An electric energy metering SOC chip and a communication method

Abstract

The application provides an electric energy metering SOC chip and a communication method, and relates to the technical field of communication, wherein the system comprises a group of communication modules, a dynamic frequency planning module and an intelligent communication scheduling module; the dynamic frequency planning module is used for detecting the working state of each communication module in the group of communication modules to obtain a detection result; the dynamic frequency planning module is further used for automatically adjusting the transmission power and working frequency of each communication module in the group of communication modules according to the detection result; the intelligent communication scheduling module is respectively electrically connected with each communication module in the group of communication modules; the intelligent communication scheduling module is used for selecting a target communication module from the group of communication modules according to the type of a current task, and dynamically allocating communication resources to the target communication module to transmit data corresponding to the current task. The technical scheme provided by the application improves the communication reliability and stability.

Description

Technical Field

[0001] This application relates to the field of communication technology, specifically to an energy metering SOC chip and communication method. Background Technology

[0002] With the rapid development of the power Internet of Things (IoT), the demand for high-precision energy metering and diversified wireless communication is increasing. Existing energy metering equipment is beginning to integrate multiple communication technologies to adapt to data transmission needs in different scenarios. Energy metering SOC chips in related technologies generally integrate one or a few communication modules, lacking the ability to simultaneously support multiple wireless communication standards. Furthermore, when attempting to integrate more communication modules to meet the needs of complex network environments, conflicts between various communication technologies in frequency band allocation, signal strength adjustment, and communication protocol matching lead to serious signal interference problems, affecting the accuracy and communication reliability of energy metering instruments. Summary of the Invention

[0003] To address the aforementioned technical problems, this application provides an energy metering SOC chip and a communication method.

[0004] In a first aspect, this application provides an energy metering SOC chip, comprising: a set of communication modules, a dynamic frequency planning module, and an intelligent communication scheduling module. The dynamic frequency planning module is electrically connected to each communication module in the set of communication modules, and is used to detect the operating status of each communication module in the set of communication modules and obtain detection results. The set of communication modules supports multiple wireless communication standards. The dynamic frequency planning module is also used to automatically adjust the transmission power and operating frequency of each communication module in the set of communication modules based on the detection results. The intelligent communication scheduling module is electrically connected to each communication module in the set of communication modules, and is used to select a target communication module from the set of communication modules according to the type of the current task, and dynamically allocate communication resources to the target communication module to transmit data corresponding to the current task.

[0005] By adopting the above technical solution and integrating a set of communication modules, multiple wireless communication standards are supported, meeting diverse communication needs in different application scenarios and improving the system's flexibility and applicability. The dynamic frequency planning module detects the working status of each communication module and automatically adjusts the transmission power and working frequency of each module based on the detection results, effectively avoiding signal interference between multiple communication modules and improving communication reliability and stability. The intelligent communication scheduling module selects the optimal target communication module according to the type of the current task and dynamically allocates communication resources to it, ensuring the efficiency and accuracy of data transmission and improving system resource utilization and communication efficiency.

[0006] Optionally, the power metering SOC chip also includes: a signal quality monitoring module, wherein the signal quality monitoring module is used to acquire a set of communication index parameters for each communication module to monitor the communication link quality of each communication module. The set of communication index parameters includes at least signal-to-noise ratio parameters, received signal strength indication parameters, and packet loss rate. The signal quality monitoring module is also used to activate adaptive anti-interference measures when interference signals are detected. The adaptive anti-interference measures include frequency adjustment, transmit power adjustment, retransmission mechanism, and channel frequency hopping.

[0007] By adopting the above technical solution, the signal quality monitoring module can acquire the communication indicator parameters of each communication module in real time, such as signal-to-noise ratio, received signal strength indication, and packet loss rate, thereby effectively monitoring the quality of the communication link. When interference signals are detected, the signal quality monitoring module will activate adaptive anti-interference measures, including frequency adjustment, transmit power adjustment, retransmission mechanism, and channel frequency hopping. These measures can significantly reduce signal interference and improve the stability and reliability of communication.

[0008] Optionally, the dynamic frequency planning module obtains the detection results according to a preset period, and uses a preset algorithm model to configure the working parameters of each communication module according to the detection results, so that the communication modules in a group do not interfere with each other.

[0009] By adopting the above technical solution, the dynamic frequency planning module can periodically detect the operating status of each communication module and optimize the operating parameters of these modules using a preset algorithm model. This not only ensures that there is no mutual interference between the communication modules, improving the stability and reliability of the communication system, but also enhances the overall performance of the power metering SOC chip, enabling it to maintain efficient operation in complex and ever-changing network environments.

[0010] Optionally, the intelligent communication scheduling module includes an AI prediction engine, which is used to predict the communication load of the power metering SOC chip in the future. The intelligent communication scheduling module is also used to transmit the first type of data during off-peak periods and the second type of data during peak periods based on the communication load prediction results.

[0011] By adopting the above technical solutions, the AI ​​prediction engine can predict communication load conditions over a future period, thereby optimizing the allocation of communication resources in advance. During off-peak hours, it transmits the first type of data (such as non-real-time data), reducing communication pressure during peak periods and improving the overall system stability and response speed. During peak periods, it transmits the second type of data (such as real-time data), ensuring the timely transmission of critical data and improving user experience and system performance. By predicting and intelligently scheduling communication load, the load of different communication modules can be effectively managed, avoiding data congestion during peak periods. By transmitting different types of data at different times, the priority and timing of data transmission can be optimized, improving data transmission efficiency and reliability.

[0012] Optionally, the AI ​​prediction engine builds machine learning models by regularly collecting historical communication records and seasonal electricity consumption trend data, and uses machine learning models to predict communication load.

[0013] By adopting the above technical solutions, the AI ​​prediction engine can periodically collect historical communication records and seasonal electricity consumption trend data, build machine learning models, and use these models to predict communication load conditions in the future. This enables the electricity metering SOC chip to transmit the first type of data during off-peak hours and the second type of data during peak hours, thereby optimizing the efficiency of communication resource utilization and improving the overall performance and stability of the system.

[0014] Optionally, the power metering SOC chip also includes an antenna, which is connected to each of the communication modules in a group of communication modules. The antenna adopts a layout that physically separates different frequency bands.

[0015] By adopting the above technical solution and physically separating the antenna layouts of different frequency bands, interference between different frequency bands can be significantly reduced, communication quality can be improved, and each communication module has an independent antenna, which can communicate more efficiently, avoid resource competition, reduce frequency band interference and improve communication efficiency, thereby enhancing the stability and reliability of the system and improving the user experience.

[0016] Optionally, a set of communication modules may include at least two of the following: a Bluetooth Low Energy (BLE) communication module, a WiFi communication module, a radio frequency (RF) communication module, a high-speed power line carrier (HPLC) communication module, and an LTE Cat1 communication module.

