Intelligent control system based on carrier communication

Abstract

The invention relates to the technical field of intelligent control, and discloses an intelligent control system based on carrier communication. Comprising a carrier communication module, a control center and a plurality of intelligent equipment terminals, the carrier communication module is respectively connected with the control center and the plurality of intelligent equipment terminals, and is used for realizing two-way data transmission between the control center and the plurality of intelligent equipment terminals by using a power line as a signal transmission medium; the control center is used for receiving data information transmitted by the intelligent equipment terminal through the carrier communication module, generating a corresponding control instruction according to a preset control strategy, and sending the control instruction to the corresponding intelligent equipment terminal through the carrier communication module; the plurality of intelligent equipment terminals are respectively connected with the control center through the carrier communication module, and each intelligent equipment terminal is used for receiving a control instruction sent by the control center through the carrier communication module, executing a corresponding operation according to the received control instruction, and feeding back a corresponding operation state and parameters to the control center.

Description

Technical Field

[0001] This application relates to the field of intelligent control technology, and in particular to an intelligent control system based on carrier communication. Background Technology

[0002] In the field of intelligent control, traditional intelligent device control systems typically use wireless communication (such as WiFi and Bluetooth) or dedicated communication lines (such as RS485 and Ethernet) to connect devices to the control center. Wireless communication is susceptible to signal interference and has limited coverage, and suffers from data transmission delays and security issues. Dedicated communication lines require additional cabling, increasing system deployment costs and construction complexity, and are particularly difficult to flexibly expand in existing buildings or industrial settings. While existing technologies include control systems based on power line carrier communication (using power lines as the signal transmission medium), these systems generally suffer from the following shortcomings: 1. Insufficient transmission reliability: The lack of effective anti-interference and error correction mechanisms for power line channel noise results in poor stability of bidirectional data transmission; 2. Limited centralized control capabilities: The control center can only issue simple commands and lacks the ability to deeply process, analyze, and strategically control equipment status data; 3. Low equipment management efficiency: Most intelligent device terminals only receive instructions in one direction and cannot provide real-time feedback on operating status and parameters, making it difficult to form a control closed loop, resulting in poor system real-time performance and scalability.

[0003] Currently, no existing technology discloses an intelligent control system architecture that integrates "bidirectional power line carrier communication, strategic processing by the control center, and status feedback from equipment terminals." In particular, it lacks a specific technical solution for achieving bidirectional data transmission between the control center and equipment terminals via a carrier communication module, and for generating control commands based on preset strategies to form a closed-loop control system. Therefore, there is an urgent need for an intelligent control system that can solve the above problems and possesses high real-time performance, stability, and scalability. Summary of the Invention

[0004] This application provides an intelligent control system based on carrier communication, aiming to solve the problem that there is currently no existing technology that discloses an intelligent control system architecture that integrates "power line carrier bidirectional communication, control center strategy processing, and equipment terminal status feedback", especially lacking a specific technical solution for realizing bidirectional data transmission between the control center and equipment terminals through a carrier communication module, and generating control commands based on preset strategies to form closed-loop control.

[0005] In a first aspect, embodiments of this application provide an intelligent control system based on carrier communication, including a carrier communication module, a control center, and multiple intelligent device terminals; the carrier communication module is connected to the control center and the multiple intelligent device terminals respectively, and is used to realize bidirectional data transmission between the control center and the multiple intelligent device terminals by using power lines as signal transmission media; The control center is used to receive data information transmitted from smart device terminals through a carrier communication module, process and analyze the received data information, generate corresponding control commands according to a preset control strategy, and send the control commands to the corresponding smart device terminals through the carrier communication module. Multiple intelligent device terminals are connected to the control center through a carrier communication module. Each intelligent device terminal is used to receive control commands sent by the control center through the carrier communication module, perform corresponding operations according to the received control commands, and feed back the corresponding operating status and parameters to the control center through the carrier communication module.

[0006] In some embodiments, the carrier communication module modulates the data signal to be transmitted into a high-frequency carrier signal and couples it to the power line for transmission via a coupler; the carrier communication module at the receiving end decouples the high-frequency carrier signal from the power line and demodulates it back into the original data signal; the carrier communication module performs error correction coding and anti-interference processing on the transmitted data to adapt to the noise environment of the power line channel.

[0007] In some embodiments, the control center performs protocol parsing and format standardization on the received raw data, extracts the operating parameters, status identifiers and timestamp information of the smart device terminal; and performs outlier detection and validity verification on the extracted information based on preset data verification rules to generate standardized device status data.

[0008] In some embodiments, the control center determines the control logic and execution parameters of the target device by matching standardized device status data with a preset control strategy rule base; if multiple control strategies match, the optimal control strategy is selected according to a preset priority rule, and a control instruction containing device address, operation type and parameter threshold is generated.

[0009] In some embodiments, the carrier communication module encapsulates the control command into a data packet with a unique device address identifier and transmits it to the target smart device terminal via the power line; if the target smart device terminal does not return a receipt confirmation message within a preset time, the carrier communication module automatically resends the control command until a confirmation is received or the maximum number of retransmissions is reached.

[0010] In some embodiments, each smart device terminal is physically connected to the power line through a built-in carrier communication interface and sends a device registration request to the control center when accessing the system. After receiving the registration request, the control center assigns a unique device address to each smart device terminal and establishes a mapping relationship between the device address and the power line network node.

[0011] In some embodiments, the smart device terminal performs address verification and instruction validity verification on the received control instructions. If the verification passes, it parses the operation type and parameters in the instruction; calls the corresponding device driver to execute the operation based on the parsing result, and records the real-time status parameters of the device before and after the operation is executed.

