A visual configuration method and system based on a modular AI gateway

Abstract

The application relates to a visual configuration method and system based on a modular AI gateway, and relates to the technical field of power Internet of Things; the visual configuration method comprises the following steps: identifying and analyzing a gateway module to obtain a module unique identifier and encapsulating the module unique identifier into structured data; according to the structured data, corresponding initialization drivers in a preset driver library are searched and loaded; according to the initialization drivers and the module type code in the structured data, a graphical parameter configuration guide is generated; in the running process of the graphical parameter configuration guide, the working state waveform diagram of the gateway module is collected and displayed, and the configuration result is verified; through automatic linkage of hardware plugging and visual configuration process, manual installation of drivers or searching for configuration documents is not needed, the configuration time is shortened, the deployment efficiency is improved, the project implementation cost is significantly reduced; the module state is fed back in real time during the configuration process, the effectiveness of parameter adjustment is ensured, and the number of repeated debugging times is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power Internet of Things, and in particular to a visual configuration method and system based on a modular AI gateway. BACKGROUND

[0002] In the field of edge computing devices, modular gateways are widely used in power grids, industrial control and other scenarios due to their scalability. However, the existing technology has significant defects: when a user inserts a new function module (such as a LORA communication module, a 4G communication module), the corresponding driver needs to be manually searched and installed, and the parameters (such as frequency band, transmission power) need to be configured one by one through professional documents. This process not only takes a lot of time and effort (the traditional configuration process takes more than 2 hours), but also requires the operator to have professional technical knowledge, resulting in low deployment efficiency and easy device failure due to configuration errors. In addition, the existing modular devices lack a linkage mechanism between hardware and visual interfaces, and cannot achieve automatic configuration of "plug and play", which is seriously inconsistent with the existing requirement that "modular products need to consider universality", and has become a technical bottleneck restricting the rapid promotion of gateway products.

[0003] Application No. CN202310112448.1 discloses a visual configuration soft gateway system and its implementation method. The system mainly includes a gateway configuration module that obtains gateway configuration information and saves the gateway configuration information on a deployment server; a connection module that establishes a connection relationship with an Internet of Things gateway platform according to the gateway configuration information; a first configuration module that determines the communication protocol parameters of the to-be-connected device according to the connection relationship, and configures the connector according to the communication protocol parameters; a second configuration module that configures the corresponding relationship between the connector and the to-be-connected device; and an information configuration module that configures the overall information of the to-be-connected device, the corresponding converter and the connector. The above-mentioned application greatly shortens the development cycle and maintenance cost of the Internet of Things gateway.

[0004] The existing technical solutions in the above have the following defects: 1. The professional technical knowledge of the operator cannot meet the requirements, resulting in low deployment efficiency and easy device failure due to configuration errors; 2. The existing modular gateway lacks a linkage mechanism between hardware and visual interfaces, and cannot achieve automatic configuration of "plug and play". SUMMARY

[0005] In view of the deficiencies of the existing technology, the purpose of the present application is to provide a visual configuration method and system based on a modular AI gateway, which automatically links hardware plugging and visual configuration process through a hardware-software-visualization trinity linkage configuration architecture, without the need for manual installation of drivers or searching for configuration documents, shortening the configuration time, improving the deployment efficiency, and significantly reducing the project implementation cost.

[0006] The object of the present application is achieved by the following technical solutions:

[0007] A visual configuration method based on a modular AI gateway, comprising:

[0008] Identifying and analyzing the gateway module to obtain a module unique identifier and encapsulating it as structured data;

[0009] According to the structured data, retrieving and loading the corresponding initialization driver in the preset driver library;

[0010] According to the initialization driver combined with the module type code in the structured data, a graphical parameter configuration wizard is generated;

[0011] During the operation of the graphical parameter configuration wizard, the working state waveform of the gateway module is collected and displayed, and the configuration result is verified;

[0012] According to the preset pre-checking mechanism combined with the configuration result, all working parameters are dynamically checked to generate a parameter checking result;

[0013] According to the parameter checking result, the digital twin of different gateway modules is constructed, and combined with the preset parameter adjustment strategy, the module performance under extreme scenarios is calculated;

[0014] According to the module performance combined with the environmental sensor network, a physical environment digital model is constructed, and a corresponding anti-interference configuration table is generated.

[0015] By adopting the above technical solutions, the module identifier analysis and structured encapsulation (such as I2C / SPI bus reading EEPROM data) trigger the automatic loading of the driver, combined with the graphical wizard guided parameter configuration (such as LoRa frequency band setting), the working state waveform (WebGL rendering RSSI dynamic diagram) is collected in real time and the connectivity is verified; further based on the AI pre-checking algorithm (such as decision tree parameter conflict detection) to dynamically check the parameters, the digital twin (using multi-physical field simulation engine) simulates the performance under extreme scenarios, and finally fuses the environmental sensor network (temperature and humidity / electromagnetic interference data) to construct the physical environment model, generates the anti-interference configuration table (such as dynamic power adjustment strategy), realizes the whole life cycle intelligent configuration from hardware identification to environmental adaptation, significantly improves the deployment efficiency and reliability of each gateway module.

