A Portable IoT Power Meter Box Modular Intelligent Identification and Configuration System

Abstract

This invention discloses a portable IoT power metering box modular intelligent identification and configuration system, relating to the field of IoT technology. It integrates a sensor network to collect power data in real time, and a data processing module cleans, compresses, and initially filters the data. Deep learning algorithms are used for feature extraction and abnormal power consumption detection to ensure rapid response. A user interface module provides a graphical user interface, allowing users to update configurations instantly as needed. LACP monitors link status, automatically selects the optimal link, and ensures stable data transmission. A local caching and backup module temporarily stores data during network fluctuations and automatically synchronizes it to the cloud after network recovery. An adaptive optimization module monitors system load and dynamically adjusts resource allocation to ensure efficient and stable system operation. This significantly improves the data processing speed of the power IoT metering box equipment and enhances its flexibility and stability.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and more specifically to a modular intelligent identification and configuration system for a portable IoT power metering box. Background Technology

[0002] Portable IoT-based energy metering boxes are an important component of smart grids, and the demand for modular intelligent identification and configuration systems is increasingly prominent. Traditional energy metering boxes suffer from problems such as decentralized management and data silos, making it difficult to meet the needs of modern power grids for efficient, intelligent, and flexible management. Against this backdrop, the modular intelligent identification and configuration system for portable IoT-based energy metering boxes has emerged, aiming to achieve intelligent management and configuration of energy metering boxes through integrated technological means, thereby improving the efficiency and management level of power grid operation.

[0003] Existing portable IoT power metering box modular intelligent identification and configuration systems integrate high-precision measurement, communication, and control functions through hardware and software integration and modular design. These systems can achieve online monitoring of all parameters, ensuring data accuracy and real-time performance, while supporting master station line loss analysis and meter misalignment analysis. The system uses intelligent measurement switches to communicate with the concentrator via carrier wave, uploading high-precision electrical parameters to the acquisition master station, achieving efficient data processing and convenient and flexible business applications. For example, Chinese patent CN202010484461.6 discloses a power IoT metering box device based on IoT sensing technology, including a box shell, door lock device, control device, at least one power meter, IoT sensing device, and monitoring device. This device enhances the IoT and sensing capabilities of the power IoT metering box device through the IoT sensing device, realizing functions such as holographic all-time protection monitoring and early warning, accurate identification of the topological relationship of low-voltage distribution areas, line loss analysis and judgment, proactive anti-theft, automatic identification of meter row and column relationships, remote meter calibration, and meter box environment monitoring. However, the IoT sensing device mentioned in the patent, used to acquire relevant data from power IoT metering boxes, may experience slower identification speeds when processing complex or large amounts of data. This delay can lead to decreased processing efficiency and negatively impact user experience in situations requiring rapid response, such as emergency troubleshooting or handling user complaints. Furthermore, in practical applications, the system may be limited by hardware or software constraints, preventing flexible adjustments based on actual needs. This may prevent the system from meeting the customized requirements of different users or scenarios, limiting its application scope. Simultaneously, IoT communication technology can be affected by various factors such as the environment, equipment, or network operators, resulting in insufficient network stability. Network instability can lead to data transmission delays or losses, affecting the system's real-time monitoring and data analysis functions. Moreover, network failures can also cause serious consequences such as system paralysis or data loss. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention discloses a portable IoT power metering box modular intelligent identification and configuration system to solve the problems mentioned in the background technology.

[0005] The present invention adopts the following technical solution:

[0006] A portable IoT power metering box modular intelligent identification and configuration system includes: a data processing module, which is used to periodically read power metering data through a sensor network, and clean, compress, encode and preliminarily filter the collected raw data. The processed data is transmitted to the identification and analysis module through the internal message bus AMQP and recorded in a log file.

[0007] The identification and analysis module is used to monitor the data stream of the data processing module in real time through a zero-copy message queue. It uses a deep anomaly detection algorithm to extract features from the received processed data, classify and identify power consumption patterns, and detect abnormal power consumption behaviors. The identification results and anomaly detection results are then transmitted to the user interface module.

[0008] The user interface module is used to load configuration file templates and initialize the user interface. It provides a graphical interface through the document object model, allowing users to update the configuration database in real time during configuration operations.

[0009] The multi-link communication module is used to periodically check the status of each communication link through the Link Aggregation Control Protocol (LACP). Based on the detection results, it automatically selects the optimal communication link through multi-mode communication methods and redundancy mechanisms, switches to the backup link when the quality of the current link deteriorates, sends data to the cloud server using the selected link, and records the reason for the failure and attempts to resend when the transmission fails.

[0010] The local caching and backup module is used to temporarily store data in the local cache area through the SQLite database and mark it as pending synchronization when network fluctuations are detected based on the communication link status detection results of the multi-link communication module. After the network returns to normal, the cached data is automatically uploaded to the cloud server through a retransmission request, and the cached data record is cleared after the upload is successful.

[0011] An adaptive optimization module is used to monitor system load in real time and automatically adjust resource allocation strategies. The adaptive optimization module includes a system status monitoring submodule, a load assessment submodule, a resource scheduling submodule, and an online learning submodule. The output of the system status monitoring submodule is connected to the input of the load assessment submodule. The output of the load assessment submodule is connected to the input of the resource scheduling submodule. The output of the resource scheduling submodule is connected to the input of the online learning submodule.

