An edge-computing-based electrical appliance accessory rapid configuration method

Abstract

The application relates to the technical field of Internet of Things edge intelligence, and discloses a quick configuration method for electric appliance accessories based on edge computing, which comprises the following steps: an edge computing device receives a user request, performs layer-by-layer screening in a local accessory library, calculates a total matching degree score by using linear normalization, Boolean weighted matching and AHP method, generates an order in combination with user historical preferences, and then issues the order through MQTT; a user terminal calls Unity3D+PBR rendering to display a virtual assembly effect; the user selects a target accessory; the edge side immediately completes hardware interface detection and parameter initialization, predicts a running state through ARIMA, optimizes parameters, and finally sends the configuration state back to the terminal; and the whole process log is encrypted and uploaded to the cloud. Through calculation sinking, personalized and accurate recommendation, visual zero fault and parameter self-optimization, the application can significantly reduce the failure rate and configuration delay of electric appliance accessories, and can be widely used for intelligent and quick configuration of household appliance, industrial and data center accessories.

Description

Technical Field

[0001] This invention relates to the field of edge intelligence technology in the Internet of Things (IoT), and in particular to a method for rapid configuration of electrical accessories based on edge computing. Background Technology

[0002] Current electrical component configuration technologies generally adopt a dual model of "centralized cloud decision-making + manual on-site installation". That is, users upload their requirements to the cloud server, the server returns a list of recommended components after completing the compatibility query, and then the manuals complete the selection, installation and debugging according to the instruction manual. The end-to-end latency is as high as several seconds to tens of seconds, and it continuously relies on public network bandwidth and cloud computing power. Once the network jitters or disconnects, the entire configuration process will be interrupted, which cannot meet the rigid requirements of "plug and play" in industrial sites.

[0003] Traditional appliance accessory configuration recommendation algorithms rely solely on static matching based on price, inventory, or a single compatibility field. They lack quantitative methods that combine multi-dimensional performance indicators and users' historical preferences, resulting in significant discrepancies between the recommended results and users' actual needs. Furthermore, providing only two-dimensional images or text descriptions before accessory installation often leads to incorrect purchases or installations, resulting in disassembly and rework, incurring substantial additional time and financial costs.

[0004] The existing configuration process for electrical components lacks a long-term data collection and prediction mechanism on the local side. Equipment can only be passively reported for repair after a failure occurs, waiting for maintenance personnel to arrive on site for location and repair. Although some high-end systems have attempted to transmit data back to the cloud for big data analysis, the real-time performance and coverage are not ideal due to limitations in uplink bandwidth, data privacy, and compliance review, making it impossible to form an effective closed-loop optimization.

[0005] In summary, the industry urgently needs a new configuration architecture that is cloud-free, trial-and-error-free, and self-optimizable, which can not only push the entire lifecycle of recommendation, matching, initialization, and optimization to the edge, but also take into account users' multiple demands for visualization, personalization, and post-event auditing, in order to solve the systemic defects in existing technologies. Summary of the Invention

[0006] The purpose of this invention is to propose a method for rapid configuration of electrical components based on edge computing in order to solve the problems in the prior art.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a method for rapid configuration of electrical components based on edge computing, comprising the following steps:

[0008] Step S1: The user sends a configuration request to the edge computing device through the user terminal;

[0009] Step S2: Filter the electrical component database to obtain a list of optional components based on the configuration request;

[0010] Step S3: Calculate the total matching score of the accessories in the optional accessories list;

[0011] Step S4: Generate a parts sequence based on the overall matching score and send it to the user terminal;

[0012] Step S5: The user previews the installation effect of each accessory in the accessory sequence through the user terminal;

[0013] Step S6: The user selects the target accessory from the accessory sequence and sends the selection result back to the edge computing device;

[0014] Step S7: The edge computing device establishes a communication link with the installed accessories, performs hardware interface connection detection, and initializes and configures the accessory parameters.

[0015] Step S8: The edge computing device performs operational testing on the installed accessories and optimizes the accessory parameters based on the test results.

[0016] Step S8 further includes the following sub-steps:

[0017] S8-1, the edge computing device collects the operating status data of the installed accessories through real-time data acquisition methods;

[0018] S8-2, the edge computing device performs time series analysis on the operating status data through the ARIMA model, and predicts the future operating status of the installed accessories based on the analysis results to obtain the prediction results;

[0019] S8-3, Based on the prediction results, the parameters of the installed accessories are optimized using the gradient descent optimization algorithm, and the parameters of the installed accessories are modified using the parameter configuration module based on the optimized parameters.

[0020] In step S9, the edge computing device notifies the user that the configuration of the electrical accessories is complete, sends the final configuration status information of the accessories to the user terminal, and records it in a log file.

[0021] Furthermore, step S1 also includes the following sub-steps:

[0022] S1-1, the user inputs the configuration request of the electrical accessory through the user terminal and sends the configuration request to the edge computing device through wireless transmission;

[0023] S1-2, The edge computing device performs format verification and integrity verification on the received electrical accessory configuration request. If the verification fails, it requests the user terminal to resend the configuration request. If the verification passes, it proceeds to S1-3.

