Methods and systems for suppressing noise interference

Abstract

This application relates to a method and system for suppressing noise interference. The method includes: acquiring noise interference data of the common ground loop of a target area of ​​an electronic device through an edge node, and sending the noise interference data to a data forwarding node; sending the noise interference data to a central processing node through the data forwarding node; determining, based on the noise interference data and a pre-built noise database, the target component generating the noise interference data and the corresponding filtering configuration information of the noise interference data through the central processing node, and sending the filtering configuration information to the edge node; and filtering the noise interference data generated by the target component based on the filtering configuration information through the edge node to suppress noise interference. This application can suppress voltage noise generated under a central + edge node architecture, thereby improving the user experience.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and specifically to a method and system for suppressing noise interference. Background Technology

[0002] Most vehicles currently use a central + regional control architecture. Under this architecture, regional control requires too many hardware I / O connections, resulting in long wiring harnesses and high costs.

[0003] Therefore, the central + edge node architecture has emerged. The central node is responsible for all data processing, while the edge nodes have no microcontrollers, have abundant I / O ports, and have extremely simple wiring harnesses. However, under this architecture, after the PCB is designed with a common board and common ground, when a high-power motor (such as a car window motor) starts or reverses, the transient current will generate voltage noise on the common ground path, which will bring significant electromagnetic interference problems and thus affect the user's driving experience. Summary of the Invention

[0004] This application provides a method and system for suppressing noise interference, which can suppress voltage noise generated under a central + edge node architecture, thereby improving the user's driving experience.

[0005] This application provides a noise interference suppression method applied to an electronic device. The electronic device includes a central processing node, at least one edge node, and a data forwarding node, with the data forwarding node connected to both the central processing node and the edge node. The method includes: By using edge nodes, noise interference data of the common ground loop in the target area of ​​electronic devices is obtained, and the noise interference data is sent to the data forwarding node; Noise interference data is sent to the central processing node via data forwarding nodes; The central processing node, based on noise interference data and a pre-built noise database, determines the target component that generates the noise interference data, as well as the corresponding filtering configuration information, and sends the filtering configuration information to the edge nodes. The filtering configuration information is used to indicate the filtering strategy for suppressing the noise interference data. By using edge nodes, noise interference data generated by the target component is filtered based on the filtering configuration information to suppress noise interference.

[0006] Based on the aforementioned technical means, this application utilizes edge nodes to acquire noise data of the target area in real time, and through a central processing node, performs precise component tracing and filtering strategy matching based on a noise database. Furthermore, the edge nodes perform localized filtering processing, thereby achieving rapid location and differentiated suppression of noise sources. Compared with the unified filtering or post-incident investigation methods in related technologies, this application can improve the real-time performance, accuracy, and adaptability of noise suppression, effectively ensuring the electromagnetic compatibility and long-term operational reliability of electronic equipment and electronic systems. In some embodiments, a central processing node, based on noise interference data and a pre-built noise database, determines the target component generating the noise interference data, as well as the corresponding filtering configuration information for the noise interference data, including: The first noise interference data is obtained by performing a fast Fourier transform on the noise interference data through the central processing node. The noise spectrum features are obtained by extracting the spectral features of the first noise interference data through the central processing node. The central processing node identifies the target component generating the noise interference data, as well as the corresponding filtering configuration information, based on the noise spectrum characteristics and noise database.

[0007] Based on the aforementioned technical means, this application performs fast Fourier transform and spectral feature extraction on noise interference data through a central processing node, transforming complex voltage fluctuation signals in the time domain into quantifiable frequency domain feature parameters. These parameters are then accurately matched with a pre-built noise database, enabling precise identification of noise sources and generation of targeted filtering strategies. This allows for the rapid identification of specific components generating interference and the matching of optimal filtering configurations, laying a solid data foundation for subsequent precise suppression. In some embodiments, the noise database includes preset spectral characteristics; through a central processing node, based on the noise spectral characteristics and the noise database, the target component generating the noise interference data, and the filtering configuration information corresponding to the noise interference data, are determined, including: The noise spectrum features are matched with preset spectrum features through the central processing node to obtain the matching result; Based on the matching results, the target component that generates the noise interference data and the filtering configuration information are determined.

[0008] Based on the aforementioned technical means, this application can quickly and accurately locate the specific target component causing interference by matching the noise spectrum features extracted in real time with the preset spectrum features in the noise database, and retrieve the corresponding filter configuration information based on the matching results; furthermore, the distribution of filter configuration based on the accurate matching results ensures the pertinence and effectiveness of the suppression measures, and provides a reliable basis for the subsequent realization of precise filtering.

[0009] In some embodiments, based on the matching results, the target component generating the noise interference data and the filtering configuration information are determined, including: Based on the matching results, the target component identifier corresponding to the noise spectrum characteristics is determined from the noise database; Based on the interference level of the target component identification and noise spectrum characteristics, the corresponding filtering strategy is queried from the noise database; wherein, the filtering strategy includes at least the filter type and the target frequency band; Determine the corresponding filter configuration information based on the filter type and target frequency band.

[0010] Based on the aforementioned technical means, this application first identifies the target component based on the matching results, and then, combined with the interference level of the noise spectrum characteristics, queries the database for differentiated filtering strategies—matching different filter types and target frequency bands for interference of varying severity, ultimately generating specific filtering configuration information. This avoids a "one-size-fits-all" filtering approach, employing low-cost, lightweight filtering for minor interference and initiating higher-order filtering schemes for severe interference, thus optimizing system resource allocation. Secondly, the introduction of interference levels provides a basis for priority scheduling of the central processing node, ensuring timely suppression of high-level, high-risk noise. Thirdly, by dynamically linking interference levels with filtering strategies, the suppression intensity can be adaptively adjusted according to the actual interference strength, minimizing the computational and power consumption of filtering processing while ensuring electromagnetic compatibility, achieving an optimal balance between performance and resource consumption.

[0011] In some embodiments, the edge node includes a sampling resistor module, a differential amplifier module, and an analog-to-digital converter module; the edge node is used to acquire noise interference data of the common ground loop in the target area of ​​the electronic device, including: The voltage fluctuation signal on the common ground loop of the target area of ​​the electronic device is acquired by the sampling resistor module to obtain the initial analog voltage signal; The initial analog voltage signal is differentially amplified using a differential amplifier module to obtain an amplified analog noise signal. The analog-to-digital converter module continuously samples the amplified analog noise signal at a preset high sampling rate to obtain noise interference data.

[0012] Based on the aforementioned technical means, this application deploys a sampling resistor module, a differential amplifier module, and an analog-to-digital converter module at the edge node. The sampling resistor module is connected in series in the common ground loop, which can directly convert transient current fluctuations into voltage signals, achieving near-end coupling and lossless capture of noise sources and ensuring the integrity of the original signal. Secondly, the differential amplifier module effectively eliminates common-mode interference on the common ground loop through a high common-mode rejection ratio, and only amplifies the differential-mode signal that reflects the real noise, significantly improving the signal-to-noise ratio and acquisition accuracy of weak noise signals. Finally, the analog-to-digital converter module continuously samples at a preset high sampling rate, ensuring that high-frequency noise components can be completely recorded, avoiding signal aliasing and information loss. The edge node can acquire high-fidelity, high-resolution noise interference data, providing a high-quality data foundation for the subsequent accurate spectrum analysis and component identification of the central processing node, thereby ensuring the reliability and effectiveness of the entire noise suppression system from the source. In some embodiments, sending noise interference data to a data forwarding node includes: After acquiring the noise interference data, the interference level corresponding to the noise interference data is determined through the edge nodes; If the interference level is less than the preset level threshold, the local preset basic filter is invoked through the edge node to suppress the noise interference data; If the interference level is greater than or equal to the preset level threshold, the interference level and noise interference data are associated and packaged through the edge node and sent to the data forwarding node. The noise interference data is sent to the central processing node through the data forwarding node, including: The data forwarding nodes forward the associated and packaged interference level and noise interference data to the central processing node.

[0013] Based on the aforementioned technical means, this application determines the interference level at edge nodes. If the interference level is less than a preset threshold, it indicates that the interference intensity is low, and the local basic filter can be used directly for noise suppression. If the interference level is greater than or equal to the preset threshold, it indicates that the interference intensity is high, and the interference level can be associated with the noise interference data and packaged before uploading. This provides a priority scheduling basis for data forwarding nodes, ensuring that high-level interference data is forwarded first and low-level data is merged for processing, effectively alleviating network congestion. At the same time, it enables the central processing node to perform hierarchical processing based on the level label, initiating precise source tracing and powerful filtering configuration for high-level data, and simplifying the processing of low-level data. This significantly reduces the computational load of the central node while ensuring timely response to high-risk noise. In some embodiments, the noise interference data is analyzed through the edge nodes to obtain interference characteristic parameters; By using edge nodes, the interference feature parameters are input into a pre-trained lightweight interference classification model, and the interference feature parameters are nonlinearly mapped and weighted to obtain the interference score. By using edge nodes, the interference level corresponding to the noise interference data is determined based on the interference score and multiple preset level threshold ranges.

