Intelligent internet-of-things control management system based on commercial operation

Abstract

The invention relates to the technical field of internet-of-things control management, in particular to an intelligent internet-of-things control management system based on commercial operation, which comprises a data acquisition and alignment module, a strategy generation and authority module and an instruction execution and exception handling module. The data acquisition and alignment module is used for acquiring an equipment state, an environment parameter and a user instruction, and completing standardized processing and space-time alignment to generate a unified data set; the strategy generation and permission module generates a control strategy according to a service rule, divides permissions according to user roles, and generates a role instruction set; and the instruction execution and exception handling module issues an instruction to the equipment, monitors an execution state, and triggers rollback, work order generation and permission verification mechanisms when an exception occurs. According to the invention, high-precision sensing, intelligent control and safe and efficient Internet of Things equipment management are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things control and management, and in particular to a smart Internet of Things control and management system based on commercial operation. Background Art

[0002] With the development of smart commerce, intelligent lighting and Internet of Things technologies, more and more commercial scenarios have introduced Internet of Things-based control systems. By deploying various sensors and control terminals, real-time perception and response control of equipment operating status, environmental parameters and user behaviors are achieved. Such systems are usually applied to scenarios such as shopping malls, chain stores, advertising light boxes, etc., for energy-saving control, safety monitoring, remote management and data-driven operation optimization. To improve operation efficiency and user experience, it is urgent to build a control system with high-precision perception, intelligent strategy generation and multi-role collaboration capabilities.

[0003] Existing Internet of Things control systems generally have the following problems: First, the heterogeneity of multi-source data is strong, lacking a unified standardization processing and spatio-temporal alignment mechanism, resulting in insufficient data fusion accuracy. Second, control strategies mostly rely on static rules and cannot be intelligently adjusted according to real-time environment and changes in the flow of people, with slow response and poor flexibility. Third, the device exception response mechanism is imperfect, and there is no closed-loop processing for failed instructions or abnormal states, posing potential safety hazards. In addition, the permission management is relatively rough, lacking effective control and auditing of the operation behaviors of multi-role users, and it is difficult to meet the requirements of large-scale deployment and multi-role collaboration. Summary of the Invention

[0004] The present invention provides a smart Internet of Things control and management system based on commercial operation, which improves the perception accuracy, control intelligence and operation and maintenance security of Internet of Things devices in commercial scenarios.

[0005] A smart Internet of Things control and management system based on commercial operation includes a data collection and alignment module, a strategy generation and permission module, and an instruction execution and exception handling module, wherein; The data collection and alignment module real-time collects multi-source data related to Internet of Things control through an Internet of Things device interface, including device status data, environmental parameters and user operation instructions. Among them, the device status data includes the tilt angle, temperature, humidity, and brightness value of the light box, and the environmental parameters include light intensity and pedestrian flow density. By performing standardization processing on the collected multi-source data and based on a spatio-temporal feature alignment algorithm, the time-series data of different devices are mapped according to a unified timestamp and spatial coordinates to generate a standardized Internet of Things data set; The policy generation and permission module generates dynamic control policies according to the standardized Internet of Things data set, combined with a preset business rule library, through a policy engine, including device operation mode switching, brightness and color temperature adjustment, warning threshold correction, and maintenance work order triggering conditions. At the same time, based on a multi-level permission mapping model, the dynamic control policy instructions are classified by user role (administrator, store user, maintenance master) to generate a role-adapted instruction set; The instruction execution and exception handling module distributes the role-adapted instruction set to the corresponding Internet of Things devices for execution, and monitors the execution status in real time. When it detects that the instruction execution fails or the device status is abnormal, it triggers an exception handling mechanism, including device control exceptions, maintenance work order triggering, and permission overstep operation interception.

[0006] Optionally, the data collection and alignment module includes: Multi-source data collection: Real-time collection of multi-source data through the API interface and sensor interface of Internet of Things devices. Among them, device status data is obtained through the three-axis acceleration sensor, temperature and humidity sensor, and light sensor built in the light box, environmental parameters are obtained through independently deployed light sensors and infrared people counters, and user operation instructions are captured through the interaction interface of the client application; Standardization processing: Perform standardization processing on the collected multi-source data; Spatio-temporal alignment: Perform spatio-temporal alignment on the standardized multi-source data based on the spatio-temporal feature alignment algorithm; Data set output: Store the spatio-temporally aligned multi-source data as a standardized Internet of Things data set, and its data structure includes device ID, timestamp, spatial coordinates, data fields, and quality control identifiers.