[0016] The present application further provides that the specific steps of identifying and analyzing the gateway module to obtain a module unique identifier and encapsulating it as structured data include:

[0017] Monitoring the external interface slot to obtain a fluctuation signal and identifying the gateway module;

[0018] According to the fluctuation signal, a communication bus is initialized, and a data transmission channel is established;

[0019] According to a preset storage mapping table, the gateway module is address-positioned, and a resolution reading instruction is sent;

[0020] According to the resolution reading instruction combined with the data transmission channel, data of the gateway module is extracted to obtain a module unique identifier;

[0021] The module unique identifier is checked, and if the check fails, a retransmission request is sent to the gateway module;

[0022] Otherwise, the module unique identifier is encapsulated to obtain structured data.

[0023] By adopting the above technical solution, by monitoring the electrical fluctuation signal (using FFT spectrum feature matching algorithm) of the external interface slot in real time, the communication bus is dynamically initialized and the special transmission channel is established, the resolution instruction is sent after the gateway module is positioned based on the storage mapping table (preloaded physical address-logical address mapping relationship), the integrity of the module unique identifier is guaranteed through CRC-16 cyclic redundancy check, and finally the structured data is encapsulated, realizing the automatic registration of the module with hardware plug-in identification and zero manual intervention, and significantly improving the deployment efficiency and anti-interference reliability of the industrial field gateway.

[0024] The application further provides that: the specific steps of retrieving and loading the corresponding initialization driver program in the preset driver library according to the structured data include:

[0025] According to a preset resolution extraction mechanism, the structured data is extracted to obtain a complete identifier, and decrypted into a hardware version number and a manufacturer code identifier;

[0026] According to a preset driver mapping table, the complete identifier is matched, if the matching is successful, the corresponding driver file path is obtained combined with the hardware version number;

[0027] Otherwise, according to the manufacturer code identifier combined with the wildcard rule, all driver file paths of the same series are obtained;

[0028] According to all the driver file paths, the preset driver library is driven to search, and all initialization driver programs are obtained;

[0029] Information of all the initialization driver programs is extracted to obtain a compatible version interval and program version information; the program version information includes a major version number, a minor version number and a patch level;

[0030] According to the program version information, the complete identifier is matched and arranged in descending order, and the compatible version interval is combined to obtain a corresponding initialization driver;

[0031] According to the built-in initialization entry function, the initialization driver is called, and an operation interface is registered;

[0032] According to the operation interface, a working parameter is transmitted to the initialization driver, and a drive boot instance is preloaded in combination with a digital signature; the working parameter includes a working frequency band, a transmission power and an air speed.

[0033] By adopting the above technical scheme, the complete identifier is extracted and decrypted from the structured data into a hardware version number and a manufacturer code identifier by using a preset interpretation extraction mechanism (such as regular expression analysis) and a decryption algorithm (such as AES), a hash matching is performed by using a drive mapping table (if the matching is successful, an accurate drive path is obtained in combination with the hardware version number, otherwise, all paths of the same series are obtained based on the manufacturer code identifier by using a wildcard rule), all initialization drivers are obtained by searching the drive library, a compatible version interval and program version information (primary / secondary / patch level) are extracted, a version similarity algorithm (such as cosine distance) is used to calculate the matching degree and arrange in descending order (such as quicksort), the optimal drive is screened in combination with the compatible interval, an operation interface is registered by calling a built-in initialization entry function, a working parameter (working frequency band, transmission power, air speed) is transmitted and a digital signature (such as RSA) is verified to pre-load a drive boot instance, automatic, high compatibility and safe and reliable drive dynamic loading is realized, and the system initialization efficiency and anti-interference ability are significantly improved.

[0034] The application is further provided as follows: the specific steps of generating a graphical parameter configuration guide according to the initialization driver and the module type code in the structured data include:

[0035] According to the structured data, a drive mapping table is searched to obtain a module type code;

[0036] According to the module type code, data of the initialization driver is extracted to obtain a configuration description file;

[0037] According to a preset metadata parser in combination with a parameter type, the configuration description file is mapped to obtain a graphical parameter control and a parameter linkage skeleton;

[0038] According to the functional domain in combination with the graphical parameter control, a blank page is filled to generate a plurality of configuration guide pages;

[0039] According to the parameter linkage skeleton in combination with a drive boot instance, all the configuration guide pages are associated and fused to generate a graphical parameter configuration guide.

