A low-code management system and method based on AIot Internet of Things

Abstract

The present invention discloses a low-code management system and method based on the AIot Internet of Things, relating to the technical field of code management. The present invention includes a template learning and optimization module, a component dynamic adaptation module, a requirement conversion module, a code annotation generation module, and a code compatibility conversion module; the template learning and optimization module is used to automatically refine optimization points and update template settings by analyzing historical cases and user data, so as to improve the applicability of the template in actual applications. In the present invention, through the template learning and optimization module, in-depth analysis of historical cases and user data is realized, optimization points are automatically refined and template settings are updated, significantly improving the applicability and development efficiency of the template in actual applications. Through the automated template update process, manual intervention is reduced, development costs are lowered, and at the same time, the stability and compatibility of the system are maintained.

Description

Technical Field

[0001] The present invention relates to the technical field of code management, and specifically to a low-code management system and method based on AIot Internet of Things. Background Art

[0002] With the rapid development of Internet of Things (IoT) technology, more and more devices are connected to the Internet, enabling data collection, exchange, and analysis. This connection includes not only traditional computers and smartphones but also extends to various sensors, smart home appliances, industrial equipment, etc. The application scenarios of the Internet of Things are becoming increasingly extensive, covering everything from smart homes, smart cities to industrial automation and agricultural monitoring. However, with the diversification of IoT devices and the complexity of application scenarios, traditional software development methods face many challenges;

[0003] Driven by IoT technology, the diversity of devices and the complexity of application scenarios pose challenges to traditional software development methods. Frequent device updates and diversification require the management system to quickly adapt to new devices, while traditional manual coding methods cannot meet the requirements of rapid iteration and deployment.

[0004] To solve the above defects, a technical solution is provided now. Summary of the Invention

[0005] The purpose of the present invention is to solve the problems of management complexity caused by the diversity and frequent updates of IoT devices, low efficiency and high cost of traditional development methods, and challenges in code maintainability and security of IoT systems, and to propose a low-code management system and method based on AIot Internet of Things.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A low-code management system based on AIot Internet of Things, comprising:

[0008] A template learning and optimization module, which is used to automatically extract optimization points and update template settings by analyzing historical cases and user data to improve the applicability of the template in actual applications;

[0009] A component dynamic adaptation module, which is used to identify new access device information and automatically adjust component configurations, and adapt component functions in real time according to device and business changes during application operation;

[0010] A requirement conversion module, which uses natural language processing technology to parse user requirement texts and convert them into low-code development operation instructions and logical frameworks for rapid development;

[0011] The code annotation generation module is used to automatically add annotations to the generated code according to the parsed requirements, improving the readability and maintainability of the code and facilitating subsequent development and maintenance work;

[0012] The code compatibility conversion module is used to identify low-code files in different formats and convert them into system-compatible formats, while performing security vulnerability repair and compatibility optimization processing.

[0013] Furthermore, the execution process of the template learning and optimization module is as follows:

[0014] Data collection phase: Collect data of successfully developed and running Internet of Things application cases from the system database. At the same time, collect the operation records of users during the use of the system, including template selection preferences, component usage frequencies, and operation information on modifying templates;

[0015] Sort and organize the collected data according to the dimensions of application type, function module, and device type;

[0016] Analysis and refinement phase: Use data analysis algorithms to analyze the functional structures of various cases, identify the composition methods of core functional modules in different applications and the logical relationships between them;

[0017] By mining the user operation records, analyze the rules for users to adjust templates when facing different business requirements;

[0018] Combining the results of functional structure analysis and user behavior pattern mining, extract optimization points for improving template applicability, including adjustments to template default functions, optimization of interface layouts, and improvement of initial parameter settings;

[0019] Template update phase: According to the extracted optimization points, formulate several template update plans. The plans include the specific content and steps for adjusting each part of the template, ensuring that the updated template meets the functional optimization requirements while maintaining the stability and compatibility of the system;

[0020] Test the several formulated template update plans, determine the priorities of the template update plans, and select the template update plan with the highest priority;

[0021] Update the template according to the template update plan with the highest priority. During the update process, test and verify the template to ensure that the new template can meet the expectations in terms of function and will not introduce new errors or problems. After the update is completed, store the new template in the template library for users to use in subsequent development.

[0022] Furthermore, the specific operation steps for the template learning and optimization module to determine the priorities of the template update plans are as follows:

[0023] By collecting evaluation parameters for comprehensive analysis, where the evaluation parameters include: function prompt amplitude, degree of user experience improvement, and development cost, and obtaining the improvement value, improvement ratio value, and cost-saving value through separate analysis of the evaluation parameters, calibrating the obtained improvement value, improvement ratio value, and cost-saving value as tc, gt, and cj, and substituting them into the following formula after normalization: ZPA = tc×ο1 + gt×ο2 + cj×ο3 to obtain the comprehensive judgment value ZPA, where ο1, ο2, and ο3 are the preset weight coefficients of the improvement value, improvement ratio value, and cost-saving value respectively;

[0024] Sort the different comprehensive judgment values obtained from several formulated template update plans in descending order, and select the template update plan with the largest comprehensive judgment value as the template update plan with the highest priority.