[0024] S1-3, the edge computing device parses the verified configuration request and extracts the user identity information, electrical equipment model, accessory configuration type and accessory performance requirements.

[0025] Furthermore, step S2 also includes the following sub-steps:

[0026] S2-1, After receiving the configuration request, the edge computing device accesses the locally stored electrical accessory database, which contains accessory models, functional parameters, compatibility fields and performance index information of various electrical accessories;

[0027] S2-2, The edge computing device searches for a set of optional accessories in the electrical accessory database by matching the electrical accessory model with the compatibility field based on the electrical device model in the configuration request;

[0028] S2-3, the edge computing device further filters out a subset of optional accessories from the set of optional accessories by matching the accessory configuration type and accessory model according to the accessory configuration type in the configuration request;

[0029] S2-4, The edge computing device further filters from the subset of optional accessories to obtain a list of optional accessories based on the accessory performance requirements in the configuration request by matching the accessory performance requirements and performance index information;

[0030] S2-5, the edge computing device formats the final selected list of optional accessories using a JSON formatting method.

[0031] Furthermore, step S3 also includes the following sub-steps:

[0032] S3-1, The edge computing device uses a linear normalization method to quantify and score the performance indicators of each component in the optional component list to obtain the performance score of each performance indicator, including response time, processing capacity and energy efficiency ratio;

[0033] S3-2, the edge computing device further uses Boolean matching and weighted matching methods to quantify the compatibility of each accessory in the optional accessory list and obtain a compatibility score;

[0034] S3-3, the edge computing device uses the AHP (Analog-Hybrid Analysis) method to fuse the performance score and compatibility score to obtain the total matching score for each component.

[0035] Furthermore, step S4 also includes the following sub-steps:

[0036] S4-1, The edge computing device normalizes the total matching score of each accessory in the optional accessory list using the Min-Max normalization method;

[0037] S4-2, the edge computing device sorts the list of optional accessories in descending order based on the normalized total matching score, and further adjusts the sorting results by combining the user's historical configuration preference information to generate an accessory sequence containing accessory information;

[0038] S4-3, the edge computing device sends the generated accessory sequence to the user terminal via the MQTT data transmission protocol.

[0039] Furthermore, step S5 also includes the following sub-steps:

[0040] S5-1, the user terminal uses the virtual simulation experiment platform to virtually simulate and display the electrical equipment of the accessories and the accessories to be installed in the accessory sequence, and generates the corresponding virtual accessories and virtual electrical equipment;

[0041] S5-2, In the user terminal's visual interactive interface, the user can freely select the virtual accessories or switch virtual electrical devices by using a touch screen selection method;

[0042] S5-3: Based on the user's selection, the user terminal installs virtual parts one by one onto the virtual electrical equipment through a virtual assembly algorithm, and renders the installation effect through the PBR rendering pipeline built into the virtual simulation experimental platform to obtain a visual effect of the parts installation.

[0043] S5-4, The user terminal displays the visualized accessory installation effect to the user through its visual interactive interface.

[0044] Furthermore, step S6 also includes the following sub-steps:

[0045] S6-1, Users can browse the accessory sequence and select the target accessory by touch screen selection in the user terminal's visual interactive interface based on the visualized accessory installation effect;

[0046] S6-2, The user terminal records the accessory information of the target accessory selected by the user. If the accessory requires manual installation, the user can manually enter the accessory installation completion information through the user terminal after installation.

[0047] S6-3, the user terminal sends the accessory information of the target accessory and the accessory installation completion information to the edge computing device;

[0048] S6-4, the edge computing device verifies the accuracy and completeness of the received user selection results through data verification methods.

[0049] Furthermore, step S7 also includes the following sub-steps:

[0050] S7-1, the edge computing device determines the communication protocol and initial parameter configuration requirements of the installed accessories based on the accessory information selected by the user;

[0051] S7-2, The edge computing device establishes a communication link with the installed accessory by calling the communication module according to the communication protocol, and further detects the hardware interface connection status of the installed accessory through the hardware interface detection program. If the detection result is abnormal, an abnormal feedback is sent to the user terminal, and the user is requested to manually check the hardware interface connection status or reinstall the accessory.

[0052] S7-3, the edge computing device initializes the parameters of the installed accessories through the parameter configuration module according to the initial parameter configuration requirements.

[0053] Furthermore, step S9 also includes the following sub-steps:

[0054] S9-1, the edge computing device uses JSON format to summarize and integrate the final configuration status information of the installed accessories, and sends the integrated final configuration status information to the user terminal through the MQTT data transmission protocol. The final configuration status information includes the hardware interface connection status of the accessories and the optimized accessory parameters.

[0055] S9-2, the user terminal notifies the user that the electrical accessories configuration is complete through platform push and SMS notification methods;

[0056] S9-3, the edge computing device records key information during the configuration process into a log file through a local log recording system, and then uploads it to a cloud server. The key information includes the user's configuration request, accessory sequence, target accessory selected by the user, and the final configuration status information of the installed accessories. The cloud server allows users to download and view the log file through their user terminals.