[0040] By adopting the technical scheme, the module type code is determined through the structured data retrieval driving mapping table determining module, and the configuration description file is generated according to the initialization driving program; the description file is mapped into the graphical parameter control and the parameter linkage skeleton by using the metadata parser combined with the parameter type; the control is filled into the blank page to form the configuration guide page based on the function domain; and finally, the parameter linkage skeleton is associated with the driving guide instance, the dynamic fusion and the logical series connection of multiple pages are realized, and the graphical parameter configuration guide is automatically generated. The configuration process is zero-code visualized through data driving and meta-model analysis, the development complexity is greatly reduced, the consistency and real-time performance of the cross-page control logic are ensured by using the parameter linkage skeleton, and the parameter configuration efficiency and accuracy of the embedded device or the industrial software are significantly improved.

[0041] The application is further provided as follows: the specific steps of mapping the configuration description file according to the preset metadata parser combined with the parameter type to obtain the graphical parameter control and the parameter linkage skeleton include:

[0042] The configuration description file is parsed according to the preset metadata parser, and the working parameters and the dependent condition expression are extracted;

[0043] The working parameters are analyzed according to the dependent condition expression, and the parameter relationship graph is constructed;

[0044] The working parameters are mapped into the initial graphical control according to the parameter type;

[0045] The dependent condition expression is compiled according to the preset relationship parser, the linkage execution logic is obtained, and the parameter monitoring network is established;

[0046] The parameter monitoring network is monitored and updated according to the parameter relationship graph combined with the parameter modification event, and the parameter network skeleton is obtained;

[0047] The parameter network skeleton is fitted according to the linkage execution logic, and the parameter linkage skeleton is obtained;

[0048] The initial graphical control is optimized according to the screen size, and the graphical parameter control is obtained.

[0049] By adopting the technical scheme, the working parameters and dependent expressions in the configuration are extracted by the metadata parser, a parameter relationship graph is constructed and mapped to an initial control, the dependent expressions are compiled into linkage execution logic by the relationship parser, a parameter monitoring network is established, when a parameter modification event is triggered, a parameter network skeleton is generated by dynamically updating the network, and a parameter linkage skeleton is fitted through the linkage logic, and finally the control layout is optimized in combination with the screen size. Through the fusion of graph modeling (parameter relationship graph), event-driven monitoring (parameter monitoring network) and dynamic compilation technology (dependent expression compilation), the automatic linkage of the dependent relationship between parameters and the adaptive rendering of visual controls are realized, and the maintainability and interface response efficiency of the complex configuration system are significantly improved.

[0050] The application further provides that, during the operation of the graphical parameter configuration wizard, the specific steps of collecting and displaying the working state waveform diagram of the gateway module and verifying the configuration result include:

[0051] When the graphical parameter configuration wizard is running, different gateway modules are parsed according to a preset protocol parser, and a plurality of customized decoding interfaces are obtained;

[0052] The running data of the gateway module is sampled according to a preset parameter sampling strategy, and module state metadata is obtained;

[0053] The module state metadata is classified according to data sources, and a plurality of category index metadata are obtained;

[0054] The index metadata is waveform-converted according to a time sequence, and a working state waveform diagram is obtained;

[0055] The gateway module is verified according to a preset detection logic, a feedback mode of the graphical parameter control is recorded, and the feedback mode is compared with a corresponding standard feedback state;

[0056] If the feedback mode is consistent with the standard feedback state, it indicates that the parameter network skeleton is correct, and the gateway module is subjected to instantaneous load testing, boundary value scanning and regression comparison according to a preset automatic testing sequence, and the module power consumption is calculated;

[0057] If the module power consumption is greater than a power consumption limit threshold, the associated parameters are located according to the parameter network skeleton, and the corresponding associated parameters are corrected, and a three-dimensional configuration verification table is generated; the three-dimensional configuration verification table includes a pass item, a warning item, a failure item, an abnormal waveform, a performance comparison and a log segment.

[0058] By adopting the above technical solution, a graphical wizard drives the protocol parser to generate a customized decoding interface. Based on the parameter sampling strategy, the gateway module's operating data is collected and classified into indicator metadata. The data is then converted into a status monitoring graph through time series waveform conversion. The consistency between the graphical control feedback mode and the standard state is verified using preset detection logic. If they match, an automated test sequence (including instantaneous load, boundary value scanning, and regression comparison) is initiated to calculate the module's power consumption. If the power consumption exceeds the limit, the associated parameters are located and corrected through the parameter network skeleton. Finally, a three-dimensional configuration verification table (including pass / warning / failure items, abnormal waveforms, performance comparison, and log fragments) is output. This achieves closed-loop visual verification and intelligent parameter tuning of the gateway configuration, significantly improving the deployment efficiency and reliability of complex systems.

