A power quality perception device interaction model construction method, device and equipment

Abstract

This invention relates to the field of energy storage and smart grid technology, and discloses a method, apparatus, and device for constructing an interaction model for power quality sensing devices. The invention generates encrypted self-describing broadcast messages through a device self-describing layer; decrypts the self-describing broadcast messages through a unified semantic layer, retrieves a pre-stored semantic mapping dictionary based on the decryption result, generates a data parsing plugin, and performs online device registration based on the data parsing plugin; receives raw measurement messages through a cloud-edge-device transmission adaptation layer, converts the raw measurement messages into data packets of a unified semantic format using the data parsing plugin, and performs fragmentation processing on the data packets; when the device status changes, the self-describing broadcast messages are updated, and the online device registration information in the three-layer architecture is updated based on the updated self-describing broadcast messages, and the updated three-layer architecture is used as the interaction model for power quality sensing devices. This invention achieves adaptive interaction throughout the entire device lifecycle.

Description

Technical Field

[0001] This invention relates to the field of energy storage and smart grid technology, specifically to a method, apparatus, and equipment for constructing an interactive model of power quality sensing devices. Background Technology

[0002] In practical applications, power quality sensing devices from different manufacturers typically employ their own proprietary communication protocols, custom measurement messages, and differentiated data definitions. When various heterogeneous power quality sensing devices are connected to an intelligent monitoring platform on a large scale, the number of nodes is often extremely large, creating an urgent need for massive concurrent node access and unified interaction of underlying data.

[0003] However, existing technologies face multiple technical challenges when handling the interaction and access of power quality sensing devices with massive numbers of nodes: First, due to the lack of a unified model architecture, devices from different manufacturers suffer from severe data silos, and the data semantics of native measurement messages are incompatible, making direct cross-vendor data fusion impossible; second, existing technologies heavily rely on manual intervention for device access and ledger configuration, which is extremely time-consuming when dealing with massive numbers of nodes, making it difficult to meet dynamic adaptive requirements; finally, existing data interaction interfaces lack elastic processing capabilities when facing high-concurrency data streams from massive nodes, easily leading to data congestion between the underlying devices and the cloud monitoring platform, failing to meet the real-time requirements of large-scale power quality sensing device data transmission. Summary of the Invention

[0004] This invention provides a method, apparatus, and device for constructing an interactive model for power quality sensing devices, in order to solve the problems of being unable to directly perform cross-vendor data fusion, being unable to meet the adaptive requirements of device registration, configuration, and status changes, and being unable to meet the data transmission requirements of large-scale power quality sensing devices.

[0005] In a first aspect, the present invention provides a method for constructing an interaction model for power quality sensing devices, based on a three-layer architecture, comprising a device self-description layer, a unified semantic layer, and a cloud-edge-device transmission adaptation layer; the method includes: Encrypted self-describing broadcast messages are generated through the device self-describing layer; The self-describing broadcast message is decrypted through a unified semantic layer. Based on the decryption result, a pre-stored semantic mapping dictionary is retrieved to generate a data parsing plugin. The device is then registered online based on the data parsing plugin. The cloud-edge-device transmission adaptation layer receives native measurement messages, uses a data parsing plugin to convert the native measurement messages into data packets with a unified semantic format, and performs fragmentation processing on the data packets. When the device status changes, the self-describing broadcast message is updated. Based on the updated self-describing broadcast message, the device online registration information in the three-layer architecture is updated, and the updated three-layer architecture is used as the interaction model for power quality sensing devices.

[0006] Beneficial effects: This invention is based on a three-layer architecture comprising a device self-description layer, a unified semantic layer, and a cloud-edge-device transmission adaptation layer. The device self-description layer generates encrypted self-description broadcast messages, which are then decrypted at the unified semantic layer. Based on the decryption result, a pre-stored semantic mapping dictionary is retrieved to automatically generate a data parsing plugin. This plugin is then used for online device registration, breaking down data silos between devices from different manufacturers and eliminating the tedious manual code development and configuration required in traditional methods. Simultaneously, the cloud-edge-device transmission adaptation layer converts raw measurement messages into unified semantic format data packets using the data parsing plugin and performs fragmentation, resolving data congestion issues caused by massive concurrent transmission across nodes. Furthermore, when device status changes, the online device registration information in the three-layer architecture is updated by updating the self-description broadcast messages. The updated three-layer architecture serves as the interaction model for power quality sensing devices, enabling plug-and-play functionality across the entire lifecycle of massive heterogeneous devices, from access and data interoperability to dynamic adaptation, significantly reducing system development and maintenance costs.

[0007] In one optional implementation, generating an encrypted self-describing broadcast message through the device self-describing layer includes: At the device self-description layer, obtain the fixed templates for self-description messages built into different devices; The device metadata is obtained by filling the corresponding self-description message fixed template with the current attribute information of each device. The device metadata is encrypted using a preset key to obtain a self-describing broadcast message.

[0008] Beneficial effects: By obtaining fixed templates for self-description messages built into different devices at the device self-description layer, and filling them with device metadata based on the current attribute information of each device, the underlying data reporting format of heterogeneous devices is unified and standardized, enabling automatic reporting upon device power-on; at the same time, the device metadata is encrypted using a preset key to obtain self-description broadcast messages, ensuring the security of data transmission between power quality sensing devices and the monitoring platform, and preventing sensitive metadata from being stolen or tampered with during broadcasting.

[0009] In one optional implementation, the self-describing broadcast message is decrypted through a unified semantic layer, and a pre-stored semantic mapping dictionary is retrieved based on the decryption result to generate a data parsing plugin, including: The self-describing broadcast message is decrypted by a unified semantic layer to obtain the decryption result, which is the original device metadata. Extract the device manufacturer identifier and native protocol type from the decryption results; Based on the device manufacturer's identifier and the native protocol type, a pre-stored semantic mapping dictionary is retrieved to obtain the semantic mapping relationship; Based on semantic mapping relationships, standardized device metadata is extracted from the decryption results and a data parsing plugin is generated.

[0010] Beneficial effects: By decrypting self-describing broadcast messages through a unified semantic layer, the decryption results are obtained, and the device manufacturer identifier and native protocol type are accurately extracted. Then, the semantic mapping relationship is obtained by retrieving the pre-stored semantic mapping dictionary, realizing automatic matching between the private message keywords of each manufacturer and the standard encoding. Furthermore, based on the semantic mapping relationship, standardized device metadata is extracted and a data parsing plugin is generated, enabling the system to automatically complete the translation from the underlying private protocol to the standard protocol. The entire process does not require manual writing of parsing drivers, shortening the device adaptation cycle from several days to minutes.

[0011] In one optional implementation, online device registration is performed according to the data parsing plugin, including: Online registration is performed based on the standardized equipment metadata and the parsing rules of the data parsing plugin to obtain the equipment ledger; Based on the equipment register, determine the corresponding data storage partition.

[0012] Beneficial effects: Based on the standardized equipment metadata and the parsing rules of the data parsing plugin, online registration is automatically performed to obtain the equipment ledger, and the corresponding data storage partition is determined according to the equipment ledger, realizing the full automation of cloud-based equipment file management and storage resource scheduling. This avoids the high time consumption and error-prone problems caused by manual entry of ledgers, and meets the engineering requirements of rapid and large-scale registration and online deployment when a large number of devices are connected.

[0013] In an alternative implementation, after converting the raw measurement messages into packets of a uniform semantic format using a data parsing plugin, the method further includes: When communication is interrupted, the parsing rules and data packets are saved locally to obtain offline cached data packets; After communication is restored, the offline cached data packets are retransmitted.