[0025] Further, the specific operation steps for the template learning and optimization module to obtain the improvement value, improvement ratio value, and cost-saving value through separate analysis of the evaluation parameters are as follows:

[0026] Function prompt amplitude: It includes the number of new functions added, the degree of business process optimization, and the improvement of data processing efficiency. Among them, the number of new functions added is obtained by counting the number of new function modules in each update plan; the degree of business process optimization is evaluated by the degree of optimization of the existing business process in the update plan, specifically quantified by calculating the process simplification ratio and denoted as the simplification ratio; the improvement of data processing efficiency is determined by measuring the improvement of data processing speed after template update, specifically by comparing the time required to process the same amount of data before and after the update, and calculating the time difference; after normalizing the obtained number of new functions added, simplification ratio, and time difference, use the number of new functions added, simplification ratio, and time difference as the side lengths of a triangle to establish a bottom triangle, and then use the correction coefficient as the height to establish a triangular prism model, calculate the surface area of this triangular prism model, and denote it as the improvement value, and use this improvement value as the standard to measure the function improvement amplitude;

[0027] Degree of user experience improvement, improvement of operation simplicity: By means of user testing or simulation operations, statistically calculate the average task time of the operation steps required to complete the standard task and denote it as the completion time; improvement of interface friendliness: By inviting users to rate the updated template interface, calculate the score difference after the update; accuracy of error prompts: By counting the number of errors encountered by users during the operation after the update, combined with all operation steps, calculate the error ratio; after normalizing the obtained completion time, score difference, and number of errors, calculate the ratio of the score difference to the sum of the completion time and the number of errors, denoted as the improvement ratio value, and use this improvement ratio value as the measure of the degree of user experience improvement;

[0028] Development cost, proportion of development time reduction: Estimate the proportion of time reduction required to develop a new application using the updated solution compared to the time required to develop it using the old template; Manpower cost savings: Calculate the manpower cost savings during the development and maintenance processes after implementing the updated solution. Specifically, quantify it by comparing the reduction in the number of developers and working hours before and after the update. Normalize the obtained reduction proportion, reduction in the number of developers, and reduction in working hours, and then sum them to obtain a savings value. Use this savings value as the standard for measuring the savings in development costs.

[0029] Furthermore, the operation process of the component dynamic adaptation module is as follows:

[0030] When a new device attempts to access the system, establish a connection with the device through the communication protocol of the device access layer, and obtain the basic information of the device, including device type, manufacturer information, device model, and communication interface parameters. At the same time, initiate a detection instruction to obtain the functional parameters of the device, including data acquisition accuracy, supported instruction set, and data transmission frequency range information;

[0031] Conduct feature analysis on the collected device information and match it with the existing device feature models in the component library. When there is a similar model, perform an adaptation plan according to the existing adaptation solution and in combination with the parameters of the current device;

[0032] According to the adaptation plan result, select appropriate components from the component library or adjust the parameter configuration of the existing components, including: for the data acquisition component, adjust the data reception buffer size and parsing algorithm parameters according to the data format and transmission frequency of the device; for the display component, adjust the interface layout, font size, and chart style according to the screen resolution and display characteristics of the device;

[0033] During the operation of the application, continuously monitor the changes in device status, business rule adjustments, and data traffic fluctuations. Specifically, set monitoring points in the system to obtain the device operation data, business logic execution status, and network transmission data volume information in real time;

[0034] When a change is detected, analyze the impact of the change on the component functions; if new data processing logic is added to the business rules, determine whether new components need to be added or the connection relationship between the existing components needs to be adjusted;

[0035] According to the adaptation decision result, perform real-time adjustment on the components, including dynamically loading new components, modifying the operation parameters of the components, and re-establishing the communication connection operations between the components, to ensure that the system can still operate stably and provide accurate functional services under the circumstances of device and business changes.

[0036] Furthermore, the judgment process of the similar models in the component dynamic adaptation module is as follows:

[0037] When performing feature analysis on the collected device information and matching it with the existing device feature models in the component library, the feature vector of the device is calculated for cosine similarity with the feature vector corresponding to each device feature model in the component library through the following formula: According to the preset similarity threshold τ, when the cosine similarity cos(θ) is greater than or equal to the preset similarity threshold τ, it is determined that there is a similar model;

[0038] When no similar model is found, a new device adaptation rule generation mechanism is started. The machine learning algorithm is used to classify and summarize the device information to generate a preliminary adaptation strategy. Specifically, through a classification algorithm based on a neural network, including a multi-layer perceptron, the device information is classified to determine which type of device feature model in the component library is the most matched. The preset number of input layer nodes is n, the number of hidden layer nodes is h, and the number of output layer nodes is m. For the input device information feature vector the calculation through the hidden layer is: where is the weight matrix from the input layer to the hidden layer, is the bias vector of the hidden layer, f is the activation function, and the calculation by the output layer is In the formula is the weight matrix from the hidden layer to the output layer, is the bias vector of the output layer, g is the activation function of the output layer; according to the result of the output layer the category with the highest probability is selected as the category of the matching device feature model.

[0039] Furthermore, the execution steps of the requirement conversion module include:

[0040] B1. Natural language preprocessing:

[0041] The natural language text input by the user is tokenized, splitting the sentence into individual words or phrases, and each word is tagged with its part of speech and the corresponding part of speech is also marked; the stop words in the text that have no actual meaning are filtered, including function words and general vocabulary irrelevant to the requirement;

[0042] B2. Semantic understanding and analysis:

[0043] The key entities in the text are identified, including device types, operation objects, and actions. Using a pre-trained entity recognition model, combined with a domain dictionary and context information, the entities and their attribute information are extracted; the semantic relationships between the entities are analyzed to determine the logical structure of the requirement;

[0044] B3. Operation instruction generation:

[0045] According to the semantic understanding result, search for matching functional components in the component library; generate operation instructions for calling these components, and build the connection and interaction logic framework between the components according to the requirement logic, determine the data flow and triggering conditions, and form preliminary low-code development operation instructions and logic framework for subsequent development use.