[0012] In some embodiments, the intelligent device terminal collects its own operating status data in real time, including voltage, current, operating mode and fault codes; the collected data is packaged into status feedback information according to a preset data format and sent to the control center through the carrier communication module in a timed reporting or event-triggered manner.

[0013] In some embodiments, the control center has a built-in machine learning model, which is trained based on historical equipment operation data and the execution effect of control strategies to generate a predictive model of equipment operation status. When processing and analyzing real-time equipment status data, the predictive model is used to predict the future operation status of the equipment, and the execution parameters of control commands are dynamically adjusted in combination with preset energy consumption optimization or fault prevention strategies.

[0014] In some embodiments, the smart device terminal has a built-in adaptive adjustment algorithm. When a control command is executed, the device's operating parameters are collected in real time after the operation. If the operating parameters are detected to deviate from the expected range of the command, the device's operating parameters are automatically adjusted through the adaptive adjustment algorithm, and the adjustment results and deviation data are fed back to the control center so as to update the preset control strategy.

[0015] This invention utilizes existing power lines as the signal transmission medium, eliminating the need for additional dedicated communication lines and reducing hardware investment and construction complexity. It achieves bidirectional data transmission through a carrier communication module, and combined with anti-interference processing and error correction mechanisms, ensures stable communication under power line channels. The control center processes and analyzes equipment status data to generate strategic control commands, supporting multi-device collaborative management and dynamic adjustment, thus improving the system's intelligence level. Intelligent device terminals provide real-time feedback on operating status and parameters, enabling the control center to optimize control strategies based on actual operating conditions, achieving efficient and safe equipment operation. Through device registration and address allocation mechanisms, it supports flexible access to new devices, making it suitable for large-scale intelligent device cluster management.

[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the structure of an intelligent control system based on carrier communication that uses a self-focusing optical fiber coupling scheme, according to an embodiment of this application.

[0019] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0022] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0023] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0024] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0025] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0026] In the field of intelligent control, traditional intelligent device control systems typically use wireless communication (such as WiFi and Bluetooth) or dedicated communication lines (such as RS485 and Ethernet) to connect devices to the control center. Wireless communication is susceptible to signal interference and has limited coverage, and suffers from data transmission delays and security issues. Dedicated communication lines require additional cabling, increasing system deployment costs and construction complexity, and are particularly difficult to flexibly expand in existing buildings or industrial settings. While existing technologies include control systems based on power line carrier communication (using power lines as the signal transmission medium), these systems generally suffer from the following shortcomings: 1. Insufficient transmission reliability: The lack of effective anti-interference and error correction mechanisms for power line channel noise results in poor stability of bidirectional data transmission; 2. Limited centralized control capabilities: The control center can only issue simple commands and lacks the ability to deeply process, analyze, and strategically control equipment status data; 3. Low equipment management efficiency: Most intelligent device terminals only receive instructions in one direction and cannot provide real-time feedback on operating status and parameters, making it difficult to form a control closed loop, resulting in poor system real-time performance and scalability.

[0027] Currently, no existing technology discloses an intelligent control system architecture that integrates "bidirectional power line carrier communication, strategic processing by the control center, and status feedback from equipment terminals." In particular, it lacks a specific technical solution for achieving bidirectional data transmission between the control center and equipment terminals via a carrier communication module, and for generating control commands based on preset strategies to form a closed-loop control system. Therefore, there is an urgent need for an intelligent control system that can solve the above problems and possesses high real-time performance, stability, and scalability.

[0028] To resolve the above issues, please refer to... Figure 1This application provides an intelligent control system based on carrier communication, including a carrier communication module, a control center, and multiple intelligent device terminals. The carrier communication module is connected to the control center and the multiple intelligent device terminals respectively, and is used to realize bidirectional data transmission between the control center and the multiple intelligent device terminals using power lines as signal transmission media. The control center is used to receive data information transmitted from the intelligent device terminals through the carrier communication module, process and analyze the received data information, generate corresponding control commands according to a preset control strategy, and send the control commands to the corresponding intelligent device terminals through the carrier communication module. The multiple intelligent device terminals are connected to the control center through the carrier communication module, and each intelligent device terminal is used to receive the control commands sent by the control center through the carrier communication module, execute the corresponding operation according to the received control commands, and feed back the corresponding operating status and parameters to the control center through the carrier communication module.

[0029] Specifically, this system constructs a bidirectional communication network using Power Line Communication (PLC) technology. Combined with strategic processing at the control center and real-time feedback from device terminals, it forms a closed-loop control system of "data acquisition - analysis and decision-making - command execution - status feedback." The PLC communication module is used for: Media multiplexing: Utilizing existing power lines to simultaneously transmit power and data signals, eliminating the need for additional wiring and reducing deployment costs. Bidirectional data transmission: Supporting command issuance (downlink) from the control center to device terminals and status reporting (uplink) from device terminals to the control center, achieving full-duplex communication. Anti-interference and error correction mechanisms: Dedicated signal processing algorithms are designed to address power line channel noise (such as harmonic interference, multipath fading, and impedance mismatch).

[0030] Modulation and demodulation techniques employ orthogonal frequency division multiplexing (OFDM) or spread spectrum modulation (such as DSSS) to modulate data signals onto a high-frequency carrier (e.g., the 10kHz-500kHz band), avoiding power frequency interference (50Hz / 60Hz). A built-in adaptive equalizer compensates for channel attenuation in real time and dynamically adjusts transmission parameters (such as modulation scheme and transmit power). CRC checksums or forward error correction (FEC) codes are added to data frames; the receiver detects and corrects errors. If error correction fails, an ARQ (Automatic Retransmission Request) is triggered to ensure data integrity. The system includes a coupler (separating power and carrier signals), a modem (PLC Modem), signal amplification and filtering circuitry, and supports various power line types (single-phase / three-phase).

