Supervision, inspection and management integrated platform based on artificial intelligence

Abstract

The invention relates to the technical field of AI supervision, in particular to a supervision, inspection and management integrated platform based on artificial intelligence, which is characterized in that an Internet of Things device acquires structured and unstructured data in real time, extracts time, place and subject information, acquires an original data set, unifies a format, calculates a task emergency degree and a personnel skill matching degree through an algorithm, and obtains a task emergency degree and a personnel skill matching degree; the method comprises the following steps: extracting a distribution path, adopting AR equipment and a voice recognition technology, superposing a check list and historical records in real time, extracting numerical values and signature information, obtaining a structured evidence packet, storing an evidence through a block chain, extracting violation mode characteristics, adopting a task completion rate and a violation detection rate, positioning a system bottleneck through causal analysis, and obtaining optimization parameters. And automatically updating to a model and rule base to obtain a supervision, inspection and management integrated platform. The problems that data integration of the Internet of Things is difficult and correlation analysis cannot be performed, an evidence chain is easy to tamper and incomplete, potential risks are difficult to actively identify, and a platform lacks a feedback optimization mechanism are solved.

Description

Technical Field

[0001] This invention belongs to the field of AI regulatory technology, specifically involving an integrated platform for supervision, inspection and management based on artificial intelligence. Background Technology

[0002] In today's complex and ever-changing business environment, efficient and accurate supervision and management of enterprises is crucial. However, existing supervision and management models have many limitations. On the one hand, there are serious deficiencies in data utilization. Although IoT devices can collect large amounts of structured and unstructured data in real time, the lack of effective integration methods results in fragmented data, inconsistent formats, and difficulty in correlation analysis, failing to fully realize the value of the data and providing comprehensive and accurate data support for supervision and inspection. On the other hand, risk warning capabilities are weak. Traditional methods struggle to accurately build risk prediction models based on historical data and domain knowledge through machine learning. Dynamic risk scoring is inaccurate and cannot be continuously updated to adapt to changes in the enterprise, leading to untimely and inaccurate risk warnings and difficulty in identifying potential risks in advance. In terms of resource allocation, existing models lack scientific algorithm support, making it difficult to accurately calculate the urgency of tasks and the matching degree of personnel skills. The unreasonable allocation path leads to low resource utilization efficiency, affecting the conduct of supervision and inspection work. Regarding evidence chain management, evidence obtained through traditional methods is easily tampered with, and its integrity cannot be guaranteed, failing to provide reliable evidence for supervision and inspection. Furthermore, the identification of potential risks relies heavily on human experience, making it difficult to leverage advanced technologies such as knowledge graphs to uncover abnormal correlations between enterprise, personnel, and transaction data. This hinders the timely discovery of potential risk lists and prevents proactive intervention. Simultaneously, the lack of effective feedback and optimization mechanisms prevents the identification of system bottlenecks based on task completion rates and violation detection rates, the generation of improvement suggestions, and the automatic updating of models and rule bases, hindering the continuous optimization of the supervision and inspection management platform. Therefore, the development of an integrated supervision and inspection management platform is urgently needed.