[0014] Beneficial effects: Addressing the challenges of unstable network communication, this invention introduces an edge-level caching mechanism. When communication is interrupted, parsing rules and data packets are stored locally to obtain offline cached data packets, enabling the edge side to independently complete data standardization processing even during offline periods. After communication is restored, the offline cached data packets are retransmitted, preventing data loss due to network failures and fully ensuring the integrity and traceability of full-cycle power quality monitoring data.

[0015] In one alternative implementation, the data packets are fragmented, including: Count the total number of currently connected online nodes in the cloud-edge-device transmission adaptation layer; When the total number of online nodes is greater than or equal to the preset concurrency threshold, the data packet is split into multiple data fragments.

[0016] Beneficial effects: By counting the total number of currently connected online nodes in the cloud-edge-device transmission adaptation layer, and when the total number of online nodes is greater than or equal to a preset concurrency threshold, the data packets are automatically split into multiple data fragments, thus constructing a dynamic rate limiting and concurrent transmission strategy. This mechanism effectively balances the computing load, avoids data congestion on a single communication channel, and stably ensures the efficiency and real-time performance of data processing when tens of thousands of massive nodes are online simultaneously.

[0017] In one optional implementation, the device online registration information in the three-layer architecture is updated according to the updated self-describing broadcast message, and the updated three-layer architecture is used as the power quality sensing device interaction model, including: The updated self-describing broadcast message is decrypted using a unified semantic layer to obtain the updated decryption result; Based on the updated decryption results, the semantic mapping dictionary is retrieved to obtain the updated data parsing plugin and the updated standardized device metadata; Based on the updated data parsing plugin and the updated standardized equipment metadata, the equipment ledger and data storage partitions are updated to complete the update of the online registration information of the equipment in the three-layer architecture, and the updated three-layer architecture is used as the interaction model of the power quality sensing equipment.

[0018] Beneficial effects: Addressing the operational characteristics of frequent additions, reductions, expansions, or module replacements in distributed resources, when equipment changes, the updated self-describing broadcast message is decrypted using a unified semantic layer to obtain the updated decryption result. This retrieves the updated data parsing plugin and the updated standardized equipment metadata, subsequently updating the equipment ledger and data storage partitions. This process achieves automated, dynamic, and adaptive updates to the online registration information of devices in the three-tier architecture, using it as the updated interaction model for power quality sensing devices. The entire process requires no manual intervention, significantly reducing the cost of long-term, large-scale operation and maintenance.

[0019] Secondly, the present invention provides a power quality sensing device interaction model construction system, comprising: The system consists of device terminals, a cloud monitoring platform, and an edge gateway. The device terminals are equipped with a device self-description layer, the edge gateways are equipped with a unified semantic layer, and the edge gateways and the cloud monitoring platform are equipped with a cloud-edge-device transmission adaptation layer. The device terminal is used to generate encrypted self-describing broadcast messages through the device self-describing layer; The edge gateway is used to receive self-describing broadcast messages through the device self-describing layer; decrypt the self-describing broadcast messages through the unified semantic layer, retrieve the pre-stored semantic mapping dictionary based on the decryption result, generate a data parsing plugin, and upload the data parsing plugin to the cloud monitoring platform; The cloud-based monitoring platform is used to complete online device registration based on the data parsing plugin; The edge gateway is also used to receive native measurement messages through the cloud-edge-device transmission adaptation layer, and uses a data parsing plugin to convert the native measurement messages into data packets with a unified semantic format. After fragmenting the data packets, they are uploaded to the cloud monitoring platform. When the device status changes, the device terminal is also used to update the self-describing broadcast message, the edge gateway is also used to update the data parsing plugin according to the updated self-describing broadcast message, and the cloud monitoring platform is also used to update the device's online registration information according to the updated data parsing plugin.

[0020] Thirdly, this invention provides a device for constructing an interaction model for power quality sensing devices, based on a three-layer architecture, which includes a device self-description layer, a unified semantic layer, and a cloud-edge-device transmission adaptation layer; including: The generation module is used to generate encrypted self-describing broadcast messages through the device self-describing layer; The registration module is used to decrypt self-describing broadcast messages through a unified semantic layer, retrieve a pre-stored semantic mapping dictionary based on the decryption result, generate a data parsing plugin, and perform online device registration based on the data parsing plugin. The conversion module is used to receive native measurement messages through the cloud-edge-device transmission adaptation layer, and uses a data parsing plugin to convert the native measurement messages into data packets with a unified semantic format, and to perform fragmentation processing on the data packets. The update module is used to update the self-describing broadcast message when the device status changes, update the device online registration information in the three-layer architecture according to the updated self-describing broadcast message, and use the updated three-layer architecture as the interaction model of power quality sensing devices.

[0021] Fourthly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform a method for constructing an interactive model of a power quality sensing device as described in the first aspect or any corresponding embodiment.

[0022] Fifthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute a method for constructing an interaction model of a power quality sensing device according to the first aspect or any corresponding embodiment described above.

[0023] In a sixth aspect, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute a method for constructing an interactive model of a power quality sensing device according to the first aspect or any corresponding embodiment described above. Attached Figure Description

[0024] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the first process of a method for constructing an interaction model of a power quality sensing device according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the second process of a method for constructing an interaction model of a power quality sensing device according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the third process of a method for constructing an interaction model of a power quality sensing device according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the fourth process of a method for constructing an interaction model for a power quality sensing device according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the fifth process of a method for constructing an interaction model for a power quality sensing device according to an embodiment of the present invention; Figure 6 This is a structural block diagram of an interactive model construction device for power quality sensing equipment according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0027] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0028] When large-scale access of massive heterogeneous power quality sensing devices in large-scale new energy power distribution systems is required, the relevant technologies suffer from several technical defects, such as data silos across devices due to vendor proprietary protocols, difficulty in dynamically adapting device access and status changes due to reliance on complex manual configuration, and the high risk of communication congestion caused by massive concurrent data streams.

[0029] The device self-description layer enables devices to autonomously generate encrypted self-description broadcast messages, achieving proactive reporting and secure transmission of underlying metadata. Secondly, the unified semantic layer decrypts the messages and retrieves pre-stored semantic mapping dictionaries, automatically generating a data parsing plugin and completing online device registration. This eliminates the need for manual coding and manual data entry, completely removing cross-vendor data semantic barriers. Thirdly, the cloud-edge-device transmission adaptation layer receives native measurement messages, calls the data parsing plugin to convert them into unified semantic format data packets, and introduces a data packet fragmentation mechanism to resolve data congestion bottlenecks in high-concurrency scenarios with massive nodes, ensuring real-time transmission. Finally, an adaptive closed-loop mechanism is established. When device status changes, the self-description broadcast message is updated in conjunction with the online device registration information in the three-layer architecture. Ultimately, the updated three-layer architecture serves as the interaction model for power quality sensing devices across massive nodes, enabling plug-and-play, efficient interoperability, and stable interaction throughout the entire lifecycle of massive heterogeneous devices.

[0030] According to an embodiment of the present invention, an embodiment of a method for constructing an interactive model of a power quality sensing device is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0031] This embodiment provides a method for constructing an interaction model for a power quality sensing device, which can be used in the aforementioned electronic devices. Figure 1 This is a schematic diagram of the first process of a method for constructing an interaction model for a power quality sensing device according to an embodiment of the present invention, as shown below. Figure 1 As shown, the process includes the following steps: Step S101: Generate an encrypted self-describing broadcast message through the device self-describing layer.