[0031] The control center is used for: Data processing and analysis: receiving real-time device terminal status data, and performing storage, visualization, anomaly detection, and trend prediction. Policy-based control: generating dynamic control policies based on preset rules (such as threshold triggering, timing logic) or machine learning models. Device management: supporting device registration, address allocation, firmware upgrades, and maintaining device network topology and status lists.

[0032] The technical architecture is layered as follows: Data Layer: Deploys a real-time database (such as Redis) to store real-time device status (voltage, current, temperature, etc.), and historical data is stored in a relational database (such as MySQL) or a time-series database (such as InfluxDB). The data interface supports standardized protocols (such as MQTT and HTTP) and is compatible with third-party system access. Processing Layer: Status Monitoring Module: Detects device faults through threshold judgment (such as device temperature exceeding safe values) or pattern recognition (such as abnormal fluctuations in motor speed). Data Analysis Module: Employs statistical analysis (such as mean and variance) or AI algorithms (such as LSTM to predict device energy consumption) to generate optimization strategies (such as peak-shifting power consumption and collaborative device scheduling).

[0033] Strategy layer: Preset control strategies support flexible configuration (such as custom logic rules through a visual interface). When the strategy is executed, a control frame containing device address, operation command, and parameters is generated (such as "Device ID=0x01, command=adjust power, parameter=50%").

[0034] Intelligent device terminals are used for: Command parsing and execution: receiving commands from the control center and driving actuators (such as motors, relays, and sensors) to operate. Status acquisition and feedback: collecting operating parameters (such as voltage, current, temperature, and operating time) in real time and actively or passively reporting them to the control center.

[0035] The main control unit includes a microcontroller (such as STM32 or ESP32) with an integrated carrier communication module driver, which parses instructions and controls peripherals. Sensors and actuators include: Sensors: current transformers, temperature sensors, pressure sensors, etc., which collect real-time equipment status data. Actuators: relays, frequency converters, regulating valves, etc., which respond to control commands (such as switching, speed regulation, and temperature regulation).

[0036] The feedback mechanism includes: proactive feedback: reporting status data at preset intervals (e.g., once per second). Event-triggered feedback: immediately reporting emergency data when the status is abnormal (e.g., overload, temperature exceeding limits) or parameter changes exceed thresholds.

[0037] By deploying a carrier communication host (including a PLC modem and network interface) in the control center, the system connects to the power line and the local area network; the intelligent device terminals have built-in carrier communication modules and directly connect to the same power network. When the device is powered on for the first time, it registers with the control center through an automatic addressing protocol. The control center assigns a unique device ID (such as a 16-bit address code) and establishes a "device ID-power line address" mapping table.

[0038] The control center inputs device type, parameter thresholds, and control strategies (such as "when device A current > 120% of rated value, issue a shutdown command and notify the administrator") through the management interface. The device terminal has its firmware burned, including carrier communication drivers, command parsing programs, and sensor data acquisition modules.

[0039] The data transmission and control closed-loop implementation includes: Downlink command flow (control center → device terminal): The control center generates a command frame (format: [device ID][command code][parameter][checksum]), which is modulated into a high-frequency signal through the carrier communication module and coupled to the power line. The target device terminal's carrier module demodulates the signal, verifies data integrity, and then the main control unit parses the command and drives the actuator to operate.

[0040] Uplink feedback process (device terminal → control center): The device terminal collects status data, encapsulates it into a feedback frame (format: [device ID][status code][parameter 1][parameter 2]…[checksum]), and uploads it to the control center via the carrier module. The control center parses the data, updates the device status list, and triggers the data analysis module to execute strategy matching (such as optimizing the scheduling plan based on energy consumption data).

[0041] An example of closed-loop control includes: Scenario: Automatic speed adjustment when an industrial motor's temperature is too high. The motor terminal sensor detects a temperature >80℃, triggering an emergency feedback; the control center receives the data, determines that the threshold has been exceeded, and generates a "reduce speed to 70%" command; the command is sent to the motor terminal to execute the speed adjustment operation; the motor provides new speed and temperature data, the control center confirms that the status has returned to normal, and the closed loop is complete.

[0042] By periodically scanning the power line channel, the system dynamically selects the subcarrier with the least interference (in OFDM mode) or adjusts the transmission rate (e.g., reducing the rate to improve reliability when noise is high). Commands and sensitive data (such as device parameters) are encrypted with AES to prevent malicious tampering or eavesdropping. The control center sets the number of command retransmissions (e.g., 3 times); if the device does not acknowledge receipt, it automatically retransmits the command. The device terminal has a built-in watchdog; in case of an anomaly, it restarts and re-registers.

[0043] The system supports plug-and-play functionality; new devices automatically register upon connection to the power grid, and the control center dynamically updates the device list without requiring manual configuration of network parameters. The carrier communication module supports multiple PLC standards (such as HomePlug and G3-PLC) and provides API interfaces, ensuring compatibility with third-party devices and platforms (such as IoT cloud platforms and SCADA systems).

[0044] This system requires no additional wiring, reducing deployment costs and is especially suitable for the renovation of existing buildings; two-way communication and error correction mechanisms improve transmission reliability, and closed-loop control enables real-time monitoring of equipment status; the control center's strategic processing supports complex scenarios (such as multi-device collaboration, energy consumption optimization, and fault self-healing).

