LNG receiving station data collaborative processing system and method based on edge computing

Abstract

The application provides an LNG receiving station data collaborative processing system and method based on edge computing. First, distributed collection data containing the operation data stream of each monitoring area equipment and the area association identifier are acquired. Then, the data is dynamically distributed according to the area association identifier, data shards and collaborative processing strategies are generated, the strategies cover data interaction permissions and processing priorities. Then, cross-node hierarchical collaborative processing is performed on the data shards according to the data shards and the collaborative processing strategies, the area collaborative results are obtained, the cross-area fusion rules are called to globally associate and integrate the area collaborative results, global operation collaborative features are generated, the global operation collaborative features are input into the equipment linkage regulation system, and equipment collaborative regulation instructions are generated. The data processing efficiency and real-time performance are effectively improved, accurate equipment linkage regulation is realized, and the safety and intelligent level of LNG receiving station operation are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of edge computing, in particular to an LNG receiving station data collaborative processing system and method based on edge computing. BACKGROUND

[0002] In the operation and management of a liquefied natural gas (LNG) receiving station, efficient data processing and collaborative regulation are crucial. The traditional data processing method for LNG receiving stations usually adopts a centralized data acquisition and processing architecture. Under the above architecture, the device operation data of all monitoring areas are transmitted to a central server for processing. However, with the continuous expansion of the LNG receiving station scale and the increasing number of monitoring devices, the above centralized processing method faces many challenges.

[0003] On the one hand, the centralized transmission of a large amount of data will cause a sharp increase in network bandwidth pressure, and may lead to data transmission delay or even loss, seriously affecting the real-time and integrity of the data. On the other hand, the central server needs to process a large amount of data, which requires a very high server performance. Once the server fails or encounters a performance bottleneck, the data processing and regulation system of the entire receiving station will be paralyzed, and the device operation state cannot be monitored and regulated in a timely manner, increasing the safety risk of the receiving station operation.

[0004] In addition, the existing data processing method lacks effective dynamic distribution and collaborative processing mechanism when processing distributed collection data, and it is difficult to realize efficient integration and global correlation analysis of regional data, which cannot fully utilize the value of data and limits the intelligent operation level of the LNG receiving station. SUMMARY

[0005] In view of the above-mentioned problems, in combination with the first aspect of the present application, the present application embodiment provides an LNG receiving station data collaborative processing method based on edge computing, which comprises:

[0006] acquiring distributed collection data of the LNG receiving station, the distributed collection data comprising device operation data streams of each monitoring area and regional association identifiers;

[0007] performing dynamic distribution on the distributed collection data according to the regional association identifiers, generating data shards and shard corresponding collaborative processing strategies, the collaborative processing strategies comprising data interaction permissions and processing priorities;

[0008] performing hierarchical collaborative processing across nodes on the data shards according to the collaborative processing strategies to obtain regional collaborative results, the regional collaborative results comprising device operation state association sequences of each region;

[0009] The preset cross-region fusion rule is called to globally associate and integrate all the regional collaborative results, and a global operation collaborative feature of the LNG receiving station is generated.

[0010] The global operation collaborative feature is input into the equipment linkage regulation system to generate an equipment collaborative regulation instruction containing regional linkage logic.

[0011] In another aspect, the embodiment of the present application also provides an LNG receiving station data collaborative processing system based on edge computing, which comprises a processor and a machine readable storage medium, the machine readable storage medium is connected with the processor, the machine readable storage medium is used for storing programs, instructions or codes, and the processor is used for executing the programs, instructions or codes in the machine readable storage medium to realize the above method.

[0012] Based on the above aspects, the embodiment of the present application acquires distributed collection data containing device operation data streams and regional association identifiers of each monitoring region, and performs dynamic distribution according to the regional association identifiers to generate data shards and corresponding collaborative processing strategies, effectively avoiding the problems of data transmission congestion and center server performance bottleneck in traditional centralized processing, performing hierarchical collaborative processing on the data shards across nodes according to the collaborative processing strategies, giving full play to the computing power of edge nodes, realizing parallel processing of regional data, and greatly improving the data processing efficiency. The preset cross-region fusion rule is called to globally associate and integrate the regional collaborative results, and a global operation collaborative feature of the LNG receiving station is generated, so that the data of each region can be deeply fused to mine the potential association between the data, and finally the global operation collaborative feature is input into the equipment linkage regulation system to generate an equipment collaborative regulation instruction, realizing precise linkage regulation between devices, effectively improving the operation safety and stability of the LNG receiving station, reducing the operation cost, and improving the overall intelligent operation level of the receiving station. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 is an execution flow diagram of the LNG receiving station data collaborative processing method based on edge computing provided by the embodiment of the present application.

[0014] Figure 2 is a schematic diagram of exemplary hardware and software components of the LNG receiving station data collaborative processing system based on edge computing provided by the embodiment of the present application. DETAILED DESCRIPTION

[0015] The present application will be specifically described below in conjunction with the drawings of the specification, Figure 1 is a flow diagram of the LNG receiving station data collaborative processing method based on edge computing provided by an embodiment of the present application, and the LNG receiving station data collaborative processing method based on edge computing will be described in detail below.

[0016] Step S110: Obtain distributed collection data of the LNG receiving station, the distributed collection data including equipment operation data streams of each monitoring area and area association identifier.

[0017] In this embodiment, the LNG receiving station includes multiple monitoring areas, and different types of equipment are distributed in each monitoring area, and the equipment continuously generates operation data. In order to realize comprehensive monitoring of the entire LNG receiving station, it is necessary to obtain distributed collection data from the edge nodes of each monitoring area.

[0018] The area association identifier is identification information for distinguishing different monitoring areas, which is composed of multiple parts, including area geographic coordinate range information, main equipment type code in the area, and hierarchical coding of the area in the overall layout of the receiving station. For example, the area association identifier of a certain monitoring area may include the latitude and longitude range of the area, the type code of the main equipment in the area being a cryogenic pump, and the hierarchical coding of the area belonging to the first partition of the receiving station.

[0019] The equipment operation data stream is composed of data collected by sensors and data collection devices installed on each device in real time. These data cover various operation parameters of the equipment, such as the speed of the cryogenic pump, the inlet and outlet pressure, the operating temperature, the on-off state of the valve, the on-off time, the flow of the pipeline, the pressure fluctuation, etc. Each piece of equipment operation data corresponds to a specific timestamp to reflect the operating state of the equipment at different times.

[0020] In the process of obtaining distributed collection data, it is necessary to realize data aggregation through edge gateways in each monitoring area. The edge gateway is connected with the equipment in the area through wired or wireless communication, reads the operation data from the equipment according to the preset collection frequency, and binds these data with the corresponding area association identifier to form complete distributed collection data. At the same time, in order to protect the privacy and security of the data, the equipment operation data stream can be encrypted during data transmission, and the symmetric encryption algorithm is used to encrypt the data content, and only the authorized edge node can decrypt and obtain the original data.

[0021] Step S120: Perform dynamic distribution of the distributed collection data according to the area association identifier, generate data shards and shard corresponding cooperative processing strategies, the cooperative processing strategies including data interaction permissions and processing priorities.

[0022] After obtaining the distributed collection data, the next step is to dynamically distribute these data according to the area association identifier. The hierarchical coding and geographic coordinate range in the area association identifier are the main basis for dynamic distribution.

[0023] Firstly, the hierarchical coding in the region association identifier is parsed to determine the hierarchical relationship of each monitoring region in the overall structure of the LNG receiving station. For example, a first-level partition contains multiple second-level partitions, and each second-level partition contains several third-level partitions, and so on. According to the above hierarchical relationship, the distributed collection data is preliminarily divided into different data sets corresponding to the first-level partitions.