[0017] By adopting the above technical solution, this energy metering SOC chip can support multiple wireless communication standards, including but not limited to Bluetooth Low Energy (BLE), WiFi, RF, high-speed power line carrier (HPLC), and LTE Cat1 communication modules. The combination of these communication modules allows the energy metering SOC chip to flexibly select the most suitable communication method in different application scenarios, improving communication reliability and flexibility.

[0018] In a second aspect of this application, a communication method for an energy metering SOC chip is also provided, applied in the aforementioned energy metering SOC chip, comprising: a dynamic frequency planning module detecting the operating status of each communication module in a group of communication modules and obtaining a detection result; the dynamic frequency planning module adjusting the transmission power and operating frequency of each communication module in the group of communication modules according to the detection result; and an intelligent communication scheduling module selecting a target communication module from the group of communication modules according to the type of the current task, and dynamically allocating communication resources to the target communication module to transmit data corresponding to the current task.

[0019] By adopting the above technical solutions, the dynamic frequency planning module can monitor the working status of each communication module in real time and automatically adjust the transmission power and operating frequency of each communication module based on the detection results. This effectively avoids signal interference between multiple communication modules and improves the communication reliability and accuracy of the power metering SOC chip. The intelligent communication scheduling module selects the optimal communication module according to the type of different tasks and dynamically allocates communication resources to it, ensuring efficient transmission of different types of data and improving the overall performance and response speed of the system.

[0020] Optionally, the intelligent communication scheduling module selects a target communication module from a set of communication modules based on the task type of the current task, including: pre-establishing a matching relationship between different task types and communication module types; and, upon receiving the current task, selecting a target communication module from the matching relationship that matches the task type of the current task.

[0021] By adopting the above technical solution, a matching relationship between different task types and communication module types is established in advance to ensure that each task type can be matched with the most suitable communication module, avoiding data transmission delays or failures caused by improper selection of communication modules. When the current task is received, the intelligent communication scheduling module selects the target communication module that matches the current task type from the matching relationship, thereby achieving accurate allocation of communication resources and improving the system's response speed and processing capacity.

[0022] Optionally, the power metering SOC chip also includes: a signal quality monitoring module, wherein the signal quality monitoring module is used to acquire a set of communication index parameters for each communication module to monitor the communication link quality of each communication module, and the set of communication index parameters includes at least a signal-to-noise ratio parameter, a received signal strength indication parameter, and a packet loss rate; the above method also includes: when interference signals are detected, the signal quality monitoring module activates adaptive anti-interference measures, wherein the adaptive anti-interference measures include frequency adjustment, transmit power adjustment, retransmission mechanism, and channel frequency hopping.

[0023] By adopting the above technical solution, the signal quality monitoring module can acquire the communication index parameters of each communication module in real time, such as signal-to-noise ratio, received signal strength indication, and packet loss rate, thereby effectively monitoring the communication link quality of each communication module. When interference signals are detected, the signal quality monitoring module will immediately activate adaptive anti-interference measures, including frequency adjustment, transmit power adjustment, retransmission mechanism, and channel frequency hopping, which significantly improves the stability and reliability of the communication system and reduces the impact of signal interference on the accuracy of power metering.

[0024] In a third aspect of this application, an electronic device is also provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor executes the program to implement the method steps of any of the above claims.

[0025] In a fourth aspect of this application, a computer-readable storage medium is also provided, which stores instructions that, when executed, perform the method steps of any of the above claims.

[0026] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages:

[0027] 1. The dynamic frequency planning module detects the working status of each communication module and automatically adjusts the transmission power and working frequency of each module based on the detection results, effectively avoiding signal interference between multiple communication modules and improving communication reliability and stability; the intelligent communication scheduling module selects the optimal target communication module according to the type of the current task and dynamically allocates communication resources to it, ensuring the efficiency and accuracy of data transmission.

[0028] 2. When interference signals are detected, the signal quality monitoring module will activate adaptive anti-interference measures, which can significantly reduce signal interference and improve the stability and reliability of communication.

[0029] 3. By predicting communication load and intelligently scheduling, the load of different communication modules can be effectively managed, avoiding data congestion during peak periods and improving the efficiency and reliability of data transmission. Attached Figure Description

[0030] Figure 1 This application provides a structural framework for an energy metering SOC chip. Figure 1 ;

[0031] Figure 2 This application provides a structural framework for an energy metering SOC chip. Figure 2 ;

[0032] Figure 3 This is a flowchart of a communication control method for an energy metering SOC chip provided in an embodiment of this application;

[0033] Figure 4 This is a schematic diagram of a multifunctional communication power metering SOC chip provided in an embodiment of this application. Detailed Implementation

[0034] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0035] In the description of the embodiments in this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Specifically, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0036] In the description of the embodiments of this application, the term "multiple" means two or more. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0037] The following is in conjunction with the appendix Figures 1-4 The embodiments of this application will be described.

[0038] This application provides an energy metering SOC chip, referring to... Figure 1 , Figure 1 This application provides a structural framework for an energy metering SOC chip. Figure 1 The system includes: a set of communication modules, a dynamic frequency planning module, and an intelligent communication scheduling module, among which,

[0039] The dynamic frequency planning module is electrically connected to each communication module in a group of communication modules. The dynamic frequency planning module is used to detect the working status of each communication module in the group of communication modules and obtain the detection results. The group of communication modules supports multiple wireless communication standards. The dynamic frequency planning module is also used to automatically adjust the transmission power and working frequency of each communication module in the group of communication modules according to the detection results.

[0040] The intelligent communication scheduling module is electrically connected to each of the communication modules in a group. The intelligent communication scheduling module is used to select the target communication module from the group of communication modules according to the type of the current task, and dynamically allocate communication resources to the target communication module to transmit data corresponding to the current task.

[0041] In the above embodiments, by integrating a set of communication modules, multiple wireless communication standards are supported, meeting diverse communication needs in different application scenarios and improving the system's flexibility and applicability. The dynamic frequency planning module detects the working status of each communication module and automatically adjusts the transmission power and operating frequency of each module based on the detection results, effectively avoiding signal interference between multiple communication modules and improving communication reliability and stability. The intelligent communication scheduling module selects the optimal target communication module according to the type of the current task and dynamically allocates communication resources to it, ensuring efficient and accurate data transmission and improving system resource utilization and communication efficiency. The energy metering SOC chip communicates with external devices through a set of communication modules.

[0042] The dynamic frequency planning module detects the operating status of the communication module and automatically adjusts its transmit power and operating frequency based on the detection results. This helps resolve conflicts in frequency band allocation and signal strength adjustment. The intelligent communication scheduling module selects the target communication module based on the type of the current task and dynamically allocates communication resources to it. This helps optimize the utilization of communication resources and improve communication efficiency. By reducing signal interference and optimizing the utilization of communication resources, this energy metering SOC chip can improve communication performance and accuracy, meeting the requirements of high-precision energy metering.

