A real-time data dynamic fusion method, device, equipment and storage medium

Abstract

The application discloses a real-time data dynamic fusion method and device, equipment and a storage medium. The method comprises the following steps: a change callback function of a source node in a memory address tree in a target data fusion scene is listened to, and when the change callback function is triggered, a target source node and corresponding change parameter data of the target source node are determined; the change parameter data of the target source node is stored in a local cache, and a target fusion node having a data fusion correlation relationship with the target source node is obtained based on an inverted dependency mapping table; a fusion script expression corresponding to the target fusion node is obtained from the memory address tree, and the change parameter data of the target source node is extracted from the local cache; a fusion data update result of the target fusion node is determined based on the fusion script expression corresponding to the target fusion node and the change parameter data of the target source node; and the fusion parameter data of the target fusion node in the memory address tree is updated by using the fusion data update result.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, device, and storage medium for real-time dynamic data fusion. Background Technology

[0002] In the fields of Industrial Internet of Things (IIoT) and smart manufacturing, underlying devices and sensors, such as PLCs (Programmable Logic Controllers), DCSs (Distributed Control Systems), and edge gateways, continuously generate massive amounts of heterogeneous real-time data. Typically, this data exists in discrete point-to-point form, and a single data point often cannot directly reflect the overall operating status of the equipment or production line. For example, the total energy consumption of equipment may need to be calculated from multiple points, such as the current of device A and the voltage of device B.

[0003] Currently, in common industry data fusion processes, the computational logic for a large amount of derived data is hard-coded into host computer software or service backends. If the location of field equipment or the calculation formula changes, it is often necessary to modify the code, compile, and restart the service, which cannot meet the flexible and ever-changing business needs of industrial field data. Some solutions introduce big data stream processing engines for streaming computing, which consumes a lot of server resources and is not suitable for deployment on edge gateways or lightweight servers. In addition, traditional methods use timers to poll and capture data from each location before aggregation. This leads to invalid queries of outdated data, wasting CPU (Central Processing Unit) performance resources, and also fails to guarantee the absolute real-time performance, effectiveness, and accuracy of data fusion. Summary of the Invention

[0004] This invention provides a real-time data dynamic fusion method, apparatus, device, and storage medium to achieve dynamic updates of data fusion, ensure the real-time nature of data fusion updates, improve the efficiency and accuracy of data fusion updates, and meet the flexible and ever-changing business needs of data.

[0005] According to one aspect of the present invention, a real-time data dynamic fusion method is provided, the method comprising: In the target data fusion scenario, the change callback function of the source node in the memory address tree constructed based on the unified information model is monitored, and when the change callback function is triggered, the target source node and its corresponding change parameter data are determined; the memory address tree contains at least one source node constructed based on the device parameter data of multi-source heterogeneous devices based on different transmission protocols, and at least one fusion node constructed based on the fusion parameter data between the multi-source heterogeneous devices. The changed parameter data of the target source node is stored in a pre-built local cache, and the target fusion node with data fusion relationship with the target source node is obtained based on the pre-built inverted dependency mapping table; Obtain the fusion script expression corresponding to the target fusion node from the memory address tree, and extract the change parameter data of the target source node from the local cache; Based on the changed parameter data of the target source node and the fusion script expression corresponding to the target fusion node, the fusion data update result of the target fusion node is determined; The fusion parameter data of the target fusion node in the memory address tree is updated using the fusion data update result.

[0006] According to another aspect of the present invention, a real-time data dynamic fusion apparatus is provided, the apparatus comprising: The change data determination module is used to listen to the change callback function of the source node in the memory address tree constructed based on the unified information model in the target data fusion scenario, and determine the target source node and its corresponding change parameter data when the change callback function is triggered. The memory address tree contains at least one source node constructed based on the device parameter data of multi-source heterogeneous devices based on different transmission protocols, and at least one fusion node constructed based on the fusion parameter data between the multi-source heterogeneous devices. The cache writing module is used to store the changed parameter data of the target source node into a pre-built local cache, and to obtain the target fusion node with data fusion relationship with the target source node based on the pre-built inverted dependency mapping table; The parameter extraction module is used to obtain the fusion script expression corresponding to the target fusion node from the memory address tree, and to extract the change parameter data of the target source node from the local cache; The fusion result determination module is used to determine the fusion data update result of the target fusion node based on the changed parameter data of the target source node and the fusion script expression corresponding to the target fusion node; The data update module is used to update the fusion parameter data of the target fusion node in the memory address tree using the fusion data update result.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the real-time data dynamic fusion method according to any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the real-time data dynamic fusion method according to any embodiment of the present invention.