[0012] As a further technical solution of the present invention, the data processing module's processing method is as follows: The data acquisition process is periodically triggered by a timer mechanism; the power metering data is read from the sensor network using the SPI serial communication protocol; and the read raw data is stored in a memory-mapped area in binary format. Based on the acquired data, the data processing module verifies the validity of the acquired raw data using a data verification algorithm, compresses and encodes the data using a compression algorithm, and performs initial data filtering using a sliding window filtering algorithm. The processed data is transmitted to the identification and analysis module via the internal message bus AMQP. During the data processing process, the data processing module also records log files via the syslog service, including data acquisition timestamps, data cleaning results, compression encoding details, and data transmission status.

[0013] As a further technical solution of the present invention, the zero-copy message queue transmits the data processed by the data processing module from the kernel space to the shared memory area managed by the message queue through the direct memory access mechanism. In the shared memory area, the zero-copy message queue stores and manages the data stream through a circular buffer. When the identification and analysis module arrives with new data, it immediately triggers the corresponding processing function to read the data from the shared memory area through event notification or polling methods.

[0014] As a further technical solution of the present invention, the zero-copy message queue also ensures the correctness and reliability of data reading in a multi-threaded or multi-process environment through locking mechanisms, version control or transaction processing methods during the data reading process.

[0015] As a further technical solution of the present invention, after receiving the data stream, the deep anomaly recognition algorithm automatically learns and extracts high-dimensional features from the data through a convolutional neural network, including the temporal sequence, periodicity, and potential abnormal pattern information of the electricity consumption data; the convolutional neural network performs multi-level abstract representation of the input features through a forward propagation mechanism and optimizes the model parameters through a backpropagation algorithm; then, the deep anomaly recognition algorithm classifies the extracted features into electricity consumption patterns through a support vector machine; based on the classified data, a normal electricity consumption pattern is modeled through an autoencoder, and the reconstruction error is used to determine whether the input data deviates from the normal pattern. When the reconstruction error exceeds a preset threshold, the algorithm determines that there is abnormal electricity consumption behavior and triggers an alarm mechanism through an event-driven programming model.

[0016] As a further technical solution of the present invention, the deep anomaly identification algorithm records and analyzes the results through the system log system syslog during the anomaly detection process, including anomaly type, occurrence time, and duration information, and measures the difference between abnormal data and normal data through a density-based clustering method to calculate the degree of anomaly.

[0017] As a further technical solution of the present invention, the operation method of the user interface module includes: the user selects the required functional module, the user adds or deletes the functional module by dragging and dropping, dynamically generates a configuration file according to the user's selection and applies the configuration file to the system to make the new configuration effective, and saves the configuration file for subsequent modification or rollback.

[0018] As a further technical solution of the present invention, the multi-link communication module first establishes a link aggregation group by initializing the LACP protocol, aggregating multiple physical links into a single logical link, and periodically sends hop messages through the LACP protocol's link detection mechanism to detect the communication status of each communication link, including signal strength and packet loss rate indicators. If a decrease in signal strength or an increase in packet loss rate is detected in a certain link, the status change of that link is recorded, and link selection logic is triggered. During data transmission, the multi-link communication module switches between multiple communication modes through a multi-mode communication protocol switching mechanism, including Wi-Fi, Bluetooth, and cellular networks. During data transmission, when the multi-link communication module detects a decrease in the quality of the current link or a transmission failure, it ensures the continuity and stability of data transmission through a redundant link switching mechanism. When data transmission fails, the multi-link communication module records failure information through a system log management system, including the time, cause, and link status information of each transmission failure, and stores the failure information in a local log file. After storage, it attempts to resend data based on the recorded information through a retry mechanism and error recovery algorithm until the data is successfully transmitted.

[0019] As a further technical solution of the present invention, the system status monitoring submodule includes a system status acquisition unit, a performance monitoring unit, and a log recording unit; the system status acquisition unit is used to monitor system call status in real time; the performance monitoring unit is used to monitor system resources in real time, including CPU, memory, and disk I / O indicators; the log recording unit is used to record and store monitoring data; the load assessment submodule includes a load calculation unit and a PID controller unit; the load calculation unit calculates the current system load using statistical methods, and assesses indicators including CPU utilization and memory utilization; the PID controller unit adjusts the resource allocation strategy based on the deviation between the current load and the target load using a PID controller algorithm; the resource scheduling submodule includes process priority adjustment. The system comprises a process priority adjustment unit, a CPU time slice allocation unit, and a memory management unit. The process priority adjustment unit dynamically adjusts process priorities using a nice value-based adjustment method. The CPU time slice allocation unit dynamically allocates CPU time slices using a dynamic scheduling algorithm. The memory management unit efficiently manages memory using the Least Recently Used (LRU) algorithm. The online learning submodule includes a data collection unit, a model training unit, and a policy generation unit. The data collection unit collects historical load data. Based on historical data, the model training unit trains a future load prediction model using a reinforcement learning algorithm and outputs the load prediction to the policy generation unit. The policy generation unit generates future resource allocation policies using decision trees to ensure efficient system operation under different load conditions.

[0020] The positive and beneficial effects of this invention are as follows:

[0021] 1. This invention employs a highly efficient data processing module, which can quickly clean, compress, encode, and initially filter the collected raw data, significantly improving data processing speed. The identification and analysis module uses a deep anomaly recognition algorithm to achieve rapid feature extraction and power consumption pattern recognition from the processed data, reducing instances of slow identification speeds. This is particularly beneficial in emergency fault diagnosis or handling user complaints, improving processing efficiency and enhancing user experience.