[0003] Existing technologies suffer from several shortcomings, including fragmented data integration making correlation analysis difficult, inaccurate risk warnings, inefficient resource allocation, easily tampered and incomplete evidence chains, difficulty in proactively identifying potential risks, and a lack of feedback and optimization mechanisms that hinder continuous platform upgrades and improvements. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides an integrated supervision and inspection management platform based on artificial intelligence. This platform solves problems such as difficulties in integrating IoT data and the inability to perform correlated analysis, lack of accuracy and dynamism in risk warnings, low utilization efficiency due to unreasonable resource allocation, easily tampered and incomplete evidence chains, difficulty in proactively identifying potential risks, and the lack of a feedback and optimization mechanism within the platform. To achieve the above objectives, this invention adopts the following technical solution: The AI-based integrated supervision and inspection management platform includes: a data integration module for real-time collection of structured and unstructured data from IoT devices, extraction of time, location, and subject information, acquisition of raw datasets, unified formatting, and obtaining data capable of correlation analysis; a dynamic optimization module for using historical data and domain knowledge, extracting enterprise size and historical violation characteristics through machine learning, constructing a risk prediction model, obtaining dynamic risk scores, continuously updating, and achieving accurate risk warning capabilities; a resource allocation module for calculating task urgency and personnel skill matching through algorithms, extracting allocation paths, obtaining scheduling schemes, and pushing them to terminals for efficient resource utilization; and a solid evidence chain. The system comprises four modules: a data processing module and an intelligent identification module. The former utilizes AR devices and voice recognition technology to overlay checklists and historical records in real time, extracting numerical and signature information to obtain a structured evidence package. This evidence is then stored on the blockchain to create an immutable and complete chain of evidence. The latter employs a knowledge graph to integrate enterprise, personnel, and transaction data. Through graph algorithms, it mines abnormal relationships, extracts violation pattern characteristics, obtains a list of potential risks, and pushes it to the regulatory authorities for proactive intervention. The former uses task completion rate and violation detection rate to locate system bottlenecks through causal analysis, extracts improvement suggestions, obtains optimization parameters, and automatically updates the model and rule base, resulting in an integrated platform for supervision, inspection, and management.

[0005] Furthermore, the data integration module includes: a data acquisition submodule, used to acquire structured and unstructured data in real time using IoT sensors and smart terminals through multi-protocol adaptation technology; a spatiotemporal preprocessing submodule, used to perform preliminary cleaning of raw data using edge computing components, extract the time of event occurrence through a timestamp synchronization algorithm, and extract spatial coordinates by combining GPS positioning and geofencing technology; a subject identification submodule, used to extract unique identifiers of subjects from text and images through an entity recognition model; and a data normalization submodule, used to integrate scattered data sources according to a preset data model, obtain a raw dataset containing the three elements of time, location, and subject, and unify it into a standardized JSON format to obtain data that can be correlated and analyzed.

[0006] Furthermore, the dynamic optimization module includes: a feature extraction submodule, used to extract key features from enterprise registration information, operating data, and past violation records using feature engineering methods, based on historical enforcement data accumulated by the regulatory system and domain knowledge compiled by industry experts; a risk modeling submodule, used to construct a dynamic risk prediction model using ensemble learning algorithms, obtaining initial model parameters through historical data training and cross-validation, and applying them to real-time data streams to generate dynamic risk scores; and a dynamic early warning submodule, used to continuously absorb new data by combining an online learning mechanism, automatically adjust model weights, obtain prediction results synchronized with actual risk trends, and achieve accurate risk early warning capabilities covering the entire lifecycle.

[0007] Furthermore, the resource allocation module includes: a task scheduling submodule, used to calculate the urgency of tasks using real-time collected task data and personnel status information through a multi-objective optimization algorithm; a resource optimization submodule, used to extract resource allocation paths using graph theory shortest path algorithms and dynamically avoid conflicts by combining geographical location and current load; a scheme encapsulation submodule, used to encapsulate the calculation results into a structured scheduling scheme, which includes task execution order, personnel division of labor, and time window; and an instruction issuance submodule, used to push the scheme to the mobile terminal in real time through a message middleware, synchronously updating the system resource occupancy status to ensure efficient resource utilization.

[0008] Furthermore, the evidence chain solidification module includes: an AR inspection submodule, used to overlay dynamic inspection lists and historical inspection records onto the field of view using a smart terminal device integrating AR display and voice interaction through real-time rendering technology; an information extraction submodule, used to trigger the data extraction process using voice commands, and accurately extract key values, timestamps, and responsible person signature information from the inspection forms by combining OCR optical recognition and handwritten signature parsing algorithms; a data sealing submodule, used to encapsulate the extracted raw data, on-site environmental images, and equipment status data into a structured evidence package, and generate a unique digital fingerprint through hash encryption; and an evidence storage submodule, used to upload the encrypted evidence package to the consortium blockchain network to complete distributed evidence storage, synchronously recording the evidence storage time and node information to obtain an immutable and complete evidence chain.

