Penetration-type supervision method and system based on market subject digital twinning

Abstract

The invention discloses a penetrating supervision method and system based on market subject digital twinning, belongs to the technical field of market supervision and digital twinning, and aims to solve the technical problem of how to realize full-flow penetrating supervision of multi-source data and improve supervision accuracy and efficiency. Comprising the steps of performing data preprocessing on multi-source heterogeneous data, and constructing a unified data pool; performing 1: 1 three-dimensional modeling on the physical space of the market subject to obtain a digital twin model capable of real-time interaction; acquiring market subject state data in real time, synchronizing the state data to the digital twin model, checking the dynamic operation state of the market subject through the digital twin model, and marking an abnormal state; performing parallel computing on a large-scale scene through a parallel visual rendering algorithm, simulating a risk evolution path in combination with a simulation deduction and algorithm integration technology, and generating a risk level and early warning information; and generating a processing task based on the risk level and the early warning information, and feeding back a processing result to the digital twinborn model.

Description

Technical Field

[0001] This invention relates to the field of market supervision and digital twin technology, specifically to a penetrating supervision method and system based on the digital twins of market entities. Background Technology

[0002] The core of existing digital twin technology in data processing and modeling lies in the integration and lightweight presentation of multi-source heterogeneous data. Through parametric modeling technology for multi-source heterogeneous data, it is possible to integrate multi-dimensional data such as geographic information, IoT sensing, and business applications to accurately depict three-dimensional space and topological semantic relationships. At the same time, by leveraging texture-based LOD oblique imagery and BIM automatic lightweighting technology, the problem of loading large-scale model data with lag can be solved, improving operational efficiency while ensuring model accuracy.

[0003] In terms of scene rendering and interaction, the key technologies focus on efficient visualization and real-time interaction. Scene culling technology reduces invalid rendering through methods such as view frustum culling and occlusion removal, while hierarchical paging scheduling technology dynamically loads data to optimize memory usage; parallel visualization rendering algorithms utilize multi-core hardware and GPU parallel architecture to improve the real-time performance and smoothness of large-scale scene rendering; and multi-dynamic high real-time interaction technology achieves accurate mapping and bidirectional interaction between physical entities and digital models, supporting full lifecycle state awareness.

[0004] In terms of application support, the technology focuses on multi-scenario integration and end-to-end empowerment. By unifying coordinates and accuracy standards, it achieves integrated adaptation of city-level, park-level, and device-level scenarios. Combined with simulation and algorithm integration capabilities, it supports the entire process from data collection and modeling to application development. At the same time, relying on multi-screen adaptation and multi-terminal integration technology, it breaks through the limitations of usage scenarios and provides visualized, interactive, and decision-making twin service support for various industries.

[0005] How to achieve end-to-end, penetrating supervision of multi-source data and improve the accuracy and efficiency of supervision is a technical problem that needs to be solved. Summary of the Invention

[0006] The technical objective of this invention is to address the above-mentioned shortcomings by providing a penetrating regulatory method and system based on digital twins of market entities, thereby solving the technical problem of how to achieve full-process penetrating regulation of multi-source data and improve the accuracy and efficiency of regulation.

[0007] This invention provides a penetrating regulatory method based on digital twins of market entities, comprising the following steps:

[0008] Multi-source regulatory data processing: Collect multi-source heterogeneous data, perform data preprocessing on the multi-source heterogeneous data, perform data cleaning, format conversion and attribute association operations through data preprocessing, perform multi-source heterogeneous data parameterization modeling and dynamic fusion on the preprocessed data to obtain integrated data, and build a unified data pool based on the integrated data;

[0009] Construction of digital twin models for market entities: Based on the oblique image of texture LOD and BIM automation lightweight technology, the physical space of market entities is modeled in 1:1 3D, and the geometric, physical, behavioral and rule attributes are fused to obtain a digital twin model that can be interacted in real time.

[0010] Real-time dynamic perception and virtual-real linkage: Based on multi-dynamic high real-time interaction technology, the status data of market entities is collected in real time through IoT sensing devices and the status data is synchronized to the digital twin model. The dynamic operating status of market entities can be viewed through the digital twin model, and abnormal status can be marked to obtain real-time status data and abnormal markings.