[0046] Further, the execution steps of the code annotation generation module are as follows:

[0047] Obtain the parsed structured requirement data from the requirement conversion module, extract key information, including business functions, operation processes, data sources and flows, involved devices and components; classify and organize this information for subsequent association with different parts of the code;

[0048] According to the extracted key requirement information, plan the structure and content points of the annotation, and determine the types of annotations to be added at positions such as the beginning of the code, function definitions, and key logic nodes. For example, add an overall function overview annotation at the beginning of the code; add function input and output parameter and function description annotations at function definitions; add judgment condition meaning annotations at conditional judgment statements;

[0049] According to the planned annotation content, combine the syntax structure and format specifications of the code to generate accurate and clear annotation text. Use the code parsing tool to locate the code lines or code blocks where annotations need to be added, and accurately insert the annotation text. For variable definitions, annotate the purpose and value range of the variable; for code logic blocks, annotate the implemented business logic and purpose;

[0050] Optimize the format of the generated annotations to make the annotations conform to the unified annotation style specifications of the team or project. Check the integrity and accuracy of the annotations to ensure the consistency between the annotations and the code, and avoid the situation where the annotations do not match the actual code function. At the same time, unify and standardize the terms and expressions in the annotations to improve the readability of the annotations; the process of checking annotation consistency is as follows:

[0051] Let the set of code statements be S = {s1, s2,..., s p}, and the corresponding set of annotations be A = {a1, a2,..., a p}; p is the number of code statements, that is, the number of elements in the code statement set S; for each code statement s1 and its corresponding annotation a1, calculate their consistency index C(s i , a i ), using the semantic similarity calculation method, convert the code statement and the annotation text into semantic vectors respectively. Let the semantic vector of the code statement s i be and the semantic vector of the annotation a i be where i is an index variable;

[0052] Then the consistency index where

[0053] where q is the vector dimension; j is another index variable;

[0054] If the consistency index C(s i , a i ) is lower than the set threshold, then mark that the code statement and the comment are inconsistent and need to be corrected.

[0055] Furthermore, the execution steps of the code compatibility conversion module are as follows:

[0056] Read the low-code file to be converted, analyze the header information, file structure characteristics, and code syntax features of the file, and identify the original format type of the file by comparing with various known low-code format standards;

[0057] For the identified format, start the corresponding syntax parser, decompose the original code into syntax units, establish a mapping relationship from the original syntax units to the target syntax units according to the syntax rules and data structures of this system, and perform preliminary code conversion;

[0058] During the conversion process, use a security scanning tool to perform static analysis on the converted code, check whether there are common security vulnerabilities in the code, and mark and record the discovered vulnerabilities;

[0059] According to the vulnerability marking information, combine the security vulnerability repair library and best practice methods to repair the code vulnerabilities. At the same time, for the parts of the code that are incompatible with this system, perform compatibility adjustments, including modifying the call parameters of the components in the original code according to the specifications of this system;

[0060] Perform functional testing and compatibility testing on the repaired and optimized code, simulate different operating scenarios, ensure that the converted code can run correctly in this system and can work in coordination with other modules and components of the system. When problems are found during the testing, return to the previous steps for correction until passing the test verification.

[0061] A low-code management method based on AIot Internet of Things includes the following steps:

[0062] S1. Template Learning and Optimization Module: First, data collection is carried out, covering past Internet of Things application cases and user operation records. After classification and sorting, it enters the analysis and extraction stage. In this stage, through algorithms, the case structure and user behavior are analyzed to extract optimization points that can improve the template applicability. Then, multiple update plans are formulated based on these optimization points, and the priorities of each plan are comprehensively evaluated. Finally, the optimal plan is selected to update the template to ensure that the template meets the system requirements in terms of function and compatibility;

[0063] S2. Component Dynamic Adaptation: When a new device is connected, first obtain the device information and adapt it to the component library model to determine the initial configuration of the component. During the system operation, continuously monitor the changes of the device and business. Once a change is detected, immediately analyze its impact on the component function, and then dynamically adjust the parameters and connection relationships of the component to ensure the stable operation and function accuracy of the system;

[0064] S3. Requirement Transformation: For the requirement text input by the user, first perform natural language preprocessing, including word segmentation, part-of-speech tagging, and stop-word filtering. Then enter the semantic understanding and analysis stage to identify key entities and semantic relationships, and construct the logical framework of the requirement. Finally, according to the analysis results, search for matching components in the component library and generate corresponding operation instructions and logical frameworks;

[0065] S4. Code Comment Generation: After obtaining information from step S3, extract the key points of the requirement and classify and sort them to plan the structure and content of the comment. Then generate the comment text according to the plan and insert it into the corresponding code position. After inserting the comment, optimize and check the format of the comment to ensure the integrity, accuracy, and consistency of the comment with the code;

[0066] S5. Code Compatibility Transformation: At the beginning, read the low-code file. By analyzing the header, structure, and syntax characteristics of the file, identify the original format. Then start the syntax parser for code conversion for this format. During the conversion process, use a security scanning tool to check for vulnerabilities and mark them. After fixing the vulnerabilities and adjusting the compatibility according to the marked information, conduct functional and compatibility tests on the converted code, simulate various running scenarios. If problems occur, return to the corresponding steps for correction until the code passes the test.

[0067] Compared with the prior art, the beneficial effects of the present invention are:

[0068] (1) In the present invention, through the template learning and optimization module, in-depth analysis of historical cases and user data is realized, automatically extracting optimization points and updating the template settings, significantly improving the applicability and development efficiency of the template in practical applications. Through the automated template update process, manual intervention is reduced, development costs are lowered, and at the same time, the stability and compatibility of the system are maintained;

[0069] (2) In the present invention, the component dynamic adaptation module can intelligently identify the information of newly connected devices and automatically adjust the component configuration to adapt to the changes in devices and services. This real-time component adaptation function ensures that the system can still operate stably and provide accurate functional services in the case of device and service changes, enhancing the flexibility and scalability of the system;

[0070] (3) In the present invention, the requirement conversion module uses natural language processing technology to convert the user requirement text into low-code development operation instructions and logical frameworks, greatly accelerating the development speed. The code comment generation module improves the readability and maintainability of the code, while the code compatibility conversion module ensures that low-code files in different formats can run seamlessly in this system, enhancing the compatibility and security of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings;

[0072] Figure 1 It is the overall block diagram of the system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0073] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts belong to the scope of protection of the present invention.