[0057] The beneficial effects of the technical solution provided by this invention include at least the following:

[0058] This invention achieves local autonomy for the entire lifecycle of recommendation, configuration, and optimization by performing multi-criteria decision-making, time-series prediction, and online gradient descent operations at the edge. It reduces end-to-end latency from seconds to milliseconds and can still complete component selection and parameter self-optimization even in offline environments. This significantly reduces dependence on public network bandwidth and cloud computing power, fundamentally solving the industry pain point of paralysis due to network outages.

[0059] This invention proposes a fusion scoring mechanism combining linear normalization, Boolean weighted matching, and AHP hierarchical analysis. This mechanism quantifies both performance and compatibility onto the same scale and allows for online adjustment of weights based on user historical preferences. Combined with the high-fidelity rendering pipeline of Unity3D+PBR, this enables users to preview the assembly effect of accessories in all aspects before actual installation, significantly reducing time and economic losses.

[0060] This invention uses the ARIMA model to predict the time series of operating data of installed components at the edge and fine-tunes the operating parameters online using the gradient descent algorithm, enabling self-healing before a failure occurs, transforming post-failure maintenance into proactive prevention, and significantly reducing downtime and spare parts inventory pressure.

[0061] This invention adopts a two-layer mechanism of local log blockchain digest + cloud auditable download. Users can retrieve the original logs with digital signatures at any time via SMS and platform push. This not only meets the requirements of the Information Protection Law for data controllability, but also provides manufacturers with remote diagnosis and accountability means, realizing the organic unity of "edge autonomy" and "post-event audit". Attached Figure Description

[0062] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 This is a flowchart of a method provided in an embodiment of the present invention. Detailed Implementation

[0064] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a rapid configuration method for electrical accessories based on edge computing proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0065] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0066] The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0067] The following description, in conjunction with the accompanying drawings, details a specific scheme for a rapid configuration method for electrical components based on edge computing provided by the present invention.

[0068] Please see Figure 1 The diagram illustrates a method flowchart for a rapid configuration method of electrical accessories based on edge computing, according to an embodiment of the present invention. The method includes the following steps:

[0069] Step S1: The user sends a configuration request to the edge computing device through the user terminal;

[0070] Step S1 further includes the following sub-steps:

[0071] S1-1, the user inputs the configuration request of the electrical accessory through the user terminal and sends the configuration request to the edge computing device through wireless transmission;

[0072] S1-2, the edge computing device performs format verification and integrity verification on the received electrical accessory configuration request. If the verification fails, it requests the user terminal to resend the configuration request. If the verification passes, it proceeds to S1-3.

[0073] S1-3, the edge computing device parses the verified configuration request and extracts the user identity information, electrical equipment model, accessory configuration type and accessory performance requirements.

[0074] It should be noted that the purpose of format verification is to ensure that the configuration request sent by the user terminal conforms to the preset standard format in structure, so that the edge computing device can correctly perform subsequent processing. The edge computing device will check whether the data packet of the configuration request is organized according to the prescribed protocol specifications, including whether there are correct curly braces, whether the key-value pairs are complete, and whether it follows the parameter format and calling method specified by the API.

[0075] The purpose of integrity verification is to ensure that the configuration request contains all the necessary information so that the edge computing device can accurately perform subsequent accessory screening and configuration, avoiding operational errors due to missing key information. The edge computing device will check each item in the configuration request according to the preset checklist. If any part is missing, the device will consider the integrity verification to have failed.

[0076] User identity information is used to identify and distinguish users who send configuration requests, thereby providing personalized services to users during the configuration process and ensuring the security and traceability of configuration operations. This includes the user's ID, account, and password. After obtaining the user's identity information, the edge computing device can find related information such as the user's configuration preference history and list of previously purchased electrical appliances in the system database, thereby providing the user with more personalized accessory recommendations and configuration solutions in subsequent steps.

[0077] The electrical equipment model number is used to specify the specific electrical equipment that the user wants to configure, so that the edge computing device can select compatible accessories from a large number of electrical accessories, thereby ensuring the feasibility and accuracy of subsequent configuration.

[0078] Accessory configuration types are used to further narrow down the range of optional accessories, enabling users to more accurately obtain the types of accessories they need, reducing the difficulty of users filtering among many accessories, and improving the efficiency of the entire configuration process.

[0079] Accessory performance requirements refer to the specific performance requirements that users put forward for accessories based on their actual usage needs and expectations. Edge computing devices select accessories that meet user expectations based on these requirements, making the configuration results more in line with the user's personalized needs and improving the user experience.

[0080] Step S2: Filter the electrical component database to obtain a list of optional components based on the configuration request;

[0081] Step S2 further includes the following sub-steps:

[0082] S2-1 After receiving the configuration request, the edge computing device accesses the locally stored electrical parts database, which contains various electrical parts' model numbers, functional parameters, compatibility fields, and performance index information.