[0032] Specifically, the device self-description layer refers to the functional layer deployed in the device's local firmware, which has a built-in standardized self-description message template. A self-description broadcast message is a broadcast data packet automatically generated by the device after filling in metadata based on the built-in template in the local firmware. An encrypted self-description broadcast message is a secure broadcast message generated after encrypting the metadata using an encrypted method.

[0033] After the device is powered on and completes its self-test, it automatically reads the standardized self-description message template built into the local firmware, fills in the device's current metadata to generate a self-description broadcast message, and uses a lightweight encryption method to generate an encrypted self-description broadcast message for broadcasting, enabling the device to report externally autonomously without manual configuration.

[0034] Step S102: Decrypt the self-describing broadcast message through the unified semantic layer, retrieve the pre-stored semantic mapping dictionary based on the decryption result, generate a data parsing plugin, and register the device online based on the data parsing plugin.

[0035] Specifically, the unified semantic layer refers to the functional layer deployed on the edge gateway, which builds a standardized semantic ontology library across the entire domain. The decryption result refers to the plaintext data containing device manufacturer and native fields extracted after decrypting encrypted self-describing broadcast messages. The semantic mapping dictionary refers to a mapping database that records the correspondence between manufacturer-private message keywords and standard encodings. The data parsing plugin refers to a rule plugin that automatically generates built-in private field to standard encoding logic based on the mapping relationship. Online device registration refers to the process of synchronizing device attributes with parsing rules and automatically completing the creation of an online ledger.

[0036] Upon receiving an encrypted self-describing broadcast message, the device manufacturer and native measurement fields are extracted by decrypting it using a pre-stored key through a unified semantic layer. Based on this, the corresponding mapping module for the manufacturer is retrieved from the pre-stored semantic mapping dictionary, and a data parsing plugin with built-in private fields and standard encoding conversion logic is automatically generated. Then, the device attributes and the rules of the data parsing plugin are uploaded synchronously, automatically completing the online registration of the device and achieving fully automated access without human intervention.

[0037] Step S103: Receive the native measurement message through the cloud-edge-device transmission adaptation layer, convert the native measurement message into a data packet with a unified semantic format using a data parsing plugin, and perform fragmentation processing on the data packet.

[0038] Specifically, the cloud-edge-device transmission adaptation layer refers to the functional layer deployed between the edge gateway and the cloud monitoring platform, which performs message specification encapsulation and concurrent transmission control. Native measurement messages refer to measurement data messages continuously collected and uploaded by the device, containing proprietary protocol formats and native fields. Unified semantic format data packets refer to standard data packets that convert native proprietary fields into standardized encoding and data formats using a data parsing plugin. Data fragmentation refers to a rate-limiting mechanism that splits massive data streams into multiple fragments for synchronous processing by parallel computing units.

[0039] The device continuously uploads native measurement messages. After receiving the native measurement messages through the cloud-edge-device transmission adaptation layer, it calls the automatically generated data parsing plugin to convert the native measurement messages into data packets with a unified semantic format in real time. When the number of online nodes reaches a preset threshold, the data packets with the unified semantic format are fragmented and distributed to multiple groups of parallel computing units for synchronous processing to avoid single-channel data congestion.

[0040] Step S104: When the device status changes, update the self-describing broadcast message, update the device online registration information in the three-layer architecture according to the updated self-describing broadcast message, and use the updated three-layer architecture as the power quality sensing device interaction model.

[0041] Specifically, equipment status changes include changes in operating status such as the addition of new measurement points, replacement of adjustment modules, or offline restart. Updated self-describing broadcast messages refer to the latest self-describing data messages regenerated and encrypted by the equipment after a status change. Equipment online registration information refers to the equipment ledger and data parsing rules recorded and maintained in the three-layer architecture. The power quality sensing equipment interaction model refers to a standardized interaction system that solidifies the equipment self-describing, unified semantic mapping, and fragmented transmission collaborative logic in the three-layer architecture.

[0042] When the device status changes due to expansion of measurement points, replacement of regulation modules, or offline restart, the device automatically regenerates and sends an updated self-describing broadcast message; the unified semantic layer automatically updates the data parsing plugin and links to update the online registration information of the devices in the three-layer architecture, enabling the system to automatically adapt to the dynamic increase or decrease of distributed resources in the distribution network, and using the three-layer architecture after dynamic rule updates as the overall interaction model of power quality sensing devices.

[0043] This embodiment provides a method for constructing an interaction model for power quality sensing devices. The invention is based on a three-layer architecture comprising a device self-description layer, a unified semantic layer, and a cloud-edge-device transmission adaptation layer. The device self-description layer generates encrypted self-description broadcast messages, which are then decrypted at the unified semantic layer. Based on the decryption result, a pre-stored semantic mapping dictionary is retrieved to automatically generate a data parsing plugin. This plugin is then used for online device registration, breaking down data silos between devices from different manufacturers and eliminating the tedious manual code development and configuration required in traditional methods. Simultaneously, the cloud-edge-device transmission adaptation layer converts raw measurement messages into data packets with a unified semantic format using the data parsing plugin and performs fragmentation on these packets, resolving data congestion issues caused by concurrent transmission across massive nodes. Furthermore, when device status changes, the online device registration information in the three-layer architecture is updated by updating the self-description broadcast messages. Finally, the updated three-layer architecture serves as the interaction model for power quality sensing devices, enabling plug-and-play functionality across the entire lifecycle of massive heterogeneous devices, from access and data interoperability to dynamic adaptation, significantly reducing system development and maintenance costs.

[0044] This embodiment provides a method for constructing an interaction model for a power quality sensing device, which can be used in the aforementioned electronic devices. Figure 2 This is a schematic diagram of the second process of a method for constructing an interaction model for a power quality sensing device according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps: Step S201: Generate an encrypted self-describing broadcast message through the device self-describing layer.

[0045] Specifically, step S201 includes: Step S2011: In the device self-description layer, obtain the fixed template of the self-description message built into different devices.

[0046] Specifically, different devices include power quality sensing terminals, photovoltaic inverters, energy storage converters, and SVG flexible regulation devices. A self-describing message fixed template refers to a message data structure template pre-written and formatted in the device's factory firmware. It includes six essential metadata fields: unique device identifier, capability set field, native communication parameters, measurement baseline parameters, device hierarchical attributes, and resource capacity limit.

[0047] After the device completes the power-on and self-test process, it automatically reads the pre-burned self-description message fixed template in its local firmware through the device's self-description layer to obtain a standardized message format framework for organizing its own parameters.

[0048] Step S2012: Fill in the corresponding self-description message fixed template according to the current attribute information of each device to obtain device metadata.

[0049] Specifically, current attribute information refers to the inherent operating parameters of the equipment under its factory configuration and current installation conditions. This includes a globally unique device ID, manufacturer's unified social credit code, hardware model code, capability set field distinguishing monitoring and regulation capabilities, factory native communication protocol, local communication port, default sampling period, native data scaling factor, measurement accuracy, and measurement unit for each indicator, equipment level attributes for distribution area terminals, feeder control devices, or substation main equipment, as well as the maximum number of measurement points and the upper limit of active power regulation capacity or reactive power regulation capacity. Equipment metadata refers to the complete plaintext data set generated after fully mapping and filling the corresponding fields of the self-description message fixed template with the above current attribute information.

[0050] The device automatically collects and reads its current attribute information, maps and fills in the content item by item according to the six categories of fields preset in the self-description message fixed template, and assembles and generates device metadata that can fully express the device's identity, communication characteristics and measurement and adjustment capabilities.

