Intelligent equipment linkage configuration system and method

Abstract

The invention relates to the technical field of equipment management, in particular to an intelligent equipment linkage configuration system and method, and the system comprises an equipment management module, a linkage rule configuration module, a rule engine module, an energy-saving strategy binding module and an authority safety module. The equipment management module uniformly registers and manages the Internet of Things equipment; the linkage rule configuration module provides a visual interface and analyzes a natural language condition generation rule in combination with a fuzzy logic algorithm; the rule engine module monitors equipment state triggering linkage in real time; the energy-saving strategy binding module uses a dynamic algorithm to optimize a power utilization threshold value; the authority security module realizes dynamic authorization through hierarchical authority and a two-dimensional code; the diagnosis self-healing module locates and repairs a fault based on a multi-modal algorithm; and the energy consumption analysis module adopts a normalized model to quantify the energy-saving effect. According to the invention, the problems of complex configuration and poor compatibility of a traditional system are solved, intelligent and secure linkage of equipment is realized through multi-module cooperation and algorithm innovation, and the reliability and resource utilization efficiency of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of device management, and specifically provides an intelligent device linkage configuration system and method. Background Art

[0002] With the rapid development of the Internet of Things technology, intelligent devices have been widely used in the fields of smart home, smart industry, intelligent building, etc. The linkage control between devices has become the key to realizing intelligent scenarios, but there are still many deficiencies in the existing technologies, making it difficult to meet complex application requirements.

[0003] In the field of smart home, users expect to achieve automated scenarios such as "automatically turning on the lights and adjusting the curtains when the light dims" through linkage rules. However, the traditional linkage configuration method relies on fixed programming logic and lacks a visual operation interface, making it difficult for ordinary users to configure complex linkage rules by themselves. At the same time, the communication protocols of devices from different manufacturers vary greatly, such as Zigbee, Bluetooth, Wi-Fi, etc., which are not compatible with each other, resulting in difficulties in device linkage and low system integration.

[0004] In intelligent industrial production, a large number of sensors, controllers, and actuators need to work together to ensure the efficient operation of the production line. The fault diagnosis of existing systems is mostly based on a single index threshold judgment, such as only judging faults by the over-high temperature of the device, and it is impossible to accurately identify the fault type and root cause by integrating multi-source data (such as vibration, current, etc.), which is prone to misjudgment or missed judgment. Moreover, there is no automatic repair ability after a fault occurs, relying on manual troubleshooting and handling, resulting in a long production interruption time and large economic losses.

[0005] In terms of energy management, with the increasing urgency of enterprises' energy conservation and emission reduction needs, the refined management of device energy consumption has become the key. Traditional energy-saving strategies often adopt fixed timing switches or unified energy consumption thresholds, and cannot dynamically adjust according to the actual operating state of the device and environmental changes. For example, during the operation of industrial equipment, the energy consumption of the device varies significantly at different times and under different working conditions, and it is difficult for a fixed threshold to achieve the best energy-saving effect, resulting in energy waste or unstable device operation.

[0006] Therefore, an intelligent device linkage configuration system and method are proposed to solve the above problems. Summary of the Invention

[0007] The purpose of the present invention is to provide an intelligent device linkage configuration system and method to solve the problems raised in the above background art.

[0008] To achieve the above purpose, the present invention provides the following technical solutions:

[0009] An intelligent device linkage configuration system includes:

[0010] The device management module is used to register and manage multiple Internet of Things devices. Each device includes at least a device unique identifier, a device type, a communication protocol type, and peripheral configuration information.

[0011] The linkage rule configuration module provides a visual operation interface and receives user-defined linkage rules. The linkage rules include a triggering device, a linked device, a triggering condition expression, an execution action instruction, and a delay time parameter.

[0012] The rule engine module monitors the device status data of the triggering device in real time. When the device status data meets the triggering condition expression, it issues an execution action instruction to the linked device according to the delay time parameter.

[0013] The energy-saving strategy binding module dynamically binds an energy-saving control strategy to the linkage rule. The energy-saving control strategy includes an electricity safety threshold, a timing task rule, and an energy consumption end condition.

[0014] The permission security module generates a permission QR code associated with the linkage rule. The permission QR code contains a device operation permission set and a validity period. The user who scans the permission QR code obtains the device operation permission within the authorized range.

[0015] As a preferred solution, the linkage rule configuration module includes:

[0016] The instruction cascading unit supports configuring multi-level sub-instructions in a single linkage rule. Each level of sub-instruction independently sets a delay time and a triggering condition.

[0017] The expression parsing unit uses an improved fuzzy logic parsing algorithm to convert the natural language conditions input by the user into an executable logical expression.

[0018] The improved fuzzy logic parsing algorithm calculates the condition satisfaction degree through an S-type function. The input value of the function is the difference between the real-time device status variable value and the user-defined threshold.

[0019] The slope of the function is dynamically adjusted by the product of the sensitivity coefficient and the reciprocal of the standard deviation of the historical state data.

[0020] The membership degree value with an output value range of [0,1] is used to quantify the condition satisfaction degree.

[0021] As a preferred solution, the energy-saving strategy binding module includes:

[0022] The electricity safety threshold generation unit automatically generates an optimal safety threshold using a dynamic threshold optimization algorithm.

[0023] The dynamic threshold optimization algorithm is based on the arithmetic mean of the historical energy consumption data as the base value.

[0024] Increase the product of the safety factor and the standard deviation of historical energy consumption data as the initial offset;

[0025] Introduce a time decay compensation factor to dynamically decay the offset, which is calculated by the natural logarithm function of the number of days the device has been running;

[0026] The timing task configuration unit is used to set the time range and action instructions for periodic execution;

[0027] The energy consumption termination unit automatically terminates the associated linkage rules when the real-time energy consumption data meets the preset conditions.

[0028] As a preferred solution, the permission security module includes:

[0029] The permission grading unit divides the device operation permissions into menu-level permissions, interface-level permissions, and instruction-level permissions;

[0030] The dynamic authorization unit dynamically activates the operation permissions according to the user identity scanning the permission QR code;

[0031] The aging control unit automatically recovers the authorized permissions after the expiration of the validity period.

[0032] As a preferred solution, it also includes a diagnosis and self-healing module. When the linkage rule execution fails:

[0033] Analyze the cause of the failure based on the device historical data packets and adopt a multi-modal fault diagnosis algorithm:

[0034] Calculate the absolute deviation of the current value of each data feature from the historical normal mean, divide it by the historical standard deviation, and then multiply it by the feature weight coefficient;

[0035] Accumulate the weighted deviation values of all features;

[0036] Superimpose the product of the device status sequence information entropy and the entropy value influence coefficient;

[0037] Output the comprehensive fault probability score;

[0038] Call the preset repair strategy library to generate a solution;

[0039] Automatically resend the control instruction or notify the designated user.

[0040] As a preferred solution, the rule engine module issues control instructions through the protocol adaptation layer:

[0041] When the device communication protocol is the serial port protocol, encapsulate the control instruction into a Modbus-RTU format data packet and append the CRC16 check code;

[0042] When the device communication protocol is TCP / UDP protocol, convert the control instruction into a hexadecimal string and transmit it through the Socket channel.

[0043] As an optimized solution, it further includes an energy consumption analysis module to count the energy-saving data after the execution of the linkage rules:

[0044] Use a normalized energy-saving evaluation model to calculate the energy-saving efficiency:

[0045] Obtain the basic efficiency value by dividing the actual saved power by the baseline energy consumption before the execution of the linkage rules;

[0046] Introduce an environmental temperature compensation factor, which is the reciprocal of 1 plus the absolute value of the deviation between the environmental temperature and the reference temperature divided by the reference temperature;

[0047] Multiply the basic efficiency value by the temperature compensation factor to obtain the final energy-saving efficiency;

[0048] Generate a visualization report, including the saved power, energy-saving efficiency, and a heat map of abnormal device alarms.

[0049] As an optimized solution, the linkage rule configuration module supports drag-and-drop operations:

[0050] Construct a device linkage topology diagram by dragging device icons;

[0051] Generate a multi-level execution action chain by dragging instruction icons.

[0052] An intelligent device linkage configuration method, which configures the linkage of intelligent devices through an intelligent device linkage configuration system.

