Signal processing method and device, storage medium and electronic equipment

Abstract

The invention discloses a signal processing method and device, a storage medium and electronic equipment. The method comprises the following steps: in a gateway system of a target vehicle, any regional gateway determines a corresponding target sampling rate according to network load information of the target vehicle, acquires key signals of the target vehicle according to a signal acquisition strategy, and acquires non-key signals of the target vehicle according to the signal acquisition strategy and the target sampling rate; the regional gateway sends the key signal and the non-key signal to a central brain under the condition of clock synchronization with the central brain in the gateway system, so that the central brain uploads the key signal and the non-key signal to a cloud; wherein the key signal and the non-key signal are used for the cloud to diagnose the network fault of the target vehicle. Through the technical scheme provided by the invention, the balance between the network load and the data quality can be considered, different signal acquisition modes are adopted for key signals and non-key signals, and the signal acquisition effect in a vehicle distributed gateway scene is optimized.

Description

Technical Field

[0001] This application belongs to the field of data processing technology, and in particular relates to a signal processing method, apparatus, storage medium and electronic device. Background Technology

[0002] As vehicle intelligence continues to develop, the vehicle's electronic and electrical architecture is also constantly evolving. In distributed gateway scenarios, a central brain corresponds to multiple regional gateways, forming a complex network environment with multiple gateways and subnets.

[0003] Diagnosing network faults in vehicles requires analyzing network fault chains using vehicle signals. However, under dynamically changing network load conditions, it is difficult to achieve high-quality signal acquisition using conventional signal acquisition methods, ultimately affecting the fault diagnosis results. Summary of the Invention

[0004] The embodiments of this application provide a signal processing method, apparatus, storage medium, and electronic device that can consider the balance between network load and data quality, adopt different signal acquisition methods for critical and non-critical signals, and optimize the signal acquisition effect in vehicle distributed gateway scenarios.

[0005] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0006] According to a first aspect of the embodiments of this application, a signal processing method is provided, applied to a regional gateway of a gateway system of a target vehicle, the gateway system including a central brain and at least two regional gateways, the method comprising:

[0007] Determine the corresponding target sampling rate based on the network load information of the target vehicle;

[0008] The key signals of the target vehicle are collected according to the signal acquisition strategy, and the non-key signals of the target vehicle are collected according to the target sampling rate according to the signal acquisition strategy.

[0009] While synchronized with the central brain's clock, critical and non-critical signals are sent to the central brain for uploading to the cloud; among them, critical and non-critical signals are used by the cloud to diagnose network faults of the target vehicle.

[0010] In some embodiments of this application, based on the foregoing scheme, key and non-key signals are sent to the central brain in synchronization with the central brain's clock, so that the central brain can upload them to the cloud, including:

[0011] If a central timestamp from the central brain is received, the clock deviation information between the regional gateway and the central brain is determined based on the central timestamp and the local clock of the regional gateway, and clock synchronization with the central brain is performed based on the clock deviation information.

[0012] Key and non-key signals are sent to the central brain via data packets, which are then uploaded to the cloud by the central brain; the data packets carry a timestamp indicating that the regional gateway sent the data packets.

[0013] In some embodiments of this application, based on the aforementioned scheme, clock deviation information between the regional gateway and the central brain is determined according to the central timestamp and the local clock of the regional gateway, and clock synchronization with the central brain is performed according to the clock deviation information, including:

[0014] Determine the transmission delay between the regional gateway and the central brain;

[0015] Based on transmission delay, central timestamp, and local clock of the regional gateway, determine the clock deviation information between the regional gateway and the central brain;

[0016] The local clock of the area gateway is updated based on the clock skew information to synchronize the area gateway's clock.

[0017] In some embodiments of this application, based on the aforementioned scheme, the network load information includes the CAN bus load rate, and the target sampling rate is negatively correlated with the CAN bus load rate;

[0018] Key and non-key signals are sent to the central brain via data packets, which then uploads them to the cloud. This includes:

[0019] Based on the actual signal frequency of the key signal, the key signal is sent to the central brain via data packets;

[0020] The signal upload frequency of non-critical signals is determined based on the CAN bus load rate, and non-critical signals are sent to the central processing unit via data packets based on the signal upload frequency. The signal upload frequency is negatively correlated with the CAN bus load rate, and the signal upload frequency is lower than the actual signal frequency.

[0021] In some embodiments of this application, based on the foregoing scheme, before sending critical and non-critical signals to the central brain in synchronization with the central brain's clock, for the central brain to upload to the cloud, the method further includes:

[0022] If the central brain is in a dormant state, key signals are stored in the buffer of the regional gateway so that the key signals in the buffer can be sent to the central brain after the central brain is awakened.

[0023] In some embodiments of this application, based on the foregoing scheme, the method further includes:

[0024] Receive new signal acquisition strategies from the cloud;

[0025] If the first version identifier corresponding to the current signal acquisition policy of the area gateway is different from the second version identifier corresponding to the new signal acquisition policy, then the new signal acquisition policy will replace the current signal acquisition policy.

[0026] According to a second aspect of the embodiments of this application, a signal processing method is provided, applied to the central brain of a gateway system of a target vehicle, the gateway system including the central brain and at least two regional gateways, the method comprising:

[0027] While synchronizing with the clocks of the regional gateways of the target vehicle, the system receives critical and non-critical signals sent by each regional gateway. The critical signals are acquired by each regional gateway based on the signal acquisition strategy, while the non-critical signals are acquired by each regional gateway based on the signal acquisition strategy and the target sampling rate. The target sampling rate is determined by each regional gateway based on the network load information of the target vehicle.

[0028] Key and non-key signals are uploaded to the cloud so that the cloud can diagnose network faults in the target vehicle.