[0051] Step S2013: Encrypt the device metadata using a preset key to obtain a self-describing broadcast message.

[0052] Specifically, the preset key refers to an AES128 (Advanced Encryption Standard 128, where 128 indicates a key length of 128 bits) symmetric encryption key that is pre-written into the terminal's local storage medium at the factory and matches the edge gateway's built-in decryption key. Encryption processing refers to the computational process of converting plaintext data using the AES128 lightweight encryption algorithm.

[0053] The device calls the preset key stored locally and executes the AES128 lightweight encryption algorithm to encrypt the assembled device metadata, obtains a self-describing broadcast message, and broadcasts the self-describing broadcast message to the edge gateway through the local communication interface to ensure the security of device parameters in the transmission link.

[0054] Step S202 involves decrypting the self-describing broadcast message using a unified semantic layer, retrieving a pre-stored semantic mapping dictionary based on the decryption result, generating a data parsing plugin, and then registering the device online using the data parsing plugin. For details, please refer to [link to relevant documentation]. Figure 1 Step S102 of the illustrated embodiment will not be described again here.

[0055] Step S203: The native measurement messages are received through the cloud-edge-device transmission adaptation layer. A data parsing plugin is used to convert the native measurement messages into data packets with a unified semantic format, and the data packets are then fragmented. For details, please refer to [link to details]. Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0056] Step S204: When the device status changes, update the self-describing broadcast message, update the device online registration information in the three-layer architecture according to the updated self-describing broadcast message, and use the updated three-layer architecture as the power quality sensing device interaction model. For details, please refer to... Figure 1 Step S104 of the illustrated embodiment will not be described again here.

[0057] This embodiment provides a method for constructing an interaction model for power quality sensing devices. By obtaining fixed templates for self-description messages built into different devices at the device self-description layer, and filling them with device metadata based on the current attribute information of each device, the method achieves the unification and standardization of the underlying data reporting format of heterogeneous devices, enabling automatic reporting upon device power-on. Simultaneously, by encrypting the device metadata using a preset key to obtain self-description broadcast messages, the method ensures the security of data transmission between the power quality sensing devices and the monitoring platform, preventing sensitive metadata from being stolen or tampered with during the broadcast process.

[0058] This embodiment provides a method for constructing an interaction model for a power quality sensing device, which can be used in the aforementioned electronic devices. Figure 3 This is a schematic diagram of the third process of a method for constructing an interaction model for a power quality sensing device according to an embodiment of the present invention, as shown below. Figure 3 As shown, the process includes the following steps: Step S301: Generate an encrypted self-describing broadcast message through the device self-describing layer. For details, please refer to [link to details]. Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0059] Step S302: Decrypt the self-describing broadcast message through the unified semantic layer, retrieve the pre-stored semantic mapping dictionary based on the decryption result, generate a data parsing plugin, and register the device online based on the data parsing plugin.

[0060] Specifically, step S302 includes: Step S3021: Decrypt the self-describing broadcast message through the unified semantic layer to obtain the decryption result, which is the original device metadata.

[0061] Specifically, decryption refers to the operation where the edge gateway uses the locally pre-stored decryption key to perform reverse reconstruction calculations on the received ciphertext message. Raw device metadata refers to the plaintext, un-standardized encoded, original metadata of the device as presented by the decryption result.

[0062] After receiving a self-describing broadcast message sent by the device, the edge gateway uses the unified semantic layer to call the locally preset decryption key to perform reverse decryption calculation, and restores the decryption result containing the original private parameter configuration of the device. This decryption result is the original device metadata.

[0063] Step S3022: Extract the device manufacturer identifier and native protocol type from the decryption result.

[0064] Specifically, the equipment manufacturer identifier refers to the code information used to uniquely identify the equipment manufacturer, including the manufacturer's unified social credit code or manufacturer model code. The native protocol type refers to the physical and communication protocol type that the equipment uses by default at the factory, including Modbus (Modbus Protocol, industrial fieldbus communication protocol), DL / T645 "Multifunctional Energy Meter Communication Protocol", IEC61850 protocol (Communication Networks and Systems for Power Utility Automation, power automation communication standard), or MQTT protocol (Message Queuing Telemetry Transport).

[0065] By parsing the fields of the original device metadata obtained through decryption using a unified semantic layer, the device manufacturer identifier representing the device manufacturer and the native protocol type representing the device's native communication protocol can be located and extracted.

[0066] Step S3023: Based on the device manufacturer identifier and native protocol type, retrieve the pre-stored semantic mapping dictionary to obtain the semantic mapping relationship.

[0067] Specifically, the semantic mapping dictionary refers to a database that is pre-stored independently on the edge gateway in modules according to manufacturer, recording the one-to-one correspondence between manufacturer-specific message keywords and power quality standard codes, as well as native data scaling factors and unit conversion formulas. The semantic mapping relationship refers to the field conversion logic, unit conversion rules, and numerical scaling rules specific to the device manufacturer and the native protocol type, matched and retrieved from the semantic mapping dictionary.

[0068] Using the extracted device manufacturer identifier and native protocol type as index keywords, the corresponding manufacturer mapping module is retrieved from the pre-loaded and stored semantic mapping dictionary to obtain a semantic mapping relationship that fully matches the device.

[0069] Step S3024: Based on the semantic mapping relationship, extract standardized device metadata from the decryption result and generate a data parsing plugin.

[0070] Specifically, standardized equipment metadata refers to the set of standard parameters obtained by transforming the original private parameters in the decryption results according to semantic mapping relationships. The standard parameter set includes monitoring-type codes and regulation-type codes, among which the monitoring-type codes include voltage deviation codes. Second harmonic distortion rate coding 5th harmonic distortion coding Three-phase voltage imbalance coding Short-time voltage flicker encoding In addition, voltage sag index codes and regulation type codes include reactive power regulation output codes. Photovoltaic active power output coding Energy storage charging and discharging power coding Tap switch adjustment gear coding And the coding of active mitigation indicators.

[0071] Based on the semantic mapping relationship obtained from the retrieval, a data parsing plugin is automatically generated for real-time translation of private data streams of this type of equipment; at the same time, the semantic mapping relationship is used to standardize and translate the original measurement and adjustment parameters in the decryption results, and extract and generate standardized equipment metadata in a standardized format.

[0072] Step S3025: Register online according to the standardized equipment metadata and the parsing rules of the data parsing plugin to obtain the equipment ledger.

[0073] Specifically, parsing rules refer to the logic for converting private fields to standard encoding, unit conversion formulas, and numerical scaling ratios encapsulated within the data parsing plugin. Online registration refers to the process of synchronizing the standardized configuration information of devices with the data parsing logic and entering it into the cloud database. Device ledgers refer to data archives established in the cloud monitoring platform that record the globally unique ID, hierarchical attributes, monitoring capabilities, adjustment capacity, and corresponding parsing rules of each device.

[0074] The standardized equipment metadata and the parsing rules of the data parsing plugin are synchronously uploaded to the cloud monitoring platform. The cloud monitoring platform automatically parses and enters the data into the resource database, completing the online registration without manual input and automatically creating the corresponding equipment ledger.

[0075] Step S3026: Determine the corresponding data storage partition based on the equipment ledger.

[0076] Specifically, data storage partitioning refers to the dedicated time-series data storage area and retrieval channel allocated in the cloud monitoring platform database for specific access devices.

