A smart IoT control and management system based on commercial operations

Abstract

The present invention relates to the field of IoT control and management technology, and specifically to a smart IoT control and management system based on commercial operations, comprising a data acquisition and alignment module, a policy generation and permissions module, and an instruction execution and exception handling module. The data acquisition and alignment module is used to collect device status, environmental parameters, and user instructions, and performs standardization processing and spatiotemporal alignment to generate a unified data set. The policy generation and permissions module generates control policies based on business rules, divides permissions by user role, and generates role-based instruction sets. The instruction execution and exception handling module sends instructions to devices, monitors execution status, and triggers rollback, work order generation, and permission verification mechanisms in the event of an anomaly. This invention achieves high-precision perception, intelligent control, and safe and efficient IoT device management.

Description

Technical Field

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

[0002] With the development of smart business, intelligent lighting and IoT technologies, IoT-based control systems are being introduced into an increasing number of commercial scenarios. By deploying various sensors and control terminals, real-time perception and response control of equipment operating status, environmental parameters and user behavior are achieved. Such systems are usually used in shopping malls, chain stores, advertising light boxes and other scenarios to achieve energy-saving control, security monitoring, remote management and data-driven operation optimization. In order to improve operational 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 IoT control systems generally have the following problems: First, multi-source data is highly heterogeneous and lacks a unified standardized processing and spatiotemporal 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 environmental and human flow changes, resulting in slow response and poor flexibility. Third, the device abnormality response mechanism is imperfect, and there is a lack of closed-loop processing for command failures or abnormal status, posing a security risk. In addition, the authority management is relatively rough, lacking effective control and auditing of the operating behaviors of multi-role users, making it difficult to meet the needs of large-scale deployment and multi-role collaboration. Summary of the Invention

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

[0005] A smart IoT control and management system based on commercial operations, comprising a data acquisition and alignment module, a policy generation and authority module, and an instruction execution and exception handling module, wherein;

[0006] The data acquisition 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. 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 crowd density. By standardizing the collected multi-source data and mapping the time series data of different devices to a unified timestamp and spatial coordinates based on the spatiotemporal feature alignment algorithm, a standardized IoT data set is generated.

[0007] The policy generation and permission module generates dynamic control policies through a policy engine based on standardized IoT data sets and a preset business rule library. These policies include device operation mode switching, brightness and color temperature adjustment, warning threshold correction, and maintenance work order triggering conditions. Furthermore, based on a multi-level permission mapping model, dynamic control policy instructions are classified by user role (administrator, store user, maintenance technician), generating a role-adapted instruction set.

[0008] The instruction execution and exception handling module sends the role adaptation 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 the exception handling mechanism, including device control abnormality, maintenance work order triggering, and unauthorized operation interception.

[0009] Optionally, the data acquisition and alignment module includes:

[0010] Multi-source data acquisition: Real-time multi-source data is collected through the API and sensor interfaces of IoT devices. Device status data is acquired through the light box's built-in three-axis accelerometer, temperature and humidity sensor, and light sensor. Environmental parameters are acquired through independently deployed light sensors and infrared crowd counters. User operation commands are captured through the client application's interactive interface.

[0011] Standardization processing: perform standardization processing on the collected multi-source data;

[0012] Spatiotemporal alignment: Spatiotemporal alignment of standardized multi-source data based on spatiotemporal feature alignment algorithm;

[0013] Dataset output: The multi-source data after spatiotemporal alignment is stored as a standardized IoT dataset, whose data structure includes device ID, timestamp, spatial coordinates, data fields, and quality control identifiers.

[0014] Optionally, the standardization process includes:

[0015] Data format conversion: unify the collected multi-source data into JSON format;

[0016] Unit standardization: standardize temperature units to degrees Celsius, brightness units to lumens, tilt angles to degrees, and crowd density to unit area / minute;

[0017] Outlier filtering: Eliminate multi-source data that exceeds the preset physical range.

[0018] Optionally, the spatiotemporal alignment includes:

[0019] Timestamp alignment: Network Time Protocol (NTP) is used to synchronize all device clocks to the millisecond level;

[0020] Spatial coordinate mapping: converting device location into a unified geographic coordinate system (WGS84) through geo-fencing technology;

[0021] Time series data integration: Generate an indexed time series data table structure based on device ID, timestamp, and spatial coordinates.

[0022] Optionally, the policy generation and authority module includes:

[0023] Strategy Engine: Receives standardized IoT data sets, loads a preset business rule library, and generates a dynamic control strategy instruction set based on real-time multi-source data matching business rules, including instructions for switching equipment operating modes, adjusting brightness and color temperature, modifying warning thresholds, and generating maintenance work orders.