[0045] The system achieves two-way interaction between "command issuance and status feedback" through power lines, forming a control closed loop; the modular design of the control center (separation of data, processing, and strategy layers) supports flexible expansion and algorithm upgrades; and the system ensures data transmission stability by dynamically responding to the complex noise environment of power lines.

[0046] The system's application scenarios include: smart buildings: centralized monitoring and energy management of equipment such as lighting, air conditioning, and elevators; industrial control: production line equipment status monitoring, remote operation and maintenance, and fault early warning; smart parks: low-cost networking and collaborative control of charging piles, sensor networks, and environmental control equipment.

[0047] Through the above technical solutions, this system achieves intelligent control with high real-time performance, stability and scalability, breaking through the limitations of traditional wireless communication and dedicated lines, and providing a new paradigm for the application of power line carrier technology in the field of intelligent control.

[0048] In some embodiments, the carrier communication module modulates the data signal to be transmitted into a high-frequency carrier signal and couples it to the power line for transmission via a coupler; the carrier communication module at the receiving end decouples the high-frequency carrier signal from the power line and demodulates it back into the original data signal; the carrier communication module performs error correction coding and anti-interference processing on the transmitted data to adapt to the noise environment of the power line channel.

[0049] The carrier communication module loads data signals onto the power line for transmission using high-frequency modulation technology. It also integrates error correction coding and anti-interference mechanisms to solve the problem of transmission instability caused by power line channel noise (such as harmonics and multipath attenuation).

[0050] Signal modulation and coupling include: At the transmitting end: The digital signal is converted into a high-frequency carrier signal (e.g., 10-500kHz band) using an OFDM modulator. The carrier signal is then superimposed onto the power line's frequency current using a coupler (capacitive / inductive coupling circuit), achieving "power + data" co-line transmission. At the receiving end: The power line frequency signal and the high-frequency carrier signal are separated by a decoupler and then demodulated back to the original digital signal.

[0051] Error correction coding involves adding FEC (Forward Error Correction) codes (such as Reed-Solomon codes) to the data frame before transmission, increasing the redundancy check bits at a 1:3 ratio. The receiving end corrects single-bit or multi-bit errors using a decoding algorithm. A CRC (Cyclic Redundancy Check) code is also added; the receiving end verifies data integrity, and if the verification fails, a retransmission is triggered.

[0052] The anti-interference processing uses a built-in adaptive equalizer to collect the channel impulse response in real time and dynamically adjust the filter coefficients to compensate for signal attenuation; it uses spread spectrum technology (DSSS) to expand the signal spectrum and reduce the impact of narrowband interference; and it periodically scans the channel noise distribution and automatically switches to low-noise subcarriers (OFDM mode).

[0053] In some embodiments, the control center performs protocol parsing and format standardization on the received raw data, extracts the operating parameters, status identifiers and timestamp information of the smart device terminal; and performs outlier detection and validity verification on the extracted information based on preset data verification rules to generate standardized device status data.

[0054] The control center performs protocol parsing, format standardization, and validity verification on the raw data reported by the devices to generate standardized status data, thus resolving the problem of heterogeneous data from multiple devices.

[0055] It supports multi-protocol parsing engines (such as custom PLC protocols, Modbus, DL / T 645), identifies the protocol identifier in the data frame header based on the device type (such as sensor, actuator), and extracts the payload. It converts different formats of operating parameters (such as temperature units ℃ / ℉, current units A / mA) into a unified standard format (such as JSON / Protobuf), with fields including: device ID, parameter type, value, status identifier (normal / warning / fault), and timestamp.

[0056] Outlier detection and validity verification include: threshold verification: comparing parameter values ​​with preset upper and lower limits of the device (e.g., motor current ≤ 150% of rated current); exceeding the limit is marked as an anomaly; timestamp continuity check: if the timestamp interval between adjacent data frames exceeds twice the acquisition cycle, it is determined as invalid data (possibly due to packet loss); logical consistency check: if the device status is "off", the power parameter should be close to 0; otherwise, it is marked as contradictory and a device status check is triggered. Standardized output data is generated and stored in a real-time database for subsequent analysis modules to use.

[0057] In some embodiments, the control center determines the control logic and execution parameters of the target device by matching standardized device status data with a preset control strategy rule base; if multiple control strategies match, the optimal control strategy is selected according to a preset priority rule, and a control instruction containing device address, operation type and parameter threshold is generated.

[0058] The control center matches standardized status data with a preset policy rule base, supports priority filtering when multiple policies conflict, and generates control commands that can be executed by the device.

[0059] The policy rule base construction includes: rules adopt the IF-THEN structure, for example: Rule 1: IF device temperature > 80℃ THEN send a "stop" command and push an alarm; Rule 2: IF the period is the peak power period and the device power > 5kW THEN adjust the power to 70%; rules are stored in the database and contain fields: rule ID, trigger condition, execution action, priority (1-10, the larger the value, the higher the priority).

[0060] Real-time status data is input into the rule engine (such as Drools), and the activation rules are quickly filtered through the pattern matching algorithm (RETE algorithm). If multiple rules are triggered (such as the device temperature exceeding the limit and being in the peak power period), the highest priority rule is selected for execution, and the secondary rules are queued to wait for the status to change and then be rematched.

[0061] The instruction format is defined as: [Device address (2 bytes)][Operation type (1 byte, such as 0x01=adjustment, 0x02=query)][Parameter length (1 byte)][Parameter value (N bytes)][CRC check (2 bytes)]; Example: The instruction to adjust the power to 50% for device 0x001 is: 0x00 0x01 0x01 0x32 (50%) 0xAB 0xCD.