[0007] Optionally, the standardization processing includes: Data format conversion: Unify the collected multi-source data into JSON format; Unit standardization: Unify the temperature unit to Celsius, the brightness unit to lumens, the tilt angle to degrees, and the pedestrian flow density to unit area / minute; Outlier filtering: Eliminate multi-source data that exceeds the preset physical range.

[0008] Optionally, the spatio-temporal alignment includes: Timestamp alignment: Synchronize all device clocks to the millisecond level using the Network Time Protocol (NTP); Spatial coordinate mapping: Convert the device location to a unified geographic coordinate system (WGS84) through geofencing technology; Time series data integration: Generate an indexed time series data table structure according to device ID, timestamp, and spatial coordinates.

[0009] Optionally, the policy generation and permission module includes: Policy engine: Receives the standardized IoT data set, loads the preset business rule library, and matches based on real-time multi-source data and business rules to generate a dynamic control policy instruction set, including device operation mode switching instructions, brightness and color temperature adjustment instructions, warning threshold correction instructions, and maintenance work order generation instructions; Permission mapping: Configures a multi-level permission mapping model, defines the correspondence between user roles and instruction operation permissions, and filters the dynamic control policy instruction set by permissions to generate a role-adapted instruction set; Instruction set generation: Classifies and stores the role-adapted instruction set according to user roles.

[0010] Optionally, the business rule library includes: Ambient light-brightness mapping table: Defines the correspondence between the brightness and color temperature of the light box under different light intensities; People flow density-device mode association matrix: Dynamically switches the device operation mode (energy-saving mode / full power mode) according to the real-time people flow density; Warning threshold priority list: Sets the warning thresholds and priorities of multi-source data according to device types; Work order trigger condition rules: Include device offline timeout (exceeding 15 minutes), continuous anomalies in multi-source data (such as temperature > 50 °C for 5 minutes), and manual fault reporting instructions.

[0011] Optionally, the correspondence between the user role and the instruction operation permission includes: Administrator: Has all permissions, including policy rule modification, device parameter configuration, and work order priority adjustment; Store user: Has limited permissions, including device on / off, mode switching, and brightness fine-tuning; Maintenance technician: Has bound permissions, including work order reception, device reset, and parameter calibration.

[0012] Optionally, the instruction execution and exception handling module includes: Instruction distribution: Pushes the role-adapted instruction set to the corresponding device through the IoT communication protocol (MQTT / CoAP), establishes an instruction execution queue, and attaches a unique device identifier and an execution priority label to each instruction; Status monitoring: Receives the instruction confirmation signal and the real-time multi-source data feedback from the device in real time, and determines the instruction execution status through the timeout detection mechanism; Exception handling: When device control exceptions, continuous failures, or unauthorized operations occur, automatically performs a safe rollback, generates an exception report or a maintenance work order, and implements permission interception.

[0013] Optionally, the exception handling includes: Device control exception handling: Automatically roll back to the preset safe mode, including turning off high-power devices, switching to the basic brightness, and generating a device exception report, including a fault code, an exception timestamp, and a snapshot of device parameters, and pushing the report to the administrator terminal through the APP interface; Maintenance work order triggering: When continuous exceptions are detected (such as the temperature exceeding the standard for 3 consecutive monitoring cycles) or the device is offline for an excessive time (>30 minutes), a maintenance work order is generated. The maintenance work order is automatically associated with the device location information (WGS84 coordinates), abnormal data segments, and historical maintenance records, and is assigned to the maintenance technician's terminal through the work order system, and the device control permission is locked; Interception of unauthorized operation: Verify the matching of the user role and the instruction permission label before the instruction is executed, intercept unauthorized instructions (such as a store user attempting to modify the warning threshold) in real time, and record the violation operation log, including the operator ID, the instruction content, and the interception time.