[0024] Permission mapping: Configure a multi-level permission mapping model, define the correspondence between user roles and instruction operation permissions, and perform permission filtering on the dynamic control strategy instruction set to generate a role-adapted instruction set;

[0025] Instruction set generation: The role adaptation instruction set is stored according to the user role classification.

[0026] Optionally, the business rule library includes:

[0027] Ambient light-brightness mapping table: defines the correspondence between light box brightness and color temperature under different light intensities;

[0028] Crowd density-device mode association matrix: Dynamically switches equipment operation modes (energy-saving mode / full power mode) based on real-time crowd density;

[0029] Warning threshold priority list: Set warning thresholds and priorities for multi-source data by device type;

[0030] Work order triggering rules include: equipment offline timeout (more than 15 minutes), continuous abnormalities in multi-source data (such as temperature > 50°C for 5 minutes), and manual fault reporting instructions.

[0031] Optionally, the correspondence between the user role and the instruction operation authority includes:

[0032] Administrator: Has full permissions, including policy rule modification, device parameter configuration, and work order priority adjustment;

[0033] Store users: have limited permissions, including device power on / off, mode switching, and brightness adjustment;

[0034] Maintenance technician: has binding permissions, including receiving work orders, resetting equipment, and parameter calibration.

[0035] Optionally, the instruction execution and exception handling module includes:

[0036] Instruction issuance: The role adaptation instruction set is pushed to the corresponding device through the IoT communication protocol (MQTT / CoAP), and an instruction execution queue is established. A unique device identifier and execution priority tag are attached to each instruction.

[0037] Status monitoring: Receives command confirmation signals and multi-source data from devices in real time, and determines the command execution status through a timeout detection mechanism.

[0038] Exception handling: When equipment control exceptions, continuous failures, or unauthorized operations occur, security rollbacks are automatically performed, exception reports or maintenance work orders are generated, and permission interception is implemented.

[0039] Optionally, the exception handling includes:

[0040] Device control exception handling: Automatically roll back to the preset safety mode, including shutting down high-power devices and switching to basic brightness, and generating a device exception report including fault code, exception timestamp and device parameter snapshot, and push the report to the administrator terminal through the APP interface;

[0041] Maintenance work order triggering: When continuous anomalies are detected (such as temperature exceeding the standard for three consecutive monitoring cycles) or the device is offline for a timeout (>30 minutes), a maintenance work order is generated. The maintenance work order is automatically associated with the device positioning information (WGS84 coordinates), abnormal data fragments, and historical maintenance records. The work order is then assigned to the maintenance technician's terminal through the work order system and the device control authority is locked.

[0042] Intercepting unauthorized operations: Verify the matching between the user role and the instruction permission label before the instruction is executed, intercept unauthorized instructions (such as store users attempting to modify the warning threshold) in real time, and record the illegal operation log, including operator ID, instruction content and interception time.

[0043] Beneficial effects of the present invention:

[0044] This invention builds a unified data acquisition and alignment module, integrates multi-source information such as device status, environmental parameters and user operations, and achieves consistency between data structure and spatial-temporal index through standardized processing and spatiotemporal alignment, thereby greatly improving the accuracy and timeliness of the IoT perception layer. The strategy engine generates differentiated control strategies based on dynamic rule matching, which can adapt to environmental changes and pedestrian flow dynamics in complex commercial scenarios in real time, realize intelligent linkage between device operation mode and lighting parameters, and significantly improve the intelligence and adaptability of system response.

[0045] The present invention accurately sends control instructions to different user roles through the permission mapping mechanism, and combines device status feedback with result consistency verification to ensure the accuracy of instruction execution. When equipment abnormalities, instruction failures or over-limit states occur, the system can automatically trigger safe rollback operations and early warning prompts. At the same time, it combines multi-condition trigger rules to generate maintenance work orders, and links maintenance personnel to achieve closed-loop fault processing, effectively reducing safety hazards caused by equipment out of control and enhancing the reliability and stability of system operation.