[0011] Risk simulation and intelligent early warning: Based on real-time status data, corresponding anomaly markers, and historical data in a unified data pool, parallel visualization rendering algorithms are used to perform parallel computing on large-scale scenarios. Combined with simulation and algorithm integration technology, the risk evolution path is simulated to generate risk levels and early warning information.

[0012] Closed-loop handling and collaborative supervision: Based on risk level and early warning information, handling tasks are generated, pushed to law enforcement terminals and market entity systems, and task progress is synchronized in real time. Standardized handling process templates are constructed, and the handling process is visualized and tracked by digital twins. The handling results are fed back to the digital twin model.

[0013] As a preferred option, multi-source heterogeneous data includes market entity registration, business licenses, special equipment parameters, IoT sensing data, and historical violation records, among which IoT sensing data includes temperature, humidity, and equipment vibration values.

[0014] As a preferred approach, during the real-time dynamic perception and virtual-real linkage process, abnormal states are alerted in real time through visual cues, including flashing and color changes.

[0015] As a preferred approach, when simulating the risk evolution path by combining simulation and algorithm integration technologies, a pre-configured risk assessment model is used to simulate the risk evolution path. The risk assessment model includes a special equipment failure prediction model, which is used to predict the time period from abnormal equipment operation to failure.

[0016] As a preferred approach, in the closed-loop disposal and collaborative supervision process, the location of key issues related to the disposal task is located through a digital twin model.

[0017] Secondly, the present invention provides a penetrating regulatory system based on digital twins of market entities, including a multi-source regulatory data processing module, a digital twin model construction module for market entities, a real-time dynamic perception and virtual-real linkage module, a risk simulation and intelligent early warning module, and a closed-loop disposal and collaborative supervision module.

[0018] The multi-source regulatory data processing module is used to perform the following: collect multi-source heterogeneous data, perform data preprocessing on the multi-source heterogeneous data, perform data cleaning, format conversion and attribute association operations through data preprocessing, perform multi-source heterogeneous data parameterization modeling and dynamic fusion on the preprocessed data to obtain integrated data, and build a unified data pool based on the integrated data.

[0019] The market entity digital twin model construction module is used to perform the following: based on texture LOD oblique images and BIM automation lightweight technology, the physical space of the market entity is modeled in 1:1 3D, and the geometric, physical, behavioral and rule attributes are fused to obtain a digital twin model that can be interacted in real time.

[0020] The real-time dynamic perception and virtual-real linkage module is used to perform the following: Based on multi-dynamic high real-time interaction technology, it collects market entity status data in real time through IoT sensing devices and synchronizes the status data to the digital twin model. It then uses the digital twin model to view the dynamic operating status of the market entity and marks abnormal statuses to obtain real-time status data and abnormal markings.

[0021] The risk simulation and intelligent early warning module is used to perform the following: based on real-time status data and corresponding anomaly markers, as well as historical data in a unified data pool, it performs parallel computation on large-scale scenarios through parallel visualization rendering algorithms, and combines simulation and algorithm integration technology to simulate the risk evolution path and generate risk levels and early warning information.

[0022] The closed-loop disposal and collaborative supervision module is used to perform the following: generate disposal tasks based on risk level and early warning information, push disposal tasks to law enforcement terminals and market entity systems, synchronize task progress in real time, build standardized disposal process templates, combine digital twins to realize the visual tracking of the disposal process, and feed the disposal results back to the digital twin model.

[0023] As a preferred option, multi-source heterogeneous data includes market entity registration, business licenses, special equipment parameters, IoT sensing data, and historical violation records, among which IoT sensing data includes temperature, humidity, and equipment vibration values.

[0024] As a preferred approach, during the real-time dynamic perception and virtual-real linkage process, abnormal states are alerted in real time through visual cues, including flashing and color changes.

[0025] As a preferred approach, when simulating the risk evolution path by combining simulation and algorithm integration technologies, a pre-configured risk assessment model is used to simulate the risk evolution path. The risk assessment model includes a special equipment failure prediction model, which is used to predict the time period from abnormal equipment operation to failure.