[0062] In some embodiments, the carrier communication module encapsulates the control command into a data packet with a unique device address identifier and transmits it to the target smart device terminal via the power line; if the target smart device terminal does not return a receipt confirmation message within a preset time, the carrier communication module automatically resends the control command until a confirmation is received or the maximum number of retransmissions is reached.

[0063] By using device address identification, acknowledgment, and automatic retransmission, control commands are reliably delivered to the terminal, thus resolving the packet loss problem in power line transmission.

[0064] Packet encapsulation and addressing include: adding a unique device address (such as a 16-bit ID) when encapsulating control commands; all devices in the power line network receive the signal, but only the target address device parses and processes it, while other devices discard it; address allocation is shown in Example 5, and broadcast addresses (0xFFFF) are supported for sending commands in bulk (such as system time synchronization).

[0065] Upon receiving and verifying the instruction, the device terminal immediately returns an ACK confirmation frame (containing the instruction ID). After sending the instruction, the control center starts a timer (e.g., 500ms). If no ACK is received within the timeout period, the instruction is retransmitted, up to a maximum of 3 times (configurable). If the retransmission still fails, the device is marked offline and a fault procedure is triggered. The instruction contains an incrementing sequence number (4 bytes). The device terminal caches the 10 most recent instruction sequence numbers. Duplicate instructions with the same sequence number are discarded to avoid redundant operations (e.g., multiple opening and closing of relays).

[0066] In some embodiments, each smart device terminal is physically connected to the power line through a built-in carrier communication interface and sends a device registration request to the control center when accessing the system. After receiving the registration request, the control center assigns a unique device address to each smart device terminal and establishes a mapping relationship between the device address and the power line network node.

[0067] New devices are automatically registered upon connection, and the control center assigns a unique address and establishes a network mapping, thus solving the problem of managing and addressing multiple devices.

[0068] The registration process includes: after the device is powered on, it sends a registration request frame (containing device type, vendor ID, and hardware version) through the carrier module; after receiving the frame, the control center queries the address pool (initialized to 0x0001-0xFEFF), allocates the minimum available address, and returns a registration response frame (containing the allocated device ID and network parameters).

[0069] Mapping relationships are established by maintaining a device registry, with fields including: Device ID, powerline physical address (MAC-like address based on PLC protocol), Device Type, Last Communication Time, and Status (Online / Offline). Manual addition of existing devices is supported: Device ID and physical address are entered through the management interface to force binding. Devices offline for more than 30 minutes (configurable) are marked as "offline." Upon re-entry, a registration request is sent; if the original address is unused, it is reused; otherwise, a new address is assigned.

[0070] In some embodiments, the smart device terminal performs address verification and instruction validity verification on the received control instructions. If the verification passes, it parses the operation type and parameters in the instruction; calls the corresponding device driver to execute the operation based on the parsing result, and records the real-time status parameters of the device before and after the operation is executed.

[0071] The device terminal verifies the legality of the instructions to ensure operational safety, and records the status changes after execution to form a control closed loop.

[0072] Command verification includes: Address verification: Compare the device ID in the command with its own ID, and discard if they do not match; Legality verification: Check whether the operation type is supported (e.g., the sensor only supports the "query" command and does not support "control"), and whether the parameter value is within the device's capability range (e.g., the brightness parameter of the dimming device needs to be 0-100%).

[0073] After parsing the operation type, the instruction execution calls the corresponding driver (such as relay driver, frequency converter API). Before execution, the current state is recorded as a "pre-execution snapshot". After execution, wait 50ms (device response delay) and collect the new state as "post-execution data" for feedback to the control center.

[0074] The security mechanism includes: critical commands (such as "stop" and "reset") require double verification: in addition to legality verification, the control center is required to send a secondary confirmation command containing a random verification code, and the device terminal executes the command after comparing the locally generated verification code with the actual code.

[0075] In some embodiments, the intelligent device terminal collects its own operating status data in real time, including voltage, current, operating mode and fault codes; the collected data is packaged into status feedback information according to a preset data format and sent to the control center through the carrier communication module in a timed reporting or event-triggered manner.

[0076] The device terminal collects multi-dimensional status data in real time, supports scheduled reporting and event-triggered reporting, and ensures that the control center can keep track of the device status in real time.

[0077] Data acquisition includes: basic parameters: voltage, current, frequency (acquired through power metering chip); operating mode (manual / automatic), running time (recorded by counter); fault parameters: fault code (e.g., 0x01 = overload, 0x02 = communication timeout), error occurrence timestamp.

[0078] The reporting mechanism includes: timed reporting: data is packaged and sent according to a preset period (e.g., 1 second / time for industrial equipment, 1 minute / time for lighting equipment), and the period can be configured remotely by the control center; event-triggered reporting: when the parameter change exceeds the threshold (e.g., temperature change >5℃) or the fault code is updated, the current task is immediately interrupted and an emergency feedback frame is sent, which has a higher priority than timed reporting.

[0079] The data packaging format reduces transmission load by using a compact binary format, such as: [Device ID (2B)], [Data type (1B, 0x01=normal state, 0x02=fault)], [Time stamp (4B, Unix timestamp)], [Parameter 1 (2B)], [Parameter 2 (2B)]...[CRC (2B)].