[0014] Based on the aforementioned technical means, by deploying a lightweight machine learning model locally at the edge node to classify and judge noise interference, compared to uploading all raw data to the central processing node for processing, the communication load between the data forwarding node and the central processing node is significantly reduced, thus lowering the system bandwidth requirements. By completing the interference level determination locally at the edge node, the round-trip delay between data upload and command issuance is avoided, enabling real-time or near-real-time output of the interference level and improving the system's response speed. The use of multi-dimensional feature nonlinear mapping and weighted calculation based on machine learning models significantly improves the accuracy and robustness of interference level discrimination compared to the single threshold or simple linear weighting methods used in existing technologies.

[0015] In some embodiments, noise interference data generated by the target component is filtered based on filtering configuration information via edge nodes, including: By analyzing and processing the filter configuration information through edge nodes, the target filter type and corresponding filter parameters corresponding to the filter configuration information are obtained. By configuring the target filter in the edge node according to the target filter type and filtering parameters, the configured target filter is obtained. By using edge nodes and based on the configured target filter, noise interference data generated by the target component is filtered.

[0016] Based on the aforementioned technical means, this application ensures that the central decision is accurately understood and losslessly transformed at the edge by analyzing and processing the filter configuration information. Furthermore, it dynamically configures the target filters in the edge nodes according to the parsed filter types and parameters, realizing on-demand allocation and real-time reconstruction of filter resources. This allows the same hardware platform to flexibly cope with various types of noise interference. Finally, based on the configured filters, it processes the noise data generated by the target components in real time, completing the suppression loop at the location closest to the noise source. This not only effectively blocks the transmission of noise to sensitive circuits through the common ground loop, ensuring the real-time performance and effectiveness of the suppression measures, but also avoids large-scale uploading of raw data through localized processing, significantly reducing the communication load of the vehicle network and the computational pressure on the central node. In some embodiments, after filtering the noise interference data of the target component based on filtering configuration information through edge nodes, the method further includes: The central processing node associates and stores the noise interference data, the noise spectrum characteristics corresponding to the noise interference data, and the filtering configuration information corresponding to the noise interference data to obtain the target data packet. The noise database is updated based on the target data packets through the central processing node, resulting in the updated noise database.

[0017] Based on the aforementioned technical means, this application continuously optimizes and expands the noise database using real-world case data, resulting in richer preset spectral characteristics and more precise filtering strategy configuration. This moves beyond a static knowledge base at the factory, allowing the database to accumulate experience and evolve over time. Ultimately, the accuracy of noise identification and suppression will continuously improve with mileage, achieving continuous evolution of the vehicle's electromagnetic compatibility and long-term reliability optimization.

[0018] This application provides a noise interference suppression system, which includes a central processing node, at least one edge node, and a data forwarding node, wherein the data forwarding node is connected to both the central processing node and the edge node; the system includes: Edge nodes are used to acquire noise interference data of the common ground loop in the target area of ​​electronic devices and send the noise interference data to the data forwarding nodes; Data forwarding nodes are used to send noisy and interfering data to the central processing node; The central processing node is used to determine the target component that generates the noise interference data and the corresponding filtering configuration information based on the noise interference data and a pre-built noise database, and then send the filtering configuration information to the edge nodes; wherein, the filtering configuration information is used to indicate the filtering strategy for suppressing the noise interference data. Edge nodes are used to filter noise interference data generated by target components based on filtering configuration information in order to suppress noise interference.

[0019] This application provides an electronic device, which includes a noise interference suppression system for implementing the steps in any of the above methods.

[0020] The beneficial effects of this application are: (1) This application utilizes edge nodes to acquire noise data of the target area in real time, and through the central processing node, performs accurate component tracing and filtering strategy matching based on the noise database. Furthermore, the edge nodes perform localized filtering processing, thereby realizing rapid location and differentiated suppression of noise sources. Compared with the unified filtering or post-incident investigation methods in related technologies, this application can improve the real-time performance, accuracy and adaptability of noise suppression, and effectively ensure the electromagnetic compatibility and long-term operational reliability of electronic equipment and electronic systems.

[0021] (2) This application performs fast Fourier transform and spectral feature extraction on noise interference data through a central processing node, transforming the complex voltage fluctuation signal in the time domain into quantifiable frequency domain feature parameters, and then accurately matching it with a pre-built noise database. This enables accurate identification of noise sources and generation of targeted filtering strategies, thereby quickly locking down the specific components that generate interference and matching them with the optimal filtering configuration, laying a solid data foundation for subsequent accurate suppression.

[0022] (3) This application can quickly and accurately locate the specific target component that causes interference by matching the noise spectrum features extracted in real time with the preset spectrum features in the noise database, and retrieve the corresponding filter configuration information based on the matching result; furthermore, the filter configuration based on the accurate matching result ensures the pertinence and effectiveness of the suppression measures, and provides a reliable basis for the subsequent realization of accurate filtering. (4) This application first identifies the target component based on the matching results, and then queries the database for differentiated filtering strategies based on the interference level of the noise spectrum characteristics. Different filter types and target frequency bands are matched for different levels of interference, and finally specific filtering configuration information is generated. This avoids the "one-size-fits-all" filtering process. For minor interference, a low-cost lightweight filter is used, while for severe interference, a high-order filtering scheme is activated, thus achieving optimized allocation of system resources. Secondly, the introduction of interference levels provides a basis for priority scheduling of the central processing node, ensuring that high-level and high-risk noise can be suppressed in a timely manner. Thirdly, by dynamically associating the interference level with the filtering strategy, the suppression intensity can be adaptively adjusted according to the actual interference intensity. While ensuring electromagnetic compatibility, the system's computing power and power consumption are minimized, achieving the best balance between performance and resource consumption.

[0023] (5) This application deploys a sampling resistor module, a differential amplifier module, and an analog-to-digital converter module at the edge node. The sampling resistor module is connected in series in the common ground loop, which can directly convert transient current fluctuations into voltage signals, realize near-end coupling and lossless capture of noise sources, and ensure the integrity of the original signal. Secondly, the differential amplifier module effectively eliminates common-mode interference on the common ground loop through high common-mode rejection ratio, and only amplifies the differential-mode signal that reflects the real noise, which significantly improves the signal-to-noise ratio and acquisition accuracy of weak noise signals. Finally, the analog-to-digital converter module continuously samples at a preset high sampling rate, which ensures that high-frequency noise components can be completely recorded, avoids signal aliasing and information loss, and enables the edge node to obtain high-fidelity and high-resolution noise interference data, providing a high-quality data foundation for the subsequent accurate spectrum analysis and component identification of the central processing node, thereby ensuring the reliability and effectiveness of the entire noise suppression system from the source.

[0024] (6) By determining the interference level at the edge node, if the interference level is less than the preset level threshold, it indicates that the interference intensity is small, and the local basic filter can be used directly for noise suppression; if the interference level is greater than or equal to the preset level threshold, it indicates that the interference intensity is large, and the interference level can be associated with the noise interference data and packaged and uploaded, providing a priority scheduling basis for the data forwarding node, ensuring that high-level interference data is forwarded first and low-level data is merged and processed, effectively alleviating network congestion; at the same time, the central processing node can perform hierarchical processing based on the level label, start accurate source tracing and powerful filtering configuration for high-level data, and simplify the processing of low-level data, thereby significantly reducing the computational load of the central node while ensuring timely response to high-risk noise. (7) By deploying a lightweight machine learning model locally on the edge node to classify and judge noise interference, compared with uploading all the original data to the central processing node for processing, the communication load between the data forwarding node and the central processing node is greatly reduced, and the system bandwidth requirement is reduced. By completing the interference level judgment locally on the edge node, the round-trip delay between data upload and command issuance is avoided, and the real-time or near-real-time output of the interference level is realized, which improves the system response speed. The use of multi-dimensional feature nonlinear mapping and weighted calculation based on machine learning model significantly improves the accuracy and robustness of interference level judgment compared with the use of a single threshold or simple linear weighting in the existing technology.

[0025] (8) By analyzing and processing the filter configuration information, the central decision is ensured to be accurately understood and losslessly transformed at the edge. Then, the target filter in the edge node is dynamically configured according to the parsed filter type and parameters, realizing the on-demand allocation and real-time reconstruction of filter resources, enabling the same hardware platform to flexibly cope with various types of noise interference. Finally, the noise data generated by the target component is processed in real time based on the configured filter, and the suppression loop is completed at the location closest to the noise source. This not only effectively blocks the transmission of noise to sensitive circuits through the common ground loop, ensuring the real-time performance and effectiveness of the suppression measures, but also avoids the large-scale uploading of the original data through localized processing, significantly reducing the communication load of the vehicle network and the computing pressure of the central node.