[0009] This invention's technical solution involves callback monitoring of source nodes in a memory address tree, storing the changed parameter data of the monitored source nodes in a local cache, and obtaining target fusion nodes with data fusion relationships with the target source nodes based on an inverted dependency mapping table. It then obtains the fusion script expression for the target fusion node and extracts the changed parameter data of the target source node from the local cache. Based on the changed parameter data of the target source node, it determines the fusion data update result based on the fusion script expression and uses the fusion data update result to update the fusion parameter data of the target fusion node in the memory address tree. This technical solution solves the problems of resource waste and inability to ensure real-time performance associated with periodic traversal and polling. It achieves dynamic updates of data fusion, ensuring real-time data fusion updates. It automates the complete fusion data update automatically without requiring personnel to modify code or other configurations, improving the efficiency and accuracy of data fusion updates without manual intervention, thus meeting the business needs of flexible and ever-changing data scenarios.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1A This is a flowchart of a real-time data dynamic fusion method provided in Embodiment 1 of the present invention; Figure 1B This is a schematic diagram of a memory address tree containing source nodes according to Embodiment 1 of the present invention; Figure 1CThis is a schematic diagram of a memory address tree containing fusion nodes according to Embodiment 1 of the present invention; Figure 2A This is a flowchart illustrating a method for constructing a memory address tree according to Embodiment 2 of the present invention; Figure 2B This is a flowchart illustrating a real-time data dynamic fusion method according to Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the structure of a real-time data dynamic fusion device provided in Embodiment 3 of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the real-time data dynamic fusion method of the present invention. Detailed Implementation

[0013] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0014] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0015] Example 1 Figure 1A This is a flowchart of a real-time data dynamic fusion method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where real-time data dynamic fusion is performed in industrial scenarios where equipment parameter data is flexible and changeable. The method can be executed by a real-time data dynamic fusion device, which can be implemented in hardware and / or software and can be configured in an electronic device.

[0016] Furthermore, the real-time data dynamic fusion method of this embodiment can be deployed on edge gateways or lightweight servers, achieving real-time dynamic fusion of multi-source heterogeneous parameter data in scenarios such as industrial settings without consuming significant server resources. Figure 1A As shown, the method includes: S110. In the target data fusion scenario, listen to the change callback function of the source node in the memory address tree constructed based on the unified information model, and when the change callback function is triggered, determine the target source node and its corresponding change parameter data.

[0017] The memory address tree contains at least one source node constructed from device parameter data of multi-source heterogeneous devices based on different transmission protocols, and at least one fusion node constructed from fusion parameter data between the multi-source heterogeneous devices.

[0018] S120. Store the changed parameter data of the target source node in a pre-built local cache, and obtain the target fusion node with data fusion relationship with the target source node based on the pre-built inverted dependency mapping table.

[0019] S130. Obtain the fusion script expression corresponding to the target fusion node from the memory address tree, and extract the change parameter data of the target source node from the local cache.

[0020] S140. Based on the changed parameter data of the target source node and the fusion script expression corresponding to the target fusion node, determine the fusion data update result of the target fusion node.

[0021] S150. Update the fusion parameter data of the target fusion node in the memory address tree using the fusion data update results.

[0022] The target data fusion scenario can be any scenario with a large demand for data fusion, such as the Industrial Internet of Things (IIoT) scenario or the smart manufacturing scenario. For example, in the IIoT scenario, there are a large number of underlying devices and sensor devices, such as PLCs, DCSs and edge gateways, which are multi-source heterogeneous devices. Each industrial device continuously generates massive amounts of device parameter data, such as discrete data such as current, voltage and flow rate. This data usually exists in the form of points (Tag / Node).

[0023] However, it should be noted that single equipment parameter data often cannot directly reflect the overall operating status of industrial equipment or production lines. The overall operating status is usually reflected by a series of integrated parameter indicators, such as total equipment energy consumption and fan health indicators. These integrated parameter indicators typically require data fusion from multiple equipment parameter points. For example, total equipment energy consumption requires calculations from multiple points, including the current parameter value of equipment A and the voltage parameter value of equipment B.

[0024] When the device parameter data at a certain point changes, the corresponding fused data also needs to change. For example, if the current parameter value of device A in the above example is updated, the total energy consumption of the device should also be updated accordingly. However, the existing method uses a timer polling method to periodically capture the device parameter data of each device, and then performs data aggregation to obtain fused data. This results in the need to poll and query data that has not been updated, causing unnecessary waste of CPU resources. At the same time, it cannot guarantee the absolute real-time performance of data fusion, and the real-time performance is limited by the polling cycle.

[0025] Furthermore, because existing fusion processes hard-code the computational logic for large amounts of derived data into host computer software or the backend, changes in the equipment locations of industrial field devices or the calculation formulas used for fusion computation often require modifying the formula code, recompiling, and restarting the service, failing to meet the flexible and ever-changing business needs of industrial scenarios. In the logical computation process, specialized big data stream processing engines such as Flink are often introduced for streaming computation. These components not only consume significant server resources but also cannot be deployed on edge gateways or lightweight servers. Moreover, the computation results and the original gateway data are often stored in different systems, increasing the complexity of system integration.

[0026] Therefore, to address the aforementioned issues, this embodiment provides a lightweight, high real-time performance, and dynamically configurable real-time data fusion method, resolving the problems of difficulty in modifying data derivation logic, large computational latency, and the disconnect between computational results and original data in existing methods. Firstly, to achieve unified management of data from multi-source heterogeneous devices in the target data fusion scenario, the discrete points of these devices can be standardized and attached to a unified address space, thereby constructing a unified data carrier for subsequent data fusion. Based on this, this embodiment pre-constructs a memory address tree based on a unified information model.

[0027] Optionally, if the target data fusion scenario is an industrial automation scenario, the unified information model can be the standard information model of the Object Connectivity and Embedding for Process Control Unified Architecture (OPC UA). It should be noted that for other data fusion scenarios, the unified information model can be a standardized information model similar to the unified architecture.