[0074] It should be understood that the terms "including" and "comprising" used in the specification and claims of this disclosure indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0075] It should also be understood that the terms used in this disclosure specification are only for the purpose of describing specific embodiments and are not intended to limit this disclosure. As used in this disclosure specification and claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms. It should be further understood that the term "and / or" used in this disclosure specification and claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0076] Such as Figure 1As shown in the figure, a low-code management system based on the AIot Internet of Things includes a template learning and optimization module, a component dynamic adaptation module, a requirement conversion module, a code annotation generation module, and a code compatibility conversion module;

[0077] The template learning and optimization module is used to automatically extract optimization points and update the template settings by analyzing historical cases and user data, so as to improve the applicability of the template in actual applications;

[0078] Data collection stage: Collect data of Internet of Things application cases that have been successfully developed and run in the past from the system database. These data cover Internet of Things projects involved in various industry fields such as smart home, industrial manufacturing, and agricultural monitoring. At the same time, collect the operation records of users during the use of the system, including template selection preferences, component usage frequencies, and operation detail information on modifying templates; Classify and organize the collected data according to dimensions such as application type, function module, and device type. For example, classify all application cases involving temperature sensor management into one category and cases related to intelligent lighting control into another category for targeted analysis in the future;

[0079] Analysis and extraction stage: Use data analysis algorithms to analyze the functional structures of various cases, identify the composition methods of core functional modules in different applications and the logical relationships between them. For example, in a smart home management application, analyze how functional modules such as device control and scene linkage work together, and find frequently occurring and effective functional combination patterns; Through in-depth mining of user operation records, find the rules for users to adjust templates when facing different business requirements. For example, count what the first template setting adjustments are usually made by users when facing a multi-device management scenario, whether it is the device grouping rule or the data display method; Combine the results of functional structure analysis and user behavior pattern mining to extract optimization points that can improve the applicability of the template. These optimization points include adjustments to the default functions of the template, optimization of the interface layout, and improvement of initial parameter settings. For example, if it is found that most users always need to manually add a specific data analysis chart when using a certain type of device monitoring template, then this chart can be considered as a default component of the template and added;

[0080] Template update stage: According to the extracted optimization points, formulate several template update plans. The plans include the specific content and steps for adjusting each part of the template to ensure that the updated template meets the functional optimization requirements and can maintain the stability and compatibility of the system; Test the several formulated template update plans to determine the priority of the template update plans, and conduct comprehensive analysis by collecting evaluation parameters. The evaluation parameters include:

[0081] Function Prompt Amplitude: It includes the number of new functions added, the degree of business process optimization, and the improvement in data processing efficiency. Among them, the number of new functions added is obtained by counting the number of new function modules in each update plan; the degree of business process optimization is evaluated by assessing the optimization degree of the update plan for the existing business process, specifically quantified by calculating the process simplification ratio and denoted as the simplification ratio value; the improvement in data processing efficiency is determined by measuring the improvement in data processing speed after template update, specifically by comparing the time required to process the same amount of data before and after the update and calculating the time difference; after normalizing the obtained number of new functions added, simplification ratio value, and time difference, use the number of new functions added, simplification ratio value, and time difference as the side lengths of a triangle to establish a bottom triangle, and then use the correction coefficient as the height to establish a triangular prism model, calculate the surface area of this triangular prism model, and denote it as the improvement value. Use this improvement value as the standard to measure the function improvement amplitude; Degree of User Experience Improvement, Improvement in Operational Simplicity: Through user testing or simulation operations, count the average task time of the operation steps required to complete the standard task and denote it as the completion time; Improvement in Interface Friendliness: Invite users to rate the updated template interface and calculate the rating difference after the update; Accuracy of Error Prompt: Based on the number of errors encountered by users during the operation after the update, combined with all operation steps, calculate the error ratio; After normalizing the obtained completion time, rating difference, and number of errors, calculate the ratio of the rating difference to the sum of the completion time and the number of errors, denoted as the improvement ratio value, and use this improvement ratio value as the standard to measure the degree of user experience improvement; Development Cost, Proportion of Development Time Shortened: Estimate the proportion of the development time shortened for developing a new application compared to the old template after the implementation of the update plan; Manpower Cost Savings: Calculate the manpower cost saved during the development and maintenance processes after the implementation of the update plan, specifically quantified by comparing the reduction in the number of developers and working hours before and after the update. After normalizing the obtained proportion of time shortened, reduction in the number of developers, and reduction in working hours, sum them up to obtain the savings value, and use this savings value as the standard to measure the savings in development cost;

[0082] Respectively calibrate the obtained improvement value, improvement ratio value, and savings value as tc, gt, and cj. After normalization, substitute them into the following formula: ZPA = tc×ο1 + gt×ο2 + cj×ο3 to obtain the comprehensive judgment value ZPA, where ο1, ο2, and ο3 are the preset weight coefficients of the improvement value, improvement ratio value, and savings value respectively; Sort the different comprehensive judgment values ZPA obtained from several formulated template update plans in descending order, and select the template update plan with the largest comprehensive judgment value as the template update plan with the highest priority.

[0083] Update the template according to the template update plan with the highest priority. During the update process, test and verify the template to ensure that the new template can meet the expectations in terms of functionality and will not introduce new errors or problems. After the update is completed, store the new template in the template library for users to use in subsequent development.

[0084] The component dynamic adaptation module is used to identify the information of newly connected devices and automatically adjust the component configuration, and adapt the component functions in real time according to the changes of devices and services during the application operation;

[0085] When a new device attempts to access the system, establish a connection with the device through the communication protocol of the device access layer, and obtain the basic information of the device, including device type, manufacturer information, device model, communication interface parameters. At the same time, initiate a deep detection instruction to obtain the functional parameters of the device, including data acquisition accuracy, supported instruction set, data transmission frequency range information; perform feature analysis on the collected device information and match it with the existing device feature models in the component library. When there is a similar model, refer to the existing adaptation plan and combine the unique parameters of the current device to make an adaptation plan; the judgment process of the similar model is as follows:

[0086] When performing feature analysis on the collected device information and matching it with the existing device feature models in the component library, the feature vector of the device (including information such as device type, functional parameters, etc.) and the feature vector corresponding to each device feature model in the component library Perform cosine similarity calculation through the following formula: According to the preset similarity threshold τ, when the cosine similarity cos(θ) is greater than or equal to the preset similarity threshold τ, it is determined that there is a similar model;