[0059] Secondly, the present invention also provides a visual configuration system based on a modular AI gateway, employing the following technical solution:

[0060] A visual configuration method based on a modular AI gateway, used in the aforementioned visual configuration system based on a modular AI gateway, includes:

[0061] The hardware layer is used to detect the insertion signals of different gateway modules, transmit hardware information to the main control board, and integrate sensors and standardized interfaces in modular slots.

[0062] The software layer is used to build the driver library and configuration engine; the driver library is used to pre-store the drivers for all the gateway modules, and the configuration engine is used to generate corresponding parameter configuration logic according to the module type.

[0063] The visualization layer is used to graphically display the guided process of the parameter configuration logic through an interactive interface that includes a module status display area, a parameter configuration wizard area, and a real-time waveform feedback area.

[0064] The monitoring layer is used to monitor the guidance process of the parameter configuration logic, extract abnormal configuration points, and generate corresponding abnormal handling logs in conjunction with the physical environment digital model.

[0065] By adopting the above technical solution, the modular slots and sensors at the hardware layer detect the insertion signal of the gateway module in real time and transmit hardware information. The software layer calls the pre-stored driver library based on the dynamic loading algorithm, while the configuration engine uses the rule matching algorithm to automatically generate parameter configuration logic adapted to different module types. The visualization layer uses the graphics rendering algorithm to dynamically display the configuration wizard process in three dimensions in the interactive interface (including status display, parameter configuration guidance and real-time waveform feedback). The monitoring layer relies on the anomaly detection algorithm to analyze the configuration process in real time, and performs digital twin comparison with the physical environment digital model to automatically locate anomalies and generate structured processing logs. Through algorithm collaboration, the entire lifecycle intelligent management of modules is realized, which is plug-and-play, has zero configuration coding, and enables anomaly self-diagnosis.

[0066] In a third aspect, the present application provides an electronic device, comprising:

[0067] one or more processors;

[0068] a memory for storing one or more programs;

[0069] When the one or more programs are executed by the one or more processors, the one or more processors implement the method of any of the above solutions.

[0070] In a fourth aspect, the present application provides a storage medium, wherein the storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by a processor to implement the method of visual configuration based on a modular AI gateway as in the above solutions.

[0071] In summary, the present application has the following beneficial technical effects:

[0072] The hardware plug-in is automatically linked with the visual configuration process, without the need for manual installation of drivers or searching for configuration documents, the configuration time is shortened, the deployment efficiency is improved, and the project implementation cost is significantly reduced;

[0073] The visual guide avoids parameter omission or errors, and reduces the configuration error rate;

[0074] The modular AI gateway state is fed back in real time during the configuration process, ensuring the effectiveness of parameter adjustment and reducing the number of repeated debugging. BRIEF DESCRIPTION OF DRAWINGS

[0075] Figure 1 is a flowchart of a visual configuration method of one embodiment of the present application.

[0076] Figure 2 is a flowchart of a visual configuration method of one embodiment of the present application.

[0077] Figure 3 is a flowchart of a visual configuration method of one embodiment of the present application.

[0078] Figure 4 is a structural diagram of a visual configuration system of one embodiment of the present application. DETAILED DESCRIPTION

[0079] The present application will be further described in detail below with reference to the accompanying drawings. EMBODIMENTS

[0080] Reference Figure 1A visual configuration method based on a modular AI gateway is disclosed, comprising:

[0081] S1: identifying and analyzing the gateway module to obtain a module unique identifier and encapsulating it as structured data;

[0082] S2: retrieving and loading the corresponding initialization driver in the preset driver library according to the structured data;

[0083] S3: generating a graphical parameter configuration wizard according to the initialization driver in combination with the module type code in the structured data;

[0084] S4: collecting and displaying the working state waveform diagram of the gateway module during the running of the graphical parameter configuration wizard, and verifying the configuration result;

[0085] S5: dynamically verifying all working parameters according to the preset pre-checking mechanism in combination with the configuration result to generate a parameter verification result;

[0086] S6: constructing digital twins of different gateway modules according to the parameter verification result, and calculating the module performance under extreme scenarios in combination with the preset parameter adjustment strategy;

[0087] S7: constructing a physical environment digital model according to the module performance in combination with the environmental sensor network, and generating a corresponding anti-interference configuration table.