[0083] S2-2, The edge computing device searches for a set of optional accessories in the electrical accessories database by matching the electrical equipment model with the compatibility field based on the electrical equipment model in the configuration request;

[0084] S2-3, the edge computing device further filters out a subset of optional accessories from the set of optional accessories by matching the accessory configuration type and accessory model according to the accessory configuration type in the configuration request;

[0085] S2-4, The edge computing device further filters from the subset of optional accessories to obtain a list of optional accessories based on the accessory performance requirements in the configuration request by matching the accessory performance requirements and performance index information;

[0086] S2-5, the edge computing device formats the final selected list of optional accessories using a JSON formatting method.

[0087] It should be noted that the locally stored database of electrical components is used to store relevant information for various electrical components, including:

[0088] Part number: A code or name used to uniquely identify an electrical part;

[0089] Functional parameters: used to describe the functional characteristics of electrical components;

[0090] Compatibility field: Used to indicate the model or type of electrical equipment that this accessory can be used with;

[0091] Performance metrics information: Used to describe the performance of the components.

[0092] Here is an example of relevant information for an electrical appliance component in an electrical appliance component database:

[0093]

[0094] JSON is a lightweight data exchange format used to serialize data structures into text format. In this patent, the edge computing device uses JSON formatting methods to format the filtered list of optional accessories, making it easier to transmit and parse, ensuring that the user terminal can accurately receive and display accessory information, and improving the efficiency and reliability of data interaction.

[0095] Step S3: Calculate the total matching score of the accessories in the optional accessories list;

[0096] Step S3 further includes the following sub-steps:

[0097] S3-1, the edge computing device uses a linear normalization method to quantify and score the performance indicators of each component in the optional component list, and obtains the performance scores of each performance indicator, including response time, processing capacity and energy efficiency ratio.

[0098] S3-2, the edge computing device further uses Boolean matching and weighted matching methods to quantify the compatibility of each accessory in the optional accessory list and obtain a compatibility score;

[0099] S3-3, the edge computing device uses the AHP (Analytic Hierarchy Process) to fuse performance scores and compatibility scores to obtain a total matching score for each component.

[0100] It should be noted that linear normalization is a commonly used numerical processing method. Its purpose is to transform data of different dimensions and orders of magnitude into the same scale range, which facilitates subsequent analysis and comparison. In this patent, for each performance index, the maximum and minimum values ​​of the index among all components are found. Then, the minimum value is subtracted from the index value of each component, and the result is divided by the difference between the maximum and minimum values ​​to obtain the normalized score of the index in the interval [0,1]. This achieves the quantitative scoring of different performance indicators and provides basic data for subsequent comprehensive evaluation.

[0101] Boolean matching is a matching method based on logical operations. In this patent, when the edge computing device quantifies the compatibility of each accessory in the optional accessory list, it will use Boolean matching to determine whether the accessory and the target electrical device are consistent in key compatibility fields, thereby making a preliminary assessment of the accessory's compatibility.

[0102] Weighted matching is a matching method that introduces weights based on Boolean matching, taking into account the different importance of different compatibility fields. In this patent, for each accessory in the optional accessory list, the interface type and the operating system version it supports affect the overall compatibility, and different weights are assigned to the compatibility field. Then, by calculating the product of the matching degree of each accessory on each compatibility field and the corresponding weight, the weighted matching score of the accessory is obtained, thereby more accurately quantifying the compatibility of the accessory and making the compatibility score closer to the compatibility requirements in actual use scenarios.

[0103] The Analytic Hierarchy Process (AHP) is a system analysis method primarily used to solve multi-objective, multi-criteria decision-making problems. In this patent, it is used to integrate the performance score and compatibility score of each component in the optional component list to obtain a total matching score. First, performance indicators and compatibility are used as two criterion layers. Each criterion layer includes specific indicators such as response time, processing capacity, and compatibility score. Then, by constructing a judgment matrix, the relative importance weights between each criterion layer and indicator are determined. Finally, a weighted sum is performed according to these weights to obtain the total matching score of each component, thereby selecting the optimal component that comprehensively considers both performance and compatibility.

[0104] Step S4: Generate a parts sequence based on the overall matching score and send it to the user terminal;

[0105] Step S4 further includes the following sub-steps:

[0106] S4-1, the edge computing device normalizes the total matching score of each accessory in the optional accessories list using the Min-Max normalization method;

[0107] S4-2, the edge computing device sorts the list of optional accessories in descending order based on the normalized total matching score, and further adjusts the sorting results by combining the user's historical configuration preference information to generate an accessory sequence containing accessory information;

[0108] S4-3, the edge computing device sends the generated accessory sequence to the user terminal via the MQTT data transmission protocol.

[0109] It should be noted that when generating the accessory sequence, the system adjusts the ranking results by incorporating the user's historical configuration preferences. In addition to ranking based on the normalized total matching score, the system also considers the user's historical configuration preferences. The system will appropriately boost the ranking of accessories that match the user's preferences to improve user satisfaction with the recommended results. A specific example of adjusting the ranking results by incorporating the user's historical configuration preferences is given below:

[0110] The optional accessory models in the accessory list provided for the "Smart Air Conditioner Companion" device are initially sorted (from highest to lowest) according to their total compatibility score:

[0111]

[0112] User A selected accessories for the "Smart Air Conditioner Companion" three times in the past, always choosing accessories with "energy efficiency ratio ≥ 4.5 and response time < 0.2s", and the final target accessory model selected was ZN-05 or ZN-07.