[0077] Based on the unique identifier and hierarchical attributes of the newly created equipment ledger, the cloud monitoring platform automatically divides and binds the corresponding data storage partitions in the database, pre-allocating storage resources for the persistent storage of real-time measurement data and algorithm calls of the equipment.

[0078] Step S303: The native measurement messages are received through the cloud-edge-device transmission adaptation layer. A data parsing plugin is used to convert the native measurement messages into data packets with a unified semantic format, and the data packets are then fragmented. For details, please refer to [link to details]. Figure 2 Step S203 of the illustrated embodiment will not be described again here.

[0079] Step S304: When the device status changes, update the self-describing broadcast message, update the device online registration information in the three-layer architecture according to the updated self-describing broadcast message, and use the updated three-layer architecture as the power quality sensing device interaction model. For details, please refer to... Figure 2 Step S204 of the illustrated embodiment will not be described again here.

[0080] This embodiment provides a method for constructing an interaction model for power quality sensing devices. It decrypts self-describing broadcast messages using a unified semantic layer, accurately extracting device manufacturer identifiers and native protocol types. Then, it retrieves semantic mapping relationships from a pre-stored semantic mapping dictionary, achieving automatic matching between keywords in each manufacturer's private messages and standard encodings. Furthermore, it extracts standardized device metadata based on this semantic mapping relationship and generates a data parsing plugin, enabling the system to automatically translate underlying private protocols into standard protocols. The entire process requires no manual writing of parsing drivers, reducing the device adaptation cycle from days to minutes. Based on the standardized device metadata and the parsing rules of the data parsing plugin, it automatically performs online registration to obtain a device ledger and determines the corresponding data storage partition based on the device ledger, achieving full automation of cloud-based device file management and storage resource scheduling. This avoids the high time consumption and error-prone nature of manual ledger entry, meeting the engineering requirements for rapid and large-scale registration and deployment when connecting massive numbers of devices.

[0081] This embodiment provides a method for constructing an interaction model for a power quality sensing device, which can be used in the aforementioned electronic devices. Figure 4 This is a schematic diagram of the fourth process of a method for constructing an interaction model for a power quality sensing device according to an embodiment of the present invention, as shown below. Figure 4 As shown, the process includes the following steps: Step S401: Generate an encrypted self-describing broadcast message through the device self-describing layer. For details, please refer to [link to details]. Figure 3 Step S301 of the illustrated embodiment will not be described again here.

[0082] Step S402: Decrypt the self-describing broadcast message through a unified semantic layer, retrieve the pre-stored semantic mapping dictionary based on the decryption result, generate a data parsing plugin, and perform online device registration based on the data parsing plugin. For details, please refer to [link to relevant documentation]. Figure 3 Step S302 of the illustrated embodiment will not be described again here.

[0083] Step S403: Receive the native measurement message through the cloud-edge-device transmission adaptation layer, convert the native measurement message into a data packet with a unified semantic format using a data parsing plugin, and perform fragmentation processing on the data packet.

[0084] Specifically, step S403 includes: Step S4031: When communication is interrupted, the parsing rules and data packets are saved locally to obtain offline cached data packets.

[0085] Specifically, communication interruption refers to an offline failure where the wide area network link between the edge gateway and the cloud monitoring platform is physically disconnected or unreachable. Local storage refers to the operation of the edge gateway using its local storage media to persistently store dynamic data in a non-volatile manner. Offline cached data packets refer to unified semantic format data packets that are persistently cached locally by the edge gateway during network outages, containing semantic mapping rules for all devices in the region and translated from the rolling real-time measurement data of the past 5 minutes.

[0086] When a network communication interruption is detected between the edge gateway and the cloud monitoring platform, the edge gateway continues to independently standardize and translate the device's native measurement messages on-site using the locally deployed data parsing plugin. The translated data packets and the corresponding parsing rules of the data parsing plugin are then persistently stored in the edge's local storage to generate offline cached data packets.

[0087] Step S4032: After communication is restored, the offline cached data packets are retransmitted.

[0088] Specifically, communication restoration refers to the re-establishment of the wide area network connection between the edge gateway and the cloud monitoring platform and the restoration of normal data communication. Retransmission refers to the operation where the edge gateway completely and synchronously uploads the offline cached data packets accumulated during the offline period to the cloud monitoring platform in batch packaging.

[0089] Once the network connectivity detection mechanism confirms that communication has been restored, the edge gateway automatically reads the offline cached data packets stored locally and uploads the supplementary data to the cloud monitoring platform in batches according to the time sequence, ensuring the integrity and no loss of the monitoring records throughout the entire cycle.

[0090] Step S4033: Count the total number of currently connected online nodes in the cloud-edge-device transmission adaptation layer.

[0091] Specifically, the total number of online nodes refers to the real-time total number of all sensing and control nodes in the current interactive network that are connected to the network and uploading measurement data in real time.

[0092] Through the connection management mechanism of the cloud-edge-device transmission adaptation layer, the online communication status of each access device is polled and monitored in real time, and the total number of online nodes connected in the current interactive network is dynamically counted and updated.

[0093] Step S4034: When the total number of online nodes is greater than or equal to the preset concurrency threshold, the data packet is split into multiple data fragments.

[0094] Specifically, the preset concurrency threshold refers to a critical value for the node size pre-set based on the monitoring platform's data throughput limit and real-time processing cycle indicators. In actual implementation, this concurrency threshold is set to 25,000 nodes. Splitting refers to the rate-limiting control logic that divides a massive, continuous single data stream into several independent data blocks. Data sharding refers to the structured standard JSON sub-data packets generated after splitting, which can be independently distributed and processed in parallel.

[0095] The cloud-edge-device transmission adaptation layer compares the current total number of online nodes with the preset concurrency threshold. When the total number of online nodes is greater than or equal to the preset concurrency threshold, a dynamic fragmentation and rate limiting mechanism is automatically triggered to split the aggregated large-scale data packets into multiple independent data fragments, so as to distribute them to multiple sets of parallel computing units in the cloud for synchronous concurrent processing and avoid single-channel data congestion.

[0096] Step S404: When the device status changes, update the self-describing broadcast message, update the device online registration information in the three-layer architecture according to the updated self-describing broadcast message, and use the updated three-layer architecture as the power quality sensing device interaction model. For details, please refer to... Figure 3 Step S304 of the illustrated embodiment will not be described again here.

[0097] This embodiment provides a method for constructing an interactive model for power quality sensing devices. Addressing the issue of unstable network communication, this invention introduces an edge-level caching mechanism. When communication is interrupted, parsing rules and data packets are saved locally to obtain offline cached data packets, allowing the edge side to independently complete data standardization processing during offline periods. After communication is restored, the offline cached data packets are retransmitted, preventing data loss due to network failures and fully ensuring the integrity and traceability of power quality monitoring data throughout the entire lifecycle. Furthermore, by statistically analyzing the total number of currently connected online nodes in the cloud-edge-device transmission adaptation layer, and when this total number of online nodes is greater than or equal to a preset concurrency threshold, data packets are automatically split into multiple data fragments, constructing a dynamic rate limiting and concurrent transmission strategy. This mechanism effectively balances the computing load, avoids data congestion on a single communication channel, and stably ensures the efficiency and real-time performance of data processing when tens of thousands of nodes are online simultaneously.

[0098] This embodiment provides a method for constructing an interaction model for a power quality sensing device, which can be used in the aforementioned electronic devices. Figure 5 This is a schematic diagram of the fifth step in a method for constructing an interaction model for a power quality sensing device according to an embodiment of the present invention. Figure 5 As shown, the process includes the following steps: Step S501: Generate an encrypted self-describing broadcast message through the device self-describing layer. For details, please refer to [link to details]. Figure 4 Step S401 of the illustrated embodiment will not be described again here.

