Multi-sensor acquisition method and system for key signal acquisition

Abstract

The invention relates to the technical field of sensor data transmission, in particular to a multi-sensor acquisition method and a multi-sensor acquisition system for acquiring key signals, which are characterized in that the priority of each sensor signal is determined according to application scenes and requirements, and the sensor signals influencing system key functions or relating to important monitoring targets are divided into high priorities; other signals are divided into low priorities, and the high priorities can be further divided into emergency key signals and common key signals; the method has the beneficial effects that the sensor signals are subjected to priority division, and different transmission modes are adopted for the signals with different priorities, so that key signals can be transmitted to the receiving end in the shortest time and are preferentially processed, and the accuracy and timeliness of signal processing are greatly improved; key signal delay caused by transmission of a large number of non-key signals is avoided, and key information can be captured in time in a large-data-volume long-distance transmission scene.

Description

Technical Field

[0001] The present invention relates to the technical field of sensor data transmission, and specifically to a multi-sensor acquisition method and system for obtaining key signals. Background Art

[0002] With the rapid development of sensor technology, various types of sensors have been widely used in different fields, such as industrial production, medical monitoring, environmental protection, etc. Sensors can sense various physical quantities in the external environment, such as temperature, pressure, strain, etc., convert these signals into electrical signals, and then further process and analyze them through a data acquisition system. In this process, how to efficiently and accurately acquire and transmit sensor signals, process large-scale data, and respond to control requirements has become a core issue in many engineering technologies.

[0003] In a sensor network, the amount of data is huge and redundant, and the real-time requirement for transmission and processing is very high. Therefore, how to preferentially transmit key signals and avoid redundancy and delay in the data transmission process is particularly important. For example, fault signals in industrial production, vital signs in medical monitoring, etc. all belong to key signals that need to be preferentially processed. Although existing sensor acquisition schemes can achieve signal acquisition and basic data transmission, they often cannot meet the requirements of real-time and reliability in an environment of high redundancy and large traffic signals.

[0004] Currently, most sensor network architectures adopt a centralized data processing method, which has problems such as large data throughput, high resource consumption, and large transmission delay. Existing solutions mainly focus on improving sensor performance and data transmission methods, such as using technologies like wireless sensor networks (WSN)[1], Internet of Things (IoT)[2], etc. to improve the efficiency and reliability of signal transmission. However, how to reasonably divide signal priorities, select appropriate transmission methods, and design an efficient data acquisition and processing system is still a technical problem to be solved urgently.

[0005] In addition, there are also some challenges in the design of the data acquisition system. Existing data acquisition systems usually include sensors, acquisition devices, and storage devices, but in actual applications, there is still much room for optimization in issues such as how to effectively manage and control sensors through software and control systems, how to achieve real-time data monitoring, and how to optimize resource scheduling.

[0006] Therefore, a multi-sensor acquisition method for obtaining key signals is proposed. By preferentially transmitting key signals, performing real-time analysis and response control, it can effectively improve the signal transmission efficiency, optimize the resource management of the data acquisition system, and ensure the real-time response of key tasks. This method can be widely applied in fields such as industrial monitoring and medical monitoring, greatly improving the stability and response ability of the system. Summary of the Invention

[0007] The object of the present invention is to provide a multi-sensor acquisition method and system for obtaining key signals, so as to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: A multi-sensor acquisition method for obtaining key signals, including the following steps:

[0009] S1: According to the application scenario and requirements, clarify the priority of each sensor signal, classify the sensor signals that affect the key functions of the system or involve important monitoring targets as high-priority, and classify the remaining signals as low-priority, and the high-priority can be further divided into emergency key signals and ordinary key signals;

[0010] S2: Construct a data convergence point, and various sensing data are first transmitted to this convergence point;

[0011] S3: At the convergence point, select an appropriate determination method according to different application scenarios to analyze the signals, and distinguish key signals from non-key signals;

[0012] S4: For high-priority key signals, adopt a real-time transmission method to ensure that they can be transmitted to the receiving end in the shortest time; for low-priority non-key signals, adopt a non-real-time transmission method and transmit them when the transmission resources are idle. Transmit the key signals in the convergence point to the server side for priority processing first, and temporarily store the low-priority signals in the convergence point.

