigned by the central cloud platform to each data management task. The target scope field, execution requirement field, and other related fields are integrated and extracted into task parameters. The task parameters clearly include key information such as the specific execution content of the data management task, the range of edge computing nodes to be covered, and the execution time requirements.

[0022] Following preset instruction encoding rules, the extracted task identifier is used as the header identification information of the instruction. The task parameters are embedded into the instruction body according to a fixed field order and format requirements. Each field is distinguished by a preset exclusive separator to avoid field confusion. During the encoding process, preset character mapping rules are strictly followed to transform the original information of the task identifier and task parameters into instruction character sequences that can be directly recognized by edge computing nodes, ultimately forming a unified and complete task execution instruction for the central cloud platform.

[0023] By invoking the generated task execution instructions through a stable cloud-edge communication link, and based on the edge computing node range specified in the task parameters, a temporary dedicated communication connection is established with each edge computing node one by one. During the connection establishment process, the legitimacy of the nodes is confirmed through a preset authentication mechanism. After a successful connection, a status collection request containing the task identifier and specific status collection requirements is sent to the corresponding edge computing node. The status collection request clearly informs the edge computing node of the network access type information and real-time computing load status information that needs to be fed back. During the transmission process, the transmission status of the communication link is monitored in real time to ensure that the status collection request is accurately and completely transmitted to each target edge computing node.

[0024] The system continuously monitors communication links with each edge computing node. Once an edge computing node completes its own status acquisition, it receives a response message returned by the node via the same communication link. This response message contains the edge computing node's unique identifier and corresponding status data. The received response message is then parsed hierarchically. First, the node identifier information in the message header is extracted to correspond to a specific edge computing node. Then, the status data portion of the message body is feature-decomposed. Based on preset feature correspondence rules, feature information representing the network access type is directly separated from the status data. This feature information indicates the network access type of the target oil and gas production area. Simultaneously, feature information representing the real-time computing load status is also separated. This feature information indicates the real-time computing load status of the target oil and gas production area, ensuring that the response data from each edge computing node can be accurately decomposed and both types of core information can be completely obtained.

[0025] Through a standardized and detailed execution process, the task identifier and task parameters of the central cloud platform are accurately obtained and standard task execution instructions are formed. This ensures that status collection requests are effectively transmitted to the target edge computing nodes. At the same time, response messages are accurately received and decomposed to comprehensively obtain the network access type and real-time computing load status of the target oil and gas production area. This provides accurate and complete basic data for subsequent assessment of the collaborative capabilities of edge computing nodes, ensuring the smooth progress of subsequent cloud-edge collaborative data management.

[0026] In S2, a nodal capability profile of the target oil and gas production area is obtained, including: By analyzing the network access type, the latency stability index and bandwidth index of the target oil and gas production area can be obtained. The real-time computing load status is decomposed in a structured manner to obtain the central processing unit utilization rate and memory utilization rate of the target oil and gas production area. The collaborative capability quantification value of edge computing nodes is determined based on latency stability indicators, bandwidth indicators, CPU utilization indicators, and memory utilization indicators. Based on the quantified value of collaborative capability, edge computing nodes are classified into different levels to obtain the collaborative capability level of edge computing nodes; By comprehensively outlining the characteristics of latency stability indicators, bandwidth indicators, CPU utilization indicators, memory utilization indicators, and collaborative capability levels, a node capability profile of the target oil and gas production area is obtained.

[0027] The formula for calculating the quantification value of collaborative capability is: ; Where C represents the quantified value of collaborative capability, and B represents the bandwidth indicator. This indicates the preset reference bandwidth specification. This indicates the CPU utilization rate. D represents the memory utilization metric, and D represents the latency stability metric. This represents the preset reference delay stability index. This represents an exponential function.

[0028] The collected network access type-related data were systematically sorted out, and the core characteristics such as the time fluctuation pattern and the upper limit of data transmission rate during the data transmission process were analyzed. Information reflecting the stability of network transmission delay was extracted through clear feature identification rules to form a delay stability index for the target oil and gas production area. At the same time, information on the amount of data that the network can stably transmit per unit time was extracted to form a bandwidth index for the target oil and gas production area.

[0029] Based on the core components of the computing load, the real-time collected computing load status data is structured and broken down, focusing on the CPU's operating status and extracting the proportion of its currently used resources to the total resources to obtain the CPU utilization rate index of the target oil and gas production area. At the same time, the proportion of memory currently occupied to the total memory resources is extracted to obtain the memory utilization rate index of the target oil and gas production area.

