Method of processing industrial data, computing device, storage medium and program product

Abstract

The present application relates to a method, a computing device, a storage medium and a program product for processing industrial data. The method comprises obtaining target industrial data completed in a target transmission cycle. The method further comprises determining a filtering strategy corresponding to the data characteristics of the target industrial data based on the data characteristics of the target industrial data, the data characteristics including one or more of frequency distribution information, trend, abnormal state and noise parameter. The method further comprises filtering the target industrial data based on the filtering strategy. In this way, the filtering strategy designed for the data characteristics can more effectively filter the data, reduce unnecessary calculation and resource consumption, and thus improve the overall data processing efficiency.

Description

Technical Field

[0001] This invention relates to the field of data processing, and more specifically, to methods, computing devices, computer storage media, and computer program products for processing industrial data. Background Technology

[0002] In industrial systems, video surveillance systems, and other systems, sensors and other devices collect large amounts of data, which are often complex in type. Transmitting this data enables information sharing between different devices, systems, or users, promoting collaboration and decision-making. Filtering the transmitted data removes invalid, redundant, or erroneous information, thereby improving data quality and accuracy. This is crucial for subsequent data analysis and decision-making.

[0003] Traditional methods for processing industrial data primarily involve filtering the data using conventional filters. However, traditional filters require fixed parameters and cannot automatically adjust to changes in the data, resulting in slow processing speeds and a lack of flexibility.

[0004] In summary, the shortcomings of traditional methods for processing industrial data are that the filtering methods are relatively fixed and cannot adequately meet the filtering needs of all data. Summary of the Invention

[0005] The embodiments of the present invention propose a method, computing device, computer storage medium, and computer program product for processing industrial data, which can set different filtering strategies for different data characteristics corresponding to different industrial data, thereby making the filtering method more flexible and the filtering applicable to a wider range of scenarios.

[0006] In a first aspect of the invention, a method for processing industrial data is provided. The method includes acquiring target industrial data that has been transmitted within a target transmission cycle. The method further includes determining a filtering strategy corresponding to data characteristics of the target industrial data, wherein the data characteristics include at least one or more of frequency distribution information, trends, abnormal states, and noise parameters. Furthermore, the method includes filtering the target industrial data based on the filtering strategy.

[0007] In a second aspect of the invention, an electronic device is provided. The electronic device includes one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the method provided according to the first aspect of the invention.

[0008] In a third aspect of the invention, a computer-readable storage medium is provided. The computer-readable storage medium stores computer-executable instructions, which are executed by a processor to implement the method provided according to a first aspect of the invention.

[0009] According to a fourth aspect of the present invention, a computer program product is provided, comprising machine-executable instructions that, when executed, cause a machine to perform the method provided in the first aspect of the present invention.

[0010] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of the present invention, nor is it intended to restrict the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0011] The above and other features, advantages, and aspects of the various embodiments of the present invention will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0012] Figure 1 A schematic diagram of an example environment in which several embodiments of the present invention may be implemented is shown;

[0013] Figure 2 A flowchart of a method for processing industrial data according to some embodiments of the present invention is shown;

[0014] Figure 3 Schematic diagrams for data filtering according to some embodiments of the present invention;

[0015] Figure 4 A schematic diagram for switching transmission paths according to some embodiments of the present invention is shown;

[0016] Figure 5 A schematic diagram of data synchronization transmission according to some embodiments of the present invention is shown;

[0017] Figure 6 A block diagram of an apparatus for processing data according to some embodiments of the present invention is shown; and

[0018] Figure 7 A block diagram of a device that can implement several embodiments of the present invention is shown. Detailed Implementation

[0019] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the invention. It should be understood that the accompanying drawings and embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the invention.

[0020] In the description of embodiments of the present invention, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0021] As mentioned above, traditional filtering methods are typically designed with fixed methods or parameters. These methods or parameters remain unchanged once the filtering strategy is designed and determined, and cannot be dynamically adjusted according to changes in the input data. However, the characteristics of the collected data are often complex and variable. Therefore, if the same filtering method is used to filter different types of data, it may exhibit significant limitations, such as the inability to effectively remove certain types of noise or interference.

[0022] Therefore, embodiments of the present invention propose a method for processing industrial data. The method includes acquiring target industrial data that has been transmitted within a target transmission cycle. The method further includes determining a filtering strategy corresponding to the data characteristics of the target industrial data. Furthermore, the method includes filtering the target industrial data based on the filtering strategy.

[0023] This approach allows for the provision of optimal filtering strategies for different types of industrial data based on their characteristics, resulting in more accurate and reliable filtering effects, and offering greater flexibility in filtering methods. Furthermore, it reduces unnecessary computation and resource consumption, thereby improving overall data processing efficiency.

[0024] Figure 1 A schematic diagram of an example environment 100 in which various embodiments of the present invention may be implemented is shown. For example... Figure 1 As shown, environment 100 includes a data acquisition system 102, data transmission paths 104-2 and 104-4, a processor 106 connected to the data acquisition system 102 via the data transmission paths, and a filtering system 108 connected to the processor 106. The data acquisition system 102 may contain multiple acquisition devices for acquiring various types of data. Figure 1 (Not shown in the image), for example, it may include acquisition device 102-1 and acquisition device 102-2. The filtering system 108 may include multiple filters of different types, such as filter 108-1, filter 108-2, and filter 108-3. The multiple filters can operate in series or in parallel. Series operation allows for progressive refinement of the data, and each filter can suppress specific noise or interference.