[0013] Preferably, in step S1, in the industrial production monitoring scenario, the sensor signals corresponding to the fault signals are defined as key signals; in the medical monitoring scenario, the heartbeat and blood pressure signals related to vital signs are defined as key signals.

[0014] Preferably, it further includes the following steps for designing the acquisition system architecture:

[0015] S201: Design a data acquisition layer, so that the data source obtains various data from external sensors or data files and transmits the data to the server side;

[0016] S202: Design a data processing layer to realize data transfer and storage and have data erasure and data read-back functions;

[0017] S203: Design a data analysis layer, which is responsible for model management, and uses the model to call the GPU and CPU to use algorithms to clean, analyze and mine the data to obtain a small amount of valuable data, and the memory and GPU in the system are expandable;

[0018] S204: Design the system resource scheduling. The resources are used to perform data collection and data statistics tasks at regular intervals. When it is determined through resource monitoring that the requirements are met, GPU or CPU resources are allocated and the model is called for data analysis. When the requirements are not met, a waiting queue is created for the next resource allocation.

[0019] Preferably, it further includes the following steps for implementing the data collection software functions:

[0020] Develop the storage function, allowing users to select the location and format for data storage;

[0021] Develop the power-on and power-off functions, enabling users to control the power-on operation of the data collection device through the software interface;

[0022] Develop the monitoring function, providing an interface for real-time monitoring of the status and operation of the data collection device, and displaying the device connection status, storage space, and power information;

[0023] Develop the data collection control function, providing the control functions for starting and stopping data collection, and displaying the real-time status of data collection, namely the collection rate and data volume information;

[0024] Develop the device connection management function, allowing users to manage the connections of multiple data collection devices. The software automatically identifies the connected devices and displays their status and relevant information. Users can manually add or remove devices and set the device connection priority parameters;

[0025] Develop the log recording function, recording the operations and events during the software operation, including device connection, data storage, and erasure operations.

[0026] Preferably, in step S3, different signal analysis and determination methods are adopted for different application scenarios. For example, in the industrial production monitoring scenario, it is determined whether a signal is a key signal according to the preset fault feature threshold; in the medical monitoring scenario, it is determined whether a signal is a key signal according to the normal range threshold of the vital sign signal.

[0027] A system for a multi-sensor acquisition method for obtaining key signals includes:

[0028] Signal priority division module: Based on the application scenario and requirements, clarify the priority of each sensor signal. The sensor signals that affect the key functions of the system or involve important monitoring targets are classified as high-priority, and the remaining signals are classified as low-priority. Moreover, the high-priority can be further divided into emergency key signals and ordinary key signals; in the industrial production monitoring scenario, the sensor signals corresponding to the fault signals are defined as key signals; in the medical monitoring scenario, the heartbeat and blood pressure signals related to vital signs are defined as key signals;

[0029] Data Aggregation and Analysis Module: Build a data aggregation point to receive various sensing data, and select an appropriate judgment method according to different application scenarios to analyze the signals and distinguish key signals from non-key signals;

[0030] Signal Transmission Module: For high-priority key signals, use real-time transmission to ensure that they can be transmitted to the receiving end in the shortest time; for low-priority non-key signals, use non-real-time transmission and transmit them when the transmission resources are idle. Transmit the key signals in the aggregation point to the server side for priority processing first, and temporarily store the low-priority signals in the aggregation point;

[0031] Acquisition System Architecture Module: Data Acquisition Layer: The data source obtains various data from external sensors or data files and transmits the data to the server side; Data Processing Layer: Realize data transfer and storage and have data erasure and data read-back functions; Data Analysis Layer: Responsible for model management, use the model to call the GPU and CPU to use algorithms for data cleaning, analysis and mining to obtain a small amount of valuable data, and the memory and GPU in the system are scalable; System Resource Scheduling Layer: The resources are used to periodically execute specific tasks such as data acquisition and data statistics. When it is determined by resource monitoring that the requirements are met, allocate GPU or CPU resources and call the model for a series of data analysis work. If not, create a waiting queue for the next resource allocation;