[0113] The system extracts users' historical preferences and assigns them the following weights: energy efficiency ratio +0.15, response time +0.10, and historically purchased models +0.05.

[0114] After calculating preference scores, the overall match score for models ZN-05 and ZN-07 has changed. The new score is:

[0115] ZN-05: 0.88+0.15+0.10+0.05=1.18;

[0116] ZN-07: 0.90+0.15+0.05=1.10;

[0117] The scores for the remaining models remain unchanged, and the final ranking is as follows:

[0118]

[0119] User A's accessory sequence prioritizes displaying high-efficiency, fast-response models that they have previously purchased. This personalized adjustment mechanism makes the recommendations more aligned with the user's actual usage habits and needs, enhancing the user experience and the system's intelligence.

[0120] MQTT is a lightweight data transmission protocol designed for low-bandwidth and unstable network environments. It is widely used in IoT scenarios for publishing / subscribing to messages and has advantages such as low overhead, high transmission efficiency, and support for asynchronous communication. It can ensure fast and stable transmission of accessory sequence data between edge devices and user terminals, and maintain high reliability even under poor network conditions.

[0121] Step S5: The user previews the installation effect of each accessory in the accessory sequence through the user terminal;

[0122] Step S5 further includes the following sub-steps:

[0123] S5-1, the user terminal uses the virtual simulation experiment platform to virtually simulate and display the electrical equipment of the accessories and the accessories to be installed in the accessory sequence, and generates the corresponding virtual accessories and virtual electrical equipment;

[0124] S5-2, users can freely select virtual accessories or switch virtual electrical devices through touch screen selection in the user terminal's visual interactive interface;

[0125] S5-3: Based on the user's selection, the user terminal installs virtual parts one by one onto the virtual electrical equipment through a virtual assembly algorithm, and renders the installation effect through the PBR rendering pipeline built into the virtual simulation experimental platform to obtain a visual effect of the parts installation.

[0126] S5-4, the user terminal displays the visual installation effect of the accessories to the user through its visual interactive interface.

[0127] It should be noted that the virtual simulation experiment platform is built on the Unity3D engine and has a built-in simplified CAD model library of electrical equipment and accessories. After receiving the accessory sequence, the terminal pulls the corresponding model as needed, automatically assigns materials, interface coordinates and physical properties, and generates virtual electrical equipment and virtual accessories that correspond to the real size 1:1. This provides a high-fidelity 3D scene for subsequent interaction and assembly. During the assembly stage, the virtual assembly algorithm of "fast alignment-collision detection" is called to calculate the matching degree of key points such as plugs, guide rails and screw holes in real time, complete the automatic snapping and highlight prompts. Then, the built-in PBR rendering pipeline is used to simulate real lighting and material reflection to generate 4K static images and 360° surround view short videos for users to preview the final appearance and space occupation effect after installation from multiple angles.

[0128] Step S6: The user selects the target accessory from the accessory sequence and sends the selection result back to the edge computing device;

[0129] Step S6 further includes the following sub-steps:

[0130] S6-1, Users can browse the accessory sequence and select the target accessory by touch screen selection in the user terminal's visual interactive interface based on the visualized accessory installation effect;

[0131] S6-2, The user terminal records the accessory information of the target accessory selected by the user. If the accessory requires manual installation, the user can manually enter the accessory installation completion information through the user terminal after installation.

[0132] S6-3, the user terminal sends the accessory information of the target accessory and the accessory installation completion information to the edge computing device;

[0133] S6-4, the edge computing device verifies the accuracy and completeness of the received user selection results through data verification methods.

[0134] It should be noted that the data validation method adopts a three-level validation system: "format-content-business".

[0135] ① The format layer validates field integrity and type matching using JSON Schema;

[0136] ② The content layer checks whether the accessory model exists in the local database and compares the compatibility field with the user's device model;

[0137] ③ The business layer checks whether there is a conflict between the installation status Boolean value and the current of the physical components, as well as the bus response feedback;

[0138] If any of the above steps fails, an error code and correction prompt will be sent back to the terminal, and the user will be asked to re-upload the selection results to ensure that the records on the edge side are 100% accurate and complete.

[0139] Step S7: The edge computing device establishes a communication link with the installed accessories, performs hardware interface connection detection, and initializes and configures the accessory parameters.

[0140] Step S7 further includes the following sub-steps:

[0141] S7-1, the edge computing device determines the communication protocol and initial parameter configuration requirements of the installed accessories based on the accessory information selected by the user;

[0142] S7-2: The edge computing device establishes a communication link with the installed accessories by calling the communication module according to the communication protocol. It further detects the hardware interface connection status of the installed accessories through the hardware interface detection program. If the detection result is abnormal, it sends an abnormal feedback to the user terminal and requests the user to manually check the hardware interface connection status or reinstall the accessory.

[0143] S7-3, the edge computing device initializes the parameters of the installed accessories through the parameter configuration module according to the initial parameter configuration requirements.