[0080] In some embodiments, the control center has a built-in machine learning model, which is trained based on historical equipment operation data and the execution effect of control strategies to generate a predictive model of equipment operation status. When processing and analyzing real-time equipment status data, the predictive model is used to predict the future operation status of the equipment, and the execution parameters of control commands are dynamically adjusted in combination with preset energy consumption optimization or fault prevention strategies.

[0081] The control center uses historical data to train predictive models, combines them with real-time data to predict equipment status, dynamically optimizes control strategies, and improves the system's intelligence level.

[0082] The model training process includes: Data preprocessing: Extracting equipment operating parameters, control commands, and fault records from historical databases, normalizing them, and dividing them into training sets (80%) and validation sets (20%); Model selection: Using LSTM neural networks for energy consumption prediction and random forest classifiers for fault prediction, trained using the TensorFlow / PyTorch framework; Model update: Retraining is automatically triggered weekly, and if the accuracy of the validation set drops by more than 5%, a manual review process is triggered.

[0083] Real-time forecasting and strategy adjustment: By inputting real-time status data into the equipment operation status prediction model, the model outputs predicted values ​​for parameters such as temperature and energy consumption for the next hour. Combined with preset strategies (such as "starting energy-saving mode 30 minutes in advance when predicted energy consumption exceeds the threshold"), the control command parameters are adjusted (such as changing the power adjustment target from 50% to 45%). Example: Based on the load prediction model trained on historical air conditioning data, when high temperature weather is predicted, the operating power of multiple air conditioners is adjusted in advance to avoid grid overload.

[0084] In some embodiments, the smart device terminal has a built-in adaptive adjustment algorithm. When a control command is executed, the device's operating parameters are collected in real time after the operation. If the operating parameters are detected to deviate from the expected range of the command, the device's operating parameters are automatically adjusted through the adaptive adjustment algorithm, and the adjustment results and deviation data are fed back to the control center so as to update the preset control strategy.

[0085] The device terminal adjusts its operating parameters in real time using an adaptive algorithm to compensate for command execution deviations, forming a dual control system consisting of local and global closed loops.

[0086] Deviation detection and adaptive adjustment are achieved by collecting parameters (such as motor speed) in real time after executing control commands and calculating the deviation from the target value of the command (such as a target of 1000 rpm and an actual speed of 950 rpm, resulting in a deviation of -5%). The built-in PID adaptive algorithm dynamically adjusts the control parameters according to the deviation (such as increasing the inverter output voltage) until the parameters enter the allowable range (such as ±2%).

[0087] Feedback and strategy updates are achieved by feeding back deviation data and adjustment parameters during the adjustment process to the control center (e.g., "Equipment 0x001, power adjustment command deviation +3%, self-adjustment parameter A=120"). The control center collects feedback data from multiple devices, analyzes common deviations (e.g., a batch of frequency converters generally have adjustment lag), and updates the preset control strategy for this type of equipment (e.g., advance compensation of 5% adjustment amount).

[0088] Safety boundary limits are set by adaptive adjustment to set hard limits (such as motor speed adjustment not exceeding ±20% of the rated value) to avoid equipment damage caused by algorithm malfunction; if the self-adjustment fails to meet the standard for 3 consecutive times, a fault report is triggered, requesting the control center to intervene.

[0089] In some embodiments, by introducing reinforcement learning (RL) algorithms, the control center can autonomously learn the optimal control strategy in complex power line network environments, solving dynamic scenarios (such as power grid load fluctuations and changes in equipment aging characteristics) that are difficult to handle by traditional rule bases.

[0090] State space and action definition include: State: containing real-time device parameters (voltage, current, temperature), grid status (peak and valley periods, real-time electricity price, line load rate), and historical control effects (energy consumption, equipment life loss); Action: a set of control commands (adjusting power, switching operating modes, sleep / wake-up), with each action accompanied by a parameter range (e.g., power adjustment step size ±5%). Reward function: Multi-objective weighted design: reward = α × energy saving benefit + β × equipment lifespan gain - γ × control delay - δ × number of policy conflicts (α / β / γ / δ are configurable weights, balancing energy consumption, equipment health, response speed, and stability). The reinforcement learning architecture uses a deep Q-network (DQN) or policy gradient algorithm (PPO). An experience replay buffer stores historical (state-action-reward-next state) data. Environment simulator: A virtual environment is built based on historical power grid data and equipment models for offline training, avoiding the impact of online learning on the actual system. Periodic synchronization mechanism: The trained policy model (.h5 file) is pushed to the control center every 24 hours, replacing some inefficient rules.

[0091] Online application scenarios for charging pile clusters: Based on real-time electricity prices and grid load, the charging power of each charging pile is dynamically adjusted, prioritizing full charging during off-peak hours and avoiding overload during peak hours. After optimization through the RL algorithm, the overall charging efficiency is improved.

[0092] In some embodiments, federated learning technology is used to aggregate data from multiple devices to train a global model without disclosing the original data of user devices, thereby resolving the contradiction between data privacy of distributed devices and the generalization ability of the model.

[0093] The federated learning process includes: initialization: the control center distributes the initial global model (such as the device fault prediction model) to each smart terminal; local training: the terminal trains the model using its own historical data (containing only parameters after anonymizing the device ID) and uploads gradient update parameters (not the original data). Federated aggregation: The control center aggregates the model parameters of each device using the FedAvg algorithm to generate a new global model, which is then returned to the terminal for continued training; Privacy protection: Differential privacy (DP) technology is used to add noise during gradient uploading to ensure that parameter aggregation does not leak the data features of a single device.

[0094] In smart home scenarios, each household's smart meter data is used to train energy consumption prediction models locally. The control center then aggregates this data to generate universal energy-saving strategies that are suitable for different apartment types, thus protecting users' electricity privacy while improving the universality of the strategies.

