Data filtering method and device, computer device, storage medium and program product
By performing proportional filtering, edge detection, and PI filtering on the data collected by the collaborative robot, the problem of high processor resource consumption was solved, and data processing capabilities were improved without distortion or loss of keyframes.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHENZHEN HANS ROBOT CO LTD
- Filing Date
- 2023-10-08
- Publication Date
- 2026-06-19
AI Technical Summary
Collaborative robots experience high processor resource consumption when processing real-time data, especially when processing large data streams such as video or audio, leading to a shortage of computing resources.
A multi-layer filtering method is adopted, including proportional filtering, edge detection and PI filtering. By acquiring the data to be filtered from the acquisition device, proportional filtering, edge detection and PI filtering are performed to obtain the target floating-point data, thereby reducing the processor resource consumption.
Without distortion or loss of keyframes, it effectively reduces the computational resource burden on the processor, improves data processing capabilities, and ensures data readability and recognition efficiency.
Smart Images
Figure CN117314799B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of data processing technology, and in particular to a data filtering method, apparatus, computer equipment, storage medium, and program product. Background Technology
[0002] The real-time data acquisition devices equipped with collaborative robots, such as cameras and recorders, acquire data that is too detailed and complex. During the operation of collaborative robots, the processor needs to perform multiple types of data analysis and driving tasks. The processing of data streams such as video or audio, which consume a lot of memory, will put a lot of pressure on the processor of the robot device to consume computing resources. Summary of the Invention
[0003] Therefore, it is necessary to provide a data filtering method, apparatus, computer equipment, storage medium, and program product that can alleviate the resource consumption problem of collaborative robots, optimize the resource consumption rate of real-time processing, and obtain usable data without distortion or loss of keyframes, in order to address the above-mentioned technical problems.
[0004] Firstly, this application provides a data filtering method, including:
[0005] Acquire the data to be filtered from the acquisition device, wherein the data to be filtered is a continuous readable data stream;
[0006] The data to be filtered is proportionally filtered according to a preset proportional coefficient to obtain a first data stream;
[0007] Edge detection is performed on the first data stream to obtain multiple detection feature points;
[0008] The data streams of each detected feature point are integrated to obtain multiple second data streams;
[0009] The target floating-point data is obtained by performing PI filtering on each second data stream according to the preset PI coefficient.
[0010] In one embodiment, acquiring the data to be filtered from the acquisition device includes:
[0011] Collect raw data according to the first frame rate;
[0012] The raw data collected within a preset time period is used as the data to be filtered.
[0013] In one embodiment, the frame rate of the first data stream is a second frame rate, wherein the second frame rate is less than the first frame rate.
[0014] In one embodiment, the preset scaling factor is the minimum scaling factor that ensures the first data stream is not distorted.
[0015] In one embodiment, the edge detection of the first data stream to obtain multiple detection feature points includes:
[0016] Detect the rising edge or falling edge state in the first data stream;
[0017] Multiple detection feature points are determined based on the rising edge state and the falling edge state.
[0018] In one embodiment, the data streams of each detected feature point are integrated to obtain multiple second data streams, including:
[0019] Based on the type of the detected feature points and the time range to which the detected feature points belong, multiple second data streams are integrated to obtain them.
[0020] Secondly, this application also provides a data filtering device, comprising:
[0021] An acquisition module is used to acquire data to be filtered, wherein the data to be filtered is a continuous readable data stream;
[0022] The first filtering module is used to perform proportional filtering on the data to be filtered according to a preset proportional coefficient to obtain a first data stream.
[0023] The edge detection module is used to perform edge detection on the first data stream to obtain multiple detection feature points;
[0024] The second filtering module is used to integrate the data streams of each detected feature point to obtain multiple second data streams;
[0025] The third filtering module is used to perform PI filtering on each second data stream according to the preset PI coefficient to obtain the target floating-point data.
[0026] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the data filtering method described in the first aspect.
[0027] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the data filtering method described in the first aspect.
[0028] Fifthly, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the data filtering method described in the first aspect.