[0032] Data Acquisition Software Module: Storage Function Unit: Allow users to select the data storage location and format; Power-on and Power-off Function Unit: Enable users to control the power-on operation of the data acquisition device through the software interface; Monitoring Function Unit: Provide an interface for real-time monitoring of the status and operation of the data acquisition device, and display the device connection status, storage space, and battery information; Data Acquisition Control Unit: Provide control functions for starting and stopping data acquisition and display the real-time status of data acquisition, that is, acquisition rate and data volume information; Device Connection Management Unit: Allow users to manage the connection of multiple data acquisition devices. The software automatically identifies the connected devices and displays their status and relevant information. Users can manually add or remove devices and set parameters such as device connection priorities; Log Record Unit: Record the operations and events during the software operation, including device connection, data storage, and erasure operations.

[0033] Preferably, in the data aggregation and analysis module, different signal analysis and judgment methods are adopted according to different application scenarios. In the industrial production monitoring scenario, judge whether the signal is a key signal according to the preset fault feature threshold; in the medical monitoring scenario, judge whether the signal is a key signal according to the normal range threshold of the vital sign signal.

[0034] Preferably, in the signal transmission module, for the real-time transmission mode, a high-speed and stable communication protocol is adopted, such as an optimized version of the TCP / IP protocol for real-time transmission, to ensure the timeliness and accuracy of critical signal transmission; for the non-real-time transmission mode, a data compression algorithm is used to compress low-priority signals before transmission to improve transmission efficiency.

[0035] Preferably, in the acquisition system architecture module, the models used in the data analysis layer include but are not limited to machine learning models and deep learning models, and the models can be trained and optimized according to the actual application scenarios to improve the accuracy and efficiency of data analysis; when allocating resources in the system resource scheduling layer, a dynamic resource allocation algorithm is adopted to make real-time adjustments according to the urgency of the current task and the resource usage situation.

[0036] Preferably, in the data acquisition software module, when the device connection management unit automatically identifies the connected devices, it uses the unique device identifier for identification and establishes a communication connection between the device and the software; when setting the device connection priority, it classifies according to the importance and real-time requirements of the signals collected by the device, and the devices with higher priority have higher precedence in data acquisition and transmission.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] The multi-sensor acquisition method and system for critical signal acquisition proposed by the present invention, by dividing the priorities of sensor signals and adopting different transmission methods for signals with different priorities, ensure that critical signals can be transmitted to the receiving end and given priority processing in the shortest time, greatly improving the accuracy and timeliness of signal processing, avoiding delays of critical signals caused by the transmission of a large number of non-critical signals, and being able to capture critical information in a large data volume and long-distance transmission scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a block diagram of the system of the present invention;

[0040] Figure 2 It is an architecture diagram of the system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] In order to clearly and completely describe the objectives, technical solutions of the present invention, and make the advantages more clearly understood, the following further details the embodiments of the present invention with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are some embodiments of the present invention, rather than all embodiments, and are only used to explain the embodiments of the present invention, not to limit the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0042] Example 1, the present invention provides a technical solution: a multi-sensor acquisition method for obtaining key signals, including the following steps:

[0043] S1: Multi-sensor signal transmission scheme

[0044] S101: Signal priority division

[0045] According to the application scenario and requirements, clarify which sensor signals belong to key signals. For example, in industrial production monitoring, the sensor signals corresponding to fault signals can be defined as key signals; in medical monitoring, signals such as heartbeat and blood pressure related to vital signs are key signals. Divide these key signals into high priority, and non-key signals into low priority. The priority levels can also be further subdivided, such as dividing high priority into emergency key signals and ordinary key signals.