[0028] This embodiment also provides a method for constructing a memory address tree. In one optional embodiment, the change callback function of the source node in the memory address tree constructed based on the unified information model is monitored. Before determining the target source node and its corresponding change parameter data when the change callback function is triggered, the device parameter data of each multi-source heterogeneous device with different transmission protocols in the target data fusion scenario is determined. According to the transmission protocol of each multi-source heterogeneous device, its own device parameter data is converted to the protocol standard format to generate parameter structure data of the unified information model. The source node of the memory address tree is constructed according to the parameter structure data of each multi-source heterogeneous device with different transmission protocols. And, the fusion node of the memory address tree is constructed according to the fusion parameter data between the multi-source heterogeneous devices. A memory address tree including at least one source node and at least one fusion node is generated.

[0029] For example, different underlying protocol stacks can be invoked to collect device parameter data from various heterogeneous devices using different transmission protocols. For instance, the underlying protocol stacks may include Modbus / TCP (Modbus Transmission Control Protocol), Modbus / RTU (Modbus Remote Terminal Unit), MQTT (Message Queuing Telemetry Transport), Profinet (Process Field Network), and dedicated protocols for DCS (Distributed Control System).

[0030] Taking industrial scenarios as an example, different heterogeneous devices from various sources may have the same or different device parameter data, such as current, voltage, flow rate, and temperature. Device parameter data is collected based on the corresponding transmission protocols of these heterogeneous devices. Using OPC UA as an example of a unified information model, heterogeneous devices convert their own device parameter data into the OPC UA standard format based on their own transmission protocols, obtaining parameter structure data, including values, data types, timestamps, and quality stamps.

[0031] The source node of the memory address tree is constructed based on the parameter structure data of each heterogeneous device from various sources under each transmission protocol. The memory address tree is a pre-constructed hierarchical address space. In one optional embodiment, constructing the source node of the memory address tree based on the parameter structure data of each heterogeneous device from various sources under each transmission protocol includes: pre-constructing the root node of the memory address tree, and constructing protocol nodes according to each transmission protocol; the protocol nodes are child nodes of the root node; constructing device nodes according to each heterogeneous device from various sources under the corresponding transmission protocol; the device nodes are child nodes of the protocol nodes corresponding to their respective transmission protocols; and constructing source nodes according to the parameter structure data of the corresponding heterogeneous devices; the source nodes are child nodes of the device nodes corresponding to their respective heterogeneous devices.

[0032] Specifically, a root node of the memory address tree is pre-constructed. Protocol nodes are then built under the root node according to the transmission protocol. The number of protocol nodes corresponds to the number of transmission protocols; that is, separate protocol nodes are built for different transmission protocols, such as Modbus protocol nodes, MQTT protocol nodes, and DCS protocol nodes. Device nodes are then built for each heterogeneous device from a different source, based on the corresponding transmission protocol. These device nodes are child nodes of the corresponding protocol nodes. For example, if the Modbus protocol corresponds to a heat treatment furnace device, then a heat treatment furnace device node is built under the Modbus protocol node.

[0033] Based on the parameter structure data of the corresponding multi-source heterogeneous equipment, source nodes are constructed, which are child nodes of the corresponding equipment nodes. For example, if the multi-source heterogeneous equipment is a heat treatment furnace, and the parameters corresponding to the heat treatment furnace include temperature, then temperature is used as a child node of the heat treatment furnace equipment's equipment node, resulting in the source node corresponding to temperature. The configuration information of the current source node is configured based on the parameter structure data, including value, data type, data quality, and timestamp, stored in a format such as {Value: DataValue, Data Type: Float, Data Quality: Good, Timestamp: T1}.

[0034] like Figure 1BThe diagram illustrates a memory address tree structure containing source nodes. Taking an industrial scenario as an example, protocol nodes, as child nodes of the root node, have a number related to the number of transmission protocol types included in the industrial scenario. Different transmission protocol types correspond to their own type of protocol nodes, such as Modbus protocol nodes, MQTT protocol nodes, and DCS protocol nodes. For any given protocol node, there are multiple heterogeneous devices belonging to its own transmission protocol. For example, under a Modbus protocol node, there are heat treatment furnace devices, flow sensors, and power distribution equipment. The heat treatment furnace devices, flow sensors, and power distribution equipment each correspond to their own device nodes, and the Modbus protocol node is their parent node. For any given device node, its corresponding source node is the device parameters it contains. For example, if the device parameter of a flow sensor is flow rate, then flow rate corresponds to its own source node. The parameter structure data corresponding to the flow rate parameter can be: {flow rate value: DataValue, data type: Float, data quality: Good, timestamp: T1}, which can be configured as parameter structure data in the flow rate source node.

[0035] The above technical solution is based on the OPC UA unified information model to build a standardized memory address tree, which can uniformly model and standardize the multi-source heterogeneous discrete data generated by various transmission protocols and various multi-source heterogeneous devices in the corresponding scenarios. It solves the conflict between data silos and protocol barriers between different devices or systems, realizes the structured, standardized and unified management of device location data, provides unified addressing for subsequent real-time data fusion and computing and standardized data, and reduces the complexity of multi-source data integration.