[0009] Furthermore, the intelligent identification module includes: a graph construction submodule, used to integrate enterprise registration information, personnel relationship networks, and transaction flow data using multi-source data pipelines to construct a dynamically updated entity association knowledge graph; a graph anomaly detection submodule, used to deeply mine hidden abnormal association paths in the graph through community discovery algorithms and graph neural network models; a risk identification submodule, used to extract feature parameters of violations using pattern recognition technology and generate a structured potential risk list in combination with a business rule engine; and a risk push submodule, used to push the risk list to the regulatory terminal through a real-time message channel, simultaneously triggering an early warning workflow to achieve proactive intervention.

[0010] Furthermore, the feedback and optimization module includes: a source analysis submodule, used to analyze indicator fluctuations and locate data delays and rule conflicts by using dual-dimensional indicator data of task completion rate and violation detection rate from task execution records and through causal inference algorithms; an intelligent efficiency improvement submodule, used to extract targeted improvement suggestions using natural language generation technology, covering scheduling strategy adjustments and model parameter optimization directions; a task allocation submodule, used to convert suggestions into executable optimization parameters and synchronize them to the risk prediction model and task allocation rule base through an automated pipeline; and an automatic optimization submodule, used to complete parameter updates, trigger full-link regression testing, and obtain an integrated supervision and inspection management platform with automatic optimization capabilities and continuous indicator improvement.

[0011] Furthermore, the feature extraction submodule is used to utilize the historical full-volume enforcement data accumulated by the regulatory system over a long period of time, combined with the domain knowledge system compiled by authoritative industry experts, to process the data in stages through feature engineering, perform structured analysis on enterprise registration information, extract features such as registered capital and years of establishment, simultaneously capture business scope and transaction frequency information from operating data, and statistically analyze the number of violations and penalty types from past violation records. Through feature normalization and correlation analysis, a set of key features including quantitative values ​​of enterprise size, business type codes, historical violation frequency, and severity classification is extracted.

[0012] Furthermore, the graph construction submodule is used to build a multi-source data pipeline using a distributed data acquisition framework, and to access enterprise registration information from the industrial and commercial system and personnel relationship networks from third-party platforms in real time through API interfaces. It uses a data cleaning engine to standardize heterogeneous data, extracts unique enterprise identifiers, personnel identification codes and transaction serial numbers as association anchors, and constructs a dynamically updated entity association knowledge graph.

[0013] In the technical solution provided by this invention, the data integration module is used for real-time collection of structured and unstructured data by IoT devices, extraction of time, location, and subject information, acquisition of raw datasets, unification of format, and obtaining data that can be correlated and analyzed; the dynamic optimization module is used to use historical data and domain knowledge, extract enterprise size and historical violation characteristics through machine learning, construct a risk prediction model, obtain dynamic risk scores, continuously update them, and obtain accurate risk warning capabilities; the resource allocation module is used to calculate task urgency and personnel skill matching degree through algorithms, extract allocation paths, obtain scheduling schemes, and push them to the terminal to achieve efficient resource utilization; the evidence chain solidification module is used for... This invention employs AR devices and voice recognition technology to extract numerical and signature information by overlaying checklists and historical records in real time, obtaining a structured evidence package. This package is then stored on blockchain to create an immutable and complete chain of evidence. An intelligent identification module uses knowledge graphs to integrate enterprise, personnel, and transaction data, employing graph algorithms to uncover abnormal relationships, extract violation pattern characteristics, obtain a list of potential risks, and push this list to regulatory authorities for proactive intervention. A feedback and optimization module uses task completion rates and violation detection rates to locate system bottlenecks through causal analysis, extract improvement suggestions, obtain optimization parameters, and automatically update the model and rule base, resulting in an integrated platform for supervision, inspection, and management. This invention addresses the problems of difficulty in integrating IoT data for correlation analysis, lack of accuracy and dynamism in risk warnings, low utilization efficiency due to unreasonable resource allocation, easily tampered and incomplete evidence chains, difficulty in proactively identifying potential risks, and the lack of feedback and optimization mechanisms in the platform. Attached Figure Description

[0014] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention.