[0024] Then, the data sets in each first-level partition are further subdivided in combination with the geographic coordinate ranges in the region association identifier. For regions with adjacent geographic coordinate ranges or device associations, their data will be allocated to nearby processing nodes to reduce the delay of data interaction.

[0025] Through the above dynamic distribution process, multiple data shards are generated, each corresponding to the device operation data of one or more monitoring regions with close associations.

[0026] While generating data shards, a corresponding collaborative processing strategy needs to be generated for each data shard. The determination of data interaction permissions is based on the device association relationship between monitoring regions and the sensitivity of data. For example, there is a direct material transfer relationship between the devices in two monitoring regions, so the data shards corresponding to these two regions have high data interaction permissions and can share some operation data with each other; while for regions containing sensitive operation parameters (such as parameters related to process secrecy), the data interaction permissions of the data shards corresponding to these regions will be limited, allowing interaction only with a few key data shards.

[0027] The processing priority is determined according to the real-time requirements of the data and the importance of the data to the safe operation of the LNG receiving station. For device operation data with high real-time requirements and direct impact on the safety of the receiving station, such as pressure and temperature data of storage tanks, the processing priority of the corresponding data shard is set to real-time level; for some data with slightly lower real-time requirements but important to device maintenance and optimized operation, such as cumulative running time and energy consumption data of devices, the processing priority is set to quasi-real-time level; while for historical archive data, device static parameters and other data with low real-time requirements, the processing priority is set to regular level.

[0028] Step S121: parsing the region association identifier in the distributed collection data, establishing a mapping relationship table between regions and nodes, the mapping relationship table containing region coding, node identifier and node load state.

[0029] When parsing the region association identifier in the distributed collection data, the region coding information needs to be extracted. The region coding is uniformly prepared by the receiving station and is used to uniquely identify each monitoring region. It contains information such as the level and geographic location of the region.

[0030] Before establishing the mapping relationship table between the regions and the nodes, the load states of the edge nodes need to be monitored. The node load state includes the CPU usage, memory occupancy, network bandwidth occupancy and other parameters of the node. The edge nodes periodically send their load state information to the central control node, and the central control node collects and summarizes these information.

[0031] Then, according to the location of the monitoring region represented by the region code and the data processing requirements, and the load state of each edge node, each region code is associated with the node identifier of the appropriate edge node. For example, a certain monitoring region is closer to a certain edge node, and the current load of the edge node is lower, so the region code is corresponded to the node identifier of the edge node, and recorded in the mapping relationship table.

[0032] The mapping relationship table is updated in real time, and when the load state of the edge node changes greatly, such as the CPU usage of a certain node exceeds the preset threshold, the central control node will re-adjust the mapping relationship between the regions and the nodes to ensure the efficiency of data processing.

[0033] Step S122: According to the mapping relationship table and the node load state, the distributed collection data is split into multiple data shards by region, and each data shard contains the device running data stream and region code of the corresponding region.

[0034] According to the corresponding relationship between the region code and the node identifier in the mapping relationship table, and the load state of each node, the distributed collection data is split. For the edge node with lower node load, more regional data can be allocated; and for the node with higher load, the amount of regional data allocated is appropriately reduced.

[0035] In the splitting process, the device running data streams belonging to the same region code are concentrated together to form a data shard. Each data shard contains not only the device running data stream, but also the corresponding region code, so that the monitoring region to which the data shard belongs can be clearly determined in the subsequent processing process.

[0036] For example, the region code A in the mapping relationship table corresponds to the node identifier X, and the node X has a lower load, so all the device running data streams with the region code A are integrated into a data shard and allocated to the node X for processing; the region code B corresponds to the node identifier Y, and the node Y has a medium load, so the data shards formed by the device running data streams with the region code B are split according to the set proportion and allocated to the node Y and the adjacent node Z with lower load.

[0037] Step S123: Based on the region importance and the real-time requirement of the device running data stream of the data shard, a processing priority is allocated to each data shard, and the processing priority is divided into real-time level, quasi-real-time level and regular level.

[0038] The importance of a region of a data shard is determined by the role of the region in the overall operation of the LNG receiving station. For example, the region of the storage tank storing LNG, the region of the main pipeline responsible for LNG transportation, and the like belong to regions of high importance; and the region of some auxiliary facilities has relatively low importance.

[0039] The real-time requirement of a device operation data stream is determined according to the urgency of the device operation state reflected by the data. For example, the real-time requirement of a data stream reflecting a device failure warning is high, and the real-time requirement of a data stream reflecting device daily maintenance statistics is low.

[0040] The processing priority of each data shard is allocated by comprehensively considering the importance of the region and the real-time requirement of the device operation data stream. The data shard with high importance and high real-time requirement is allocated to the real-time level; the data shard with high importance and medium real-time requirement is allocated to the quasi-real-time level; and the data shard with low importance or low real-time requirement is allocated to the regular level.

[0041] For example, the data shard of the device operation data stream reflecting the sharp change of the tank pressure in the tank region is allocated to the real-time level because of its high importance and high real-time requirement; the data shard of the data stream reflecting the progress of the regular loading and unloading operation in the loading and unloading region is allocated to the quasi-real-time level because of its high importance and medium real-time requirement; and the data shard of the historical operation record data of the device in the auxiliary region is allocated to the regular level because of its low importance and low real-time requirement.

[0042] Step S124: According to the physical connection relationship between regions and the data interaction requirement, the data interaction permission of each data shard is set, which includes a list of interactable nodes and a data sharing range.

[0043] The physical connection relationship between regions refers to the actual connection between devices in different monitoring regions, such as pipeline connection, circuit connection, and the like. For example, the tank region and the pipeline region have a direct pipeline connection, so there is a strong data interaction requirement between the two regions.

[0044] The data interaction requirement is determined based on the correlation of device operation. When the device operation state of one region affects the device operation of another region, there is a data interaction requirement between the data shards corresponding to the two regions.

[0045] According to the physical connection relationship between regions and the data interaction requirement, it is determined which edge nodes each data shard can interact with to form a list of interactable nodes. At the same time, the range of data sharing is determined, that is, which types of device operation data can be shared between these nodes.

[0046] For example, the data slice of the storage tank area has strong interaction requirements with the data slice of the pipeline area, so the list of interactive nodes of the data slice of the storage tank area includes the edge nodes of the pipeline area, and the data sharing range includes the liquid level and pressure of the storage tank. The data slice of the auxiliary area has weak interaction requirements, so the list of interactive nodes may not include the edge nodes of the auxiliary area, or only a small amount of non-critical data is allowed to be shared.

[0047] Step S125: Combine the data slice, processing priority, and data interaction permission into a collaborative processing strategy, so that each data slice corresponds to a unique collaborative processing strategy.

[0048] After completing the division of the data slice, the allocation of the processing priority, and the setting of the data interaction permission, the three parts are combined to form the collaborative processing strategy corresponding to each data slice.

[0049] The collaborative processing strategy is a structured information set, in which the data slice specifies the data content to be processed, the processing priority specifies the order of data processing, and the data interaction permission limits the interaction range and object of the data in the processing process.

[0050] The collaborative processing strategy corresponding to each data slice is unique, because different data slices differ in data content, processing requirements, and interaction requirements. Through the above unique correspondence, it can be ensured that each data slice can be processed according to its own strategy, improving the pertinence and efficiency of data processing.

[0051] Step S130: According to the collaborative processing strategy, perform cross-node hierarchical collaborative processing on the data slice to obtain a regional collaborative result, which includes the sequence of the running states of the devices of each region.

[0052] After obtaining the collaborative processing strategy corresponding to each data slice, cross-node hierarchical collaborative processing can be performed on the data slice according to the strategy. Hierarchical collaborative processing refers to collaborative processing at different processing levels according to the processing priority of the data slice.