[0043] In this embodiment, the SOC chip automatically adjusts the transmit power and operating frequency of each communication module through a dynamic frequency planning module, effectively reducing signal interference caused by frequency band overlap and improving communication quality. The intelligent communication scheduling module can flexibly select the most suitable communication method based on the characteristics of different tasks and rationally allocate communication resources, improving the overall efficiency of the system. It not only solves the interference problem in multi-mode communication but also achieves efficient resource utilization through intelligent scheduling, enhancing the performance of the entire system.

[0044] The dynamic frequency planning module primarily monitors the basic operating status of each communication module, such as whether it is transmitting or receiving data and its current operating frequency. Based on the detected operating status, the dynamic frequency planning module automatically adjusts the transmit power and operating frequency of each communication module to avoid interference between them. During system initialization and daily operation, the dynamic frequency planning module performs preliminary frequency allocation and power adjustments to ensure that each communication module operates normally without significant interference; its main purpose is to prevent multiple communication modules from operating simultaneously on the same frequency band, thereby reducing internal interference. Operating status includes whether each communication module is currently in standby or connected state, data transmission state, or power consumption state.

[0045] The aforementioned set of communication modules may include any two or more combinations of Bluetooth Low Energy (BLE) communication modules, WiFi communication modules, RF communication modules, high-speed power line carrier (HPLC) communication modules, and LTE Cat1 communication modules. Regarding how to monitor the operating status of each communication module in real time and automatically adjust the transmit power and operating frequency, at the hardware level: the SOC chip has a dedicated RF front-end circuit that can monitor the signal strength and quality of different frequency bands. These front-end circuits are closely connected to each communication module and can capture the signals emitted by each module as well as the electromagnetic noise of the surrounding environment. At the software level: a dedicated monitoring program runs inside the chip. This program can periodically (e.g., once per second) read information from the RF front-end circuit, including but not limited to received signal strength, signal-to-noise ratio, and bit error rate, to determine the current communication status and electromagnetic environment. When the monitoring program detects strong interference or degraded signal quality in a certain frequency band, it triggers an optimization process. This process first attempts to reduce the transmit power of the communication module operating on that frequency band to reduce potential interference to other frequency bands. If reducing the power still cannot improve the communication quality, it will consider adjusting the module's operating frequency to another, cleaner frequency band. For example, in a typical residential environment, Wi-Fi networks usually operate in the 2.4 GHz band. If severe interference is detected in this band, the SOC chip may switch the Wi-Fi module to the 5 GHz band and simultaneously increase the transmit power of the BLE module to compensate for the weakened local signal coverage caused by the Wi-Fi module's frequency change. For instance, the operating frequency of the BLE communication module could be adjusted from 2.4 GHz to 2.48 GHz, and the operating frequency of the RF communication module from 433 MHz to 868 MHz.

[0046] The intelligent communication scheduling unit determines which communication technology should be prioritized for which task based on the current task type (e.g., data upload, remote control command execution) and the characteristics of each communication technology (e.g., bandwidth, latency). For example, when a large amount of data needs to be transmitted quickly, the system will choose high-bandwidth Wi-Fi or HPLC technology; while for low-bandwidth operations such as device status queries, the lower-power BLE technology can be used. Dynamic adjustment example: Suppose at a certain moment, a user requests to check the status of all smart appliances in their home, which involves the exchange of numerous small data packets. The intelligent communication scheduling unit will first quickly establish a connection with each device using BLE technology, and then use Wi-Fi technology for efficient data transmission. Additionally, at night when power is low, the scheduling unit may choose to suspend unnecessary data synchronization operations and instead concentrate resources to ensure the normal operation of critical services (such as security alarm systems). The data transmitted between the power metering SOC chip and external devices may include power data, event log information, status information, configuration parameters, and security information.

[0047] In an optional embodiment, the power metering SOC chip further includes: a signal quality monitoring module, wherein the signal quality monitoring module is used to acquire a set of communication index parameters for each communication module to monitor the communication link quality of each communication module, the set of communication index parameters including at least a signal-to-noise ratio parameter, a received signal strength indication parameter, and a packet loss rate; the signal quality monitoring module is also used to activate adaptive anti-interference measures when interference signals are detected, wherein the adaptive anti-interference measures include frequency adjustment, transmit power adjustment, retransmission mechanism, and channel frequency hopping.

[0048] In the above embodiments, the signal quality monitoring module can acquire the communication index parameters of each communication module in real time, such as signal-to-noise ratio parameters, received signal strength indication parameters, and packet loss rate, thereby effectively monitoring the quality of the communication link. When interference signals are detected, the signal quality monitoring module will activate adaptive anti-interference measures, including frequency adjustment, transmit power adjustment, retransmission mechanism, and channel frequency hopping. These measures can significantly reduce signal interference and improve the stability and reliability of communication.

[0049] Existing power metering SOC chips typically lack real-time monitoring of communication link quality, which may lead to undetected communication quality degradation in harsh electromagnetic environments. The power metering SOC chip in this embodiment also includes a signal quality monitoring module, such as... Figure 2As shown, this signal quality monitoring module can monitor the quality of the communication link in real time, ensuring the stability and reliability of communication. In complex electromagnetic environments, various interference sources may cause communication interruptions or data loss. The signal quality monitoring module can activate adaptive anti-interference measures when interference signals are detected. By adjusting frequency, transmit power, retransmission mechanisms, and channel frequency hopping, it reduces the impact of interference and ensures normal communication. Through real-time monitoring of communication link quality and adaptive anti-interference measures, this SOC chip can significantly improve the reliability and stability of communication, especially in complex and variable electromagnetic environments. The data provided by the signal quality monitoring module can be used by other modules to further optimize communication parameters and resource allocation, improving the overall system performance.

[0050] In practical applications, the signal quality monitoring module can collaborate with the dynamic frequency planning module and the intelligent communication scheduling module. For example, when the signal quality monitoring module detects a deterioration in communication link quality, it can notify the dynamic frequency planning module to adjust the operating frequency or transmit power of the communication module, or notify the intelligent communication scheduling module to switch communication modules or adjust communication resources. The dynamic frequency planning module aims to optimize the overall performance of the communication modules and avoid frequency band conflicts, while the signal quality monitoring module aims to ensure the stability and reliability of the communication link by initiating adaptive anti-interference measures to address specific interference problems. The signal quality monitoring module focuses on monitoring the communication link quality of each communication module, including key indicators such as signal-to-noise ratio (SNR), received signal strength indication (RSSI), and packet loss rate. When external interference signals are detected, the signal quality monitoring module will initiate a series of adaptive anti-interference measures, including frequency adjustment, transmit power adjustment, retransmission mechanisms, and channel frequency hopping. During system operation, the signal quality monitoring module continuously monitors the quality of the communication link to ensure high-quality communication under any circumstances. Its main purpose is to detect and respond to external interference signals to ensure the stability and reliability of communication.

[0051] In an optional embodiment, the dynamic frequency planning module obtains the detection results according to a preset period, and uses a preset algorithm model to configure the working parameters of each communication module according to the detection results, so that the communication modules in a group of communication modules do not interfere with each other.