[0029] In summary, this application proposes a data filtering method, apparatus, computer device, storage medium, and program product, comprising: acquiring data to be filtered from a data acquisition device, wherein the data to be filtered is a continuous readable data stream; performing proportional filtering on the data to be filtered according to a preset proportional coefficient to obtain a first data stream; performing edge detection on the first data stream to obtain multiple detection feature points; integrating the data streams of each detection feature point to obtain multiple second data streams; and performing PI filtering on each second data stream according to a preset PI coefficient to obtain target floating-point data. The data filtering method provided by this application is based on three-layer filtering processing, which can process floating-point data that is easy for calculators to perform subsequent analysis and processing without video distortion or loss of keyframes, effectively improving the data processing capability of collaborative robots. Attached Figure Description
[0030] Figure 1 This is a diagram illustrating the application environment of a data filtering method in one embodiment;
[0031] Figure 2 This is a flowchart illustrating a data filtering method in one embodiment;
[0032] Figure 3 This is a flowchart illustrating the steps of acquiring the data to be filtered from the acquisition device in one embodiment;
[0033] Figure 4 This is a flowchart illustrating the steps of performing edge detection on a first data stream to obtain multiple detected feature points in one embodiment.
[0034] Figure 5 This is a structural block diagram of a data filtering device in one embodiment;
[0035] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation
[0036] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0037] The data filtering method provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or located in the cloud or on other network servers. Terminal 102 can be, but is not limited to, robotic devices, computer devices, laptops, smartphones, tablets, IoT devices, or portable wearable devices. Terminal 102 should at least have audio and video acquisition and data processing functions, capable of acquiring streaming data such as video and audio, and performing calculations on the data streams to obtain analytical floating-point data or other processor-recognizable data. In practical applications, terminal 102 acquires video or audio data streams through acquisition devices, processes the data streams into processor-recognizable data, and then further uploads the recognizable data to server 104 for subsequent analysis and processing. Server 104 can be implemented using a standalone server or a server cluster composed of multiple servers.
[0038] In one embodiment, such as Figure 2 As shown, a data filtering method is provided, which is applied to... Figure 1 Taking the application environment in [the document] as an example, the following steps are included:
[0039] S201, acquire the data to be filtered from the acquisition device, wherein the data to be filtered is a continuous readable data stream.
[0040] Specifically, in this embodiment, the acquisition device is a video or audio acquisition device connected to terminal 102, or a device built into terminal 102. For example, the acquisition device can be a camera or a recorder. It should be noted that terminal 102 also includes at least a processor capable of meeting the corresponding computing power requirements, such as an Intel processor.
[0041] In this embodiment, the data to be filtered is readable data. The video or audio data stream acquired by the acquisition device will be processed into complete byte data to obtain data that can be analyzed and processed.
[0042] In a specific embodiment, the data to be filtered should include at least one detectable feature point. Specifically, this detectable feature point is a feature point that can be retrieved and compared based on the rising or falling edge of the data stream. It should be noted that the setting of the detectable feature point needs to be configured according to the actual application scenario. For example, a person clenching their fist in a video can be set as a detectable feature point.
[0043] This embodiment does not specifically limit the detection feature points, but can be any feature points that a robot device needs to identify in the result data when performing high-precision image processing or other recognition tasks.
[0044] S202, Perform proportional filtering on the data to be filtered according to the preset proportional coefficient to obtain the first data stream.
[0045] Specifically, the specifications of the data to be filtered vary depending on the type of equipment. For example, video data captured by a camera may be 120 frames per second. In this case, the total file size of the data to be filtered is relatively large. For robotic devices, processor resources are limited, and reading 120 frames of video data requires a significant amount of processor computing resources. Therefore, proportional filtering is needed on this portion of the data to be filtered to alleviate the resource consumption pressure on the robotic device's processor as much as possible.
[0046] Based on multiple performance tests, this embodiment can obtain preset ratio coefficients corresponding to various types of processors, and store the preset ratio coefficients corresponding to each type of processor in the memory so as to match the application according to the processor type.
[0047] For example, for a Celeron J1900 processor (Intel(R) Celeron(R) CPU J1900 @1.99GHz), the image recognition accuracy is 1mm, and 0.75 can be used as the preset scaling factor.
[0048] In practical applications, the preset scaling factor is the minimum scaling factor that ensures the first data stream is not distorted. When obtaining the preset scaling factor based on performance tests, the total amount of data in the data stream should be minimized while ensuring the filtered data is not distorted, thereby reducing the processor's computational resource consumption.
[0049] It should be noted that different types of processors may have different preset ratio coefficients. The preset ratio coefficients need to be configured according to the actual application scenario. This will not be elaborated here.
[0050] After filtering the data based on a preset scaling factor, a first data stream with a relatively low total data volume can be obtained. It should be noted that the first data stream should at least include target detection feature points. In practical applications, when the robot performs the corresponding analysis task, it will set specified target detection feature points. The determination of the preset scaling factor also needs to consider ensuring that the target detection feature points in the first data stream can be detected during subsequent edge detection.
[0051] S203, perform edge detection on the first data stream to obtain multiple detection feature points.