[0026] As a preferred option, the closed-loop handling and collaborative supervision module is used to locate the position of key issues related to the handling task through a digital twin model.

[0027] The penetrating supervision method and system based on digital twins of market entities of the present invention has the following advantages: it realizes the integration of multi-source heterogeneous supervision data, breaks down data silos, constructs a 1:1 digital model of market entities, accurately maps physical status, connects with animal and sensor data in real time, dynamically updates operational status, predicts risks through simulation and deduction, provides intelligent early warning, forms a closed-loop handling process, improves supervision efficiency, achieves the penetrating supervision effect of "overall view on one screen, real-time perception, accurate early warning, and efficient handling", and realizes the transformation from passive response to proactive prevention and control. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] The invention will be further described below with reference to the accompanying drawings.

[0030] Figure 1 Example 1 is a flowchart of a penetrating supervision method based on digital twins of market entities. Detailed Implementation

[0031] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments are not intended to limit the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0032] This invention provides a method and system for penetrating supervision based on digital twins of market entities, which addresses the technical problem of how to achieve full-process penetrating supervision of multi-source data and improve the accuracy and efficiency of supervision.

[0033] Example 1:

[0034] This invention provides a penetrating regulatory method based on digital twins of market entities, comprising four steps: multi-source regulatory data processing, construction of digital twin models of market entities, real-time dynamic perception and virtual-real linkage, risk simulation and intelligent early warning, and closed-loop disposal and collaborative supervision.

[0035] Step S100 Multi-source regulatory data processing: Collect multi-source heterogeneous data, perform data preprocessing on the multi-source heterogeneous data, perform data cleaning, format conversion and attribute association operations through data preprocessing, perform multi-source heterogeneous data parameterization modeling and dynamic fusion on the preprocessed data to obtain integrated data, and build a unified data pool based on the integrated data.

[0036] The multi-source heterogeneous data includes market entity registration, business licenses, special equipment parameters, IoT sensing data, and historical violation records. The IoT sensing data includes temperature, humidity, and equipment vibration values.

[0037] This step relies on multi-source heterogeneous data parametric modeling technology to integrate multi-dimensional data such as market entity registration, business licenses, special equipment parameters, IoT sensing data (e.g., temperature, humidity, equipment vibration values), and historical violation records. Through data cleaning, format conversion, and attribute fusion, it eliminates data fragmentation and format heterogeneity issues, constructing a unified regulatory data resource pool. Leveraging its "end-to-end connectivity" feature, it achieves full-chain management of data from collection and access to fusion, ensuring data consistency and real-time updates.

[0038] In the specific implementation process, regulatory personnel query the full integrated data of the target market entities, filter by dimensions such as entity name and industry type, and obtain a full life cycle data view (such as a complete record from registration to each inspection).

[0039] Step S200: Construction of digital twin model of market entity: Based on the tilted image of texture LOD and BIM automation lightweight technology, the physical space of the market entity is modeled in 1:1 three dimensions, and the geometric, physical, behavioral and rule attributes are fused to obtain a digital twin model that can be interacted in real time.

[0040] This step, based on integrated data, employs texture-based LOD-based oblique imagery and BIM automatic lightweighting technology to create a 1:1 3D model of the physical space of market entities (such as factory layout and equipment location). Lightweighting processes compress the model data volume (e.g., compressing a GB-level BIM model to MB-level) to avoid loading delays. Combined with multi-dimensional modeling technology, it achieves the fusion of model "geometric-physical-behavioral-rule" attributes (e.g., associating an elevator model with its rated load capacity, number of runs, and fault triggering rules).

[0041] In practical applications, regulatory personnel can use the digital twin model to zoom in and rotate the model to view details (such as clicking on the device model to display its parameters and historical status) and intuitively grasp the main physical structure and core attributes.

[0042] Step S300 Real-time dynamic perception and virtual-real linkage: Based on multi-dynamic high real-time interaction technology, the status data of market entities is collected in real time through IoT sensing devices and the status data is synchronized to the digital twin model. The dynamic operating status of market entities is viewed through the digital twin model, and abnormal status is marked to obtain real-time status data and abnormal markings.