[0015] Figure 1 This is a schematic diagram of the first embodiment of the integrated supervision and inspection management platform based on artificial intelligence in this invention.

[0016] Figure 2 This is a schematic diagram of the second embodiment of the integrated supervision and inspection management platform based on artificial intelligence in this invention.

[0017] Figure 3 This is a schematic diagram of the third embodiment of the integrated supervision, inspection and management platform based on artificial intelligence in this invention.

[0018] Figure 4 This is a schematic diagram of the fourth embodiment of the integrated supervision, inspection and management platform based on artificial intelligence in this invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0020] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0021] An integrated supervision and inspection management platform based on artificial intelligence, such as Figure 1 As shown, it includes: a data integration module, used for real-time collection of structured and unstructured data from IoT devices, extracting time, location, and subject information, obtaining raw datasets, unifying formats, and obtaining data that can be correlated and analyzed; a dynamic optimization module, used to use historical data and domain knowledge, extract enterprise size and historical violation characteristics through machine learning, build a risk prediction model, obtain dynamic risk scores, continuously update them, and obtain accurate risk warning capabilities; a resource allocation module, used to calculate task urgency and personnel skill matching through algorithms, extract allocation paths, obtain scheduling schemes, and push them to the terminal to achieve efficient resource utilization; and an evidence chain solidification module, used to use A... The R-device and voice recognition technology, through real-time overlay of checklists and historical records, extract numerical and signature information to obtain a structured evidence package, which is then stored on the blockchain to obtain an immutable and complete chain of evidence; the intelligent recognition module uses knowledge graphs to integrate enterprise, personnel, and transaction data, mines abnormal relationships through graph algorithms, extracts violation pattern characteristics, obtains a list of potential risks, and pushes it to the regulatory end for proactive intervention; the feedback and optimization module uses task completion rate and violation detection rate to locate system bottlenecks through causal analysis, extracts improvement suggestions, obtains optimization parameters, and automatically updates them to the model and rule base, resulting in an integrated platform for supervision, inspection, and management.

[0022] like Figure 2As shown, in this embodiment, the data acquisition submodule is used to collect structured and unstructured data in real time using IoT sensors and smart terminals through multi-protocol adaptation technology; the spatiotemporal preprocessing submodule is used to perform preliminary cleaning of the raw data using edge computing components, extract the time of event occurrence through a timestamp synchronization algorithm, and extract spatial coordinates by combining GPS positioning and geofencing technology; the subject identification submodule is used to extract unique identifiers of subjects from text and images through an entity recognition model; and the data normalization submodule is used to integrate scattered data sources according to a preset data model to obtain a raw dataset containing the three elements of time, location, and subject, and unify it into a standardized JSON format to obtain data that can be correlated and analyzed.

[0023] The data acquisition submodule, leveraging multi-protocol adaptation technology, enables real-time and comprehensive acquisition of both structured and unstructured data, ensuring data integrity. The spatiotemporal preprocessing submodule utilizes edge computing and various technologies to accurately extract spatiotemporal information of events, providing a spatiotemporal benchmark for subsequent analysis. The subject identification submodule can quickly extract unique identifiers of subjects from text and images, clearly identifying data-related objects. The data normalization submodule integrates scattered data into a standardized JSON format raw dataset, enabling correlated data analysis and significantly improving data processing efficiency and analytical accuracy.