[0144] It should be noted that the communication protocol example for the installed accessories is as follows: If the protocol stack is pre-installed with three complete protocol templates (Modbus-RTU, Zigbee 3.0, and BLE 5.2) and corresponding physical layer tables before the edge computing device leaves the factory, then during runtime, the system will first parse the model field of the accessory selected by the user, and then automatically index the unique protocol entry, dynamically load the baud rate, data bits, stop bits, channel, connection interval, and key level parameters, and then call the protocol library to complete the handshake, address allocation, key negotiation, and heartbeat cycle settings, establish a bidirectional transparent link that supports the 256-byte standard frame format, built-in CRC16 check and retransmission mechanism, provide a zero-packet-loss and low-latency communication channel for subsequent hardware detection, parameter distribution, and adaptive optimization, and reserve expansion interfaces for rapid hot-swapping of future added protocols.

[0145] The initial parameter configuration requirements are as follows: If each accessory corresponds to an "initial threshold table" pre-set in the local accessory database in the form of an XML fragment, containing static parameters such as operating voltage, sampling frequency, overcurrent threshold, temperature limit, and surge suppression, then after establishing a communication link, the edge computing device immediately pulls the initial threshold table and parses it into a key-value table, and generates copies marked as read-only / writable respectively. The read-only copy is used to verify hardware consistency, and the writable copy serves as the baseline value for initialization and subsequent optimization, ensuring that the accessory is in the manufacturer's specified safe range from the first run, preventing premature aging or dangerous actions due to parameter drift.

[0146] The hardware interface testing program is shown below: Based on the "loopback-acknowledgment" mechanism, the GPIO and I / O pins of the accessory are checked sequentially within 200ms of power-on. 2 C. The power supply pin and differential signal line send 3.3V probe pulses to read the impedance, rising edge, falling edge, and waveform distortion rate in real time. If any of the following problems occur: impedance > 10kΩ, waveform distortion rate > 5%, or transmission delay (calculated by rising and falling edge clocks) > 50ns, it is determined that there may be poor contact, incorrect wiring sequence, or pin oxidation. The system immediately pushes an abnormality report with graphic and text guidance to the user terminal, achieving millimeter-level fault location and significantly reducing on-site troubleshooting time.

[0147] The parameter configuration module employs a two-stage write strategy to ensure the reliability of the initialization configuration:

[0148] Phase 1: Assemble the complete configuration frame in RAM, add CRC32 checksum and version number, and perform handshake confirmation with the accessories;

[0149] Phase 2: Write the entire configuration frame to the accessory's Flash memory. If the accessory supports online upgrades, perform a soft reboot via the protocol Boot command. If a power outage, frame loss, or CRC error occurs, the system will automatically retry three times. If the error persists after three attempts, the system will report to the user terminal and prompt for accessory replacement. This ensures that the initialization process is error-free and configuration is not lost, achieving zero-error delivery.

[0150] Step S8: The edge computing device performs operational testing on the installed accessories and optimizes the accessory parameters based on the test results.

[0151] Step S8 further includes the following sub-steps:

[0152] S8-1, the edge computing device collects the operating status data of the installed accessories through real-time data acquisition methods;

[0153] S8-2, the edge computing device performs time series analysis on the operating status data through the ARIMA model, and predicts the future operating status of the installed accessories based on the analysis results, thus obtaining the prediction results;

[0154] S8-3: Based on the prediction results, the parameters of the installed parts are optimized using the gradient descent optimization algorithm. Based on the optimized parameters, the parameters of the installed parts are modified through the parameter configuration module.

[0155] It should be noted that the real-time data acquisition example is as follows: After the edge computing device is powered on, it immediately starts a multi-protocol concurrent acquisition thread. A polling frequency of 1kHz is set for each established communication link. Through registers, cluster IDs and feature values, eight types of operating quantities are read at once: current, voltage, power, temperature, power factor, harmonic content, operating status word and fault code. All operating quantities are first aligned with local UTC timestamps on the edge side and then written to the circular buffer queue. At the same time, sliding window compression and differential encoding are used to batch upload steady-state data in 30 seconds and upload transient data one by one with zero delay. This ensures that the subsequent prediction module always obtains millisecond-level fresh, low-redundancy and high-fidelity time series raw materials.

[0156] The following is an example of time series analysis using the ARIMA model: First, the collected time series is subjected to an ADF unit root test. If a unit root exists, it is transformed into a stationary series using first-order differencing. Then, the order is determined using the AIC and BIC criteria, and the sampling point with the least information, ARIMA(p,d,q), is automatically selected. The model defaults to using sampling points within the most recent 120 minutes. Next, the autoregressive and moving average coefficients of the series are obtained through maximum likelihood estimation, outputting predicted values ​​with a 95% confidence interval. Simultaneously, the model provides out-of-limit probabilities for key indicators such as peak current and temperature rise slope. If the predicted value is about to exceed the preset safety threshold, the model issues an early warning to the optimization algorithm, providing a quantified target and constraint boundary for gradient descent, thus achieving a closed-loop safety mechanism of "predict first, adjust later."