[0025] For example, after filtering the data, filter 108-1 can send the filtered data to filter 108-2, which further filters the data and sends the second-filtered data to filter 108-3. The filtered data is then output after further filtering by filter 108-3. In other embodiments, filter 108-1 can filter data acquired by data acquisition system 102, and filter 108-2 can filter data acquired by another data acquisition system. Alternatively, filters 108-1 and 108-2 can filter data separately to obtain different outputs. In some embodiments, the filter type may include, but is not limited to, low-pass filters, high-pass filters, band-pass filters, simple moving average filters, weighted moving average filters, Kalman filters, and median filters. Different types of filters have different filtering characteristics.

[0026] In some embodiments of the present invention, the data transmission path can be a wired data transmission path or a wireless data transmission path. A wired data transmission path can be a path that transmits data through a physical medium or transmission medium such as a physical cable or optical fiber. The transmission medium can include twisted-pair cables, coaxial cables, serial cables (e.g., RS-422, RS-232, and RS-485), etc. For example, a wireless transmission path can be a path that transmits data using wireless technologies such as radio waves, infrared rays, and microwaves. The wireless transmission methods can include short-range wireless communication methods (e.g., Bluetooth, Wi-Fi, and Near Field Communication (NFC)) and long-range wireless communication methods (e.g., wireless bridges, shortwave communication, and spread spectrum microwaves).

[0027] In practical applications, data transmission paths or channels may be long or the transmission environment may be complex. Different transmission paths correspond to different scenarios or states, and different types of data are suitable for different transmission paths or channels. Based on this, the optimal transmission path can be dynamically selected according to the data characteristics and the state of the data transmission path (e.g., selecting a better transmission path from data transmission path 104-1 and data transmission path 104-2) for transmitting the data. In some embodiments, the transmission path used to transmit the data can be determined based on the path state and data characteristics. The path state of the data transmission path may include transmission delay, transmission error rate, operating status (whether it can work normally), signal strength, etc. The data characteristics may include data type, data size, data priority, etc.

[0028] In some embodiments of the present invention, when the path status of data transmission path 104-1 is abnormal and the path status of data transmission path 104-2 is normal, data transmission path 104-2 can be used as the data transmission channel. Alternatively, when both data transmission paths 104-1 and 104-2 are in normal status and data transmission path 104-1 has lower transmission latency, data transmission path 104-1 can be used as the data transmission channel. For high-priority data with high real-time requirements (such as audio and video data in video calls), paths with lower transmission latency and lower transmission error rates can be selected to ensure smooth data transmission and high-quality presentation; for low-priority data with low real-time requirements (such as non-urgent emails or file transfers), paths with lower transmission costs or less resource consumption can be selected.

[0029] In this way, the optimal transmission path can be dynamically selected, thereby better adapting to different network environments and data transmission needs, and further improving the efficiency and quality of data transmission.

[0030] In some embodiments of the present invention, after receiving the transmitted data, in order to improve data processing efficiency and ensure data accuracy, the data can be filtered to determine the filtered data. Filtering is the operation of removing specific frequency bands from the signal. The processor 106 can determine a filtering strategy suitable for the data based on its characteristics. The filtering strategy indicates the filtering method and filtering parameters. The filtering method can be the filtering steps and the filter used for filtering. Filtering parameters can include the center frequency, cutoff frequency, and filter window size corresponding to the filtering. It is understood that the filter parameters can be dynamically adjusted based on data characteristics.

[0031] like Figure 1As shown, after receiving the filtering strategy sent by the processor 106, the filtering system 106 filters the data according to the filtering method and filtering parameters indicated by the filtering strategy. For example, a low-pass filter can be used to filter the data first, and then a simple moving average filter can be used to filter the filtered data again.

[0032] In this way, filtering methods and parameters can be rationally selected based on the characteristics of the data, thereby optimizing the filtering effect and improving the accuracy and reliability of the data.

[0033] Figure 2 A flowchart of a method 200 for processing data according to some embodiments of the present invention is shown. Method 200 can be... Figure 1 The processor 106 in the system executes the commands. For example... Figure 2 As shown in block 202, method 200 may include acquiring target industrial data that has been transmitted during the target transmission cycle. It can be understood that the data acquisition device (e.g., Figure 1 The acquisition devices 102-1 and 102-2 shown can acquire multiple industrial data points by setting a preset acquisition frame rate. These multiple data points constitute the industrial data acquired in one acquisition cycle. This industrial data can be transmitted through a data transmission path, stored in a memory, or filtered and stored or displayed by the processor 106. The transmission cycle refers to the time interval between the industrial data transmission from the acquisition device to the processor 106 or the memory.