[0024] like Figure 3 As shown in this embodiment, the feature extraction submodule is used to extract key features from enterprise registration information, operating data, and past violation records using feature engineering methods, based on historical enforcement data accumulated by the regulatory system and domain knowledge compiled by industry experts; the risk modeling submodule is used to build a dynamic risk prediction model using ensemble learning algorithms, obtain initial model parameters through historical data training and cross-validation, and apply them to real-time data streams to generate dynamic risk scores; the dynamic early warning submodule is used to continuously absorb new data by combining online learning mechanisms, automatically adjust model weights, obtain prediction results synchronized with actual risk trends, and obtain accurate risk early warning capabilities covering the entire life cycle.

[0025] The feature extraction submodule leverages historical law enforcement data and domain knowledge, employing feature engineering to accurately extract key enterprise characteristics, thus laying a solid data foundation for risk assessment. The risk modeling submodule utilizes ensemble learning algorithms to construct dynamic models, which, after training and cross-validation with historical data, generate dynamic risk scores, enabling preliminary quantitative predictions of risk. The dynamic early warning submodule, combined with an online learning mechanism, continuously absorbs new data and automatically adjusts model weights, ensuring that prediction results closely synchronize with actual risk trends. This provides enterprises with accurate risk warnings covering the entire lifecycle, effectively helping them proactively prevent risks and achieve stable development.

[0026] like Figure 4As shown, in this embodiment, the task scheduling submodule is used to calculate the urgency of tasks by using real-time collected task data and personnel status information and a multi-objective optimization algorithm; the resource allocation submodule is used to extract resource allocation paths using graph theory shortest path algorithm and dynamically avoid conflicts by combining geographical location and current load; the scheme encapsulation submodule is used to encapsulate the calculation results into a structured scheduling scheme, which includes task execution order, personnel division of labor, and time window; and the instruction issuance submodule is used to push the scheme to the mobile terminal in real time through message middleware and update the system resource occupancy status synchronously to enable efficient resource utilization.

[0027] The task scheduling submodule leverages real-time data and multi-objective optimization algorithms to accurately calculate task urgency, providing a basis for rational scheduling. The resource allocation submodule uses graph theory's shortest path algorithm, combined with geographical and load information, to dynamically allocate resources, effectively avoiding conflicts and improving resource allocation rationality. The scheme encapsulation submodule transforms the calculation results into a structured scheduling scheme, clearly defining the task execution order, personnel division of labor, and time windows, enhancing the scheme's operability. The instruction issuance submodule pushes the scheme and updates resource status in real time through a message middleware, ensuring timely information transmission, achieving efficient resource utilization, and improving overall task execution efficiency and quality.

[0028] In this embodiment, the AR inspection submodule is used to overlay dynamic inspection lists and historical inspection records onto the field of view using a smart terminal device that integrates AR display and voice interaction, through real-time rendering technology; the information extraction submodule is used to trigger the data extraction process using voice commands, and combine OCR optical recognition and handwritten signature parsing algorithms to accurately extract key values, timestamps, and responsible person signature information from the inspection forms; the data sealing submodule is used to encapsulate the extracted raw data, on-site environmental images, and equipment status data into a structured evidence package, and generate a unique digital fingerprint through hash encryption; the evidence storage submodule is used to upload the encrypted evidence package to the consortium blockchain network to complete distributed evidence storage, synchronously record the evidence storage time and node information, and obtain an immutable and complete evidence chain.

[0029] The AR inspection submodule utilizes smart terminal devices to overlay dynamic lists and historical records with real-time rendering technology, allowing inspectors to intuitively obtain information and improving the efficiency and accuracy of on-site inspections. The Information Extraction submodule, triggered by voice commands, combines multiple algorithms to accurately extract key information from forms, reducing manual data entry errors. The Data Sealing submodule encapsulates various types of data into structured evidence packages and uses hash encryption to generate unique fingerprints, ensuring data integrity and security. The Evidence Storage submodule uploads the encrypted evidence packages to the consortium blockchain network for distributed evidence storage, recording relevant information to form an immutable chain of evidence.