[0088] The implementation principle of the embodiment is as follows: first, the unique identifier (such as device_id) of the gateway module is safely identified through a dynamic token authentication mechanism (access_token), and is encapsulated as structured data; the matching initialization program (supporting SQLServer, MySQL, HTTP, MQTT, etc. protocols) in the preset driver library is loaded according to the data, and a graphical configuration wizard is generated in combination with the module type code; during the running of the wizard, the module working state waveform diagram is collected and visualized through a real-time stream processing engine, and the data of heterogeneous devices such as PLC is dynamically analyzed by using a multi-protocol adaptation layer; after the configuration result is dynamically verified by a pre-checking mechanism (including boundary value / collision detection), a digital twin is constructed to simulate the performance under extreme scenarios (such as high load, electromagnetic interference); finally, a physical environment digital model is constructed by fusing environmental sensor network data, an anti-interference configuration table (such as communication retry threshold, power consumption optimization scheme) is generated by a parameter adjustment strategy engine, and a configuration version rollback mechanism is introduced to ensure fault tolerance, so as to finally realize a safe, compatible and real-time gateway intelligent configuration closed loop. EMBODIMENT

[0089] The step S1 comprises:

[0090] The external interface slot is subjected to load monitoring to obtain a fluctuation signal, and a gateway module is identified;

[0091] The communication bus is initialized according to the fluctuation signal, and a data transmission channel is established;

[0092] The gateway module is subjected to address positioning according to a preset storage mapping table, and a parsing reading instruction is sent;

[0093] The gateway module is subjected to data extraction according to the parsing reading instruction in combination with the data transmission channel, and a module unique identifier is obtained;

[0094] The module unique identifier is subjected to verification, and if the verification fails, a retransmission request is sent to the gateway module;

[0095] Otherwise, the module unique identifier is encapsulated to obtain structured data.

[0096] The implementation principle of the embodiment is as follows: through real-time high-precision load monitoring of the external interface slot, the system captures a fluctuation signal to identify a gateway module, and initializes a communication bus based on the signal to establish an encrypted data transmission channel, and integrates a dynamic token authentication mechanism to prevent unauthorized access; subsequently, address positioning is performed using a preset storage mapping table, a plurality of industrial protocols such as Modbus, CAN, and MQTT are supported to ensure protocol compatibility, and a parsing reading instruction is sent; the module unique identifier is efficiently extracted through the channel, while CRC verification and data integrity verification are performed to prevent data damage; if the verification fails, the system automatically triggers a retransmission request, and fault tolerance processing is realized in combination with timeout control and abnormal log recording to avoid system crash; after the verification succeeds, the unique identifier is encapsulated as structured data, millisecond-level response is realized through optimization of a signal processing algorithm, and environmental sensor network data is fused to dynamically adjust monitoring parameters to cope with physical interference such as temperature and humidity, and finally a reliable and adaptive module identification and data encapsulation process is completed to ensure efficient operation of the system in a complex industrial environment. EMBODIMENT

[0097] The step S2 comprises:

[0098] The structured data is extracted according to a preset interpretation extraction mechanism to obtain a complete identifier, and is decrypted into a hardware version number and a manufacturer code identifier;

[0099] The complete identifier is matched according to a preset driver mapping table, and if the matching succeeds, the corresponding driver file path is obtained in combination with the hardware version number;

[0100] Otherwise, all driver file paths of the same series are obtained according to the manufacturer code identifier in combination with a wildcard rule;

[0101] According to all the drive file paths, a preset drive library is driven to search and obtain all initialized drivers;

[0102] Information is extracted from all the initialized drivers to obtain a compatible version interval and program version information; the program version information includes a major version number, a minor version number and a patch level;

[0103] According to the program version information, the matching degree of the complete identifier is calculated and arranged in descending order, and combined with the compatible version interval, the corresponding initialized driver is obtained;

[0104] According to the built-in initialization entry function, the initialized driver is called and the operation interface is registered;

[0105] According to the operation interface, the working parameters are transmitted to the initialized driver, and the drive boot instance is preloaded in combination with the digital signature; the working parameters include the working frequency band, the transmission power and the air speed.

[0106] The implementation principle of the embodiment is: through a preset interpretation extraction mechanism, a complete identifier is extracted from structured data and decrypted into a hardware version number and a manufacturer code identification, and a digital signature verification is strengthened to ensure data integrity; then, based on the drive mapping table, matching is performed, when successful, the drive file path is obtained in combination with the hardware version number, and when failed, all paths of the same series are obtained by using the manufacturer code identification and the wildcard rule, and automatic retry and timeout control are integrated to handle exceptions; then, all initialized drivers are searched from a preset drive library, the performance is optimized through a cache mechanism, and the compatible version interval and the program version information (including the major version number, the minor version number and the patch level) are extracted; the version matching degree is calculated and arranged in descending order, the optimal driver is selected in combination with the compatible interval, dynamic updating is supported to adapt to new devices; after calling the built-in initialization entry function to register the operation interface, the working parameters (such as the working frequency band, the transmission power and the air speed) are transmitted through the interface, the parameters are dynamically adjusted in combination with the environmental sensor data, and the digital signature is verified to preload the drive boot instance, finally, a safe, efficient and self-adaptive drive initialization and operation process is realized. Embodiment

[0107] Reference Figure 2 , the step S3 comprises:

[0108] S31: According to the structured data, a drive mapping table is searched to obtain a module type code;

[0109] S32: According to the module type code, data is extracted from the initialized driver to obtain a configuration description file;

[0110] S33: mapping the configuration description file according to a preset metadata parser combined with a parameter type to obtain a graphical parameter control and a parameter linkage skeleton;

[0111] S34: filling a blank page according to a function domain combined with the graphical parameter control to generate a plurality of configuration guide pages;

[0112] S35: associating and fusing all the configuration guide pages according to the parameter linkage skeleton combined with a driving guide instance to generate a graphical parameter configuration wizard.