[0022] 2. The user interface module provides a graphical interface, allowing users to update the configuration database in real time, enabling the system to be flexibly adjusted according to actual needs. This modularity and user-customizable configuration capability solves the problem of hardware or software limitations that may exist in existing technologies, enabling the system to meet the customized needs of different users or different scenarios and expanding the application scope of the system.

[0023] 3. The multi-link communication module periodically checks the status of each communication link through the Link Aggregation Control Protocol (LACP) and automatically selects the optimal communication link. It switches to a backup link when the quality of the current link deteriorates, enhancing network stability. The local caching and backup module temporarily stores data in a local cache during network fluctuations and automatically synchronizes it to the cloud server after the network recovers, reducing the risk of data transmission delays or loss and improving the system's real-time monitoring and data analysis capabilities.

[0024] 4. The adaptive optimization module monitors the system load in real time and automatically adjusts resource allocation strategies, improving the system's adaptability and resource optimization capabilities under different workloads. This adaptive optimization mechanism helps the system maintain efficient and stable operation when facing different workloads and environmental changes, avoiding system paralysis or performance degradation caused by excessive load. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the overall architecture of a portable IoT power metering box modular intelligent identification and configuration system according to the present invention;

[0026] Figure 2 This is a schematic diagram illustrating the working principle of the multi-link communication module of the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] In this invention, such as Figure 1 As shown: A modular intelligent identification and configuration system for a portable IoT power metering box includes: a data processing module, which is used to periodically read power metering data through a sensor network, and clean, compress, encode and preliminarily filter the collected raw data. The processed data is transmitted to the identification and analysis module through the internal message bus AMQP and recorded in a log file.

[0029] The identification and analysis module is used to monitor the data stream of the data processing module in real time through a zero-copy message queue. It uses a deep anomaly detection algorithm to extract features from the received processed data, classify and identify power consumption patterns, and detect abnormal power consumption behaviors. The identification results and anomaly detection results are then transmitted to the user interface module.

[0030] The user interface module loads configuration file templates and initializes the user interface. It provides a graphical interface through the Document Object Model (DOM), allowing users to update the configuration database in real-time during configuration operations. The user interface module's operation methods include: user selection of desired functional modules; user addition or deletion of functional modules via drag-and-drop; dynamic generation of configuration files based on user selections; application of the configuration files to the system to make the new configuration effective; and saving the configuration files for later modification or rollback. In specific implementation...

[0031] The multi-link communication module is used to periodically check the status of each communication link through the Link Aggregation Control Protocol (LACP). Based on the detection results, it automatically selects the optimal communication link through multi-mode communication methods and redundancy mechanisms, switches to the backup link when the quality of the current link deteriorates, sends data to the cloud server using the selected link, and records the reason for the failure and attempts to resend when the transmission fails.

[0032] The local caching and backup module is used to temporarily store data in the local cache area through the SQLite database and mark it as pending synchronization when network fluctuations are detected based on the communication link status detection results of the multi-link communication module. After the network returns to normal, the cached data is automatically uploaded to the cloud server through a retransmission request, and the cached data record is cleared after the upload is successful.

[0033] An adaptive optimization module is used to monitor system load in real time and automatically adjust resource allocation strategies. The adaptive optimization module includes a system status monitoring submodule, a load assessment submodule, a resource scheduling submodule, and an online learning submodule. The output of the system status monitoring submodule is connected to the input of the load assessment submodule. The output of the load assessment submodule is connected to the input of the resource scheduling submodule. The output of the resource scheduling submodule is connected to the input of the online learning submodule.

[0034] The data processing module transmits processed data to the identification and analysis module via the internal AMQP message bus and logs the data. The identification and analysis module then transmits the identification results and anomaly detection results to the user interface module. The input end of the identification and analysis module listens to the data stream from the data processing module in real time via a zero-copy message queue. The user interface module allows users to update the configuration database instantly during configuration operations. It also allows the input end to load configuration file templates, initialize the user interface, and receive the identification and anomaly detection results from the identification and analysis module. The multi-link communication module sends data to the cloud server using the selected link, records the reason for failure, and attempts to resend if transmission fails. The input end of the multi-link communication module is based on the pending synchronization status data from the local cache and backup module, as well as the result data from the identification and analysis module. The local cache and backup module automatically uploads cached data to the cloud server via a retransmission request after the network returns to normal. Based on the communication link status detection results from the multi-link communication module, the local cache and backup module temporarily stores data in its local cache using an SQLite database when network fluctuations are detected. The adaptive optimization module monitors the system load in real time. Its inputs include the outputs of the system status monitoring submodule, load assessment submodule, resource scheduling submodule, and online learning submodule.