[0029] In some embodiments of this application, based on the aforementioned scheme, critical and non-critical signals are uploaded to the cloud for the cloud to diagnose network faults of the target vehicle, including:

[0030] Based on the sending timestamps corresponding to each regional gateway, the key signals and non-key signals sent by each regional gateway are sorted separately to obtain the key signal sequence and non-key signal sequence.

[0031] Key signal sequences and non-key signal sequences are uploaded to the cloud so that the cloud can diagnose network faults in the target vehicle.

[0032] In some embodiments of this application, based on the foregoing scheme, the method further includes:

[0033] If a first wake-up message is received, the system switches from sleep mode to working mode, and determines the target function corresponding to the first wake-up message, as well as the regional gateway and target subnet associated with the target function.

[0034] If the associated area gateway is in a dormant state, a second wake-up message is sent to the associated area gateway so that the associated area gateway can collect critical and non-critical signals based on the target subnet segment.

[0035] According to a third aspect of the embodiments of this application, a signal processing apparatus is provided, comprising a regional gateway configured in a gateway system of a target vehicle, the gateway system including a central brain and at least two regional gateways, the apparatus comprising:

[0036] The sampling rate determination module is used to determine the corresponding target sampling rate based on the network load information of the target vehicle.

[0037] The signal acquisition module is used to acquire key signals of the target vehicle according to the signal acquisition strategy, and to acquire non-key signals of the target vehicle according to the target sampling rate based on the signal acquisition strategy.

[0038] The signal uploading module is used to send critical and non-critical signals to the central brain in sync with the central brain's clock, so that the central brain can upload them to the cloud. Among them, the critical and non-critical signals are used by the cloud to diagnose network faults of the target vehicle.

[0039] According to a fourth aspect of the embodiments of this application, a signal processing apparatus is provided, which is configured in the central brain of a gateway system of a target vehicle. The gateway system includes the central brain and at least two regional gateways. The apparatus includes:

[0040] The signal receiving module is used to receive critical and non-critical signals sent by each area gateway while synchronizing with the clock of each area gateway of the target vehicle. The critical signals are collected by each area gateway based on the signal acquisition strategy, and the non-critical signals are collected by each area gateway based on the signal acquisition strategy and the target sampling rate. The target sampling rate is determined by each area gateway based on the network load information of the target vehicle.

[0041] The signal upload module is used to upload critical and non-critical signals to the cloud so that the cloud can diagnose network faults of the target vehicle.

[0042] According to a fifth aspect of the embodiments of this application, a computer-readable storage medium is provided, which stores computer program instructions that, when loaded and executed by a processor, implement the steps of the method as described in any one of the first and second aspects above.

[0043] According to a sixth aspect of the embodiments of this application, an electronic device is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the method as described in any one of the first and second aspects above.

[0044] According to a seventh aspect of the embodiments of this application, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method as described in any one of the first and second aspects above.

[0045] In this application, within the target vehicle's gateway system, any regional gateway determines a corresponding target sampling rate based on the target vehicle's network load information. It then collects key signals from the target vehicle according to a signal acquisition strategy, and collects non-key signals from the target vehicle according to the same strategy and the target sampling rate. This regional gateway, synchronized with the central processing unit's clock, sends both key and non-key signals to the central processing unit for uploading to the cloud. The key and non-key signals are used by the cloud to diagnose network faults in the target vehicle. The technical solution provided in this application balances network load and data quality, employing different signal acquisition methods for key and non-key signals to optimize signal acquisition performance in a distributed gateway scenario for vehicles.

[0046] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0047] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0048] Figure 1 A schematic diagram of a scenario in which the signal processing method of the embodiments of this application can be applied is shown;

[0049] Figure 2 A flowchart of a signal processing method applied to a regional gateway in an embodiment of this application is shown;

[0050] Figure 3 A detailed flowchart illustrating the sending of critical and non-critical signals to the central brain is shown in an embodiment of this application.

[0051] Figure 4 Another detailed flowchart illustrating the transmission of critical and non-critical signals to the central brain in an embodiment of this application is shown;

[0052] Figure 5 A detailed flowchart of the signal acquisition strategy update in an embodiment of this application is shown;

[0053] Figure 6 A flowchart of a signal processing method applied to the central brain in an embodiment of this application is shown;

[0054] Figure 7This application illustrates a detailed flowchart of uploading critical and non-critical signals to the cloud in an embodiment of the present application.

[0055] Figure 8 A detailed flowchart of gateway system wake-up in an embodiment of this application is shown;

[0056] Figure 9 Another flowchart of the signal processing method in an embodiment of this application is shown;

[0057] Figure 10 A block diagram of a signal processing device configured in a regional gateway in an embodiment of this application is shown;

[0058] Figure 11 A block diagram of a signal processing device configured in the central brain is shown in an embodiment of this application;

[0059] Figure 12 A schematic diagram of the structure of an electronic device in an embodiment of this application is shown. Detailed Implementation

[0060] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0061] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0062] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0063] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0064] To enable those skilled in the art to better understand this application, firstly, in conjunction with Figure 1 A brief description of the application scenarios involved in this application is provided.

[0065] See Figure 1 The diagram illustrates a scenario where the signal processing method described in the embodiments of this application can be applied.

[0066] The target vehicle's gateway system includes a central brain 101 and multiple regional gateways 102 and 103. The central brain 101 has certain data processing capabilities and can communicate with each regional gateway via the network, coordinating the functions of the corresponding regional controllers. The central brain 101 can also communicate with the relevant cloud 100, uploading signals collected by each regional gateway to the cloud 100 to enable network fault diagnosis of the target vehicle.