[0030] By referencing preset reference bandwidth and reference latency stability standards, the actual bandwidth metrics are compared with the reference bandwidth standards to obtain relative bandwidth performance. This is combined with the CPU utilization rate reflecting the availability of processing resources and the memory utilization rate reflecting the availability of storage resources. Furthermore, the degree of fit between the latency stability metrics and the reference latency stability standards is considered. By comprehensively weighing the impact weight of each metric on collaborative capabilities, a specific value that accurately reflects the collaborative capabilities of edge computing nodes is formed, namely, the quantitative value of the collaborative capabilities of edge computing nodes.

[0031] The network access type of the target oil and gas production area is analyzed. By extracting features and performing quantitative analysis on network access-related data, a bandwidth index reflecting the data transmission carrying capacity of the area is obtained. At the same time, by statistically analyzing and quantifying the network transmission time fluctuations, a delay stability index is obtained.

[0032] The real-time computing load status of the target oil and gas production area is decomposed in a structured manner. By collecting the CPU's operating data and performing normalization and quantization, the CPU utilization rate is obtained. Similarly, by collecting memory usage data and performing normalization and quantization, the memory utilization rate is obtained.

[0033] The preset reference bandwidth index is a standard value set in advance to measure the pass rate of bandwidth index, which combines the regular needs of oil and gas production data management with industry standards.

[0034] The preset reference delay stability index is a standard value set in advance to measure the qualified level of delay stability index, which combines the conventional delay requirements of oil and gas production data transmission with industry standards.

[0035] By dividing the bandwidth index by the product of the preset reference bandwidth index, the CPU utilization index, and the OnePlus memory utilization index, a value reflecting the matching degree between network transmission capacity and computing resource usage is obtained.

[0036] Calculate the absolute value of the difference between the delay stability index and the preset reference delay stability index, and then divide the absolute value by the preset reference delay stability index to obtain the relative deviation between the delay stability index and the standard value.

[0037] The negative value of the obtained relative deviation is substituted into the exponential function for calculation. The exponential function is calculated by using the negative value as the exponent and performing a power operation with the natural constant as the base, to obtain a value that reflects the degree to which the delay stability meets the standard.

[0038] Multiply the previously obtained value reflecting the matching degree between network transmission capacity and computing resource usage by the value reflecting the degree of compliance with latency stability standards, and finally obtain a quantitative value that fully reflects the collaborative capability of edge computing nodes under the combined effect of network transmission performance and computing resource status.

[0039] The larger the bandwidth index, which reflects the data transmission carrying capacity, the larger the value obtained after dividing by the product of the preset reference bandwidth index, the CPU utilization index, and the OnePlus memory utilization index, the greater the final quantitative value of the collaborative capability.

[0040] The higher the CPU utilization rate, the larger the product of the preset reference bandwidth, CPU utilization rate, and OnePlus memory utilization rate, the smaller the value obtained by dividing the bandwidth rate by the CPU utilization rate, and the smaller the final quantified value of the collaborative capability.

[0041] The higher the memory usage rate, the larger the result of OnePlus's memory usage rate. The larger the product of the preset reference bandwidth rate, the CPU usage rate, and this result, the smaller the value obtained by dividing the bandwidth rate by this value, and the smaller the final quantified value of the collaborative capability.

[0042] The smaller the absolute value of the difference between the latency stability index and the preset reference latency stability index, the smaller the relative deviation obtained by dividing the absolute value by the preset reference latency stability index. The larger the value obtained by substituting the negative value into the exponential function, the larger the final quantitative value of the collaborative capability.

[0043] A clearly defined range of collaborative capability levels is predefined, with each range corresponding to a unique level identifier. The obtained quantified value of the collaborative capability of the edge computing node is compared with these predefined ranges one by one to determine the specific range in which the quantified value is located, and then it is mapped to the corresponding level identifier to obtain the collaborative capability level of the edge computing node.

[0044] The system comprehensively integrates the specific performance of latency stability indicators, the actual values ​​of bandwidth indicators, the specific proportions of CPU utilization indicators, the specific proportions of memory utilization indicators, and the attribution of collaborative capability levels. It presents a complete picture from three key dimensions: network transmission performance, computing resource utilization status, and collaborative capability level, forming a set that can comprehensively and accurately reflect the core characteristics related to edge computing node collaboration, and obtains a node capability profile of the target oil and gas production area.

[0045] By systematically extracting key indicators, accurately quantifying collaborative capabilities, and classifying them into levels, the resulting node capability profile can fully and accurately reflect the core characteristics of edge computing nodes, providing a reliable basis for the precise assembly of subsequent cloud-edge collaborative protocols and effectively ensuring the pertinence and effectiveness of the cloud-edge collaborative data management process.