[0053] It can be seen from the technical solutions provided by the present invention above that for an intelligent device linkage configuration system and method provided by the present invention, the beneficial effects are:

[0054] Highly intelligent and flexible configuration: The linkage rule configuration module supports visual drag-and-drop operations and natural language condition parsing. Users can quickly define complex linkage logics without professional programming knowledge; through instruction cascading and fuzzy logic algorithms, the system can adapt to diverse scenario requirements, such as the automatic scene switching in smart homes and the collaborative control of devices in industrial production, greatly improving the intelligence level and usability flexibility of the system;

[0055] Enhanced security guarantee and permission management: The permission security module adopts a hierarchical permission control and dynamic authorization mechanism, refines the device operation permissions to the menu level, interface level, and instruction level, and combines permission QR codes with time limit control to ensure that only authorized users can perform legal operations within the specified time; this effectively prevents illegal access and unauthorized operations, safeguards device and data security, and meets the strict requirements of enterprises for information security;

[0056] Ensure the stable and reliable operation of the system: The diagnostic self-healing module can quickly and accurately locate equipment failures through multimodal fault diagnosis algorithms, and automatically call preset repair strategies for processing, realizing autonomous repair of faults or timely notifying operation and maintenance personnel; This significantly reduces system downtime, improves equipment availability and system stability, and reduces the risk of production interruption caused by faults;

[0057] Significantly improve energy-saving efficiency: The energy-saving strategy binding module uses dynamic threshold optimization algorithms and timed task rules, combined with real-time energy consumption monitoring and automatic termination mechanisms, to achieve refined management of equipment energy consumption; By adaptively adjusting the electricity safety threshold and peak-shaving operation strategies, while ensuring the normal operation of equipment, it significantly reduces energy consumption, saves operating costs for enterprises, and conforms to the development trend of green energy conservation;

[0058] Implement data-driven decision-making: The energy consumption analysis module quantitatively analyzes the energy-saving effect after the execution of linkage rules through a normalized energy-saving evaluation model and visual reports, and intuitively displays energy consumption data, energy-saving efficiency, and abnormal alarm information; This provides clear decision-making basis for users, facilitates optimizing equipment configuration and linkage strategies, realizes continuous improvement of energy management, and improves resource utilization efficiency;

[0059] Reduce system operation and maintenance costs: The present invention reduces manual intervention and maintenance workload through automated fault diagnosis and repair, intelligent permission management, and energy-saving strategies; At the same time, the modular design and standardized interfaces make the system easy to expand and upgrade, reduce long-term operation and maintenance costs, and improve the economic benefits and competitiveness of enterprises. Brief Description of the Drawings

[0060] Figure 1 It is a schematic diagram of the overall structure of an intelligent device linkage configuration system of the present invention. Detailed Embodiments

[0061] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0062] In order to better understand the above technical solutions, the above technical solutions will be described in detail below in conjunction with the drawings of the specification and specific embodiments.

[0063] As Figure 1 shown, the embodiment of the present invention provides an intelligent device linkage configuration system, including:

[0064] The device management module is used to register and manage multiple Internet of Things devices. Each device includes at least a device unique identifier, a device type, a communication protocol type, and peripheral configuration information;

[0065] The linkage rule configuration module provides a visual operation interface and receives user-defined linkage rules. The linkage rules include a triggering device, a linked device, a trigger condition expression, an execution action instruction, and a delay time parameter;

[0066] The rule engine module monitors the device status data of the triggering device in real time. When the device status data meets the trigger condition expression, it sends an execution action instruction to the linked device according to the delay time parameter;

[0067] The energy-saving strategy binding module dynamically binds an energy-saving control strategy to the linkage rule. The energy-saving control strategy includes an electricity safety threshold, a timing task rule, and an energy consumption end condition;

[0068] The permission security module generates a permission QR code associated with the linkage rule. The permission QR code contains a device operation permission set and a validity period. The user who scans the permission QR code obtains the device operation permission within the authorized range.

[0069] In this embodiment, the device management module serves as the basic support unit of the intelligent device linkage configuration system. Like the "digital butler" of the system, it is responsible for the full life cycle management of various Internet of Things devices, ensuring the accuracy and traceability of device information. The following will elaborate in detail from aspects of function overview, sub-module composition, and technical principle:

[0070] I. Overall function overview:

[0071] The device management module undertakes core tasks such as the registration, information storage, status monitoring, and permission management of Internet of Things devices; through a unified management platform, it realizes the centralized management of device unique identifiers, device types, communication protocol types, and peripheral configuration information; it not only provides basic data for linkage rule configuration, but also can monitor the device online status and performance indicators in real time, timely detect device anomalies, provide data support for device maintenance, upgrade, and optimization, and ensure the stable operation and efficient collaboration of devices in the system;

[0072] II. Sub-module composition and functions:

[0073] (I) Device registration and information entry unit:

[0074] Device Registration: Supports multiple device access methods, including manual entry, batch import, automatic scanning and discovery, etc.; When a new device accesses the system, it can be registered by manually inputting the device unique identifier (such as MAC address, device ID), device type (such as sensor, controller, actuator), communication protocol type (serial port protocol, TCP / UDP protocol, etc.) and peripheral configuration information (such as sensor range, actuator control parameters); Batch device registration can also be achieved by importing an Excel sheet; For devices that support the automatic discovery protocol, the system can automatically scan and identify the devices and guide the user to complete the registration process;

[0075] Information Completion and Update: Allows users to modify and supplement the information of registered devices; For example, when the device replaces the communication module resulting in a change in the protocol type, or when the peripheral configuration parameters change after the device is upgraded, the user can update the device information in a timely manner to ensure the accuracy and timeliness of the device data in the system;

[0076] (II) Device Information Storage and Retrieval Unit:

[0077] Database Storage: Uses a structured database (such as MySQL) to store device information, creates a device information table, including fields such as device unique identifier, device type, communication protocol type, peripheral configuration information, registration time, update time, etc.; Through the index technology of the database, the query and retrieval efficiency of device information is improved;

[0078] Quick Retrieval: Provides diverse retrieval methods, and users can retrieve device information according to single or multiple conditions combinations such as device unique identifier, device type, communication protocol type, etc.; For example, operation and maintenance personnel can quickly retrieve all sensor devices using the TCP / UDP protocol, which is convenient for unified management and maintenance of specific types of devices;

[0079] (III) Device Status Monitoring and Warning Unit:

[0080] Real-time Status Monitoring: Collaborates with the rule engine module to obtain the running status data of devices in real time, such as device online / offline status, working voltage, signal strength, running duration, etc.; Displays the device status in the form of charts, indicator lights, etc. through a visual interface, and operation and maintenance personnel can intuitively understand the device operation situation;

[0081] Abnormal Warning: Sets device status thresholds, and when the device running status data exceeds the preset thresholds, the system automatically triggers the warning mechanism; For example, when the device working voltage is lower than the safety threshold, or the continuous offline time exceeds 10 minutes, the system sends warning information to the management personnel via email, text message or system pop-up window, and highlights the abnormal devices on the visual interface for timely handling;

[0082] (IV) Device Permission Management Unit:

[0083] Permission allocation: Link with the permission security module to allocate device operation permissions to different users or user groups; support fine-grained allocation of menu-level permissions (such as whether to allow viewing the device list), interface-level permissions (such as whether to allow calling the device status query interface), and instruction-level permissions (such as whether to allow sending control instructions to the device); for example, ordinary users only have the permission to view the device status, while senior operation and maintenance personnel can have the permissions to control the device and modify parameters.

[0084] Permission auditing: Record the operation behaviors of users on the device, including information such as operation time, operation content, and operated device, to form an operation log; through auditing the operation log, the user operations can be traced to ensure the security and compliance of device operations.

[0085] III. Key technical principles:

[0086] (1) Device registration and information management principle:

[0087] Based on the Internet of Things device access standard protocols (such as MQTT, CoAP), realize the automatic discovery and registration of devices; adopt database management technology to structurally store device information, and ensure the integrity and accuracy of device information through data dictionaries and data verification mechanisms; utilize database indexing and query optimization technologies to improve the retrieval efficiency of device information and meet the system's requirement for rapid access to device information.