[0030] In this embodiment, the graph construction submodule is used to integrate enterprise registration information, personnel relationship networks, and transaction flow data using multi-source data pipelines to construct a dynamically updated entity association knowledge graph; the graph anomaly detection submodule is used to deeply mine hidden abnormal association paths in the graph through community discovery algorithms and graph neural network models; the risk identification submodule is used to extract feature parameters of violations using pattern recognition technology and generate a structured potential risk list in combination with a business rule engine; and the risk push submodule is used to push the risk list to the regulatory terminal through a real-time message channel, simultaneously triggering an early warning workflow to achieve proactive intervention.

[0031] The graph construction submodule integrates multi-source data to build a dynamic knowledge graph, clearly presenting the relationships between enterprise entities and providing comprehensive data support for risk analysis. The graph anomaly detection submodule leverages community discovery algorithms and graph neural networks to deeply mine hidden abnormal relationship paths, effectively identifying potential risk points. The risk identification submodule uses pattern recognition and a business rule engine to accurately extract violation characteristics and generate a risk list, improving the accuracy and efficiency of risk identification. The risk push submodule pushes risk lists through real-time messaging channels and triggers early warning workflows, helping regulators to intervene promptly and proactively, curbing risks in their early stages and ensuring market order stability.

[0032] In this embodiment, the source analysis submodule uses task completion rate and violation detection rate data from task execution records to analyze indicator fluctuations and locate data delays and rule conflicts through causal inference algorithms; the intelligent efficiency improvement submodule uses natural language generation technology to extract targeted improvement suggestions, covering scheduling strategy adjustments and model parameter optimization directions; the task allocation submodule converts the suggestions into executable optimization parameters and synchronizes them to the risk prediction model and task allocation rule base through an automated pipeline; and the automatic optimization submodule completes parameter updates, triggers full-link regression testing, and obtains an integrated supervision and inspection management platform with automatic optimization capabilities and continuous indicator improvement.

[0033] The source analysis submodule uses dual-dimensional indicator data combined with causal inference algorithms to accurately pinpoint issues such as data delays and rule conflicts during task execution, providing clear directions for optimization. The intelligent efficiency improvement submodule utilizes natural language generation technology to provide targeted improvement suggestions, helping to enhance scheduling and model performance. The task allocation submodule transforms these suggestions into executable parameters and synchronizes them to relevant libraries, ensuring the effective implementation of optimization measures. The automatic optimization submodule updates parameters and triggers regression testing, enabling the platform to automatically optimize, achieve continuous indicator improvement, and build an efficient, intelligent, and constantly evolving integrated platform for supervision, inspection, and management, thereby improving regulatory effectiveness.

[0034] In this embodiment, the feature extraction submodule is used to utilize the historical full-volume enforcement data accumulated by the regulatory system over a long period of time, combined with the domain knowledge system compiled by authoritative industry experts, to process the data in stages through feature engineering. It performs structured parsing of enterprise registration information, extracts features such as registered capital and years of establishment, and simultaneously captures business scope and transaction frequency information from operating data. It also counts the number of violations and penalty types from past violation records, and extracts a set of key features including quantitative values ​​of enterprise size, business type codes, historical violation frequency, and severity levels through feature normalization and correlation analysis.

[0035] The feature extraction submodule leverages the comprehensive historical enforcement data and authoritative domain knowledge accumulated over the long term by the regulatory system to conduct feature engineering processing in stages, ensuring the systematic and in-depth nature of data mining. It accurately analyzes and extracts enterprise registration information, operational data, and past violation records, comprehensively covering key information dimensions of enterprises. Through feature normalization and correlation analysis, it effectively integrates data and extracts a set of key features, including information on enterprise size, business type, and historical violations. This provides rich, accurate, and highly correlated data support for subsequent risk assessment and regulatory decisions, helping to improve the accuracy and effectiveness of regulation. In this embodiment, the graph construction submodule is used to build a multi-source data pipeline using a distributed data acquisition framework. It accesses enterprise registration information from the industrial and commercial system and personnel relationship networks from third-party platforms in real time through API interfaces. It uses a data cleaning engine to standardize heterogeneous data, extracts unique enterprise identifiers, personnel identification codes, and transaction serial numbers as association anchors, and constructs a dynamically updated entity association knowledge graph.