[0014] Advantages of the present invention: In the present invention, by constructing a unified data collection and alignment module, multi-source information such as device status, environmental parameters, and user operations is integrated, and through standardized processing and spatio-temporal alignment, the consistency of the data structure and the spatio-temporal index is achieved, greatly improving the accuracy and timeliness of the IoT perception layer. The policy engine generates differentiated control policies based on dynamic rule matching, can adapt to environmental changes and human flow dynamics in complex commercial scenarios in real time, and realizes the intelligent linkage of device operation modes and lighting parameters, significantly enhancing the intelligence and adaptive ability of system response.

[0015] In the present invention, control instructions are accurately sent to different user roles through the permission mapping mechanism, and combined with device status feedback and result consistency verification, the execution accuracy of the instructions is ensured. When device exceptions, instruction failures, or over-limit states occur, the system can automatically trigger safety rollback operations and warning prompts, and at the same time generate maintenance work orders based on multi-condition trigger rules, and link maintenance personnel to achieve closed-loop fault handling, effectively reducing potential safety hazards caused by device out-of-control and enhancing the reliability and stability of system operation.

[0016] In the present invention, through the permission constraints of roles, real-time interception and logging of unauthorized operations can be achieved, improving the operation security of the system. Through systematic work order generation, automatic association of device location and historical maintenance data, automatic dispatching and closed-loop tracking of device fault handling are realized, greatly improving the device operation and maintenance efficiency, reducing the manual management cost, and providing an efficient, intelligent, and auditable IoT control management solution for multi-terminal and multi-role collaboration scenarios. Description of the Drawings

[0017] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 Schematic diagram of the system function modules of the embodiments of the present invention; Figure 2 Schematic diagram of the policy generation and permission module of the embodiments of the present invention. Detailed implementation manners

[0019] The following will describe the present invention in detail in conjunction with the drawings and specific embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; moreover, the drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0020] As Figure 1 - Figure 2 shown, a smart IoT control and management system based on commercial operation includes a data acquisition and alignment module, a policy generation and permission module, and an instruction execution and exception handling module, wherein; The data acquisition and alignment module includes: 1. Multi-source data acquisition: Real-time acquisition of multi-source data related to device operation, environmental status, and user operations through the API interfaces and sensor interfaces of IoT devices, specifically including: (1) Device status data: Collected by sensors installed inside the light box, including tilt angle (collected by a three-axis acceleration sensor), temperature, humidity (collected by a temperature and humidity sensor), and brightness value (collected by a photosensitive sensor); (2) Environmental parameters: Collected by independent sensors deployed at the boundaries of the monitoring scene, including light intensity (obtained by a light sensor) and pedestrian flow density (obtained by an infrared human detection counter); (3) User operation instructions: Captured through the human-computer interaction interface of the client application (App / Web), such as manual control commands, scene switching requests, etc.; 2. Standardization processing: Uniformly standardize the collected multi-source data, specifically including: (1) Data format conversion: Uniformly convert multi-source data uploaded by different IoT devices (such as XML, CSV, raw serial port byte stream) into a JSON structure; (2) Unit standardization: Uniformly convert the temperature to , uniformly convert the brightness to lumens, represent the tilt angle in degrees, and represent the pedestrian flow density in units of area / minute; (3)Outlier filtering: Physical range threshold filtering logic is adopted, expressed as: ; Where, is the filtered valid value, is the original sensor reading, and are the physical upper and lower limits of this parameter respectively; 3. Spatiotemporal alignment: Achieve the consistency of time-series data and the unification of spatial references between different devices, specifically including: (1)Timestamp alignment: All devices use the Network Time Protocol (NTP) for time synchronization, and the error is controlled at the millisecond level. The time synchronization error is expressed as: ; Where, is the device local clock time, is the standard time source time, is the synchronization error, and the target is ; (2)Spatial coordinate mapping: Map the original position of the device (such as local coordinates, relative reference point) to the global coordinate system (WGS84) through geofencing. If the original coordinate is relative to the local coordinate , the conversion formula is expressed as: ; Where, and are the latitude and longitude (WGS84) after mapping respectively, and are the coordinates of the reference origin respectively, and are the relative coordinates, is the radius of the earth; (3)Time-series data integration: All aligned multi-source data are integrated into a table structure with time and space indexes according to the following indexing rules, expressed as: ; Where, is the unique device identifier, is the aligned timestamp, is the standardized spatial coordinate (WGS84), is the set of observed value fields (such as temperature, humidity, etc.), is the data quality identifier (such as whether it is missing, whether it is interpolated), is the total number of standardized multi-source data records that have been collected and processed; 4. Dataset output: The aligned multi-source data is stored in the form of a structured dataset to form a standardized IoT dataset.