[0113] The implementation principle of the embodiment is as follows: based on a structured data retrieval driving mapping table to obtain a module type code, after ensuring the safety of the data source through digital signature verification, a configuration description file is extracted from an initialization driving program according to the module type code; then, a preset metadata parser combined with a parameter type (such as an enumeration value and a numerical value range) maps the description file into a graphical parameter control (such as a slider and a drop-down menu) and a parameter linkage skeleton (defining the logical dependency relationship between the controls), and embeds real-time verification rules to strengthen the parameter legality; then, the graphical parameter control is dynamically filled into a blank page according to a function domain to generate a plurality of configuration guide pages, a responsive layout is adopted to adapt to different terminals, and the performance is optimized through a lazy loading technology; finally, the parameter linkage skeleton combined with a driving guide instance associates and fuses all the pages to generate a unified graphical parameter configuration wizard, the user state is automatically saved during the process and abnormal rollback is supported, the efficiency and robustness of the configuration process are ensured, and finally, a safe, intuitive and adaptive hardware driving parameter configuration is realized. EMBODIMENT

[0114] Referring to Figure 3 , the step S33 comprises:

[0115] S331: analyzing the configuration description file according to a preset metadata parser to extract working parameters and a dependent condition expression;

[0116] S332: analyzing all the working parameters according to the dependent condition expression to construct a parameter relationship graph;

[0117] S333: mapping all the working parameters according to a parameter type to generate initial graphical controls;

[0118] S334: compiling the dependent condition expression according to a preset relationship parser to obtain linkage execution logic and establish a parameter monitoring network;

[0119] S335: monitoring and updating the parameter monitoring network according to the parameter relationship graph combined with a parameter modification event to obtain a parameter network skeleton;

[0120] S336: fitting the parameter network skeleton according to the linkage execution logic to obtain a parameter linkage skeleton;

[0121] S337: optimizing the initial graphical control according to the screen size to obtain a graphical parameter control.

[0122] The implementation principle of the embodiment is that: after the configuration description file is safely checked by the preset metadata parser, the working parameters and the dependent condition expression are extracted, and the logic legality is ensured through syntax checking; then the expression is parsed to construct a parameter relationship graph with loop detection, and an initial graphical control is generated according to the parameter type mapping; at the same time, the relationship parser compiles the expression into linkage execution logic in real time, and establishes a parameter monitoring network based on event throttling and dependent pruning; when the user triggers the parameter modification event, the parameter relationship graph is dynamically updated and the parameter network skeleton is generated, and then the parameter linkage skeleton is formed through the fitting of the linkage logic and the skeleton; finally, the initial control is optimized for touch hot area and response reorganization in combination with the screen size, pixel density and direction, to generate a cross-terminal adaptive graphical parameter control, which supports log tracking and abnormal rollback throughout the process, and realizes high-robustness hardware parameter dynamic configuration. Embodiment

[0123] The step S4 comprises:

[0124] When the graphical parameter configuration wizard is running, different gateway modules are parsed according to a preset protocol parser to obtain a plurality of customized decoding interfaces;

[0125] The running data of the gateway module is sampled according to a preset parameter sampling strategy to obtain module state metadata;

[0126] The module state metadata is classified according to the data source to obtain a plurality of class index metadata;

[0127] The index metadata is waveform converted according to a time sequence to obtain a working state waveform graph;

[0128] The gateway module is verified according to a preset detection logic, the feedback mode of the graphical parameter control is recorded, and the corresponding standard feedback state is compared;

[0129] If the feedback mode is consistent with the standard feedback state, it indicates that the parameter network skeleton is correct, and the gateway module is tested for instantaneous load, boundary value scanning and regression comparison according to a preset automatic test sequence, and the module power consumption is calculated;

[0130] If the module power consumption is greater than the power consumption limit threshold, the associated parameter positioning is performed according to the parameter network skeleton, and the corresponding associated parameter is corrected to generate a three-dimensional configuration verification table; the three-dimensional configuration verification table includes a pass item, a warning item, a failure item, an abnormal waveform, a performance comparison, and a log segment.