[0043] During real-time dynamic perception and virtual-real linkage, abnormal states are alerted in real time through visual cues, including flashing and color changes.

[0044] This step utilizes multi-dynamic high real-time interactive technology to collect dynamic data of market entities (such as production workshop temperature and equipment operating frequency) in real time through IoT sensing devices (sensors, cameras) and synchronize it to the digital twin model, achieving millisecond-level linkage between physical state and digital model. Based on the "sensory interconnection" capability, a "state indicator-event modeling" association rule is established (such as setting "cold chain temperature > 8℃ triggers an abnormal event") to drive the model to dynamically update state indicators (such as highlighting abnormal areas).

[0045] Regulatory personnel can use digital twin models to view the dynamic operating status of market entities (such as real-time equipment parameter curves and heat maps of pedestrian flow density), and abnormal conditions can be alerted in real time through visual prompts (flashing, color changes).

[0046] Step S400 Risk Simulation and Intelligent Early Warning: Based on real-time status data, corresponding anomaly markers, and historical data in a unified data pool, parallel computing is performed on large-scale scenarios through parallel visualization rendering algorithms. Combined with simulation and algorithm integration technology, the risk evolution path is simulated to generate risk levels and early warning information.

[0047] When simulating the risk evolution path by combining simulation and algorithm integration technology, a pre-configured risk assessment model is used to simulate the risk evolution path. The risk assessment model includes a special equipment failure prediction model, which is used to predict the time period from abnormal equipment operation to failure.

[0048] This step, based on integrated historical data and real-time status, utilizes parallel visualization rendering algorithms to perform parallel computations on large-scale scenarios (such as multi-entity collaborative risks within a region), ensuring simulation efficiency (rendering frame rate ≥ 30 frames / second for million-level triangle models). Combining simulation and algorithm integration technologies, a built-in risk assessment model (such as a special equipment fault prediction algorithm) is incorporated to simulate risk evolution paths (such as the time cycle from abnormal equipment operation to failure), generating risk levels and early warning information.

[0049] In this step, regulators input parameters (such as "assuming the equipment continues to operate above the temperature"), view the results of risk evolution simulations (such as "the probability of failure reaches 70% within 12 hours"), and assist in formulating priority handling strategies.

[0050] Step S500 Closed-Loop Disposal and Collaborative Supervision: Based on risk level and early warning information, disposal tasks are generated, and the disposal tasks are pushed to law enforcement terminals and market entity systems. The task progress is synchronized in real time, a standardized disposal process template is constructed, and the disposal process is visualized and tracked by digital twins. The disposal results are fed back to the digital twin model.

[0051] In the closed-loop disposal and collaborative supervision process, the location of key issues related to the disposal task is located through a digital twin model.

[0052] This step relies on collaborative control and task management technologies to generate handling tasks for early warning events. These tasks are then pushed to law enforcement terminals and market entity systems via interfaces, with real-time synchronization of task progress (e.g., "dispatch - on-site inspection - rectification - re-inspection"). A standardized handling process template is built using the "scenario editing" function, and a digital twin model is used to achieve visualized tracking of the handling process. Finally, the handling results are fed back to the model to update the risk status.

[0053] In this step, regulators assign tasks and designate responsible persons, locate the problem using a digital twin model, and monitor the progress of the handling in real time (such as rectification certificates uploaded by law enforcement personnel). After the handling is completed, the risk level of the main entity is updated to the model.

[0054] The method in this embodiment applies digital twin technology to achieve penetrating supervision in the field of market regulation. It can collect multi-source data such as registration, business licenses, IoT sensing, and violation records of market entities, and integrate and standardize them using multi-source heterogeneous data parametric modeling technology to form a unified regulatory data pool. Based on this data pool, a 1:1 digital twin model of the market entity is constructed using texture LOD oblique imagery and BIM automatic lightweight technology, and its attribute information and behavioral rules are associated. Real-time data is accessed through the Internet of Things, and real-time linkage between physical entities and digital models is achieved through multi-dynamic high real-time interaction technology, dynamically updating the operating status and marking anomalies. Parallel visualization rendering algorithms and simulation inference technology are used to simulate risk evolution and generate early warnings by combining historical and real-time data. Finally, disposal tasks are generated through collaborative control and task management technology, progress is tracked, and results are fed back to the model to form a closed loop, and the disposal results flow back to the data pool for continuous optimization.