[0088] (2) Device status monitoring principle:

[0089] Communicate with the device through the rule engine module, and collect device status data at a preset frequency; adopt data encryption and secure transmission protocols (such as TLS) to ensure the security during data transmission; utilize real-time data processing technologies (such as stream computing) to perform real-time analysis on the collected device status data, and combine with preset thresholds to achieve rapid judgment and early warning of device abnormal states.

[0090] (3) Device permission management principle:

[0091] Based on the RBAC (Role-Based Access Control) model, associate users with roles and roles with permissions to achieve flexible permission allocation; through technical means such as permission QR codes and tokens, realize the dynamic granting and revocation of user permissions; adopt digital signature and identity authentication technologies to ensure the legality and security of user operations and prevent illegal users from operating on the device.

[0092] In this embodiment, the linkage rule configuration module includes:

[0093] Instruction cascading unit, which supports configuring multi-level sub-instructions in a single linkage rule, and each level of sub-instruction independently sets the delay time and trigger conditions.

[0094] An expression parsing unit that uses an improved fuzzy logic parsing algorithm to convert the natural language conditions input by the user into executable logical expressions;

[0095] The improved fuzzy logic parsing algorithm calculates the condition satisfaction degree through an S-shaped function, and the input value of this function is the difference between the real-time device status variable value and the user-defined threshold;

[0096] The slope of the function is dynamically adjusted by the product of the sensitivity coefficient and the reciprocal of the standard deviation of the historical state data;

[0097] Output the membership degree value in the range of [0,1], which is used to quantify the degree of condition satisfaction;

[0098] Furthermore, the linkage rule configuration module, as the "intelligent center" of the intelligent device linkage configuration system, undertakes the key task of transforming user requirements into device linkage logic; through visual interaction and intelligent algorithms, it enables users to conveniently define device linkage rules and achieve automated collaboration between devices; the following elaborates in detail from the functional architecture, core units, technical principles, and working processes:

[0099] I. Overall function overview:

[0100] The linkage rule configuration module provides an intuitive visual operation interface, supporting users to flexibly define device linkage logic, including trigger devices, linked devices, trigger condition expressions, execution action instructions, and delay time parameters; the module can not only meet simple one-to-one device linkage requirements, but also achieve multi-device and multi-level intelligent linkages through instruction cascading and complex logic parsing; at the same time, combined with the expression parsing algorithm and drag-and-drop operation, it reduces the user configuration threshold, improves the efficiency and accuracy of rule configuration, and lays a foundation for the system to achieve automated scenario control;

[0101] II. Composition and functions of sub-modules:

[0102] (1) Visual interaction unit:

[0103] Device and instruction drag-and-drop operations: Provide a graphical interface where users can quickly construct a device linkage topology diagram by dragging device icons, intuitively presenting the trigger and triggered relationships between devices; generate a multi-level execution action chain by dragging instruction icons, and support setting parameters such as delay time and priority for each action; for example, users can connect the temperature sensor icon with the air conditioner icon to define that when the temperature exceeds the threshold, the air conditioner automatically turns on the cooling mode;

[0104] Parameter Visualization Configuration: For parameters such as trigger conditions and execution actions, interactive components such as drop-down menus, input boxes, and sliders are provided; users can directly set the trigger condition threshold (such as the light intensity is lower than 50 Lux), the execution action instruction (such as the motor speed is adjusted to 80%), and the delay time (such as execute 3 seconds after triggering), and what you see is what you get during the operation process;

[0105] (2) Instruction Cascade Unit:

[0106] Multi-level Sub-instruction Configuration: Support nesting multi-level sub-instructions in a single linkage rule, and each level of sub-instruction can independently set the delay time and trigger conditions; for example, in a security scenario, when the door and window sensor triggers an alarm, the first-level sub-instruction can be set to immediately start the audible and visual alarm, and the second-level sub-instruction is executed after a 5-second delay to send an alarm message to the property security system;

[0107] Instruction Logic Arrangement: Provide logical operators such as "AND", "OR", and "NOT", and users can combine multiple sub-instructions to form complex logic; for example, set "when the temperature sensor exceeds the high temperature threshold and the smoke sensor detects smoke, start the sprinkler system and ventilation equipment at the same time" to achieve multi-condition linkage control;

[0108] (3) Expression Parsing Unit:

[0109] Natural Language to Logic Expression: Adopt an improved fuzzy logic parsing algorithm to convert the natural language conditions input by users (such as "turn on the dehumidifier when the humidity is high") into executable logic expressions; the core formula is: (where, represents the condition satisfaction membership function, and the output value range is [0,1], which is used to quantify the degree of condition satisfaction; represents the real-time device status variable value, such as the current humidity value; represents the threshold parameter defined by the user, such as the humidity threshold; represents the adaptive slope factor, and the calculation formula is ; where, is the standard deviation of historical state data, which reflects the degree of data fluctuation, is a configurable sensitivity coefficient, which is used to adjust the sensitivity of condition judgment);

[0110] Expression Verification and Optimization: Conduct syntax checks and rationality verification on the generated logic expressions to avoid linkage failures caused by expression errors; at the same time, optimize the expressions based on historical execution data, dynamically adjust the thresholds and slope factors, and improve the accuracy of rule execution;

[0111] (4) Rule Storage and Management Unit:

[0112] Rule database storage: Store the linkage rules configured by users in a structured database, record information such as rule names, triggering devices, linked devices, triggering condition expressions, execution action instructions, and delay times, and establish an index to support fast retrieval;

[0113] Rule version management: Support version control for linkage rules. When users modify the rules, new versions are automatically generated, and historical version records are retained for easy traceability and rollback; for example, when the newly configured rules are abnormal, they can be quickly restored to the historical valid version;

[0114] Rule import / export: Provide rule import / export functions. Users can export the configured rules as files for reuse in different projects or systems; they can also import external rule files to quickly complete rule configuration and improve work efficiency;

[0115] III. Key technical principles:

[0116] (I) Visual interaction technical principle:

[0117] Based on HTML5 Canvas or vector graphics technology, realize the dragging, connection, and layout rendering of device and instruction icons; capture user operations (such as clicking, dragging, releasing) through the event listening mechanism, and update the linkage topology diagram and execution action chain in real time; adopt data binding technology to associate graphical operations with underlying rule data to ensure that the operation results are accurately reflected in the rule configuration;

[0118] (II) Instruction cascading and logic orchestration principle:

[0119] Design a multi-level sub-instruction architecture based on the state machine model. Each sub-instruction corresponds to a state node, and the instruction execution order is controlled through state transition conditions (delay time, triggering condition); logical operators implement conditional combination judgment through Boolean algebra operations to ensure the correct execution of complex linkage logic;

[0120] (III) Expression parsing and optimization principle:

[0121] Use natural language processing (NLP) technology to perform semantic analysis on user input, extract keywords and condition information; combine fuzzy logic algorithms to convert semantic information into mathematical expressions, and dynamically adjust the sensitivity of condition judgment through an adaptive slope factor; through machine learning algorithms (such as reinforcement learning), optimize the expression parameters based on rule execution feedback to achieve the self-adaptability of the rules;

[0122] IV. Working process of the module:

[0123] (I) Initialization stage:

[0124] After the linkage rule configuration module is started, it loads the visual interface component library, the expression parsing algorithm model, and the connection configuration of the rule storage database;

[0125] Obtain the list of registered devices from the device management module, generate device icons in the visual interface, and provide basic data for users to configure rules;

[0126] (2) Rule configuration stage:

[0127] User operation input: The user drags and drops device icons and instruction icons through the visual interface, and sets parameters such as trigger conditions, execution actions, and delay times;

[0128] Expression parsing and generation: The expression parsing unit converts the natural language conditions input by the user into logical expressions, performs syntax checking and optimization on the expressions, and generates executable rule logic;

[0129] Instruction cascading arrangement: The instruction cascading unit arranges the execution order and conditions of instructions according to the multi-level sub-instructions and logical operators set by the user to form complete linkage logic;

[0130] (3) Rule storage stage:

[0131] The rule storage and management unit formats the configured linkage rules, records the detailed information and version numbers of the rules;

[0132] Store the rule data in the database, establish an index, and generate a backup of the rule configuration file at the same time to ensure the security and recoverability of the rule data;

[0133] (4) Rule update and management stage:

[0134] Users can modify, delete, or copy the configured rules; when modifying the rules, the system automatically generates a new version and retains the historical version records;

[0135] Support the import / export operations of rules. Users can import local rule files into the system or export rule files for sharing and backup;

[0136] (5) End stage:

[0137] When the system stops running or receives a stop instruction, the linkage rule configuration module stops the user interaction operation, closes the database connection, releases system resources, and saves the current unfinished rule configuration progress.