[0053] For real-time level data slices, processing is required at the highest processing level, and real-time data interaction and collaboration between nodes are required. Quasi-real-time level data slices are processed at an intermediate level, and the real-time requirement for data interaction is relatively reduced. The conventional level data slice is processed at the basic level, mainly relying on independent node processing, and only a small amount of data interaction is performed when necessary.

[0054] In the processing process, each edge node exchanges data with the nodes in the interactive node list according to the data interaction permission in the cooperative processing strategy, and processes the data fragments cooperatively. Through the above cross-node cooperative processing, the device operation data of multiple related areas can be integrated, and the correlation between the device operation states can be mined, and finally the area cooperative result containing the correlation sequence of the device operation states of each area is formed.

[0055] Step S131: distribute the data fragments and the corresponding cooperative processing strategy to the associated nodes, the cooperative processing strategy including data interaction permission and processing priority, the data interaction permission including an interactive node list and a data sharing range, and the processing priority being divided into real-time level, quasi-real-time level and regular level.

[0056] When distributing the data fragments and the corresponding cooperative processing strategy, each data fragment and its cooperative processing strategy is sent to the corresponding associated node according to the corresponding relationship between the area code and the node identifier in the mapping relationship table.

[0057] After receiving the data fragments and the cooperative processing strategy, the associated node can analyze the cooperative processing strategy to determine the data interaction permission and the processing priority of the data fragments. The interactive node list indicates which nodes the associated node can interact with, the data sharing range specifies the types and contents of the shareable data, and the processing priority determines the position of the data fragment in the node processing queue.

[0058] For example, a real-time level data fragment and its cooperative processing strategy are distributed to node M, and node M analyzes and learns that it can interact with node N and node P, can share real-time pressure and temperature of the device, and needs to process the data fragment first.

[0059] Step S132: sort the received data fragments according to the processing priority, process the real-time level data fragments first, then the quasi-real-time level data fragments, and finally the regular level data fragments.

[0060] After receiving multiple data fragments, the associated node can sort them in the processing queue according to the processing priority of each data fragment. The processing queue is a task list used to arrange the order of data processing within the node.

[0061] First, all real-time level data fragments are arranged at the front of the queue, which need to be processed first to ensure timely response to the real-time operation state of the device. Then, the quasi-real-time level data fragments are arranged after the real-time level data fragments, and are processed after the real-time level data fragments are processed or during the processing gap. Finally, the regular level data fragments are arranged at the end of the queue, and are processed after the first two types of data fragments are processed.

[0062] In the sorting process, if multiple data shards of the same priority appear, a secondary sorting can be further performed according to the importance of the region corresponding to the data shard or the data receiving time, to optimize the processing efficiency.

[0063] Step S133: For the data shard with the processing priority of the real-time level, a real-time data request is sent to the associated nodes according to the interactable node list in the data interaction permission, and the real-time data of the associated region returned by the associated nodes is received.

[0064] When the data shard with the processing priority of the real-time level enters the processing queue and starts processing, the associated nodes will first check the interactable node list in the data interaction permission in the cooperative processing strategy.

[0065] Then, the real-time data request information is generated according to the node identifier in the interactable node list. The real-time data request information includes the identifier of the node, the type of data to be requested (determined based on the data sharing range), and the timestamp of the request, etc.

[0066] The generated real-time data request information is sent to each associated node in the interactable node list. After receiving the real-time data request, the associated nodes can immediately extract the corresponding real-time data of the associated region from the real-time level data shard being processed by the associated nodes, and return the data to the node sending the request.

[0067] For example, the interactable node list of the real-time level data shard processed by node A includes node B and node C, node A will send a real-time data request to node B and node C to request the real-time running data related to the data shard, and node B and node C will return the relevant real-time data in their respective regions to node A after receiving the request.

[0068] Step S1331: Extract the interactable node list contained in the data interaction permission in the cooperative processing strategy, and determine the associated node identifier that needs to be interacted.

[0069] When processing the real-time level data shard, the interactable node list is first extracted from the data interaction permission in the cooperative processing strategy. The interactable node list is a list containing multiple node identifiers, and the edge nodes corresponding to these node identifiers have a real-time data interaction demand with the monitoring region to which the data shard processed by the current processing node belongs.

[0070] By analyzing the interactable node list, the edge nodes represented by each node identifier in the interactable node list are determined, and the associated node identifiers that need to be interacted are determined. These associated node identifiers are recorded separately as the target objects for sending the real-time data request subsequently.

[0071] Step S1332: generating a real-time data request instruction according to the associated node identifiers, the real-time data request instruction containing a request node identifier, a region code of the data shard, and a request timestamp.

[0072] After the associated node identifiers are determined, the real-time data request instruction is generated according to the identifiers. The request node identifier is the self-identifier of the edge node currently processing the data shard, which is used to let the associated nodes know which node the request comes from.

[0073] The region code of the data shard is contained in the request instruction, so that the associated nodes can know which region the real-time data related to needs to be returned. The request timestamp records the time when the request instruction is generated, which is used by the associated nodes to judge the timeliness of the request and to perform time synchronization when the data is returned.

[0074] The real-time data request instruction is organized in a specific format to ensure that the associated nodes can correctly parse the information in it.

[0075] Step S1333: sending the real-time data request instruction to all the associated nodes in the list of interactable nodes, and waiting for the responses of the associated nodes.

[0076] After the real-time data request instruction is generated, it is sent to all the associated nodes in the list of interactable nodes through the communication network between nodes. The communication network uses a reliable transmission protocol to ensure that the instruction can accurately and timely reach the associated nodes.

[0077] After the sending is completed, the current processing node enters a waiting state to wait for the responses of the associated nodes. During the waiting process, the node monitors the state of the communication link. If the instruction sending fails, a retransmission operation can be performed to improve the success rate of the request.

[0078] Step S1334: receiving the associated region real-time data returned by the associated nodes, the associated region real-time data containing the real-time level data shards processed by the associated nodes and the corresponding region codes.

[0079] After receiving the real-time data request instruction, the associated nodes can extract the relevant associated region real-time data according to the region code in the instruction and the real-time level data shards processed by themselves, and return these data to the request node together with the region code of the associated nodes.

[0080] The current processing node continuously monitors the communication port, and when the data returned by the associated nodes is received, the data is received and temporarily stored. The returned associated region real-time data can contain data sent by multiple associated nodes, and each data unit corresponds to a specific region code, so that subsequent data matching and processing can be performed.

[0081] Step S1335: Perform regional encoding verification on the received associated area real-time data, timestamp align the associated area real-time data that passes the verification with the real-time level data slice of itself, and generate a time-synchronized associated data set.

[0082] After receiving the associated area real-time data, it needs to be verified for regional encoding. The verification process is to compare the regional encoding in the returned data with the regional encoding of the data slice contained in the real-time data request instruction, to check whether there is an expected association between the two, to ensure that the received data is from the correct associated area.

[0083] For the associated area real-time data that passes the verification, the next step is to perform timestamp alignment. By comparing the timestamp of the associated area real-time data with the timestamp of the data in the real-time level data slice of itself, the data with similar time are matched to keep them synchronized in the time dimension.

[0084] After timestamp alignment, the real-time level data slice data of itself and the associated area real-time data that passes the verification are integrated together to form a time-synchronized associated data set.

[0085] Step S134: Perform real-time state association analysis on the basis of the data slice and the associated area real-time data, and generate a real-time state association sub-sequence.

[0086] With the time-synchronized associated data set, real-time state association analysis can be performed. The analysis process mainly mines the association between the devices in the area to which the data slice belongs and the devices in the associated area in terms of running state.

[0087] By analyzing the change trend and change time point of the device running parameters in the associated data set, the mutual influence and association rules of the running states of different devices are found out. For example, when the pressure in the storage tank area shows an upward trend, the valve opening degree in the pipeline area may change accordingly, and the above change relationship will be captured and recorded.