[0036] After constructing the source nodes of the memory address tree, the fusion nodes of the memory address tree are constructed based on the fusion parameter data between the various heterogeneous devices, generating a memory address tree that includes at least one source node and at least one fusion node. The fusion node can be used to store the parameter fusion calculation results. For example, there is a multi-parameter fusion indicator: a wind turbine health indicator. This fusion parameter indicator depends on wind turbine speed, wind turbine vibration, and wind turbine temperature. Since wind turbine speed, wind turbine vibration, and wind turbine temperature are deployed on different source nodes, fusion nodes can be dynamically created by configuring fusion calculation rules.

[0037] In one optional embodiment, constructing a fusion node in a memory address tree based on fusion parameter data among various multi-source heterogeneous devices includes: determining fusion parameter data under the target data fusion scenario, and determining the device parameter data and fusion script expression required to calculate the fusion parameter data; constructing a fusion node in the memory address tree based on the fusion parameter data, and generating a node identifier for the fusion node; configuring information for the fusion node based on the multi-source heterogeneous devices corresponding to the device parameter data required to calculate the fusion parameter data, the node identifier and data type of the fusion node, and the fusion script expression.

[0038] For example, taking an industrial scenario as the target data fusion example, the fusion parameter data may include fan health indicators, motor insulation health levels, bearing remaining life, production line unit energy consumption, and pump group health indicators. The equipment parameter data required to calculate different fusion parameter data are different. For example, the fan health indicator depends on equipment parameter data such as fan speed, fan vibration, and fan temperature; the bearing remaining life depends on equipment parameter data such as vibration acceleration, bearing temperature, and equipment operating speed.

[0039] The fusion script expressions required to calculate different fusion parameter data vary. These expressions can be specific mathematical formulas or calculation rules. For example, the fusion script expression for wind turbine health indicators could be: 100 (Excellent) when the wind turbine speed is greater than 950 RPM, the wind turbine vibration is less than 4.5 mm / s, and the wind turbine temperature is less than 55℃; 80 (Good) when the wind turbine vibration is not less than 4.5 mm / s and not greater than 8 mm / s, and the wind turbine speed and temperature are within the preset healthy speed and temperature ranges; and 0 (Fault or Abnormal) when any indicator exceeds the preset alarm threshold, such as when the wind turbine speed is not greater than 950 RPM, the wind turbine vibration is not less than 8 mm / s, or the wind turbine temperature is not less than 55℃. Another example is the fusion parameter for production line unit energy consumption. The fusion script expression can be: in, Total energy consumption during the production line cycle; This represents the total energy consumption during the production line cycle.

[0040] Based on the fusion parameter data, a fusion node is constructed in the memory address tree, and a node identifier for the fusion node is generated. For example, the fusion node can be created at the same level as the source node in the memory address tree, or it can be created under a child node of the root node. The creation position of the fusion node in the memory address tree is not limited in this embodiment. For instance, taking the creation of a fusion node as a child node of the root node as an example... Figure 1C The diagram shows a memory address tree structure containing fusion nodes.

[0041] A unique node identifier (NodeID) is generated and configured within the memory address tree for each fusion node to establish a direct memory write channel. Based on the multi-source heterogeneous devices corresponding to the device parameter data required for calculating the fusion parameter data, the node identifier and data type of the fusion node, and the fusion script expression, the fusion node is configured with information. Specific configuration information can be {NodeID, data type, NodeID and node name of the associated source node, fusion script expression}. For example, the configuration information can be expressed in JSON (JavaScript Object Notation) format.

[0042] An inverted dependency mapping table is pre-constructed to quickly locate the parameters to be merged and their associated device parameter data during subsequent updates of fusion parameter data. In one optional embodiment, the inverted dependency mapping table is constructed as follows: traversing the configuration information of the fusion nodes in the memory address tree, determining at least one source node and its corresponding node identifier that each fusion node depends on during the calculation of fusion parameter data; generating a key-value pair mapping relationship using the node identifiers of the source nodes that the fusion nodes depend on as keys and the node identifiers of the fusion nodes that the source nodes depend on as values, and constructing the inverted dependency mapping table based on the mapping relationship.

[0043] Specifically, by traversing each fusion node in the memory address tree and considering the configuration information of each fusion node, the node identifiers and names of the source nodes upon which the fusion parameter data of the fusion node depends during the calculation process can be determined. A key-value pair mapping relationship is generated, using the node identifiers of the source nodes upon which the fusion node depends as keys and the node identifiers of the fusion nodes upon which the source nodes depend as values. For example, taking the fusion nodes as fan health indicators and fan airflow indicators, the source nodes upon which the fan health indicator depends are fan speed, fan vibration, and fan temperature. The source nodes upon which the fan airflow indicator depends are fan speed, fan volumetric flow rate, and fan impeller diameter. The generated key-value pair mapping relationship can be seen as follows: Key: Node identifier of the fan speed source node; Value: Node identifiers corresponding to the fan health index fusion node and the fan air volume index fusion node, respectively. Key: Node identifier of the wind turbine vibration source node; Value: Node identifier of the wind turbine health index fusion node. Key: Node identifier of the fan temperature source node; Value: Node identifier of the fan health index fusion node. Key: Node identifier of the fan volume flow rate source node; Value: Node identifier of the fan air volume index fusion node. Key: Node identifier of the source node for the fan impeller diameter; Value: Node identifier of the fusion node for the fan air volume index.