[0052] Specifically, after obtaining a first data stream with a relatively small total amount of data, edge detection is performed on the first data stream to detect rising or falling edges that meet the characteristics of target detection feature points.
[0053] In practical applications, the rising and falling edges associated with multiple detection feature points can be stored in memory, which facilitates comparison based on the first data stream after it is acquired.
[0054] If the rising and falling edges of the first data stream are found to match the shape characteristics of the target detection feature points, then it can be determined that the first data stream includes detection feature points corresponding to the corresponding detection feature point type.
[0055] In specific implementation, there are at least two feasible implementation methods for the edge detection step of the first data stream. First, perform edge detection on the first data stream and output all detected feature points that match the detection feature point database, i.e., output all feature points that conform to a preset rising edge shape feature or a preset falling edge shape feature. Second, perform edge detection on the first data stream and output detected feature points that match a specified target detection feature point, i.e., output all feature points that conform to the rising edge shape feature or falling edge shape feature corresponding to the specified target detection feature point.
[0056] S204, integrate the data streams of each detected feature point to obtain multiple second data streams.
[0057] Specifically, when the first data stream includes multiple detection feature points, the partial data stream corresponding to each detection feature point can be integrated to obtain the second data stream corresponding to each detection feature point.
[0058] In specific embodiments, different processing methods can be used for subsequent filtering of data streams with different detection feature points.
[0059] Therefore, by integrating the data streams of different detection feature points into multiple second data streams, subsequent PI filtering can be performed based on these second data streams.
[0060] S205, perform PI filtering on each second data stream according to the preset PI coefficient to obtain the target floating-point data.
[0061] In a specific embodiment, the edge detection layer and the PI filtering layer constitute a small closed-loop control. The detected feature points obtained from edge detection can be used as either input signals or feedforward signals. The PI layer integrates the data streams corresponding to the detected feature points to obtain a second data stream. The second data stream is then subjected to PI filtering to eliminate oscillations and steady-state errors.
[0062] Due to the nature of small-scale closed-loop control, the integrated second data stream will introduce a certain steady-state error compared to the true value. This embodiment uses PI filtering on different second data streams based on a preset PI coefficient, which can improve system response speed and eliminate steady-state error without increasing system resource overhead.
[0063] For example, taking the action of opening a hand as the target detection feature point, after edge detection is performed on the first data stream by the edge detection layer, the hand opening detection feature point in the first data stream can be detected. After the PI layer integrates to obtain the second data stream corresponding to the detected feature point, PI filtering is performed on the second data stream to obtain the target floating-point data corresponding to the detected feature point. The target floating-point data can be any data in the range of 0-1. The target floating-point data represents the degree of hand opening; when the target floating-point data is 0, it means that the hand is in a clenched fist state, and when the target floating-point data is 1, it means that the hand is in a fully open state.
[0064] In some embodiments, the specific value of the target floating-point data will vary depending on the type of the detected feature point corresponding to the second data stream. For example, when the detected feature point is an arm raising or lowering action in a continuous image, the target floating-point data is only 0 or 1, where 0 indicates that the arm is raised and 1 indicates that the arm is lowered. When the detected feature point is a jumping action, the target floating-point data can be used to represent the height above the ground.
[0065] In summary, this embodiment proposes a data filtering method. By performing proportional filtering, edge detection, and PI filtering on streaming data, target floating-point data suitable for subsequent analysis by an edge processor can be obtained. The robotic device can identify the execution level of detection feature points based on the target floating-point data and determine whether a specified program needs to be executed based on this execution level. The data filtering method proposed in this embodiment can effectively alleviate the computational resource consumption pressure on the processor and ensure the recognition efficiency of target detection feature points.
[0066] In one embodiment, such as Figure 3 As shown, the data to be filtered obtained from the acquisition device includes:
[0067] S301, collect raw data according to the first frame rate;
[0068] S302, acquire the raw data collected within a preset time period as the data to be filtered.
[0069] In a specific embodiment, the first frame rate can be the set frame rate for the acquisition device to collect raw data. Since the raw data stream acquired by the acquisition device can be directly saved in video or audio mode without other analysis and processing, the raw data acquired by the acquisition device has high accuracy.
[0070] The filtering method proposed in this embodiment is mainly for streaming data. When acquiring the data to be filtered, it is necessary to collect the original data within a preset time range as the data to be filtered.
[0071] In one embodiment, the frame rate of the first data stream is a second frame rate, wherein the second frame rate is less than the first frame rate.
[0072] In practical applications, the second frame rate of the first data stream obtained after proportional filtering by a preset scaling factor is lower than the first frame rate. This ensures that the first data stream obtained after proportional filtering can effectively reduce the total amount of data that the processor needs to process, alleviating the pressure on the processor's computing resources.