[0095] The device terminal adds a federated learning client module to support breakpoint resume (such as caching gradient parameters when the network is interrupted and re-transmitting them after recovery); the control center is set to manage model version numbers, and only accepts gradient updates that differ from the current global model version by ≤3, to prevent interference from outdated parameters.

[0096] In some embodiments, by constructing a digital twin for each smart device and driving the dynamic evolution of the virtual model through real-time data, the remaining life of the device (RUL) can be predicted and pre-failure maintenance can be performed, reducing the risk of unplanned downtime.

[0097] Physical Model: A multiphysics simulation model is constructed based on equipment CAD drawings and physical parameters (such as motor torque-speed curves); Data-Driven Model: An LSTM-Attention hybrid network is used to input real-time vibration, temperature, and current data, and output the health index (0-100, <30 triggers maintenance warning) of key components (such as bearings and capacitors); Twin Mapping: The real-time status of the equipment is synchronized every 50ms, and the simulation data and measured data are fused through Kalman filtering to reduce noise interference.

[0098] When the twin model predicts that the remaining lifespan of a device is less than 72 hours or the health index drops sharply (>20% / hour), a three-level response is automatically triggered: ① Level 1: Issue a self-repair command (such as cleaning dust from the heat dissipation holes); ② Level 2: Schedule backup equipment to come online and perform planned downtime maintenance; ③ Level 3: Immediately shut down the device and push a work order to the operation and maintenance platform, along with a fault location prediction (accuracy ±10cm).

[0099] The visualization interface displays a 3D twin model of the device on a large screen in the control center, and renders temperature cloud maps and stress distribution in real time. Maintenance personnel can simulate the effects of different maintenance schemes (such as life extension prediction after component replacement) by dragging and dropping operations.

[0100] In some embodiments, for novel faults that cannot be identified by traditional threshold detection, an autoencoder is used to learn the feature representation of the normal state of the device, and unsupervised anomaly detection is achieved by reconstructing the error, thereby improving the robustness of the system.

[0101] Model construction and training include: Input layer: all operating parameters of the device (such as voltage, current, frequency, and operating mode, totaling 12 dimensions); Encoding layer: compressed to 3-dimensional latent variables through two fully connected layers (capturing core features); Decoding layer: restores the original 12-dimensional parameters, with the training objective being to minimize the mean squared error of reconstruction (MSE); Normal data training: collect fault-free data for 30 days after the device leaves the factory, and iteratively train until the validation set MSE < 0.05.

[0102] The online detection mechanism calculates the reconstruction error between the current data and the normal state by inputting real-time data into the autoencoder. When the error exceeds the dynamic threshold (normal mean + 3σ) for three consecutive detection cycles (e.g., 30 seconds), it is judged as an unknown anomaly and triggers: ① The device terminal starts a safety mode (locks the current state to prevent deterioration); ② The control center marks the abnormal data and triggers the manual review process, while adding the abnormal samples to the federated learning system and updating the global anomaly feature library.

[0103] The model is automatically fine-tuned monthly using newly collected normal data to prevent normal feature drift caused by equipment aging and ensure long-term detection accuracy.

[0104] In some embodiments, to address the information overload problem in large-scale device networks, an attention mechanism is introduced, enabling the control center to dynamically focus on the status of key devices and improve decision-making efficiency in complex scenarios.

[0105] Attention model design includes: Query vector: the target of the current decision (e.g., "high-energy-consuming equipment should be controlled first when the power grid is overloaded"); Key vector: equipment characteristics (power level, load rate, whether it is a critical business equipment); Value vector: real-time status data of the equipment; the equipment weight is calculated by scaling dot product attention: attention_score = softmax(Query*Key / sqrt(d_k))*Value; (d_k is the dimension of the key vector, and the higher the weight of the equipment, the higher the priority in the decision).

[0106] Application scenarios include: Power grid peak load response: When a line load rate > 90% is detected, the control center generates a "load reduction" query vector. The attention mechanism automatically filters out the current highest-power, non-critical equipment (such as non-production lighting and redundant heating devices), prioritizing the issuance of shutdown commands, reducing the response time from 30 seconds of traditional polling to 5 seconds; Fault cascading effect suppression: When a device trips due to a fault, the attention model quickly locates the affected upstream and downstream devices based on the device connection graph (a predefined power line network adjacency matrix), adjusting their protection parameters in advance to prevent fault propagation. System integration: A "critical level" field (levels 0-5, with level 5 being core production equipment) is added to the device metadata as a priori weight input for the attention model; Visual attention heatmaps are supported, allowing maintenance personnel to view the weight distribution of each device in each decision, assisting in strategy auditing.

[0107] This invention utilizes existing power lines as the signal transmission medium, eliminating the need for additional dedicated communication lines and reducing hardware investment and construction complexity. It achieves bidirectional data transmission through a carrier communication module, and combined with anti-interference processing and error correction mechanisms, ensures stable communication under power line channels. The control center processes and analyzes equipment status data to generate strategic control commands, supporting multi-device collaborative management and dynamic adjustment, thus improving the system's intelligence level. Intelligent device terminals provide real-time feedback on operating status and parameters, enabling the control center to optimize control strategies based on actual operating conditions, achieving efficient and safe equipment operation. Through device registration and address allocation mechanisms, it supports flexible access to new devices, making it suitable for large-scale intelligent device cluster management.