[0046] S102: Transmission method selection

[0047] In experiments with a large amount of data, in the experiment, various sensing data first enter a convergence point, where key signal analysis is performed. The analysis method can select an appropriate determination method according to different application scenarios. For high-priority key signals, a real-time transmission method is adopted to ensure that the signals can be transmitted to the receiving end in the shortest time. For low-priority non-key signals, a non-real-time transmission method is adopted, and the transmission is carried out when the transmission resources are idle. In this way, the key signals in the convergence point are preferentially transmitted to the server side for priority processing, while the low-priority signals are temporarily stored in the convergence point.

[0048] S2: Acquisition system architecture

[0049] S201: Data acquisition layer design

[0050] The data source obtains various data from external sensors or data files, and then transmits the data to the server side.

[0051] S202: Data processing layer design

[0052] Perform data transfer and have data erasure and data read-back functions.

[0053] S203: Data analysis layer design

[0054] Responsible for model management, and then using the model to call the GPU and CPU to use algorithms for data cleaning, analysis (signal processing), and mining. Finally, only a small amount of valuable data is obtained. To cope with complex real-world situations, the memory and GPU in the system are scalable.

[0055] S204: System resource scheduling design

[0056] Resources are generally used to perform specific tasks at regular intervals, such as data collection and data statistics. When it is determined in resource monitoring that the required resources can be satisfied, GPU or CPU resources are allocated and the model is called to perform a series of data analysis tasks. If the resources cannot be satisfied, a waiting queue needs to be created for the next step of resource allocation.

[0057] S3: Data acquisition software

[0058] The data acquisition software connects to all parts of the equipment required for data acquisition. The acquisition software includes the following functions:

[0059] (1) Storage function: Allows users to select the location and format of data storage.

[0060] (2) Power-on and power-off function: Users can control the power-on operation of the data acquisition device through the software interface.

[0061] (3) Monitoring function: Used to monitor the status and operation of the data acquisition device in real time. Provide a monitoring interface to display information such as the connection status, storage space, and power of the device.

[0062] (4) Data acquisition control: Provide control functions for starting and stopping data acquisition. And display the real-time status of data acquisition, such as acquisition rate, data volume, etc.

[0063] (5) Device connection management: Provide device connection management functions, allowing users to manage the connections of multiple data acquisition devices. The software can automatically identify the connected devices and display their status and related information. Users can manually add or remove devices and set parameters such as device connection priorities.

[0064] (6) Log recording: Record operations and events during the software operation, including device connection, data storage, erase operations, etc.

[0065] In the second embodiment, based on the first embodiment, a multi-sensor acquisition system for obtaining key signals is proposed, which consists of sensors, data acquisition devices, data processing units, response control units, etc. The sensors are responsible for collecting signal data in the environment. The data acquisition devices transmit the collected data to the data processing units. The data processing units perform real-time processing and analysis on the data to achieve the monitoring and analysis of sensor events. At the same time, the response control units execute corresponding control and measures according to the monitoring results.

[0066] The specific composition is as follows:

[0067] (1) Sensors: The acquisition platform is usually equipped with various types of sensors for collecting the generated signals. The sensor types include strain sensors, pressure sensors, acceleration sensors, etc. These sensors can capture signals such as strain, pressure waves, and vibrations generated by sensor events and convert them into electrical signals for subsequent processing and analysis.

[0068] (2) Data acquisition devices: Data acquisition devices are used to convert the signals collected by sensors into digital data and store and transmit them. It usually includes data acquisition cards, data acquisition modules, or embedded data acquisition systems, etc. These devices can efficiently collect and process a large amount of data.

[0069] (3) Communication devices: To achieve real-time data transmission and remote control, the acquisition platform is usually equipped with corresponding communication devices, such as wireless communication modules, acoustic communication devices, etc. These devices can perform reliable data transmission and communicate and interact with the control center.