[0087] When no similar model is found, start the new device adaptation rule generation mechanism, use machine learning algorithms to classify and summarize the device information, and generate a preliminary adaptation strategy; specifically, through a classification algorithm based on neural networks, including a multi-layer perceptron, used to classify the device information to determine which type of device feature model in the component library is the most matched. The preset number of input layer nodes is n (corresponding to the feature dimension of the device information), the number of hidden layer nodes is h, and the number of output layer nodes is m (corresponding to the number of categories of device feature models in the component library). For the input device information feature vector The calculation through the hidden layer is: Where is the weight matrix from the input layer to the hidden layer, is the bias vector of the hidden layer, f is the activation function, and the calculation of the output layer is In the formula is the weight matrix from the hidden layer to the output layer, is the bias vector of the output layer, and g is the activation function of the output layer; According to the result of the output layer Select the category with the highest probability as the matching device feature model category;

[0088] According to the adaptation planning result, select appropriate components from the component library or adjust the parameter configuration of the existing components. For example, for the data acquisition component, adjust parameters such as the data reception buffer size and parsing algorithm according to the device's data format and transmission frequency; for the display component, adjust the interface layout, font size, and chart style according to the device's screen resolution and display characteristics; during the application operation, continuously monitor the device status changes, business rule adjustments, and data traffic fluctuations. Specifically, by setting monitoring points in the system, real-time obtain the device operation data, business logic execution status, and network transmission data volume information;

[0089] When a change is detected, analyze the impact of the change on the component function. For example, when the device data transmission frequency increases, determine whether the data caching strategy needs to be adjusted; if a new data processing logic is added to the business rule, determine whether new components need to be added or the connection relationship between the existing components needs to be adjusted; according to the adaptation decision result, make real-time adjustments to the components, including dynamically loading new components, modifying the running parameters of the components, and re-establishing the communication connection operation between the components, to ensure that the system can still operate stably and provide accurate function services under the changes of the device and business.

[0090] The requirement conversion module uses natural language processing technology to parse the user requirement text and convert it into low-code development operation instructions and logical frameworks for rapid development; The process is as follows:

[0091] Natural language preprocessing: Perform word segmentation on the natural language text input by the user, split the sentence into individual words or phrases, and label the part of speech for each word. For example, for the sentence "Monitor the temperature in the factory workshop and alarm", after word segmentation, we get "Monitor", "factory workshop", "temperature", "and", "alarm", and at the same time label their corresponding parts of speech. This helps to accurately understand the grammar structure and semantics of the text in the following; Filter out the stop words without actual meaning in the text, such as common function words like "of", "is", "in" and some general words irrelevant to the requirements. After this step, the text is more refined and highlights the key information;

[0092] Semantic Understanding and Analysis: Identify key entities in the text, such as device types ("temperature sensor"), operation objects ("workshop"), actions ("monitor", "alarm"). Utilize a pre-trained entity recognition model, combined with a domain dictionary and context information, to accurately extract entity and its attribute information; Analyze the semantic relationships between entities to determine the logical structure of the requirements. For example, determine the conditional relationship between "monitor temperature" and "alarm", that is, when the temperature meets specific conditions, the alarm action is triggered. Clearly represent the requirement logic by constructing a semantic relationship graph or a similar data structure;

[0093] Operation Instruction Generation: Based on the semantic understanding results, search for matching functional components in the component library. For example, for the "temperature monitoring" requirement, match components such as temperature data acquisition components and threshold judgment components; for the "alarm" requirement, match components such as message notification components; Generate operation instructions for calling these components, and construct the connection and interaction logic framework between components according to the requirement logic, determine the data flow direction and triggering conditions, forming a preliminary low-code development operation instruction and logic framework for subsequent development.

[0094] The code annotation generation module is used to automatically add annotations to the generated code according to the parsed requirements, improving the readability and maintainability of the code, and facilitating subsequent development and maintenance work;

[0095] Obtain the parsed structured requirement data from the requirement conversion module, extract key information, including business functions, operation processes, data sources and flows, and involved devices and components; Classify and organize this information for subsequent accurate association with different parts of the code; According to the extracted key requirement information, plan the structure and content points of the annotations, and determine the types of annotations to be added at positions such as the beginning of the code, function definitions, and key logic nodes. For example, add an overall function overview annotation at the beginning of the code; add function input and output parameter and function description annotations at function definitions; add judgment condition meaning annotations at conditional judgment statements;

[0096] According to the planned annotation content, combined with the syntax structure and format specifications of the code, generate accurate and clear annotation text. Use a code parsing tool to locate the code lines or code blocks where annotations need to be added, and accurately insert the annotation text. For variable definitions, annotate the purpose and value range of the variable; for code logic blocks, annotate the implemented business logic and purpose; Optimize the format of the generated annotations to make the annotations conform to the unified annotation style specification of the team or project. Check the integrity and accuracy of the annotations to ensure the consistency between the annotations and the code, and avoid situations where the annotations do not match the actual code function. At the same time, unify and standardize the terms and expressions in the annotations to improve the readability of the annotations; The process of checking annotation consistency is as follows:

[0097] Let the set of code statements be S = {s1, s2, …, s p}, and the corresponding set of comments be A = {a1, a2, …, a p}; p is the number of code statements, that is, the number of elements in the set of code statements S; for each code statement s1 and its corresponding comment a1, calculate the consistency index C(s i , a i ). Using the semantic similarity calculation method, convert the code statement and the comment text into semantic vectors respectively (using a pre-trained language model or a rule-based semantic representation method). Let the semantic vector of the code statement s i be and the semantic vector of the comment a i be where i is an index variable;

[0098] Then the consistency index where

[0099] q is the vector dimension; j is an index variable used to traverse the dimensions of the vector when calculating the dot product of the semantic vectors. It starts from 1 and ends at the vector dimension q, indicating that when calculating the semantic similarity, the elements in the j-th dimension of the vector are operated on. If the consistency index C(s i , a i ) is lower than the set threshold, then mark that the code statement and the comment are inconsistent and need to be corrected.

