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

Abstract

The invention relates to a method, a computing device, a storage medium and a program product for processing industrial data. The method comprises the following steps: acquiring target industrial data which is transmitted in a target transmission period; the method further includes determining a filtering strategy corresponding to a data feature based on the data feature of the target industrial data, the data feature at least including one or more of frequency distribution information, a trend, an abnormal state and a noise parameter. The method further comprises the step of filtering the target industrial data based on the filtering strategy. In this way, the filtering strategy designed for the data features can filter the data more effectively, unnecessary calculation and resource consumption are reduced, and therefore the overall data processing efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and more particularly to a method, a computing device, a computer storage medium and a computer program product for processing industrial data. Background Art

[0002] In industrial systems, video surveillance systems or other systems, sensors or other devices will collect a large amount of data, which is large in volume and complex in type. Transmitting this data enables different devices, systems or users to share information, promote collaboration and decision-making. Filtering the transmitted data can remove invalid, redundant or erroneous data, thereby improving the quality and accuracy of the data. This is crucial for subsequent data analysis and decision-making.

[0003] Traditional methods for processing industrial data mainly include filtering data using traditional filters. Traditional filters require fixed parameters when processing data, cannot automatically adjust according to data changes, and have slow processing speeds and lack flexibility.

[0004] In summary, the shortcomings of the traditional methods for processing industrial data are that the filtering method is relatively fixed and cannot meet the filtering requirements of all data. Summary of the invention

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

[0006] In a first aspect of the present invention, a method for processing industrial data is provided. The method includes acquiring target industrial data that has completed transmission in a target transmission cycle. The method also includes determining a filtering strategy corresponding to the data feature based on data features of the target industrial data, wherein the data feature includes at least one or more of frequency distribution information, trend, abnormal state, and noise parameter. In addition, the method also includes filtering the target industrial data based on the filtering strategy.

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

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

[0009] According to a fourth aspect of the present invention, there is provided a computer program product, comprising machine executable instructions, which, when executed, enable a machine to perform the method provided by the first aspect of the present invention.

[0010] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other features, advantages and aspects of the embodiments of the present invention will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein: Figure 1 A schematic diagram illustrating an example environment in which various embodiments of the present invention may be implemented; Figure 2 A flowchart of a method for processing industrial data according to some embodiments of the present invention is shown; Figure 3 Schematic diagram for filtering data according to some embodiments of the present invention; Figure 4 A schematic diagram for switching a transmission path according to some embodiments of the present invention is shown; Figure 5 A schematic diagram showing synchronous data transmission according to some embodiments of the present invention is shown; Figure 6 A block diagram showing an apparatus for processing data according to some embodiments of the present invention; and Figure 7 A block diagram is shown of a device in which various embodiments of the present invention may be implemented. DETAILED DESCRIPTION

[0012] Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

[0013] In the description of the embodiments of the present invention, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part 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.

[0014] As mentioned above, traditional filtering methods usually have fixed methods or parameters when they are designed. These methods or parameters remain unchanged after the filtering strategy is designed and determined, and cannot be dynamically adjusted according to changes in input data. The data features corresponding to the collected data are often complex and changeable. Therefore, if the same filtering method is used to filter different types of data, it may show obvious limitations, such as the inability to effectively remove certain types of noise or interference.

[0015] To this end, an embodiment of the present invention provides a method for processing industrial data. The method includes acquiring target industrial data that has completed transmission in a target transmission cycle. The method also includes determining a filtering strategy corresponding to the data feature based on the data feature of the target industrial data. In addition, the method also includes filtering the target industrial data based on the filtering strategy.

[0016] In this way, the optimal filtering strategy can be provided for different industrial data according to the characteristics of industrial data, so as to obtain a more accurate and reliable filtering effect, and the filtering method is more flexible. In addition, unnecessary calculations and resource consumption can be reduced, thereby improving the overall data processing efficiency.

[0017] Figure 1 1 is a schematic diagram of an example environment 100 in which various embodiments of the present invention may be implemented. Figure 1 As shown, in the environment 100, a data acquisition system 102, a data transmission path 104-2, a data transmission path 104-4, a processor 106 connected to the data acquisition system 102 through the data transmission path, and a filter system 108 connected to the processor 106. The data acquisition system 102 may include multiple acquisition devices for acquiring multiple types of data ( Figure 1 1 and 102-2. The filter system 108 may include a plurality of filters, and the types of the filters may be different, for example, filter 108-1, filter 108-2, and filter 108-3. The plurality of filters may work in series or in parallel. The series operation may allow the data to be processed in a step-by-step manner, and each filter may suppress specific noise or interference.