[0052] In the above embodiments, the dynamic frequency planning module can periodically detect the operating status of each communication module and optimize the operating parameters of these modules using a preset algorithm model. This not only ensures that the communication modules do not interfere with each other, improving the stability and reliability of the communication system, but also enhances the overall performance of the power metering SOC chip, enabling it to maintain efficient operation in complex and ever-changing network environments. Specifically: the dynamic frequency planning module acquires detection results at preset intervals, ensuring real-time monitoring of communication module status changes; based on the detection results, it optimizes the operating parameters of the communication modules using a preset algorithm model, avoiding errors and delays caused by manual adjustments; and by precisely configuring the operating parameters of each communication module, it ensures that multiple communication modules work collaboratively in the same system without interfering with each other, improving communication efficiency and stability.

[0053] By configuring the dynamic frequency planning module and the preset algorithm model, it is ensured that each communication module operates stably without interference, thereby improving communication stability. Since the communication modules do not interfere with each other, communication resources can be utilized more effectively, improving communication efficiency and throughput. This embodiment enhances the adaptability and stability of the energy metering SOC chip in complex communication environments, thereby improving the reliability of the entire system. Obtaining detection results through preset cycles ensures that the system can promptly detect and handle potential interference problems. Configuring the operating parameters of the communication modules using preset algorithm models enables intelligent parameter adjustment, improving the system's adaptability and flexibility. Periodic detection and configuration through the dynamic frequency planning module ensures that the communication modules do not interfere with each other, improving the overall performance and reliability of the system.

[0054] For example, if the Bluetooth Low Energy (BLE) communication module's detection results are: currently operating in the 2.4GHz band, with a low signal-to-noise ratio (SNR) and a Received Signal Strength Indication (RSSI) of -70dBm, indicating slight interference, the configuration adjustments could include: frequency adjustment, changing the BLE communication module's operating frequency to the 2.48GHz band to avoid the current interfering frequency; transmit power adjustment, appropriately increasing the BLE communication module's transmit power from 10dBm to 13dBm to improve signal strength; and retransmission mechanism, enabling the data packet retransmission mechanism to ensure data transmission reliability.

[0055] When the WiFi communication module detects that it is currently operating in the 2.4GHz band, with a high signal-to-noise ratio (SNR) and a received signal strength indication (RSSI) of -50dBm, but multiple WiFi devices operating on the same frequency band are causing channel congestion, the following configuration adjustments can be made: frequency adjustment, changing the WiFi communication module's operating frequency to the 5GHz band to avoid 2.4GHz band congestion; channel selection, choosing a less frequently used 5GHz channel, such as channel 36; and transmission rate adjustment, reducing the transmission rate to improve transmission stability.

[0056] When the RF communication module's detection results are: currently operating in the 433MHz band, with a low signal-to-noise ratio (SNR) and a received signal strength indication (RSSI) of -60dBm, indicating strong external interference, the configuration adjustments can include: frequency adjustment, adjusting the RF communication module's operating frequency to the 868MHz band to avoid the current interfering frequency band; transmit power adjustment, appropriately increasing the RF communication module's transmit power from 10dBm to 15dBm to improve signal strength; and channel hopping, enabling the channel hopping mechanism to dynamically switch operating frequency bands and reduce the impact of interference.

[0057] When the HPLC communication module's detection results are: currently operating in the 1MHz to 30MHz frequency band, with a high signal-to-noise ratio (SNR) and a received signal strength indication (RSSI) of -40dBm, but with power line noise interference, configuration adjustments can include: frequency adjustment, adjusting the HPLC communication module's operating frequency to a less noisy band, such as 10MHz to 20MHz; filter settings, enabling a stronger filter to reduce power line noise interference; and data packet length adjustment, reducing the data packet length to decrease transmission time and improve anti-interference capability.

[0058] When the LTECat1 communication module's detection results are: currently operating in the 800MHz band, with a high signal-to-noise ratio (SNR) and a Received Signal Strength Indication (RSSI) of -75dBm, but with occasional packet loss, the configuration adjustments could include: frequency adjustment (keeping the current operating frequency unchanged, as the 800MHz band has good coverage); transmit power adjustment (appropriately increasing the LTECat1 communication module's transmit power from 23dBm to 26dBm to improve signal strength); retransmission mechanism (enabling the data packet retransmission mechanism to ensure data transmission reliability); and QoS settings (optimizing QoS (Quality of Service) settings to prioritize the transmission of important data).

[0059] The aforementioned preset algorithm model can be either the "interference minimization algorithm" or the "channel utilization maximization algorithm," both of which are commonly used wireless communication optimization algorithms. The interference minimization algorithm aims to reduce mutual interference between different communication modules by adjusting their operating frequencies and transmit power, thereby improving overall communication quality. The channel utilization maximization algorithm aims to maximize channel utilization by rationally allocating the bandwidth and frequency of each communication module, thereby improving overall communication efficiency.

[0060] In an optional embodiment, the intelligent communication scheduling module includes an AI prediction engine, which is used to predict the communication load of the power metering SOC chip in the future. The intelligent communication scheduling module is also used to transmit the first type of data during off-peak periods and the second type of data during peak periods based on the communication load prediction results.

[0061] In the above embodiments, the AI ​​prediction engine can predict communication load over a future period, thereby optimizing the configuration of communication resources in advance. During off-peak hours, it transmits the first type of data (such as non-real-time data), reducing communication pressure during peak hours and improving the overall system stability and response speed. During peak hours, it transmits the second type of data (such as real-time data), ensuring the timely transmission of critical data and improving user experience and system performance. By predicting and intelligently scheduling communication load, the load of different communication modules can be effectively managed, avoiding data congestion during peak periods. By transmitting different types of data at different times, the priority and timing of data transmission can be optimized, improving the efficiency and reliability of data transmission.

[0062] In the absence of an AI prediction engine, the communication scheduling of power metering SOC chips may be based on fixed time intervals or simple threshold judgments, which may lead to waste or insufficiency of communication resources. During peak communication periods, important data may be delayed or lost due to network congestion, while during off-peak periods, network resources may not be fully utilized.

[0063] In practical applications, AI prediction engines can predict future communication load using machine learning algorithms (such as time series forecasting and regression analysis). These technologies have been widely applied in other fields and are highly mature. Existing power metering SOC chips often employ static or simple dynamic strategies for communication resource management, which cannot be optimized based on future communication load, easily leading to resource waste or communication congestion. AI prediction engines can more accurately predict future communication load, enabling more rational scheduling decisions. During off-peak hours, transmitting data with low real-time requirements (Type 1) can fully utilize network resources and avoid waste. During peak hours, prioritizing the transmission of data with high real-time requirements (Type 2) ensures timely transmission of critical data, improving system reliability and stability. This technical solution can improve the communication efficiency and resource utilization of power metering SOC chips, reduce communication costs, and enhance user experience. Off-peak hours refer to periods with lighter communication loads, such as nighttime or non-working hours, while peak hours refer to periods with heavier communication loads, such as daytime or working hours. The first category of data can include non-urgent, low-priority data, such as routine electricity metering data and log data. The second category of data can include urgent, high-priority data, such as fault alarm data and real-time control commands.