[0073] In one embodiment, such as Figure 4 As shown, edge detection is performed on the first data stream to obtain multiple detection feature points, including:
[0074] S401, detect the rising edge state or falling edge state in the first data stream;
[0075] S402 determines multiple detection feature points based on the rising edge state and the falling edge state.
[0076] In a specific embodiment, the rising edge state or falling edge state of the detection feature points that the robot device needs to identify can be stored in the memory in advance. During edge detection, multiple detection feature points included in the first data stream are determined by comparing the rising edge state and falling edge state of the first data stream with the preset rising edge state and falling edge state stored in the memory.
[0077] In one embodiment, the data streams of each detected feature point are integrated to obtain multiple second data streams, including:
[0078] Based on the type of the detected feature points and the time range to which the detected feature points belong, multiple second data streams are integrated to obtain them.
[0079] Specifically, when collecting data to be filtered, a data stream within a certain time period needs to be collected as the data to be filtered. After proportional filtering of the data to be filtered, the total amount of data in the first data stream is less than the total amount of data to be filtered, but the time points corresponding to each part of the data in the first data stream correspond to the time points corresponding to each part of the data in the data to be filtered.
[0080] During edge detection, partial rising edge data and falling edge data of the corresponding detection feature point can be detected. The time range to which the rising edge data or falling edge data belonging to the same detection feature point belongs is determined, and a portion of the data within that time range is integrated to form a second data stream. It should be noted that the first data stream includes multiple second data streams.
[0081] In practical applications, the same PI coefficient is configured for the second data streams that belong to the same type of detection feature points, so that corresponding filtering processing can be performed on different second data streams to improve the accuracy of the target floating-point data obtained in this embodiment.
[0082] In a more detailed embodiment, implementing the data filtering method proposed in this embodiment requires at least one acquisition device and one processor. The processor is configured with a proportional filtering layer, an edge detection layer, and a PI filtering layer. After the acquisition device acquires streaming data, the proportional filtering layer in the processor performs proportional filtering on the streaming data. That is, proportional filtering of the data to be filtered according to a preset proportional coefficient yields a corresponding first data stream. In specific implementations, if the data to be filtered is video data, the frame rate of the first data stream can be controlled to around 60 frames. After obtaining the first data stream, the edge detection layer in the processor processes the first data stream, determining the detection feature points in the first data stream based on the detected rising and falling edge shapes. The PI filtering layer integrates the data corresponding to the detection feature points to obtain second data streams corresponding to different detection feature points, and performs PI filtering on each second data stream according to the corresponding preset PI coefficient to obtain target floating-point data that meets the processing requirements of the processor. In practical applications, the meaning of the target floating-point data can be determined according to the type of detection feature points.
[0083] In summary, this application proposes a data filtering method that performs multi-layer filtering on the data stream acquired by the acquisition device. While ensuring data readability and preventing distortion, it minimizes the computational resource burden on the processor. Furthermore, by performing edge detection on the first data stream, this embodiment ensures that keyframes are not lost during the filtering process. Moreover, the data filtering method proposed in this embodiment differs from traditional co-processing by performing multi-layer filtering at the data's underlying layers rather than the surface layer, thus mitigating the impact of noise on the accuracy of the data results to a certain extent.
[0084] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.
[0085] Based on the same inventive concept, this application also provides a data filtering device for implementing the data filtering method described above. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations in one or more data filtering device embodiments provided below can be found in the limitations of the data filtering method described above, and will not be repeated here.
[0086] In one embodiment, such as Figure 5 As shown, a data filtering device 500 is provided, including: an acquisition module 510, a first filtering module 520, an edge detection module 530, a second filtering module 540, and a third filtering module 550, wherein:
[0087] The acquisition module 510 is used to acquire the data to be filtered, wherein the data to be filtered is a continuous readable data stream.
[0088] The first filtering module 520 is used to perform proportional filtering on the data to be filtered according to a preset proportional coefficient to obtain the first data stream.
[0089] The edge detection module 530 is used to perform edge detection on the first data stream to obtain multiple detection feature points.
[0090] The second filtering module 540 is used to integrate the data streams of each detected feature point to obtain multiple second data streams.
[0091] The third filtering module 550 is used to perform PI filtering on each second data stream according to the preset PI coefficient to obtain the target floating-point data.
[0092] In one embodiment, the acquisition module 510 is specifically used to acquire raw data according to a first frame rate; and acquire the raw data acquired within a preset time period as the data to be filtered.