[0034] In box 204, method 200 may include determining a filtering strategy corresponding to the data characteristics of the target industrial data. The data characteristics include at least one or more of the following: frequency distribution information, trends, anomalies, and noise parameters. These data characteristics can be used to characterize the industrial data from a certain dimension. For example, data characteristics may include, but are not limited to, data type, frequency characteristics (e.g., low frequency, high frequency, bandpass), frequency distribution information, data trends and stationarity, whether the data contains outliers, and noise parameters (e.g., noise level, noise type). Different industrial data have different data characteristics, and therefore different applicable filtering methods. Based on this, a reasonable filtering strategy applicable to the industrial data can be determined according to its data characteristics. The filtering strategy may include relevant information used for filtering, such as the filter used, the filtering algorithm, the filtering parameters corresponding to the filter, and the filtering steps (e.g., the filter used in each step, the filtering algorithm used in each step, etc.). In one example, a filtering strategy can be determined based on one data characteristic or by comprehensively considering multiple data characteristics. For example, a first filter can be determined based on the frequency distribution of the industrial data, a second filter based on the outliers of the industrial data, and a third filter based on the trends of the industrial data. The first filter, the second filter, and the third filter can filter the target data sequentially.

[0035] In box 206, method 200 may include filtering the target industrial data based on a filtering strategy. For example, the target industrial data may be filtered using a filter indicated by the filtering strategy according to the filtering method indicated by the filtering strategy, thereby determining the filtered data. It should be noted that the filtering strategy can be flexibly adjusted according to the characteristics and requirements of the industrial data to adapt to different application scenarios. Therefore, the method of filtering the data can also be flexibly adjusted.

[0036] By selecting appropriate filtering methods based on the characteristics of industrial data in this way, noise can be effectively removed and the data smoothed. Because the filtering strategy changes according to variations in data characteristics, it allows for flexible and reasonable filtering of various types of industrial data, ensuring filtering accuracy and providing users with accurate and reliable data support.

[0037] In some embodiments, the data characteristics of the target industrial data may include the frequency distribution of the industrial data. For example, a time-domain signal can be converted into a frequency-domain signal using Fourier transform, and the frequency distribution of the data can be determined based on the frequency-domain signal. Further, the frequency distribution of the data can be determined based on the amplitude and phase of the signal at different frequencies in the frequency-domain signal. Here, the frequency distribution can be the occurrence of different frequencies. In some examples, a spectrogram can be used to represent the frequency distribution of the data. The spectrogram can be used to display the amplitude (or power) of the signal at different frequencies. On the spectrogram, the horizontal axis represents frequency, and the vertical axis represents amplitude (or power). Based on this, the spectrogram can directly reflect the frequency distribution of the signal.

[0038] In some embodiments of the present invention, based on the frequency distribution of the target industrial data, the main frequencies and their relative magnitudes in the target industrial data can be determined. A corresponding filter can be selected based on the characteristics of the frequencies. For example, if the frequencies in the target industrial data below a preset frequency threshold (e.g., 0.1Hz, 0.2Hz, etc., values ​​set by the user) have a high intensity (e.g., strong low-frequency components), a low-pass filter can be selected to filter the target industrial data. The low-pass filter can be a filter that allows low-frequency signals to pass while attenuating or blocking high-frequency signals; for example, it can be used to retain low-frequency signals or filter out low-frequency noise. There are various types of low-pass filters, such as Butterworth filters, Chebyshev filters, and Bessel filters. In some embodiments of the present invention, the cutoff frequency, passband width, stopband attenuation, and other parameters of the filter can be determined based on the low-frequency components, and a suitable low-pass filter can be selected.

[0039] In some embodiments, the presence of significantly increased amplitude components in the high-frequency region can be determined based on the frequency distribution of the target industrial data. If the amplitude of the high-frequency components is significantly higher than that of other frequency components (e.g., the difference between the amplitude of the high-frequency components and the amplitude of other frequencies is greater than a preset difference) and these high-frequency components are not part of the signal itself (e.g., these high-frequency components exceed the normal frequency range of the signal or do not conform to the expected characteristics of the signal), then it can be determined that the high-frequency noise in the industrial data is significant. When it is determined that the high-frequency noise in the target industrial data is significant, a high-pass filter can be selected to filter the target industrial data. A high-pass filter is a filter that allows high-frequency signals to pass through while attenuating or blocking low-frequency signals. In some embodiments of the present invention, parameters such as the filter's cutoff frequency, passband width, and stopband attenuation can be determined based on the high-frequency components, and a suitable low-pass filter can be selected.

[0040] In other embodiments, the presence of a specific frequency peak (e.g., a peak significantly higher than surrounding frequencies) in the spectrum corresponding to the target industrial data can be determined based on the frequency distribution of the target industrial data. If a specific frequency peak exists, a bandpass filter can be selected to filter the target data. A bandpass filter is a filter that allows signals within a specific frequency range to pass through while attenuating or blocking signals of other frequencies. In some embodiments, the relevant parameters of the bandpass filter can be determined based on the frequency peak. For example, the center frequency of the bandpass filter can be determined based on the frequency peak. The bandwidth of the bandpass filter is determined based on the frequency range (the frequency range surrounding the frequency peak).

[0041] By selecting an appropriate filtering method based on the frequency distribution of industrial data in this way, various types of data can be filtered flexibly and reasonably, thereby ensuring the accuracy of filtering and providing users with more accurate data.