[0064] In an optional embodiment, the AI ​​prediction engine builds a machine learning model by periodically collecting historical communication records and seasonal electricity consumption trend data, and uses the machine learning model to predict communication load.

[0065] In the above embodiments, the AI ​​prediction engine can periodically collect historical communication records and seasonal electricity consumption trend data, build a machine learning model, and use the model to predict communication load in the future. This enables the electricity metering SOC chip to transmit the first type of data during off-peak hours and the second type of data during peak hours, thereby optimizing the efficiency of communication resource utilization and improving the overall performance and stability of the system.

[0066] By regularly collecting historical communication records and seasonal electricity consumption trend data, a more comprehensive machine learning model can be built, which can significantly improve the accuracy of communication load prediction. More accurate prediction results enable the intelligent communication scheduling module to allocate communication resources more rationally and improve resource utilization. Optimized communication resource management and scheduling can improve system stability and reliability and enhance user experience.

[0067] By leveraging accumulated data, AI prediction engines can more accurately identify patterns in communication load changes, providing a reliable basis for subsequent load predictions. Machine learning models trained on extensive historical data can effectively capture patterns in communication load changes, improving prediction accuracy. Accurate load prediction helps to allocate communication resources in advance, avoiding communication congestion during peak periods and ensuring the timely transmission of important data. Based on prediction results, non-urgent data is prioritized for transmission during off-peak periods, while critical data is processed centrally during peak periods, thereby balancing system load and improving communication efficiency and user experience.

[0068] The workflow of the AI ​​prediction engine includes: collecting historical communication data, including communication time, data type, and data volume; training a prediction model using machine learning algorithms (such as time series prediction and regression analysis) to predict future communication load; predicting communication load over a period of time based on the trained model; and the intelligent communication scheduling module deciding to transmit the first type of data during off-peak periods and the second type of data during peak periods based on the communication load prediction results.

[0069] Historical communication records can include communication time, data type, data volume, and communication success rate over a past period. Collecting historical communication records helps to understand the periodicity, volatility, and anomalies of communication load, providing data support for building accurate machine learning models. Seasonal electricity consumption trend data can include electricity consumption trends in different seasons and time periods. This data can be obtained from power grid companies or users. For example, seasonal electricity consumption trend data can include monthly electricity consumption, total monthly electricity consumption, reflecting the impact of seasonal changes on electricity consumption; peak and off-peak electricity consumption periods, daily or weekly peak and off-peak periods, helping to understand the periodic changes in electricity demand; the relationship between temperature and electricity consumption, correlation analysis between temperature changes and electricity consumption, revealing the impact of seasonal factors on electricity consumption; holiday electricity consumption patterns, electricity consumption trends and patterns during holidays, reflecting the impact of special events on electricity consumption; and user electricity consumption behavior, user electricity consumption habits and behavior patterns in different seasons, such as using air conditioning in summer and using heating in winter.

[0070] Model building includes: data preprocessing, which involves cleaning and normalizing the collected historical communication records and seasonal electricity consumption trend data; feature extraction, which extracts features related to communication load, such as time, data type, data volume, season, and weather; model selection, which involves choosing appropriate machine learning algorithms, such as time series forecasting (ARIMA, LSTM, etc.), regression analysis (linear regression, random forest, etc.), and neural networks (deep learning models); model training, which involves training the selected model using historical data and optimizing the model parameters; and model validation, which involves evaluating the predictive accuracy of the model through methods such as cross-validation.

[0071] Based on the aforementioned historical communication records and seasonal electricity consumption trend data, machine learning models can predict future communication load conditions. Specific prediction results can include: future communication data volume prediction (based on historical communication data trends and seasonal electricity consumption trends, predicting the amount of communication data to be generated over a future period); peak and off-peak communication period prediction (combining historical communication records and user electricity consumption behavior to predict peak and off-peak periods of future communication activities, providing decision support for intelligent communication scheduling); communication network stability prediction (by analyzing historical communication success rates and error rates, predicting the stability and reliability of the future communication network, providing timely warnings of potential communication failures); and resource demand prediction (based on the predicted communication load conditions, predicting future demand for communication resources (such as bandwidth and power), providing a basis for resource allocation and optimization). These prediction results can help the intelligent communication scheduling module of the electricity metering SOC chip make more rational decisions, optimize the allocation and utilization of communication resources, and improve the overall performance and efficiency of the system. Simultaneously, it also provides grid operators with more accurate electricity consumption forecasts and scheduling data, contributing to the intelligent management and optimization of the power grid.

[0072] In an optional embodiment, the power metering SOC chip further includes an antenna, which is connected to each of the communication modules in a group of communication modules, wherein the antenna adopts a layout that physically separates different frequency bands.

[0073] In the above embodiments, by physically separating the antenna layouts of different frequency bands, interference between different frequency bands can be significantly reduced, communication quality can be improved, each communication module has an independent antenna, communication can be carried out more efficiently, resource competition can be avoided, frequency band interference can be reduced and communication efficiency can be improved, the stability and reliability of the system can be enhanced, and the user experience can be improved.

[0074] The antenna in this embodiment supports multiple frequency bands and reduces inter-band interference through physical separation. For example, a multi-band antenna array can be used, with each frequency band corresponding to an independent antenna element. Advanced material shielding measures further reduce the possibility of signal cross-interference. In practical applications, each antenna element can be physically separated to ensure they do not interfere with each other spatially. Physical separation can be achieved through antenna spacing, shielding materials, etc. Each communication module is connected to its corresponding antenna element via an independent RF front-end, ensuring that the signal transmission of each communication module is not interfered with by other modules. For example, antenna element 1 supports a BLE communication module operating in the 2.4GHz band; antenna element 2 supports a WiFi communication module operating in the 2.4GHz and 5GHz bands; antenna element 3 supports an RF communication module operating in the 433MHz and 868MHz bands; antenna element 4 supports an HPLC communication module operating in the 1MHz to 30MHz band; and antenna element 5 supports an LTE Cat1 communication module operating in the 800MHz band. Optionally, antenna elements 1-5 can be located at different positions on the PCB board.

[0075] In one optional embodiment, a set of communication modules includes at least two of the following: a Bluetooth Low Energy (BLE) communication module, a WiFi communication module, a radio frequency (RF) communication module, a high-speed power line carrier (HPLC) communication module, and an LTE Cat1 communication module.

[0076] In the above embodiments, the energy metering SOC chip supports multiple wireless communication standards, including but not limited to Bluetooth Low Energy (BLE), WiFi, RF, high-speed power line carrier (HPLC), and LTE Cat1 communication modules. The combination of these communication modules allows the energy metering SOC chip to flexibly select the most suitable communication method in different application scenarios, improving communication reliability and flexibility. For example, Bluetooth Low Energy (BLE) is suitable for short-range, low-power communication scenarios; WiFi is suitable for scenarios requiring high-speed data transmission; RF is suitable for long-range, low-power communication needs; high-speed power line carrier (HPLC) is suitable for efficient communication within power systems; and LTE Cat1 is suitable for wide-area network communication, ensuring long-distance, stable data transmission capabilities. This multi-mode communication support significantly improves the applicability and performance of the energy metering system.