[0100] The code compatibility conversion module is used to identify low-code files in different formats and convert them into a system-compatible format, and at the same time perform security vulnerability repair and compatibility optimization processing;

[0101] Read the low-code file to be converted, analyze the header information, file structure characteristics, and code syntax characteristics of the file, and accurately identify the original format type of the file by comparing with various known low-code format standards; for the identified format, start the corresponding syntax parser, decompose the original code into syntax units such as variable declarations, function definitions, and logical control statements, and establish a mapping relationship from the original syntax units to the target syntax units according to the syntax rules and data structures of this system to perform preliminary code conversion;

[0102] During the conversion process, a security scanning tool is used to perform static analysis on the converted code to check for common security vulnerabilities in the code, such as buffer overflows, SQL injection vulnerabilities, improper permission management, etc. For the discovered vulnerabilities, they are marked and relevant information is recorded. According to the vulnerability marking information, referring to the security vulnerability repair library and best practice methods, the code vulnerabilities are repaired. At the same time, for the parts of the code that are incompatible with this system, such as component call methods, data type differences, etc., compatibility adjustments are made. For example, the call parameters of specific components in the original code are modified according to the specifications of this system. Functional testing and compatibility testing are carried out on the repaired and optimized code, simulating various operating scenarios to ensure that the converted code can run correctly in this system and can work in coordination with other modules and components of the system. If problems are found during the testing, return to the previous steps for correction until it passes the test verification.

[0103] A low-code management method based on the AIot Internet of Things includes the following steps:

[0104] Template learning and optimization module: First, data collection is carried out, covering past Internet of Things application cases and user operation records. After classification and sorting, it enters the analysis and extraction stage. In this stage, through algorithms, the case structure and user behavior are analyzed to extract optimization points that can improve the applicability of the template. Then, multiple update plans are formulated based on these optimization points, and the priorities of each plan are comprehensively evaluated. Finally, the optimal plan is selected to update the template to ensure that the template meets the system requirements in terms of function and compatibility.

[0105] Component dynamic adaptation: When a new device is connected, first obtain the device information and adapt it to the component library model to determine the initial configuration of the component. During the operation of the system, continuously monitor the changes in the device and business. Once a change is detected, immediately analyze its impact on the component function, and then dynamically adjust the parameters and connection relationships of the component to ensure the stable operation and functional accuracy of the system.

[0106] Requirement conversion: For the requirement text input by the user, first perform natural language preprocessing, including word segmentation, part-of-speech tagging, and stop word filtering, to better understand the text semantics. Subsequently, enter the semantic understanding and analysis stage to identify key entities and semantic relationships, and construct the logical framework of the requirement. Finally, based on the analysis results, search for matching components in the component library to generate corresponding operation instructions and logical frameworks, providing a basis for subsequent development.

[0107] Code comment generation: After obtaining information from the requirements conversion step, extract the key requirements points, classify and organize them, plan the structure and content of the comments accordingly, then generate accurate comment text according to the plan and insert it into the corresponding code positions. After inserting the comments, optimize and check the comment format to ensure the integrity, accuracy, and consistency with the code, thereby improving the readability and maintainability of the code;

[0108] Code compatibility conversion: At the beginning, read the low-code file, identify its original format by analyzing the file's header, structure, and syntax characteristics, then start a syntax parser for code conversion for that format. During the conversion process, use a security scanning tool to check for vulnerabilities and mark them. After fixing the vulnerabilities and adjusting compatibility based on the marked information, conduct functional and compatibility tests on the converted code, simulate various running scenarios. If problems occur, return to the corresponding steps for correction until the code passes the test, ensuring that it can run correctly in the system and work in coordination with other modules.

[0109] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation manners. Obviously, many modifications and variations can be made according to the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A low-code management system based on the AIot Internet of Things, characterized in that, It includes: A template learning and optimization module, which is used to automatically refine optimization points and update template settings by analyzing historical cases and user data, so as to improve the applicability of the template in actual applications; A component dynamic adaptation module, which is used to identify newly connected device information and automatically adjust component configurations, and adapt component functions in real time according to device and business changes during application operation; A requirement conversion module, which uses natural language processing technology to parse user requirement texts and convert them into low-code development operation instructions and logical frameworks for rapid development; A code comment generation module, which is used to automatically add comments to the generated code according to the parsed requirements, improve code readability and maintainability, and facilitate subsequent development and maintenance work; A code compatibility conversion module, which is used to identify low-code files in different formats and convert them into system-compatible formats, and at the same time perform security vulnerability repair and compatibility optimization processing; The execution process of the template learning and optimization module is as follows: Data collection stage: Collect data of successfully developed and running Internet of Things application cases from the system database. At the same time, collect operation records of users during the use of the system, including template selection preferences, component usage frequencies, and operation information on modifying templates; Classify and organize the collected data according to dimensions of application type, functional module, and device type; Analysis and refinement stage: Use data analysis algorithms to analyze the functional structures of various cases, identify the composition methods of core functional modules in different applications and the logical relationships between them; By mining user operation records, analyze the rules for users to adjust templates when facing different business requirements; Combining the results of functional structure analysis and user behavior pattern mining, extract optimization points for improving template applicability, including adjustments to template default functions, optimizations of interface layouts, and improvements to initial parameter settings; Template update stage: According to the extracted optimization points, formulate several template update plans. The plans include specific contents and steps for adjusting each part of the template, ensuring that the updated template meets the functional optimization requirements while maintaining the stability and compatibility of the system; Test the several formulated template update plans, determine the priorities of the template update plans, and select the template update plan with the highest priority; Update the template according to the template update plan with the highest priority. During the update process, test and verify the template to ensure that the new template can meet expectations in terms of function and will not introduce new errors or problems. After the update is completed, store the new template in the template library for users to use in subsequent development.