[0042] In some embodiments of the present invention, the data characteristics of the target industrial data may further include the trend of change of the target industrial data. The trend of change of the target industrial data can be used to characterize whether the industrial data changes over time and how it changes over time. For example, the trend of change of the target industrial data can be a stationary trend (the statistical characteristics of the data, such as the mean or variance, do not change over time), a non-stationary trend (linear trend, non-linear trend), etc. Among them, a linear trend can refer to the target industrial data showing a stable increasing or decreasing trend over time. In some embodiments of the present invention, the trend of change of the target industrial data can be determined based on the average value or other statistical values ​​of the industrial data. For example, the trend of change of the target industrial data can be determined based on the moving average value of the target industrial data. The moving average value can be the arithmetic mean of a certain number of data selected sequentially in the target industrial data. It can be understood that, depending on the weight of each element used in the calculation process (e.g., the degree of influence of data at different time points on the current value), it can be divided into simple moving average and weighted moving average. Correspondingly, the moving average value can include simple moving average and weighted moving average. After determining the trend of change of the target industrial data, the corresponding target filter and the filtering parameters of the target filter can be determined. For example, when the target industrial data exhibits a linear trend and has low noise, a simple moving average filter can be used as the target filter. The simple moving average filter smooths the data and reduces noise by calculating the average of multiple data points within a certain window. When the target industrial data shows a clear trend and fluctuates significantly, a weighted moving average filter (e.g., assigning different weights to different data points, such as giving more weight to recent data) or an exponential smoothing filter can be used as the target filter. When the target industrial data has no clear trend and fluctuates significantly, a Kalman filter can be used as the target filter.

[0043] In practical applications, due to acquisition errors such as measurement errors, data entry errors, or the inherent variability of the data itself, some erroneous or outlier values ​​may exist in the acquired data. To determine more accurate data, these erroneous or outlier values ​​need to be filtered. Therefore, in some embodiments of the present invention, the data characteristics of the target industrial data may further include whether the target industrial data contains outliers or spikes, and the number of outliers or spikes. For example, statistical algorithms can be used to detect outliers or spikes in the target industrial data. Outliers refer to data points in the target industrial data that are significantly different or deviate from other data points. Spikes refer to data points in the target data that suddenly increase or decrease to extremely high or low levels. In some embodiments of the present invention, the statistical algorithm may include the z-score algorithm, the interquartile range (IQR) algorithm, etc. The z-score algorithm can be used to measure the degree of deviation of a value from the mean of its dataset.

[0044] In some embodiments of the present invention, when it is determined that there are spikes or extreme values ​​in the target industrial data, a median filter can be used as the target filter. The median filter examines the sampled values ​​in the target industrial data and calculates the median as the output using an observation window composed of an odd number of sampled values. In this way, the median filter can effectively remove outliers from the target industrial data while retaining other characteristics of the data, thereby improving the accuracy and reliability of the data. Furthermore, if the number of outliers or spikes is large (e.g., greater than a preset threshold), the median filter can be combined with other smoothing filters (e.g., mean filter, Gaussian filter, etc.) to further smooth the data and reduce noise. Besides the median filter, other smoothing filters, such as [list of other filters], can also be used to further smooth the data and reduce noise.

[0045] In some embodiments of the present invention, the data characteristics of the target industrial data may further include the noise level of the target industrial data. The noise level can be used to assess the intensity and characteristics of noise in the target industrial data. The noise level can be determined based on the variance or noise figure of the target industrial data. Variance is a statistic used to measure the degree of fluctuation in the target industrial data. High variance (e.g., greater than a preset variance threshold) indicates a high noise level in the target industrial data; low variance (e.g., less than a preset variance threshold) indicates a low noise level in the target industrial data. In some embodiments, the noise figure can be determined based on the ratio of signal power to noise power. It is understood that after determining the noise level of the target industrial data, a corresponding target filter can be determined based on this noise level. For example, if the noise level of the target industrial data is low and it mainly consists of high-frequency noise, a low-pass filter can be selected to filter out these high-frequency components; if the noise level of the target industrial data is low and it mainly consists of low-frequency noise, a high-pass filter can be selected to filter out these low-frequency components. When the noise level in the industrial data is high, more complex filtering algorithms (e.g., bandpass filters, notch filters, or combined filters) can be used to filter the target data, thereby more effectively removing noise from the target data.

[0046] In some embodiments, multiple target filters and their corresponding filtering parameters can be determined based on multiple data characteristics. That is, the filtering strategy can include multiple target filters, which can operate in series. For example, multiple target filters can be used sequentially to filter target data to achieve the best filtering effect. For instance, target industrial data can first be filtered through one of the multiple target filters (e.g., a bandpass filter), and then through another of the multiple target filters (e.g., a simple moving average filter). In this way, the advantages of different filters can be fully utilized to meet more complex filtering requirements, improving the signal-to-noise ratio and clarity of the signal.