[0021] The policy generation and authority module includes: 1. Policy engine: Receives standardized IoT data sets and loads the preset business rule library, which includes: (1) Ambient light-brightness mapping table: defines different light intensities Lower target brightness With color temperature The corresponding relationship is expressed as: ; For example: ; ; (2) Crowd density-device mode association matrix: Assume that the crowd density is , the mode switching rule is expressed as: ; in, The device operation mode. is the mode switching threshold (5 people ); (3) Warning threshold priority list: define device type The warning thresholds and processing priorities for different multi-source data are expressed as: ; in, For the The warning thresholds of the monitoring indicators are set as follows (temperature is set to 50, tilt angle is set to 15, humidity is set to 90, brightness is set to 100, light intensity is set to 1000, and crowd density is set to 20). The corresponding priority weights are: the smaller the value, the higher the priority (temperature is set to 1, tilt angle is set to 2, humidity is set to 3, brightness is set to 4, light intensity is set to 5, and crowd density is set to 6). The total number of monitoring parameters involved in early warning judgment;

[0022] (4) Work order triggering condition rule: Set a certain indicator in the standardized IoT data set Continuously exceeding the threshold Duration That is, the alarm condition is triggered, expressed as: ; in, is the time threshold for continuous exceeding of the limit; Specific warning triggering example conditions are as follows: temperature: And duration Minutes; Tilt angle: And duration Minutes; Humidity: And duration Minutes; Brightness: And duration Minutes; Light intensity: And duration Minutes; Pedestrian flow density: And duration Minutes; Device offline: No signal reported for Minutes; User reporting: The user actively submits a reporting instruction, which is immediately triggered; (5)Policy instruction set generation: By matching the trigger condition rules with the real-time data, a dynamic control policy instruction set is generated, including device operation mode switching instructions (such as switching the air conditioner to the energy-saving mode), brightness and color temperature adjustment instructions (such as adjusting to 600 lm / 4000 K), warning threshold correction instructions (such as adjusting the tilt alarm threshold according to the trend model), and maintenance work order generation instructions (issuing tasks when the work order trigger conditions are met); 2. Permission mapping: The permission mapping unit is used to configure a multi-level permission model and implement role adaptation of the instruction set, specifically including: (1)Permission role definition: Administrator: Has all permissions, including policy rule modification, device parameter configuration, and work order priority adjustment; Store user: Has limited permissions, such as device on / off, mode switching, and brightness fine-tuning; Maintenance technician: Has bound permissions, such as work order reception, device reset, and parameter calibration; (2)Permission filtering function definition: Let the policy instruction be set , the role permission be set , and the role-adapted instruction set be , that is, filter the instruction set according to the role permission set to retain the instructions that have permission to execute; 3. Instruction set generation: Classify, store, and encapsulate the role-adapted instruction set according to the user role.