[0070] (4) Storage devices: The collected data needs to be stored for subsequent processing and analysis. Therefore, the acquisition platform is usually equipped with a certain capacity of storage devices, such as hard disks, solid-state drives (SSDs), memory cards, etc. These storage devices can stably store a large amount of data and need to have a certain degree of waterproof and shock-resistant capabilities in some environments.

[0071] (5) Software and control systems: The acquisition platform is usually equipped with corresponding software and control systems for real-time monitoring and control of the acquisition process and for data processing and analysis. These software and systems can achieve the control and management of sensors and data acquisition devices, as well as real-time processing and analysis of the collected data, so as to achieve the monitoring and identification of key sensor events.

[0072] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-sensor acquisition method for obtaining key signals, characterized in that: It includes the following steps: S1: According to the application scenario and requirements, clarify the priorities of each sensor signal. Classify the sensor signals that affect the key functions of the system or involve important monitoring targets as high-priority signals, and classify the remaining signals as low-priority signals. Moreover, high-priority signals can be further divided into emergency critical signals and ordinary critical signals; S2: Construct a data aggregation point to which various sensing data is first transmitted; S3: At the aggregation point, select an appropriate determination method according to different application scenarios to analyze the signals and distinguish key signals from non-key signals; S4: For high-priority key signals, adopt a real-time transmission method to ensure that they can be transmitted to the receiving end in the shortest time; for low-priority non-key signals, adopt a non-real-time transmission method to transmit them when the transmission resources are idle. Transmit the key signals in the aggregation point to the server side for priority processing first, and temporarily store the low-priority signals in the aggregation point.

2. The multi-sensor acquisition method for obtaining key signals according to claim 1, wherein: In step S1, in the industrial production monitoring scenario, define the sensor signals corresponding to the fault signals as key signals; in the medical monitoring scenario, define the heartbeat and blood pressure signals related to vital signs as key signals.

3. The multi-sensor acquisition method for obtaining key signals according to claim 2, wherein: It also includes the following steps for designing the acquisition system architecture: S201: Design the data acquisition layer to obtain various data from external sensors or data files as the data source and transmit the data to the server side; S202: Design the data processing layer to realize data transfer and storage and have the functions of data erasure and data readback; S203: Design the data analysis layer to be responsible for model management, and use the model to call the GPU and CPU to use algorithms for data cleaning, analysis and mining to obtain a small amount of valuable data, and the memory and GPU in the system can be expanded; S204: Design the system resource scheduling. The resources are used to execute data acquisition and data statistics specific tasks regularly. When it is determined by resource monitoring that the requirements are met, allocate GPU or CPU resources and call the model for data analysis. When the requirements are not met, create a waiting queue for the next resource allocation.

4. A multi-sensor acquisition method for key signal acquisition according to claim 3, characterized in that: It also includes the following steps for implementing the functions of the data acquisition software: Develop the storage function to allow users to select the location and format of data storage; Develop the power-on and power-off functions to enable users to control the power-on operation of the data acquisition device through the software interface; Develop the monitoring function to provide an interface for real-time monitoring of the status and operation of the data acquisition device, and display the device connection status, storage space, and power information; Develop the data acquisition control function to provide control functions for starting and stopping data acquisition and display the real-time status of data acquisition, that is, the acquisition rate and data volume information; Develop the device connection management function to allow users to manage the connections of multiple data acquisition devices. The software automatically identifies the connected devices and displays their status and relevant information. Users can manually add or remove devices and set the device connection priority parameters; Develop the log recording function to record the operations and events during the software operation, including device connection, data storage, and erasure operations.

5. A multi-sensor acquisition method for obtaining key signals according to claim 4, characterized in that: In step S3, different signal analysis and determination methods are adopted for different application scenarios. For example, in the industrial production monitoring scenario, it is determined whether a signal is a critical signal according to a preset fault feature threshold; in the medical monitoring scenario, it is determined whether a signal is a critical signal according to the normal range threshold of vital sign signals.