[0047] Figure 3 A schematic diagram for data filtering according to some embodiments of the present invention is shown. For example... Figure 3As shown, target data 302 can be filtered four times, for example, using four filters (such as filter 304-1, filter 304-2, filter 304-3, and filter 304-4). Filter 304-1 can be a low-pass filter determined based on the frequency distribution of target data 302, with its filtering parameters corresponding to the frequency distribution. Filter 304-2 can be a simple moving average filter determined based on the changing trend of target data 302, with its filtering parameters corresponding to the changing trend. In some embodiments, filter 304-3 can be a median filter determined based on outliers in target data 302, with its filtering parameters corresponding to the outliers. Filter 304-4 can be a Kalman filter determined based on the noise level of target data 302, with its filtering parameters corresponding to the noise level. After four filtering operations, filtered data 306 can be determined. In this way, various types of filters can be used to filter the target data in various aspects and dimensions, thereby obtaining more accurate and reliable data.

[0048] In some embodiments of the present invention, after acquiring target data, the data acquisition system needs to transmit the target data to the target location via a transmission path. Transmission paths generally come in various types (e.g., wireless or wired transmission paths). To select the optimal target transmission path from multiple transmission paths for transmitting the target data, the target transmission path can be determined based on its transmission parameters and path status. The transmission parameters of the transmission path can characterize its transmission capability, and may include, but are not limited to, signal strength (e.g., the quality of wireless communication between currently connected devices and the acquisition device), latency (e.g., transmission path latency), bandwidth (e.g., maximum data transmission rate), transmission error parameters (e.g., transmission error rate), and the number of transmission failures. The path status can characterize whether the transmission path can transmit data normally; for example, the path status can include a normal state, an abnormal state, or a fault state. In some embodiments, the target transmission path can also be determined based on the priority of the transmission paths. For example, the user can set priorities for multiple transmission paths according to actual transmission needs; for instance, the priority of wired transmission paths can be set higher than that of wireless transmission paths.

[0049] In some embodiments of the present invention, if only one transmission path among multiple transmission paths is in a normal state while the others are in an abnormal state, that transmission path can be used as the target transmission path. If multiple transmission paths are in a normal state, the target transmission path can be determined based on the transmission parameters of the normal transmission paths. For example, the transmission path with the highest signal strength among the multiple transmission paths can be used as the target transmission path. In other embodiments, the transmission capabilities of the transmission paths can be comprehensively considered by combining multiple transmission parameters to determine the transmission path with stronger transmission capabilities. For example, the total score of the transmission paths can be determined based on multiple scores and values ​​determined by multiple transmission parameters. The transmission path with the highest score from the total score is selected as the target transmission path.

[0050] In some embodiments of the present invention, to determine a more accurate target transmission path, different weight values ​​can be assigned to multiple transmission parameters according to actual transmission requirements. The weight value can characterize the degree of influence of the transmission parameter on the evaluation of the transmission path's capability. For example, the transmission score of the transmission path can be determined based on the weighted sum of the values ​​corresponding to multiple transmission parameters. In one example, the transmission score of the transmission path can be determined using the following formula: Path_Score = (Signal_Strength × Weight_Signal) + (1 / Latency × Weight_Latency) + (Bandwidth × Weight_Bandwidth) - (Error_Rate × Weight_Error). Here, Path_Score can be the score of the transmission path, Signal_Strength can be the signal strength of the transmission path, Weight_Signal can be the weight value of the signal strength, Latency can be the transmission delay of the transmission path, Weight_Latency can be the weight value corresponding to the transmission delay, Bandwidth can be the bandwidth of the transmission path, and Weight_Bandwidth can be the weight value corresponding to the bandwidth. Error_Rate can be the transmission error rate of the transmission path (e.g., the ratio of the number of transmission errors to the total number of transmissions), and Weight_Error can be the weight value corresponding to this transmission error rate. After determining the transmission score corresponding to each transmission path using the above formula, the largest transmission score can be determined from multiple transmission scores, and the transmission path corresponding to the largest transmission score can be used as the target transmission path.

[0051] In this way, the target transmission path can be determined simply and intuitively using transmission parameters, thereby ensuring the accuracy and reliability of data transmission.

[0052] In some embodiments, the transmission status of a transmission path can be determined based on the signal strength and transmission error parameters of the transmission path. Signal strength refers to the power of the signal received by the receiving end, such as a memory or processor. In some embodiments, the signal strength of the transmission path can be obtained using signal testing equipment or software. Transmission error parameters refer to parameters such as error rate and packet loss rate that occur during data transmission; these parameters reflect the reliability and stability of data transmission along the transmission path. In some embodiments, transmission error parameters can be obtained using network monitoring tools (e.g., network packet capture tools) or protocol analysis tools.

[0053] In some embodiments of the present invention, different transmission scenarios have different requirements for the transmission path. For example, in a high-quality transmission scenario, a high signal strength is required for the transmission path. Based on this, the state of the transmission path can be determined by comparing the signal strength with a preset strength threshold. The preset strength threshold can be set by the user according to the transmission scenario or transmission requirements, and there can be one or more preset signal strength thresholds. When there are multiple preset signal strength thresholds, the state of the transmission path can be divided into multiple states to meet data transmission requirements under various conditions. In some embodiments, if the signal strength of the transmission path is lower than the preset strength threshold, it can be determined that the signal quality of the transmission path is poor, which may not meet the transmission requirements or pose a potential risk. Based on this, the transmission state of the transmission path can be considered an abnormal state. If the signal strength of the transmission path is higher than the preset strength threshold, it can be determined that the signal quality of the transmission path is good and can meet the transmission requirements. Based on this, the transmission state of the transmission path can be considered a normal state.