[0067] Specifically, each regional gateway determines the corresponding target sampling rate based on the network load information of the target vehicle. It then collects the target vehicle's critical signals according to the signal acquisition strategy, and also collects the target vehicle's non-critical signals according to the same strategy and the target sampling rate. While synchronized with the central brain 101's clock, it sends both critical and non-critical signals to the central brain 101. The central brain 101 then uploads the critical and non-critical signals from each regional gateway to the cloud 100, which uses these signals to diagnose network faults in the target vehicle.

[0068] Cloud 100 can be implemented using a standalone server or a server cluster consisting of multiple servers. Cloud 100 can inherit a data storage system to store key and non-key signals from different vehicles to be processed.

[0069] In one exemplary embodiment, refer to Figure 2 The flowchart of the signal processing method in this embodiment is shown. It is applied to the area gateway of the gateway system of the target vehicle. The gateway system includes a central brain and at least two area gateways, which are described in detail below:

[0070] Step 201: Determine the corresponding target sampling rate based on the network load information of the target vehicle.

[0071] The target vehicle's gateway system includes a central processing unit and multiple regional gateways, each capable of independently executing the steps of the signal processing method provided in this embodiment. An example of one of the regional gateways will be used for illustration:

[0072] The regional gateway acquires the network load information of the target vehicle in real time and determines the target sampling rate corresponding to signal acquisition based on the network load information. The network load information of the target vehicle characterizes its network bandwidth usage. Different regional gateways can acquire the same network load information simultaneously. A higher network bandwidth usage rate indicated by the network load information indicates a greater network load, allowing for a larger target sampling rate to minimize the impact of signal acquisition on the network load.

[0073] Optionally, the target sampling rate can be set to a minimum of 10Hz.

[0074] Optionally, different regional gateways may obtain their respective network load information at the same time, and the network load information corresponding to different regional gateways may be different.

[0075] Step 202: Collect key signals of the target vehicle according to the signal acquisition strategy, and collect non-key signals of the target vehicle according to the target sampling rate based on the signal acquisition strategy.

[0076] The signal acquisition strategy is issued from the cloud. The signal acquisition strategy defines the specific types of key and non-key signals to be acquired by each regional gateway, as well as the corresponding signal acquisition methods.

[0077] The area gateway categorizes collectable vehicle signals into critical signals and non-critical signals. Critical signals include, but are not limited to, wake-up messages and network management messages, while non-critical signals include, but are not limited to, sensor signals. The area gateway employs different signal acquisition methods for critical and non-critical signals: it collects as many critical signals as possible, while sampling non-critical signals.

[0078] Specifically, the regional gateway analyzes the signal acquisition strategy, determines the specific types and acquisition methods of key and non-key signals, and then acquires all key signals based on the signal acquisition strategy, as well as samples non-key signals based on the signal acquisition strategy and target sampling rate.

[0079] Step 203: While synchronized with the central brain's clock, send critical and non-critical signals to the central brain for uploading to the cloud.

[0080] Each regional gateway in the gateway system must synchronize its clock with the central brain's clock. Only when the clocks are synchronized can the collected critical and non-critical signals be sent to the central brain. Understandably, each regional gateway is configured with an independent local clock. If the local clocks of different regional gateways are inconsistent, the central brain will have difficulty quickly distinguishing the order in which critical and non-critical signals were collected from different regional gateways. By using the central brain's time as the standard, synchronizing each regional gateway's clock with the central brain allows the central brain to perform preliminary analysis of the critical and non-critical signals sent from different regional gateways, improving data quality.

[0081] Specifically, after confirming that the regional gateway has completed the clock synchronization process with the central brain, it sends the collected key and non-key signals to the central brain, which then uploads them to the cloud. The key and non-key signals are used by the cloud to diagnose network faults in the target vehicle. That is, the cloud can comprehensively analyze the key and non-key signals collected by each regional gateway to determine if the target vehicle has a network fault. Furthermore, when a network fault exists, the cloud can analyze the network fault chain by combining key and non-key signals to pinpoint the specific faulty object and its cause.

[0082] It should be noted that the area gateway can periodically execute the steps of the above signal processing method. Each time a new cycle begins, the area gateway reacquires real-time network load information, determines the target sampling rate for the new cycle, and then collects key and non-key signals until the cycle ends.

[0083] In this application, any regional gateway in the target vehicle's gateway system determines a corresponding target sampling rate based on the target vehicle's network load information. It then collects key signals of the target vehicle according to a signal acquisition strategy, and collects non-key signals of the target vehicle according to the target sampling rate and the same strategy. This regional gateway, synchronized with the central brain's clock in the gateway system, sends both key and non-key signals to the central brain for uploading to the cloud. The key and non-key signals are used by the cloud for diagnosing network faults in the target vehicle. The technical solution provided in this application balances network load and data quality by employing different signal acquisition methods for key and non-key signals—namely, collecting all key signals and sampling non-key signals based on network load—thus optimizing signal acquisition performance in a distributed gateway scenario for vehicles.

[0084] Based on the above embodiments, in an exemplary embodiment, see [link to example]. Figure 3 The flowchart illustrating the detailed process of sending key and non-key signals to the central brain in an embodiment of this application is shown, specifically including:

[0085] Step 301: If the central timestamp of the central brain is received, the clock deviation information between the regional gateway and the central brain is determined based on the central timestamp and the local clock of the regional gateway, and clock synchronization is performed with the central brain based on the clock deviation information.

[0086] The central timestamp of the central brain is based on the master clock deployed in the central brain. Before sending critical and non-critical signals to the central brain, the regional gateway needs to confirm whether the clock synchronization process with the central brain has been completed. When the regional gateway receives the central timestamp, it can enter the clock synchronization process.