[0088] During the analysis process, the device running parameters can be continuously monitored, and when the running parameters of a device exceed the normal range or mutate, the running parameters of the devices in the associated area can be immediately checked for corresponding changes. Through the above method, the real-time state association between devices is determined.

[0089] These association relationships are sorted in chronological order to form a real-time state association sub-sequence. Each element in the real-time state association sub-sequence contains the associated device identifier, the respective running state parameter, and the time information of the state association, which can clearly reflect the state association between devices in real time.

[0090] Step S1341: Extract the device state identifier and state change time in the device running data stream contained in the self data slice, and generate a self state event sequence, which contains the device identifier, state value and change time.

[0091] From the device running data stream in the self data slice, filter out the identification information that can reflect the device state. These device state identifiers can be the model code of the device, the installation location code, etc., which are used to uniquely determine the specific device.

[0092] At the same time, extract the time when the state changes and the state value at the corresponding time in the device running data stream. The state value can be the running parameter value of the device, such as pressure value, temperature value, and speed value, etc. It can also be the running mode identifier of the device, such as running, stopping, standby, etc.

[0093] Arrange the extracted device identifier, state value and change time in chronological order to form a self state event sequence. For example, the low-temperature pump A in the area to which the self data slice belongs has a state value of speed increase at a certain change time, which will be recorded in the self state event sequence.

[0094] Step S1342: Extract the device state identifier and state change time in the device running data stream contained in the associated area real-time data, and generate an associated state event sequence, which contains the device identifier, state value and change time.

[0095] For the device running data stream in the associated area real-time data, the same method as extracting the self state event sequence is used to extract the device state identifier, state change time and state value.

[0096] These device state identifiers correspond to the devices in the associated area, and through these identifiers, different associated devices can be distinguished. These information is also arranged in chronological order to generate an associated state event sequence.

[0097] For example, the valve B in the associated area has a state value of opening degree increase at a certain change time, which will be recorded in the associated state event sequence.

[0098] Step S1343: Merge and sort the self state event sequence and the associated state event sequence according to the change time to generate a merged event sequence.

[0099] Arrange the self state event sequence and the associated state event sequence together, and sort them according to the change time. Compare the change time of each event in the two sequences, and rearrange all the events in chronological order.

[0100] If the change time of two events is the same or very close (within a preset time error range), they are considered as events at the same time point, and arranged in the order of device identification.

[0101] Through the above merging and sorting operation, a merged event sequence containing all device state events of the self region and the associated region is formed, which can fully reflect the state changes of all related devices within a period of time.

[0102] Step S1344: Analyze the device association of adjacent state events in the merged event sequence to determine the device pairs with linkage relationship and the corresponding state change rules.

[0103] In the merged event sequence, adjacent state events are viewed one by one to analyze whether there is association between the devices involved in these events. Device association can be determined by physical connection relationship between devices, upstream and downstream relationship in process, etc.

[0104] For example, the two adjacent state events involve a cryogenic pump and a pipeline valve connected to it, and since they have a physical connection, it can be considered that there is an association between the two devices.

[0105] For devices with association, further analyze their state change conditions to find out the state change rules. For example, after the speed of the cryogenic pump increases, the opening of the pipeline valve increases, and the above regular change is the state change rule of the two devices.

[0106] Combine the devices with linkage relationship into device pairs and record the corresponding state change rules.

[0107] Step S1345: Based on the state change rules, combine the state events with linkage relationship in the merged event sequence into associated event groups, each of which contains at least two state events of associated devices.

[0108] According to the determined state change rules, find the state events in the merged event sequence that meet the rules. Combine those state events with linkage relationship together to form associated event groups.

[0109] Each associated event group contains at least two state events from different devices (which can be devices in the self region and the associated region, or devices between the associated regions), and there is an explicit linkage relationship between these events, which meets the corresponding state change rules.

[0110] For example, the state event of increasing the speed of the cryogenic pump and the state event of increasing the opening of the pipeline valve meet the state change rules of both, and they are combined into an associated event group.

[0111] Step S1346: The associated event groups are arranged in chronological order to generate a real-time state association sub-sequence, each element in the real-time state association sub-sequence being an associated event group.

[0112] All the associated event groups formed are arranged in chronological order according to their time in the merged event sequence to form a real-time state association sub-sequence.

[0113] Each element in the real-time state association sub-sequence is an associated event group, and through these elements, the time evolution process of the state association between different devices in real time can be clearly presented, reflecting the dynamic association of the device running state.

[0114] Step S135: For the data shard with the processing priority of the quasi-real-time level, based on the data sharing range in the data interaction permission, the historical collaborative data of the associated region is obtained, and quasi-real-time association analysis is performed on the self data shard to generate a quasi-real-time state association sub-sequence.

[0115] When the data shard with the processing priority of the quasi-real-time level starts processing, the associated node determines the type of historical collaborative data of the associated region that needs to be obtained according to the data sharing range of the data interaction permission in the collaborative processing strategy.

[0116] The historical collaborative data is the collaborative processing result of the associated region when processing the data shard of the quasi-real-time level or the real-time level in the past period of time. These data have been stored and sorted and contain the associated information of the device running state.

[0117] The associated node requests to obtain the corresponding historical collaborative data by communicating with the edge node of the associated region. The edge node of the associated region extracts the historical collaborative data from the local storage according to the type and range of the requested data and returns it.

[0118] After obtaining the historical collaborative data, quasi-real-time association analysis is performed on the device running data stream of the self data shard. The analysis process is similar to the real-time state association analysis, but the time span is larger, mainly focusing on the association rules of the device running state in a longer period of time.

[0119] Through analyzing the change trend, periodicity, etc. of the device state in the self data shard data and the historical collaborative data, the association relationship is found out, and then a quasi-real-time state association sub-sequence is generated. The quasi-real-time state association sub-sequence is also composed of multiple associated event groups, which reflect the association of the device running state in the quasi-real-time situation.

[0120] Step S136: For the data shard with the processing priority of the regular level, the data shard is processed independently to generate a regular state association sub-sequence.

[0121] The data slice of the regular level has the lowest processing priority and usually does not need to interact with other areas. Therefore, the associated nodes can be processed independently when processing the data slice of the regular level.

[0122] The independent processing process mainly analyzes the running data flow of the device of the data slice itself and mines the state correlation between the devices in the area. By analyzing the change of the device running parameters, the sequence of device running and the like, the correlation between the devices in the area is determined.

[0123] For example, the relationship between the opening and closing sequence of different valves in the area and the change of the pipeline pressure is analyzed to find the correlation mode. The correlation is sorted into a correlation event group in time sequence, and the correlation event group is arranged to form a regular state correlation subsequence.

[0124] Step S137: The real-time state correlation subsequence, the quasi-real-time state correlation subsequence and the regular state correlation subsequence are spliced in time sequence to form a device running state correlation sequence of each area as a regional coordination result.

[0125] After generating the real-time state correlation subsequence, the quasi-real-time state correlation subsequence and the regular state correlation subsequence respectively, the three subsequences need to be integrated.

[0126] The integration process is spliced in time sequence, that is, according to the time information of the correlation event group in each subsequence, they are placed in the correct time position. For the correlation event groups overlapping in time, the merging processing can be performed to retain the complete correlation information.

[0127] After splicing, a complete device running state correlation sequence covering data of different processing priorities is formed, which reflects the running state correlation of the devices in the corresponding area at different time scales and is stored and used as a regional coordination result.

[0128] Step S140: A preset cross-area fusion rule is called to globally integrate all the regional coordination results of the areas to generate a global running coordination feature of the LNG receiving station.

[0129] After obtaining the regional coordination results of all the areas, the results need to be globally integrated to obtain the global running coordination feature of the entire LNG receiving station. This process is realized by calling a preset cross-area fusion rule.