[0054] In other embodiments, the state of the transmission path can also be determined based on a comparison between the transmission error parameter and a preset error parameter threshold. This comparison result can be used to characterize the reliability and stability of data transmission along the transmission path. For example, if the transmission error parameter is lower than or equal to the preset error parameter threshold, it can be determined that the data transmission reliability and stability of the path are good and can meet business requirements. Based on this, the transmission state of the transmission path can be considered normal.

[0055] In other embodiments of the present invention, to determine a more accurate transmission status, it is necessary to comprehensively consider multiple factors and weigh their impact. For example, even if the signal strength meets the requirements, if the transmission error parameter is too high, it may lead to a decrease in data transmission quality or failure to complete the transmission task. In some embodiments, if both the signal strength and the transmission error parameter meet the requirements, the path status of the transmission path can be determined to be normal. If either the signal strength or the transmission error parameter does not meet the requirements, the path status of the transmission path can be determined to be abnormal. In other embodiments, different weight values ​​can be assigned to the signal strength and the transmission error parameter according to their importance, and the path status of different transmission paths can be determined based on the sum of their weights. Alternatively, different priorities can be set for the signal strength and the transmission error parameter; if the parameter with higher priority meets the requirements, the transmission status of the transmission path can be determined to be normal.

[0056] Since the state of the transmission path can change at any time, to ensure transmission stability, the state of the transmission path can be monitored in real time, and redundancy switching can be triggered when a state change occurs. For example, if the state of the target transmission path used to transmit the target data changes to an abnormal state, the transmission path needs to be switched. For instance, the transmission path with the highest transmission score among the remaining transmission paths can be used to transmit the remaining data.

[0057] Figure 4 A schematic diagram illustrating a method for switching transmission paths according to some embodiments of the present invention is shown. For example... Figure 4 As shown, based on the data characteristics of the data to be transmitted 402, the transmission path used for data transmission can be determined as transmission path A. Transmission path A can transmit the data to be transmitted 402 to the memory 404. If the state of transmission path A changes from normal to abnormal, the transmission path can be switched to transmission path B, which is in a normal state. Transmission path B can continue to transmit the remaining data to the memory 404. After switching transmission paths, the number of transmission failures of transmission path A can be recorded through logs or other methods. In one example, a counter can be used to record the number of transmission failures of the transmission path. The counter increments each time the path is switched (due to a fault).

[0058] It is understandable that if the transmission status of transmission path B also becomes abnormal, data transmission is suspended, and troubleshooting is performed on both transmission paths A and B to enable normal data transmission. In some embodiments, if the transmission status of transmission path A returns to normal, when the primary path is troubleshooted and restored to normal, it can be determined whether the Path_Score of transmission path A exceeds the Path_Score of the currently used backup path, i.e., transmission path B. If so, the system automatically switches back to the primary path, i.e., transmission path A, and data transmission resumes via the primary path.

[0059] In this way, the optimal transmission path can be dynamically selected, and the continuity, stability, and reliability of data transmission can be guaranteed by providing redundant switching.

[0060] In some embodiments of the present invention, to further improve the reliability of data transmission, data can be transmitted using a dual-path synchronous transmission method. For example, data can be transmitted simultaneously via a wired transmission path and a wireless transmission path, ensuring that even if one transmission path fails, the other transmission path can continue to transmit data. Furthermore, ideally, both transmission paths can operate simultaneously, thereby providing a higher total bandwidth than a single transmission path. It should be noted that these two transmission paths can be physically completely independent or logically independent (e.g., using different network protocols or frequency bands).

[0061] Figure 5 A schematic diagram illustrating data synchronization transmission according to some embodiments of the present invention is shown. For example... Figure 5 As shown, the data to be transmitted 502 can be backed up to determine backup data 504. Backup data 504 is exactly the same as data to be transmitted 502. Data to be transmitted 502 can be transmitted through transmission path A (where the transmission score of transmission path A can be the highest among multiple transmission paths), and backup data 504 can be transmitted through transmission path B (where the transmission score of transmission path B can be the second highest among multiple transmission paths).

[0062] It should be noted that in dual-path transmission, since data is sent simultaneously through two paths, the memory 506 may receive redundant data packets. To handle these redundant data packets, the memory 506 can use timestamps to deduplicate the received data. The timestamp can be time information represented digitally, providing accurate time proof for any electronic document or data packet. When the memory 506 receives different data packets simultaneously or within a preset time interval from transmission path A and transmission path B, the timestamps of the same data packets can be compared. Based on the order or consistency of the timestamps, the earliest or latest data packet is selected and retained, while redundant data packets are discarded.

[0063] This dual-path data transmission method improves data transmission reliability and fault tolerance. Furthermore, deduplication ensures data reliability while saving storage resources.

[0064] It should be noted that data transmission and filtering can be recorded using logs. For example, each time a transmission path is selected for data transmission, the content transmitted can be recorded. This includes the selected transmission path, the selection time, the selection criteria (such as transmission score, various thresholds involved, etc.), and other relevant system status information. Alternatively, when switching to another transmission path, information related to the switch can also be recorded, such as the transmission path before and after the switch, the switch time, and the reason for the switch. In other embodiments, when a previously marked "unavailable" transmission path is restored, restoration information can also be recorded. This includes the restored path, restoration time, restoration operation, and the system status after restoration.