[0026] (9) By continuously optimizing and expanding the noise database using real-world case data, the preset spectral characteristics become richer and the filtering strategy configuration becomes more accurate. This means that it is no longer limited to the static knowledge base at the factory, but can continuously accumulate experience and evolve itself as the usage time increases; ultimately, the accuracy of noise identification and the suppression effect will continuously improve with the increase of mileage, realizing the continuous evolution of the vehicle's electromagnetic compatibility and the optimization of long-term reliability. Attached Figure Description

[0027] Figure 1 A flowchart illustrating a noise interference suppression method provided in this application embodiment. Figure 1 ; Figure 2 A flowchart illustrating a noise interference suppression method provided in this application embodiment. Figure 2 ; Figure 3 A schematic diagram of an optional structural composition of the noise interference suppression system provided in the embodiments of this application; Figure 4 A schematic diagram of an optional structural composition of the noise interference suppression system provided in the embodiments of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0028] It should be noted that the terms "first" and "second" mentioned above are only used to distinguish between different options and do not represent the degree of superiority or inferiority of the options or their priority in the implementation process. Detailed Implementation

[0029] The embodiments of this application will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be understood that the preferred embodiments are only for illustrating this application and are not intended to limit the scope of protection of this application.

[0030] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0031] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0032] In the following description, the terms "first, second, third" are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0033] In this embodiment, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, object A and / or object B can represent three situations: object A exists alone, object A and object B exist simultaneously, and object B exists alone.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0035] It should be noted that the electronic device in this application can be any device that may generate electromagnetic interference, such as a vehicle, an airplane, or an intelligent robot. This application uses a vehicle as an example for illustration.

[0036] Currently, most vehicles adopt a central + regional control architecture. Under the current central + regional control architecture, the regional control has too many hardware inputs / outputs (I / O), the wiring harness is long and the cost is high, the regional control uses high-specification chips (which are also more expensive), and the central system receives data processed by the regional control, without raw data to support a large number of artificial intelligence (AI) models.

[0037] Based on this, the central + edge node architecture has emerged. The central node is the logic and data hub of the whole vehicle, responsible for processing all uploaded data. The edge nodes have no microcontroller unit (MCU), but have abundant I / O ports, support protocol conversion to transmit a large amount of data from the end side to the central node, and are directly placed at the actuator position, with extremely simple wiring harnesses.

[0038] However, this architecture presents a common technical problem: in areas like doors, where edge nodes connect to all door motors, lock motors, rearview mirror adjustment motors, Hall sensors, hard switches, buttons, and other hardware, a shared circuit board (PCB) ground design can cause transient currents to generate voltage noise on the ground path when high-power motors (such as window motors) start or reverse. This noise can couple to other signal channels (such as Hall sensors and button detection circuits), leading to false triggering, signal distortion, or even control failure, resulting in significant electromagnetic interference (EMI) problems.

[0039] In related technologies, an automatic window lowering control method for electric windows and its anti-EMI interference topology circuit have been proposed. However, this is not a solution based on a central + edge node architecture. It describes using hardware circuitry to add an operational amplifier U5 to amplify the drive motor current in two stages, forming the ripple signal required by the microcontroller to control the window operation. While it can suppress noise in specific frequency bands, it cannot identify interference sources, relies on local MCU processing, has a slow response, and cannot coordinate across channels. Furthermore, it cannot achieve active interference identification and system-level optimization at the MCU edge node.

[0040] Based on this, this application provides a noise interference suppression method applied to a vehicle. The vehicle includes a central processing node, at least one edge node, and a data forwarding node, with the data forwarding node connected to both the central processing node and the edge node. The method includes: acquiring noise interference data of the common ground loop of a target area of ​​the vehicle through the edge node and sending the noise interference data to the data forwarding module; sending the noise interference data to the central processing node through the data forwarding node; determining, based on the noise interference data and a pre-built noise database, the target component generating the noise interference data and the corresponding filtering configuration information based on the noise interference data through the central processing node, and sending the filtering configuration information to the edge node; wherein the filtering configuration information is used to indicate a filtering strategy for suppressing the noise interference data; and filtering the noise interference data generated by the target component based on the filtering configuration information through the edge node to suppress the noise interference. In this way, this application utilizes edge nodes to acquire noise data of the target area in real time, and through the central processing node, performs accurate component tracing and filtering strategy matching based on the noise database. Furthermore, the edge nodes perform localized filtering processing, thereby realizing rapid location and differentiated suppression of noise sources. Compared with the unified filtering or post-event investigation methods in related technologies, this application can improve the real-time performance, accuracy and adaptability of noise suppression, and effectively ensure the electromagnetic compatibility and long-term operational reliability of vehicle electronic systems.

[0041] The technical solutions in the embodiments of this application will now be clearly and completely described with reference to the accompanying drawings.

[0042] It should be noted that the noise interference suppression method provided in the embodiments of this application can be executed by an electronic device, which may include, but is not limited to, cars, sports cars, SUVs, commercial vehicles, engineering vehicles, airplanes, intelligent robots, etc. The electronic device includes at least a central processing node, at least one edge node, and a data forwarding node, with the data forwarding node connected to both the central processing node and the edge node.

[0043] Figure 1 A flowchart illustrating a noise interference suppression method provided in this application embodiment. Figure 1,like Figure 1 As shown, it may include S101 to S104, wherein: S101, through the edge node, obtains the noise interference data of the common ground loop of the target area of ​​the electronic device, and sends the noise interference data to the data forwarding node.

[0044] Here, the target area of ​​the electronic device refers to multiple areas divided according to the spatial location of the electronic device; for example, the vehicle can be divided into a front left area, a rear left area, a front right area, and a rear right area, wherein the front left area may include various motors, lights, and other drive modules on the front left door.

[0045] In other words, the target area can be the left front door area, and the edge nodes can be connected to all the door motors, lock motors, rearview mirror adjustment motors, Hall sensors, hard switches, buttons and other hardware in the door area. After the PCB is designed with a common board and common ground, when a high-power motor (such as a window motor) starts or reverses, the transient current may generate voltage noise on the common ground path. This noise is the noise interference data on the common ground loop.

[0046] In other words, noise interference data refers to the voltage drop across the common ground impedance caused by the operating current (especially transient inrush current) of multiple loads (especially high-power inductive loads such as motors) sharing the same ground path in the common ground design of electronic equipment systems. This voltage drop causes instantaneous changes in the ground reference level, resulting in interference signals such as voltage or current spikes and oscillations.

[0047] Here, an edge node refers to a hardware unit deployed in various target areas of the vehicle (such as the left front door) that has the capability to collect and perform preliminary data processing. It is directly connected to the PCB circuits of various electronic components (including motors, sensors, switches, etc.) within its area, enabling it to monitor electrical parameters (such as voltage and current) on the common ground loop in real time and extract noise interference data. Edge nodes possess certain computing and communication capabilities, and are responsible for preprocessing the collected data before sending it to the next-level data forwarding node.

[0048] A data forwarding node is a central hub responsible for aggregating data from one or more edge nodes. It is typically deployed in the central location of the vehicle or exists as a domain controller. It receives noise interference data reported by each edge node and may aggregate, fuse, compress, or further analyze the data before forwarding it to a higher-level in-vehicle central computing platform or vehicle-to-everything (V2X) cloud. For example, a data forwarding node can be a regional gateway.

[0049] Understandably, each target area of ​​the vehicle can be connected to at least one edge node to acquire noise interference data of the target area.

[0050] In some embodiments, edge nodes can acquire noise interference data on the common ground loop of the vehicle target area, add a hardware timestamp to each set of sampled data, encapsulate data packets using the SOME / IP protocol, and upload them to the area gateway via Time-Sensitive Networking (TSN) Ethernet.

[0051] It should be noted that edge nodes can upload data via event triggering or periodically via a preset period; this application embodiment does not impose any limitations on this.

[0052] It should also be noted that, since the impact of noise generated by different components on vehicle functional safety, communication stability and user experience varies significantly during vehicle operation, the priority of noise interference data acquired from different components is different. For example, the priority of noise data generated by key actuator components can be set to high priority, while the priority of noise data generated by static or low-power components can be set to low priority.

[0053] For example, transient noise during window motor startup, if interfering with the Hall sensor's pulse signal, may cause the anti-pinch function to mis-trigger (injuring a passenger) or malfunction. Therefore, noise data generated by such components needs to be prioritized for collection and reporting by edge nodes so that the system can perform timely algorithm compensation or fault warnings. Another example is hard switch state changes, button backlight LEDs (Light Emitting Diodes, LEDs), or Hall sensors. These components themselves generate relatively small current changes, and the resulting ground noise is usually weak. Their noise data is primarily used as background reference or as auxiliary data during full-vehicle noise spectrum analysis; therefore, their collection and reporting priority can be relatively low. Next, this application will describe several ways to obtain noise interference data of the target area.