[0046] In S3, corresponding cloud-edge collaboration protocols are equipped for edge computing nodes, including: Based on the node capability profile, edge computing nodes are categorized into the corresponding protocol adaptation groups; Logically encapsulate the protocol adaptation group to obtain the basic protocol framework for edge computing nodes; A quantitative assessment of node capabilities is conducted to obtain the network comprehensive utility index and computing resource surplus of edge computing nodes. Based on the network comprehensive utility index and the abundance of computing resources, the configurable parameters in the basic protocol framework are assigned values ​​to obtain the parameter set of the edge computing node; By binding the parameter set and the basic protocol framework, the cloud-edge collaboration protocol for edge computing nodes is obtained, including: Extract the heartbeat interval, data fragment size, and retransmission mechanism from the parameter set. The heartbeat interval is the time interval between the cloud platform and the edge computing node sending heartbeat signals. Configure the corresponding triggering period in the basic protocol framework based on the heartbeat interval; The data fragment size is used as the largest transmission unit in the basic protocol framework; The retransmission mechanism is optimized to obtain the response strategy of the basic protocol framework; The triggering period, maximum transmission unit, and response strategy are integrated into a cloud-edge collaboration protocol for edge computing nodes.

[0047] Based on the latency stability indicators, bandwidth indicators, CPU utilization indicators, memory utilization indicators, and collaborative capability levels included in the node capability profile, clear classification rules are set. The rules specify the characteristic range of each indicator and its correspondence with the protocol adaptation group. Each indicator feature of each edge computing node is compared with the classification rules one by one, and edge computing nodes that meet the same rule conditions are classified into the same protocol adaptation group, ensuring that each node can be classified into the corresponding protocol adaptation group.

[0048] For each protocol adaptation group, the core communication logic, basic data interaction process, and essential functional modules required by the edge computing nodes in that group are sorted out. These core elements are integrated and encapsulated according to a fixed logical structure. The basic responsibilities of each functional module, the calling order between modules, and the basic path of data transmission are clarified, forming a basic protocol framework for edge computing nodes with a unified basic structure that can adapt to the core needs of the nodes in that group.

[0049] Based on the various indicators in the node capability profile, a fixed quantitative evaluation standard is established. The latency stability indicator and bandwidth indicator are comprehensively evaluated according to the standard to obtain a network comprehensive utility indicator that can reflect the overall network transmission performance of the node. By calculating the ratio of the difference between the total resources of the central processing unit and the occupied resources to the total resources, and the ratio of the difference between the total resources of the memory and the occupied resources to the total resources, and combining the weight allocation of the two, the computing resource surplus of the edge computing node is obtained.

[0050] The types and value ranges of configurable parameters in the basic protocol framework are clearly defined. These ranges directly correspond to the quantitative results of the network comprehensive utility index and the surplus of computing resources. The configuration direction related to transmission in the parameters is determined according to the level of the network comprehensive utility index. The specific values ​​related to resource allocation in the parameters are set according to the amount of surplus computing resources. Each configurable parameter is assigned a precise value that is adapted to the actual capabilities of the node, thus forming a parameter set for the edge computing node.

[0051] From the generated parameter set, the three key parameters of heartbeat interval, data fragment size and retransmission mechanism are accurately extracted according to the preset identification information of the parameters. The extraction process strictly follows the storage structure of the parameter set to ensure that the extracted parameter information is complete and consistent with the original data in the set.

[0052] The extracted heartbeat interval value is directly applied to the basic protocol framework. A trigger period that corresponds exactly to this value is set in the heartbeat detection function module of the framework. This trigger period determines the time interval for sending the heartbeat signal in the framework, ensuring that the framework can perform heartbeat detection according to the set period and maintain the connection status monitoring between the cloud and the edge.

[0053] The extracted data fragment size is directly specified as the maximum transmission unit of the data transmission module in the basic protocol framework. This clarifies the maximum amount of data that the framework can carry in a single transmission. All data transmitted through this framework must be processed according to the requirements of this maximum transmission unit to ensure that data transmission matches the carrying capacity of the node.

[0054] Based on the extracted retransmission mechanism and combined with the network comprehensive utility index of the node, the time threshold for retransmission triggering, the upper limit of the number of retransmissions, and the priority ranking of retransmitted data are adjusted. The optimized retransmission mechanism can adapt to the network transmission characteristics of the node and transform it into a data transmission response strategy in the basic protocol framework, clarifying the receiver's response method after data transmission and the processing flow when no response is received.

[0055] The pre-configured trigger period, maximum transmission unit, and response strategy are embedded into the corresponding functional modules of the basic protocol framework to ensure that each parameter is precisely connected to the corresponding module of the framework. Through fixed integration logic, the various parts are organically combined to form a cloud-edge collaboration protocol for edge computing nodes with a complete structure and parameter configuration adapted to the node capabilities.