[0035] Furthermore, the data processing module's processing method is as follows: It periodically triggers the data acquisition process using a timer mechanism, reads electricity metering data from the sensor network using the SPI (Serial Peripheral Interface) protocol, and stores the raw data in binary format in a memory-mapped area. Based on the acquired data, the data processing module verifies the validity of the raw data using a data verification algorithm, compresses and encodes the data using a compression algorithm, and performs initial data filtering using a sliding window filtering algorithm. The processed data is transmitted to the identification and analysis module via the internal message bus AMQP. During the data processing process, the data processing module also records log files using the syslog service, including data acquisition timestamps, data cleaning results, compression and encoding details, and data transmission status. In specific implementation, the data processing module periodically triggers the data acquisition process using a timer mechanism, reads electricity metering data from the sensor network using the SPI (Serial Peripheral Interface) protocol, and stores the raw data in binary format in a memory-mapped area. Based on the collected data, the data processing module verifies the validity of the raw data using a data verification algorithm, compresses and encodes the data using a compression algorithm (such as LZW compression), and performs preliminary data filtering using a sliding window filtering algorithm. The processed data is then transmitted to the identification and analysis module via the internal message bus AMQP (Advanced Message Queuing Protocol). During data processing, the data processing module also records log files via syslog service, including data collection timestamps, data cleaning results, compression and encoding details, and data transmission status.

[0036] In practical operation, the data processing module first uses a timer mechanism to periodically trigger the data acquisition process, ensuring regular data updates and improving data timeliness and accuracy. It then uses the SPI protocol to read power metering data from the sensor network, ensuring efficient and reliable data transmission. Secondly, raw data is stored in a memory-mapped area, reducing data copying overhead and increasing processing speed. A data verification algorithm validates the collected raw data, ensuring only valid data proceeds to the next step, reducing unnecessary workload in subsequent processing. LZW compression is used to compress and encode the data, reducing bandwidth requirements and improving transmission efficiency. A sliding window filtering algorithm performs preliminary filtering, removing noise and outliers, improving data quality. Finally, the processed data is transmitted to the identification and analysis module via the AMQP protocol, ensuring data transmission security and reliability. Throughout the process, detailed log files are recorded using the syslog service, facilitating subsequent troubleshooting and performance analysis.

[0037] Compared with existing technologies, the advantages and features of the data processing module in the portable IoT energy metering box modular intelligent identification and configuration system mainly include: First, the data acquisition process is periodically triggered by a timer mechanism, ensuring regular data updates and timeliness, thus improving the system's response speed. Second, data transmission is performed using the SPI protocol, improving data transmission efficiency and reliability. Third, memory mapping technology reduces data copying overhead, increasing data processing speed. Data verification algorithms, compression algorithms, and sliding window filtering algorithms ensure data validity, reduce data volume, and improve data quality, thereby enhancing the overall system performance. Finally, data transmission is performed using the AMQP protocol, ensuring data transmission security and reliability, and detailed log files are recorded through the syslog service, facilitating subsequent troubleshooting and performance optimization. These advantages and features enable the data processing module to perform excellently in the portable IoT energy metering box modular intelligent identification and configuration system, effectively processing large amounts of data and ensuring the system's efficient and stable operation.

[0038] Furthermore, the zero-copy message queue uses a direct memory access mechanism (DMA) to transfer data processed by the data processing module from kernel space to a shared memory area managed by the message queue. Within the shared memory area, the zero-copy message queue stores and manages the data stream using a circular buffer. The identification and analysis module triggers the corresponding processing function immediately upon the arrival of new data using event notification or polling methods to read the data from the shared memory area. Furthermore, during data reading, the zero-copy message queue also uses locking mechanisms, version control, or transaction processing methods to ensure the correctness and reliability of data reading in a multi-threaded or multi-process environment. In specific implementations, the zero-copy message queue uses a direct memory access mechanism (DMA) to transfer data processed by the data processing module from kernel space to the shared memory area managed by the message queue. The DMA mechanism allows direct data transfer between kernel space and user space, avoiding the performance loss caused by multiple memory copies in traditional data copying processes. Within the shared memory area, the zero-copy message queue stores and manages the data stream using a circular buffer. A circular buffer is an efficient memory management structure that can reuse a fixed amount of memory space, thereby reducing memory fragmentation and frequent memory allocation operations. In addition, zero-copy message queues ensure the correctness and reliability of data reading in multi-threaded or multi-process environments through locking mechanisms (such as mutexes or read-write locks), version control (such as atomic operations), or transaction processing methods (such as two-phase commit).

[0039] Zero-copy message queues offer significant advantages in practical applications. Firstly, by using DMA (Distributed Memory Access) to transfer data from kernel space to user space, the number of copies between different memory regions is reduced, greatly improving data transfer efficiency. The circular buffer design makes data storage and management more efficient, reducing memory fragmentation and thus improving memory utilization. Furthermore, locking mechanisms, version control, or transaction processing ensure the correctness and reliability of data reads, guaranteeing data consistency and integrity even in multi-threaded or multi-process environments. This design not only improves the system's concurrent processing capabilities but also enhances its stability and reliability. Therefore, the application of zero-copy message queues in a portable IoT power metering box modular intelligent identification and configuration system can significantly improve data processing speed and overall system performance.

[0040] In practical implementation, the logical operation of the zero-copy message queue in the modular intelligent identification and configuration system of the portable IoT power metering box is as follows: First, the data processing module transfers the processed data from the kernel space to the shared memory area managed by the message queue through a DMA mechanism. The data is stored in a circular buffer that uses a fixed amount of memory in a cyclical manner, avoiding frequent memory allocation and release operations. When new data arrives, the identification and analysis module immediately triggers the corresponding processing function through event notification or polling methods to read the data from the shared memory area. During the data reading process, the zero-copy message queue ensures the correctness and reliability of data reading in a multi-threaded or multi-process environment through locking mechanisms, version control, or transaction processing methods. Throughout the process, data transmission and storage are highly efficient, reducing unnecessary memory copying and management overhead, and ensuring the real-time performance and reliability of data processing.