[0130] The cross-area fusion rule is formulated based on the overall layout of the LNG receiving station, the global correlation between the devices and the running management requirements, and contains the judgment standard of area correlation, the evaluation method of event correlation and the extraction dimension of global features.

[0131] By applying these rules, the device operation state association sequence of each region is analyzed to find the association relationship of device operation states between different regions, and the scattered regional coordination results are integrated into a global and unified operation coordination feature.

[0132] Step S141: Collect the regional coordination results of all regions, which include the device operation state association sequence of each region, and each device operation state association sequence includes a group of association events arranged in chronological order, and each group of association events includes the state events of at least two associated devices.

[0133] The central control node sends a regional coordination result collection request to the edge nodes of all regions, and the request clearly specifies the time range and data format to be collected.

[0134] After receiving the request, each edge node arranges the generated regional coordination results according to the specified format, and then sends them to the central control node.

[0135] The central control node summarizes and stores the received regional coordination results to ensure that the results of all regions are collected completely. In the collected regional coordination results, the device operation state association sequence of each region maintains the original time sequence and association event group structure, and each association event group clearly records the state events of the related devices.

[0136] Step S142: Analyze the cross-region fusion rules to extract the regional association weight, event association threshold, and global feature dimension included in the cross-region fusion rules. The regional association weight is used to represent the importance of different regions in global association, the event association threshold is used to judge the association of events in different regions, and the global feature dimension is used to define the composition of the global operation coordination feature.

[0137] The central control node analyzes the pre-set cross-region fusion rules to extract the three key parameters of regional association weight, event association threshold, and global feature dimension.

[0138] The regional association weight is determined according to the importance of each region in the LNG receiving station. The weight value of an important region is relatively high, and it occupies a larger proportion in the global association integration.

[0139] The event association threshold is a critical value for judging whether the events of different regions are associated. When the association degree of events of two regions exceeds the threshold, it is considered that they are associated.

[0140] The global feature dimension specifies the specific content and dimension of the global operation coordination feature, such as event occurrence frequency, regional coverage, etc.

[0141] Step S143: Based on the region correlation weight, the device running state correlation sequence of each region is weighted to generate a weighted state correlation sequence.

[0142] For the device running state correlation sequence of each region, the region correlation weight corresponding to the region is used for weighting processing. The weighting processing mainly adjusts the state values in the correlation event group.

[0143] Specifically, each state value in the correlation event group is multiplied by the region correlation weight of the region to obtain a weighted state value. In this way, the state value of an important region has greater influence in subsequent analysis.

[0144] After weighting the state values of all correlation event groups, the original time sequence and correlation structure are maintained to form the weighted state correlation sequence of the region.

[0145] Step S1431: Extract the device running state correlation sequence of each region, which includes correlation event groups arranged in time sequence, and each correlation event group includes state events of at least two associated devices.

[0146] From all the collected regional coordination results, the device running state correlation sequence of each region is extracted one by one. The correlation event groups in these sequences are arranged in time sequence, and each correlation event group contains at least two state events of associated devices, each state event has a clear device identifier, state value and time information.

[0147] During the extraction process, the integrity of each sequence can be checked to ensure that no critical information is missing.

[0148] Step S1432: Obtain the region correlation weight in the cross-region fusion rule, which assigns a weight value to each region, and the sum of the weight values of all regions is 1.

[0149] From the parsed cross-region fusion rule, extract the region correlation weight assigned to each region. These weight values are determined based on factors such as the functional importance of each region in the receiving station, the number of devices, data influence, etc., and the sum of all region weight values is 1 to ensure the rationality and normalization of weight distribution.

[0150] For example, the weight value of the storage tank region may be relatively high, while the weight value of the auxiliary facility region may be relatively low.

[0151] Step S1433: Add a region weight mark to each correlation event group in the device running state correlation sequence of each region, and the value of the region weight mark is equal to the region correlation weight value of the region.

[0152] In the device running state association sequence of each region, a region weight mark is added to each association event group. The value of the region weight mark is the region association weight value corresponding to the region, which is used to identify the importance of the association event group in the global association integration.

[0153] After adding the region weight mark, the association event group contains not only the state event information of the device, but also the corresponding weight information.

[0154] Step S1434: The state value of each state event in the association event group is weighted to obtain a weighted state value, which is equal to the product of the original state value and the region weight mark.

[0155] For the state value of each state event in each association event group, it is multiplied by the region weight mark of the association event group to obtain a weighted state value.

[0156] For example, the region weight mark of a certain association event group is 0.3, and the original state value of a certain state event is a certain pressure value. Then the weighted state value is the product of the pressure value and 0.3.

[0157] Through the above weighting calculation, the state values of different regions are adjusted according to their regional importance.

[0158] Step S1435: Replace the original state value with the weighted state value to form an association event group containing the weighted state value.

[0159] Replace the original state value in the association event group with the calculated weighted state value, and keep other information such as device identifier, time information, etc. unchanged to form a new association event group containing the weighted state value.

[0160] In this way, the state value in the association event group carries the weight information of the regional importance, and can better reflect its actual influence in the global association.

[0161] Step S1436: Keep all association event groups containing weighted state values in chronological order to generate the weighted state association sequence of the region.

[0162] Arrange all the weighted association event groups in the original time order to maintain their time association, thereby generating the weighted state association sequence of the region.

[0163] The weighted state association sequence not only retains the original time structure and event association relationship, but also reflects the importance difference of the region through weighting processing.

[0164] Step S144: Calculate the similarity of the associated event groups in the weighted state association sequence of different regions. When the similarity is greater than the event association threshold, it is determined that the corresponding associated event group exists global association.

[0165] For the weighted state association sequence of different regions, the associated event groups are selected for pairwise comparison, and the similarity between them is calculated. The calculation of the similarity is based on multiple factors such as the device identifier, the weighted state value, the time information, etc. in the associated event group.

[0166] For example, by comparing whether the devices involved in the two associated event groups have an association relationship, whether the change trend of the weighted state value is similar, whether there is a certain association in time, etc., the similarity is calculated comprehensively.

[0167] The calculated similarity is compared with the event association threshold. If the similarity is greater than the event association threshold, it is considered that the two associated event groups have global association; otherwise, it is considered that they do not have global association.

[0168] Step S145: The associated event groups with global association are integrated to generate a global associated event chain, which contains multiple regional associated event groups and the causal relationship between events.

[0169] For the associated event groups determined to have global association, integration processing is needed. In the integration process, the sequence and causal relationship between them can be determined according to the time information and device association relationship of the associated event groups.

[0170] These associated event groups are connected according to the time sequence and causal relationship to form a global associated event chain containing multiple regional associated event groups. The global associated event chain can clearly show the global association and causal relationship between the device running states of different regions.

[0171] Step S1451: Screen out the associated event group pairs with a similarity greater than the event association threshold. Each associated event group pair contains two associated event groups from different regions.

[0172] From the comparison results of all the associated event groups of different regions, the associated event group pairs with a similarity greater than the event association threshold are screened out. Each of the above associated event group pairs consists of two associated event groups from different regions, and they have strong global association.

[0173] In the screening process, the similarity value and the region information involved in each associated event group pair can be recorded.

[0174] Step S1452: Analyze the time sequence of the two associated event groups in the associated event group pair to determine the sequence of event occurrence.

[0175] For each pair of associated event groups, the time information of the two associated event groups (e.g. the earliest time or the average time of the state events in the associated event group) is extracted, and their time sequence is compared.

[0176] According to the comparison result of the time information, the time sequence of the two associated event groups is determined, i.e. which associated event group occurs first and which occurs later.

[0177] The determination of the time sequence is an important basis for analyzing the causal relationship of events.

[0178] Step S1453: Based on the time sequence of the event occurrence and the physical connection relationship of the devices in the associated event groups, the causal relationship type between the two associated event groups is determined, including direct causality, indirect causality and no causality.