[0077] SOC chips, by integrating multiple communication modules, offer richer communication options to meet the needs of various application scenarios. Dynamic frequency planning and signal quality monitoring modules reduce interference between different communication modules, improving communication reliability and stability. For example, when an energy metering SOC chip is used in smart homes, the BLE communication module can connect to smart sockets, smart bulbs, and other devices for low-power local control, while the WiFi communication module uploads data to the cloud for remote monitoring and control. In smart grid applications, the HPLC communication module utilizes power lines for data transmission, enabling remote meter reading from smart meters, while the LTE Cat1 communication module uploads data to the grid management system for wide-area remote monitoring. In industrial automation applications, the RF communication module can remotely monitor the status of factory equipment for long-distance data transmission, while the WiFi communication module uploads data to the factory's central control system for centralized data management. Furthermore, when used for remote monitoring, the LTE Cat1 communication module uploads data to a remote server for wide-area remote monitoring, while the RF communication module transmits data in outdoor environments for long-distance monitoring.

[0078] In an optional embodiment, the aforementioned power metering SOC chip further includes a security encryption module, wherein the security encryption module is used to encrypt and decrypt data transmitted by a group of communication modules to ensure data security.

[0079] During data transmission, the security encryption module encrypts the data according to a preset encryption algorithm and decrypts it at the receiving end to protect the privacy and integrity of the data.

[0080] In an optional embodiment, the above-mentioned power metering SOC chip further includes an energy management module, wherein the energy management module is used to monitor and manage the power consumption of the power metering SOC chip, and to optimize the power consumption of a set of communication modules.

[0081] In the above embodiments, the energy management module is integrated into the SOC chip and connected to each communication module. It is used to acquire the operating status and power consumption data of the communication modules and to send power adjustment commands to them. For example, the overall power consumption data of the energy metering SOC chip is collected in real time by an energy consumption sensor, and the power consumption data of each communication module, including its current operating status, is obtained through the communication interface. The collected energy consumption data and the operating status of the communication modules are recorded in memory or external storage for subsequent analysis and optimization. The time of each power adjustment, the power consumption data before and after the adjustment, and the operating status of the communication modules are also recorded. Specific ways to optimize the power consumption of communication modules include: Low-power mode: During off-peak periods or when there are no tasks, switch the communication module to low-power mode to reduce unnecessary energy consumption; for example, when there is no data transmission, switch the BLE communication module to sleep mode, retaining only the necessary listening functions; when there is no network connection, switch the WiFi communication module to power-saving mode to reduce the frequency of scanning and broadcasting; when there is no data transmission, turn off the transmitter of the RF communication module, leaving only the receiver; when there is no data transmission, reduce the sampling frequency and transmission rate of the HPLC communication module; when there is no data transmission, switch the LTE Cat1 communication module to idle mode to reduce the network connection retention time. Dynamic power adjustment mode: Based on communication requirements, the transmission power of the communication module is dynamically adjusted to reduce unnecessary energy consumption. For example, the BLE communication module reduces transmission power for short-distance transmission and appropriately increases transmission power for long-distance transmission; the WiFi communication module reduces transmission power when the signal quality is good and appropriately increases transmission power when the signal quality is poor; the RF communication module reduces transmission power when the signal quality is good and appropriately increases transmission power when the signal quality is poor; the HPLC communication module reduces transmission power when the signal quality is good and appropriately increases transmission power when the signal quality is poor; the LTE Cat1 communication module reduces transmission power when the signal quality is good and appropriately increases transmission power when the signal quality is poor.

[0082] This application also provides a communication control method for an energy metering SOC chip, which is applied to the aforementioned energy metering SOC chip. Figure 3 This is a flowchart of a communication control method for an energy metering SOC chip provided in an embodiment of this application. The process includes:

[0083] Step S301: The dynamic frequency planning module detects the working status of each communication module in a group of communication modules and obtains the detection results;

[0084] Step S302: The dynamic frequency planning module adjusts the transmission power and operating frequency of each communication module in a group of communication modules according to the detection results.

[0085] In step S303, the intelligent communication scheduling module selects a target communication module from a group of communication modules according to the type of the current task, and dynamically allocates communication resources to the target communication module to transmit data corresponding to the current task.

[0086] Through the above steps, the dynamic frequency planning module can monitor the working status of each communication module in real time and automatically adjust the transmission power and operating frequency of each communication module based on the detection results. This effectively avoids signal interference between multiple communication modules and improves the communication reliability and accuracy of the power metering SOC chip. The intelligent communication scheduling module selects the optimal communication module according to the type of different tasks and dynamically allocates communication resources to it, ensuring efficient transmission of different types of data and improving the overall performance and response speed of the system.

[0087] In related technologies, the transmit power and operating frequency of communication modules in power metering SOC chips are often fixed, unable to be dynamically adjusted according to actual needs. This results in low communication performance and resource utilization. Furthermore, they are often based on static priorities or fixed communication protocols, failing to allow for flexible resource allocation according to task type. The method in this embodiment, through dynamic frequency planning, can flexibly adjust the transmit power and operating frequency according to the operating status of the communication modules and actual needs, thereby improving communication quality and efficiency, and reducing interference and energy consumption. The intelligent communication scheduling module can dynamically allocate resources according to task type, ensuring that critical tasks are prioritized, while optimizing the overall utilization of communication resources. The dynamic frequency planning module detects the operating status of each communication module and adjusts the transmit power and operating frequency based on the detection results; the intelligent communication scheduling module selects the target communication module according to the current task type and dynamically allocates communication resources. By detecting and adjusting the dynamic frequency planning module, interference between different communication modules can be significantly reduced, improving communication quality. The intelligent communication scheduling module selects the target communication module based on the type of the current task and dynamically allocates communication resources, which can make more rational use of communication resources and improve communication efficiency. Reducing communication interference and optimizing resource allocation can enhance the stability and reliability of the system and improve the user experience.

[0088] In an optional embodiment, the intelligent communication scheduling module selects a target communication module from a group of communication modules based on the task type of the current task, including: pre-establishing a matching relationship between different task types and communication module types; and, upon receiving the current task, selecting a target communication module from the matching relationship that matches the task type of the current task.

[0089] In the above embodiments, a matching relationship between different task types and communication module types is pre-established to ensure that each task type can be matched with the most suitable communication module, avoiding data transmission delays or failures caused by improper selection of communication modules; when the current task is received, the intelligent communication scheduling module selects the target communication module that matches the current task type from the matching relationship to achieve accurate allocation of communication resources and improve the system's response speed and processing capabilities.