[0018] For example, after filter 108-1 filters the data, the filtered data can be sent to filter 108-2, filter 108-2 further filters the filtered data, and sends the secondary filtered data to filter 108-3, which is filtered and output by filter 108-3. In other embodiments, filter 108-1 can filter the data collected by data acquisition system 102, and filter 108-2 can filter the data collected by another data acquisition system. Or filter 108-1 and filter 108-2 can filter the data separately to obtain different outputs. In some embodiments, the type of filter can include but is not limited to low-pass filter, high-pass filter, band-pass filter, simple moving average filter, weighted moving average filter, Kalman filter and median filter. Different types of filters have different filtering characteristics.

[0019] In some embodiments of the present invention, the data transmission path may be a wired data transmission path or a wireless data transmission path. Among them, the wired data transmission path may be a path for data transmission through a tangible medium or transmission medium such as a physical cable or optical fiber. Among them, the transmission medium may include twisted pair, coaxial cable, serial cable (such as RS-422, RS-232 and RS-485), etc. For example, a wireless transmission path may be a path for data transmission using wireless technologies such as radio waves, infrared rays, and microwaves. Among them, the wireless transmission method may include short-range wireless communication methods (such as Bluetooth, wireless broadband (Wi-Fi) and near field communication (Near Field Communication, NFC) and other methods) and long-range wireless communication methods (such as wireless bridges, short-wave communications and spread spectrum microwaves, etc.).

[0020] In actual applications, the path or channel of data transmission 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 transmission channels. On this basis, the optimal transmission path can be dynamically selected according to the data characteristics of the data and the state of the data transmission path (for example, a better transmission path is selected from the data transmission path 104-1 and the data transmission path 104-2) for transmitting the data. In some embodiments, the transmission path for transmitting the data can be determined according to the path state of the data transmission path and the data characteristics. Among them, the path state of the data transmission path may include transmission delay, transmission error rate, working state (whether it can work normally), signal strength, etc. The data characteristics of the data may include the type of data, the size of the data, the priority of the data, etc.

[0021] In some embodiments of the present invention, when the path state of the data transmission path 104-1 is abnormal and the path state of the data transmission path 104-2 is normal, the data transmission path 104-2 can be used as a channel for transmitting data. Alternatively, when the path states of the data transmission path 104-1 and the data transmission path 104-2 are both normal and the transmission delay of the data transmission path 104-1 is lower, the data transmission path 104-1 can be used as a channel for transmitting data. For data with high priority and high real-time requirements (such as audio and video data in video calls), a path with low transmission delay and low transmission error rate can be selected to ensure smooth data transmission and high-quality presentation; for data with low priority and low real-time requirements (such as non-urgent emails or file transfers), a path with lower transmission cost or less resource occupation can be selected.

[0022] In this way, the optimal transmission path can be dynamically selected, thereby better adapting to different network environments and data transmission requirements, and further improving the efficiency and quality of data transmission.

[0023] In some embodiments of the present invention, after receiving the data that has been transmitted, in order to improve the efficiency of data processing and ensure the accuracy of the data, the data can be filtered to determine the filtered data. Among them, filtering is an operation to filter out a specific band frequency in a signal. The processor 106 can determine a filtering strategy suitable for the data based on the characteristics of the data. Among them, the filtering strategy is used to indicate the filtering method and filtering parameters of the data. Among them, the filtering method can be the steps of filtering and the filter used for filtering. The filtering parameters may include the center frequency, cutoff frequency, filtering window size, etc. corresponding to the filtering. It can be understood that the filtering parameters of the filter can be dynamically adjusted based on the data characteristics.

[0024] like Figure 1 As shown, after receiving the filtering strategy sent by the processor 106, the filtering system 106 filters the data according to the filtering mode and filtering parameters indicated by the filtering strategy. For example, the data may be first filtered using a low-pass filter, and then the filtered data may be filtered again using a simple moving average filter.

[0025] In this way, the filtering method and parameters can be reasonably selected based on the characteristics of the data, thereby optimizing the filtering effect and improving the accuracy and credibility of the data.