[0087] Specifically, the regional gateway does not use the conventional RTC (Real-Time Clock). Instead, it uses the default local clock after initialization. When the regional gateway receives the central timestamp, it can compare the central timestamp with the local clock to determine the clock deviation information between the regional gateway and the central brain, and then perform time calibration to complete the clock synchronization process.

[0088] Optionally, when the regional gateway is in a dormant state, it receives a wake-up message from the central brain, and the central timestamp is used to indicate the time when the wake-up message was sent.

[0089] Optionally, the transmission delay between the regional gateway and the central brain is determined; based on the transmission delay, the central timestamp, and the local clock of the regional gateway, clock deviation information between the regional gateway and the central brain is determined; and the local clock of the regional gateway is updated based on the clock deviation information to synchronize the clock of the regional gateway.

[0090] It is understandable that the transmission delays between different regional gateways and the central brain are inconsistent. It is necessary to pre-determine the transmission delay between the regional gateway and the central brain, and then determine the local time when the regional gateway receives the central timestamp based on the local clock of the regional gateway. Combined with the sending time of the central brain indicated by the central timestamp, the clock deviation information between the regional gateway and the central brain is determined, and then the local clock of the regional gateway is updated to achieve clock synchronization with microsecond-level precision.

[0091] For example, when the regional gateway is synchronized with the central brain clock, the central timestamp plus the transmission delay should be equal to the local time when the regional gateway receives the central timestamp. When the regional gateway and the central brain clock are not synchronized, the clock deviation information between the regional gateway and the central brain can be obtained by reverse derivation and calculation. The clock deviation information is represented by Δt, which can be a positive or negative number. The clock deviation information is added to the local clock to complete the clock synchronization process.

[0092] Step 302: Send key and non-key signals to the central brain via data packets so that the central brain can upload them to the cloud.

[0093] The regional gateway packages and sends both critical and non-critical signals to the central control unit, and the data packets include a timestamp indicating when the regional gateway sent the data packets. This timestamp is based on the local clock deployed on the regional gateway (which has been synchronized).

[0094] Correspondingly, after the central brain receives critical and non-critical signals sent by different regional gateways, the order of reception time cannot represent the order of collection time due to the inconsistent transmission delay between the different regional gateways and the central brain. The central brain can directly determine the order of collection time of critical and non-critical signals sent by different regional gateways based on the sending timestamp carried by the data packets. Thus, the critical and non-critical signals are uploaded to the cloud in the order of collection time, which is beneficial for cloud analysis of network fault chains.

[0095] This application provides a method for synchronizing the clocks of regional gateways and the central brain. Instead of using RTC, the regional gateways use local clocks, which can reduce costs to some extent. The central brain can periodically broadcast clock synchronization messages carrying a central timestamp to each regional gateway, and can also send wake-up messages carrying a central timestamp to each regional gateway when waking it up, thereby sending the central timestamp to each regional gateway. Then, each regional gateway determines the clock deviation information and completes clock synchronization to achieve clock synchronization of the entire gateway system. Furthermore, each regional gateway sends critical and non-critical signals to the central brain through data packets carrying a sending timestamp, so that the central brain can sort the critical and non-critical signals sent by each regional gateway based on the sending timestamp and upload them to the cloud. This is beneficial for the cloud to analyze the complete network fault chain and complete network fault diagnosis.

[0096] Based on the above embodiments, in an exemplary embodiment, see [link to example]. Figure 4 The flowchart illustrating the detailed process of sending key and non-key signals to the central brain in an embodiment of this application is shown, specifically including:

[0097] Step 401: Based on the actual signal frequency of the key signal, the key signal is sent to the central brain via data packets.

[0098] The actual signal frequency refers to the actual vehicle signal frequency. The area gateway will upload all critical signals according to the actual signal frequency, such as uploading newly acquired critical signals every 10ms or 20ms.

[0099] When a regional gateway sends a critical signal to the central brain, it can package the critical signal into a data packet, send the data packet to the central brain, and carry a sending timestamp in the data packet.

[0100] Step 402: Determine the signal upload frequency of non-critical signals based on the CAN bus load rate, and send the non-critical signals to the central brain via data packets based on the signal upload frequency.

[0101] It is understandable that network load information includes the CAN (Controller Area Network) bus load rate, and the target sampling rate is negatively correlated with the CAN bus load rate. That is, the target sampling rate is determined based on the bus load rate of the target vehicle. Optionally, a preset threshold for the CAN bus load rate, such as 80%, can be provided. Only when the CAN bus load rate is higher than 80% will non-critical signals be sampled. Furthermore, the higher the CAN bus load rate, the lower the target sampling rate, and the fewer non-critical signals are collected.

[0102] In this embodiment, the CAN bus load rate is considered not only during signal acquisition but also during signal uploading. The uploading frequency of non-critical signals is negatively correlated with the CAN bus load rate, and the uploading frequency is lower than the actual signal frequency. That is, the higher the CAN bus load rate, the lower the uploading frequency of non-critical signals, which is lower than the uploading frequency of critical signals. For example, newly acquired non-critical signals are uploaded every 500ms, 1s, or 10s.

[0103] When a regional gateway sends a non-critical signal to the central brain, it can package the non-critical signal into a data packet, send the data packet to the central brain, and carry a sending timestamp in the data packet.

[0104] In this application, different signal uploading methods are adopted for critical signals and non-critical signals. Specifically, critical signals are uploaded according to the actual vehicle signal frequency, while the uploading frequency of non-critical signals is reduced considering network load, thereby optimizing the signal uploading effect in the vehicle distributed gateway scenario.