[0054] In one possible implementation, noise interference data can be indirectly obtained by monitoring the current changes at the power drive terminals of various components within the target area. Specifically, edge nodes are connected to the current sampling pins of each motor driver to acquire the current waveforms on the phase lines in real time during motor operation. Since the voltage noise on the common ground loop is directly related to the load current change rate, the voltage noise characteristics on the common ground loop can be deduced by analyzing the distortion, spikes, and harmonic components of the current waveform. This method is suitable for intelligent power devices that already integrate current sensing functionality, without requiring additional hardware sampling circuitry.

[0055] In another possible implementation, the edge node may include, but is not limited to, a sampling resistor module, a differential amplifier module, and an analog-to-digital converter module; through the edge node, noise interference data of the common ground loop in the target area of ​​the electronic device is acquired, including: The voltage fluctuation signal on the common ground loop of the target area of ​​the electronic device is acquired by the sampling resistor module to obtain the initial analog voltage signal; The initial analog voltage signal is differentially amplified using a differential amplifier module to obtain an amplified analog noise signal. The analog-to-digital converter module continuously samples the amplified analog noise signal at a preset high sampling rate to obtain noise interference data.

[0056] Here, the sampling resistor module refers to a high-precision (±1%), low-resistance (typically in the milliohm range, e.g., 10mΩ), ≥1W resistor connected in series in the common-ground loop of the vehicle's target area. According to Ohm's law, when a transient current flows through the common-ground loop, a small voltage drop will be generated across this resistor. The function of this module is to convert the common-ground loop current fluctuations, which are difficult to measure directly, into a voltage signal that is easy to process. The initial analog voltage signal refers to the raw voltage waveform directly converted by the sampling resistor module without any processing.

[0057] A differential amplifier module is a signal conditioning circuit consisting of a high-precision operational amplifier and an external matching resistor network. Its core feature is a high common-mode rejection ratio (CMRR), effectively suppressing common interference signals on the sampling line. This module is specifically designed to process weak differential signals from the sampling resistor module. Differential amplification involves connecting the voltage signals across the sampling resistor (positive and negative terminals) to the two input terminals of the differential amplifier. The amplifier amplifies the difference between these two signals while canceling out the common-mode noise. This process effectively extracts and amplifies the pure differential-mode voltage component caused by transient currents, while eliminating common-mode radiated interference from the environment. The amplified analog noise signal refers to the voltage signal after conditioning and amplitude amplification by the differential amplifier module. At this point, the signal amplitude has been increased to the optimal input range of the analog-to-digital converter (e.g., 0-3.3V or 0-5V), and the signal-to-noise ratio is significantly improved.

[0058] An analog-to-digital converter (ADC) module refers to a high-speed analog-to-digital conversion circuit integrated inside or external to an edge node microcontroller. Its function is to convert continuous analog signals into discrete digital quantities. Understandably, ADC modules can have high sampling rates (e.g., tens of thousands to millions of samples per second), such as a sampling rate ≥ 100 kilosamples per second (kSPS) and a resolution of 12 bits, in order to capture high-frequency components contained in transient noise.

[0059] In this way, by deploying sampling resistor modules, differential amplifier modules, and analog-to-digital converter modules at the edge nodes, with the sampling resistor modules connected in series in the common ground loop, transient current fluctuations can be directly converted into voltage signals, achieving near-end coupling and lossless capture of noise sources and ensuring the integrity of the original signal. Secondly, the differential amplifier module effectively eliminates common-mode interference on the common ground loop through its high common-mode rejection ratio, amplifying only the differential-mode signal reflecting the true noise, significantly improving the signal-to-noise ratio and acquisition accuracy of weak noise signals. Finally, the analog-to-digital converter module continuously samples at a preset high sampling rate, ensuring that high-frequency noise components are completely recorded, avoiding signal aliasing and information loss. The edge nodes can acquire high-fidelity, high-resolution noise interference data, providing a high-quality data foundation for the subsequent accurate spectrum analysis and component identification of the central processing node, thus ensuring the reliability and effectiveness of the entire noise suppression system from the source. Noise interference data refers to the final output result after digital processing by the analog-to-digital converter module. This is a discrete numerical sequence (usually in binary code form) representing the change of noise waveform amplitude over time. This data accurately quantifies the time-domain waveform, amplitude, pulse width, and occurrence time of voltage fluctuations on the common-ground loop, serving as the foundational raw data for subsequent fault diagnosis, spectrum analysis, and interference tracing.

[0060] S102 sends noise interference data to the central processing node through the data forwarding node.

[0061] In this embodiment of the application, after the edge node acquires the noise interference data and uploads it to the data forwarding node, the data forwarding node can receive the noise interference data from one or more edge nodes, and can add a gateway local timestamp to the noise interference data, and forward it to the central processing node through the backbone TSN network.

[0062] For example, noise interference data can be encapsulated using the SOME / IP protocol at the edge node to obtain noise data packets. Correspondingly, after receiving the SOME / IP data packets, the data forwarding node can add a gateway local timestamp (for link delay analysis) and forward it to the central processing node through the backbone TSN network.

[0063] S103, through the central processing node, based on the noise interference data and the pre-built noise database, determines the target component that generates the noise interference data, as well as the corresponding filtering configuration information, and sends the filtering configuration information to the edge nodes.

[0064] The filtering configuration information is used to indicate the filtering strategy for suppressing noise interference data. The filtering strategy refers to the signal processing algorithm used to attenuate or eliminate noise in a specific frequency band to ensure that the circuit (such as audio bus, sensor signal) is not interfered with.

[0065] Here, the central processing node is responsible for large-scale data processing, pattern recognition, decision-making, and overall management. It possesses greater computing power and can run complex algorithms (such as machine learning and spectrum comparison) to process noisy data aggregated from multiple edge nodes.

[0066] Here, the pre-built noise database can be created by artificially making each component work individually on a laboratory bench or in a vehicle, recording their noise spectrum, extracting features (for example, the characteristic frequency of a window motor is 500Hz, and that of a door lock motor is 1.2kHz), and storing them in the database.

[0067] For example, the noise database may include, but is not limited to, the spectral characteristics of different components and the filtering configuration information corresponding to different components.

[0068] It should be noted that the filter configuration information can be a command package, which contains specific filter strategy parameters. For example: Filter type: Finite Impulse Response (FIR) or Infinite Impulse Response (IIR); Filter mode: Low-pass, High-pass, Band-stop (notch filter); Filter parameters: Cutoff frequency, stopband attenuation, window type, order; Target object: Specify which target component (e.g., car window motor)'s signal channel is being filtered.

[0069] In this embodiment, after receiving noise interference data from the edge nodes, the central processing node can process the noise interference data to obtain spectral features, and compare these spectral features with the spectral features in pre-constructed noise data to determine the target component generating the noise interference data. Further, the central processing node can determine the corresponding filtering configuration information based on the matched component and noise type, and send the filtering configuration information to the edge nodes.

[0070] For example, after receiving noise interference data from the edge nodes, the central processing node can process the noise interference data to obtain its spectral characteristics. If it finds a peak in the noise data at 800Hz and the waveform characteristics match the characteristics of "left front window motor stall" in the database, then the "left front window motor" is the target component. Furthermore, the central processing node can determine the corresponding filtering configuration information. For example, the filtering configuration information could be: for 800Hz noise, a notch filter with a center frequency of 800Hz can be enabled.

[0071] S104 uses edge nodes to filter noise interference data generated by the target component based on filtering configuration information in order to suppress noise interference.

[0072] In this embodiment, when the edge node receives the filtering configuration information sent by the central processing node, it can parse the filtering configuration information and convert the configuration information (such as "turn on notch filter, center frequency 800Hz") into specific algorithm parameters. When the target component (such as the window motor) is working, the edge node will continuously collect real-time noise interference data. Before the noise interference data enters the subsequent transmission or application logic, it will be filtered based on the parsed filtering configuration information. Furthermore, the signal obtained at the output end has eliminated the interference components of specific frequency bands and retained the normal control or monitoring signal, thereby suppressing noise interference.

[0073] In this embodiment, noise data of the target area is acquired in real time by edge nodes, and the noise source is accurately traced and matched with filtering strategies based on the noise database by the central processing node. Furthermore, localized filtering is performed by the edge nodes, thereby realizing rapid location and differentiated suppression of noise sources. Compared with the unified filtering or post-incident investigation methods in related technologies, this application can improve the real-time performance, accuracy and adaptability of noise suppression, and effectively ensure the electromagnetic compatibility and long-term operational reliability of vehicle electronic systems. In some embodiments, the above-described S103, "determining the target component generating the noise interference data and the corresponding filtering configuration information based on the noise interference data and the pre-built noise database through the central processing node," may include the following steps: S1031, through the central processing node, performs a fast Fourier transform on the noise interference data to obtain the first noise interference data.