[0026] Figure 2 1 shows a flow chart of a method 200 for processing data according to some embodiments of the present invention. The method 200 may be Figure 1 The processor 106 in the embodiment executes. Figure 2As shown, at block 202, method 200 may include acquiring target industrial data transmitted in a target transmission cycle. It is understood that a data acquisition device (e.g. Figure 1 The acquisition device 102-1, acquisition device 102-2, etc. shown in the figure can acquire multiple industrial data points at a preset acquisition frame rate, and the multiple industrial data points can constitute the industrial data acquired in one acquisition cycle. The industrial data can be transmitted through a data transmission path, stored by a memory, or stored or displayed after filtering by the processor 106. The transmission cycle can refer to the time interval for the industrial data to be transmitted from the acquisition device to the processor 106 or the memory.

[0027] In box 204, method 200 may include determining a filtering strategy corresponding to the data feature based on the data feature of the target industrial data, and the data feature includes at least one or more of frequency distribution information, trend, abnormal state and noise parameter. Among them, the data feature can be used to characterize the characteristics of industrial data from a certain dimension. For example, the data feature may include but is not limited to data type, frequency characteristics (such as low frequency, high frequency, bandpass), frequency distribution information, data trend and stability, whether the data contains abnormal values, data noise parameters (such as noise level, noise type, etc.). Different industrial data correspond to different data features, and the applicable filtering methods are also different. On this basis, a reasonable filtering strategy applicable to the industrial data can be determined according to the data features of the industrial data. Among them, the filtering strategy may include relevant information for filtering, such as the filter used for filtering, the filtering algorithm, the filtering parameters corresponding to the filter, and the steps of filtering (such as the filter used for each step of filtering, the filtering algorithm used for each step, etc.). In an example, the filtering strategy can be determined according to one data feature, or multiple data features can be comprehensively considered, and the filtering strategy can be determined according to multiple data features. For example, the first filter can be determined according to the frequency distribution of the industrial data, the second filter can be determined according to the abnormal value of the industrial data, and the third filter can be determined according to the trend of the industrial data. The first filter, the second filter, and the third filter may filter the target data in sequence.

[0028] In block 206, method 200 may include filtering the target industrial data based on the filtering strategy. For example, the target industrial data may be filtered using the filter indicated by the filtering strategy in the filtering manner indicated by the filtering strategy, thereby determining the filtered data. It should be noted that the filtering strategy may be flexibly adjusted according to the characteristics and requirements of the industrial data to adapt to different application scenarios. Therefore, the manner in which the data is filtered may also be flexibly adjusted.

[0029] In this way, by selecting the appropriate filtering method according to the characteristics of industrial data, the noise in the industrial data can be effectively removed and the data can be smoothed. Since the filtering strategy changes according to the changes in data characteristics, it can flexibly and reasonably filter various industrial data, thereby ensuring the accuracy of filtering and providing users with accurate and reliable data support.

[0030] In some embodiments, the data features of the target industrial data may include the frequency distribution of the industrial data. For example, the time domain signal can be converted into a frequency domain signal by Fourier transformation, 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 according to the amplitude and phase of the signal in the frequency domain signal at different frequencies. Among them, the frequency distribution can be the situation where different frequencies appear. In some examples, a spectrogram can be used to represent the frequency distribution of the data. Among them, the spectrogram can be used to display the amplitude (or power) of the signal at different frequencies. On the spectrogram, the horizontal axis represents the frequency and the vertical axis represents the amplitude (or power). On this basis, the spectrogram can directly reflect the frequency distribution of the signal.

[0031] In some embodiments of the present invention, based on the frequency distribution of the target industrial data, it is possible to determine which frequencies are mainly contained in the target industrial data and the relative size of the frequencies. A corresponding filter can be selected according to the characteristics of the frequency. For example, if the intensity corresponding to the frequency below the preset frequency threshold (for example, 0.1Hz, 0.2Hz, etc., which is set by the user) in the target industrial data is large (for example, there is a strong low-frequency component), a low-pass filter can be selected to filter the target industrial data. Among them, the low-pass filter can be a filter that allows low-frequency signals to pass through and attenuates or blocks high-frequency signals, for example, it can be used to retain low-frequency signals and filter out low-frequency noise. There can be many types of low-pass filters, for example, 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 according to the low-frequency component, and a suitable low-pass filter can be selected.