[0041] Compared with existing technologies, the advantages and features of zero-copy message queues in the modular intelligent identification and configuration system for portable IoT energy metering boxes mainly include: First, zero-copy data transmission is achieved through the DMA mechanism, greatly reducing the number of data copies between kernel space and user space, and improving data transmission efficiency. Second, the design of the circular buffer makes data storage and management more efficient, reducing memory fragmentation and improving memory utilization. Furthermore, locking mechanisms, version control, or transaction processing methods ensure the correctness and reliability of data reading, enhancing system stability and reliability. These advantages and features make zero-copy message queues perform excellently in the modular intelligent identification and configuration system for portable IoT energy metering boxes, effectively handling large amounts of data and ensuring the system's efficient and stable operation.

[0042] Furthermore, upon receiving the data stream, the deep anomaly detection algorithm automatically learns and extracts high-dimensional features from the data using a convolutional neural network, including the temporal sequence, periodicity, and potential abnormal pattern information of the electricity consumption data. The convolutional neural network performs multi-level abstract representation of the input features through a forward propagation mechanism and optimizes the model parameters through a backpropagation algorithm. Next, the deep anomaly detection algorithm classifies the extracted features into electricity consumption patterns using a support vector machine. Based on the classified data, a normal electricity consumption pattern is modeled using an autoencoder, and the reconstruction error is used to determine whether the input data deviates from the normal pattern. When the reconstruction error exceeds a preset threshold, the algorithm determines that abnormal electricity consumption behavior exists and triggers an alarm mechanism through an event-driven programming model. Furthermore, during the anomaly detection process, the deep anomaly detection algorithm records and analyzes the results through the system log (syslog system), including anomaly type, occurrence time, and duration information. It also measures the difference between abnormal and normal data using a density-based clustering method to calculate the degree of anomaly. In its implementation, the deep anomaly detection algorithm automatically learns and extracts high-dimensional features from the data using a Convolutional Neural Network (CNN), including the temporal sequence, periodicity, and potential abnormal patterns of electricity consumption data. The CNN uses a forward propagation mechanism to perform multi-level abstract representation of the input features and optimizes the model parameters through backpropagation to improve the accuracy of feature extraction. Next, the algorithm uses a Support Vector Machine (SVM) to classify the extracted features into electricity consumption patterns. Based on the classified data, the algorithm models normal electricity consumption patterns using an autoencoder and judges whether the input data deviates from the normal pattern based on the reconstruction error. When the reconstruction error exceeds a preset threshold, the algorithm determines that abnormal electricity consumption behavior exists. Finally, the algorithm records the analysis results through a system log system (Syslog) and uses a density-based clustering method to measure the difference between abnormal and normal data and calculate the degree of anomaly.

[0043] In practical applications, deep anomaly detection algorithms first employ convolutional neural networks to automatically learn and extract high-dimensional features from data. This enables the algorithm to capture complex and subtle anomaly patterns, improving the accuracy and robustness of anomaly detection. Secondly, support vector machines are used to classify power consumption patterns, effectively categorizing power consumption data into different patterns and providing a foundation for subsequent anomaly detection. Thirdly, an autoencoder models normal power consumption patterns, and reconstruction errors are used to determine whether input data deviates from normal patterns. This method identifies data points that significantly differ from normal patterns, allowing for timely detection of abnormal behavior. Finally, the system logs the analysis results, and density-based clustering measures the differences between abnormal and normal data, making the anomaly detection results highly interpretable and easier for maintenance personnel to understand and implement appropriate measures. This design not only improves the system's detection accuracy but also enhances its maintainability and usability.

[0044] In its implementation, the deep anomaly detection algorithm first receives a data stream and automatically learns and extracts high-dimensional features from the data using a convolutional neural network (CNN), including the temporal sequence, periodicity, and potential abnormal patterns of electricity consumption data. Next, the CNN uses a forward propagation mechanism to perform multi-level abstract representation of the input features and optimizes the model parameters through a backpropagation algorithm to improve the accuracy of feature extraction. Then, the algorithm uses a support vector machine (SVM) to classify the extracted features into electricity consumption patterns. Based on the classified data, the algorithm models normal electricity consumption patterns using an autoencoder and judges whether the input data deviates from the normal pattern based on the reconstruction error. When the reconstruction error exceeds a preset threshold, the algorithm determines that abnormal electricity consumption behavior exists and triggers an alarm mechanism through an event-driven programming model. During the anomaly detection process, the algorithm records the analysis results through a system log system, including anomaly type, occurrence time, and duration information, and uses a density-based clustering method to measure the difference between abnormal and normal data and calculate the degree of anomaly.

[0045] Compared to existing technologies, deep anomaly detection algorithms automatically learn and extract high-dimensional features from data through convolutional neural networks, enabling them to capture complex and subtle anomaly patterns and improving the accuracy and robustness of anomaly detection. Secondly, the use of support vector machines for electricity consumption pattern classification enhances classification accuracy and generalization ability. Furthermore, by modeling normal electricity consumption patterns using an autoencoder and assessing whether input data deviates from normal patterns based on reconstruction errors, this method effectively identifies data points that significantly differ from normal patterns, promptly detecting abnormal behavior. Finally, the system logs the analysis results, and density-based clustering measures the differences between abnormal and normal data, making the anomaly detection results highly interpretable and easy for maintenance personnel to understand and implement appropriate measures. These advantages and features make deep anomaly detection algorithms perform exceptionally well in portable IoT power metering box modular intelligent identification and configuration systems, effectively processing large amounts of data and ensuring the system's efficient and stable operation.