[0065] In some embodiments of the present invention, logs can be collected periodically by a log management tool and stored in a secure and accessible location. For example, by analyzing the logs, users (such as administrators) can identify which paths are more reliable and which are more prone to failure. Based on this information, users can adjust the weight values ​​and thresholds in the path selection algorithm to optimize its performance. For instance, if the number of transmission failures on a certain transmission path frequently exceeds a threshold, users can reduce the weight value of that transmission path or increase its threshold to reduce reliance on that transmission path.

[0066] In this way, logs can be used to make the data transmission and filtering process visible, making it easier for users to identify problems in the data transmission and filtering process more clearly.

[0067] Figure 6 A block diagram of an apparatus 600 for processing data according to some embodiments of the present invention is shown. Figure 6 As shown, the device 600 includes a target data acquisition unit 802, configured to acquire target industrial data that has been transmitted during a target transmission cycle. The device 600 also includes a filtering strategy determination unit 604, configured to determine a filtering strategy corresponding to the data characteristics of the target industrial data, wherein the data characteristics include at least one or more of frequency distribution information, trends, abnormal states, and noise parameters. The device 600 further includes a filtering unit 606, configured to filter the target data based on the filtering strategy.

[0068] It is understood that by using the apparatus 60 of the present invention, at least one of the many advantages that can be achieved by the methods or processes described above can be realized.

[0069] In some embodiments, the filtering strategy determination unit 604 is further configured to: determine a target filter corresponding to the frequency distribution information based on the mapping relationship between frequency distribution information and filter type; determine the filtering parameters of the target filter based on the frequency distribution information; and determine the corresponding filtering strategy based on the target filter and the filtering parameters.

[0070] In some embodiments, the filtering strategy determination unit 604 is further configured to: acquire the average value of the target data within a preset time period; determine the trend of change corresponding to the target data based on the average value; determine the target filter and the filtering parameters of the target filter based on the trend; and determine the corresponding filtering strategy based on the target filter and the filtering parameters.

[0071] In some embodiments, the filtering strategy determination unit 604 is further configured to: determine whether a specific value exists in the target data; determine the number of specific values ​​in response to the existence of specific values ​​in the target data; determine the corresponding target filter and the filtering parameters of the target filter based on the specific values ​​and the number of specific values; and determine the corresponding filtering strategy based on the target filter and the filtering parameters.

[0072] In some embodiments, the filtering strategy determination unit 604 is further configured to: in response to the number of specific values ​​being greater than a preset number threshold, use the median filter as the target filter.

[0073] In some embodiments, the filtering strategy determination unit 604 is further configured to: determine the corresponding target filter and the filtering parameters of the target filter based on the comparison result of the noise parameters and the preset noise threshold; and determine the corresponding filtering strategy based on the target filter and the filtering parameters.

[0074] In some embodiments, the apparatus 600 further includes a transmission unit configured to: determine a target transmission path based on transmission parameters of a plurality of candidate transmission paths and corresponding path states; and transmit target data within a target transmission period based on the target transmission path.

[0075] In some embodiments, the transmission unit is further configured to: acquire the signal strength and / or transmission error parameters of the candidate transmission path; and determine the path status of the candidate transmission path based on the comparison result of the signal strength with a preset strength threshold and / or the comparison result of the transmission error parameters with a preset error parameter threshold.

[0076] In some embodiments, the multiple candidate transmission paths include a first transmission path and a second transmission path, and the transmission unit is further configured to: obtain a first transmission parameter corresponding to the first transmission path and a second transmission parameter corresponding to the second transmission path; determine a first transmission score of the first transmission path based on the first transmission parameter; determine a second transmission score of the second transmission path based on the second transmission parameter; and determine a target transmission path based on the first transmission score and the second transmission score.

[0077] In some embodiments, the target transmission path is a first transmission path, and the transmission unit is further configured to: switch the target transmission path to a second transmission path and record the switching information in response to the path status of the first transmission path being an abnormal state.

[0078] In some embodiments, the transmission unit is further configured to: switch the target transmission path to the second transmission path and record the switching information in response to the path state of the first transmission path returning to normal and the first transmission score of the first transmission path being greater than the second transmission score.

[0079] In some embodiments, the transmission parameters include at least one of the following parameters: signal strength, delay, bandwidth, transmission error parameters, priority, and number of transmission failures.

[0080] Figure 7 A schematic block diagram of an example device 700 that can be used to implement embodiments of the present invention is shown. As shown, device 700 includes a computing unit (i.e., CPU 701) that can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (i.e., ROM 702) or loaded from storage unit 708 into random access memory (i.e., RAM 703). Various programs and data required for the operation of device 700 may also be stored in RAM 703. CPU 701, ROM 702, and RAM 703 are interconnected via bus 704. Input / output interfaces (i.e., I / O interfaces 705) are also connected to bus 704.