[0179] The causal relationship type between the two associated event groups is determined in combination with the time sequence of the associated event group pair and the physical connection relationship of the devices in the associated event group.

[0180] If there is a direct physical connection between the device in the associated event group occurring first and the device in the associated event group occurring later, and the state change of the former can directly lead to the state change of the latter, then it is determined as a direct causal relationship.

[0181] If there is no direct physical connection between the devices in the two associated event groups, but they are indirectly associated through other devices, and the state change of the former indirectly leads to the state change of the latter through the intermediate devices, then it is determined as an indirect causal relationship.

[0182] If there is neither a direct physical connection nor an indirect association between the two associated event groups, or the time sequence cannot support the causal inference, then it is determined as no causal relationship.

[0183] Step S1454: The associated event groups with direct causality or indirect causality are connected in time sequence to form a local associated event chain.

[0184] After determining the causal relationship type between the associated event group pair, for those associated event groups with direct causality or indirect causality, they are connected in their time sequence. For example, if the associated event group X occurs before the associated event group Y, and there is a direct causal relationship between them, then X is arranged before Y to form the connection relationship of X-Y. A plurality of the above associated event groups are sequentially connected according to the time sequence and the causal relationship, which constitutes a local associated event chain. Each local associated event chain focuses on a specific causal association path and contains a series of associated event groups associated with each other.

[0185] Step S1455: merging all local correlation event chains, eliminating duplicate correlation event groups, and generating a global correlation event chain containing multiple regions and the causal relationship between events.

[0186] After obtaining multiple local correlation event chains, they need to be merged. First, check if there are duplicate correlation event groups in different local correlation event chains, i.e., the same correlation event group appears in multiple local correlation event chains. For duplicate correlation event groups, only one instance is retained to avoid redundancy in the global correlation event chain.

[0187] Then, according to the time sequence and causal relationship of each local correlation event chain, they are integrated into a unified whole. For local correlation event chains that exist in cross or correlation, they are connected through common correlation event groups to form a global correlation event chain that covers a wider range and contains multiple regions of correlation event groups. In this global correlation event chain, the causal relationship between each correlation event group is clearly recorded, and the global correlation of the entire LNG receiving station can be fully presented.

[0188] Step S146: according to the global feature dimension, extracting features from the global correlation event chain to generate global operation coordination features containing event occurrence frequency, regional coverage, and causal relationship strength.

[0189] After generating the global correlation event chain, it is necessary to extract relevant feature information according to the requirements of the global feature dimension to form the global operation coordination features. The global feature dimension specifies the types of features to be extracted, including event occurrence frequency, regional coverage, and causal relationship strength. Through the extraction of these features, the information contained in the global correlation event chain can be converted into feature data that can be used for analysis and control.

[0190] Step S1461: determine that the global feature dimension contains event occurrence frequency dimension, regional coverage dimension, and causal relationship strength dimension.

[0191] Before feature extraction, first clarify the specific content of the global feature dimension, i.e., the event occurrence frequency dimension, the regional coverage dimension, and the causal relationship strength dimension. These three dimensions respectively reflect the global operation coordination of the LNG receiving station from different angles. The event occurrence frequency dimension focuses on the frequency of correlation event groups, the regional coverage dimension reflects the spatial range of correlation events, and the causal relationship strength dimension reflects the closeness of the correlation between events.

[0192] Step S1462: For the event occurrence frequency dimension, count the number of times each associated event group in the global associated event chain appears within a preset time window, and calculate the average number of occurrences per unit time as the event occurrence frequency.

[0193] First, a preset time window is set, which can be a day, a week, or other time period determined according to the operating characteristics of the LNG receiving terminal. Then, each associated event group in the global associated event chain is traversed, and the number of times each associated event group appears within the preset time window is counted.

[0194] For each associated event group, its occurrence count is divided by the duration of a preset time window to obtain the average occurrence count per unit time. This average occurrence count is the event frequency of that associated event group. The event frequencies of all associated event groups together constitute the content of the global operational coordination characteristics in the event frequency dimension. This frequency information can reflect the activity level of different associated event groups over time.

[0195] Step S1463: For the region coverage dimension, extract the region codes involved in all associated event groups in the global associated event chain, merge duplicate region codes to obtain a region set, calculate the minimum bounding polygon based on the geographic coordinates of the region set, and use the vertex coordinates and area of ​​the minimum bounding polygon as the region coverage.

[0196] Iterate through all associated event groups in the global associated event chain and extract the region code involved in each associated event group. These region codes represent the regional location where the associated event group occurs. Summarize all extracted region codes and merge duplicate region codes to obtain a region set containing all unique region codes.

[0197] For each region code in the region set, its corresponding geographic coordinates (such as the latitude and longitude of the region center or the region boundary) are obtained. Based on these geographic coordinates, geometric calculation methods are used to determine the smallest bounding polygon that can encompass all regions. The vertex coordinates of this smallest bounding polygon accurately describe its spatial location and shape, while its area reflects the size of the region coverage. The vertex coordinates and area of ​​the smallest bounding polygon are used as feature data of the region coverage, constituting the content of the global operational collaborative features in the region coverage dimension.

[0198] Step S1464: For the causal relationship strength dimension, based on the causal relationship type between events in the global associated event chain, assign a strength value to each causal relationship type, and calculate the average of the causal relationship strength values ​​of all adjacent event pairs as the overall causal relationship strength.

[0199] Based on the type of causal relationship (direct causation, indirect causation) between events in the global chain of related events, a corresponding strength value is assigned to each type. For example, the strength value of a direct causal relationship is higher than that of an indirect causal relationship. The specific value is determined based on the tightness of the causal relationship and the degree of impact on the operation of the receiving station.

[0200] Then, iterate through all adjacent pairs of related events (i.e., event pairs with direct connections) in the global event chain, obtain the causal relationship type for each pair, and find the corresponding strength value. Sum all these strength values ​​and divide by the number of adjacent event pairs to obtain the average causal relationship strength value of all adjacent event pairs. This average value is the overall causal relationship strength. The overall causal relationship strength reflects the overall tightness of the causal relationships between events in the global event chain and is an important component of the global operational coordination characteristics in the causal relationship strength dimension.

[0201] Step S1465: Arrange the event occurrence frequency, regional coverage, and overall causal relationship strength in the order of the global feature dimensions to generate global operational collaborative features.

[0202] The extracted event frequency, regional coverage, and overall causal strength are arranged in the order specified in the global feature dimensions. For example, the feature data of each dimension are combined in the order of event frequency, regional coverage, and causal strength to form a complete global operational collaborative feature.

[0203] This global operational coordination feature is a multi-dimensional feature set that contains key information reflecting the overall operational coordination of the LNG receiving terminal.

[0204] Step S150: Input the global operation coordination feature into the device linkage control system to generate a device coordination control command containing regional linkage logic.

[0205] Global operational coordination characteristics comprehensively reflect the interrelationship of equipment operation in various areas of the LNG receiving terminal. When input into the equipment linkage control system, these characteristics enable the system to formulate reasonable equipment coordination control commands. The equipment linkage control system stores a large number of equipment control rules and strategies, formulated based on the receiving terminal's operating procedures, equipment characteristics, and safety requirements. By matching and analyzing the global operational coordination characteristics with these rules and strategies, equipment coordination control commands incorporating regional linkage logic can be generated, enabling coordinated control and optimized operation of the entire LNG receiving terminal equipment.

[0206] Step S151: Input the global operation coordination feature into the feature parsing unit of the device linkage control system to obtain the global associated event chain and the event occurrence frequency, regional coverage and causal relationship strength contained in the global operation coordination feature.