[0131] The implementation principle of the embodiment is: a gateway customized protocol is loaded through a dynamic adaptation layer of a protocol parser, a decoding interface is generated, and a sampling strategy is dynamically adjusted according to a working condition perception engine to obtain classified index metadata; then, time series data are waveform-converted and fault-labeled in combination with an abnormal waveform feature library to generate a working state waveform graph; meanwhile, an interaction of a graphical control is quantified into a time delay / precision index by a detection logic, a parameter network skeleton is verified for correctness by comparison with a multi-dimensional similarity of a standard state; if the verification is passed, an automatic test sequence is started to perform load scanning and regression comparison, when the module power consumption is out of limit, an associated parameter is positioned by calling a parameter-power consumption mapping model and an optimal solution is generated, finally, a three-dimensional configuration verification table containing abnormal waveform labeling, performance comparison and AI optimization log is output, and a closed-loop intelligent parameter adjustment of a gateway module is realized.

[0132] In the embodiment, a 2U standard rack-mounted body is adopted, a domestic chip RX3588 is used as a core, a very rich interface type is provided, a highly blockized design is adopted, POE network, storage, serial port, small current sampling, input and output signal control, 4G / 5G communication, LORA, WIFI, Bluetooth and other interfaces are deployed on different function modules, the product expandability is enhanced, different function combinations are convenient for users to select, and the product maintainability is greatly improved; all module drivers are managed by using a unified interface specification (such as a Linuxudev rule) to ensure the compatibility when a new module is connected; a preset configuration wizard template is provided for different types of modules (communication type, sampling type and control type), and the corresponding template is called by using a module type code; millisecond-level data transmission between a main control board and a visual interface is realized by using a WebSocket protocol to ensure that the waveform feedback has no delay. Embodiment

[0133] Reference Figure 4 A visual configuration system based on a modular AI gateway is applied to the fault detection method and includes the following.

[0134] A hardware layer is used to detect different gateway module insertion signals, transmit hardware information to a main control board, and integrate sensors and standardized interfaces in the modular slot.

[0135] A software layer is used to build a driver library and a configuration engine; the driver library is used to prestore the drivers of all the gateway modules, and the configuration engine is used to generate corresponding parameter configuration logic according to the module type.

[0136] A visualization layer is configured to graphically display a guiding process of the parameter configuration logic through an interactive interface including a module state display area, a parameter configuration wizard area, and a real-time waveform feedback area.

[0137] A monitoring layer is configured to monitor the guiding process of the parameter configuration logic, extract an abnormal configuration point, and generate a corresponding abnormal handling log in combination with a physical environment digital model.

[0138] The implementation principle of the embodiment is that the hardware layer pre-processes the insertion signals and running data of the gateway module in real time through integrated sensors and edge computing units, and transmits the signals and data to the main control board through a standardized interface; the software layer calls a driving library matching module types, generates an anti-interference parameter configuration logic in combination with a knowledge graph by a configuration engine, and dynamically projects a configuration path on the modular slot through an AR guiding technology of the visualization layer; the monitoring layer synchronously collects configuration process data, compares abnormal configuration points by using a physical environment digital model, and generates a diagnosis log, which is fed back to the knowledge graph to optimize prediction rules, so as to finally form a closed-loop configuration verification system of "signal sensing-intelligent guiding-exception self-healing-model iteration", and realize zero-contact type adjustment and predictive maintenance of the gateway module. Embodiment

[0139] An electronic device includes:

[0140] One or more processors;

[0141] A memory for storing one or more programs;

[0142] When the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any of the above solutions. Embodiment

[0143] A storage medium, in which at least one instruction, at least one program, a code set, or an instruction set is stored, is loaded and executed by a processor to implement a visualization configuration method based on a modular AI gateway as in the above solutions.

[0144] The embodiments of the specific implementation are the preferred embodiments of the present application, not limited to the protection scope of the present application, and therefore: any equivalent changes made in the structure, shape, and principle of the present application should be covered within the protection scope of the present application.