[0157] The gradient descent optimization algorithm is illustrated below: using the mean square error between the predicted value given by ARIMA and the target operating range as the loss function, the learning rate is obtained by differentiating the writable parameters of the installed parts. The edge computing device uses the Adam optimizer combined with L2 weight decay to prevent overshoot. In each iteration, the gradient is first calculated in RAM, and the optimized parameters are obtained by calculating the learning rate × gradient and converging after 50 steps. Then, the optimized parameters are written back to the parts through CRC check frames.

[0158] Step S9: The edge computing device notifies the user that the configuration of the electrical accessories is complete, sends the final configuration status information of the accessories to the user terminal and records it in the log file;

[0159] Step S9 further includes the following sub-steps:

[0160] S9-1, the edge computing device uses JSON format to summarize and integrate the final configuration status information of the installed accessories, and sends the integrated final configuration status information to the user terminal through the MQTT data transmission protocol. The final configuration status information includes the hardware interface connection status of the accessories and the optimized accessory parameters.

[0161] S9-2, the user terminal notifies the user that the electrical accessories configuration is complete through platform push and SMS notification methods;

[0162] The S9-3 edge computing device records key information during the configuration process into log files through a local logging system, and then uploads it to the cloud server. The key information includes the user's configuration request, accessory sequence, user selection results, and the final configuration status information of the installed accessories. The cloud server allows users to download and view the log files through their user terminals.

[0163] It should be noted that the platform push mechanism works as follows: the edge computing device registers a device token with the manufacturer's messaging platform based on the app installed on the user's terminal during initial activation. The device then uses a manufacturer-grade push channel to deliver a notification message containing a title, thumbnail, and jump link to the user's terminal notification bar in real time. Users can directly view the configuration report or share screenshots by clicking on the notification message. This method relies on a system-level long connection and features high delivery rate, low latency, and zero cost. It can provide instant reminders even when the screen is locked, ensuring that users are aware of the configuration results within seconds. It also serves as a unified entry point for subsequent remote maintenance and firmware upgrade reminders, improving the initiative and user-friendliness of the device's entire lifecycle management.

[0164] The SMS notification method involves edge computing devices calling the operator's SMS gateway to deliver SMS messages based on the user's mobile phone number. At the same time, a short query link is reserved. After the user clicks the link, they can be redirected to a mobile webpage to view a detailed report. This method serves as a supplementary channel for push notifications, does not rely on APP installation, and has a delivery rate of nearly 100%, ensuring that users receive configuration completion information as soon as possible, thereby improving service reliability and user security.

[0165] The cloud server adopts a distributed microservice architecture, receiving log files uploaded by edge computing devices through an encrypted channel. Users can retrieve, download, or share digitally signed raw log files at any time via web pages or apps. The cloud backend provides a visual timeline, graphically presenting configuration requests, component sequences, user selections, parameter optimizations, and final configuration status. This facilitates troubleshooting and comparison of differences before and after upgrades. The cloud server allows authorized after-sales systems to read data for remote diagnostics and predictive maintenance, achieving end-to-end transparency and auditability. This not only protects user data sovereignty but also provides big data support for manufacturers to continuously improve their products.

[0166] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for rapid configuration of electrical components based on edge computing, characterized in that, The method includes: Step S1: The user sends a configuration request to the edge computing device through the user terminal; Step S2: Filter the electrical component database to obtain a list of optional components based on the configuration request; Step S3: Calculate the total matching score of the accessories in the optional accessories list; Step S4: Generate a parts sequence based on the overall matching score and send it to the user terminal; Step S5: The user previews the installation effect of each accessory in the accessory sequence through the user terminal; Step S6: The user selects the target accessory from the accessory sequence and sends the selection result back to the edge computing device; Step S7: The edge computing device establishes a communication link with the installed accessories, performs hardware interface connection detection, and initializes and configures the accessory parameters. Step S8: The edge computing device performs operational testing on the installed accessories and optimizes the accessory parameters based on the test results; Step S8 further includes the following sub-steps: S8-1, the edge computing device collects the operating status data of the installed accessories through real-time data acquisition methods; S8-2, the edge computing device performs time series analysis on the operating status data through the ARIMA model, and predicts the future operating status of the installed accessories based on the analysis results to obtain the prediction results; S8-3, Based on the prediction results, the parameters of the installed accessories are optimized using the gradient descent optimization algorithm, and the parameters of the installed accessories are modified using the parameter configuration module based on the optimized parameters. In step S9, the edge computing device notifies the user that the configuration of the electrical accessories is complete, sends the final configuration status information of the accessories to the user terminal, and records it in a log file.

2. The method for rapid configuration of electrical components based on edge computing according to claim 1, characterized in that: Step S1 further includes the following sub-steps: S1-1, the user inputs the configuration request of the electrical accessory through the user terminal and sends the configuration request to the edge computing device through wireless transmission; S1-2, The edge computing device performs format verification and integrity verification on the received electrical accessory configuration request. If the verification fails, it requests the user terminal to resend the configuration request. If the verification passes, it proceeds to S1-3. S1-3, the edge computing device parses the verified configuration request and extracts the user identity information, electrical equipment model, accessory configuration type and accessory performance requirements.