[0036] The graph construction submodule utilizes a distributed data acquisition framework to build a multi-source data pipeline. Through API interfaces, it can access data from industrial and commercial systems and third-party platforms in real time, ensuring the timeliness and comprehensiveness of information. The data cleaning engine standardizes heterogeneous data, effectively resolving issues related to data format and semantic differences. Using unique enterprise identifiers as anchor points, a dynamically updated entity-relationship knowledge graph is constructed, clearly presenting the complex relationships between enterprises, individuals, and transactions. This not only provides an intuitive and comprehensive data view for subsequent risk analysis and regulatory decisions but also updates in real time as data changes, ensuring the accuracy and timeliness of the analysis.

[0037] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. An integrated supervision and inspection management platform based on artificial intelligence, characterized in that: The AI-based integrated supervision and inspection management platform includes: The data integration module is used for IoT devices to collect structured and unstructured data in real time, extract time, location, and subject information, obtain raw datasets, unify the format, and obtain data that can be correlated and analyzed. The dynamic optimization module is used to extract enterprise size and historical violation characteristics through machine learning by using historical data and domain knowledge, build a risk prediction model, obtain a dynamic risk score, continuously update it, and obtain accurate risk warning capabilities. The resource allocation module is used to calculate the urgency of tasks and the matching degree of personnel skills through algorithms, extract allocation paths, obtain scheduling plans, and push them to the terminal to achieve efficient resource utilization. The evidence chain solidification module is used to extract numerical and signature information by overlaying checklists and historical records in real time using AR devices and voice recognition technology, obtain a structured evidence package, and obtain an immutable and complete evidence chain through blockchain storage. The intelligent identification module is used to integrate enterprise, personnel and transaction data using knowledge graphs, mine abnormal relationships through graph algorithms, extract the characteristics of violation patterns, obtain a list of potential risks, and push it to the regulatory authorities to achieve proactive intervention; The feedback and optimization module is used to identify system bottlenecks through causal analysis by using task completion rate and violation detection rate, extract improvement suggestions, obtain optimization parameters, and automatically update them to the model and rule base to obtain an integrated platform for supervision, inspection and management.

2. The integrated supervision and inspection management platform based on artificial intelligence according to claim 1, characterized in that, The data integration module includes: The data acquisition submodule is used to collect structured and unstructured data in real time using IoT sensors and smart terminals through multi-protocol adaptation technology. The spatiotemporal preprocessing submodule is used to perform preliminary cleaning of raw data using edge computing components, extract the time of event occurrence through timestamp synchronization algorithm, and extract spatial coordinates by combining GPS positioning and geofencing technology. The subject recognition submodule is used to extract unique identifiers of subjects from text and images using an entity recognition model. The data unification submodule is used to integrate scattered data sources according to a preset data model, obtain the original dataset containing three elements: time, location, and subject, unify it into a standardized JSON format, and obtain data that can be correlated and analyzed.

3. The integrated supervision and inspection management platform based on artificial intelligence according to claim 1, characterized in that, The dynamic optimization module includes: The feature extraction submodule is used to extract key features from enterprise registration information, operating data and past violation records by using historical law enforcement data accumulated by the regulatory system and domain knowledge sorted out by industry experts, through feature engineering methods. The risk modeling submodule is used to build a dynamic risk prediction model using ensemble learning algorithms. It obtains initial model parameters through training and cross-validation of historical data and applies them to real-time data streams to generate dynamic risk scores. The dynamic early warning submodule is used to continuously absorb new data by combining online learning mechanisms, automatically adjust model weights, obtain prediction results that are synchronized with actual risk trends, and obtain accurate risk early warning capabilities covering the entire life cycle.