[0056] By accurately classifying, encapsulating, assigning parameters, and integrating optimizations based on node capability profiles, the assembled cloud-edge collaboration protocol is highly compatible with the actual capabilities of edge computing nodes, ensuring the stability and efficiency of data transmission and interaction between the cloud and the edge, and providing reliable protocol support for the smooth implementation of subsequent cloud-edge collaborative data management.

[0057] In S4, the synchronization rules for the target oil and gas production area are obtained, including: Receive the cloud-edge collaboration protocol uploaded by the edge computing node, and decapsulate the cloud-edge collaboration protocol to obtain the protocol configuration parameter set and protocol framework identifier of the target oil and gas production area; In the central cloud platform, based on the protocol framework identifier, the protocol configuration parameter set is structured and parsed to obtain the synchronization triggering conditions, synchronization direction and data format requirements of the target oil and gas production area; The synchronization triggering conditions, synchronization direction, data format requirements and node capability profiles are correlated and matched to obtain the preliminary synchronization strategy for the target oil and gas production area. The initial synchronization strategies are merged, and time and resource conflicts in the merged synchronization strategies are eliminated to obtain the synchronization rules for the target oil and gas production area.

[0058] The cloud-edge collaboration protocol uploaded by edge computing nodes is received through a pre-defined secure transmission channel between the cloud and the edge. This channel employs a fixed encryption transmission mechanism to ensure the integrity and security of the protocol data. The protocol is then decapsulated according to its pre-defined encapsulation structure. First, the identifier field in the protocol header is parsed to extract the protocol framework identifier used to distinguish the protocol type. Then, the main body of the protocol is disassembled layer by layer to extract a complete data set containing various configuration information, namely the protocol configuration parameter set for the target oil and gas production area.

[0059] In the protocol parsing module of the central cloud platform, based on the extracted protocol framework identifier, the corresponding preset parsing template is invoked. This template corresponds one-to-one with the protocol framework identifier, clarifying the meaning of the fields and parsing logic of the protocol configuration parameter set. Following the requirements of the parsing template, the protocol configuration parameter set is structurally decomposed. Specific condition information for triggering data synchronization is extracted from the parameter set to form the synchronization triggering conditions for the target oil and gas production area; the direction definition information for data transmission is extracted to form the synchronization direction for the target oil and gas production area; and the format specification information for data storage and transmission is extracted to form the data format requirements for the target oil and gas production area.

[0060] The node capability profiles of the target oil and gas production area are retrieved from previous records. The synchronization triggering conditions are matched with the latency stability and bandwidth indicators in the node capability profiles to ensure that the triggering conditions are compatible with the network transmission capabilities of the nodes. The synchronization direction is matched with the collaborative capability level in the node capability profiles to ensure that the data transmission direction meets the collaborative interaction needs of the nodes. The data format requirements are matched with the CPU utilization and memory utilization indicators in the node capability profiles to ensure that the nodes can efficiently process data of the corresponding format. These matched information are integrated to form a preliminary synchronization strategy for the target oil and gas production area.

[0061] According to the preset strategy fusion rules, the preliminary synchronization strategies corresponding to multiple edge computing nodes are integrated, prioritizing the retention of strategies with the highest compatibility with the core capabilities of the nodes to form a unified fusion strategy. The fusion strategy undergoes comprehensive verification to check for resource conflicts such as multiple nodes competing for the same network or computing resources within the same time period, as well as time conflicts such as overlapping synchronization execution times in different strategies. For resource conflicts, resource allocation is adjusted based on resource availability in the node capability profile; for time conflicts, the execution order is set according to the importance of the synchronization tasks, ultimately forming a conflict-free synchronization rule for the target oil and gas production area that is compatible with all target nodes.

[0062] Through standardized protocol reception and parsing processes, precise association matching, and conflict resolution operations, the generated synchronization rules are fully adapted to the actual capabilities of edge computing nodes, ensuring that subsequent data synchronization processes have clear execution guidelines. This effectively improves the orderliness and efficiency of cloud-edge data synchronization in oil and gas production scenarios, and provides precise rule support for the construction of data synchronization channels and resource scheduling channels.

[0063] In S5, data synchronization channels and resource scheduling channels are built between edge computing nodes, including: According to the synchronization rules, a unique session identifier is assigned to each edge computing node; Based on the session identifier, a secure handshake is performed on the edge computing nodes to obtain the initial peer connection of the edge computing nodes; The initial peer-to-peer connection is tunneled to obtain the logical channel of the edge computing node; According to the synchronization rules, the logical channel is divided into a data synchronization sub-channel and a resource scheduling sub-channel for edge computing nodes; Based on synchronization rules, the data synchronization sub-channel and resource scheduling sub-channel are format-encapsulated to obtain the data synchronization channel and resource scheduling channel of the edge computing node.