[0108] It should be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. It should be understood that when an element or layer is referred to as “on,” “adjacent to,” “connected to,” or “coupled to” other elements or layers, it may be directly on, adjacent to, connected to, or coupled to other elements or layers, or there may be intervening elements or layers. Conversely, when an element is referred to as “directly on,” “directly adjacent to,” “directly connected to,” or “directly coupled to” other elements or layers, there are no intervening elements or layers. It should be understood that although the terms first, second, third, etc., may be used to describe various elements, components, areas, layers, and / or portions, these elements, components, areas, layers, and / or portions should not be limited by these terms. These terms are merely used to distinguish one element, component, area, layer, or portion from another element, component, area, layer, or portion. Therefore, without departing from the teachings of this application, the first element, component, area, layer, or portion discussed below may be referred to as a second element, component, area, layer, or portion.

[0109] Spatial relation terms such as “below,” “under,” “below,” “under,” “above,” “above,” etc., are used herein for convenience of description to describe the relationship between one element or feature shown in the figure and other elements or features. It should be understood that, in addition to the orientation shown in the figure, spatial relation terms are intended to also include different orientations of the device in use and operation. For example, if the device in the figure is flipped, then the element or feature described as “below,” “under,” or “below” other elements or features will be oriented “above” other elements or features. Therefore, the exemplary terms “below” and “under” can include both above and below orientations. The device may be otherwise oriented (rotated 90 degrees or otherwise) and the spatial descriptive terms used herein will be interpreted accordingly.

[0110] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. When used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising” and / or “including,” when used in this specification, identify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups. When used herein, the term “and / or” includes any and all combinations of the associated listed items.

[0111] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0112] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An intelligent control system based on carrier communication, characterized in that, It includes a carrier communication module, a control center, and multiple intelligent device terminals; the carrier communication module is connected to the control center and the multiple intelligent device terminals respectively, and is used to realize bidirectional data transmission between the control center and the multiple intelligent device terminals by using power lines as signal transmission medium. The control center is used to receive data information transmitted from smart device terminals through a carrier communication module, process and analyze the received data information, generate corresponding control commands according to a preset control strategy, and send the control commands to the corresponding smart device terminals through the carrier communication module. Multiple intelligent device terminals are connected to the control center through a carrier communication module. Each intelligent device terminal is used to receive control commands sent by the control center through the carrier communication module, perform corresponding operations according to the received control commands, and feed back the corresponding operating status and parameters to the control center through the carrier communication module.

2. The intelligent control system based on carrier communication according to claim 1, characterized in that, The carrier communication module modulates the data signal to be transmitted into a high-frequency carrier signal, which is then coupled to the power line for transmission via a coupler. The carrier communication module at the receiving end decouples the high-frequency carrier signal from the power line and demodulates it back into the original data signal. The carrier communication module performs error correction coding and anti-interference processing on the transmitted data to adapt to the noise environment of the power line channel.

3. The intelligent control system based on carrier communication according to claim 1, characterized in that, The control center performs protocol parsing and format standardization on the received raw data, extracts the operating parameters, status identifiers, and timestamp information of the smart device terminals, and performs outlier detection and validity verification on the extracted information based on preset data verification rules to generate standardized device status data.

4. The intelligent control system based on carrier communication according to claim 1, characterized in that, The control center matches standardized equipment status data with a preset control strategy rule base to determine the control logic and execution parameters of the target equipment. If multiple control strategies match, the optimal control strategy is selected according to preset priority rules, and a control instruction containing the equipment address, operation type, and parameter thresholds is generated.

5. The intelligent control system based on carrier communication according to claim 1, characterized in that, The carrier communication module encapsulates control commands into data packets with unique device address identifiers and transmits them to the target smart device terminal via power lines. If the target smart device terminal does not return a receipt confirmation message within a preset time, the carrier communication module automatically resends the control commands until a confirmation is received or the maximum number of retransmissions is reached.

6. The intelligent control system based on carrier communication according to claim 1, characterized in that, Each smart device terminal is physically connected to the power line through a built-in carrier communication interface. When accessing the system, it sends a device registration request to the control center. After receiving the registration request, the control center assigns a unique device address to each smart device terminal and establishes a mapping relationship between the device address and the power line network node.

7. The intelligent control system based on carrier communication according to claim 1, characterized in that, The intelligent device terminal performs address verification and instruction validity verification on the received control commands. If the verification is successful, it parses the operation type and parameters in the command; based on the parsing result, it calls the corresponding device driver to execute the operation, and records the real-time status parameters of the device before and after the operation is executed.

8. The intelligent control system based on carrier communication according to claim 1, characterized in that, The intelligent device terminal collects its own operating status data in real time, including voltage, current, operating mode and fault codes; the collected data is packaged into status feedback information according to the preset data format and sent to the control center through the carrier communication module in the form of timed reporting or event triggering.

9. The intelligent control system based on carrier communication according to claim 1, characterized in that, The control center has a built-in machine learning model, which is trained based on historical equipment operation data and the execution effect of control strategies to generate a predictive model of equipment operation status. When processing and analyzing real-time equipment status data, the predictive model is used to predict the future operation status of the equipment, and the execution parameters of control commands are dynamically adjusted in combination with preset energy consumption optimization or fault prevention strategies.

10. The intelligent control system based on carrier communication according to claim 1, characterized in that, The intelligent device terminal has a built-in adaptive adjustment algorithm. When a control command is executed, the device's operating parameters are collected in real time after the operation. If the operating parameters are detected to deviate from the expected range of the command, the device's operating parameters are automatically adjusted through the adaptive adjustment algorithm, and the adjustment results and deviation data are fed back to the control center to update the preset control strategy.

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