[0138] In this embodiment, the rule engine module issues control instructions through the protocol adaptation layer:

[0139] When the device communication protocol is the serial port protocol, the control instruction is encapsulated into a Modbus-RTU format data packet and appended with a CRC16 check code;

[0140] When the device communication protocol is the TCP / UDP protocol, the control instruction is converted into a hexadecimal string and transmitted through a Socket channel;

[0141] Furthermore, as the "decision-making brain" of the intelligent device linkage configuration system, the rule engine module is responsible for real-time monitoring of device status and triggering linkage actions according to preset rules; through its efficient event handling mechanism and protocol adaptation ability, it ensures the intelligent collaboration between devices and is the core component to achieve the automated operation of the system; the following elaborates in detail from the functional architecture, core technologies, and working processes:

[0142] I. Overall Function Overview:

[0143] The rule engine module collects the status data of the triggering device in real time, compares it with the trigger condition expression defined by the linkage rule configuration module, and when the condition is met, issues an execution instruction to the linked device according to the set parameters; the module has high-performance event handling capabilities, supports parallel execution and dynamic update of multiple rules, and at the same time solves the communication problems of heterogeneous devices through the protocol adaptation layer to ensure the linkage compatibility between cross-protocol devices;

[0144] II. Composition and Functions of Sub-modules:

[0145] (I) Real-time Data Acquisition Unit:

[0146] Multi-source data access: Through a message queue (such as Kafka) or a real-time data stream interface, it is linked with the device management module to obtain the status data of the triggering device in real time, including sensor readings, device online status, operating parameters, etc.; supports both timed acquisition and event-driven modes to meet the monitoring requirements of different devices;

[0147] Data preprocessing: Cleans, converts the format, and standardizes the collected raw data, removes outliers and noise data, and ensures the accuracy of the data input for rule judgment; for example, performs moving average filtering on the temperature sensor data to eliminate instantaneous fluctuation interference;

[0148] (II) Rule Matching Engine Unit:

[0149] Condition expression evaluation: Based on an improved fuzzy logic algorithm, it calculates the satisfaction degree of the device status data for the trigger condition expression in real time; uses the formula: (where is the membership function of the condition satisfaction degree, and the output value range is [0,1]; represents the real-time device status variable value; represents the threshold parameter defined by the user; Represents the adaptive slope factor, and its calculation formula is ; where is the standard deviation of historical state data, reflecting the degree of data fluctuation, is the configurable sensitivity coefficient);

[0150] Multi-rule parallel processing: Adopts rule priority sorting and parallel computing framework, supports simultaneous evaluation of multiple linkage rules, and ensures the rule processing efficiency in high-concurrency scenarios; for example, in the intelligent park system, it can simultaneously process linkage rules in multiple fields such as environmental monitoring, security alarm, and energy management;

[0151] (III) Instruction execution scheduling unit:

[0152] Delayed execution control: According to the delay time parameter configured by the linkage rule, schedule the execution instruction at a fixed time; Adopts the time wheel algorithm to achieve high-precision delayed task management, and ensures that the instruction is executed accurately according to the set time;

[0153] Execution result feedback: Receive the execution feedback information of the linked device, and update the rule execution status; If the execution fails, trigger the diagnostic self-healing module to conduct fault troubleshooting and repair, forming a closed-loop control;

[0154] (IV) Protocol adaptation layer:

[0155] Multi-protocol conversion: Provide standardized instruction encapsulation and parsing for devices with different communication protocol types; For example:

[0156] When the device communication protocol is the serial port protocol, encapsulate the control instruction into a Modbus-RTU format data packet and append the CRC16 check code;

[0157] When the device communication protocol is the TCP / UDP protocol, convert the control instruction into a hexadecimal string and transmit it through the Socket channel;

[0158] Communication link management: Maintain the communication connection with each device and monitor the link status in real time; When a communication interruption is detected, automatically trigger the reconnection mechanism, and cache the untransmitted instructions to the local queue, and send them in order after the link is restored;

[0159] III. Key technical principles:

[0160] (I) Real-time event processing principle:

[0161] Based on the reactive programming model (such as the Reactor pattern), adopts non-blocking I / O and event-driven architecture to achieve high-throughput processing of device status data; Cache hot rules and device status through an in-memory database (such as Redis) to reduce disk I / O overhead and improve the rule matching speed;

[0162] (2) Fuzzy logic decision principle:

[0163] Using fuzzy mathematics theory, map the device status data to the satisfaction value in the interval [0, 1], and solve the limitations of traditional binary logic in complex scenarios; dynamically adjust the steepness of the condition curve through the adaptive slope factor k to make the rule judgment more suitable for the actual application scenario; for example, in the temperature and humidity linkage control, the threshold sensitivity can be automatically adjusted according to the season change;

[0164] (3) Protocol adaptation technology principle:

[0165] Design the protocol adapter using the strategy pattern, and implement a unified interface for different communication protocols; through the instruction template configuration, convert the abstract control instruction into a data packet in the specific protocol format; for example, convert the instruction of "turn on the air conditioner" into the Modbus register write instruction for a specific air conditioner device;

[0166] IV. Working process of the module:

[0167] (1) Initialization stage:

[0168] Load all linkage rule configurations from the rule storage database, parse the trigger condition expression and the execution action instruction, and establish a rule index;

[0169] Initialize the communication connection with the device management module, and subscribe to the status data update event of the trigger device;

[0170] Start the protocol adaptation layer, and initialize the connection pool and encoding / decoding tool for each communication protocol;

[0171] (2) Data acquisition and monitoring stage:

[0172] Obtain the real-time status data of the trigger device from the device management module according to the preset frequency or event trigger method;

[0173] Clean, format convert and standardize the acquired data to ensure the data quality;

[0174] (3) Rule matching stage:

[0175] Input the preprocessed device status data into the rule matching engine, and evaluate the satisfaction degree of the trigger conditions of each linkage rule;

[0176] For the rules that meet the trigger conditions, sort them according to the priority and generate a queue of instructions to be executed;

[0177] (4) Instruction execution stage:

[0178] The instruction execution scheduling unit schedules the instruction queue regularly according to the delay time parameter;

[0179] Convert the execution instructions into the protocol format supported by the target device through the protocol adaptation layer and send them to the associated devices.

[0180] Receive the execution feedback from the device and update the rule execution status record; if the execution fails, trigger the fault handling process.

[0181] (5) Abnormal handling stage:

[0182] When a communication interruption or instruction execution failure is detected, cache the unfinished instructions in the local persistent queue.

[0183] Call the diagnostic self-healing module to analyze the cause of the fault and attempt automatic repair, such as reconnecting the device, switching redundant links, etc.

[0184] If the fault persists, send an alarm notification to the system administrator.

[0185] (6) End stage:

[0186] After receiving the system shutdown instruction, stop the data collection and rule matching tasks.

[0187] Empty the instruction queue to be executed to ensure that all instructions are processed or properly saved.

[0188] Close the communication connections with each module and release the system resources.

[0189] In this embodiment, the energy-saving policy binding module includes:

[0190] An electricity safety threshold generation unit that automatically generates an optimal safety threshold using a dynamic threshold optimization algorithm.

[0191] The dynamic threshold optimization algorithm uses the arithmetic mean of historical energy consumption data as the base value.

[0192] Add the product of the safety factor and the standard deviation of historical energy consumption data as the initial offset.

[0193] Introduce a time decay compensation factor to dynamically decay the offset, which is calculated by the natural logarithm function of the number of device operation days.

[0194] A timing task configuration unit for setting the time range and action instructions for periodic execution.

[0195] An energy consumption termination unit that automatically terminates the associated linkage rules when the real-time energy consumption data meets the preset conditions.