[0074] Here, the Fast Fourier Transform (FFT) can be used to convert a signal from the time domain (horizontal axis is time) to the frequency domain (horizontal axis is frequency). In other words, a waveform where "voltage fluctuates over time" can be decomposed into the superposition of different frequency sine waves and the amplitude (intensity) of each frequency component can be calculated. The first noise interference data refers to the data obtained after the FFT transformation; it is not a voltage value that changes over time, but a spectrum.

[0075] For example, the FFT parameters can be set as follows: for instance, the sampling rate can be set to 100kSPS to satisfy the sampling theorem; the number of FFT points can be set to 1024; and the frequency range can be set to 0–50kHz to cover the main frequency band of motor interference.

[0076] S1032, through the central processing node, extracts the spectral features of the first noise interference data to obtain the noise spectral features.

[0077] Here, spectral feature extraction refers to automatically identifying and extracting representative key information that uniquely identifies a noise source from the obtained spectrum. This may include, but is not limited to, finding peak points in the spectrum (the frequencies with the largest amplitudes), calculating the precise value of the peak frequency, measuring the bandwidth of the peak (3dB bandwidth), analyzing the harmonic distribution (amplitude relationship of integer multiples of the peak frequency), and calculating the spectral centroid.

[0078] Noise spectrum features refer to the extracted feature vectors used to characterize the essential properties of the noise.

[0079] For example, spectral feature extraction refers to extracting the frequency component with the largest amplitude from the first noise interference data. For instance, for a car window motor, the frequency component with the largest amplitude is 1-3kHz; for a door lock motor, the frequency component with the largest amplitude is 5-8kHz; and for a rearview mirror motor, the frequency component with the largest amplitude is 2-4kHz.

[0080] S1033, through the central processing node, determines the target component that generates noise interference data, as well as the corresponding filtering configuration information, based on the noise spectrum characteristics and noise database.

[0081] In some embodiments, S1033 may further include the following steps: S10331, through the central processing node, matches the noise spectrum characteristics with the preset spectrum characteristics to obtain the matching result.

[0082] Preset spectrum features refer to the standard spectrum features stored in a pre-built noise database for each component (such as window motor and door lock motor). These feature vectors can be collected and extracted on a test bench or in a real vehicle by operating each component individually.

[0083] In one possible implementation, the central processing node compares the extracted real-time noise spectrum features with the preset spectrum features in the database one by one. The features are treated as vectors, and the distance or angle between the two in multi-dimensional space is calculated. The closer the distance (or the closer the similarity is to 1), the higher the matching degree.

[0084] Another possible implementation method is to use rule matching, that is, if-then logic. For example: if the main frequency is between 800Hz and 830Hz and there is a significant second harmonic with an amplitude > 0.4V, then it is determined to be "left front window motor".

[0085] Here, the matching result refers to the output based on the preset matching algorithm, which can be a list containing similarity or confidence.

[0086] For example, the matching results could be: Candidate component A: left front window motor, matching degree: 98%; Candidate component B: left rearview mirror motor, matching degree: 45%; Candidate component C: unknown noise source, matching degree: 10%.

[0087] S10332, Based on the matching results, determine the target component that generates the noise interference data and the filtering configuration information.

[0088] In some embodiments, decisions can be made based on the matching results. For example, the candidate component with the highest matching degree and exceeding a preset threshold (e.g., >90%) can be selected as the target component. If all matching degrees are low, a re-acquisition process can be triggered.

[0089] Furthermore, after identifying the target component, the central processing node can use the target component as an index to search in the noise database; the noise database not only stores the characteristics of each component, but also pre-associates the filtering parameters for the noise of that component, i.e., filtering configuration information.

[0090] For example, after determining that the target component is the "left front window motor", the record corresponding to the "left front window motor" can be found in the noise database, which contains the following pre-stored information: recommended filter: second-order IIR notch filter; center frequency: 820Hz (automatically adjusted or preset according to characteristics); stopband width: 50Hz; attenuation depth: -20dB; and this set of filter configuration information is packaged and prepared to be sent to the corresponding edge node.

[0091] In this way, by matching the noise spectrum features extracted in real time with the preset spectrum features in the noise database, this application can quickly and accurately locate the specific target component that causes interference, and retrieve the corresponding filter configuration information based on the matching result; furthermore, the distribution of filter configuration based on the accurate matching result ensures the pertinence and effectiveness of the suppression measures, and provides a reliable basis for the subsequent implementation of precise filtering.

[0092] In this embodiment, the present application matches the noise spectrum features extracted in real time with the preset spectrum features in the noise database, thereby quickly and accurately locating the specific target component that causes interference, and retrieving the corresponding filter configuration information based on the matching result; furthermore, the distribution of filter configuration based on the accurate matching result ensures the pertinence and effectiveness of the suppression measures, and provides a reliable basis for the subsequent implementation of accurate filtering.

[0093] In some embodiments, the step of "determining the target component that generates noise interference data and the filtering configuration information based on the matching result" in S10332 above may include the following steps: S201, Based on the matching results, determine the target component identifier corresponding to the noise spectrum characteristics from the noise database.

[0094] Here, the target component identifier can be used to uniquely identify the ID or name of a specific hardware component.

[0095] In some embodiments, after determining the matching result, the central processing node can determine the candidate components with matching degree as the target components from the matching result list, and determine the component identifier of the target component.

[0096] S202: Based on the target component identification and the interference level of the noise spectrum characteristics, query the corresponding filtering strategy from the noise database.

[0097] The filtering strategy includes at least the filter type and the target frequency band.

[0098] Here, the interference level of the noise spectrum characteristics is used as a quantitative grading index to characterize the severity of noise interference.

[0099] For example, the interference level can be classified according to the noise amplitude (voltage magnitude), duration, and scope of influence (whether it affects critical buses or safety functions); it can also be classified according to the type of target component that generates the noise interference, but this application embodiment does not limit this.

[0100] The filter type can include, but is not limited to, low-pass filters, high-pass filters, band-stop filters (notch filters), adaptive filters, etc. The target frequency band refers to the frequency range that needs to be suppressed. For example, the target frequency band can be 800Hz-850Hz or a center frequency of 820Hz with a bandwidth of 50Hz.

[0101] In some embodiments, after determining the interference level of the target component identifier and noise spectrum characteristics, the target component information can be queried in the noise database based on the target component identifier as an index. Further, the processing schemes corresponding to different interference levels of the same target component can be queried based on the interference level as an index. For example: if the interference level is level one, the filtering strategy could be "no processing" or "enable simple software smoothing filtering"; if the interference level is level two, the filtering strategy could be "enable a band-stop filter to filter out the main frequency and its first harmonic"; if the interference level is level three, the filtering strategy could be "enable a high-order band-stop filter and simultaneously notify the power management module to adjust the power supply". Afterwards, the queried filtering strategy (including filter type and target frequency band) can be output.

[0102] S203 determines the corresponding filter configuration information based on the filter type and target frequency band.

[0103] Among them, the filtering configuration information is used to characterize the specific parameters that transform the filtering strategy at the logical level into hardware / software executable parameters, which is the set of executable instructions that are finally sent to the edge nodes.

[0104] For example, the filter configuration information can be represented as a set of specific binary values ​​or register configuration words. For example: filter_type = 0x02 (representing a notch filter); coeff_a1 = 0.1234, coeff_a2 = 0.4567 (filter coefficients); sample_rate = 10000 (sampling rate configuration); notch_freq = 820 (notch frequency).

[0105] In some embodiments, the filter coefficients can be calculated using a preset standard formula based on the filter type (e.g., a second-order IIR notch filter), the target frequency band (820Hz), and the known system sampling rate; alternatively, a predefined mapping table can be used to map the target frequency band to specific hardware register configuration values ​​(e.g., setting the cutoff frequency register of a programmable filter chip). Furthermore, these calculated parameters can be packaged according to a communication protocol format to generate the final filter configuration information, which is then sent to the edge nodes.

[0106] In this embodiment, the application first identifies the target component based on the matching results, and then queries the database for differentiated filtering strategies based on the interference level of the noise spectrum characteristics. Different filter types and target frequency bands are matched for interference of varying severity, ultimately generating specific filtering configuration information. This avoids a "one-size-fits-all" approach to filtering, using low-cost lightweight filtering for minor interference and initiating higher-order filtering schemes for severe interference, thus optimizing system resource allocation. Secondly, the introduction of interference levels provides a basis for priority scheduling of the central processing node, ensuring timely suppression of high-level, high-risk noise. Thirdly, by dynamically linking interference levels with filtering strategies, the suppression intensity can be adaptively adjusted according to the actual interference strength, ensuring electromagnetic compatibility while minimizing the computational and power consumption of filtering processing, achieving an optimal balance between performance and resource consumption.