[0105] Based on the above embodiments, in an exemplary embodiment, when synchronized with the central brain clock, key signals and non-key signals are sent to the central brain so that the central brain can upload them to the cloud. If the central brain is in a dormant state, the key signals are stored in the buffer of the regional gateway so that the key signals in the buffer can be sent to the central brain after the central brain is awakened.

[0106] Once the target vehicle's controller enters a sleep state, it can be woken up via the CAN bus. For example, when one of the target vehicle's controllers senses an external operation, the corresponding area gateway sends a wake-up message to the central control unit (CCU). Upon receiving the wake-up message, the CCU switches from sleep to active mode. Furthermore, the CCU can determine the target function corresponding to the wake-up message and send wake-up messages to other area gateways associated with the target function that are currently in sleep mode, thereby scheduling the controllers corresponding to those area gateways to implement the target function.

[0107] When the central brain is in a dormant state, the regional gateway stops sending critical and non-critical signals to the central brain and stores the critical signals in a separate buffer. Then, when the central brain is in an active state, the critical signals stored in the buffer are sent to the central brain in a timely manner.

[0108] In this application, the regional gateway can store key signals in a cache to prevent the central brain from being unable to receive key signals during dormancy or wake-up, thus avoiding the loss of key signals. Then, when the central brain is awakened, the stored key signals are sent to the central brain. For non-key signals, the method in this embodiment can be omitted to reduce the uploading of invalid data.

[0109] Based on the above embodiments, in an exemplary embodiment, see [link to example]. Figure 5 The flowchart illustrating the detailed signal acquisition strategy update in this embodiment of the application is shown, specifically including:

[0110] Step 501: Receive the new signal acquisition strategy sent from the cloud.

[0111] The cloud can distribute signal acquisition strategies to the central brain and regional gateways. Optionally, the cloud can directly distribute the corresponding signal acquisition strategies to the central brain and regional gateways, or the cloud can distribute the signal acquisition strategies to the central brain, which in turn distributes the corresponding signal acquisition strategies to each regional gateway. Accordingly, the regional gateways receive the new signal acquisition strategies.

[0112] Step 502: If the first version identifier corresponding to the current signal acquisition strategy of the area gateway is different from the second version identifier corresponding to the new signal acquisition strategy, then the new signal acquisition strategy shall replace the current signal acquisition strategy.

[0113] Each signal acquisition strategy has a unique version identifier. The version identifier of the signal acquisition strategy currently used by the area gateway is the first version identifier, and the version identifier of a new signal acquisition strategy received by the area gateway is the second version identifier. If the first version identifier and the second version identifier are the same, it means that the area gateway does not need to update the signal acquisition strategy. If the first version identifier and the second version identifier are different, it means that the area gateway needs to update the signal acquisition strategy, that is, to replace the currently used signal acquisition strategy with the new one.

[0114] In this application, the cloud can issue signal acquisition strategies to the vehicle and manage the overall signal acquisition and uploading process. When the overall data requirements change, the vehicle signal acquisition and uploading process can be dynamically adjusted based on the signal acquisition strategies.

[0115] In one exemplary embodiment, refer to Figure 6 The flowchart of the signal processing method in this embodiment is shown. It is applied to the central brain of the gateway system of the target vehicle. The gateway system includes the central brain and at least two regional gateways, which are described in detail below:

[0116] Step 601: While synchronizing with the clocks of the regional gateways of the target vehicle, receive critical and non-critical signals sent by each regional gateway.

[0117] Among them, key signals are collected by each area gateway based on the signal acquisition strategy, while non-key signals are collected by each area gateway based on the signal acquisition strategy and the target sampling rate; the target sampling rate is determined by each area gateway based on the network load information of the target vehicle.

[0118] Step 602: Upload critical and non-critical signals to the cloud so that the cloud can diagnose network faults of the target vehicle.

[0119] In this application, considering the balance between network load and data quality, different signal acquisition methods are adopted for critical and non-critical signals. That is, all critical signals are acquired, and non-critical signals are sampled based on network load to optimize the signal acquisition effect in the vehicle distributed gateway scenario.

[0120] Based on the above embodiments, in an exemplary embodiment, see [link to example]. Figure 7 The flowchart illustrating the detailed process of uploading critical and non-critical signals to the cloud in an embodiment of this application is shown, specifically including:

[0121] Step 701: Based on the sending timestamps corresponding to each regional gateway, sort the key signals and non-key signals sent by each regional gateway to obtain the key signal sequence and non-key signal sequence.

[0122] With each regional gateway synchronized with the central brain's clock, the central brain receives critical and non-critical signals transmitted by each regional gateway via data packets. These data packets carry a timestamp indicating their transmission. Since each regional gateway has completed its clock synchronization process, the time error between their local clocks reaches an accuracy of ±1 microsecond.

[0123] The central brain can determine the order of transmission of critical and non-critical signals from each regional gateway based on the transmission timestamp, and then sort them according to the order of transmission time to obtain the critical signal sequence and the non-critical signal sequence.

[0124] Step 702: Upload the critical signal sequence and non-critical signal sequence to the cloud so that the cloud can diagnose network faults of the target vehicle.

[0125] The central processing unit can periodically upload critical and non-critical signal sequences to the cloud via data packets. After receiving the critical and non-critical signal sequences, the cloud can determine the signal change trend and propagation path, thereby diagnosing network faults in the target vehicle.

[0126] Optionally, the cloud sends signal upload instructions (including key signal upload instructions and non-key signal upload instructions) to the central brain. After receiving the signal upload instructions, the central brain uploads the corresponding signal sequence to the cloud.

[0127] In this application, the central brain can reconstruct the global timeline based on the transmission timestamp, sort key signals and non-key signals to obtain homogeneous time-series data, namely key signal sequences and non-key signal sequences, thereby improving the signal acquisition quality and ensuring the accuracy and reliability of subsequent fault diagnosis based on signals.