[0207] After receiving the global operational coordination features, the feature analysis unit of the equipment linkage control system analyzes and processes them. First, it extracts the global associated event chain from the global operational coordination features. This global associated event chain contains all associated event groups and the causal relationships between them. Simultaneously, it analyzes feature data such as event frequency, regional coverage, and causal relationship strength.

[0208] During the parsing process, the feature parsing unit performs format conversion and standardization on this data to ensure that it can be correctly recognized and used by other modules of the device linkage control system. For example, it converts the vertex coordinates of the area coverage to the coordinate format used internally by the system, and standardizes the unit of event frequency to the unit specified by the system.

[0209] Step S152: Call the linkage rule library of the device linkage control system to match the regional linkage mode corresponding to the global associated event chain. The regional linkage mode includes the combination of regions to be linked and the device operation logic.

[0210] The linkage rule base of the equipment linkage control system stores a variety of preset regional linkage modes, which are summarized based on historical operating data, equipment characteristics, and process requirements. Each regional linkage mode corresponds to a specific combination of related events and causal relationships, including the combination of regions that need to be linked and the operating logic of the equipment in each region under this mode.

[0211] After parsing the global event chain, the linkage rule base can be invoked to match the global event chain with the regional linkage patterns in the rule base. During the matching process, information such as the associated event groups and causal relationships in the global event chain can be compared with the characteristics of each regional linkage pattern to find the best-matching regional linkage pattern. For example, if the global event chain reflects a specific causal relationship between the storage tank area and the pipeline area, the corresponding storage tank-pipeline linkage pattern can be found.

[0212] Step S153: Based on the frequency of occurrence of the event and the strength of the causal relationship, adjust the device operation priority in the regional linkage mode so that the device operation corresponding to the event with high causal relationship strength has a higher priority.

[0213] After determining the matching regional linkage mode, the priority of device operations within that mode needs to be adjusted based on the event frequency and causal relationship strength in the global operational coordination characteristics. Device operation priority determines the order in which multiple device operations need to be executed.

[0214] For equipment operations corresponding to frequently occurring related event groups, it indicates a higher probability of their occurrence during operation, requiring higher priority to ensure timely response. Simultaneously, equipment operations corresponding to events with strong causal relationships, due to their closer correlation and greater impact on the receiving station's operation, also require higher priority.

[0215] During the adjustment process, a priority value can be assigned to each device operation, with higher values ​​indicating higher priority. Based on a comprehensive evaluation of the frequency of events and the strength of causal relationships, the priority values ​​in the initial regional linkage mode are modified to ensure that the adjusted device operation priorities better reflect the current operational situation.

[0216] Step S154: Based on the area coverage, determine the applicable device range of the area linkage mode, and integrate the adjusted area linkage mode, device operation priority and applicable device range into a device collaborative control instruction. The device collaborative control instruction includes an instruction identifier, an execution time window and an operation step sequence.

[0217] The regional coverage area clarifies the geographical region involved in the global chain of related events. Based on this information, the applicable device range for the regional linkage mode can be determined. The applicable device range refers to the specific devices that need to be operated under this regional linkage mode; these devices are located within the area covered by the regional coverage area and are related to the devices in the related event group.

[0218] After determining the applicable equipment scope, the adjusted regional linkage mode, equipment operation priority, and applicable equipment scope are integrated to form a coordinated equipment control command. The coordinated equipment control command also includes a unique command identifier to distinguish different commands; an execution time window, which specifies the time interval within which the command needs to be executed; and an operation step sequence, which details the specific operation steps and order that each device needs to perform.

[0219] For example, the sequence of operating procedures might include specific steps such as "open valve 1 in area A, start pump 2 in area B after 5 minutes, and close valve 3 in area C when the pressure reaches a certain level." These instructions enable coordinated control of equipment in various areas of the LNG receiving terminal, ensuring its safe and efficient operation.

[0220] Figure 2The illustration shows exemplary hardware and software components of an edge computing-based LNG receiving terminal data collaborative processing system 100 that can implement the ideas of this application, according to some embodiments of this application. For example, a processor 120 can be used in the edge computing-based LNG receiving terminal data collaborative processing system 100 and to perform the functions in this application.

[0221] For example, the edge computing-based LNG receiving terminal data collaborative processing system 100 may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and various forms of storage media 140, such as a disk, ROM, or RAM, or any combination thereof. Exemplarily, the edge computing-based LNG receiving terminal data collaborative processing system 100 may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The methods of this application can be implemented according to these program instructions. The edge computing-based LNG receiving terminal data collaborative processing system 100 also includes an I / O interface 150 between the computer and other input / output devices.

[0222] Furthermore, this embodiment of the invention also provides a readable storage medium, wherein computer-executable instructions are preset in the readable storage medium, and when the processor executes the computer-executable instructions, the above-mentioned LNG receiving station data collaborative processing method based on edge computing is implemented.

[0223] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A data collaborative processing method for LNG receiving terminals based on edge computing, characterized in that, The method includes: Acquire distributed acquisition data from the LNG receiving terminal, wherein the distributed acquisition data includes equipment operation data streams and area association identifiers for each monitoring area; The distributed collected data is dynamically distributed according to the regional association identifier, generating data fragments and corresponding collaborative processing strategies for each fragment. The collaborative processing strategies include data interaction permissions and processing priorities. Based on the collaborative processing strategy, cross-node hierarchical collaborative processing is performed on the data shards to obtain regional collaborative results, which include a sequence of device operating status associations for each region. The preset cross-regional fusion rules are invoked to globally correlate and integrate the regional collaboration results of all regions, generating global operational collaboration characteristics of the LNG receiving terminal; The global operational coordination features are input into the device linkage control system to generate device coordination control instructions containing regional linkage logic. According to the collaborative processing strategy, cross-node hierarchical collaborative processing is performed on the data shards to obtain regional collaborative results. The regional collaborative results include a sequence of device operating status associations for each region, including: The data shards and corresponding collaborative processing strategies are distributed to associated nodes. The collaborative processing strategies include data interaction permissions and processing priorities. The data interaction permissions include a list of interactive nodes and a data sharing scope. The processing priorities are divided into real-time, near-real-time, and normal levels. The received data fragments are sorted into processing queues according to processing priority, with real-time data fragments processed first, then near-real-time data fragments, and finally regular data fragments. For data shards with a processing priority of real-time, according to the list of interactive nodes in the data interaction permissions, send real-time data requests to the associated nodes and receive real-time data of the associated region returned by the associated nodes; By combining its own data shards and real-time data from related regions, real-time status correlation analysis is performed to generate real-time status correlation subsequences. For data shards with a processing priority of near real-time, based on the data sharing scope in the data interaction permissions, historical collaborative data of the associated region is obtained, and near real-time correlation analysis is performed with its own data shard to generate a near real-time state correlation subsequence. For data shards with a processing priority of "normal", process the data shards independently and generate normal state-related subsequences. The real-time status association subsequence, the near-real-time status association subsequence, and the regular status association subsequence are concatenated in chronological order to form the equipment operation status association sequence of each region, which serves as the regional collaboration result.

2. The LNG receiving terminal data collaborative processing method based on edge computing according to claim 1, characterized in that, The distributed data collection is dynamically distributed according to the regional association identifier, generating data fragments and corresponding collaborative processing strategies. The collaborative processing strategies include data interaction permissions and processing priorities, including: Parse the regional association identifiers in the distributed collection data and establish a mapping table between regions and nodes. The mapping table includes region codes, node identifiers, and node load status. Based on the mapping table and node load status, the distributed collection data is divided into multiple data shards by region. Each data shard contains the device operation data stream and region code for the corresponding region. Based on the regional importance of data fragments and the real-time requirements of device operation data streams, a processing priority is assigned to each data fragment. The processing priorities are divided into real-time level, near-real-time level, and normal level. Based on the physical connections between regions and the data interaction requirements, data interaction permissions are set for each data segment. The data interaction permissions include a list of interactive nodes and a data sharing scope. Data sharding, processing priority, and data interaction permissions are combined into a collaborative processing strategy, so that each data shard corresponds to a unique collaborative processing strategy.