[0055] Example 2:

[0056] This invention discloses a penetrating regulatory system based on digital twins of market entities, comprising a multi-source regulatory data processing module, a digital twin model construction module for market entities, a real-time dynamic perception and virtual-real linkage module, a risk simulation and intelligent early warning module, and a closed-loop disposal and collaborative supervision module.

[0057] The multi-source regulatory data processing module is used to perform the following: collect multi-source heterogeneous data, perform data preprocessing on the multi-source heterogeneous data, perform data cleaning, format conversion and attribute association operations through data preprocessing, perform multi-source heterogeneous data parameterization modeling and dynamic fusion on the preprocessed data to obtain integrated data, and build a unified data pool based on the integrated data.

[0058] The multi-source heterogeneous data includes market entity registration, business licenses, special equipment parameters, IoT sensing data, and historical violation records. The IoT sensing data includes temperature, humidity, and equipment vibration values.

[0059] This module leverages multi-source heterogeneous data parametric modeling technology to integrate multi-dimensional data from market entities, including registration, business licenses, special equipment parameters, IoT sensing data (such as temperature, humidity, and equipment vibration values), and historical violation records. Through data cleaning, format conversion, and attribute fusion, it eliminates data fragmentation and format heterogeneity issues, constructing a unified regulatory data resource pool. Utilizing its "end-to-end connectivity" feature, it achieves full-chain management of data from collection and access to fusion, ensuring data consistency and real-time updates.

[0060] In the specific implementation process, regulatory personnel query the full integrated data of the target market entities, filter by dimensions such as entity name and industry type, and obtain a full life cycle data view (such as a complete record from registration to each inspection).

[0061] The market entity digital twin model construction module is used to perform the following: based on texture LOD oblique images and BIM automation lightweight technology, the physical space of the market entity is modeled in 1:1 3D, and the geometric, physical, behavioral and rule attributes are fused to obtain a digital twin model that can be interacted in real time.

[0062] This module, based on integrated data, employs texture-based LOD-based oblique imagery and BIM automatic lightweighting technology to create a 1:1 3D model of the physical space of market entities (such as factory layout and equipment location). Lightweighting processes compress the model data volume (e.g., compressing a GB-level BIM model to MB-level) to avoid loading delays. Combined with multi-dimensional modeling technology, it achieves the fusion of model "geometric-physical-behavioral-rule" attributes (e.g., associating an elevator model with its rated load capacity, number of runs, and fault triggering rules).

[0063] In practical applications, regulatory personnel can use the digital twin model to zoom in and rotate the model to view details (such as clicking on the device model to display its parameters and historical status) and intuitively grasp the main physical structure and core attributes.

[0064] The real-time dynamic perception and virtual-real linkage module is used to perform the following: Based on multi-dynamic high real-time interaction technology, it collects market entity status data in real time through IoT sensing devices and synchronizes the status data to the digital twin model. It then uses the digital twin model to view the dynamic operating status of the market entity and marks abnormal statuses to obtain real-time status data and abnormal markings.

[0065] During real-time dynamic perception and virtual-real linkage, abnormal states are alerted in real time through visual cues, including flashing and color changes.

[0066] This module utilizes multi-dynamic high real-time interactive technology to collect dynamic data of market entities (such as production workshop temperature and equipment operating frequency) in real time through IoT sensing devices (sensors, cameras) and synchronize it to the digital twin model, achieving millisecond-level linkage between physical state and digital model. Based on the "sensory interconnection" capability, it establishes "state indicator-event modeling" association rules (such as setting "cold chain temperature > 8℃ triggers abnormal event") to drive the model to dynamically update state indicators (such as highlighting abnormal areas).

[0067] Regulatory personnel can use digital twin models to view the dynamic operating status of market entities (such as real-time equipment parameter curves and heat maps of pedestrian flow density), and abnormal conditions can be alerted in real time through visual prompts (flashing, color changes).