[0064] Based on the edge computing node identifier, data interaction scope, and task association relationship specified in the synchronization rules, a preset unique identifier generation mechanism is adopted to assign a unique session identifier to each edge computing node. This identifier includes node feature code, task association code, and timestamp information, ensuring that the session identifiers of different edge computing nodes are not duplicated, and providing a unique identity credential for subsequent peer-to-peer interconnection between nodes.

[0065] Based on the assigned session identifier, the edge computing node initiating the interconnection sends a handshake request containing its own session identifier and authentication information to the target node. After receiving the request, the target node verifies the validity of the initiating node's session identifier, confirms the node's legitimacy through a preset authentication process, and returns an acknowledgment response containing its own session identifier after successful verification. The initiating node receives the acknowledgment response and completes the verification, thus forming the initial peer connection between the edge computing nodes.

[0066] A preset tunnel encapsulation protocol is used to encrypt and isolate the data transmission link of the initial peer connection. The transmission boundary, data encapsulation format and security transmission rules of the tunnel are defined. The original transmission path of the initial peer connection is encapsulated into an independent logical transmission link. This link only allows legitimate data carrying session identifiers to pass through, thus obtaining the logical channel of the edge computing node.

[0067] Referring to the data synchronization content type, transmission priority, and resource scheduling instruction type and execution requirements in the synchronization rules, a channel division standard is set. The links in the logical channel used to transmit real-time production status data, node operation health data and other synchronization data are divided into data synchronization sub-channels for edge computing nodes, and the links used to transmit scheduling data such as task execution status and resource utilization information are divided into resource scheduling sub-channels for edge computing nodes.

[0068] Based on the data format requirements, transmission rate specifications, and interaction protocol standards in the synchronization rules, a dedicated data packaging format, transmission verification algorithm, and data retransmission mechanism are configured for the data synchronization sub-channel to ensure the integrity and accuracy of synchronous data transmission. For the resource scheduling sub-channel, a format template for resource requests and responses, a priority transmission strategy, and an instruction execution feedback mechanism are configured to ensure the efficient transmission of resource scheduling instructions. Through the above format encapsulation, the data synchronization channel and resource scheduling channel of the edge computing node are finally obtained.

[0069] The S5 steps strictly follow synchronization rules to complete node interconnection and channel construction, enabling data synchronization channels and resource scheduling channels to have clear functional positioning and adaptable transmission characteristics. This achieves classified transmission and independent scheduling of different types of data, ensuring the orderliness, security, and efficiency of data interaction between edge computing nodes, and providing stable and reliable data transmission and resource scheduling support for subsequent global situational awareness aggregation.

[0070] In S6, a management view of the target oil and gas production area is obtained, including: Through the data synchronization channel, real-time production status data and node health data of edge computing nodes are received; Receive snapshots of task execution status and resource utilization of edge computing nodes through the resource scheduling channel; The real-time production status data, node health data, task execution status and resource utilization snapshots are formatted and normalized to obtain a standard aggregated data stream of edge computing nodes. Multidimensional correlation analysis was performed on the standard aggregated data stream to obtain the correlation relationships of the target oil and gas production areas. Based on the relationships, generate comprehensive management information for the target oil and gas production area; Visual mapping of integrated management information yields a management view of the target oil and gas production area.

[0071] Multidimensional correlation analysis was performed on the standard aggregated data stream to obtain the correlation relationships of the target oil and gas production areas, including: Multi-dimensional slicing of the standard aggregated data stream yields a data cube of the target oil and gas production area. High-order association mining is performed on the data cube to obtain strongly associated data pairs within the data cube; By analyzing the sequence of changes and the consistency of trends between strongly correlated data pairs, the potential causal relationship between the strongly correlated data pairs can be obtained. Cross-validation of potential causal relationships yields correlations among target oil and gas production areas.

[0072] Through the previously established data synchronization channel, the central cloud platform continuously monitors the transmitted data within the channel, receiving real-time production status data and node health data sent by each edge computing node in the target oil and gas production area according to a preset protocol. The real-time production status data covers core production information such as production data and equipment operating parameters during the oil and gas extraction process, while the node health data includes equipment status information such as the operating temperature of the node hardware and fault warning signals. During the reception process, the verification mechanism built into the channel ensures that the data is complete and without omissions.

[0073] Based on the established resource scheduling channels, the central cloud platform captures the task execution status and resource utilization snapshots fed back by each edge computing node in real time. The task execution status clearly shows the completion progress of the data management tasks currently undertaken by each edge computing node, whether the execution results meet expectations, etc. The resource utilization snapshot records the usage of core computing resources such as central processing unit and memory of the edge computing node at a specific point in time. All received data is accompanied by the corresponding node identifier and timestamp, which facilitates accurate association in the future.