[0128] Based on the above embodiments, in an exemplary embodiment, see [link to example]. Figure 8 The flowchart illustrating the details of gateway system wake-up in this embodiment of the application is shown, specifically including:

[0129] Step 801: If a first wake-up message is received, switch from sleep state to working state, and determine the target function corresponding to the first wake-up message, as well as the regional gateway and target subnet associated with the target function.

[0130] Step 802: If the associated area gateway is in a dormant state, a second wake-up message is sent to the associated area gateway so that the associated area gateway can collect key signals and non-key signals based on the target subnet segment.

[0131] The central brain can receive a wake-up message, i.e., the first wake-up message, sent by the regional gateway corresponding to any controller. After being woken up, the central brain can, based on the service-oriented functional link design, only wake up other regional gateways and target subnets associated with the target function corresponding to the first wake-up message.

[0132] Specifically, the central brain determines the target function corresponding to the first wake-up message. The central brain can identify the controller corresponding to the regional gateway that sent the first wake-up message, thereby determining the target function involved in the first wake-up message. It then identifies all regional gateways associated with the target function, as well as the specific target subnets and sensors associated with each regional gateway. The central brain sends a second wake-up message to the associated regional gateways. Correspondingly, the regional gateways, based on the second wake-up message, determine the associated target subnets and sensors and activate them for signal acquisition. For example, a remote-controlled air conditioner wakes up the central brain, which in turn wakes up the associated regional gateways, causing the associated regional gateways to activate their electric air outlets and compressors, and begin signal acquisition.

[0133] Understandably, the area gateway collects signals based on the principle of minimizing acquisition, activating only the data acquisition module of the target subnet segment associated with the target function.

[0134] Optionally, the central brain can maintain the dormant state of unrelated regional gateways and corresponding controllers to reduce invalid data collection and uploading.

[0135] In this application, the central brain can locally wake up the regional gateway and its target subnet segment, reduce the energy consumption of the target vehicle, reduce invalid data collection and uploading, and achieve precise control of network bandwidth.

[0136] To enable those skilled in the art to better understand this application as a whole, the application process of the solution in this application will be briefly described below with a specific embodiment:

[0137] See Figure 9 This illustrates another flowchart of a signal processing method in an embodiment of this application, specifically including:

[0138] Step 901: The regional gateway and the central brain receive the signal acquisition strategy sent from the cloud.

[0139] Step 902: The regional gateway determines the corresponding target sampling rate based on the CAN bus load rate of the target vehicle, and collects the key signals of the target vehicle according to the signal acquisition strategy, and collects the non-key signals of the target vehicle according to the target sampling rate according to the signal acquisition strategy.

[0140] Step 903: If the central brain is in a dormant state, the regional gateway stores the key signals in the buffer of the regional gateway so that the key signals in the buffer can be sent to the central brain after the central brain is awakened.

[0141] Step 904: The regional gateway receives the central timestamp from the central brain, determines the transmission delay between itself and the central brain, determines the clock deviation information between itself and the central brain based on the transmission delay, the central timestamp, and the local clock of the regional gateway, and updates the local clock of the regional gateway based on the clock deviation information to synchronize the clock of the regional gateway.

[0142] Step 905: The regional gateway sends the critical signals to the central brain via data packets based on the actual signal frequency of the critical signals, and determines the signal upload frequency of non-critical signals based on the CAN bus load rate, and sends the non-critical signals to the central brain via data packets based on the signal upload frequency.

[0143] Step 906: The central brain sorts the key signals and non-key signals sent by each regional gateway according to the sending timestamps corresponding to each regional gateway, and obtains the key signal sequence and non-key signal sequence.

[0144] Step 907: The central brain uploads the key signal sequence and non-key signal sequence to the cloud.

[0145] Step 908: The cloud platform diagnoses network faults of the target vehicle based on critical signal sequences and non-critical signal sequences.

[0146] In this application, the network topology of the target vehicle is implemented based on a distributed gateway system. The gateway system includes a central brain and multiple regional gateways. Each regional gateway can consider the balance between network load and data quality, adopt different signal acquisition methods for critical and non-critical signals, and complete high-precision clock synchronization with the central brain for the entire gateway system. This allows the central brain to sort the critical and non-critical signals sent by different regional gateways to obtain time-series data from the same source, and then upload it to the cloud for the cloud to diagnose network faults of the target vehicle. This is beneficial to improving the signal acquisition and uploading effect, and improving the accuracy and reliability of fault diagnosis.

[0147] The following describes an embodiment of the apparatus described in this application, which can be used to execute the signal processing method described above. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the signal processing method described above.

[0148] See Figure 10 This diagram illustrates a block diagram of a signal processing device 1000 according to an embodiment of this application. The signal processing device 1000 is configured in a regional gateway of a gateway system of a target vehicle. The gateway system includes a central brain and at least two regional gateways, specifically including:

[0149] The sampling rate determination module 1001 is used to determine the corresponding target sampling rate based on the network load information of the target vehicle.

[0150] The signal acquisition module 1002 is used to acquire key signals of the target vehicle according to the signal acquisition strategy, and to acquire non-key signals of the target vehicle according to the target sampling rate based on the signal acquisition strategy.

[0151] The signal uploading module 1003 is used to send critical and non-critical signals to the central brain in sync with the central brain's clock, so that the central brain can upload them to the cloud; among them, the critical and non-critical signals are used by the cloud to diagnose network faults of the target vehicle.

[0152] In an exemplary embodiment, the signal uploading module 1003 includes:

[0153] The clock synchronization unit is used to determine the clock deviation information between the regional gateway and the central brain based on the central timestamp and the local clock of the regional gateway if it receives the central timestamp from the central brain, and to synchronize the clock with the central brain based on the clock deviation information.