[0046] The present invention, through the permission constraints of roles, can achieve real-time interception and log recording of unauthorized operations, improve the security of system operation, and realize automatic dispatching and closed-loop tracking of equipment fault processing through systematic work order generation, equipment positioning and automatic association of historical maintenance data, greatly improving equipment operation and maintenance efficiency, reducing manual management costs, and providing an efficient, intelligent and auditable Internet of Things control and management solution for multi-terminal and multi-role collaborative scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 Schematic diagram of system function modules according to an embodiment of the present invention;

[0049] Figure 2 Schematic diagram of the policy generation and permission module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. Those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0051] like Figure 1-Figure 2 As shown, a smart IoT control and management system based on commercial operations includes a data acquisition and alignment module, a policy generation and permission module, and an instruction execution and exception handling module, wherein;

[0052] The data acquisition and alignment module includes:

[0053] 1. Multi-source data collection: Real-time collection of multi-source data related to device operation, environmental status, and user operations through the API interface and sensor interface of the IoT device, including:

[0054] (1) Equipment 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 photosensor);

[0055] (2) Environmental parameters: collected by independent sensors deployed at the edge of the monitoring scene, including light intensity (obtained by light sensors) and crowd density (obtained by infrared human detection counters);

[0056] (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.

[0057] 2. Standardization processing: Unify and standardize the collected multi-source data, including:

[0058] (1) Data format conversion: Convert multi-source data (such as XML, CSV, and raw serial port byte streams) uploaded by different IoT devices into a unified JSON structure;

[0059] (2) Unit standardization: convert temperature uniformly into , brightness is uniformly converted to lumens, tilt angle is expressed in degrees, and crowd density is expressed in unit area / minute;

[0060] (3) Outlier filtering: Use the physical range threshold filtering logic, expressed as:

[0061] ;

[0062] in, is the effective value after screening, is the raw sensor reading, 、 are the physical upper and lower limits of the parameter respectively;

[0063] 3. Spatiotemporal alignment: Achieve consistency in time series data and unified spatial references across different devices, specifically including:

[0064] (1) Timestamp alignment: All devices use the Network Time Protocol (NTP) for time synchronization, with the error controlled at the millisecond level. The time synchronization error is expressed as:

[0065] ;

[0066] in, is the local clock time of the device, is the standard time source time, is the synchronization error, the target is ;

[0067] (2) Spatial coordinate mapping: The original position of the device (such as local coordinates, relative reference point) is mapped to the global coordinate system (WGS84) through the geofence conversion. If the original coordinates are relative local coordinates , the conversion formula is expressed as:

[0068] ;

[0069] in, 、 are the mapped latitude and longitude (WGS84), 、 are the coordinates of the reference origin, 、 is the relative coordinate, is the radius of the Earth;

[0070] (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:

[0071] ;

[0072] in, Is the unique identifier of the device. is the timestamp after alignment, is the standardized space coordinate (WGS84), is a set of observation fields (such as temperature, humidity, etc.), is the data quality indicator (such as whether it is missing or interpolated), The total number of standardized multi-source data records that have been collected and processed;

[0073] 4. Dataset output: The aligned multi-source data is stored in the form of a structured dataset to form a standardized IoT dataset.

[0074] The policy generation and authority module includes:

[0075] 1. Policy Engine: Receives standardized IoT data sets and loads a pre-set business rule library, which includes:

[0076] (1) Ambient light-brightness mapping table: defines different light intensities Lower target brightness and color temperature The corresponding relationship is expressed as:

[0077] ;

[0078] For example: ;

[0079] ;

[0080] (2) Crowd density-device mode association matrix: Let the crowd density be , the mode switching rule is expressed as:

[0081] ;

[0082] in, The device operation mode. Mode switching threshold (5 people );

[0083] (3) Warning threshold priority list: define device type The warning thresholds and processing priorities for different multi-source data are expressed as:

[0084] ;

[0085] 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;

[0086] (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 triggering condition is expressed as:

[0087] ;

[0088] in, The time threshold for continuous exceeding of the limit;

[0089] Specific warning triggering example conditions are as follows:

[0090] temperature: And duration minute;

[0091] Tilt angle: And duration minute;

[0092] humidity: And duration minute;

[0093] brightness: And duration minute;

[0094] Light intensity: And duration minute;

[0095] Crowd density: And duration minute;

[0096] Device offline: No signal reporting continues minute;

[0097] User fault reporting: The user actively submits a fault reporting instruction, which is triggered immediately;

[0098] (5) Strategy instruction set generation: By matching trigger condition rules with real-time data, a dynamic control strategy instruction set is generated, including equipment operation mode switching instructions (such as switching the air conditioner to energy-saving mode), brightness and color temperature adjustment instructions (such as adjusting to 600lm / 4000K), 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);

[0099] 2. Permission mapping: The permission mapping unit is used to configure a multi-level permission model and implement role adaptation of the instruction set, including:

[0100] (1) Definition of authority role:

[0101] Administrator: Has full permissions, including policy rule modification, device parameter configuration, and work order priority adjustment;

[0102] Store users: have limited permissions, such as device power on / off, mode switching, and brightness adjustment;

[0103] Maintenance technician: has binding permissions, such as receiving work orders, resetting equipment, and calibrating parameters;

[0104] (2) Definition of permission filtering function: Let policy instructions be a set , role permissions are a collection , the role adaptation instruction set is , that is, according to the role permission set Filter the instruction set and retain the instructions that have permission to execute;

[0105] 3. Instruction set generation: The role-adapted instruction set is classified, stored, and packaged according to the user role.