[0074] A unified data format standard is pre-defined, which clearly specifies the field names, data types, unit specifications, and storage structure of the data. The received real-time production status data, node operation health data, task execution status, and resource utilization snapshots are processed one by one according to the standard. The raw data of different formats are converted into a unified format, the data types are adjusted to be consistent, the data unit expression is standardized, and the integration barriers caused by the differences in data formats of different edge computing nodes are eliminated. Then, all standardized data are integrated and sorted according to time order and node identifier to form a standard aggregated data stream of edge computing nodes.

[0075] Fixed slicing rules are set according to the time dimension, node identification dimension, and data type dimension. The time dimension is divided according to fixed time intervals, the node identification dimension is distinguished by the unique identifier of each edge computing node, and the data type dimension is classified into production data, health data, task data, and resource data. Based on these rules, the standard aggregated data stream is split and reorganized in multiple dimensions, and related data under the same dimension combination are centrally classified to construct a data cube of the target oil and gas production area with multi-dimensional information and a regular structure.

[0076] Using preset association mining rules, all data items in the data cube are traversed, the degree of association between different data items is calculated, a clear threshold for determining the association strength is set, and data items with an association strength exceeding the threshold are grouped and filtered out to form strongly associated data pairs in the data cube. These data pairs can intuitively reflect the significant association characteristics between different types of data.

[0077] For each strongly correlated data pair, trace its complete change trajectory over time, accurately record the time points when each of the two data items changes, clarify the order of change between them, and compare and analyze the direction and magnitude of change of the two data items within the same time period to determine whether they show a consistent trend. Combining the analysis results of the order of change and the consistency of the trend, infer the possible influence and being influenced relationship between the strongly correlated data pairs, that is, the potential causal relationship of the strongly correlated data pairs.

[0078] Historical data from the standard aggregated data stream and similar data from different edge computing nodes are retrieved to conduct multiple rounds of cross-validation on the inferred potential causal relationships. The stability and universality of the potential causal relationships are repeatedly verified under different time stages and different node operating scenarios. False associations caused by accidental factors are eliminated, and causal relationships that remain stable after multiple rounds of verification are retained, which are finally determined as the association relationships of the target oil and gas production areas.

[0079] Based on the established relationships, various basic data in the standard aggregated data stream are linked and integrated according to the association logic to form a complete data chain. Key information reflecting the overall operating status of the target oil and gas production area is extracted from the data chain, including the collaborative operating efficiency of each edge computing node, the overall progress of production tasks, the rationality of computing resource allocation, and the impact of node health status on the production process. This key information is classified, summarized, and structured to generate comprehensive and systematic integrated management information for the target oil and gas production area.

[0080] By setting fixed visual mapping rules, different types of data in the comprehensive management information are mapped to specific visual presentation formats. Numerical data is displayed in the form of charts, dashboards, etc., status data is presented in the form of different colored icons, etc., and relational data is presented in the form of flowcharts, topology diagrams, etc. All visual presentation elements are arranged reasonably according to the preset layout specifications to form an intuitive, clear and comprehensive management view of the target oil and gas production area, which makes it easy for staff to quickly grasp the overall operation status of the area.

[0081] By accurately receiving multiple types of data through different channels, and through a series of standardized processes such as standardized processing, multi-dimensional correlation analysis, and visual mapping, the data from edge computing nodes is fully integrated and deeply mined. The generated management view can completely present the overall operational status of the target oil and gas production area. This not only ensures the accuracy and completeness of data aggregation, but also improves the intuitiveness and readability of management information, providing comprehensive and reliable visualization support for the overall monitoring and decision-making of oil and gas production.

[0082] Example 2: Based on Example 1, before calculating the quantification value of the collaborative capability of edge computing nodes, a preprocessing step is included, which assigns oil and gas production scenario-based weights to each indicator and introduces a node health early warning correction coefficient. Furthermore, the calculation formula for the quantification value of collaborative capability is optimized based on the preprocessing results, specifically including: The target oil and gas production area is categorized into scenario types, including at least oil and gas wellhead scenarios, gathering and transportation station scenarios, and oil and gas processing plant scenarios. Based on the core data transmission and computing requirements of different scenarios, differentiated scenario-based weighting coefficients w are assigned to latency stability indicators, bandwidth indicators, CPU utilization indicators, and memory utilization indicators. D w B w Lc w Lm The sum of the four scenario-based weight coefficients is 1; In the oil and gas extraction wellhead scenario w D Take the maximum value, w in the collection and transportation station scenario B Take the maximum value, w in the oil and gas processing plant scenario. Lc With w Lm Take the maximum value; Collect hardware fault warning values ​​F of edge computing nodes h Network link failure warning value F n Hardware fault warning value F h The failure risk quantification values ​​for edge computing node CPU and memory, and the network link failure early warning value F. n To quantify the packet loss and outage risk of the node network access link, F h F n After normalization, the formula is used. The node health warning correction coefficient K is calculated, where For hardware fault weights, K represents the network link failure weight, where K∈(0,1], and the higher the node failure risk, the smaller the value of K. By incorporating the scenario-based weighting coefficient and the node health warning correction coefficient into the formula for calculating the collaborative capability quantification value, the improved formula for calculating the collaborative capability quantification value is obtained as follows: ; Differentiated weighting of indicators is applied to the three core scenarios of oil and gas production: extraction, gathering and transportation, and processing. A node health early warning correction coefficient is also introduced, enabling scenario-based and dynamic optimization of the quantitative calculation of collaborative capabilities. The optimized C more accurately reflects the actual collaborative capabilities of edge computing nodes in specific oil and gas production scenarios, providing a more scientific basis for the precise assembly of subsequent cloud-edge collaboration protocols and the formulation of synchronization rules, further improving the adaptability and reliability of cloud-edge collaborative data management across multiple oil and gas production scenarios.