[0068] The risk simulation and intelligent early warning module is used to perform the following: based on real-time status data, corresponding anomaly markers, and historical data in a unified data pool, it performs parallel computation on large-scale scenarios through parallel visualization rendering algorithms, and simulates the risk evolution path by combining simulation and algorithm integration technology to generate risk levels and early warning information.

[0069] When simulating the risk evolution path by combining simulation and algorithm integration technology, a pre-configured risk assessment model is used to simulate the risk evolution path. The risk assessment model includes a special equipment failure prediction model, which is used to predict the time period from abnormal equipment operation to failure.

[0070] Based on integrated historical data and real-time status, this module employs parallel visualization rendering algorithms to perform parallel computations on large-scale scenarios (such as multi-entity collaborative risks within a region), ensuring simulation efficiency (rendering frame rate ≥ 30 frames / second for million-level triangle models). Combining simulation and algorithm integration technologies, it incorporates a built-in risk assessment model (such as a special equipment fault prediction algorithm) to simulate risk evolution paths (such as the time cycle from abnormal equipment operation to failure), generating risk levels and early warning information.

[0071] Based on the parameters input by supervisors in this module (such as "assuming the equipment continues to operate above the temperature"), they can view the risk evolution simulation results (such as "the probability of failure reaches 70% within 12 hours") and be assisted in formulating priority handling strategies.

[0072] The closed-loop disposal and collaborative supervision module is used to perform the following: generate disposal tasks based on risk level and early warning information, push disposal tasks to law enforcement terminals and market entity systems, synchronize task progress in real time, build standardized disposal process templates, combine digital twins to realize the visual tracking of the disposal process, and feed the disposal results back to the digital twin model.

[0073] In the closed-loop disposal and collaborative supervision process, the location of key issues related to the disposal task is located through a digital twin model.

[0074] This module relies on collaborative control and task management technologies to generate handling tasks for early warning events. These tasks are then pushed to law enforcement terminals and market entity systems via interfaces, synchronizing task progress in real time (e.g., "dispatch - on-site inspection - rectification - re-inspection"). A standardized handling process template is built using the "scenario editing" function, and a digital twin model is used to achieve visualized tracking of the handling process. Finally, the handling results are fed back to the model to update the risk status.

[0075] Based on this module, regulators assign tasks and designate responsible persons, locate the problem location through the digital twin model, and view the handling progress in real time (such as rectification certificates uploaded by law enforcement personnel). After the handling is completed, the main risk level is updated to the model.

[0076] The above provides a detailed description of the penetrating supervision method and system based on digital twins of market entities provided by this invention. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A penetrating regulatory method based on digital twins of market entities, characterized in that, Includes the following steps: Multi-source regulatory data processing: Collect multi-source heterogeneous data, perform data preprocessing on the multi-source heterogeneous data, perform data cleaning, format conversion and attribute association operations through data preprocessing, perform multi-source heterogeneous data parameterization modeling and dynamic fusion on the preprocessed data to obtain integrated data, and build a unified data pool based on the integrated data; Construction of digital twin models for market entities: Based on the oblique image of texture LOD and BIM automation lightweight technology, the physical space of market entities is modeled in 1:1 3D, and the geometric, physical, behavioral and rule attributes are fused to obtain a digital twin model that can be interacted in real time. Real-time dynamic perception and virtual-real linkage: Based on multi-dynamic high real-time interaction technology, the status data of market entities is collected in real time through IoT sensing devices and the status data is synchronized to the digital twin model. The dynamic operating status of market entities can be viewed through the digital twin model, and abnormal status can be marked to obtain real-time status data and abnormal markings. Risk simulation and intelligent early warning: Based on real-time status data, corresponding anomaly markers, and historical data in a unified data pool, parallel visualization rendering algorithms are used to perform parallel computing on large-scale scenarios. Combined with simulation and algorithm integration technology, the risk evolution path is simulated to generate risk levels and early warning information. Closed-loop handling and collaborative supervision: Based on risk level and early warning information, handling tasks are generated, pushed to law enforcement terminals and market entity systems, and task progress is synchronized in real time. Standardized handling process templates are constructed, and the handling process is visualized and tracked by digital twins. The handling results are fed back to the digital twin model.