[0046] Furthermore, the multi-link communication module first establishes a link aggregation group by initializing the LACP protocol, aggregating multiple physical links into a single logical link. It then periodically sends hop packets through the LACP protocol's link detection mechanism to monitor the communication status of each link, including signal strength and packet loss rate indicators. If a decrease in signal strength or an increase in packet loss rate is detected on a link, the module records the link's status change and triggers link selection logic. During data transmission, the multi-link communication module switches between multiple communication modes, including Wi-Fi, Bluetooth, and cellular networks, through a multi-mode communication protocol switching mechanism. During data transmission, when the module detects a decrease in current link quality or transmission failure, it ensures the continuity and stability of data transmission through a redundant link switching mechanism. In the event of a data transmission failure, the multi-link communication module records failure information through a system log management system, including the time, cause, and link status information for each failure, and stores the failure information in a local log file. After storage, it attempts to resend the data based on the recorded information using a retry mechanism and error recovery algorithm until the data is successfully transmitted. In specific implementations, such as... Figure 2As shown, the multi-link communication module establishes a link aggregation group (LAG) through the Link Aggregation Control Protocol (LACP), aggregating multiple physical links into a single logical link. The LACP protocol periodically monitors the status of each communication link by sending heartbeat messages, including metrics such as signal strength and packet loss rate. When a decrease in signal strength or an increase in packet loss rate is detected on a link, the module records the status change and triggers link selection logic. The multi-link communication module also switches between various communication modes (such as Wi-Fi, Bluetooth, and cellular networks) through a multi-mode communication protocol switching mechanism to ensure the continuity and stability of data transmission. During data transmission, the module ensures the reliability of data transmission through a redundant link switching mechanism. When data transmission fails, the module manages the system log system to record the failure information and attempts to retransmit the data using a retry mechanism and error recovery algorithm until successful transmission.

[0047] In practical implementation, firstly, the multi-link communication module establishes link aggregation groups through the LACP protocol, improving the reliability of communication links. The LACP protocol detects link status by periodically sending heartbeat messages, ensuring real-time monitoring of the links. Secondly, through a multi-mode communication protocol switching mechanism, the module can flexibly switch between different communication modes, selecting the optimal communication method based on current environmental conditions, thus improving communication flexibility and adaptability. Thirdly, through a redundant link switching mechanism, when a degradation in the quality of the current link or a transmission failure is detected, the module can immediately switch to a backup link, ensuring the continuity and stability of data transmission. Finally, the system log management system records failure information and attempts to retransmit data through retry mechanisms and error recovery algorithms, ensuring that data is ultimately transmitted successfully. This design not only improves the system's communication reliability but also enhances its fault tolerance and data transmission stability.

[0048] Compared to existing technologies, the multi-link communication module establishes link aggregation groups through the LACP protocol, improving the reliability of communication links and ensuring real-time monitoring. Secondly, through a multi-mode communication protocol switching mechanism, the module can flexibly switch between different communication modes, improving communication flexibility and adaptability. Furthermore, a redundant link switching mechanism ensures the continuity and stability of data transmission, enhancing the system's fault tolerance. Finally, a system log management system records failure information, and retry mechanisms and error recovery algorithms ensure successful data transmission, improving the overall system performance and reliability. These advantages and features make the multi-link communication module perform exceptionally well in portable IoT energy metering box modular intelligent identification and configuration systems, effectively handling data transmission needs in complex environments and ensuring efficient and stable system operation.

[0049] Furthermore, the system status monitoring submodule includes a system status acquisition unit, a performance monitoring unit, and a log recording unit; the system status acquisition unit is used to monitor system call status in real time; the performance monitoring unit is used to monitor system resources in real time, including CPU, memory, and disk I / O metrics; the log recording unit is used to record and store monitoring data; the load assessment submodule includes a load calculation unit and a PID controller unit; the load calculation unit calculates the current system load using statistical methods, assessing metrics including CPU utilization and memory utilization; the PID controller unit adjusts resource allocation strategies based on the deviation between the current load and the target load using a PID controller algorithm; the resource scheduling submodule includes a process priority adjustment unit, a C... The system comprises a CPU time slice allocation unit and a memory management unit. The process priority adjustment unit dynamically adjusts process priorities using a nice value-based adjustment method. The CPU time slice allocation unit dynamically allocates CPU time slices using a dynamic scheduling algorithm. The memory management unit efficiently manages memory using the Least Recently Used (LRU) algorithm. The online learning submodule includes a data collection unit, a model training unit, and a policy generation unit. The data collection unit collects historical load data. Based on historical data, the model training unit trains a future load prediction model using a reinforcement learning algorithm and outputs the load prediction to the policy generation unit. The policy generation unit generates future resource allocation policies using decision trees to ensure efficient system operation under different load conditions. In specific implementation, the system status monitoring submodule monitors system call status in real time through a system status acquisition unit, monitors system resources in real time through a performance monitoring unit (including CPU, memory, disk I / O, etc.), and records and stores monitoring data through a log recording unit. The load assessment submodule uses a statistical method to calculate the current system load through the load calculation unit, evaluating metrics such as CPU utilization and memory utilization. It then uses a PID controller unit to adjust resource allocation strategies based on the deviation between the current load and the target load using a PID controller algorithm. The resource scheduling submodule uses a process priority adjustment unit to dynamically adjust process priorities using a nice value-based adjustment method, a CPU time slice allocation unit to dynamically allocate CPU time slices using a dynamic scheduling algorithm, and a memory management unit to efficiently manage memory using a Least Recently Used (LRU) algorithm. The online learning submodule collects historical load data through a data collection unit, trains a future load prediction model using a reinforcement learning algorithm through a model training unit, and outputs the load prediction to a policy generation unit. A decision tree algorithm is then used to generate future resource allocation strategies, ensuring efficient system operation under different load conditions. The adaptive optimization module has significant beneficial effects in practical applications. First, real-time monitoring of system status and performance ensures the transparency and traceability of system operation.Secondly, the load assessment submodule dynamically adjusts resource allocation strategies, enabling the system to automatically adjust resource allocation based on current load conditions, thus improving system response speed and efficiency. Thirdly, the resource scheduling submodule optimizes process priority, CPU time slice allocation, and memory management, improving system resource utilization and ensuring that critical tasks are prioritized. Finally, the online learning submodule predicts future load conditions and generates resource allocation strategies accordingly, enhancing the system's foresight and adaptability. This design not only improves overall system performance but also enhances system stability and reliability.