[0154] The signal uploading unit is used to send critical and non-critical signals to the central brain via data packets, so that the central brain can upload them to the cloud; wherein, the data packets carry the sending timestamp of the data packets sent by the regional gateway.

[0155] In one exemplary embodiment, the clock synchronization unit described above includes:

[0156] The delay determination subunit is used to determine the transmission delay between the regional gateway and the central brain.

[0157] The deviation determination subunit is used to determine the clock deviation information between the regional gateway and the central brain based on the transmission delay, the central timestamp, and the local clock of the regional gateway.

[0158] The clock synchronization subunit is used to update the local clock of the area gateway based on the clock deviation information in order to synchronize the clock of the area gateway.

[0159] In an exemplary embodiment, the network load information includes the CAN bus load rate, and the target sampling rate is negatively correlated with the CAN bus load rate. The aforementioned signal uploading unit includes:

[0160] The first signal uploading subunit is used to send the key signal to the central brain via data packets based on the actual signal frequency of the key signal.

[0161] The second signal uploading subunit is used to determine the signal uploading frequency of non-critical signals based on the CAN bus load rate, and to send the non-critical signals to the central brain via data packets based on the signal uploading frequency; wherein, the signal uploading frequency is negatively correlated with the CAN bus load rate, and the signal uploading frequency is lower than the actual signal frequency.

[0162] In an exemplary embodiment, the signal processing device 1000 further includes:

[0163] The pre-storage module stores key signals in the buffer of the regional gateway if the central brain is in a dormant state, so that the key signals in the buffer can be sent to the central brain after the central brain is awakened.

[0164] In an exemplary embodiment, the signal processing device 1000 further includes:

[0165] The strategy receiving module is used to receive new signal acquisition strategies sent from the cloud.

[0166] The policy update module is used to replace the current signal acquisition policy with the new one if the first version identifier corresponding to the current signal acquisition policy of the area gateway is different from the second version identifier corresponding to the new signal acquisition policy.

[0167] See Figure 11 This diagram illustrates a block diagram of a signal processing device 1100 according to an embodiment of this application. The signal processing device 1100 is configured in the central brain of the gateway system of the target vehicle. The gateway system includes a central brain and at least two regional gateways, specifically including:

[0168] The signal receiving module 1101 is used to receive key signals and non-key signals sent by each area gateway while synchronizing with the clock of each area gateway of the target vehicle; wherein, the key signals are collected by each area gateway based on the signal acquisition strategy, and the non-key signals are collected by each area gateway based on the signal acquisition strategy and the target sampling rate; the target sampling rate is determined by each area gateway based on the network load information of the target vehicle.

[0169] The signal upload module 1102 is used to upload critical and non-critical signals to the cloud so that the cloud can diagnose network faults of the target vehicle.

[0170] In an exemplary embodiment, the signal uploading module 1102 described above includes:

[0171] The first signal uploading unit is used to sort the key signals and non-key signals sent by each regional gateway according to the sending timestamp corresponding to each regional gateway, so as to obtain the key signal sequence and non-key signal sequence.

[0172] The second signal uploading unit is used to upload critical signal sequences and non-critical signal sequences to the cloud so that the cloud can diagnose network faults of the target vehicle.

[0173] In an exemplary embodiment, the signal processing device 1100 further includes:

[0174] The first wake-up module is used to switch from a sleep state to a working state if a first wake-up message is received, and to determine the target function corresponding to the first wake-up message, as well as the regional gateway and target subnet associated with the target function.

[0175] The second wake-up module is used to send a second wake-up message to the associated area gateway if the associated area gateway is in a dormant state, so that the associated area gateway can collect key signals and non-key signals based on the target subnet segment.

[0176] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing computer program instructions. When the computer program instructions are loaded and executed by a processor, they implement the steps of the signal processing method described above.

[0177] Based on the same inventive concept, this application provides an electronic device, see [link to relevant documentation]. Figure 12 The diagram shows a schematic of the structure of an electronic device in an embodiment of this application. The electronic device includes one or more memories 1204, one or more processors 1202, and at least one computer program stored in the memory 1204 and executable on the processor 1202. When the processor 1202 executes the computer program, it implements the steps of the signal processing method described above.

[0178] The bus architecture (represented by bus 1200) may include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 1202 and memory represented by memory 1204. Bus 1200 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 1205 provides an interface between bus 1200 and receiver 1201 and transmitter 1203. Receiver 1201 and transmitter 1203 may be the same element, a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 1202 is responsible for managing bus 1200 and general processing, while memory 1204 can be used to store data used by processor 1202 during operation.

[0179] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored as one or more instructions or codes on or transmitted via a computer-readable medium. Other examples and embodiments are within the scope and spirit of this application and the appended claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hardwired, or any combination thereof. Furthermore, the functional units may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit.

[0180] Based on the same inventive concept, embodiments of this application provide a computer program product, including a computer program, which, when executed by a processor, implements the steps of the signal processing method described above.

[0181] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0182] The units described as separate components may or may not be physically separate. Similarly, the components of the control device may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0183] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a 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 steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing computer program instructions, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0184] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A signal processing method, characterized in that, A regional gateway for a gateway system applied to a target vehicle, the gateway system including a central brain and at least two regional gateways, the method comprising: Determine the corresponding target sampling rate based on the network load information of the target vehicle; The key signals of the target vehicle are acquired according to the signal acquisition strategy, and the non-key signals of the target vehicle are acquired according to the target sampling rate according to the signal acquisition strategy. While synchronized with the central brain's clock, the critical signals and non-critical signals are sent to the central brain for uploading to the cloud; wherein, the critical signals and non-critical signals are used by the cloud to diagnose network faults of the target vehicle.