2. The low-code management system based on the AIot Internet of Things according to claim 1, characterized in that, The specific operation steps for the template learning and optimization module to determine the priorities of template update plans are as follows: Through collecting evaluation parameters for comprehensive analysis, where the evaluation parameters include: function prompt amplitude, user experience improvement degree, and development cost, and obtaining the improvement value, improvement ratio value, and cost saving value through separate analysis of the evaluation parameters. The obtained improvement value, improvement ratio value, and cost saving value are denoted as tc, gt, and cj respectively. After normalization processing, substitute them into the following formula: ZPA = tc×ο1 + gt×ο2 + cj×ο3 to obtain the comprehensive judgment value ZPA, where ο1, ο2, and ο3 are the preset weight coefficients of the improvement value, improvement ratio value, and cost saving value respectively; Sort the different comprehensive judgment values obtained from several formulated template update plans according to their magnitudes, and select the template update plan with the largest comprehensive judgment value as the template update plan with the highest priority.

3. A low-code management system based on the AIot Internet of Things according to claim 2, characterized in that, The specific operation steps for the template learning and optimization module to obtain the improvement value, improvement ratio value, and cost saving value through separate analysis of the evaluation parameters are as follows: Function prompt amplitude: It includes the number of new functions added, the degree of business process optimization, and the improvement of data processing efficiency. Among them, the number of new functions added is obtained by counting the number of new function modules in each update plan; The degree of business process optimization is evaluated by assessing the optimization degree of the existing business process in the update plan, specifically quantified by calculating the process simplification ratio and denoted as the simplification ratio value; the improvement of data processing efficiency is determined by measuring the improvement of data processing speed after template update, specifically by comparing the time required to process the same amount of data before and after the update, and calculating the time difference. After normalizing the obtained number of new functions added, simplification ratio value, and time difference, use the number of new functions added, simplification ratio value, and time difference as the side lengths of a triangle to establish a bottom triangle, and then use the correction coefficient as the height to establish a triangular prism model, calculate the surface area of this triangular prism model, and denote it as the improvement value. Use this improvement value as the standard to measure the function improvement amplitude; User experience improvement degree, improvement of operation simplicity: Through user testing or simulation operations, count the average task time of the operation steps required to complete the standard task and denote it as the completion time; improvement of interface friendliness: Invite users to rate the updated template interface and calculate the rating difference after the update; Accuracy of error prompt: Calculate the error ratio by combining the number of errors encountered by users during operation after the update with all operation steps. After normalizing the obtained completion time, rating difference, and number of errors, calculate the ratio of the rating difference to the sum of the completion time and the number of errors, and denote it as the improvement ratio value. Use this improvement ratio value as the standard to measure the user experience improvement degree; Development cost, shortening ratio of development time: Estimate the shortening ratio of the time required to develop a new application compared to the old template after the implementation of the update plan; labor cost savings: Calculate the labor cost saved during the development and maintenance processes after the implementation of the update plan, specifically quantified by comparing the number of developers and working hours reduced before and after the update. After normalizing the obtained shortening ratio, number of developers reduced, and working hours reduced, sum them up to obtain the cost saving value. Use this cost saving value as the standard to measure the development cost savings; 4. An AIot-based low-code management system according to claim 3, characterized in that, The operation process of the component dynamic adaptation module is as follows: When a new device attempts to access the system, a connection is established with the device through the communication protocol of the device access layer, and the basic information of the device is obtained, including device type, manufacturer information, device model, and communication interface parameters. At the same time, a detection instruction is initiated to obtain the functional parameters of the device, including data acquisition accuracy, supported instruction set, and data transmission frequency range information; Perform feature analysis on the collected device information and match it with the existing device feature models in the component library. When there is a similar model, perform an adaptation plan according to the existing adaptation scheme and in combination with the parameters of the current device; According to the adaptation plan result, select the corresponding components from the component library or adjust the parameter configuration of the existing components, including: for the data acquisition component, adjust the data reception buffer size and parsing algorithm parameters according to the data format and transmission frequency of the device; For the display component, adjust the interface layout, font size, and chart style according to the screen resolution and display characteristics of the device; During the operation of the application, continuously monitor the device status changes, business rule adjustments, and data traffic fluctuations. Specifically, by setting monitoring points in the system, obtain the device operation data, business logic execution status, and network transmission data volume information in real time; When a change is detected, analyze the impact of the change on the component functions; if a new data processing logic is added to the business rule, determine whether new components need to be added or the connection relationship between the existing components needs to be adjusted; According to the adaptation decision result, perform real-time adjustment on the components, including dynamically loading new components, modifying the operation parameters of the components, and re-establishing the communication connection operations between the components, to ensure that the system can still operate stably and provide accurate functional services under the changes of the device and business.

5. An AIot Internet of Things-based low-code management system according to claim 4, characterized in that The judgment process of the similar model in the component dynamic adaptation module is as follows: When performing feature analysis on the collected device information and matching it with the existing device feature models in the component library, the feature vector of the device is calculated for cosine similarity with the feature vector corresponding to each device feature model in the component library through the following formula: According to the preset similarity threshold τ, when the cosine similarity cos(θ) is greater than or equal to the preset similarity threshold τ, it is determined that there is a similar model; When no similar model is found, the new device adaptation rule generation mechanism is activated to classify and summarize device information using machine learning algorithms to generate a preliminary adaptation strategy. Specifically, through a classification algorithm based on neural networks, including a multi-layer perceptron, the device information is classified to determine which type of device feature model in the component library is the most matching. The preset number of input layer nodes is n, the number of hidden layer nodes is h, and the number of output layer nodes is m. For the input device information feature vector The calculation through the hidden layer is: Where is the weight matrix from the input layer to the hidden layer, is the bias vector of the hidden layer, f is the activation function, and then the calculation by the output layer is In the formula is the weight matrix from the hidden layer to the output layer, is the bias vector of the output layer, g is the activation function of the output layer; according to the result of the output layer Select the category with the highest probability as the matching device feature model category.