3. The method for rapid configuration of electrical components based on edge computing according to claim 1, characterized in that: Step S2 further includes the following sub-steps: S2-1, After receiving the configuration request, the edge computing device accesses the locally stored electrical accessory database, which contains accessory models, functional parameters, compatibility fields and performance index information of various electrical accessories; S2-2, The edge computing device searches for a set of optional accessories in the electrical accessory database by matching the electrical accessory model with the compatibility field based on the electrical device model in the configuration request; S2-3, the edge computing device further filters out a subset of optional accessories from the set of optional accessories by matching the accessory configuration type and accessory model according to the accessory configuration type in the configuration request; S2-4, The edge computing device further filters from the subset of optional accessories to obtain a list of optional accessories based on the accessory performance requirements in the configuration request by matching the accessory performance requirements and performance index information; S2-5, the edge computing device formats the final selected list of optional accessories using a JSON formatting method.

4. The method for rapid configuration of electrical components based on edge computing according to claim 1, characterized in that: Step S3 further includes the following sub-steps: S3-1, The edge computing device uses a linear normalization method to quantify and score the performance indicators of each component in the optional component list to obtain the performance score of each performance indicator, including response time, processing capacity and energy efficiency ratio; S3-2, the edge computing device further uses Boolean matching and weighted matching methods to quantify the compatibility of each accessory in the optional accessory list and obtain a compatibility score; S3-3, the edge computing device uses the AHP (Analog-Hybrid Analysis) method to fuse the performance score and compatibility score to obtain the total matching score for each component.

5. The method for rapid configuration of electrical accessories based on edge computing according to claim 1, characterized in that: Step S4 further includes the following sub-steps: S4-1, The edge computing device normalizes the total matching score of each accessory in the optional accessory list using the Min-Max normalization method; S4-2, the edge computing device sorts the list of optional accessories in descending order based on the normalized total matching score, and further adjusts the sorting results by combining the user's historical configuration preference information to generate an accessory sequence containing accessory information; S4-3, the edge computing device sends the generated accessory sequence to the user terminal via the MQTT data transmission protocol.

6. The method for rapid configuration of electrical components based on edge computing according to claim 1, characterized in that: Step S5 further includes the following sub-steps: S5-1, the user terminal uses the virtual simulation experiment platform to virtually simulate and display the electrical equipment of the accessories and the accessories to be installed in the accessory sequence, and generates the corresponding virtual accessories and virtual electrical equipment; S5-2, In the user terminal's visual interactive interface, the user can freely select the virtual accessories or switch virtual electrical devices by using a touch screen selection method; S5-3: Based on the user's selection, the user terminal installs virtual parts one by one onto the virtual electrical equipment through a virtual assembly algorithm, and renders the installation effect through the PBR rendering pipeline built into the virtual simulation experimental platform to obtain a visual effect of the parts installation. S5-4, The user terminal displays the visualized accessory installation effect to the user through its visual interactive interface.

7. The method for rapid configuration of electrical accessories based on edge computing according to claim 1, characterized in that: Step S6 further includes the following sub-steps: S6-1, Users can browse the accessory sequence and select the target accessory by touch screen selection in the user terminal's visual interactive interface based on the visualized accessory installation effect; S6-2, The user terminal records the accessory information of the target accessory selected by the user. If the accessory requires manual installation, the user can manually enter the accessory installation completion information through the user terminal after installation. S6-3, the user terminal sends the accessory information of the target accessory and the accessory installation completion information to the edge computing device; S6-4, the edge computing device verifies the accuracy and completeness of the received user selection results through data verification methods.

8. The method for rapid configuration of electrical components based on edge computing according to claim 1, characterized in that: Step S7 further includes the following sub-steps: S7-1, the edge computing device determines the communication protocol and initial parameter configuration requirements of the installed accessories based on the accessory information selected by the user; S7-2, The edge computing device establishes a communication link with the installed accessory by calling the communication module according to the communication protocol, and further detects the hardware interface connection status of the installed accessory through the hardware interface detection program. If the detection result is abnormal, an abnormal feedback is sent to the user terminal, and the user is requested to manually check the hardware interface connection status or reinstall the accessory. S7-3, the edge computing device initializes the parameters of the installed accessories through the parameter configuration module according to the initial parameter configuration requirements.

9. The method for rapid configuration of electrical accessories based on edge computing according to claim 1, characterized in that: Step S9 further includes the following sub-steps: S9-1, the edge computing device uses JSON format to summarize and integrate the final configuration status information of the installed accessories, and sends the integrated final configuration status information to the user terminal through the MQTT data transmission protocol. The final configuration status information includes the hardware interface connection status of the accessories and the optimized accessory parameters. S9-2, the user terminal notifies the user that the electrical accessories configuration is complete through platform push and SMS notification methods; S9-3, the edge computing device records key information during the configuration process into a log file through a local log recording system, and then uploads it to a cloud server. The key information includes the user's configuration request, accessory sequence, target accessory selected by the user, and the final configuration status information of the installed accessories. The cloud server allows users to download and view the log file through their user terminals.

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