[0093] In one embodiment, the edge detection module 530 is specifically used to detect rising edge states or falling edge states in the first data stream; and to determine multiple detection feature points based on the rising edge states and falling edge states.
[0094] In summary, this application proposes a data filtering device capable of performing multi-layer filtering on the data stream acquired by the acquisition device. While ensuring data readability and preventing distortion, it minimizes the computational resource burden on the processor. Furthermore, this embodiment performs edge detection on the first data stream, ensuring that keyframes are not lost during the filtering process. Moreover, the data filtering device proposed in this embodiment differs from traditional co-processing by performing multi-layer filtering at the data's underlying layer rather than its surface layer, thus mitigating the impact of noise on the accuracy of the data results to a certain extent.
[0095] Each module in the aforementioned data filtering device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.
[0096] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a data filtering method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.
[0097] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0098] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:
[0099] Acquire the data to be filtered from the acquisition device, wherein the data to be filtered is a continuous readable data stream;
[0100] The data to be filtered is proportionally filtered according to a preset proportional coefficient to obtain the first data stream;
[0101] Edge detection is performed on the first data stream to obtain multiple detection feature points;
[0102] The data streams of each detected feature point are integrated to obtain multiple second data streams;
[0103] The target floating-point data is obtained by performing PI filtering on each second data stream according to the preset PI coefficient.
[0104] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:
[0105] Acquire the data to be filtered from the acquisition device, wherein the data to be filtered is a continuous readable data stream;
[0106] The data to be filtered is proportionally filtered according to a preset proportional coefficient to obtain the first data stream;
[0107] Edge detection is performed on the first data stream to obtain multiple detection feature points;
[0108] The data streams of each detected feature point are integrated to obtain multiple second data streams;
[0109] The target floating-point data is obtained by performing PI filtering on each second data stream according to the preset PI coefficient.
[0110] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:
[0111] Acquire the data to be filtered from the acquisition device, wherein the data to be filtered is a continuous readable data stream;
[0112] The data to be filtered is proportionally filtered according to a preset proportional coefficient to obtain the first data stream;
[0113] Edge detection is performed on the first data stream to obtain multiple detection feature points;
[0114] The data streams of each detected feature point are integrated to obtain multiple second data streams;
[0115] The target floating-point data is obtained by performing PI filtering on each second data stream according to the preset PI coefficient.
[0116] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.
[0117] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0118] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0119] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.
Claims
1. A method of filtering data, characterized by, include: Acquire the data to be filtered from the acquisition device, wherein the data to be filtered is a continuous readable data stream; The data to be filtered is proportionally filtered according to a preset proportional coefficient to obtain a first data stream; the preset proportional coefficient is the minimum proportional coefficient that makes the first data stream undistorted. Edge detection is performed on the first data stream to obtain multiple detection feature points; the edge detection of the first data stream to obtain multiple detection feature points includes: detecting rising edge state or falling edge state in the first data stream; determining multiple detection feature points based on the rising edge state and the falling edge state; The data streams of each detection feature point are integrated to obtain multiple second data streams; the integration of the data streams of each detection feature point to obtain multiple second data streams includes: integrating the data streams according to the type of the detection feature point and the time range to which the detection feature point belongs; The target floating-point data is obtained by performing PI filtering on each second data stream according to the preset PI coefficient.
2. The method according to claim 1, characterized in that, The process of acquiring the data to be filtered from the acquisition device includes: Collect raw data according to the first frame rate; The raw data collected within a preset time period is used as the data to be filtered.
3. The method of claim 2, wherein, The frame rate of the first data stream is the second frame rate, wherein the second frame rate is less than the first frame rate.
4. A data filtering device, characterized by include: An acquisition module is used to acquire data to be filtered, wherein the data to be filtered is a continuous readable data stream; The first filtering module is used to perform proportional filtering on the data to be filtered according to a preset proportional coefficient to obtain a first data stream; the preset proportional coefficient is the minimum proportional coefficient that makes the first data stream undistorted. An edge detection module is used to perform edge detection on the first data stream to obtain multiple detection feature points; the edge detection module is also used to detect rising edge states or falling edge states in the first data stream; and to determine multiple detection feature points based on the rising edge states and the falling edge states. The second filtering module is used to integrate the data streams of each detection feature point to obtain multiple second data streams; the second filtering module is also used to integrate multiple second data streams according to the type of the detection feature point and the time range of the detection feature point. The third filtering module is used to perform PI filtering on each second data stream according to the preset PI coefficient to obtain the target floating-point data.
5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the data filtering method according to any one of claims 1 to 3.
6. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the data filtering method according to any one of claims 1 to 3.
7. A computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the data filtering method according to any one of claims 1 to 3.