6. An AIot Internet of Things-based low-code management system according to claim 5, characterized in that, The execution steps of the requirement conversion module include: B1. Natural language preprocessing: Perform word segmentation on the natural language text input by the user, split the sentence into individual words or phrases, and label the part of speech for each word, and at the same time label the corresponding part of speech; filter out the stop words without practical meaning in the text, including function words and general vocabulary irrelevant to the requirements; B2. Semantic understanding and analysis: Identify the key entities in the text, including device type, operation object, and action. Use the pre-trained entity recognition model, combined with the domain dictionary and context information, to extract the entity and its attribute information; analyze the semantic relationship between the entities to determine the logical structure of the requirement; B3. Operation instruction generation: According to the semantic understanding result, search for the matching functional components in the component library; generate the operation instructions for calling these components, and build the connection and interaction logic framework between the components according to the requirement logic, determine the data flow direction and trigger conditions, and form the preliminary low-code development operation instructions and logic framework for subsequent development use.

7. An AIot-based low-code management system according to claim 6, characterized in that The execution steps of the code annotation generation module are as follows: Obtain the parsed structured requirement data from the requirement conversion module, and extract the key information, including business functions, operation processes, data sources and flows, and the devices and components involved; Classify and organize this information for subsequent association with different parts of the code; According to the extracted key information of requirements, plan the structure and content key points of the comments, and determine the types of comments to be added at the beginning of the code, at the function definition, and at the logical node positions, including adding an overall function overview comment at the beginning of the code; Add comments on the input and output parameters and function descriptions at the function definition; add comments on the meaning of the judgment conditions at the conditional judgment statements; According to the planned comment content, combined with the syntax structure and format specifications of the code, generate accurate and clear comment text. Locate the code lines or code blocks that need to add comments through the code parsing tool, and accurately insert the comment text. For variable definitions, comment on the purpose and value range of the variables; for code logic blocks, comment on the business logic and purpose implemented; Optimize the format of the generated comments to make the comments conform to the unified comment style specifications of the team or project. Check the integrity and accuracy of the comments to ensure the consistency between the comments and the code, and avoid the situation where the comments do not match the actual code function. At the same time, unify and standardize the terms and expressions in the comments to improve the readability of the comments; the process of checking comment consistency is as follows: Let the set of code statements be S = {s1, s2,..., s p}, and the corresponding set of comments be A = {a1, a2,..., a p}; p is the number of code statements, that is, the number of elements in the set of code statements S; for each code statement s1 and its corresponding comment a1, calculate their consistency index C(s i , a i ), using the semantic similarity calculation method, convert the code statement and the comment text into semantic vectors respectively. Let the semantic vector of the code statement s i be and the semantic vector of the comment a i be where i is the index variable; The consistency index wherein where q is the vector dimension; j is another index variable; If the consistency index C(s i , a i ) is lower than the set threshold, then mark that the code statement and the comment are inconsistent and need to be corrected.

8. An AIot Internet of Things-based low-code management system according to claim 7, characterized in that, The execution steps of the code compatibility conversion module are as follows: Read the low-code file to be converted, analyze the header information, file structure characteristics, and code syntax characteristics of the file, and identify the original format type of the file by comparing with various known low-code format standards; For the identified format, start the corresponding syntax parser, decompose the original code into syntax units, establish a mapping relationship from the original syntax units to the target syntax units according to the syntax rules and data structures of this system, and perform preliminary code conversion; During the conversion process, use a security scanning tool to perform static analysis on the converted code to check whether there are common security vulnerabilities in the code. For the discovered vulnerabilities, mark and record them; According to the vulnerability marking information, combined with the security vulnerability repair library and best practice methods, repair the code vulnerabilities. At the same time, make compatibility adjustments for the parts of the code that are incompatible with this system, including modifying the call parameters of the components in the original code according to the specifications of this system; Conduct functional testing and compatibility testing on the repaired and optimized code, simulate different running scenarios, ensure that the converted code can run correctly in this system and can work in coordination with other modules and components of the system. When problems are found during the testing, return to the previous steps for correction until the testing is passed and verified.

9. A low-code management method based on the AIot Internet of Things, characterized in that, Apply a low-code management system based on AIot Internet of Things as described in claim 8, including the following steps: S1. Template learning and optimization module: First, conduct data collection, covering past Internet of Things application cases and user operation records. After classification and sorting, it enters the analysis and extraction stage. In this stage, through algorithms to analyze the case structure and user behavior, extract the optimization points that can improve the applicability of the template, then formulate multiple update plans based on these optimization points, and comprehensively evaluate the priorities of each plan. Finally, select the optimal plan to update the template to ensure that the template meets the system requirements in terms of function and compatibility; S2. Component Dynamic Adaptation: When a new device is connected, first obtain the device information and adapt it to the component library model to determine the initial configuration of the components. During the operation of the system, continuously monitor the changes in the device and business. Once a change is detected, immediately analyze its impact on the component functions, and then dynamically adjust the parameters and connection relationships of the components to ensure the stable operation and functional accuracy of the system; S3. Requirement Transformation: For the requirement text input by the user, first perform natural language preprocessing, including word segmentation, part-of-speech tagging, and stop-word filtering. Then enter the semantic understanding and analysis stage to identify key entities and semantic relationships, and construct the logical framework of the requirements. Finally, based on the analysis results, search for matching components in the component library and generate corresponding operation instructions and logical frameworks; S4. Code Comment Generation: After obtaining the information from step S3, extract the key points of the requirements and classify and organize them to plan the structure and content of the comments. Then generate the comment text according to the plan and insert it into the corresponding code position. After inserting the comments, optimize and check the format of the comments to ensure the integrity, accuracy, and consistency of the comments with the code; S5. Code Compatibility Transformation: At the beginning, read the low-code file. By analyzing the header, structure, and syntax characteristics of the file, identify the original format. Then start the syntax parser for code conversion for this format. During the conversion process, use a security scanning tool to check for vulnerabilities and mark them. After fixing the vulnerabilities and adjusting the compatibility according to the marked information, conduct functional and compatibility tests on the converted code, simulate various operating scenarios. If problems occur, return to the corresponding steps for correction until the code passes the test.

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