[0099] Step S502: Decrypt the self-describing broadcast message through a unified semantic layer, retrieve the pre-stored semantic mapping dictionary based on the decryption result, generate a data parsing plugin, and perform online device registration based on the data parsing plugin. For details, please refer to [link to relevant documentation]. Figure 4 Step S402 of the illustrated embodiment will not be described again here.

[0100] Step S503: The native measurement messages are received through the cloud-edge-device transmission adaptation layer. A data parsing plugin is used to convert the native measurement messages into data packets with a unified semantic format, and the data packets are then fragmented. For details, please refer to [link to details]. Figure 4 Step S403 of the illustrated embodiment will not be described again here.

[0101] Step S504: When the device status changes, update the self-describing broadcast message, update the device online registration information in the three-layer architecture according to the updated self-describing broadcast message, and use the updated three-layer architecture as the power quality sensing device interaction model.

[0102] Specifically, step S504 includes: Step S5041: Decrypt the updated self-describing broadcast message through the unified semantic layer to obtain the updated decryption result.

[0103] Specifically, the updated decryption result refers to the original metadata plaintext reflecting the latest status of the device, obtained by the edge gateway after decrypting and restoring the updated self-describing broadcast message using a pre-stored key.

[0104] When a device undergoes a state change and automatically retransmits an updated self-describing broadcast message, the edge gateway receives the message through the unified semantic layer and uses the pre-stored key to perform decryption calculations, obtaining an updated decryption result that reflects the latest measurement points and capacity characteristics of the device.

[0105] Step S5042: Based on the updated decryption results, retrieve the semantic mapping dictionary to obtain the updated data parsing plugin and the updated standardized device metadata.

[0106] Specifically, the updated data parsing plugin refers to a rule plugin that is dynamically compiled and generated with the latest private-to-standard mapping logic after re-searching the dictionary based on the latest changes in the device's measurement or adjustment capabilities. The updated standardized device metadata refers to the standard parameter set reflecting the device's current latest configuration, obtained by translating the updated decryption results using the latest mapping relationships.

[0107] Based on the manufacturer identifier and the latest test point configuration contained in the updated decryption results, the corresponding mapping module in the pre-stored semantic mapping dictionary is retrieved again, the updated data parsing plugin is regenerated, and the updated standardized device metadata is extracted and translated from it.

[0108] Step S5043: Based on the updated data parsing plugin and the updated standardized device metadata, update the device ledger and data storage partitions, complete the update of the online registration information of the devices in the three-layer architecture, and use the updated three-layer architecture as the interaction model of the power quality sensing devices.

[0109] The updated standardized equipment metadata and the updated data parsing plugin are synchronously uploaded to the cloud monitoring platform. The cloud monitoring platform automatically refreshes and modifies the previously established equipment ledger content, and adaptively adjusts or expands the corresponding data storage partitions to achieve seamless updates of the online registration information of equipment in the three-layer architecture. The dynamically updated three-layer architecture with standardized interactive capabilities is used as the power quality sensing device interaction model to achieve continuous adaptive monitoring of dynamically added or removed equipment.

[0110] This embodiment provides a method for constructing an interaction model for power quality sensing devices. Addressing the operational characteristics of frequent additions, reductions, expansions, or module replacements in distributed resources, when device changes occur, a unified semantic layer decrypts the updated self-describing broadcast message to obtain the updated decryption result. This retrieves the updated data parsing plugin and the updated standardized device metadata, thereby updating the device ledger and data storage partitions. This process achieves automated, dynamic, and adaptive updates to the online registration information of devices in a three-tier architecture, using this information as the updated interaction model for power quality sensing devices. The entire process requires no manual intervention, significantly reducing the cost of long-term, large-scale operation and maintenance.

[0111] As a specific application embodiment of the present invention, this embodiment is applied to the county-level new energy full-area monitoring scenario. In this scenario, a total of 25,200 power quality sensing devices are deployed, including 3,200 distributed photovoltaic inverters, 860 energy storage converters, and 21,140 transformer area harmonic monitoring instruments, involving 12 equipment manufacturers. The native protocol types of the equipment delivered by the factory cover Modbus protocol, DL / T645 protocol, and private MQTT protocol.

[0112] During implementation, firstly, standardized self-describing message templates are pre-installed in the local firmware of all power quality sensing devices, and the preset fields of device metadata are completed at the factory. A unified semantic mapping dictionary is deployed across the entire region's edge gateways, and independent modular mapping configuration files corresponding to the aforementioned 12 manufacturers are uploaded and loaded. A distributed parallel computing cluster is configured on the cloud monitoring platform, and a dynamic sharding transmission mechanism is enabled, with the concurrency threshold set to 25,000 nodes. Subsequently, the 25,200 power quality sensing devices in the region are powered on in batches to complete self-tests. The device self-describing layer reads the firmware's built-in template and fills in the current attribute information to generate device metadata. Using a preset key, AES128 encryption is performed to generate encrypted self-describing broadcast messages, which are then broadcast to the edge gateways. The edge gateways decrypt the encrypted self-describing broadcast messages through a unified semantic layer to obtain the decrypted results in the form of the original device metadata. After extracting the device manufacturer identifier and native protocol type, the pre-stored semantic mapping dictionary is retrieved, and the corresponding manufacturer's mapping module is matched to automatically generate a data parsing plugin and provide... The system extracts standardized device metadata and then synchronously uploads it along with the parsing rules of the data parsing plugin to the cloud monitoring platform to complete online device registration. This automatically establishes a device ledger and allocates corresponding data storage partitions. After the device continuously uploads native measurement messages, the edge gateway receives these messages through the cloud-edge-device transmission adaptation layer and calls the data parsing plugin to translate them into data packets with a unified semantic format. Since the total number of online nodes has reached 25,200, which is greater than the preset concurrency threshold of 25,000, the system automatically triggers dynamic fragmentation and rate limiting. The data packets are then split into multiple data fragments and sent to the parallel computing cluster of the cloud monitoring platform for synchronous processing in a concurrent manner. Furthermore, when a device's status changes due to module replacement, expansion of measurement points, or restart, an updated self-describing broadcast message is regenerated and sent. The edge gateway and the cloud monitoring platform work together to update the data parsing plugin, device ledger, and data storage partitions to update the online registration information of the devices in the three-layer architecture. The updated three-layer architecture serves as the interaction model for power quality sensing devices.

[0113] Actual operation tests showed that the average time for a single device to complete the connection and online registration was 1 minute and 42 seconds, with the longest time being 2 minutes and 21 seconds, fully meeting the requirement of less than or equal to 3 minutes. Under the condition of 25,200 nodes running online at full load, the real-time data processing cycle of the cloud monitoring platform was stable in the range of 7.2 seconds to 9.5 seconds, fully meeting the performance constraint of less than or equal to 10 seconds. Data from heterogeneous devices from 12 manufacturers were automatically converted to standardized encoding without the need for additional conversion programs, reducing on-site maintenance manpower by 70%.

[0114] As a specific application embodiment of the present invention, this embodiment is applied to the distributed new energy governance scenario in industrial and commercial parks. In this scenario, a total of 2,160 power quality sensing nodes are deployed, along with 532 sets of energy storage and SVG (Static Var Generator) flexible regulation devices, involving 7 equipment manufacturers. The native protocol type of the equipment is mainly the IEC61850 proprietary protocol.