[0107] In some embodiments, the above-described S101, "sending noise interference data to the data forwarding node," may include the following steps: S1011 After acquiring the noise interference data, the interference level corresponding to the noise interference data is determined through the edge nodes; In some embodiments, after collecting raw noise data, the edge node does not immediately upload all the raw data (as that would consume a lot of bandwidth). Instead, it performs preliminary analysis locally, extracts key features, and assigns a label—interference level—to the noise based on preset rules. This level will be reported along with the data or used as a basis for local decision-making.

[0108] In one possible implementation, determining the interference level corresponding to the noise interference data through edge nodes may include the following steps: S10111, extract the temporal feature parameters corresponding to the noise interference data through the edge nodes; wherein, the temporal feature parameters include at least one of amplitude peak value, mean value or effective value.

[0109] Time-domain characteristic parameters are statistical quantities calculated from the original voltage waveform that changes over time, and can reflect the amplitude characteristics of noise in the time dimension.

[0110] In some embodiments, the analog-to-digital converter of the edge node acquires a continuous noise waveform data at a high sampling rate and stores it in a buffer. Further, iterative calculations can be performed to traverse all data points in the buffer, find the maximum and minimum values, and determine the peak amplitude. The amplitude of all data points is summed and divided by the number of points to obtain the mean. The amplitude of all data points is squared, summed, divided by the number of points, and then squared to obtain the effective value. Finally, a set of time-domain feature values ​​are output.

[0111] S10112, through edge nodes, perform fast Fourier transform on the noise interference data to obtain the frequency domain feature parameters corresponding to the noise interference data; wherein, the frequency domain feature parameters include at least one of the energy spectral density or signal-to-noise ratio of the target frequency band.

[0112] In some embodiments, a fast Fourier transform can be performed on the noise interference data to obtain spectral data. S10113, based on the preset interference level mapping table, time domain characteristic parameters and frequency domain characteristic parameters, determine the interference level corresponding to the noise interference data.

[0113] Here, the preset interference level mapping table refers to the mapping relationship between characteristic parameters (input) and interference levels (output).

[0114] In some embodiments, the edge node can aggregate the time-domain feature parameters (such as peak value and RMS value) and frequency-domain feature parameters (such as target frequency band energy and signal-to-noise ratio) calculated in S10111 and S10112, and input the aggregated feature parameters into a preset interference level mapping table for matching to obtain the interference level corresponding to the noise interference data.

[0115] In another possible implementation, determining the interference level corresponding to the noise interference data through edge nodes may also include the following steps: By using edge nodes, noise interference data is analyzed to obtain interference characteristic parameters.

[0116] It should be noted that data analysis can include, but is not limited to, time-domain analysis and frequency-domain analysis. Through data analysis, statistical quantities or spectral components that can characterize the physical properties of the interference signal can be extracted as interference characteristic parameters.

[0117] It is understood that interference characteristic parameters include, but are not limited to, one or more combinations of: peak amplitude, root mean square value, peak factor, kurtosis, energy percentage of a specified frequency band, and spectral centroid.

[0118] Furthermore, through edge nodes, the interference feature parameters are input into a pre-trained lightweight interference classification model, and the interference feature parameters are nonlinearly mapped and weighted to obtain the interference score.

[0119] It should be noted that the lightweight interference classification model can be a machine learning model built based on decision trees, support vector machines, or shallow neural networks. This model is pre-trained in an offline environment using sample data labeled with real interference levels. After training, the model parameters are stored in the non-volatile memory of the edge nodes.

[0120] In some embodiments, edge nodes can use interference feature parameters as input vectors and perform forward computation through the model. The model performs nonlinear transformation and weighted summation on each feature parameter and outputs a normalized continuous value as an interference score, which is used to characterize the severity of the current noise interference.

[0121] Furthermore, by using edge nodes, the interference level corresponding to the noise interference data is determined based on the interference score and multiple preset level threshold ranges.

[0122] It is understandable that the memory of the edge node is pre-configured with multiple level threshold intervals, each level threshold interval corresponds to an interference level, and there is no overlap between adjacent level threshold intervals.

[0123] In some embodiments, the edge node can compare the output interference score with multiple level threshold intervals one by one to determine the target level threshold interval into which the interference score falls, and output the interference level corresponding to the target level threshold interval as the final interference level of the noise interference data.

[0124] In this embodiment, a lightweight machine learning model is deployed locally at the edge node to classify and judge noise interference. Compared to uploading all raw data to the central processing node for processing, this significantly reduces the communication load between the data forwarding node and the central processing node, and lowers the system bandwidth requirements. By completing the interference level determination locally at the edge node, the round-trip delay between data upload and command issuance is avoided, enabling real-time or near-real-time output of the interference level and improving the system response speed. The use of multi-dimensional feature nonlinear mapping and weighted calculation based on the machine learning model significantly improves the accuracy and robustness of interference level discrimination compared to the single threshold or simple linear weighting methods used in the prior art. S1012, if the interference level is less than the preset level threshold, the local preset basic filter is invoked through the edge node to suppress the noise interference data.

[0125] Understandably, at least one basic filter is pre-deployed in the local memory of the edge node. The basic filter is selected from one or more of the following types: first-order low-pass digital filter, median filter, and moving average filter. The filtering parameters of the basic filter are pre-calibrated and fixed according to the typical noise characteristics of the target area where the edge node is located during system deployment.

[0126] In some embodiments, the edge node can determine the target component generating the noise interference data and the corresponding filtering configuration information based on the noise interference data and a pre-built noise database. It can then select a matching base filter from at least one base filter and invoke that base filter to perform real-time filtering on the noise interference data to suppress the noise interference. After suppression is complete, the edge node does not send a reporting message to the data forwarding node.

[0127] S1013 If the interference level is greater than or equal to the preset level threshold, the interference level and noise interference data are associated and packaged through the edge node and sent to the data forwarding node.

[0128] In some embodiments, interference levels and noise interference data can be encapsulated according to a predefined format to obtain a packaged data packet.

[0129] For example, the associated packaged data packet may include the following: Frame header: identifies the beginning of a data packet; Node ID: identifies which edge node sent the data; Timestamp: records the time of data acquisition; Interference level; Data length; Number of bytes of noise data; Noise data: the original sequence of ADC sampled values; Check bit: used to check for errors during data transmission.

[0130] Correspondingly, the above-mentioned S102 "sending noise interference data to the central processing node through the data forwarding node" may include the following steps: The associated and packaged interference level and noise interference data can be forwarded to the central processing node through the data forwarding node.

[0131] In this embodiment, by determining the interference level at the edge node, if the interference level is less than a preset threshold, it indicates that the interference intensity is small, and the local basic filter can be used directly for noise suppression. If the interference level is greater than or equal to the preset threshold, it indicates that the interference intensity is large, and the interference level can be associated with the noise interference data and packaged before uploading. This provides a priority scheduling basis for data forwarding nodes, ensuring that high-level interference data is forwarded first and low-level data is merged for processing, effectively alleviating network congestion. At the same time, it enables the central processing node to perform hierarchical processing based on the level label, initiate precise source tracing and powerful filtering configuration for high-level data, and simplify processing for low-level data. This significantly reduces the computational load of the central node while ensuring timely response to high-risk noise.

[0132] In some embodiments, Figure 2 A flowchart illustrating a noise interference suppression method provided in this application embodiment. Figure 2 ,like Figure 2As shown, S104 above, "filtering the noise interference data generated by the target component based on the filtering configuration information through the edge node", may include S1041 to S1043, wherein: S1041, through edge nodes, analyzes and processes the filter configuration information to obtain the target filter type and corresponding filter parameters corresponding to the filter configuration information.

[0133] Here, filter parameters refer to the specific values ​​that enable a particular type of filter to function. It's important to note that different types of filters require different parameters. For example, for IIR / FIR filters, filter parameters can be filter coefficients (a0, a1, a2, b0, b1, b2...); for simple moving average filters, the filter parameter is the window length (N); and for notch filters, filter parameters include the center frequency, cutoff frequency, or notch depth.

[0134] In some embodiments, the edge node receives the filtering configuration information via a communication interface such as CAN / LIN / Ethernet. First, it performs a CRC check to ensure that the data is error-free during transmission. Further, the data packets can be unpacked according to a predefined communication protocol format to obtain the target filter type and corresponding filtering parameters corresponding to the filtering configuration information.

[0135] S1042, through the edge node, configure the target filter in the edge node according to the target filter type and filtering parameters to obtain the configured target filter.

[0136] S1043 filters the noise interference data generated by the target component through the edge node based on the configured target filter.