2. The penetrating supervision method based on digital twins of market entities according to claim 1, characterized in that, Multi-source heterogeneous data includes market entity registration, business licenses, special equipment parameters, IoT sensing data, and historical violation records. Among them, IoT sensing data includes temperature, humidity, and equipment vibration values.

3. The penetrating supervision method based on digital twins of market entities according to claim 1, characterized in that, During real-time dynamic perception and virtual-real linkage, abnormal states are alerted in real time through visual cues, including flashing and color changes.

4. The penetrating supervision method based on digital twins of market entities according to claim 1, characterized in that, When simulating the risk evolution path by combining simulation and algorithm integration technology, a pre-configured risk assessment model is used to simulate the risk evolution path. The risk assessment model includes a special equipment failure prediction model, which is used to predict the time period from abnormal equipment operation to failure.

5. The penetrating supervision method based on digital twins of market entities according to claim 1, characterized in that, During the closed-loop handling and collaborative supervision process, the location of key issues related to the handling task is located through a digital twin model.

6. A penetrating regulatory system based on digital twins of market entities, characterized in that, It includes a multi-source regulatory data processing module, a market entity digital twin model construction module, a real-time dynamic perception and virtual-real linkage module, a risk simulation and intelligent early warning module, and a closed-loop disposal and collaborative supervision module; The multi-source regulatory data processing module is used to perform the following: collect multi-source heterogeneous data, perform data preprocessing on the multi-source heterogeneous data, perform data cleaning, format conversion and attribute association operations through data preprocessing, perform multi-source heterogeneous data parameterization modeling and dynamic fusion on the preprocessed data to obtain integrated data, and build a unified data pool based on the integrated data. The market entity digital twin model building module is used to perform the following: based on the oblique image of texture LOD and BIM automation lightweight technology, the physical space of the market entity is modeled in 1:1 three dimensions, and the geometric, physical, behavioral and rule attributes are fused to obtain a digital twin model that can be interacted in real time. The real-time dynamic perception and virtual-real linkage module is used to perform the following: Based on multi-dynamic high real-time interaction technology, it collects market entity status data in real time through IoT sensing devices and synchronizes the status data to the digital twin model. It then uses the digital twin model to view the dynamic operating status of the market entity and marks abnormal statuses to obtain real-time status data and abnormal markings. The risk simulation and intelligent early warning module is used to perform the following: based on real-time status data and corresponding anomaly markers, as well as historical data in a unified data pool, it performs parallel computation on large-scale scenarios through parallel visualization rendering algorithms, and combines simulation and algorithm integration technology to simulate the risk evolution path and generate risk levels and early warning information. The closed-loop disposal and collaborative supervision module is used to perform the following: generate disposal tasks based on risk level and early warning information, push disposal tasks to law enforcement terminals and market entity systems, synchronize task progress in real time, build standardized disposal process templates, combine digital twins to realize the visual tracking of the disposal process, and feed the disposal results back to the digital twin model.

7. The penetrating regulatory system based on digital twins of market entities according to claim 6, characterized in that, Multi-source heterogeneous data includes market entity registration, business licenses, special equipment parameters, IoT sensing data, and historical violation records. Among them, IoT sensing data includes temperature, humidity, and equipment vibration values.

8. The penetrating regulatory system based on digital twins of market entities according to claim 6, characterized in that, During real-time dynamic perception and virtual-real linkage, abnormal states are alerted in real time through visual cues, including flashing and color changes.

9. The penetrating regulatory system based on digital twins of market entities according to claim 6, characterized in that, When simulating the risk evolution path by combining simulation and algorithm integration technology, a pre-configured risk assessment model is used to simulate the risk evolution path. The risk assessment model includes a special equipment failure prediction model, which is used to predict the time period from abnormal equipment operation to failure.

10. The penetrating regulatory system based on digital twins of market entities according to claim 6, characterized in that, The closed-loop handling and collaborative supervision module is used to locate the key issues related to the handling task through a digital twin model.

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