[0090] By pre-establishing a matching relationship between task types and communication module types, the scheduling process can be simplified, and scheduling efficiency and flexibility can be improved. Selecting the target communication module based on the task type ensures that the task is processed using the most suitable communication method, thereby improving communication quality and efficiency. This method can also reduce energy consumption and costs during communication, improving the overall performance of the equipment and user satisfaction. Pre-establishing the matching relationship between different task types and communication module types can be achieved through configuration files or a database. The intelligent communication scheduling module can identify the type of the current task through task type identifiers or task description information. Based on the pre-established matching relationship, the intelligent communication scheduling module can quickly select the most suitable target communication module. As an example, the matching relationships between different task types and communication module types include: regular data transmission with BLE communication modules, emergency alarms with LET Cat1 communication modules, large-volume data transmission with WiFi communication modules, remote monitoring with RF communication modules, and power line data transmission with HPLC communication modules.

[0091] In an optional embodiment, the power metering SOC chip further includes: a signal quality monitoring module, wherein the signal quality monitoring module is used to acquire a set of communication index parameters for each communication module to monitor the communication link quality of each communication module, and the set of communication index parameters includes at least a signal-to-noise ratio parameter, a received signal strength indication parameter, and a packet loss rate; the above method further includes: when an interference signal is detected, the signal quality monitoring module activates adaptive anti-interference measures, wherein the adaptive anti-interference measures include frequency adjustment, transmit power adjustment, retransmission mechanism, and channel frequency hopping.

[0092] In the above embodiments, the signal quality monitoring module can acquire the communication index parameters of each communication module in real time, such as signal-to-noise ratio, received signal strength indication, and packet loss rate, thereby effectively monitoring the communication link quality of each communication module. When interference signals are detected, the signal quality monitoring module will immediately activate adaptive anti-interference measures, including frequency adjustment, transmit power adjustment, retransmission mechanism, and channel frequency hopping, which significantly improves the stability and reliability of the communication system and reduces the impact of signal interference on the accuracy of power metering.

[0093] By introducing a signal quality monitoring module, the quality of the communication link can be monitored in real time, potential problems can be detected in a timely manner, and corresponding measures can be taken. Adaptive anti-interference measures can take corresponding measures according to the type and degree of interference, thereby improving the anti-interference capability of the communication system and ensuring the stability and reliability of communication.

[0094] In related technologies, power metering SOC chips often lack real-time signal quality monitoring and adaptive anti-interference capabilities. This results in the inability to take timely and effective measures when the communication link is interfered with, thus affecting the stability and reliability of communication. This embodiment uses a signal quality monitoring module to monitor the communication link quality in real time, enabling timely detection and handling of communication quality problems, improving the stability and reliability of the communication link. Through adaptive anti-interference measures, it can effectively cope with external interference and improve the anti-interference capability of communication. In practical applications, when interference signals are detected, measures can be taken such as adjusting the operating frequency of the communication module to avoid the interfering frequency band; increasing the transmission power of the communication module to improve signal strength; implementing a retransmission mechanism to ensure the reliability of data transmission; or enabling a channel frequency hopping mechanism to dynamically switch the operating frequency band and reduce the impact of interference.

[0095] Here is a specific example, such as the communication metric parameters obtained:

[0096] BLE communication module: SNR=20dB, RSSI=-70dBm, packet loss rate=5%;

[0097] WiFi communication module: SNR=25dB, RSSI=-50dBm, packet loss rate=2%;

[0098] RF communication module: SNR=18dB, RSSI=-60dBm, packet loss rate=8%;

[0099] HPLC communication module: SNR=22dB, RSSI=-40dBm, packet loss rate=3%;

[0100] LTECat1 communication module: SNR=20dB, RSSI=-75dBm, packet loss rate=4%;

[0101] The signal quality monitoring module periodically acquires the aforementioned communication metrics and monitors the communication link quality in real time. By analyzing RSSI and packet loss rate, it detects strong external interference in the RF communication module. Adaptive interference mitigation measures may include: adjusting the RF communication module's operating frequency from 433MHz to 868MHz to avoid interfering frequency bands; increasing the RF communication module's transmit power from 10dBm to 15dBm to improve signal strength; enabling a data packet retransmission mechanism to ensure data transmission reliability; and enabling a channel frequency hopping mechanism to dynamically switch operating frequency bands and reduce the impact of interference.

[0102] By using a dynamic frequency planning module and a preset algorithm model, the operating parameters of the communication modules can be dynamically configured based on the detection results, ensuring that the various communication modules do not interfere with each other and improving the overall performance of the communication system. This method can also reduce interference and conflicts during the communication process, and improve the stability and reliability of communication.

[0103] For example, the current operating status of each communication module is as follows: the BLE communication module is currently operating in the 2.4 GHz band, with a detected SNR of 20 dB and an RSSI of -70 dBm; the WiFi communication module is currently operating in the 2.4 GHz band, with a detected SNR of 25 dB and an RSSI of -50 dBm; the RF communication module is currently operating in the 433 MHz band, with a detected SNR of 18 dB and an RSSI of -60 dBm; the HPLC communication module is currently operating in the 1 MHz to 30 MHz band, with a detected SNR of 22 dB and an RSSI of -40 dBm; and the LTE Cat1 communication module is currently operating in the 800 MHz band, with a detected SNR of 20 dB and an RSSI of -75 dBm.

[0104] Based on the test results, the dynamic frequency planning module is configured using a preset algorithm model. As an example, the configuration strategy could be as follows: the BLE communication module adjusts its operating frequency to 2.48 GHz and increases its transmit power to 13 dBm; the WiFi communication module adjusts its operating frequency to 5 GHz and selects channel 36; the RF communication module adjusts its operating frequency to 868 MHz and increases its transmit power to 15 dBm; the HPLC communication module adjusts its operating frequency to 10 MHz to 20 MHz and enables a stronger filter; and the LTE Cat1 communication module increases its transmit power to 26 dBm and enables a data packet retransmission mechanism.

[0105] In an optional embodiment, the above method further includes: the dynamic frequency planning module optimizes its internal algorithm model through self-learning, specifically including: after adjusting the transmission power and operating frequency of the communication module, the dynamic frequency planning module collects and analyzes communication quality feedback data, including communication success rate, bit error rate, and latency; based on the feedback data, the dynamic frequency planning module dynamically adjusts the parameters of its internal algorithm model to optimize the configuration strategy of future communication parameters and achieve continuous improvement in communication performance.

[0106] In the above embodiments, an adaptive learning function is introduced, enabling the dynamic frequency planning module to optimize its algorithm parameters based on actual communication performance, thereby achieving continuous improvement in communication performance. This function, through a closed-loop feedback mechanism, enables the system to have the ability to learn and evolve on its own, and to better adapt to complex and ever-changing communication environments.

[0107] Obviously, the embodiments described above are only some embodiments of this application, and not all embodiments. The present application will be specifically described below with reference to specific embodiments.