[0023] The instruction execution and exception handling module includes: 1. Instruction distribution: Distribute the role-adapted instruction set to the corresponding device side, specifically including: Communication protocol support: Adopt Internet of Things communication protocols such as MQTT or CoAP to achieve low-latency, two-way communication, and push the instruction set to the corresponding device; Instruction execution queue management: Establish independent instruction execution queues for different devices, supporting instruction concurrency control and queuing priority scheduling; Meta-information attachment mechanism: Attach the following control meta-information to each instruction, including the device unique identifier and execution priority label; 2. Status monitoring: Real-time monitor the instruction execution status and device response behavior, specifically including: (1) Instruction confirmation feedback mechanism: After receiving the instruction, the device returns a confirmation signal, and the system receives and parses it; (2) Timeout detection logic: If the device does not return a confirmation within the set time seconds, it is determined that the instruction execution fails. The judgment formula is: ; Among them, is the instruction issuance timestamp, that is, the time point when the instruction is sent to the device, is the instruction confirmation timestamp, that is, the time point when the device returns a confirmation response, is the maximum allowable confirmation delay time threshold for the instruction. If it exceeds this value, it is determined as a failure, is the instruction execution failure flag bit, 1 indicates failure, and 0 indicates normal; (3) Result consistency verification mechanism: Real-time collect the sensor feedback data of the device, compare it with the target control value. If the error exceeds the tolerance range, it is determined that the device status is abnormal, expressed as: ; Among them, is the status abnormality flag bit, 1 indicates abnormality, and 0 indicates normal, is the sensor feedback value, is the instruction target value, is the allowable error threshold (temperature is set to 2, tilt angle is set to 1, humidity is set to 5%, brightness is set to 10%, light intensity is set to 50, and population density is set to 2); 3. Exception handling: (1) Device control exception handling: Rollback mechanism: When control failure or status abnormality occurs, the device automatically switches to the preset safe mode, such as turning off high-power components and setting low brightness limits; Exception report generation: The system automatically records the device exception information and generates a device exception report, including the fault code, exception occurrence timestamp, and current device parameter snapshot (such as temperature and humidity, brightness, status code); Notification Push Mechanism: Automatically push the exception report to the administrator terminal through the APP interface; (2) Maintenance Work Order Trigger Mechanism: 1) Trigger Conditions: Generate a work order automatically when any of the following conditions are met: The same exception occurs continuously for ≥ 3 monitoring cycles (e.g., the temperature exceeds the standard 3 times); The device is offline for more than 30 minutes; 2) Composition of Work Order Content: Problem device identification and geographical location information (WGS84 coordinates); Exception data segments and trend charts; Historical maintenance and fault records; 3) Distribution and Permission Control: The work order is automatically assigned to the maintenance technician's terminal through the work order system. At the same time, permission locking is imposed on the target device to prevent unauthorized control operations; (3) Permission Over - authorization Operation Interception Mechanism: 1) Pre - execution Permission Verification: Before the instruction is executed, the system verifies whether the operator's role permissions cover the current instruction permission tags; 2) Interception Processing Flow: Intercept unauthorized instructions (e.g., store users attempt to modify the warning threshold); Record the illegal operation log, including the operator ID, the content of the attempted instruction, and the interception time.

[0024] The present invention covers any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of the present invention. To enable the public to have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention. However, those skilled in the art can fully understand the present invention without these detailed descriptions. Additionally, well - known methods, processes, procedures, components, and circuits are not described in detail to avoid unnecessary confusion to the essence of the present invention.

[0025] The above - mentioned are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A smart Internet of Things control and management system based on commercial operation, characterized in that, It includes a data collection and alignment module, a policy generation and permission module, and an instruction execution and exception handling module, where; The data collection and alignment module collects multi-source data related to IoT control in real time through the IoT device interface, including device status data, environmental parameters, and user operation instructions. Among them, the device status data includes the tilt angle, temperature, humidity, and brightness value of the light box, and the environmental parameters include light intensity and pedestrian flow density. By performing standardized processing on the collected multi-source data and based on the spatio-temporal feature alignment algorithm, the time-series data of different devices are mapped according to the unified timestamp and spatial coordinates to generate a standardized IoT dataset; The policy generation and permission module generates dynamic control policies according to the standardized IoT dataset, in combination with the preset business rule library, through the policy engine, including device operation mode switching, brightness and color temperature adjustment, warning threshold correction, and maintenance work order trigger conditions. At the same time, based on the multi-level permission mapping model, the dynamic control policy instructions are graded according to the user role to generate a role-adapted instruction set; The instruction execution and exception handling module issues the role-adapted instruction set to the corresponding IoT device for execution and monitors the execution status in real time. When it detects that the instruction execution fails or the device status is abnormal, it triggers an exception handling mechanism, including device control exception, maintenance work order trigger, and permission overstep operation interception.

2. The intelligent IoT control and management system based on commercial operation according to claim 1, wherein The data collection and alignment module includes: Multi-source data collection: Collect multi-source data in real time through the API interface and sensor interface of the IoT device. Among them, the device status data is obtained through the three-axis acceleration sensor, temperature and humidity sensor, and light sensor built in the light box, the environmental parameters are obtained through the independently deployed light sensor and infrared pedestrian counter, and the user operation instructions are captured through the interaction interface of the client application; Standardized processing: Perform standardized processing on the collected multi-source data; Spatio-temporal alignment: Perform spatio-temporal alignment on the multi-source data after standardized processing based on the spatio-temporal feature alignment algorithm; Dataset output: Store the multi-source data after spatio-temporal alignment as a standardized IoT dataset, and its data structure includes device ID, timestamp, spatial coordinates, data fields, and quality control identifiers.