[0137] In some embodiments, a target filter can be determined from the edge nodes according to the target filter type, and the filter coefficients of the target filter can be configured based on the filter parameters to obtain the configured target filter; further, the noise interference data generated by the target component can be filtered based on the configured target filter to suppress the noise interference of the target component.

[0138] In this embodiment, by analyzing and processing the filter configuration information, it is ensured that the central decision is accurately understood and losslessly transformed at the edge. Then, based on the parsed filter type and parameters, the target filters in the edge nodes are dynamically configured, realizing on-demand allocation and real-time reconstruction of filter resources. This allows the same hardware platform to flexibly cope with various types of noise interference. Finally, based on the configured filters, the noise data generated by the target components is processed in real time, completing the suppression loop at the location closest to the noise source. This not only effectively blocks the transmission of noise to sensitive circuits through the common ground loop, ensuring the real-time performance and effectiveness of the suppression measures, but also avoids large-scale uploading of raw data through localized processing, significantly reducing the communication load of the vehicle network and the computational pressure on the central node. In some embodiments, after filtering the noise interference data of the target component based on filtering configuration information through edge nodes, the method further includes: The central processing node associates and stores the noise interference data, the noise spectrum characteristics corresponding to the noise interference data, and the filtering configuration information corresponding to the noise interference data to obtain the target data packet.

[0139] In some embodiments, after the edge node completes the filtering process, it can send a "processing complete" message to the central processing node, or the central node itself can begin preparing to integrate the data after issuing the filtering configuration information. The central processing node can retrieve all data related to the event from the cache or database, including noise interference data, noise spectrum characteristics, and filtering configuration information obtained from the data reported by the edge nodes, and store this data in association to generate a unique event ID (such as a hash value based on timestamp + node ID) as an index for the target data packet. Furthermore, all the above data, along with metadata such as timestamps and vehicle status, can be encapsulated into a data packet (such as JSON, binary format, or database record) and written to the local storage of the central node or uploaded to the cloud database.

[0140] The noise database is updated based on the target data packets through the central processing node, resulting in the updated noise database.

[0141] In some embodiments, after the target data packet is determined, the target data packet can be added directly to the noise database as a new case record; or, if the original data packet corresponding to the target component already exists in the noise database, the original data packet can be replaced with the target data packet.

[0142] In other embodiments, newly added target data packets can be analyzed offline or online. Cluster analysis can be used to discover that a certain noise feature recurs and is successfully suppressed using a certain filtering configuration, thereby strengthening the association weight between this feature and the component. If it is found that for the same type of noise in the same component, the filtering parameters sent multiple times are slightly different (for example, sometimes an 820Hz notch filter works well, and sometimes an 815Hz filter works well), an optimal parameter range can be statistically analyzed and updated in the noise database.

[0143] In this embodiment, by continuously optimizing and expanding the noise database using real-world case data, the preset spectral characteristics become richer and the filtering strategy configuration more precise. This allows it to move beyond the static knowledge base at the factory and continuously accumulate experience and evolve over time. Ultimately, the accuracy of noise identification and suppression will continuously improve with mileage, achieving continuous evolution of the vehicle's electromagnetic compatibility and long-term reliability optimization.

[0144] Based on the above embodiments, this application also provides a noise interference suppression system. Figure 3 A schematic diagram of an optional structural composition of the noise interference suppression system provided in the embodiments of this application is shown below. Figure 3 As shown, the noise interference suppression system 300 includes: Edge node 301 is used to acquire noise interference data of the common ground loop in the target area of ​​the electronic device and send the noise interference data to the data forwarding node; Data forwarding node 302 is used to send noise interference data to the central processing node; The central processing node 303 is used to determine the target component that generates the noise interference data and the corresponding filtering configuration information based on the noise interference data and a pre-built noise database, and to send the filtering configuration information to the edge nodes; wherein, the filtering configuration information is used to indicate the filtering strategy for suppressing the noise interference data. Edge node 301 is used to filter noise interference data generated by the target component based on the filtering configuration information in order to suppress noise interference.

[0145] Regarding edge node 301, the process of acquiring noise interference data of the common ground loop of the target area of ​​the electronic device can be referred to the specific implementation of the aforementioned step S101, and will not be repeated here.

[0146] Regarding the central processing node 303, the process of determining the target component that generates the noise interference data and the corresponding filtering configuration information based on the noise interference data and the pre-built noise database can be referred to the specific implementation of the aforementioned step S103, and will not be repeated here.

[0147] The noise interference suppression system provided in this application utilizes edge nodes to acquire noise data of the target area in real time, and through a central processing node, performs precise component source tracing and filtering strategy matching based on a noise database. Furthermore, the edge nodes perform localized filtering processing, thereby achieving rapid location and differentiated suppression of noise sources. Compared with the unified filtering or post-event investigation methods in related technologies, this application can improve the real-time performance, accuracy, and adaptability of noise suppression, effectively ensuring the electromagnetic compatibility and long-term operational reliability of electronic equipment and electronic systems. For example, Figure 4 A schematic diagram of an optional structural composition of the noise interference suppression system provided in the embodiments of this application is shown below. Figure 4 As shown, The noise interference suppression system may include a central processing node 303, a data forwarding node 302, an edge node A 3011 and actuators 401 corresponding to multiple components of the target area connected to the edge node A, an edge node B 3012 and actuators 402 corresponding to multiple components of the target area connected to the edge node B, and an edge node C 3013 and actuators 403 corresponding to multiple components of the target area connected to the edge node C.

[0148] It is understood that actuators 401, 402, and 403 may include, but are not limited to, brushed motors, stepper motors, brushless motors, and sensors.

[0149] Edge nodes can acquire noise interference data of the common ground loop in the target area of ​​electronic devices and transmit this data to data forwarding nodes via hardwired connections. Data forwarding nodes can then transmit the noise interference data to the central processing node via a low-speed network. The central processing node, based on the noise interference data and a pre-built noise database, identifies the target component generating the noise interference data and the corresponding filtering configuration information. This filtering configuration information is then transmitted to the data forwarding nodes via a high-speed gigabit network. The data forwarding nodes can transmit the filtering configuration information to the edge nodes via a low-speed network.

[0150] The following describes the application of the noise interference suppression method provided in the embodiments of this application in a real-world scenario.

[0151] This application provides a technology for detecting and separating interference caused by multiple actuators sharing a common ground in edge nodes, which solves the problem of electromagnetic interference caused by multiple motors sharing a common ground in a central + regional edge architecture, and realizes real-time detection of interference, location of interference sources, and system-level anti-interference control.

[0152] The central + edge node structure may include: 1) a central high-computing-power central brain (the central computing node in the above embodiment), which is the logical hub; 2) a regional gateway (the data forwarding node in the above embodiment), which connects the central brain and the edge nodes and is responsible for data exchange between various domains of the vehicle; and 3) edge nodes, which connect various motors, lights and other drive modules on the doors, as well as diagnostics and data collection.

[0153] Edge nodes include a low-resistance sampling resistor (Rsense) connected in series on the ground wire of the common ground load (motor, sensor) to collect low-voltage interference on the ground wire; Car window motor → power supply path → sampling resistor (10mΩ) → differential amplifier → dedicated ADC (analog-to-digital converter) channel → raw voltage data waveform; Sampling resistor Rsense: low resistance (10mΩ), high accuracy (±1%), power ≥1W, connected in series in the common ground loop of all actuators; Differential amplifier: suppresses common-mode interference and amplifies weak voltage fluctuations (typical noise amplitude: 10mV~100mV). ADC channel: edge node RCP chip, sampling rate ≥100kSPS, resolution 12bit.

[0154] Timestamp generation: Edge nodes support Ethernet, and a timestamp is added after each ADC acquisition, using TSN technology; Data packaging and uploading: Transmitted to the area gateway via TSN Ethernet (supports event-triggered uploading or periodic uploading); The edge node packages the collected raw voltage interference waveform, adds a high-precision timestamp (the edge node has a physical layer (PHY) (network communication controller and physical layer chip), and supports Ethernet), and uploads it to the regional gateway via Ethernet; Regional gateway: Data conversion / distribution, receiving noisy data packets from multiple edge nodes; forwarding them to the central processing unit via backbone Ethernet; Central Brain: Centralized data processing, analysis to locate the motor causing interference, and distribution of filtering parameters; 1. Receive Ethernet data from the regional gateway, open a data buffer for each edge node to prevent data loss, perform time synchronization calibration to ensure accurate alignment of data from multiple nodes, and perform signal preprocessing. 2. Perform Fast Fourier Transform (FFT) and spectral feature extraction on the data; The anti-interference control strategy involves the central brain dynamically distributing filtering parameters (such as cutoff frequency) based on the noise spectrum results to the regional gateway via Ethernet (ETH). The regional gateway identifies and unpacks the packets before sending them to the edge nodes. The edge nodes then automatically filter the data using programmable analog filters (adjustable RC or digital filtering front-ends). The construction and matching of the noise database involves operating each motor individually on a test bench or in a real vehicle, collecting the noise spectrum of each motor when it is working alone, extracting feature vectors, and storing them in a central database.