2. The method according to claim 1, characterized in that, The step of sending the critical signals and non-critical signals to the central brain in synchronization with the central brain's clock, so that the central brain can upload them to the cloud, includes: If a central timestamp from the central brain is received, the clock deviation information between the regional gateway and the central brain is determined based on the central timestamp and the local clock of the regional gateway, and clock synchronization with the central brain is performed based on the clock deviation information. The critical signals and non-critical signals are sent to the central brain via data packets, so that the central brain can upload them to the cloud; wherein, the data packets carry a timestamp indicating that the regional gateway sent the data packets.

3. The method according to claim 2, characterized in that, The step of determining the clock deviation information between the regional gateway and the central brain based on the central timestamp and the local clock of the regional gateway, and synchronizing the clock with the central brain based on the clock deviation information, includes: Determine the transmission delay between the regional gateway and the central brain; Based on the transmission delay, the central timestamp, and the local clock of the regional gateway, determine the clock deviation information between the regional gateway and the central brain; The local clock of the regional gateway is updated based on the clock deviation information to synchronize the clock of the regional gateway.

4. The method according to claim 2, characterized in that, The network load information includes the CAN bus load rate of the controller area network, and the target sampling rate is negatively correlated with the CAN bus load rate. The step of sending the critical signals and non-critical signals to the central brain via data packets, so that the central brain can upload them to the cloud, includes: Based on the actual signal frequency of the key signal, the key signal is sent to the central brain via data packets; The signal upload frequency of the non-critical signal is determined based on the CAN bus load rate, and the non-critical signal is sent to the central brain via data packets based on the signal upload frequency; wherein the signal upload frequency is negatively correlated with the CAN bus load rate, and the signal upload frequency is lower than the actual signal frequency.

5. The method according to claim 1, characterized in that, Before sending the critical signals and non-critical signals to the central brain in synchronization with the central brain's clock, so that the central brain can upload them to the cloud, the method further includes: If the central brain is in a dormant state, the key signals are stored in the buffer of the regional gateway so that the key signals in the buffer can be sent to the central brain after the central brain is awakened.

6. The method according to claim 1, characterized in that, The method further includes: Receive new signal acquisition strategies from the cloud; If the first version identifier corresponding to the current signal acquisition strategy of the regional gateway is different from the second version identifier corresponding to the new signal acquisition strategy, then the new signal acquisition strategy shall replace the current signal acquisition strategy.

7. A signal processing method, characterized in that, A central brain for a gateway system applied to a target vehicle, the gateway system including a central brain and at least two regional gateways, the method comprising: While synchronizing with the clocks of the regional gateways of the target vehicle, the system receives critical and non-critical signals sent by each regional gateway. The critical signals are acquired by each regional gateway based on a signal acquisition strategy, and the non-critical signals are acquired by each regional gateway based on the signal acquisition strategy and a target sampling rate. The target sampling rate is determined by each regional gateway based on the network load information of the target vehicle. The critical signals and non-critical signals are uploaded to the cloud so that the cloud can diagnose network faults of the target vehicle.

8. The method according to claim 7, characterized in that, The step of uploading the critical signals and the non-critical signals to the cloud for the cloud to diagnose network faults of the target vehicle includes: Based on the sending timestamps corresponding to each regional gateway, the key signals and non-key signals sent by each regional gateway are sorted separately to obtain the key signal sequence and non-key signal sequence. The critical signal sequence and the non-critical signal sequence are uploaded to the cloud so that the cloud can diagnose the network faults of the target vehicle.

9. The method according to claim 7, characterized in that, The method further includes: If a first wake-up message is received, the system switches from sleep mode to working mode, determines the target function corresponding to the first wake-up message, and determines the regional gateway and target subnet associated with the target function. If the associated regional gateway is in a dormant state, a second wake-up message is sent to the associated regional gateway so that the associated regional gateway can collect key and non-key signals based on the target subnet segment.

10. A signal processing apparatus, characterized in that, A regional gateway configured in a gateway system of a target vehicle, the gateway system including a central brain and at least two regional gateways, the device comprising: The sampling rate determination module is used to determine the corresponding target sampling rate based on the network load information of the target vehicle. The signal acquisition module is used to acquire key signals of the target vehicle according to the signal acquisition strategy, and to acquire non-key signals of the target vehicle according to the target sampling rate according to the signal acquisition strategy. The signal uploading module is used to send the key signals and the non-key signals to the central brain in sync with the central brain's clock, so that the central brain can upload them to the cloud; wherein, the key signals and the non-key signals are used by the cloud to diagnose network faults of the target vehicle.

11. A signal processing apparatus, characterized in that, A central brain of a gateway system configured in a target vehicle, the gateway system including a central brain and at least two regional gateways, the device comprising: The signal receiving module is used to receive critical and non-critical signals sent by each regional gateway while synchronizing with the clock of each regional gateway of the target vehicle; wherein the critical signals are collected by each regional gateway based on a signal acquisition strategy, and the non-critical signals are collected by each regional gateway based on the signal acquisition strategy and a target sampling rate; the target sampling rate is determined by each regional gateway based on the network load information of the target vehicle. The signal uploading module is used to upload the critical signals and the non-critical signals to the cloud so that the cloud can diagnose the network faults of the target vehicle.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which are loaded and executed by a processor to perform the operations performed by the method as described in any one of claims 1 to 9.

13. An electronic device comprising a processor and a memory, characterized in that, The memory stores computer program instructions that can be executed by the processor, and when the processor executes the computer program instructions, it implements the instructions of the method as described in any one of claims 1 to 9.

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