6. A system for the multi-sensor acquisition method for obtaining key signals according to claim 5, characterized in that: Including: Signal priority division module: According to the application scenario and requirements, clarify the priorities of each sensor signal. The sensor signals that affect the key functions of the system or involve important monitoring targets are classified as high-priority, and the remaining signals are classified as low-priority. Moreover, high-priority can be further divided into emergency critical signals and ordinary critical signals; in the industrial production monitoring scenario, the sensor signals corresponding to fault signals are defined as critical signals; in the medical monitoring scenario, the heartbeat and blood pressure signals related to vital signs are defined as critical signals; Data aggregation and analysis module: Build a data aggregation point, receive various sensing data, and select an appropriate determination method according to different application scenarios to analyze the signals and distinguish critical signals from non-critical signals; Signal transmission module: For high-priority critical signals, adopt a real-time transmission method to ensure that they can be transmitted to the receiving end in the shortest time; for low-priority non-critical signals, adopt a non-real-time transmission method and transmit them when the transmission resources are idle. The critical signals in the aggregation point are preferentially transmitted to the server side for priority processing, and the low-priority signals are temporarily stored in the aggregation point; Acquisition system architecture module: Data acquisition layer: The data source obtains various data from external sensors or data files and transmits the data to the server side; Data processing layer: Realize data transfer and storage and have the functions of data erasure and data read-back; Data analysis layer: Responsible for model management, use the model to call the GPU and CPU to use algorithms for data cleaning, analysis and mining to obtain a small amount of valuable data, and the memory and GPU in the system are scalable; System resource scheduling layer: The resources are used to periodically execute specific tasks such as data acquisition and data statistics. When it is determined by resource monitoring that the requirements are met, allocate GPU or CPU resources and call the model to perform a series of data analysis tasks. If the requirements cannot be met, create a waiting queue for the next resource allocation; Data acquisition software module: Storage function unit: Allow users to select the location and format of data storage; Power-on and power-off function unit: Enable users to control the power-on operation of the data acquisition device through the software interface; Monitoring function unit: Provide an interface for real-time monitoring of the status and operation of the data acquisition device, and display the connection status, storage space, and power information of the device; Data acquisition control unit: Provide the control functions of starting and stopping data acquisition and display the real-time status of data acquisition, namely the acquisition rate and data volume information; Device connection management unit: Allow users to manage the connections of multiple data acquisition devices. The software automatically identifies the connected devices and displays their status and relevant information. Users can manually add or remove devices and set the device connection priority parameters; Log recording unit: Record the operations and events during the software operation, including device connection, data storage, and erasure operations.

7. A system according to claim 6, characterized in that: In the data aggregation and analysis module, different signal analysis and determination methods are adopted for different application scenarios. In the industrial production monitoring scenario, it is judged whether the signal is a key signal according to the preset fault feature threshold; in the medical monitoring scenario, it is judged whether the signal is a key signal according to the normal range threshold of the vital sign signal.

8. A system according to claim 7, characterized in that: In the signal transmission module, for the real-time transmission mode, a high-speed and stable communication protocol is adopted, such as the optimized real-time transmission version of the TCP / IP protocol, to ensure the timeliness and accuracy of the key signal transmission; for the non-real-time transmission mode, a data compression algorithm is used to compress the low-priority signals before transmission to improve the transmission efficiency.

9. A system according to claim 8, characterized in that: In the acquisition system architecture module, the models used in the data analysis layer include but are not limited to machine learning models and deep learning models, and the models can be trained and optimized according to the actual application scenario to improve the accuracy and efficiency of data analysis; when allocating resources in the system resource scheduling layer, a dynamic resource allocation algorithm is adopted, and real-time adjustment is made according to the urgency of the current task and the resource usage situation.

10. A system according to claim 9, wherein: In the data acquisition software module, when the device connection management unit automatically identifies the connected devices, it uses the device unique identifier for identification and establishes a communication connection between the device and the software; when setting the device connection priority, it is classified according to the importance and real-time requirements of the signals collected by the devices, and the devices with higher priority have higher priority in data acquisition and transmission.

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