Claims

1. A method for visual configuration based on a modular AI gateway, characterized in that, The method comprises the following steps: Identify and parse the gateway module to obtain a module unique identifier and encapsulate it as structured data; According to the structured data, search and load the corresponding initialization driver in the preset driver library; According to the initialization driver combined with the module type code in the structured data, generate a graphical parameter configuration guide, including: According to the structured data, search the drive mapping table to obtain the module type code; According to the module type code, data extraction is performed on the initialization driver to obtain a configuration description file; According to the preset metadata parser combined with the parameter type, map the configuration description file to obtain a graphical parameter control and a parameter linkage skeleton; According to the functional domain combined with the graphical parameter control, fill in the blank page to generate a plurality of configuration guide pages; According to the parameter linkage skeleton combined with the drive guide instance, associate and integrate all the configuration guide pages to generate a graphical parameter configuration guide; During the running of the graphical parameter configuration guide, collect and display the working state waveform of the gateway module, and verify the configuration result; According to the preset pre-check verification mechanism combined with the configuration result, dynamically verify all working parameters to generate a parameter verification result; According to the parameter verification result, construct the digital twin of different gateway modules, and combined with the preset parameter adjustment strategy, calculate the module performance under extreme scenarios; According to the module performance combined with the environmental sensor network, construct a physical environment digital model, and generate a corresponding anti-interference configuration table. 2.The method of claim 1, wherein, The specific steps of identifying and parsing the gateway module to obtain a module unique identifier and encapsulating it as structured data include: Load monitoring is performed on the external interface slot to obtain a fluctuation signal, and the gateway module is identified; According to the fluctuation signal, initialize the communication bus to establish a data transmission channel; According to the preset storage mapping table, address the gateway module and send the analysis reading instruction; According to the analysis reading instruction combined with the data transmission channel, data extraction is performed on the gateway module to obtain a module unique identifier; If the verification fails, send a retransmission request to the gateway module; Otherwise, encapsulate the module unique identifier to obtain structured data. 3.The method of claim 1, wherein, The specific steps of searching and loading the corresponding initialization driver in the preset driver library according to the structured data include: According to the preset interpretation extraction mechanism, extract the structured data to obtain a complete identifier, and decrypt it into a hardware version number and a manufacturer code identification; According to the preset drive mapping table, match the complete identifier, if the matching is successful, combine the hardware version number to obtain the corresponding drive file path; Otherwise, according to the manufacturer code identification combined with the wildcard rule, obtain all drive file paths of the same series; According to all the drive file paths, search the preset driver library to obtain all initialization drivers; Extract information from all the initialization drivers to obtain a compatible version interval and program version information; the program version information includes a major version number, a minor version number and a patch level. According to the program version information, the complete identifier is matched, degree of calculation and descending arrangement, and the compatible version interval is combined to obtain the corresponding initialization driver; According to the built-in initialization entry function, the initialization driver is called, and the operation interface is registered; According to the operation interface, the working parameters are transmitted to the initialization driver, and the digital signature is combined to pre-load the drive boot instance; The working parameters include working frequency band, transmission power and air speed. 4.The method of claim 1, wherein, The specific steps of mapping the configuration description file to obtain the graphic parameter control and parameter linkage skeleton according to the preset metadata parser combined with the parameter type include: According to the preset metadata parser, the configuration description file is parsed, and the working parameters and dependent condition expressions are extracted; According to the dependent condition expression, all the working parameters are analyzed to construct a parameter relationship diagram; According to the parameter type, all the working parameters are mapped to the control to generate an initial graphic control; According to the preset relationship parser, the dependent condition expression is compiled to obtain the linkage execution logic, and the parameter monitoring network is established; According to the parameter relationship diagram combined with the parameter modification event, the parameter monitoring network is monitored and updated to obtain a parameter network skeleton; According to the linkage execution logic, the parameter network skeleton is fitted to obtain a parameter linkage skeleton; According to the screen size, the initial graphic control is optimized to obtain a graphic parameter control. 5.The method of visualizing configuration based on modular AI gateway according to claim 1, wherein, The specific steps of collecting and displaying the working state waveform diagram of the gateway module and verifying the configuration result during the operation of the graphical parameter configuration wizard include: When the graphical parameter configuration wizard is running, a plurality of customized decoding interfaces are obtained by parsing different gateway modules according to a preset protocol parser; According to the preset parameter sampling strategy, the running data of the gateway module is sampled to obtain module state metadata; According to the data source, the module state metadata is classified to obtain a plurality of category index metadata; According to the time sequence, the index metadata is waveform converted to obtain a working state waveform diagram; According to the preset detection logic, the gateway module is verified, the feedback mode of the graphic parameter control is recorded, and the corresponding standard feedback state is compared; If the feedback mode is consistent with the standard feedback state, it means that the parameter network skeleton is correct, and the gateway module is tested for instantaneous load, boundary value scanning and regression comparison according to the preset automatic test sequence, and the module power consumption is calculated; If the module power consumption is greater than the power consumption limit threshold, the associated parameters are located according to the parameter network skeleton, and the corresponding associated parameters are corrected to generate a three-dimensional configuration verification table; The three-dimensional configuration verification table includes pass items, warning items, failure items, abnormal waveforms, performance comparisons and log segments.

6. An electronic device, comprising: It includes: One or more processors; Memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1-5. 7.A storage medium having stored therein at least one instruction, at least one program, a code set or an instruction set, which are loaded and executed by a processor to implement the method for visualizing configuration of a modular AI gateway according to any one of claims 1 to 5.

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