[0044] Based on the mapping relationship between all fusion nodes and their respective source nodes in the corresponding scenario, an inverted dependency mapping table is generated. The inverted mapping table includes the mapping relationship {Key:Value} between the node identifier of the source node and the node identifier of its corresponding fusion node.

[0045] Register OPC UA value change callbacks for each source node in the memory address tree and initialize local caches. When the multi-source heterogeneous device powers on and its values ​​are updated, the change callback function is invoked, and the changed parameter data is cached.

[0046] Specifically, the system monitors the change callback functions of each source node in the memory address tree within the target data fusion scenario. When the device parameter data of a source node changes, the change callback function is triggered. The source node corresponding to the triggering change callback function is then designated as the target source node, and its changed parameter data is retrieved. For example, if the current value of the target source node changes from 0A to 50A, the changed parameter data will be 50A. A local cache is maintained in memory, and when a callback is triggered, the cache for that small subset of points is updated.

[0047] The changed parameter data of the target source node is stored in a pre-built local cache, and the target fusion node with data fusion relationship with the target source node is obtained based on the pre-built inverted dependency mapping table. For example, the inverted dependency mapping table is traversed based on the node identifier of the target source node to obtain the target fusion node corresponding to the node identifier of the target source node. The fusion script expression corresponding to the target fusion node is obtained from the memory address tree, the changed parameter data of the target source node is extracted from the local cache, and the device parameter data of other related source nodes required to calculate the target fusion node is obtained. For example, if the target source node whose parameter value has changed is the fan temperature, the corresponding target fusion nodes include the fan health index. In addition to the fan temperature, the fan speed and fan vibration are also included in the fan health index fusion calculation process. Therefore, the device parameter data of the fan speed source node and the fan vibration source node for the current time period are obtained.

[0048] Based on the changed parameter data of the target source node and the device parameter data of the associated source node in the current time period, the fusion data update result of the target fusion node is determined according to the fusion script expression corresponding to the target fusion node. For example, the changed parameter data of the target source node and the device parameter data of the associated source node in the current time period can be substituted into the fusion script expression, and the calculated parameter value can be used as the fusion data update result of the target fusion node.

[0049] In one optional embodiment, determining the fusion data update result of the target fusion node based on the changed parameter data of the target source node and the fusion script expression corresponding to the target fusion node includes: determining the associated source node related to updating the fusion parameter data of the target fusion node based on the fusion script expression corresponding to the target fusion node; obtaining the device parameter data of the associated source node from the memory address tree; and calling a pre-selected script calculation engine to calculate the fusion data update result of the target fusion node based on the changed parameter data of the target source node and the device parameter data of the associated source node and the fusion script expression corresponding to the target fusion node.

[0050] Based on the fusion script expression corresponding to the target fusion node, determine the associated source nodes that are related to updating the fusion parameter data of the target fusion node. For example, if the target source node is the fan temperature and the target fusion node is the fan health index, and the fan health index includes fan speed and fan vibration in addition to fan temperature during the fusion calculation, then the associated source nodes are fan speed and fan vibration. Obtain the device parameter data of the associated source nodes from the memory address tree.

[0051] The pre-selected script calculation engine is invoked to calculate the fusion data update result of the target fusion node based on the changed parameter data of the target source node and the device parameter data of the associated source nodes, using the fusion script expression corresponding to the target fusion node. The script calculation engine can be pre-selected by relevant technical personnel; for example, it could be an open-source JavaScript engine. The changed parameter data of the target source node and the device parameter data of the associated source nodes are injected into the context of the JavaScript engine, which then calculates the fusion data update result of the target fusion node based on the fusion script expression corresponding to the target fusion node.

[0052] Based on the node identifier of the target fusion node, the corresponding fusion data update result is bypassed and written to the target fusion node in the memory address tree. This operation reuses the unified environment, enabling the fusion node to be obtained by the upper layer system in real time using the standard OPC UA Client method or WebSocket push method.

[0053] This invention's technical solution involves callback monitoring of source nodes in a memory address tree, storing the changed parameter data of the monitored source nodes in a local cache, and obtaining target fusion nodes with data fusion relationships with the target source nodes based on an inverted dependency mapping table. It then obtains the fusion script expression for the target fusion node and extracts the changed parameter data of the target source node from the local cache. Based on the changed parameter data of the target source node, it determines the fusion data update result based on the fusion script expression and uses the fusion data update result to update the fusion parameter data of the target fusion node in the memory address tree. This technical solution solves the problems of resource waste and inability to ensure real-time performance associated with periodic traversal and polling. It achieves dynamic updates of data fusion, ensuring real-time data fusion updates. It automates the complete fusion data update automatically without requiring personnel to modify code or other configurations, improving the efficiency and accuracy of data fusion updates without manual intervention, thus meeting the business needs of flexible and ever-changing data scenarios.

[0054] Example 2 Figure 2A This is a flowchart illustrating a method for constructing a memory address tree according to Embodiment 2 of the present invention. Based on the above embodiments, this embodiment provides a preferred example for constructing a memory address tree. Figure 2A As shown, the method includes the following steps: S210. Determine the device parameter data of each multi-source heterogeneous device with different transmission protocols in the target data fusion scenario.