[0108] This application provides a multi-functional communication power metering SOC chip. To address the problem of integrating and stably operating multiple wireless communication technologies in a power metering SOC chip while avoiding signal interference, the following solution is proposed:

[0109] A multi-channel dynamic frequency planning module is designed inside the SOC chip to detect the working status of the BLE, WiFi, Cat1, RF and HPLC communication modules and the surrounding electromagnetic environment in real time, and automatically adjust the transmission power and operating frequency of each module to reduce mutual interference.

[0110] An intelligent communication scheduling mechanism is adopted to dynamically allocate communication resources based on the characteristics of each communication technology and the priority of the current task, so as to ensure the timeliness and effectiveness of critical data transmission.

[0111] By implementing the principle of isolation design, the antenna layouts of different frequency bands are physically separated, and advanced material shielding measures are combined to further reduce the possibility of signal cross-interference.

[0112] An integrated signal quality monitoring subsystem continuously assesses the quality of each communication link. Once an interference signal is detected, an adaptive anti-interference mode is immediately activated to quickly adjust parameter settings until the optimal communication state is restored.

[0113] Specific examples:

[0114] In a smart grid monitoring environment, considering that electricity meters are located in complex residential areas surrounded by radiation sources from various wireless devices, the dynamic frequency planning module collects environmental noise data every second and calculates the optimal operating parameter configuration using a preset algorithm model. For example, Wi-Fi uses channel 7, while BLE switches to a lower frequency band to minimize the avoidance of each other's main communication paths. Furthermore, whenever the load decreases at night, the HPLC's transmission rate automatically increases, utilizing idle power line bandwidth to accelerate the transmission of large amounts of data packets.

[0115] Furthermore, in order to address the challenge of designing a reasonable resource allocation strategy that coordinates the needs of various communication technologies and improves the overall performance of the chip without affecting power metering, an AI prediction engine is embedded to analyze potential peak data traffic periods in the near future and reserve bandwidth for critical communication tasks in advance, effectively preventing sudden communication congestion.

[0116] For example, during daily operation, the AI ​​prediction engine regularly collects historical communication records and seasonal electricity consumption trends to build machine learning models and predict communication load for each time period in the next few days. Based on the prediction results, the system proactively initiates large-scale data synchronization tasks during off-peak periods, while prioritizing the timely delivery of alarm messages and emergency control commands with high real-time requirements during peak periods, thus achieving intelligent communication resource scheduling.

[0117] To further enhance the security level of the chip and prevent malicious attacks and illegal intrusions, encrypted communication technology and access control lists are introduced to strengthen the auditing authority for external device access and ensure the confidentiality and integrity of communication data.

[0118] Figure 4 This is a schematic diagram of a multi-functional communication power metering SOC chip. The SOC chip integrates BLE, Wi-Fi, Cat1, RF and HPLC communication modules, all of which are connected to an MCU. The MCU is also connected to the metering module. The MCU includes the aforementioned multi-channel dynamic frequency planning module, which is used to plan the power and frequency of each communication module. It also adopts an intelligent communication scheduling mechanism to dynamically allocate communication resources, that is, the MCU also includes an intelligent communication scheduling module. The MCU also integrates a signal quality monitoring subsystem (corresponding to the aforementioned signal quality monitoring module), which is used to continuously evaluate the quality of each communication link.

[0119] Compared with the prior art, the embodiments of this application have at least the following technical effects:

[0120] 1) Because of the adoption of dynamic frequency planning and intelligent scheduling strategies, multiple communication technologies can coexist harmoniously on the same SOC chip platform, which greatly improves the reliability and accuracy of power metering instruments in complex electromagnetic environments.

[0121] 2) By leveraging the forward-looking resource allocation of the AI ​​prediction engine, not only is the invalid waiting time reduced, but the response speed of key data is also greatly improved, thereby significantly enhancing the user experience;

[0122] 3) The introduction of high-level security protection measures enables the power metering SOC chip to resist modern cybersecurity threats, ensuring the privacy and security of power data.

[0123] This application also provides a computer-readable storage medium storing instructions that, when executed, perform the steps of any of the methods described above.

[0124] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0125] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0126] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0127] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0128] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0129] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0130] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practical application.

[0131] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art that are not described in this disclosure.

Claims

1. A communication method of an electric energy metering SOC chip, characterized by, The application is applied to an electric energy metering SOC chip, and the electric energy metering SOC chip comprises a group of communication modules, a dynamic frequency planning module, an intelligent communication scheduling module and a signal quality monitoring module, wherein the dynamic frequency planning module is electrically connected with each communication module in the group of communication modules, and the group of communication modules supports multiple wireless communication standards; the intelligent communication scheduling module is electrically connected with each communication module in the group of communication modules; The signal quality monitoring module is used for acquiring a group of communication index parameters of each communication module to monitor the communication link quality of each communication module, and the group of communication index parameters at least comprises a signal-to-noise ratio parameter, a received signal strength indication parameter and a packet loss rate; The AI prediction engine in the intelligent communication scheduling module is used for predicting the communication load situation of the electric energy metering SOC chip in a future period of time, and the intelligent communication scheduling module is further used for transmitting non-real-time data in a low peak period and transmitting real-time data in a high peak period according to the communication load prediction result; The electric energy metering SOC chip further comprises an antenna, and the antenna is connected with each communication module in the group of communication modules, wherein the antenna adopts a layout mode of physically separating different frequency bands; The communication method comprises: The dynamic frequency planning module detects the working state of each communication module in the group of communication modules to obtain a detection result, wherein the dynamic frequency planning module obtains the detection result according to a preset period and configures the working parameters of each communication module according to the detection result by using a preset algorithm model, so that each communication module in the group of communication modules does not interfere with each other; the preset algorithm model is an interference minimization algorithm or a channel utilization maximization algorithm; The dynamic frequency planning module adjusts the transmission power and working frequency of each communication module in the group of communication modules according to the detection result; The intelligent communication scheduling module selects a target communication module from the group of communication modules according to the type of a current task, and dynamically allocates communication resources for the target communication module to transmit data corresponding to the current task; The intelligent communication scheduling module selects a target communication module from the group of communication modules according to the type of a current task, comprising: A matching relationship between different task types and communication module types is established in advance; In the case that the current task is received, the target communication module matched with the task type of the current task is selected from the matching relationship; The communication method further comprises: in the case that an interference signal is detected, the signal quality monitoring module starts an adaptive anti-interference measure, wherein the adaptive anti-interference measure comprises frequency adjustment, transmission power adjustment, retransmission mechanism and channel frequency hopping.

2. The communication method according to claim 1, characterized by, The AI prediction engine constructs a machine learning model by periodically collecting historical communication records and seasonal electricity consumption trend data, and predicts the communication load situation by using the machine learning model.

3. The communication method according to claim 1, characterized by, The group of communication modules comprises at least two of a Bluetooth Low Energy (BLE) communication module, a WiFi communication module, a Radio Frequency (RF) communication module, a High-speed Power Line Carrier (HPLC) communication module and a LTE Cat1 communication module.

Images