[0106] The instruction execution and exception handling module includes:

[0107] 1. Command issuance: Send the role adaptation command set to the corresponding device, including:

[0108] Communication protocol support: Use IoT communication protocols such as MQTT or CoAP to achieve low-latency, two-way communication and push instruction sets to corresponding devices;

[0109] Instruction execution queue management: Establish independent instruction execution queues for different devices, support instruction concurrency control and queue priority scheduling;

[0110] Meta-information attachment mechanism: Attach the following control meta-information to each instruction, including the device unique identifier and execution priority tag;

[0111] 2. Status monitoring: Real-time monitoring of instruction execution status and device response behavior, including:

[0112] (1) Command confirmation feedback mechanism: After receiving the command, the device returns a confirmation signal, and the system receives and analyzes it;

[0113] (2) Timeout detection logic: If the device does not If a confirmation is returned within seconds, it is judged that the instruction execution failed. The judgment formula is:

[0114] ;

[0115] in, The timestamp of the command, that is, the time when the command is sent to the device. The command confirmation timestamp, that is, the time when the device returns the confirmation response. The maximum allowed delay time threshold for instruction confirmation. If it exceeds this value, it will be judged as a failure. Instruction execution failure flag, 1 indicates failure, 0 indicates normal;

[0116] (3) Result consistency verification mechanism: The feedback data from the equipment sensors is collected in real time and compared with the target control value. If the error exceeds the tolerance range, it is determined that the equipment status is abnormal, which is expressed as:

[0117] ;

[0118] in, It is the status abnormal flag, 1 indicates abnormality, 0 indicates normality, 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 crowd density is set to 2);

[0119] 3. Exception handling:

[0120] (1) Equipment control exception handling:

[0121] Rollback mechanism: When a control failure or abnormal status occurs, the device automatically switches to a preset safety mode, such as shutting down high-power components and setting a low brightness limit;

[0122] Abnormal report generation: The system automatically records equipment abnormal information and generates equipment abnormality reports, including fault code, abnormality occurrence timestamp, and current equipment parameter snapshots (such as temperature and humidity, brightness, and status code);

[0123] Notification push mechanism: Automatically push abnormality reports to the administrator terminal through the APP interface;

[0124] (2) Maintenance work order triggering mechanism:

[0125] 1) Trigger conditions: A work order is automatically generated when any of the following conditions are met:

[0126] The same abnormality occurs continuously for ≥3 monitoring cycles (e.g. temperature exceeds the standard 3 times);

[0127] The device is offline for more than 30 minutes;

[0128] 2) Work order content composition:

[0129] The problem device's identification and geolocation information (WGS84 coordinates);

[0130] Abnormal data snippets and trend graphs;

[0131] Historical maintenance and fault records;

[0132] 3) Distribution and permission control: Work orders are automatically distributed to maintenance technician terminals through the work order system, and permission is locked on target devices to prevent unauthorized control operations.

[0133] (3) Interception mechanism for unauthorized operations:

[0134] 1) Pre-execution permission verification: Before the instruction is executed, the system verifies whether the operator role permission covers the current instruction permission tag;

[0135] 2) Interception process:

[0136] Block unauthorized commands (such as store users attempting to modify warning thresholds);

[0137] Record illegal operation logs, including operator ID, attempted instruction content, and interception time.