[0050] In specific implementation, the adaptive optimization module operates as follows in the modular intelligent identification and configuration system of the portable IoT power metering box: First, the system status monitoring submodule monitors system calls in real time through the system status acquisition unit and monitors system resources, including CPU utilization, memory usage, and disk I / O, through the performance monitoring unit. Monitoring data is recorded and stored by the log recording unit. Next, the load assessment submodule calculates the current system load through the load calculation unit and adjusts the resource allocation strategy based on the deviation between the current load and the target load through the PID controller unit. Then, the resource scheduling submodule dynamically adjusts process priorities through the process priority adjustment unit, dynamically allocates CPU time slices through the CPU time slice allocation unit, and efficiently manages memory through the memory management unit. Finally, the online learning submodule collects historical load data through the data collection unit and trains a future load prediction model through the model training unit. The load prediction is then output to the strategy generation unit, which generates future resource allocation strategies using a decision tree algorithm, ensuring efficient system operation under different load conditions.

[0051] Compared to existing technologies, the adaptive optimization module improves system transparency and traceability by monitoring system status and performance in real time. Secondly, by dynamically adjusting resource allocation strategies, the system can automatically adjust resource allocation based on current load conditions, improving system response speed and efficiency. Furthermore, by optimizing resource scheduling, it improves system resource utilization, ensuring that critical tasks are prioritized. Finally, by predicting future load conditions through online learning and generating resource allocation strategies accordingly, it enhances the system's foresight and adaptability. These advantages and features make the adaptive optimization module perform exceptionally well in the modular intelligent identification and configuration system for portable IoT energy metering boxes, effectively handling resource management needs in complex environments and ensuring efficient and stable system operation.

[0052] While specific embodiments of the present invention have been described above, those skilled in the art should understand that these specific embodiments are merely illustrative. Those skilled in the art can omit, substitute, and modify the details of the above methods and systems in various ways without departing from the principles and essence of the present invention. For example, combining the above method steps to perform substantially the same function and achieve substantially the same result according to substantially the same method falls within the scope of the present invention. Therefore, the scope of the present invention is defined only by the appended claims.

Claims

1. A portable IoT power metering box modular intelligent identification and configuration system, characterized in that: include: The data processing module is used to periodically read electricity metering data through the sensor network, and to clean, compress, encode and perform preliminary filtering on the collected raw data. The processed data is transmitted to the identification and analysis module through the internal message bus AMQP and recorded in the log file. The identification and analysis module is used to monitor the data stream of the data processing module in real time through a zero-copy message queue. It uses a deep anomaly detection algorithm to extract features from the received processed data, classify and identify power consumption patterns, and detect abnormal power consumption behaviors. The identification results and anomaly detection results are then transmitted to the user interface module. The user interface module is used to load configuration file templates and initialize the user interface. It provides a graphical interface through the document object model, allowing users to update the configuration database in real time during configuration operations. The multi-link communication module is used to periodically check the status of each communication link through the Link Aggregation Control Protocol (LACP). Based on the detection results, it automatically selects the optimal communication link through multi-mode communication methods and redundancy mechanisms, switches to the backup link when the quality of the current link deteriorates, sends data to the cloud server using the selected link, and records the reason for the failure and attempts to resend when the transmission fails. The local caching and backup module is used to temporarily store data in the local cache area through the SQLite database and mark it as pending synchronization when network fluctuations are detected based on the communication link status detection results of the multi-link communication module. After the network returns to normal, the cached data is automatically uploaded to the cloud server through a retransmission request, and the cached data record is cleared after the upload is successful. An adaptive optimization module is used to monitor system load in real time and automatically adjust resource allocation strategies. The adaptive optimization module includes a system status monitoring submodule, a load assessment submodule, a resource scheduling submodule, and an online learning submodule. The output of the system status monitoring submodule is connected to the input of the load assessment submodule. The output of the load assessment submodule is connected to the input of the resource scheduling submodule. The output of the resource scheduling submodule is connected to the input of the online learning submodule.