4. The integrated supervision and inspection management platform based on artificial intelligence according to claim 1, characterized in that, The resource allocation module includes: The task scheduling submodule is used to calculate the urgency of tasks by using real-time collected task data and personnel status information, and through a multi-objective optimization algorithm. The resource allocation submodule is used to extract resource allocation paths using graph theory shortest path algorithms and dynamically avoid conflicts by combining geographical location and current load. The scheme encapsulation submodule is used to encapsulate the calculation results into a structured scheduling scheme, which includes the task execution order, personnel division of labor, and time window. The instruction delivery submodule is used to push the solution to the mobile terminal in real time through the message middleware, and update the system resource usage status in a synchronous manner to make efficient use of resources.

5. The integrated supervision and inspection management platform based on artificial intelligence according to claim 1, characterized in that, The evidence chain solidification module includes: The AR inspection submodule is used to overlay dynamic inspection lists and historical inspection records on the field of view using smart terminal devices that integrate AR display and voice interaction through real-time rendering technology. The Information Extraction Submodule is used to trigger the data extraction process using voice commands. It combines OCR optical recognition and handwritten signature parsing algorithms to accurately extract key values, timestamps, and responsible person's signature information from the inspection form. The data sealing submodule is used to encapsulate the extracted raw data, on-site environmental images, and equipment status data into a structured evidence package, and generate a unique digital fingerprint through hash encryption. The evidence storage submodule is used to upload the encrypted evidence package to the consortium blockchain network to complete distributed evidence storage, synchronously record the evidence storage time and node information, and obtain an immutable and complete evidence chain.

6. The integrated supervision and inspection management platform based on artificial intelligence according to claim 1, characterized in that, The intelligent recognition module includes: The graph construction submodule is used to integrate enterprise registration information, personnel relationship networks and transaction flow data from multiple data sources to build a dynamically updated entity association knowledge graph. The graph anomaly detection submodule is used to deeply mine hidden abnormal correlation paths in the graph through community detection algorithms and graph neural network models; The risk identification submodule is used to extract characteristic parameters of violations using pattern recognition technology and combine them with the business rule engine to generate a structured list of potential risks. The risk push submodule is used to push the risk list to the regulatory terminal through a real-time message channel, and simultaneously trigger the early warning workflow to achieve proactive intervention.

7. The integrated supervision and inspection management platform based on artificial intelligence according to claim 1, characterized in that, The feedback and optimization module includes: The source analysis submodule is used to analyze indicator fluctuations and locate data delays and rule conflicts by using task completion rate and violation detection rate data from task execution records and causal inference algorithms. The intelligent efficiency improvement submodule is used to extract targeted improvement suggestions using natural language generation technology, covering scheduling strategy adjustments and model parameter optimization. The task allocation submodule is used to transform suggestions into executable optimization parameters and synchronize them to the risk prediction model and task allocation rule base through an automated pipeline. The automatic optimization submodule is used to complete parameter updates and trigger full-link regression testing, resulting in an integrated supervision and management platform with automatic optimization capabilities and continuous improvement of indicators.

8. The integrated supervision and inspection management platform based on artificial intelligence according to claim 3, characterized in that, The feature extraction submodule utilizes the historical enforcement data accumulated over a long period by the regulatory system, combined with domain knowledge systems compiled by authoritative industry experts. Through feature engineering, it processes the data in stages, performs structured analysis on enterprise registration information, extracts features such as registered capital and years of establishment, simultaneously captures business scope and transaction frequency information from operational data, and statistically analyzes the number of violations and penalty types from past violation records. Through feature normalization and correlation analysis, it extracts a set of key features including quantitative values ​​of enterprise size, business type codes, historical violation frequency, and severity levels.

9. The integrated supervision and inspection management platform based on artificial intelligence according to claim 6, characterized in that, The graph construction submodule is used to build a multi-source data pipeline using a distributed data acquisition framework. It accesses enterprise registration information from the industrial and commercial system and personnel relationship networks from third-party platforms in real time through API interfaces. It uses a data cleaning engine to standardize heterogeneous data, extracts unique enterprise identifiers, personnel identification codes, and transaction serial numbers as association anchors, and constructs a dynamically updated entity association knowledge graph.

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