[0083] The scenario type classification, fault warning value collection, and weight coefficient allocation in the above process can all be achieved through existing oil and gas production site sensing and monitoring equipment and cloud-edge data acquisition modules.

[0084] Example 3: A cloud-edge collaborative data management device adaptable to multiple oil and gas production scenarios, comprising: One or more processors; Memory, used to store one or more computer programs; When one or more programs are executed by one or more processors, the one or more processors perform the method in Embodiment 1 or Embodiment 2.

[0085] Example 4: A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the method in Example 1 or Example 2.

Claims

1. A cloud-edge collaborative data management method adaptable to multiple scenarios in oil and gas production, characterized by the following steps: include: S1. Respond to the data management tasks sent by the central cloud platform and simultaneously collect the network access type and real-time computing load status of edge computing nodes in the target oil and gas production area; S2. Based on network access type and real-time computing load status, evaluate the collaborative capabilities of edge computing nodes to obtain a node capability profile for the target oil and gas production area. S3. Based on the node capability profile, equip edge computing nodes with corresponding cloud-edge collaboration protocols; S4. Synchronize the cloud-edge collaboration protocol to the central cloud platform and parse the cloud-edge collaboration protocol to obtain the synchronization rules for the target oil and gas production area. S5. Based on the synchronization rules, perform peer-to-peer interconnection of edge computing nodes to build data synchronization channels and resource scheduling channels between edge computing nodes; S6. On the central cloud platform, based on the data synchronization channel and resource scheduling channel, the edge computing nodes are aggregated to obtain a management view of the target oil and gas production area.

2. The cloud-edge collaborative data management method for oil and gas production adapted to multiple scenarios as described in claim 1, characterized in that, In S1, the system responds to data management tasks sent by the central cloud platform and simultaneously collects the network access type and real-time computing load status of edge computing nodes in the target oil and gas production area, including: Receive data management tasks issued by the central cloud platform, and perform multi-dimensional analysis of the data management tasks to obtain the task identifier and task parameters of the central cloud platform; Encode the task identifier and task parameters into task execution instructions for the central cloud platform; The application task execution command sends a status acquisition request to the edge computing nodes within the target oil and gas production area. The system receives response messages from edge computing nodes and performs feature decomposition on the response messages to obtain the network access type and real-time computing load status of the target oil and gas production area.

3. The cloud-edge collaborative data management method for oil and gas production adapted to multiple scenarios as described in claim 1, characterized in that, In S2, a nodal capability profile of the target oil and gas production area is obtained, including: By analyzing the network access type, the latency stability index and bandwidth index of the target oil and gas production area can be obtained. The real-time computing load status is decomposed in a structured manner to obtain the central processing unit utilization rate and memory utilization rate of the target oil and gas production area. The collaborative capability quantification value of edge computing nodes is determined based on latency stability indicators, bandwidth indicators, CPU utilization indicators, and memory utilization indicators. Based on the quantified value of collaborative capability, edge computing nodes are classified into different levels to obtain the collaborative capability level of edge computing nodes; By comprehensively outlining the characteristics of latency stability indicators, bandwidth indicators, CPU utilization indicators, memory utilization indicators, and collaborative capability levels, a node capability profile of the target oil and gas production area is obtained.

4. The cloud-edge collaborative data management method for oil and gas production adapted to multiple scenarios as described in claim 3, characterized in that, The formula for calculating the quantification value of collaborative capability is: ； Where C represents the quantified value of collaborative capability, and B represents the bandwidth index. This indicates the preset reference bandwidth specification. This indicates the CPU utilization rate. D represents the memory utilization metric, and D represents the latency stability metric. This represents the preset reference delay stability index. This represents an exponential function.