[0196] Furthermore, as the "green steward" of the intelligent device linkage configuration system, the energy-saving strategy binding module realizes energy conservation and efficiency improvement in the device linkage scenario through dynamic strategy formulation and precise energy consumption control. It deeply integrates energy-saving control into the linkage rules, forming a closed-loop management from threshold optimization, task scheduling to energy consumption monitoring. The following is a detailed elaboration from the functional architecture, core technologies, and working processes:

[0197] I. Overall Function Overview:

[0198] The energy-saving strategy binding module dynamically binds customized energy-saving control strategies to the linkage rules, covering three core functions: power safety threshold management, timed task scheduling, and energy consumption termination control. The module analyzes the historical energy consumption data of the devices, generates adaptive safety thresholds using dynamic algorithms, and combines timed tasks to achieve energy-saving operations such as off-peak electricity use and intelligent sleep. At the same time, it real-time monitors the energy consumption data and automatically terminates high-energy-consuming linkage rules when preset conditions are met, minimizing energy consumption to the greatest extent while ensuring the normal operation of the devices.

[0199] II. Composition and Functions of Sub-Modules:

[0200] (I) Power Safety Threshold Generation Unit:

[0201] Historical Data Collection and Analysis: Extract device energy consumption data from the system historical database, including parameters such as power, electricity, and running duration, to construct a historical energy consumption dataset. Remove outliers through data cleaning to provide an accurate data basis for threshold calculation.

[0202] Dynamic Threshold Calculation: Use the dynamic threshold optimization algorithm to generate the optimal safety threshold. The calculation formula is: (where, represents the optimal safety threshold; represents the arithmetic mean of the historical energy consumption data; represents the standard deviation of the historical energy consumption data; represents the safety factor, and the value range is [1.5, 2.5], which is used to adjust the threshold looseness; represents the time decay factor, and the calculation formula is , where, represents the number of days the device has been running, reflecting the impact of the device running duration on the threshold; represents the proportion of the time from the current time point to the initial operation of the device (unit: days), which is used to dynamically adjust the changing trend of the threshold over time);

[0203] Threshold Dynamic Update: Regularly or in real-time update the safety threshold according to factors such as the number of days of device operation and real-time energy consumption fluctuations to ensure that the threshold always adapts to the actual operating state of the device; for example, at the initial stage of new device commissioning, the threshold converges rapidly with the number of days of operation; after the device operates stably, the threshold is fine-tuned according to real-time data;

[0204] (2) Scheduled Task Configuration Unit:

[0205] Task Rule Definition: Provide a visual configuration interface to support users in customizing the execution cycle (such as by day, week, month), time range (such as low valley hours at night) of scheduled tasks, and associated action instructions (such as turning off non-essential devices, reducing device power); for example, the lighting system in the office area can be set to automatically switch to the energy-saving mode from 18:00 to 8:00 the next day on weekdays;

[0206] Task Scheduling Management: Implement high-precision task scheduling based on the time wheel algorithm to ensure that scheduled tasks are triggered at the set time; support setting task priorities, and when multiple tasks conflict, the high-priority tasks are executed first;

[0207] Task Status Monitoring: Record the execution status (executed, to be executed, execution failed) of scheduled tasks in real-time and generate task execution logs; if the task execution fails, automatically trigger the retry mechanism or send an alarm notification to the administrator;

[0208] (3) Energy Consumption Termination Unit:

[0209] Real-time Energy Consumption Monitoring: Link with the rule engine module to obtain real-time device energy consumption data and the cumulative energy consumption value during the execution of linkage rules in real-time; collect real-time data from devices such as smart meters and power sensors through data interfaces (such as Modbus / TCP);

[0210] Termination Condition Judgment: Analyze and judge the real-time energy consumption data according to the preset energy consumption termination conditions (such as single-day energy consumption exceeding the threshold, continuous operation time being too long); when the termination conditions are met, trigger the rule termination process;

[0211] Linkage Rule Termination: Send a termination instruction to the rule engine module to stop the execution of associated linkage rules and adjust the device to a low-power or sleep state; at the same time, record the energy consumption termination event and related data for subsequent energy consumption analysis;

[0212] III. Key Technical Principles:

[0213] (1) Dynamic Threshold Optimization Principle:

[0214] Based on statistical theory, combined with the device operation time dimension, quantify the energy consumption fluctuation range through the mean and standard deviation of historical data; time decay factor Introduce an exponential decay model to enable the threshold to quickly adapt in the initial stage of device operation and tend to be stable in the later stage; the safety factor is used as an adjustment parameter to balance the energy-saving effect and the operational safety of the device;

[0215] (2) Scheduling principle of timed tasks:

[0216] Adopt the TimeWheel algorithm to achieve efficient management of the task queue; the TimeWheel divides time into multiple time slots, and each time slot corresponds to a task list; by rotating the pointer to traverse the time slots, when the pointer points to a certain time slot, the corresponding task is triggered to execute, realizing the scheduling of timed tasks with millisecond-level precision;

[0217] (3) Energy consumption monitoring and termination principle:

[0218] Based on data stream processing technologies (such as Flink), perform streaming calculation and analysis on real-time energy consumption data; judge the energy consumption termination conditions through a preset rule engine (such as a conditional expression based on SQL), and when the data meets the conditions, send a termination instruction to the rule engine through a message queue (such as Kafka) to achieve dynamic control of the linkage rules;

[0219] IV. Workflow of the module:

[0220] (1) Initialization stage:

[0221] Load the device historical energy consumption data and energy-saving strategy configuration parameters (such as the safety factor , time decay factor initial value) from the system database;

[0222] Establish communication connections with the rule engine module and the device management module, and subscribe to the device energy consumption data update event;

[0223] Initialize the timed task scheduler and load the configured timed task rules;

[0224] (2) Strategy calculation and configuration stage:

[0225] Threshold calculation: The electricity safety threshold generation unit calculates the optimal safety threshold according to the historical energy consumption data using the dynamic threshold optimization algorithm and synchronizes the result to the rule engine module;

[0226] Task configuration: The user defines the energy-saving timed tasks through the timed task configuration unit, sets the execution period, time range, and action instructions, and the system stores the task rules in the database and loads them into the task scheduler;

[0227] Termination condition setting: Configure the energy consumption termination conditions (such as energy consumption threshold, operation duration threshold) in the energy consumption termination unit and associate them with the corresponding linkage rules;

[0228] (3) Real-time Monitoring and Execution Phase:

[0229] Data Acquisition: Obtain device energy consumption data and the execution status of linkage rules in real time, and clean and convert the data in terms of format;

[0230] Policy Execution:

[0231] The timed task scheduler triggers the energy-saving task at the set time, and issues control instructions to the device through the rule engine module;

[0232] The energy consumption termination unit analyzes the energy consumption data in real time. When the termination condition is met, it sends a termination instruction to the rule engine module to stop the execution of the associated rules;

[0233] Status Feedback: Receive the execution feedback information of the device, and update the record of the execution status of the energy-saving policy; if the execution fails, trigger the exception handling process;

[0234] (4) Exception Handling Phase:

[0235] When the execution of the timed task fails or the energy consumption termination instruction is issued abnormally, record the fault information and try to retry automatically;

[0236] If the retry fails, send an alarm notification to the system administrator, including the fault type, occurrence time, and relevant device information;

[0237] (5) Policy Optimization Phase:

[0238] Regularly analyze the execution effect of the energy-saving policy, compare the actual energy consumption with the benchmark energy consumption, and evaluate the energy-saving efficiency;

[0239] Adjust the safety threshold, timed task rules, or energy consumption termination conditions according to the analysis results, and optimize the configuration of the energy-saving policy;

[0240] (6) End Phase:

[0241] After receiving the system shutdown instruction, stop the data acquisition and policy execution tasks;

[0242] Save the current energy-saving policy configuration and execution log, and close the communication connections with each module;

[0243] Release the system resources and complete the module shutdown.