[0055] S220. Based on the transmission protocols of each multi-source heterogeneous device, the device parameter data is converted to a standard protocol format to generate parameter structure data for a unified information model. Source node construction in the memory address tree is executed via S230A-S230C; fusion node construction in the memory address tree is executed via S240A-S240C.

[0056] S230A: The root node of the memory address tree is pre-built, and protocol nodes are built according to each transmission protocol; the protocol nodes are child nodes of the root node.

[0057] S230B: Construct device nodes according to the various heterogeneous devices under the corresponding transmission protocols; each device node is a child node of the protocol node corresponding to its respective transmission protocol.

[0058] S230C: Construct source nodes based on the parameter structure data of the corresponding multi-source heterogeneous devices; the source node is a child node of the device node corresponding to its multi-source heterogeneous device.

[0059] S240A: Determine the fusion parameter data under the target data fusion scenario, and determine the device parameter data and fusion script expression required to calculate the fusion parameter data.

[0060] The S240B constructs a fusion node in the memory address tree based on the fusion parameter data and generates a node identifier for the fusion node.

[0061] S240C configures information for fusion nodes based on the multi-source heterogeneous devices corresponding to the equipment parameter data required for computing fusion parameter data, the node identifier and data type of the fusion node, and the fusion script expression.

[0062] S250. Generate a memory address tree including at least one source node and at least one fusion node.

[0063] Figure 2B This is a flowchart illustrating a real-time dynamic data fusion method according to Embodiment 2 of the present invention. Based on the above embodiments, this embodiment provides a preferred example of a real-time dynamic data fusion method, targeting an industrial automation scenario. Figure 2B As shown, the method includes the following steps: S310. In industrial automation scenarios, listen for the change callback function of the source node in the memory address tree constructed based on the OPC UA standard information model, and when the change callback function is triggered, determine the target source node and its corresponding change parameter data.

[0064] The memory address tree contains at least one source node constructed from device parameter data of multi-source heterogeneous devices based on different transmission protocols, and at least one fusion node constructed from fusion parameter data between the multi-source heterogeneous devices.

[0065] S320. Store the changed parameter data of the target source node in a pre-built local cache, and obtain the target fusion node with data fusion relationship with the target source node based on the pre-built inverted dependency mapping table.

[0066] S330: Obtain the fusion script expression corresponding to the target fusion node from the memory address tree, and extract the change parameter data of the target source node from the local cache.

[0067] S340. Based on the fusion script expression corresponding to the target fusion node, determine the associated source node that is related to updating the fusion parameter data of the target fusion node.

[0068] S350: Obtain device parameter data of the associated source node from the memory address tree.

[0069] S360: Call the pre-selected script calculation engine to calculate the fusion data update result of the target fusion node based on the changed parameter data of the target source node and the device parameter data of the associated source node, and the fusion script expression corresponding to the target fusion node.

[0070] S370. Update the fusion parameter data of the target fusion node in the memory address tree using the fusion data update results.

[0071] The above technical solution abandons the timed traversal and polling of existing technologies, and adopts an inverted index and change callback architecture to ensure that the calculation of specific fusion node formulas dependent on that point is precisely triggered only when the value of the underlying device changes, thus saving CPU resources to the greatest extent and reducing the calculation latency to sub-millisecond levels. Business personnel can create source nodes and fusion nodes and their related configuration parameters in real time by modifying formulas and dependencies without downtime or code modification. The built-in independent script engine ensures uninterrupted operation of industrial applications. The updated data generated by the calculation is written back to the OPC UA address space, enabling a high degree of integration between the old and new data. Various front-end interfaces and systems can read the calculated fusion parameter indicators as if they were reading the underlying physical instruments, greatly reducing the complexity of the system architecture.

[0072] Furthermore, this solution can be implemented using a decentralized, lightweight architecture with extremely low resource consumption. It eliminates the need for a large middleware cluster; a single machine or industrial gateway can handle the computational tasks. The resulting data remains within its own database, natively adapting to the industrial Ethernet ecosystem. Upper-layer application software can read it seamlessly from the PLC's raw data. By establishing an inverted dependency table and registering callbacks at the data layer, zero polling overhead and precise allocation of computing power are achieved. Only changed data is calculated, avoiding the consumption of resources from a large number of points in a steady state. Calculations are triggered instantly for events accompanied by changes in physical variable values, ensuring real-time result updates.

[0073] Example 3 Figure 3 This is a schematic diagram of a real-time data dynamic fusion device provided in Embodiment 3 of the present invention. The real-time data dynamic fusion device provided in this embodiment of the present invention is applicable to situations where equipment parameter data in industrial settings are subject to flexible and changing real-time data dynamic fusion. This real-time data dynamic fusion device can be implemented in hardware and / or software, such as... Figure 3 As shown, the device includes: a change data determination module 301, a cache writing module 302, a parameter extraction module 303, a fusion result determination module 304, and a data update module 305. Among them, The change data determination module 301 is used to listen to the change callback function of the source node in the memory address tree constructed based on the unified information model in the target data fusion scenario, and determine the target source node and its corresponding change parameter data when the change callback function is triggered. The memory address tree contains at least one source node constructed based on the device parameter data of multi-source heterogeneous devices based on different transmission protocols, and at least one fusion node constructed based on the fusion parameter data between the multi-source heterogeneous devices. The cache writing module 302 is used to store the changed parameter data of the target source node into a pre-built local cache, and to obtain the target fusion node with data fusion relationship with the target source node based on the pre-built inverted dependency mapping table; The parameter extraction module 303 is used to obtain the fusion script expression corresponding to the target fusion node from the memory address tree, and to extract the change parameter data of the target source node from the local cache; The fusion result determination module 304 is used to determine the fusion data update result of the target fusion node based on the changed parameter data of the target source node and the fusion script expression corresponding to the target fusion node; The data update module 305 is used to update the fusion parameter data of the target fusion node in the memory address tree using the fusion data update result.