[0115] During implementation, the park's edge gateway preloads and stores the incremental semantic mapping configuration files corresponding to the aforementioned seven manufacturers. After the photovoltaic inverters, energy storage converters, SVG flexible regulation devices, and power quality monitoring terminals within the park are powered on in batches, encrypted self-description broadcast messages are automatically generated and broadcast externally through the device self-description layer. The park's edge gateway decrypts the messages through the unified semantic layer and retrieves the semantic mapping dictionary, automatically generating a data parsing plugin and standardized device metadata specific to the IEC61850 private protocol translation. The parsing rules of the data parsing plugin and the standardized device metadata are then uploaded to the cloud monitoring platform to automatically complete the online registration of devices and the establishment of device ledgers. During normal operation, the edge gateway continuously receives native measurement messages through the cloud-edge-device transmission adaptation layer and calls the data parsing plugin to translate the private format into data packets of the unified semantic format in real time. Since the current total number of online nodes is 2160 and has not reached the concurrency threshold of 25000 nodes, the translated data packets can be directly and quickly uploaded to the cloud monitoring platform without splitting to support local collaborative regulation and wide-area comprehensive governance.

[0116] Through practical application verification, the average adaptation time for batch access of various types of equipment in the park is only 1 minute and 15 seconds. Under the peak concurrent operation of 2160 nodes, the data processing cycle of the cloud monitoring platform is only 3.8 seconds. After carrying out comprehensive power quality management in the park based on standardized and unified semantic data, the voltage qualification rate reaches or exceeds 98.5%, the harmonic and three-phase imbalance qualification rate reaches or exceeds 99%, and all indicators meet the project acceptance requirements.

[0117] This embodiment also provides a power quality sensing device interaction model construction apparatus, which is used to implement the above embodiments and preferred embodiments, and will not be repeated for details already described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0118] This embodiment provides a device for constructing an interaction model for power quality sensing devices, based on a three-layer architecture. The three-layer architecture includes a device self-description layer, a unified semantic layer, and a cloud-edge-device transmission adaptation layer. Figure 6 As shown, it includes: The generation module 601 is used to generate encrypted self-describing broadcast messages through the device self-describing layer; The registration module 602 is used to decrypt self-describing broadcast messages through a unified semantic layer, retrieve a pre-stored semantic mapping dictionary based on the decryption result, generate a data parsing plugin, and perform online device registration based on the data parsing plugin. The conversion module 603 is used to receive native measurement messages through the cloud-edge-device transmission adaptation layer, convert the native measurement messages into data packets with a unified semantic format using a data parsing plugin, and perform fragmentation processing on the data packets. The update module 604 is used to update the self-describing broadcast message when the device status changes, update the device online registration information in the three-layer architecture according to the updated self-describing broadcast message, and use the updated three-layer architecture as the power quality sensing device interaction model.

[0119] In some alternative implementations, the generation module 601 includes: The self-describing message fixed template acquisition unit is used to acquire the self-describing message fixed templates built into different devices at the device self-describing layer. The device metadata acquisition unit is used to fill the corresponding self-describing message fixed template according to the current attribute information of each device to obtain device metadata; The device metadata encryption unit is used to encrypt device metadata using a preset key to obtain a self-describing broadcast message.

[0120] In some alternative implementations, the registration module 602 includes: The first decryption unit for self-describing broadcast messages is used to decrypt self-describing broadcast messages through a unified semantic layer to obtain the decryption result, which is the original device metadata. The identifier and protocol type extraction unit is used to extract the device manufacturer identifier and native protocol type from the decryption results; The semantic mapping relationship retrieval unit is used to retrieve the pre-stored semantic mapping dictionary based on the device manufacturer identifier and the native protocol type to obtain the semantic mapping relationship; The data parsing plugin generation unit is used to extract standardized device metadata from the decryption results and generate data parsing plugins based on semantic mapping relationships. The equipment ledger is a unit used to register online according to the standardized equipment metadata and the parsing rules of the data parsing plugin, and thus obtain the equipment ledger. The data storage partition determination unit is used to determine the corresponding data storage partition based on the equipment ledger.

[0121] In some optional embodiments, a power quality sensing device interaction model construction apparatus further includes: The offline cached data packet acquisition unit is used to save the parsing rules and data packets locally when communication is interrupted, thus obtaining the offline cached data packets; The offline cached data packet retransmission unit is used to retransmit offline cached data packets after communication is restored.

[0122] In some alternative implementations, the conversion module 603 includes: The online node total number statistics unit is used to count the total number of currently connected online nodes in the cloud-edge-device transmission adaptation layer; The packet splitting unit is used to split data packets into multiple data fragments when the total number of online nodes is greater than or equal to a preset concurrency threshold.

[0123] In some alternative implementations, the update module 604 includes: The second decryption unit for self-describing broadcast messages is used to decrypt the updated self-describing broadcast messages through a unified semantic layer to obtain the updated decryption result. The semantic mapping dictionary retrieval unit is used to retrieve the semantic mapping dictionary based on the updated decryption results, and obtain the updated data parsing plugin and the updated standardized device metadata; The power quality sensing device interaction model is obtained by a unit, which is used to update the device ledger and data storage partitions according to the updated data parsing plugin and the updated standardized device metadata, complete the update of the online registration information of the devices in the three-layer architecture, and use the updated three-layer architecture as the power quality sensing device interaction model.

[0124] The power quality sensing device interaction model construction apparatus provided in this embodiment of the invention can execute the power quality sensing device interaction model construction method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method execution. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0125] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0126] The following is a detailed reference. Figure 7This diagram illustrates a suitable structural schematic for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 701, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 702 or a program loaded from memory 708 into random access memory (RAM) 703. The RAM 703 also stores various programs and data required for the operation of the electronic device. The processor 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0127] Typically, the following devices can be connected to I / O interface 705: input devices 706 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 707 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 708 including, for example, magnetic tapes, hard disks, etc.; and communication devices 709. Communication device 709 allows electronic devices to exchange data via wireless or wired communication with other devices. Although Figure 7 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0128] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 709, or installed from a memory 708, or installed from a ROM 702. When the computer program is executed by the processor 701, it performs the functions defined in the power quality sensing device interaction model construction method of an embodiment of the present invention.

[0129] Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0130] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, it implements the power quality sensing device interaction model construction method shown in the above embodiments.

[0131] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0132] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for constructing an interactive model for power quality sensing devices, characterized in that, The method is based on a three-layer architecture, comprising a device self-description layer, a unified semantic layer, and a cloud-edge-device transmission adaptation layer; the method includes: Encrypted self-describing broadcast messages are generated through the device's self-describing layer; The self-describing broadcast message is decrypted through the unified semantic layer, and a pre-stored semantic mapping dictionary is retrieved based on the decryption result to generate a data parsing plugin. The device is then registered online based on the data parsing plugin. The cloud-edge-device transmission adaptation layer receives native measurement messages, uses the data parsing plugin to convert the native measurement messages into data packets with a unified semantic format, and performs fragmentation processing on the data packets. When the device status changes, the self-describing broadcast message is updated, the device online registration information in the three-layer architecture is updated according to the updated self-describing broadcast message, and the updated three-layer architecture is used as the power quality sensing device interaction model.

2. The method according to claim 1, characterized in that, The generation of encrypted self-describing broadcast messages through the device's self-describing layer includes: In the device self-description layer, obtain the fixed templates for self-description messages built into different devices; The device metadata is obtained by filling the corresponding self-description message fixed template with the current attribute information of each device. The device metadata is encrypted using a preset key to obtain a self-describing broadcast message.