2. The portable IoT power metering box modular intelligent identification and configuration system according to claim 1, characterized in that: The data processing module's processing method is as follows: It periodically triggers the data acquisition process using a timer mechanism, reads electricity metering data from the sensor network using the SPI serial communication protocol, and stores the raw data in binary format in a memory-mapped area. Based on the acquired data, the data processing module verifies the validity of the raw data using a data verification algorithm, compresses and encodes the data using a compression algorithm, and performs initial data filtering using a sliding window filtering algorithm. The processed data is transmitted to the identification and analysis module via the internal message bus AMQP. During the data processing process, the data processing module also records log files via the syslog service, including data acquisition timestamps, data cleaning results, compression and encoding details, and data transmission status.

3. The portable IoT power metering box modular intelligent identification and configuration system according to claim 1, characterized in that: The zero-copy message queue uses a direct memory access mechanism to transfer data processed by the data processing module from kernel space to a shared memory area managed by the message queue. In the shared memory area, the zero-copy message queue stores and manages the data stream through a circular buffer. The identification and analysis module triggers the corresponding processing function immediately upon the arrival of new data via event notification or polling to read the data from the shared memory area.

4. The portable IoT power metering box modular intelligent identification and configuration system according to claim 1, characterized in that: During the data reading process, the zero-copy message queue also ensures the correctness and reliability of data reading in a multi-threaded or multi-process environment through locking mechanisms, version control, or transaction processing methods.

5. The portable IoT power metering box modular intelligent identification and configuration system according to claim 1, characterized in that: After receiving the data stream, the deep anomaly detection algorithm automatically learns and extracts high-dimensional features from the data through a convolutional neural network, including the temporal sequence, periodicity, and potential anomaly pattern information of the electricity consumption data. The convolutional neural network performs multi-level abstract representation of the input features through a forward propagation mechanism and optimizes the model parameters through a backpropagation algorithm. Then, the deep anomaly detection algorithm classifies the extracted features into electricity consumption patterns using a support vector machine. Based on classification data, a normal power consumption pattern is modeled using an autoencoder, and the input data is judged to deviate from the normal pattern by the reconstruction error. When the reconstruction error exceeds a preset threshold, the algorithm determines that there is abnormal power consumption behavior and triggers an alarm mechanism through an event-driven programming model.

6. The portable IoT power metering box modular intelligent identification and configuration system according to claim 5, characterized in that: During the anomaly detection process, the deep anomaly identification algorithm records and analyzes the results through the system log system syslog, including anomaly type, occurrence time, and duration information. It also uses a density-based clustering method to measure the difference between abnormal and normal data and calculate the degree of anomaly.

7. The portable IoT power metering box modular intelligent identification and configuration system according to claim 1, characterized in that: The operation method of the user interface module includes: the user selects the required functional module, the user adds or deletes functional modules by dragging and dropping, dynamically generates a configuration file according to the user's selection and applies the configuration file to the system to make the new configuration effective, and saves the configuration file for subsequent modification or rollback.

8. The portable IoT power metering box modular intelligent identification and configuration system according to claim 1, characterized in that: The multi-link communication module first establishes a link aggregation group by initializing the LACP protocol, aggregating multiple physical links into a single logical link, and periodically sends jump messages through the LACP protocol's link detection mechanism to detect the communication status of each communication link, including signal strength and packet loss rate indicators. If a decrease in signal strength or an increase in packet loss rate is detected on a certain link, the status change of that link is recorded and the link selection logic is triggered. During data transmission, the multi-link communication module switches between multiple communication modes through a multi-mode communication protocol switching mechanism. The communication modes include Wi-Fi, Bluetooth, and cellular network. During data transmission, when the multi-link communication module detects a degradation in the quality of the current link or a transmission failure, it ensures the continuity and stability of data transmission through a redundant link switching mechanism. When data transmission fails, the multi-link communication module records the failure information through the system log management system, including the time, reason, and link status information of each transmission failure, and stores the failure information in a local log file; After storage is complete, the data is resent using a retry mechanism and error recovery algorithm based on the recorded information until the data is successfully transmitted.

9. A modular intelligent identification and configuration system for a portable IoT power metering box according to claim 1, characterized in that: The system status monitoring submodule includes a system status acquisition unit, a performance monitoring unit, and a log recording unit. The system status acquisition unit is used to monitor system call status in real time. The performance monitoring unit is used to monitor system resources in real time, including CPU, memory, and disk I / O metrics. The log recording unit is used to record and store monitoring data. The load assessment submodule includes a load calculation unit and a PID controller unit. The load calculation unit calculates the current system load using statistical methods and assesses metrics including CPU utilization and memory utilization. The PID controller unit adjusts resource allocation strategies based on the deviation between the current load and the target load using a PID controller algorithm. The resource scheduling submodule includes a process priority adjustment unit, a CPU time slice allocation unit, and a memory management unit. The process priority adjustment unit dynamically adjusts process priorities using a nice value-based adjustment method. The CPU time slice allocation unit dynamically allocates CPU time slices using a dynamic scheduling algorithm. The memory management unit efficiently manages memory using a Least Recently Used (LRU) algorithm. The online learning submodule includes a data collection unit, a model training unit, and a strategy generation unit. The data collection unit is used to collect historical load data. Based on historical data, the model training unit trains a future load prediction model using a reinforcement learning algorithm and outputs the load prediction to the policy generation unit. The strategy generation unit generates future resource allocation strategies through decision trees, ensuring the efficient operation of the system under different load conditions.

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