3. The intelligent IoT control and management system based on commercial operation according to claim 2, wherein The standardized processing includes: Data format conversion: Unify the collected multi-source data into JSON format; Unit standardization: Unify the temperature unit to Celsius, the brightness unit to lumen, the tilt angle to degree, and the pedestrian flow density to unit area / minute; Outlier filtering: Eliminate the multi-source data that exceeds the preset physical range.

4. An intelligent Internet of Things control and management system based on commercial operation according to claim 3, characterized in that, The spatio-temporal alignment includes: Timestamp alignment: Synchronize the clocks of all devices at the millisecond level using the Network Time Protocol; Spatial coordinate mapping: Convert the device location to a unified geographic coordinate system through the geofencing technology; Time-series data integration: Generate an indexed time-series data table structure according to the device ID, timestamp, and spatial coordinates.

5. A smart IoT control and management system based on commercial operation according to claim 1, characterized in that, The policy generation and permission module includes: Policy Engine: Receives the standardized IoT data set, loads the preset business rule library, and matches based on real-time multi-source data and business rules to generate a dynamic control policy instruction set, including device operation mode switching instructions, brightness and color temperature adjustment instructions, warning threshold correction instructions, and maintenance work order generation instructions; Permission Mapping: Configures a multi-level permission mapping model, defines the correspondence between user roles and instruction operation permissions, and filters the dynamic control policy instruction set by permissions to generate a role-adapted instruction set; Instruction Set Generation: Classifies and stores the role-adapted instruction set according to user roles.

6. The intelligent IoT control and management system based on commercial operation according to claim 5, characterized in that, The business rule library includes: Ambient Light - Brightness Mapping Table: Defines the correspondence between the brightness and color temperature of the light box under different light intensities; People Flow Density - Equipment Mode Association Matrix: Dynamically switches the device operation mode according to the real-time people flow density; Warning Threshold Priority List: Sets the warning thresholds and priorities of multi-source data according to device types; Work Order Trigger Condition Rules: Include device offline timeout, continuous anomalies in multi-source data, and manual fault reporting instructions.

7. An intelligent Internet of Things control and management system based on commercial operation according to claim 6, characterized in that, The correspondence between the user role and the instruction operation permission includes: Administrator: Has all permissions, including policy rule modification, device parameter configuration, and work order priority adjustment; Store User: Has limited permissions, including device on / off, mode switching, and brightness fine-tuning; Maintenance Technician: Has bound permissions, including work order reception, device reset, and parameter calibration.

8. A smart IoT control and management system based on commercial operation according to claim 1, characterized in that, The instruction execution and exception handling module includes: Instruction Issuance: Pushes the role-adapted instruction set to the corresponding device through the IoT communication protocol, establishes an instruction execution queue, and attaches a unique device identifier and an execution priority label to each instruction; Status Monitoring: Receives the instruction confirmation signal returned by the device and the real-time feedback multi-source data in real time, and determines the instruction execution status through the timeout detection mechanism; Exception Handling: When device control anomalies, continuous failures, or unauthorized operations occur, automatically executes a safe rollback, generates an exception report or a maintenance work order, and implements permission interception.

9. A smart IoT control and management system based on commercial operation according to claim 8, characterized in that, The exception handling includes: Device Control Anomaly Handling: Automatically rolls back to the preset safe mode, including turning off high-power devices, switching to the basic brightness, and generating a device exception report, including a fault code, an exception timestamp, and a device parameter snapshot, and pushes the report to the administrator terminal through the APP interface; Maintenance Work Order Trigger: When continuous anomalies or device offline timeout are detected, generates a maintenance work order, which is automatically associated with the device location information, abnormal data fragments, and historical maintenance records, and is assigned to the maintenance technician terminal through the work order system, and locks the device control permission; Permission Unauthorized Operation Interception: Verifies the matching of the user role and the instruction permission label before the instruction is executed, intercepts unauthorized instructions in real time, and records the violation operation log, including the operator ID, the instruction content, and the interception time.

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