3. The method according to claim 1, characterized in that, The process of decrypting the self-describing broadcast message through the unified semantic layer, retrieving a pre-stored semantic mapping dictionary based on the decryption result, and generating a data parsing plugin includes: The self-describing broadcast message is decrypted through the unified semantic layer to obtain the decryption result, which is the original device metadata. Extract the device manufacturer identifier and native protocol type from the decryption results; Based on the device manufacturer identifier and the native protocol type, a pre-stored semantic mapping dictionary is retrieved to obtain the semantic mapping relationship; Based on the semantic mapping relationship, standardized device metadata is extracted from the decryption result and a data parsing plugin is generated.

4. The method according to claim 3, characterized in that, The online device registration based on the data parsing plugin includes: Online registration is performed based on the standardized equipment metadata and the parsing rules of the data parsing plugin to obtain the equipment ledger; Based on the equipment ledger, determine the corresponding data storage partition.

5. The method according to claim 4, characterized in that, After the data parsing plugin is used to convert the raw measurement message into a data packet with a unified semantic format, the method further includes: When communication is interrupted, the parsing rules and the data packets are saved locally to obtain offline cached data packets; After communication is restored, the offline cached data packets are retransmitted.

6. The method according to claim 1, characterized in that, The fragmentation process of the data packet includes: The total number of currently connected online nodes in the cloud-edge-device transmission adaptation layer is counted. When the total number of online nodes is greater than or equal to a preset concurrency threshold, the data packet is split into multiple data fragments.

7. The method according to claim 4, characterized in that, The step of updating the online registration information of devices in the three-layer architecture according to the updated self-describing broadcast message, and using the updated three-layer architecture as the interaction model for power quality sensing devices, includes: The updated self-describing broadcast message is decrypted through the unified semantic layer to obtain the updated decryption result; Based on the updated decryption results, the semantic mapping dictionary is retrieved to obtain the updated data parsing plugin and the updated standardized device metadata; Based on the updated data parsing plugin and the updated standardized device metadata, the device ledger and the data storage partition are updated to complete the update of the online registration information of the devices in the three-layer architecture, and the updated three-layer architecture is used as the interaction model of the power quality sensing device.

8. A power quality sensing device interaction model construction system, characterized in that, The system includes: The device terminal, cloud monitoring platform, and edge gateway are configured; the device terminal is equipped with a device self-description layer, the edge gateway is equipped with a unified semantic layer, and the edge gateway and the cloud monitoring platform are equipped with a cloud-edge-device transmission adaptation layer. The device terminal is used to generate encrypted self-describing broadcast messages through the device self-describing layer; The edge gateway is used to receive the self-describing broadcast message through the device self-describing layer; decrypt the self-describing broadcast message through the unified semantic layer; retrieve the pre-stored semantic mapping dictionary based on the decryption result; generate a data parsing plugin; and upload the data parsing plugin to the cloud monitoring platform. The cloud monitoring platform is used to complete the online registration of devices based on the data parsing plugin; The edge gateway is also used to receive native measurement messages through the cloud-edge-device transmission adaptation layer, convert the native measurement messages into data packets with a unified semantic format using the data parsing plugin, and upload the data packets to the cloud monitoring platform after fragmentation. When the device status changes, the device terminal is also used to update the self-describing broadcast message, the edge gateway is also used to update the data parsing plugin according to the updated self-describing broadcast message, and the cloud monitoring platform is also used to update the device online registration information according to the updated data parsing plugin.

9. A device for constructing an interactive model for power quality sensing equipment, characterized in that, The device is executed based on a three-layer architecture, which includes a device self-description layer, a unified semantic layer, and a cloud-edge-device transmission adaptation layer; the device includes: A generation module is used to generate encrypted self-describing broadcast messages through the device's self-describing layer; The registration module is used to decrypt the self-describing broadcast message through the unified semantic layer, retrieve the pre-stored semantic mapping dictionary based on the decryption result, generate a data parsing plugin, and perform online device registration based on the data parsing plugin. The conversion module is used to receive native measurement messages through the cloud-edge-device transmission adaptation layer, convert the native measurement messages into data packets with a unified semantic format using the data parsing plugin, and perform fragmentation processing on the data packets. The update module is used to update the self-describing broadcast message when the device status changes, update the device online registration information in the three-layer architecture according to the updated self-describing broadcast message, and use the updated three-layer architecture as the power quality sensing device interaction model.

10. The apparatus according to claim 9, characterized in that, The generation module includes: The self-describing message fixed template acquisition unit is used to acquire the self-describing message fixed templates built into different devices in the device self-describing layer. The device metadata acquisition unit is used to fill the corresponding self-describing message fixed template according to the current attribute information of each device to obtain device metadata; The device metadata encryption unit is used to encrypt the device metadata using a preset key to obtain a self-describing broadcast message.

11. The apparatus according to claim 9, characterized in that, The registration module includes: The first decryption unit for self-describing broadcast messages is used to decrypt the self-describing broadcast messages through the unified semantic layer to obtain a decryption result, wherein the decryption result is the original device metadata. An identifier and protocol type extraction unit is used to extract the device manufacturer identifier and native protocol type from the decryption result; The semantic mapping relationship retrieval unit is used to retrieve a pre-stored semantic mapping dictionary based on the device manufacturer identifier and the native protocol type to obtain the semantic mapping relationship; A data parsing plugin generation unit is used to extract standardized device metadata from the decryption result and generate a data parsing plugin based on the semantic mapping relationship. The equipment ledger obtaining unit is used to register online according to the standardized equipment metadata and the parsing rules of the data parsing plugin to obtain the equipment ledger; The data storage partition determination unit is used to determine the corresponding data storage partition based on the device ledger.

12. The apparatus according to claim 11, characterized in that, A device for constructing an interactive model for power quality sensing equipment also includes: An offline cached data packet obtaining unit is used to save the parsing rules and the data packets locally when communication is interrupted, thereby obtaining an offline cached data packet; The offline cached data packet retransmission unit is used to retransmit the offline cached data packets after communication is restored.

13. The apparatus according to claim 9, characterized in that, The conversion module includes: The online node total number statistics unit is used to count the total number of currently connected online nodes in the cloud-edge-end transmission adaptation layer; The data packet splitting unit is used to split the data packet into multiple data fragments when the total number of online nodes is greater than or equal to a preset concurrency threshold.

14. The apparatus according to claim 11, characterized in that, The update module includes: The second decryption unit for self-describing broadcast messages is used to decrypt the updated self-describing broadcast message through the unified semantic layer to obtain the updated decryption result; The semantic mapping dictionary retrieval unit is used to retrieve the semantic mapping dictionary based on the updated decryption results, and obtain the updated data parsing plugin and the updated standardized device metadata. The power quality sensing device interaction model obtaining unit is used to update the device ledger and the data storage partition according to the updated data parsing plugin and the updated standardized device metadata, complete the update of the online registration information of the devices in the three-layer architecture, and use the updated three-layer architecture as the power quality sensing device interaction model.

15. An electronic device, characterized in that, include: The device includes a memory and a processor, which are interconnected and communicate with each other. The memory stores computer instructions, and the processor executes the computer instructions to perform a method for constructing an interactive model of a power quality sensing device according to any one of claims 1 to 7.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute a method for constructing an interactive model of a power quality sensing device according to any one of claims 1 to 7.

17. A computer program product, characterized in that, It includes computer instructions for causing a computer to execute a method for constructing an interactive model of a power quality sensing device according to any one of claims 1 to 7.

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