[0155] The following embodiments of this application will illustrate the implementation scheme of this application with a practical example.

[0156] For example, when a user is driving, simultaneously performing the driver's side window raising (high-power motor starts) and the rearview mirror automatic folding (low-power motor starts), since the two motors share a common ground wire, the window motor generates a large current (peak value of about 30A) at the moment of startup, causing "ground bounce" noise on the common ground path. This noise is coupled to the Hall sensor channel of the rearview mirror motor, causing Hall signal distortion; edge nodes are misjudged as "stalled"; the rearview mirror motor stops abnormally or reverses; and the user perceives it as "functional failure" or "stuttering".

[0157] 1. When an interference event occurs, the user presses the "window up" button, and at the same time the system triggers the "rearview mirror automatic folding" window motor to start. The instantaneous current rises to 30A. Due to the sudden change in current, voltage noise ("ground bounce") is generated in the common ground path. The noise frequency is concentrated in 1.8kHz~2.5kHz (window reversing noise). This noise propagates through the common ground path and affects the Hall signal acquisition of the rearview mirror motor. 2. The original noise of the edge node is sampled. The resistor Rsense (10mΩ) detects the ground voltage fluctuation. The differential amplifier amplifies the weak voltage signal to the range that the ADC can recognize. The dedicated ADC channel continuously collects the noise waveform at a sampling rate of 100kSPS. 3. High-precision timestamp marking and data packaging at edge nodes: Each set of sampled data is timestamped with hardware, data packets are encapsulated using the SOME / IP protocol, and uploaded to the regional gateway via TSN Ethernet with high priority. 4. The regional gateway forwards the data packets to the central computing unit, receives the SOME / IP data packets, adds a local timestamp to the gateway (for link latency analysis), and forwards them to the central brain through the backbone TSN network; 5. Central FFT spectrum analysis and interference localization: The central brain receives data, detrends it, applies windowing, performs FFT on the 1024 data points, searches the noise database for matching, and uses the matching results to determine the interference source. 6. The anti-interference strategy is dynamically issued. The central system activates the optimization strategy based on the interference level (high), actively filters (effective immediately), issues instructions, and configures digital filters at the edge nodes to effectively filter out interference noise, and restores the rearview mirror Hall signal to normal. 7. The system returned to normal, the window motor descended normally, and the rearview mirror motor continued to fold after a short delay, without any jamming or reversal. The user perceived that "the function was executed normally" and there were no abnormalities. 8. Data closed loop: The central system records the current interference event and updates the noise database (e.g., noise spectrum shift of the window motor at low temperatures). Supports OTA push to all vehicle models.

[0158] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages does not have to be sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0159] Based on the above embodiments, this application also provides an electronic device. Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 5 As shown, the electronic device 500 includes a noise interference suppression system 300, which is used to implement the method provided in the embodiments of this application.

[0160] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above steps / processes do not imply a sequential order of execution; the execution order of each step / process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above embodiments of this application are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0161] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0162] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components may be combined, or integrated into another system, or some features may be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0163] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0164] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0165] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory, magnetic disks, or optical disks.

[0166] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence or the part that contributes to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium 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 methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0167] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for suppressing noise interference, characterized in that, The method is applied to an electronic device, the electronic device including a central processing node, at least one edge node, and a data forwarding node, the data forwarding node being connected to the central processing node and the edge node respectively; the method includes: The edge node acquires noise interference data of the common ground loop in the target area of ​​the electronic device and sends the noise interference data to the data forwarding node. The noise interference data is sent to the central processing node through the data forwarding node; The central processing node, based on the noise interference data and a pre-built noise database, determines the target component that generates the noise interference data and the corresponding filtering configuration information, and sends the filtering configuration information to the edge node; wherein, the filtering configuration information is used to indicate the filtering strategy for suppressing the noise interference data; The noise interference data generated by the target component is filtered based on the filtering configuration information through the edge nodes to suppress the noise interference.

2. The method according to claim 1, characterized in that, The process of determining the target component generating the noise interference data and the corresponding filtering configuration information based on the noise interference data and a pre-built noise database through the central processing node includes: The first noise interference data is obtained by performing a fast Fourier transform on the noise interference data through the central processing node. The central processing node extracts spectral features from the first noise interference data to obtain noise spectral features. The central processing node, based on the noise spectrum characteristics and the noise database, determines the target component that generates the noise interference data, as well as the corresponding filtering configuration information for the noise interference data.

3. The method according to claim 2, characterized in that, The noise database includes preset spectral features; the process of determining the target component generating the noise interference data, and the corresponding filtering configuration information, based on the noise spectral features and the noise database, through the central processing node, includes: The noise spectrum features and the preset spectrum features are matched using the central processing node to obtain a matching result. Based on the matching results, the target component that generates the noise interference data and the filtering configuration information are determined.

4. The method according to claim 3, characterized in that, The step of determining the target component that generates the noise interference data and the filtering configuration information based on the matching result includes: Based on the matching results, the target component identifier corresponding to the noise spectrum characteristics is determined from the noise database; Based on the target component identifier and the interference level of the noise spectrum characteristics, a corresponding filtering strategy is queried from the noise database; wherein, the filtering strategy includes at least a filter type and a target frequency band; Based on the filter type and the target frequency band, determine the corresponding filter configuration information.

5. The method according to any one of claims 1 to 4, characterized in that, The edge node includes a sampling resistor module, a differential amplifier module, and an analog-to-digital converter module; acquiring noise interference data of the common ground loop in the target area of ​​the electronic device through the edge node includes: The voltage fluctuation signal on the common ground loop of the target area of ​​the electronic device is acquired through the sampling resistor module to obtain the initial analog voltage signal; The initial analog voltage signal is differentially amplified using the differential amplifier module to obtain an amplified analog noise signal. The amplified analog noise signal is continuously sampled at a preset high sampling rate by the analog-to-digital converter module to obtain the noise interference data.

6. The method according to any one of claims 1 to 4, characterized in that, Sending the noise interference data to the data forwarding node includes: After acquiring the noise interference data, the interference level corresponding to the noise interference data is determined through the edge nodes; If the interference level is less than a preset level threshold, then the local preset basic filter is invoked through the edge node to suppress the noise interference data; If the interference level is greater than or equal to the preset level threshold, then the interference level and the noise interference data are associated and packaged through the edge node and sent to the data forwarding node; The step of sending the noise interference data to the central processing node through the data forwarding node includes: The data forwarding node forwards the associated and packaged interference level and noise interference data to the central processing node.

7. The method according to claim 6, characterized in that, Determining the interference level corresponding to the noise interference data through the edge nodes includes: By using the edge nodes, the noise interference data is analyzed to obtain interference characteristic parameters; Through the edge nodes, the interference feature parameters are input into a pre-trained lightweight interference classification model, and the interference feature parameters are nonlinearly mapped and weighted to obtain an interference score. By using the edge nodes, the interference level corresponding to the noise interference data is determined based on the interference score and multiple preset level threshold ranges.

8. The method according to any one of claims 1 to 4, characterized in that, The step of filtering the noise interference data generated by the target component based on the filtering configuration information through the edge nodes includes: By analyzing and processing the filtering configuration information through the edge nodes, the target filter type and corresponding filtering parameters corresponding to the filtering configuration information are obtained. By using the edge nodes, the target filter in the edge nodes is configured according to the target filter type and the filtering parameters to obtain the configured target filter; The noise interference data generated by the target component is filtered through the edge node based on the configured target filter.

9. The method according to any one of claims 1 to 4, characterized in that, After filtering the noise interference data of the target component based on the filtering configuration information through the edge nodes, the method further includes: The central processing node associates and stores the noise interference data, the noise spectrum characteristics corresponding to the noise interference data, and the filtering configuration information corresponding to the noise interference data to obtain the target data packet. The noise database is updated based on the target data packet through the central processing node to obtain the updated noise database.

10. A noise interference suppression system, the noise interference suppression system comprising a central processing node, at least one edge node, and a data forwarding node, the data forwarding node being connected to the central processing node and the edge node respectively; the system comprising: The edge node is used to acquire noise interference data of the common ground loop in the target area of ​​the electronic device, and send the noise interference data to the data forwarding node; The data forwarding node is used to send the noise interference data to the central processing node; The central processing node is used to determine the target component that generates the noise interference data and the corresponding filtering configuration information based on the noise interference data and a pre-built noise database, and to send the filtering configuration information to the edge node; wherein, the filtering configuration information is used to indicate the filtering strategy for suppressing the noise interference data; The edge node is used to filter the noise interference data generated by the target component based on the filtering configuration information, so as to suppress the noise interference.

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