[0138] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0139] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A smart IoT control and management system based on commercial operations, characterized by: It includes data collection and alignment module, policy generation and permission module, and instruction execution and exception handling module, among which; The data acquisition 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. 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 crowd density. By standardizing the collected multi-source data and mapping the time series data of different devices to a unified timestamp and spatial coordinates based on the spatiotemporal feature alignment algorithm, a standardized IoT data set is generated. The policy generation and permission module generates dynamic control policies through a policy engine based on standardized IoT data sets and a preset business rule library, including equipment 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, dynamic control policy instructions are classified according to user roles to generate role-adaptive instruction sets. The instruction execution and exception handling module sends the role adaptation 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 the exception handling mechanism, including device control abnormality, maintenance work order triggering, and unauthorized operation interception; The data acquisition and alignment module includes: Multi-source data acquisition: Real-time multi-source data is collected through the API and sensor interfaces of IoT devices. Device status data is acquired through the light box's built-in three-axis accelerometer, temperature and humidity sensor, and light sensor. Environmental parameters are acquired through independently deployed light sensors and infrared crowd counters. User operation commands are captured through the client application's interactive interface. Standardization processing: perform standardization processing on the collected multi-source data; Spatiotemporal alignment: Spatiotemporal alignment of standardized multi-source data based on spatiotemporal feature alignment algorithm; Dataset output: The multi-source data after spatiotemporal alignment is stored as a standardized IoT dataset, whose data structure includes device ID, timestamp, spatial coordinates, data fields, and quality control identifiers; The policy generation and authority module includes: Strategy Engine: Receives standardized IoT data sets, loads a preset business rule library, and generates a dynamic control strategy instruction set based on real-time multi-source data matching business rules, including instructions for switching equipment operating modes, adjusting brightness and color temperature, modifying warning thresholds, and generating maintenance work orders. Permission mapping: Configure a multi-level permission mapping model, define the correspondence between user roles and instruction operation permissions, and perform permission filtering on the dynamic control strategy instruction set to generate a role-adapted instruction set; Instruction set generation: the role adaptation instruction set is stored according to the user role classification; The instruction execution and exception handling module includes: Instruction issuance: Push the role adaptation instruction set to the corresponding device through the IoT communication protocol, establish an instruction execution queue, and attach a device unique identifier and execution priority tag to each instruction; Status monitoring: Receives command confirmation signals and multi-source data from devices in real time, and determines the command execution status through a timeout detection mechanism. Exception handling: When equipment control exceptions, continuous failures, or unauthorized operations occur, security rollbacks are automatically performed, exception reports or maintenance work orders are generated, and permission interception is implemented.

2. The intelligent IoT control and management system based on commercial operation according to claim 1 is characterized in that: The standardization process includes: Data format conversion: unify the collected multi-source data into JSON format; Unit standardization: standardize temperature units to degrees Celsius, brightness units to lumens, tilt angles to degrees, and crowd density to unit area / minute; Outlier filtering: Eliminate multi-source data that exceeds the preset physical range.

3. The intelligent IoT control and management system based on commercial operation according to claim 2 is characterized in that: The spatiotemporal alignment includes: Timestamp alignment: Use the Network Time Protocol to synchronize all device clocks at the millisecond level; Spatial coordinate mapping: converting device location into a unified geographic coordinate system through geo-fencing technology; Time series data integration: Generate an indexed time series data table structure based on device ID, timestamp, and spatial coordinates.

4. The intelligent IoT control and management system based on commercial operation according to claim 3 is characterized in that: The business rule library includes: Ambient light-brightness mapping table: defines the correspondence between light box brightness and color temperature under different light intensities; Crowd density-device mode association matrix: Dynamically switch equipment operation modes based on real-time crowd density; Warning threshold priority list: Set warning thresholds and priorities for multi-source data by device type; Work order triggering condition rules: including device offline timeout, continuous abnormalities in multi-source data, and manual fault reporting instructions.

5. The intelligent IoT control and management system based on commercial operation according to claim 4 is characterized in that: The correspondence between the user role and the instruction operation authority includes: Administrator: Has full permissions, including policy rule modification, device parameter configuration, and work order priority adjustment; Store users: have limited permissions, including device power on / off, mode switching, and brightness adjustment; Maintenance technician: has binding permissions, including receiving work orders, resetting equipment, and parameter calibration.

6. The intelligent IoT control and management system based on commercial operation according to claim 5, characterized in that: The exception handling includes: Device control exception handling: Automatically roll back to the preset safety mode, including shutting down high-power devices and switching to basic brightness, and generating a device exception report including fault code, exception timestamp and device parameter snapshot, and push the report to the administrator terminal through the APP interface; Maintenance work order triggering: When continuous anomalies are detected or the device is offline for timeout, a maintenance work order is generated. The maintenance work order automatically associates the device location information, abnormal data fragments, and historical maintenance records, and is assigned to the maintenance technician's terminal through the work order system, and the device control authority is locked; Intercepting unauthorized operations: Verify the matching between the user role and the instruction permission label before the instruction is executed, intercept unauthorized instructions in real time, and record the illegal operation log, including operator ID, instruction content and interception time.

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