[0244] In this embodiment, the permission security module includes:

[0245] The permission grading unit divides the device operation permissions into menu-level permissions, interface-level permissions, and instruction-level permissions;

[0246] The dynamic authorization unit dynamically activates the operation permissions according to the user identity scanning the permission QR code;

[0247] The time-limited control unit automatically recovers the authorized permissions after the expiration of the validity period;

[0248] As the "security barrier" of the intelligent device linkage configuration system, the permission security module provides full-process and refined security protection for the system through hierarchical permission control, dynamic authorization management, and time-limited constraint mechanisms; it ensures that only legitimate users can operate the device within the authorized scope, effectively preventing the risks of data leakage and illegal control; the following elaborates in detail from the functional architecture, core units, technical principles, and working processes:

[0249] I. Overall function overview:

[0250] The permission security module is responsible for generating, managing, and operating permissions associated with device linkage rules, and realizes user identity verification and dynamic permission allocation through permission QR codes; the module divides device operation permissions into three levels: menu level, interface level, and instruction level, supports dynamic authorization based on user identity, and ensures the validity of permissions within the validity period through the time-limited control unit; in addition, the module also has a permission audit function, which completely records user operation behaviors, ensuring the security and traceability of system operations;

[0251] II. Composition and functions of sub-modules:

[0252] (I) Permission hierarchical unit:

[0253] Permission level division:

[0254] Menu-level permissions: Control users' access permissions to system function menus, such as whether to allow viewing the device management interface, linkage rule configuration interface, etc.; for example, ordinary users only have access to the device status viewing menu, while system administrators can access all function menus;

[0255] Interface-level permissions: Control permissions for system API interfaces, restricting users' calls to device status query interfaces, instruction issuing interfaces, etc.; for example, third-party applications are only authorized to call the device status query interface and are prohibited from calling the control instruction interface;

[0256] Instruction-level permissions: Subdivide and authorize specific device operation instructions, such as allowing users to perform an "open" operation on a certain device but prohibiting a "parameter modification" operation;

[0257] Permission combination configuration: Supports combining different levels of permissions to form permission templates; administrators can quickly assign corresponding permission templates according to user roles (such as ordinary users, operation and maintenance personnel, system administrators), improving the efficiency of permission management;

[0258] (II) Dynamic authorization unit:

[0259] Permission QR Code Generation: Generate a unique permission QR code for each linkage rule. The content of the QR code includes the device operation permission set (such as the specific permission lists at the menu level, interface level, and instruction level) and the validity period. For example, for the QR code generated for the device linkage rule of a certain meeting room, it is specified that the user has instruction-level permissions such as device on / off and volume adjustment during the meeting period.

[0260] Identity Authentication and Permission Activation: The user initiates an authorization request by scanning the permission QR code. The system authenticates the user's identity (such as through mobile phone number, account password, or biometric authentication). After successful authentication, the user's operation permissions are dynamically activated according to the permission set in the QR code. If the user's identity information does not match the preset authorization conditions, the authorization is refused.

[0261] Dynamic Permission Adjustment: Support the administrator to adjust the user's permissions in real time during the system operation according to user requirements or security policy changes. For example, temporarily add the parameter modification permission for a certain operation and maintenance personnel for specific devices, and the permission change takes effect immediately.

[0262] (III) Time Limit Control Unit:

[0263] Validity Period Setting: When generating the permission QR code, the administrator can customize the permission validity period, supporting settings in time units such as minutes, hours, and days. For example, for the QR code generated for the equipment inspection task, set the permission validity period to 2 hours to ensure the availability of permissions during the inspection period.

[0264] Automatic Permission Recovery: When the permission validity period ends, the system automatically triggers the permission recovery mechanism to revoke the user's operation permissions. At the same time, record the permission expiration time and the recovery operation log to prevent users from using permissions beyond the expiration date.

[0265] Validity Period Warning: Before the permission validity period is about to expire, the system sends warning notifications to the user and the administrator through pop-ups, emails, or text messages to remind the user to apply for permission extension or re-authorization in a timely manner.

[0266] (IV) Permission Audit Unit:

[0267] Operation Log Recording: Real-time record all the operation behaviors of the user on the device, including information such as operation time, operation device, operation instruction, and operation result. For example, record that the user performs the operation of "raising the temperature" on a certain air-conditioning device at a certain time and whether the operation is successful.

[0268] Log Query and Analysis: Provide a visual log query interface, supporting users to retrieve logs according to conditions such as time range, device name, and operation type. Through the analysis of operation logs, abnormal operation behaviors such as high-frequency instruction issuance and unauthorized operations can be discovered, and security alerts are generated.

[0269] Compliance Check: Regularly check the compliance of permission allocation and user operations to ensure that permission allocation complies with enterprise security policies and relevant regulatory requirements, such as meeting the permission management requirements of Information Security Protection 2.0.

[0270] 3. Key technical principles:

[0271] (1) Principle of hierarchical authority management:

[0272] Based on the RBAC (Role-Based Access Control) model, permissions are bound to roles, and users obtain corresponding permissions by assigning roles. Through the three-level permission division of menu level, interface level, and instruction level, refined control from functional access to specific operations is achieved. The database permission table is used to store permission information, and the query, assignment, and modification of permissions are achieved through SQL statements.

[0273] (2) Principles of dynamic authorization technology:

[0274] Utilize QR code encoding technology (such as QR Code) to encrypt permission information and generate a QR code. AES encryption algorithm is used to ensure the security of the QR code content. After the user scans the code, the system verifies the user's identity through OAuth2.0 or JWT (JSON Web Token) authentication mechanisms, and dynamically generates an access token based on the permission policy to temporarily grant permissions.

[0275] (3) Time control principle:

[0276] Based on the comparison between the system timestamp and the validity period of the permission, when the system time exceeds the validity period timestamp, the permission recovery logic is triggered; a scheduled task (such as the Quartz framework) is used to regularly scan for permissions that are about to expire and send early warning notifications;

[0277] (IV) Principles of authority auditing:

[0278] Through aspect-based programming (AOP) technology, logging code is embedded before and after user interface calls to automatically capture operation information and store it in a log database. The ELK (Elasticsearch, Logstash, Kibana) technology stack enables real-time analysis and visualization of logs, triggering security alerts based on pre-set rules (such as operation frequency thresholds and unauthorized operation modes).

[0279] 4. Module workflow:

[0280] (1) Initialization phase:

[0281] After the permission security module is started, the permission configuration parameters are loaded, including the default permission template, permission level definition, security policy, etc.

[0282] Establish communication connections with the device management module and linkage rule configuration module to obtain device information and linkage rule data, providing basic data for permission allocation;

[0283] Initialize the permission audit log database and create the operation log table structure;

[0284] (2) Permission configuration stage:

[0285] Creation of permission templates: Administrators create templates for menu-level, interface-level, and command-level permission combinations in the permission grading unit based on user role requirements, such as "common user template" and "operation and maintenance personnel template";

[0286] User rights allocation: assign rights templates to specific users, or customize rights configuration for individual users; rights information is stored in the database and synchronized to the dynamic authorization unit;

[0287] (3) Dynamic authorization stage:

[0288] QR code generation: Administrators generate permission QR codes for specific linkage rules, set permission sets and validity periods, and store the QR code information in the database;

[0289] Scan code authorization: The user scans the QR code, and the system verifies the user's identity. If the identity is legitimate, the user's permissions are dynamically activated according to the QR code permission set, and an access token is generated;

[0290] Permission usage: During the validity period of the permission, the user uses the access token to operate the device. The system verifies the validity of the token and the scope of permission before performing the operation.

[0291] (IV) Time control stage:

[0292] Validity period monitoring: The validity control unit regularly checks the validity period of user permissions and sends warning notifications to users and administrators when it finds that the permissions are about to expire;

[0293] Permission revocation: When permissions expire, the system automatically revokes user permissions, deletes access tokens, and records permission revocation logs;

[0294] (V) Authority audit stage:

[0295] Operation log record: The permission audit unit captures user operation information in real time and records it in the log database;

[0296] Log analysis and alerts: Regularly analyze operation logs, trigger security alerts when abnormal operations are found, and generate audit reports for administrators to review;

[0297] (6) Ending stage:

[0298] When the system stops running or receives a stop instruction, the permission security module stops operations such as permission verification and aging monitoring, closes the communication connection with other modules, saves the permission configuration and audit log data, and releases system resources.