5. The cloud-edge collaborative data management method for oil and gas production adapted to multiple scenarios as described in claim 1, characterized in that, In S3, based on node capability profiles, corresponding cloud-edge collaboration protocols are equipped for edge computing nodes, including: Based on the node capability profile, edge computing nodes are categorized into the corresponding protocol adaptation groups; Logically encapsulate the protocol adaptation group to obtain the basic protocol framework for edge computing nodes; A quantitative assessment of node capabilities is conducted to obtain the network comprehensive utility index and computing resource surplus of edge computing nodes. Based on the network comprehensive utility index and the abundance of computing resources, the configurable parameters in the basic protocol framework are assigned values ​​to obtain the parameter set of the edge computing node; By binding the parameter set and the basic protocol framework, the cloud-edge collaboration protocol of the edge computing node is obtained.

6. The cloud-edge collaborative data management method for oil and gas production adapted to multiple scenarios as described in claim 5, characterized in that, By binding the parameter set and the basic protocol framework, the cloud-edge collaboration protocol for edge computing nodes is obtained, specifically including: Extract the heartbeat interval, data fragment size, and retransmission mechanism from the parameter set. The heartbeat interval is the time interval between the cloud platform and the edge computing node sending heartbeat signals. Configure the corresponding triggering period in the basic protocol framework based on the heartbeat interval; The data fragment size is used as the maximum transmission unit in the basic protocol framework; The retransmission mechanism is optimized to obtain the response strategy of the basic protocol framework; The triggering period, maximum transmission unit, and response strategy are integrated into a cloud-edge collaboration protocol for edge computing nodes.

7. The cloud-edge collaborative data management method for oil and gas production adapted to multiple scenarios as described in claim 1, characterized in that, In S4, the synchronization rules for the target oil and gas production area are obtained, including: Receive the cloud-edge collaboration protocol uploaded by the edge computing node, and decapsulate the cloud-edge collaboration protocol to obtain the protocol configuration parameter set and protocol framework identifier of the target oil and gas production area; In the central cloud platform, based on the protocol framework identifier, the protocol configuration parameter set is structured and parsed to obtain the synchronization triggering conditions, synchronization direction and data format requirements of the target oil and gas production area; The synchronization triggering conditions, synchronization direction, data format requirements and node capability profiles are correlated and matched to obtain the preliminary synchronization strategy for the target oil and gas production area. The initial synchronization strategies are merged, and time and resource conflicts in the merged synchronization strategies are eliminated to obtain the synchronization rules for the target oil and gas production area.

8. The cloud-edge collaborative data management method for oil and gas production adapted to multiple scenarios as described in claim 1, characterized in that, In S5, data synchronization channels and resource scheduling channels are built between edge computing nodes, including: According to the synchronization rules, a unique session identifier is assigned to each edge computing node; Based on the session identifier, a secure handshake is performed on the edge computing nodes to obtain the initial peer connection of the edge computing nodes; The initial peer-to-peer connection is tunneled to obtain the logical channel of the edge computing node; According to the synchronization rules, the logical channel is divided into a data synchronization sub-channel and a resource scheduling sub-channel for edge computing nodes; Based on synchronization rules, the data synchronization sub-channel and resource scheduling sub-channel are format-encapsulated to obtain the data synchronization channel and resource scheduling channel of the edge computing node.

9. The cloud-edge collaborative data management method for oil and gas production adapted to multiple scenarios as described in claim 1, characterized in that, In S6, a management view of the target oil and gas production area is obtained, including: Through the data synchronization channel, real-time production status data and node health data of edge computing nodes are received; Receive snapshots of task execution status and resource utilization of edge computing nodes through the resource scheduling channel; The real-time production status data, node health data, task execution status and resource utilization snapshots are formatted and normalized to obtain a standard aggregated data stream of edge computing nodes. Multidimensional correlation analysis was performed on the standard aggregated data stream to obtain the correlation relationships of the target oil and gas production areas. Based on the relationships, generate comprehensive management information for the target oil and gas production area; Visual mapping of integrated management information yields a management view of the target oil and gas production area.

10. The cloud-edge collaborative data management method for oil and gas production adapted to multiple scenarios as described in claim 9, characterized in that, Multidimensional correlation analysis was performed on the standard aggregated data stream to obtain the correlation relationships of the target oil and gas production areas, specifically including: Multi-dimensional slicing of the standard aggregated data stream yields a data cube of the target oil and gas production area. High-order association mining is performed on the data cube to obtain strongly associated data pairs within the data cube; By analyzing the sequence of changes and the consistency of trends between strongly correlated data pairs, the potential causal relationship between the strongly correlated data pairs can be obtained. Cross-validation of potential causal relationships yields correlations among target oil and gas production areas.

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