3. The LNG receiving terminal data collaborative processing method based on edge computing according to claim 1, characterized in that, For data shards with a processing priority of real-time, the process involves sending real-time data requests to associated nodes according to the list of interactive nodes in the data interaction permissions, and receiving real-time data of the associated region returned by the associated nodes, including: Extract the list of interactive nodes included in the data interaction permissions of the collaborative processing strategy, and determine the identifiers of the associated nodes that need to be interacted with. A real-time data request instruction is generated based on the associated node identifier. The real-time data request instruction includes the request node identifier, the regional code of the data fragment, and the request timestamp. Send the real-time data request command to all associated nodes in the list of interactive nodes and wait for the associated nodes to respond; Receive real-time data of the associated region returned by the associated node, wherein the real-time data of the associated region includes real-time data fragments processed by the associated node and the corresponding region codes; The received real-time data of the associated region is verified by the region encoding. The real-time data of the associated region that passes the verification is timestamped with its own real-time data fragments to generate a time-synchronized associated data set. Furthermore, the step of combining its own data shards and real-time data from associated regions to perform real-time state correlation analysis and generate real-time state correlation sub-sequences includes: By combining its own data shards and real-time data from related regions, real-time status correlation analysis is performed to generate real-time status correlation subsequences. Extract the device status identifier and status change time from the device operation data stream contained in its own data shards, and generate its own status event sequence, which includes the device identifier, status value and change time; Extract the device status identifier and status change time from the device operation data stream contained in the real-time data of the associated region, and generate an associated status event sequence, wherein the associated status event sequence includes the device identifier, status value and change time; The sequence of its own state events and the sequence of associated state events are merged and sorted according to the time of change to generate a merged event sequence; Analyze the device correlation of adjacent state events in the merged event sequence to determine the device pairs with linkage relationships and the corresponding state change patterns; Based on the aforementioned state change pattern, state events with linkage relationships in the merged event sequence are grouped into associated event groups, and each associated event group contains state events of at least two associated devices. The associated event groups are arranged in chronological order to generate a real-time state associated subsequence, where each element in the real-time state associated subsequence is an associated event group.

4. The LNG receiving terminal data collaborative processing method based on edge computing according to claim 1, characterized in that, The method of invoking preset cross-regional fusion rules to globally correlate and integrate the regional collaboration results of all regions, generating global operational collaboration characteristics of the LNG receiving terminal, including: Collect the regional collaboration results of all regions. The regional collaboration results include the device operation status association sequence of each region. Each device operation status association sequence contains a group of associated events arranged in chronological order. Each group of associated events contains the status events of at least two associated devices. The cross-regional fusion rules are analyzed, and the regional association weights, event association thresholds, and global feature dimensions contained in the cross-regional fusion rules are extracted. The regional association weights are used to represent the importance of different regions in global association, and the event association thresholds are used to determine the association between events in different regions. Based on the regional association weights, the equipment operation status association sequences of each region are weighted to generate a weighted status association sequence. Calculate the similarity of associated event groups in the weighted state association sequences of different regions. When the similarity is greater than the event association threshold, it is determined that the corresponding associated event group has a global association. The globally related event groups are integrated to generate a global related event chain, which includes related event groups in multiple regions and the causal relationships between events; Based on the global feature dimension, feature extraction is performed on the global associated event chain to generate global operational collaboration features that include event occurrence frequency, regional coverage, and causal relationship strength.

5. The LNG receiving terminal data collaborative processing method based on edge computing according to claim 4, characterized in that, The step of weighting the device operation status association sequences of each region based on the regional association weight to generate a weighted status association sequence includes: Extract the device operation status association sequence of each region. The device operation status association sequence contains association event groups arranged in chronological order. Each association event group contains status events of at least two associated devices. Obtain the regional association weights in the cross-regional fusion rules. Each region is assigned a weight value, and the sum of the weight values ​​of all regions is 1. Add a regional weight tag to each associated event group in the device operation status association sequence for each region, and the value of the regional weight tag is equal to the regional association weight value of that region; The state value of each state event in the associated event group is weighted and calculated to obtain a weighted state value, which is equal to the product of the original state value and the region weight label. Replace the original state value with the weighted state value to form an associated event group containing the weighted state value; All associated event groups containing weighted state values ​​are retained in chronological order to generate a weighted state association sequence for the region.

6. The LNG receiving terminal data collaborative processing method based on edge computing according to claim 4, characterized in that, The process involves integrating globally related event groups to generate a global event chain. This global event chain includes event groups from multiple regions and the causal relationships between events, including: Filter out associated event pairs with a similarity greater than the event association threshold, and each associated event pair contains associated event pairs from two different regions; Analyze the temporal sequence of two related event groups in a pair of related event groups to determine the chronological relationship of the events. Based on the chronological order of events and the physical connection of devices in the related event groups, the causal relationship type between two related event groups is determined. The causal relationship type includes direct causation, indirect causation, and no causation. Connect related event groups with direct or indirect causal relationships in chronological order to form a local chain of related events; All local related event chains are merged to eliminate duplicate related event groups and generate a global related event chain that includes related event groups from multiple regions and the causal relationships between events.

7. The LNG receiving terminal data collaborative processing method based on edge computing according to claim 4, characterized in that, The step of extracting features from the global associated event chain based on the global feature dimension to generate global operational collaboration features including event frequency, regional coverage, and causal relationship strength includes: The global feature dimensions are determined to include the event frequency dimension, the area coverage dimension, and the causal relationship strength dimension. Regarding the frequency of event occurrence, the number of times each associated event group in the global associated event chain appears within a preset time window is counted, and the average number of occurrences per unit time is calculated as the event occurrence frequency. For the dimension of regional coverage, extract the regional codes involved in all related event groups in the global related event chain, merge duplicate regional codes to obtain a regional set, calculate the minimum bounding polygon based on the geographical coordinates of the regional set, and use the vertex coordinates and area of ​​the minimum bounding polygon as the regional coverage. Regarding the causal relationship strength dimension, based on the causal relationship type between events in the global associated event chain, a strength value is assigned to each causal relationship type, and the average causal relationship strength value of all adjacent event pairs is calculated as the overall causal relationship strength. The frequency of events, the area covered, and the overall causal relationship strength are arranged in the order of the global feature dimensions to generate global operational collaborative features.

8. The LNG receiving terminal data collaborative processing method based on edge computing according to claim 1, characterized in that, The step of inputting the global operational coordination characteristics into the device linkage control system to generate device coordination control instructions containing regional linkage logic includes: The global operational coordination features are input into the feature analysis unit of the device linkage control system to analyze and obtain the global associated event chain and the event occurrence frequency, regional coverage and causal relationship strength contained in the global operational coordination features; Call the linkage rule library of the device linkage control system to match the regional linkage mode corresponding to the global associated event chain. The regional linkage mode includes the combination of regions to be linked and the device operation logic. Based on the frequency of occurrence of the events and the strength of the causal relationship, the priority of device operation in the regional linkage mode is adjusted so that the device operation corresponding to the event with a high strength of causal relationship has a higher priority. Based on the coverage area, the applicable device range of the regional linkage mode is determined. The adjusted regional linkage mode, device operation priority, and applicable device range are integrated into a device collaborative control command. The device collaborative control command includes a command identifier, an execution time window, and a sequence of operation steps.

9. A data collaborative processing system for an LNG receiving terminal based on edge computing, characterized in that, The device includes a processor and a memory, the memory and the processor being connected. The memory is used to store programs, instructions or code, and the processor is used to execute the programs, instructions or code in the memory to implement the edge computing-based LNG receiving station data collaborative processing method as described in any one of claims 1-8.

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