[0074] This invention's technical solution involves callback monitoring of source nodes in a memory address tree, storing the changed parameter data of the monitored source nodes in a local cache, and obtaining target fusion nodes with data fusion relationships with the target source nodes based on an inverted dependency mapping table. It then obtains the fusion script expression for the target fusion node and extracts the changed parameter data of the target source node from the local cache. Based on the changed parameter data of the target source node, it determines the fusion data update result based on the fusion script expression and uses the fusion data update result to update the fusion parameter data of the target fusion node in the memory address tree. This technical solution solves the problems of resource waste and inability to ensure real-time performance associated with periodic traversal and polling. It achieves dynamic updates of data fusion, ensuring real-time data fusion updates. It automates the complete fusion data update automatically without requiring personnel to modify code or other configurations, improving the efficiency and accuracy of data fusion updates without manual intervention, thus meeting the business needs of flexible and ever-changing data scenarios.

[0075] Optionally, the device further includes: The callback triggering module is used to listen for the change callback function of the source node in the memory address tree constructed based on the unified information model, and before determining the target source node and its corresponding change parameter data when the change callback function is triggered, determine the device parameter data of each multi-source heterogeneous device with different transmission protocols in the target data fusion scenario. The format conversion module is used to convert the parameter data of each of the multi-source heterogeneous devices into the protocol standard format according to their own transmission protocols, and generate parameter structure data of the unified information model. The source node construction module is used to construct the source nodes of the memory address tree based on the parameter structure data of various heterogeneous devices from different sources according to various transmission protocols; and, The fusion node construction module is used to construct fusion nodes with memory address trees based on the fusion parameter data between various multi-source heterogeneous devices; The memory address tree generation module is used to generate a memory address tree that includes at least one source node and at least one fusion node.

[0076] Optional, source node building modules, specifically used for: A root node of the memory address tree is pre-constructed, and protocol nodes are constructed according to each of the aforementioned transmission protocols; the protocol nodes are child nodes of the root node. Based on the various heterogeneous devices from different sources under the corresponding transmission protocols, device nodes are constructed; the device node is a child node of the protocol node corresponding to its respective transmission protocol. Based on the parameter structure data of the corresponding multi-source heterogeneous device, a source node is constructed; the source node is a child node of the device node corresponding to its multi-source heterogeneous device.

[0077] Optional, the fusion node building module is specifically used for: Determine the fusion parameter data under the target data fusion scenario, and determine the device parameter data and fusion script expression required to calculate the fusion parameter data; Based on the fusion parameter data, a fusion node is constructed in the memory address tree, and a node identifier for the fusion node is generated; Based on the multi-source heterogeneous devices corresponding to the device parameter data required for calculating the fusion parameter data, the node identifier and data type of the fusion node, and the fusion script expression, the fusion node is configured with information.

[0078] Optionally, the fusion result determination module 304 is specifically used for: Based on the fusion script expression corresponding to the target fusion node, determine the associated source node that is related to updating the fusion parameter data of the target fusion node; Obtain the device parameter data of the associated source node from the memory address tree; The pre-selected script calculation engine is invoked to calculate the fusion data update result of the target fusion node based on the changed parameter data of the target source node and the device parameter data of the associated source node, and on the fusion script expression corresponding to the target fusion node.

[0079] Optionally, the inverted dependency mapping table can be constructed as follows: The configuration information of the fusion nodes in the memory address tree is traversed to determine at least one source node and its corresponding node identifier that each fusion node depends on during the calculation of fusion parameter data. A key-value pair mapping relationship is generated using the node identifiers of the source nodes that the fusion node depends on as keys and the node identifiers of the fusion nodes that the source nodes depend on as values. An inverted dependency mapping table is then constructed based on the mapping relationship.

[0080] Optionally, the target data fusion scenario is an industrial automation scenario, and correspondingly, the unified information model is the OPC UA standard information model for object connection and embedding unified architecture used for process control.

[0081] The real-time data dynamic fusion device provided in the embodiments of the present invention can execute the real-time data dynamic fusion method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0082] Example 4 Figure 4 A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0083] like Figure 4As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded from storage unit 48 into the RAM 43. The RAM 43 may also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0084] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0085] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as real-time data dynamic fusion methods.

[0086] In some embodiments, the real-time data dynamic fusion method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the real-time data dynamic fusion method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the real-time data dynamic fusion method by any other suitable means (e.g., by means of firmware).