[0299] In this embodiment, the system further includes a diagnostic self-healing module. When the linkage rule execution fails:

[0300] Analyze the cause of the failure based on the device historical data packets and adopt a multi-modal fault diagnosis algorithm:

[0301] Calculate the absolute deviation of the current value of each data feature from the historical normal mean value, divide it by the historical standard deviation, and then multiply by the feature weight coefficient;

[0302] Accumulate the weighted deviation values of all features;

[0303] Superimpose the product of the device status sequence information entropy and the entropy value influence coefficient;

[0304] Output the comprehensive fault probability score;

[0305] Call the preset repair strategy library to generate a solution;

[0306] Automatically resend the control instruction or notify the designated user;

[0307] Furthermore, the diagnostic self-healing module, as the "intelligent doctor" of the intelligent device linkage configuration system, undertakes the full-process closed-loop management task of fault detection, location, and repair; through multi-modal data analysis and adaptive repair strategies, this module can quickly identify device anomalies in complex environments and automatically execute recovery operations to ensure the continuous and stable operation of the system; the following elaborates in detail from the functional architecture, core units, and technical principles:

[0308] I. Overall function overview:

[0309] The diagnostic self-healing module focuses on fault handling when the linkage rule execution is abnormal. By real-time monitoring the device status data, it uses a multi-modal fault diagnosis algorithm to quickly locate the cause of the fault; the module has a built-in preset repair strategy library, which automatically calls the corresponding solution for different fault types, supports automatically resending control instructions, switching redundant devices, or notifying the designated user, realizes the full automation of fault handling, greatly reduces the manual operation and maintenance cost, and improves the system reliability;

[0310] II. Composition and functions of sub-modules:

[0311] (I) Multi-modal fault diagnosis unit:

[0312] Data collection and integration: Real-time obtain device operation status data from the device management module and the rule engine module, including multi-source information such as sensor output values, communication link signal strength, power parameters, and instruction execution feedback; Achieve unified collection and format conversion of heterogeneous data through data interfaces (such as MQTT, RESTful API);

[0313] Multi-modal fault diagnosis algorithm: Use the formula (where, represents the fault probability score, and the larger the value, the higher the probability of failure; is the number of monitoring indicators; represents the weight coefficient of the type of data feature, satisfying , and is used to measure the contribution degree of different indicators to fault diagnosis; represents the current device status data vector; represents the mean value of the type of feature in the historical normal state; represents the standard deviation of the historical normal state features, reflecting the degree of data fluctuation; represents the information entropy of the device status sequence , which is used to quantify the abnormality of data fluctuation. The higher the entropy value, the more unstable the data; represents the entropy value influence coefficient, with a default value of 0.2, which is used to adjust the influence weight of information entropy on the fault score);

[0314] Fault type identification: Based on the fault score result, combined with the preset fault threshold and feature library, judge the fault type (such as sensor fault, communication interruption, power supply abnormality, etc.); For example, when the sensor data deviation score and the communication link entropy value both exceed the threshold, it is diagnosed as a sensor and communication collaboration fault;

[0315] (2) Repair strategy execution unit:

[0316] Repair strategy library: Built-in multiple preset repair strategies, stored by fault type classification, including:

[0317] Device restart: For faults caused by software exceptions, automatically send a device restart instruction;

[0318] Redundancy switch: When the main device fails, switch to the standby device or link;

[0319] Parameter reset: Restore the device configuration parameters to the default values;

[0320] Manual intervention: Send an alarm notification containing the fault details to the designated operation and maintenance personnel;

[0321] Policy Matching and Execution: Based on the fault diagnosis results, match the optimal repair plan from the policy library and execute it automatically. For example, if the diagnosis is a sensor fault, first try to restart the sensor. If the restart fails, then call the redundant sensor to take over the data acquisition task.

[0322] Execution Effect Verification: After executing the repair policy, monitor the device status in real time to verify whether the fault is eliminated. If the repair fails, automatically upgrade the repair policy (such as from device restart to manual intervention), and record the reason for failure.

[0323] (3) Fault Recording and Analysis Unit:

[0324] Log Storage: Completely record information such as the fault occurrence time, fault type, diagnosis process, repair policy, and execution result, and store it in the local database. Log data is efficiently stored and retrieved using a time series database (such as InfluxDB).

[0325] Root Cause Analysis: Use historical fault data and device operation data to analyze the root cause of the fault through machine learning algorithms (such as decision trees, association rule mining), and discover potential systematic problems. For example, by analyzing multiple communication interruption faults, locate the root cause of the high load of the network switch.

[0326] Policy Optimization: Dynamically optimize the repair policy library according to the fault handling feedback. If the success rate of a specific repair policy for a certain type of fault is low, then adjust the policy priority or supplement new solutions.

[0327] In this embodiment, the system further includes an energy consumption analysis module to count the energy-saving data after the execution of the linkage rules:

[0328] Use a normalized energy-saving evaluation model to calculate the energy-saving efficiency:

[0329] Obtain the basic efficiency value by dividing the actual saved power by the baseline energy consumption before the execution of the linkage rules;

[0330] Introduce an environmental temperature compensation factor, which is 1 plus the reciprocal of the absolute value of the deviation between the environmental temperature and the reference temperature divided by the reference temperature;

[0331] Multiply the basic efficiency value by the temperature compensation factor to obtain the final energy-saving efficiency;

[0332] Generate a visual report, including the saved power, energy-saving efficiency, and a heat map of abnormal device alarms;

[0333] Furthermore, as the "energy think tank" of the intelligent device linkage configuration system, the energy consumption analysis module provides data support for system optimization and resource management by quantifying energy-saving effects and exploring energy consumption patterns. It converts complex energy consumption data into decision-making information through a scientific evaluation model and visual presentation, helping to achieve the goal of energy conservation and efficiency improvement. The following elaborates in detail from the aspects of functional architecture, core units, technical principles, working processes, and application values:

[0334] I. Overall Function Overview:

[0335] The energy consumption analysis module focuses on statistically analyzing the energy-saving data after the execution of linkage rules, quantifying the energy-saving efficiency through a normalized energy-saving evaluation model, and generating a visual report. The module obtains energy consumption data from the device management module and the rule engine module, corrects the evaluation results in combination with environmental factors, and identifies weak links in energy conservation and potential optimization points. Its output results can be directly used to guide the adjustment of energy-saving strategies, equipment selection, and resource allocation, and it is a key component to promote the green operation of the system.

[0336] II. Composition and Functions of Sub-modules:

[0337] (I) Data Acquisition and Preprocessing Unit:

[0338] Multi-source data access: Through API interfaces or message queues (such as Kafka), obtain the basic energy consumption parameters of devices (such as rated power, running duration) from the device management module, collect real-time energy consumption data (such as power consumption, power fluctuations) during the execution of linkage rules from the rule engine module, and synchronize environmental monitoring data (such as temperature, humidity).

[0339] Data cleaning and integration: Perform cleaning operations on the collected data, such as removing outliers and imputing missing values. For example, supplement missing energy consumption data for a short period through linear interpolation. Associate and integrate the device energy consumption data with linkage rules and environmental parameters to form a structured data set, providing a basis for subsequent analysis.

[0340] (II) Energy-saving Evaluation Model Unit:

[0341] Calculation of normalized energy-saving efficiency: Use the normalized energy-saving evaluation model to calculate the energy-saving efficiency. The core formula is: (where, represents the normalized energy-saving efficiency, reflecting the relative value of the actual energy-saving effect; represents the actual saved electricity, calculated by the energy consumption difference before and after the execution of the linkage rule; represents the baseline energy consumption before the execution of the linkage rule, which can be estimated based on historical data of the same period or the rated energy consumption of the device; represents the absolute value of the deviation between the environmental temperature and the reference temperature , The default is 25°C, which is used to correct the impact of environmental factors on energy consumption);

[0342] Sub-item energy consumption statistics: Support the statistics of energy consumption distribution according to equipment types (such as air conditioners, lighting, power equipment), regions (such as floors, workshops), and time dimensions (days, weeks, months), calculate the energy consumption proportion and energy-saving contribution degree of each sub-item, and help users locate high-energy-consuming links;

[0343] (3) Visual report generation unit:

[0344] Chart generation: Use visualization libraries such as ECharts and Highcharts to generate various types of charts:

[0345] Trend chart: Display the change trends of energy-saving efficiency and total energy consumption over time;

[0346] Heat map: Intuitively present the abnormal energy consumption distribution of different equipment or regions with the depth of color;

[0347] Comparison chart: Compare the energy-saving effects under different linkage rules or equipment configurations;

[0348] Report output: Automatically generate a structured energy-saving analysis report, including energy-saving efficiency statistics, abnormal energy consumption alarms, optimization suggestions, etc.; The report supports export in PDF and Excel formats, or can be pushed to management personnel via email or system messages;