[0081] Multiple components in device 700 are connected to I / O interface 705, including: input unit 706, such as keyboard, mouse, etc.; output unit 707, such as various types of monitors, speakers, etc.; storage unit 708, such as disk, optical disk, etc.; and communication unit 709, such as network card, modem, wireless transceiver, etc. Communication unit 709 allows device 700 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0082] CPU 701 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of CPU 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as method 200. For example, in some embodiments, method 200 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by CPU 701, one or more steps of method 200 described above may be performed. Alternatively, in other embodiments, CPU 701 may be configured to perform method 200 by any other suitable means (e.g., by means of firmware).

[0083] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload programmable logic devices (CPLDs), and so on.

[0084] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0085] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. Furthermore, although the operations are depicted in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the invention. Certain features described in the context of individual embodiments may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented individually or in any suitable sub-combination in multiple implementations.

[0086] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for processing industrial data, characterized in that, include: Acquire the target industrial data that has been transmitted during the target transmission cycle; Based on the data characteristics of the target industrial data, a filtering strategy corresponding to the data characteristics is determined. The filtering strategy includes the filter used in each filtering step and the filtering algorithm used in each filtering step. The data characteristics include at least one or more of frequency distribution information, trends, abnormal states, and noise parameters. The filter includes a first filter, a second filter, and a third filter. The first filter is determined based on the frequency distribution of the target industrial data, the second filter is determined based on the outliers of the target industrial data, and the third filter is determined based on the trends of the target industrial data. as well as Based on the filtering strategy, the target industrial data is filtered; The method for determining the filtering strategy corresponding to the data features includes: determining multiple target filters and filtering parameters corresponding to the multiple target filters based on multiple data features; the multiple target filters are connected in series to filter the target industrial data in sequence.

2. The method according to claim 1, characterized in that, Determining the filtering strategy corresponding to the data features includes: Based on the mapping relationship between frequency distribution information and filter type, the target filter corresponding to the frequency distribution information is determined. Based on the frequency distribution information, the filtering parameters of the target filter are determined; and Based on the target filter and the filtering parameters, the corresponding filtering strategy is determined.

3. The method according to claim 1, characterized in that, Determining the filtering strategy corresponding to the data features includes: Obtain the average value of the target industrial data over a preset time period; Based on the average value, determine the trend of change corresponding to the target industrial data; Based on the trend, a target filter corresponding to the trend and the filtering parameters of the target filter are determined; and Based on the target filter and the filtering parameters, the corresponding filtering strategy is determined.

4. The method according to claim 1, characterized in that, Determining the filtering strategy corresponding to the data features includes: Determine whether a specific value exists in the target industrial data; In response to the presence of a specific value in the target industrial data, the quantity of the specific value is determined; Based on the specific value and the quantity of the specific value, determine the corresponding target filter and the filtering parameters of the target filter; and Based on the target filter and the filtering parameters, the corresponding filtering strategy is determined.

5. The method according to claim 4, characterized in that, Determining the corresponding target filter includes: In response to the fact that the number of the specific values ​​is greater than a preset threshold, the median filter is used as the target filter.

6. The method according to claim 1, characterized in that, Determining the filtering strategy corresponding to the data features includes: Based on the comparison results between the noise parameters and the preset noise threshold, the corresponding target filter and the filtering parameters of the target filter are determined; and Based on the target filter and the filtering parameters, the corresponding filtering strategy is determined.

7. The method according to claim 1, characterized in that, The method further includes: Based on the transmission parameters and corresponding path states of multiple candidate transmission paths, the target transmission path is determined; and Based on the target transmission path, the target industrial data is transmitted within the target transmission cycle.

8. The method according to claim 7, characterized in that, The method further includes: Obtain the signal strength and / or transmission error parameters of the candidate transmission paths; and Based on the comparison result of the signal strength with a preset strength threshold and / or the comparison result of the transmission error parameter with a preset error parameter threshold, the path status of the candidate transmission path is determined.

9. The method according to claim 7, characterized in that, The plurality of candidate transmission paths includes a first transmission path and a second transmission path, and determining the target transmission path includes: Obtain the first transmission parameters corresponding to the first transmission path and the second transmission parameters corresponding to the second transmission path; Based on the first transmission parameters, determine the first transmission score of the first transmission path; Based on the second transmission parameters, determine the second transmission score of the second transmission path; The target transmission path is determined based on the first transmission score and the second transmission score.

10. The method according to claim 9, characterized in that, The target transmission path is the first transmission path, and the method further includes: In response to the abnormal state of the first transmission path, the target transmission path is switched to the second transmission path and the switching information is recorded.

11. The method according to claim 10, characterized in that, The method further includes: In response to the path status of the first transmission path returning to normal and the first transmission score of the first transmission path being greater than the second transmission score, the target transmission path is switched to the second transmission path and the switching information is recorded.

12. The method according to claim 7, characterized in that, The transmission parameters include at least one of the following parameters: signal strength, delay, bandwidth, transmission error parameters, priority, and number of transmission failures.

13. A computing device, characterized in that, include: At least one processor; as well as A memory coupled to the at least one processor and having instructions stored thereon, which, when executed by the at least one processor, cause the device to perform the method according to any one of claims 1-12.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a machine, performs the method according to any one of claims 1-12.

15. A computer program product, characterized in that, Includes machine-executable instructions that, when executed, cause the machine to perform the method according to any one of claims 1-12.

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