[0087] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0088] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0089] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0090] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0091] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0092] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0093] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0094] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A real-time data dynamic fusion method, characterized in that, include: In the context of target data fusion, the change callback function of the source node in the memory address tree constructed based on the unified information model is monitored, and when the change callback function is triggered, the target source node and its corresponding change parameter data are determined. The memory address tree contains at least one source node constructed from device parameter data of multi-source heterogeneous devices based on different transmission protocols, and at least one fusion node constructed from fusion parameter data between the multi-source heterogeneous devices. The changed parameter data of the target source node is stored in a pre-built local cache, and the target fusion node with data fusion relationship with the target source node is obtained based on the pre-built inverted dependency mapping table; Obtain the fusion script expression corresponding to the target fusion node from the memory address tree, and extract the change parameter data of the target source node from the local cache; Based on the changed parameter data of the target source node and the fusion script expression corresponding to the target fusion node, the fusion data update result of the target fusion node is determined; The fusion parameter data of the target fusion node in the memory address tree is updated using the fusion data update result.

2. The method according to claim 1, characterized in that, Before monitoring the change callback function of the source node in the memory address tree constructed based on the unified information model, and determining the target source node and its corresponding change parameter data when the change callback function is triggered, the process also includes: Determine the device parameter data of each multi-source heterogeneous device with different transmission protocols in the target data fusion scenario; Based on the transmission protocols of each of the multi-source heterogeneous devices, the parameter data of each device is converted to the protocol standard format to generate parameter structure data of a unified information model; The source nodes of the memory address tree are constructed based on the parameter structure data of each heterogeneous device from various sources according to each transmission protocol; and, The fusion nodes are constructed by building a memory address tree based on the fusion parameter data among the various heterogeneous devices from multiple sources; Generate a memory address tree that includes at least one source node and at least one merge node.

3. The method according to claim 2, characterized in that, The source node for constructing the memory address tree based on the parameter structure data of each heterogeneous device from each transmission protocol includes: A root node of the memory address tree is pre-constructed, and protocol nodes are constructed according to each of the aforementioned transmission protocols; the protocol nodes are child nodes of the root node. Based on the various heterogeneous devices from different sources under the corresponding transmission protocols, device nodes are constructed; the device node is a child node of the protocol node corresponding to its respective transmission protocol. Based on the parameter structure data of the corresponding multi-source heterogeneous device, a source node is constructed; the source node is a child node of the device node corresponding to its multi-source heterogeneous device.

4. The method according to claim 2, characterized in that, The fusion node, which constructs a memory address tree based on the fusion parameter data between various multi-source heterogeneous devices, includes: Determine the fusion parameter data under the target data fusion scenario, and determine the device parameter data and fusion script expression required to calculate the fusion parameter data; Based on the fusion parameter data, a fusion node is constructed in the memory address tree, and a node identifier for the fusion node is generated; Based on the multi-source heterogeneous devices corresponding to the device parameter data required for calculating the fusion parameter data, the node identifier and data type of the fusion node, and the fusion script expression, the fusion node is configured with information.

5. The method according to claim 1, characterized in that, The step of determining the fusion data update result of the target fusion node based on the changed parameter data of the target source node and the fusion script expression corresponding to the target fusion node includes: Based on the fusion script expression corresponding to the target fusion node, determine the associated source node that is related to updating the fusion parameter data of the target fusion node; Obtain the device parameter data of the associated source node from the memory address tree; The pre-selected script calculation engine is invoked to calculate the fusion data update result of the target fusion node based on the changed parameter data of the target source node and the device parameter data of the associated source node, and on the fusion script expression corresponding to the target fusion node.

6. The method according to claim 1, characterized in that, The inverted dependency mapping table is constructed as follows: The configuration information of the fusion nodes in the memory address tree is traversed to determine at least one source node and its corresponding node identifier that each fusion node depends on during the calculation of fusion parameter data. A key-value pair mapping relationship is generated using the node identifiers of the source nodes that the fusion node depends on as keys and the node identifiers of the fusion nodes that the source nodes depend on as values. An inverted dependency mapping table is then constructed based on the mapping relationship.

7. The method according to claim 1, characterized in that, The target data fusion scenario is an industrial automation scenario, and correspondingly, the unified information model is the OPC UA standard information model for object connection and embedding unified architecture used for process control.

8. A real-time data dynamic fusion device, characterized in that, include: The change data determination module is used to listen for the change callback function of the source node in the memory address tree built based on the unified information model in the target data fusion scenario, and determine the target source node and its corresponding change parameter data when the change callback function is triggered. The memory address tree contains at least one source node constructed from device parameter data of multi-source heterogeneous devices based on different transmission protocols, and at least one fusion node constructed from fusion parameter data between the multi-source heterogeneous devices. The cache writing module is used to store the changed parameter data of the target source node into a pre-built local cache, and to obtain the target fusion node with data fusion relationship with the target source node based on the pre-built inverted dependency mapping table; The parameter extraction module is used to obtain the fusion script expression corresponding to the target fusion node from the memory address tree, and to extract the change parameter data of the target source node from the local cache; The fusion result determination module is used to determine the fusion data update result of the target fusion node based on the changed parameter data of the target source node and the fusion script expression corresponding to the target fusion node; The data update module is used to update the fusion parameter data of the target fusion node in the memory address tree using the fusion data update result.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the real-time data dynamic fusion method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the real-time data dynamic fusion method according to any one of claims 1-7.

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