[0349] (4) Abnormal analysis and early warning unit:

[0350] Threshold alarm: Preset energy consumption abnormal thresholds (such as the daily energy consumption exceeding 120% of the benchmark value). When the real-time data triggers the threshold, the system issues an alarm through pop-up windows, text messages, etc., and highlights the abnormal equipment on the visualization interface;

[0351] Root cause analysis: Combine equipment operation logs, linkage rule execution records, and environmental data, and use association analysis algorithms to locate the reasons for abnormal energy consumption; For example, it is found that the sudden increase in air conditioner energy consumption in a certain area is due to the linkage rules not adapting to high-temperature weather;

[0352] III. Key technical principles:

[0353] (1) Normalized energy-saving evaluation principle:

[0354] Based on thermodynamics and statistics theories, by introducing an environmental temperature correction factor , eliminate the interference of environmental factors on energy consumption, making the energy-saving efficiency evaluation results more comparable; The dynamic calculation of the benchmark energy consumption (such as based on historical means, equipment rated values) ensures the adaptability of the evaluation model;

[0355] (2) Visual data presentation principle:

[0356] Adopt data mapping and graphical coding technologies to map numerical energy consumption data to chart elements (such as color, size, position); through interaction design (such as data drilling, dynamic filtering), support users to conduct in-depth analysis of energy consumption details from macro trends to specific devices;

[0357] (3) Abnormal root cause analysis principle:

[0358] Use association rule mining algorithms (such as Apriori) to analyze the correlation between energy consumption data and device status, environmental parameters, and linkage rules, and build a fault tree model to trace the abnormal root cause; combine machine learning algorithms (such as random forest) to predict energy consumption trends and discover potential anomalies in advance;

[0359] IV. Workflow of the module:

[0360] (1) Initialization stage:

[0361] After the energy consumption analysis module is started, load the energy-saving evaluation model parameters (such as reference temperature , threshold configuration), visualization templates, and data interface configuration;

[0362] Establish communication connections with the device management module, rule engine module, and environmental monitoring system, and subscribe to energy consumption data update events;

[0363] (2) Data collection and processing stage:

[0364] Collect device energy consumption data, linkage rule execution records, and environmental parameters at a preset frequency (such as once every 15 minutes);

[0365] Clean, integrate, and standardize the original data, and store it in a time series database (such as InfluxDB);

[0366] (3) Energy-saving evaluation stage:

[0367] Calculate the baseline energy consumption and the actual saved electricity ;

[0368] Substitute into the normalized energy-saving evaluation model to calculate the energy-saving efficiency , and conduct itemized statistics by device, region, and time dimensions;

[0369] (4) Visualization and report generation stage:

[0370] Generate various visualization charts based on the evaluation results and display them in real time on the system interface;

[0371] Regularly (such as daily, monthly) generate energy-saving analysis reports, summarizing key indicators, abnormal alarms, and optimization suggestions;

[0372] (V) Abnormal handling phase:

[0373] Real-time monitor the energy consumption data. When the abnormal threshold is triggered, start the alarm mechanism and record the abnormal event;

[0374] Use the root cause analysis algorithm to locate the cause of the abnormality and generate handling suggestions (such as adjusting the linkage rules and repairing the equipment);

[0375] (VI) End phase:

[0376] When the system stops running or receives a stop instruction, the energy consumption analysis module stops the data collection and calculation tasks, closes the database connection, saves the evaluation results and configuration parameters, and releases the system resources.

[0377] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent device linkage configuration system, characterized in that: Including: The device management module is used to register and manage multiple Internet of Things devices. Each device includes at least a device unique identifier, a device type, a communication protocol type, and peripheral configuration information; The linkage rule configuration module provides a visual operation interface and receives user-defined linkage rules. The linkage rules include trigger devices, linked devices, trigger condition expressions, execution action instructions, and delay time parameters; The rule engine module monitors the device status data of trigger devices in real time. When the device status data meets the trigger condition expression, it issues an execution action instruction to the linked device according to the delay time parameter; The energy-saving strategy binding module dynamically binds an energy-saving control strategy to the linkage rule. The energy-saving control strategy includes an electricity safety threshold, a timed task rule, and an energy consumption end condition; The permission security module generates a permission QR code associated with the linkage rule. The permission QR code contains a device operation permission set and a validity period. Users who scan the permission QR code obtain device operation permissions within the authorized range.

2. An intelligent device linkage configuration system according to claim 1, characterized in that: The linkage rule configuration module includes: The instruction cascading unit supports configuring multi-level sub-instructions in a single linkage rule. Each level of sub-instruction independently sets the delay time and trigger conditions; The expression parsing unit uses an improved fuzzy logic parsing algorithm to convert natural language conditions input by users into executable logical expressions; The improved fuzzy logic parsing algorithm calculates the condition satisfaction degree through an S-type function. The input value of the function is the difference between the real-time device status variable value and the user-defined threshold; The slope of the function is dynamically adjusted by the product of the sensitivity coefficient and the reciprocal of the standard deviation of historical status data; Output a membership degree value with a value range of [0,1] to quantify the condition satisfaction degree.

3. An intelligent device linkage configuration system according to claim 1, characterized in that: The energy-saving strategy binding module includes: The electricity safety threshold generation unit automatically generates an optimal safety threshold using a dynamic threshold optimization algorithm; The dynamic threshold optimization algorithm uses the arithmetic mean of historical energy consumption data as the base value; Add the product of the safety factor and the standard deviation of historical energy consumption data as the initial offset; Introduce a time decay compensation factor to dynamically decay the offset. The factor is calculated through the natural logarithm function of the number of device operation days; The timed task configuration unit is used to set the time range and action instructions for periodic execution; The energy consumption termination unit automatically terminates the associated linkage rule when the real-time energy consumption data meets the preset conditions.

4. An intelligent device linkage configuration system according to claim 1, characterized in that: The permission security module includes: The permission grading unit divides device operation permissions into menu-level permissions, interface-level permissions, and instruction-level permissions; The dynamic authorization unit dynamically activates operation permissions according to the user identity who scans the permission QR code; The validity period control unit automatically recovers the authorized permissions after the validity period ends.

5. The intelligent device linkage configuration system according to claim 1, wherein: It also includes a diagnostic self-healing module. When the linkage rule execution fails: Analyze the failure cause based on the device historical data packet and use a multi-modal fault diagnosis algorithm: Calculate the absolute deviation of the current value of each data feature from the historical normal mean, divide it by the historical standard deviation, and then multiply by the feature weight coefficient; Accumulate the weighted deviation values of all features; Superimpose the product of the device status sequence information entropy and the entropy value influence coefficient; Output a comprehensive fault probability score; Call the preset repair strategy library to generate a solution; Automatically resend control instructions or notify specified users.

6. The intelligent device linkage configuration system according to claim 1, wherein: The rule engine module issues control instructions through the protocol adaptation layer: When the device communication protocol is the serial port protocol, the control instruction is encapsulated into a Modbus-RTU format data packet and appended with a CRC16 checksum; When the device communication protocol is the TCP / UDP protocol, the control instruction is converted into a hexadecimal string and transmitted through a Socket channel.

7. An intelligent device linkage configuration system according to claim 1, characterized in that: It also includes an energy consumption analysis module to statistically analyze the energy-saving data after the execution of the linkage rules: An energy-saving evaluation model is used to calculate the energy-saving efficiency: The basic efficiency value is obtained by dividing the actual saved power by the baseline energy consumption before the execution of the linkage rules; An environmental temperature compensation factor is introduced, which is 1 plus the reciprocal of the absolute value of the deviation between the environmental temperature and the reference temperature divided by the reference temperature; The basic efficiency value is multiplied by the temperature compensation factor to obtain the final energy-saving efficiency; A visualization report is generated, including the saved power, energy-saving efficiency, and a heat map of abnormal device alarms.

8. The intelligent device linkage configuration system according to claim 1, wherein: The linkage rule configuration module supports drag-and-drop operations: Construct a device linkage topology diagram by dragging device icons; Generate a multi-level execution action chain by dragging instruction icons.

9. An intelligent device linkage configuration method, characterized in that: The method performs linkage configuration of intelligent devices based on the intelligent device linkage configuration system described in any one of claims 1-8.

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