[0032] In some embodiments, it is also possible to determine whether there is a part with a significantly increased amplitude in the high-frequency region based on the frequency distribution of the target industrial data. If the amplitude value of the high-frequency component is significantly higher than other frequency components (for example, the difference between the amplitude value of the high-frequency component and the amplitude value of other frequencies is greater than the preset difference) and these high-frequency components are not part of the signal itself (for example, these high-frequency components exceed the normal frequency range of the signal or do not conform to the expected characteristics of the signal), it can be determined that the high-frequency noise of the industrial data is obvious. In the case where it is determined that the high-frequency noise of the target industrial data is obvious, a high-pass filter can be selected to filter the target industrial data. Among them, a high-pass filter is a filter that allows high-frequency signals to pass through and attenuates or blocks low-frequency signals. In some embodiments of the present invention, the filter's cutoff frequency, passband width, stopband attenuation and other parameters can be determined based on the high-frequency component, and a suitable low-pass filter can be selected.

[0033] In other embodiments, it is also possible to determine whether there is a specific frequency peak (e.g., a peak that is significantly higher than the surrounding frequencies) in the spectrum corresponding to the target industrial data based on the frequency distribution of the target industrial data. If there is a specific frequency peak, 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 while attenuating or blocking other frequency signals. 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 around the frequency peak).

[0034] In this way, by selecting the appropriate filtering method according to the frequency distribution of industrial data, various data can be filtered flexibly and reasonably, thereby ensuring the accuracy of filtering and providing users with more accurate data.

[0035] In some embodiments of the present invention, the data features of the target industrial data may further include a change trend of the target industrial data. The change trend of the target industrial data may be used to characterize whether the industrial data changes over time and the situation of the change over time. For example, the change trend of the target industrial data may be a steady trend (the statistical characteristics of the data such as the mean or variance do not change over time), a non-steady trend (linear change trend, nonlinear change trend), etc. Among them, the linear change trend may refer to a stable increase or decrease trend of the target industrial data over time. In some embodiments of the present invention, the change trend of the target industrial data may be determined according to the average value or other statistical values ​​of the industrial data. For example, the change trend of the target industrial data may be determined according to the moving average value of the target industrial data. The moving average value may be the arithmetic mean of a certain number of data selected in sequence from the target industrial data. It can be understood that, according to the different weights of the elements used in the calculation process (for example, the degree of influence of the data at different time points on the current value), it can be divided into a simple moving average and a weighted moving average. Correspondingly, the moving average value may include a simple moving average value and a weighted moving average value. After determining the change trend of the target industrial data, the corresponding target filter and the filtering parameters of the target filter may be determined. For example, when the target industrial data has a linear trend and low noise, a simple moving average filter can be used as the target filter. The simple moving average filter can smooth the data and reduce noise by calculating the average value of multiple data points in the target industrial data within a certain window. When the target industrial data has a significant trend and large fluctuations, a weighted moving average filter (for example, different weights can be assigned to different data points, such as recent data can be assigned a greater weight) or an exponential smoothing filter can be used as the target filter. When the target industrial data has no obvious trend and large fluctuations, a Kalman filter can be used as the target filter.

[0036] In practical applications, due to acquisition errors such as measurement errors, data entry errors, or inherent variability of the data itself, some erroneous values ​​or outliers may be present in the collected data. In order to determine more accurate data, these erroneous values ​​or outliers need to be filtered. Based on this, in some embodiments of the present invention, the data features of the target industrial data may also include whether the target industrial data contains outliers or spike values ​​and the number of outliers or spike values. For example, a statistical algorithm can be used to detect outliers or spike values ​​in the target industrial data. Among them, an outlier refers to a data point that is significantly different or deviated from other data points in the target industrial data. A spike value refers to a data point that suddenly increases or decreases to an extremely high or extremely low level in the target data. In some embodiments of the present invention, the statistical algorithm may include a standard score (z-score) algorithm, an interquartile range (IQR) algorithm, and the like. Among them, the z-score algorithm can be used to measure the degree of deviation of a value from the average value of the data set to which it belongs.

[0037] In some embodiments of the present invention, when it is determined that there are spike values ​​or extreme values ​​in the target industrial data, a median filter can be used as a target filter. The median filter can check the sampled values ​​in the target industrial data and use an observation window composed of an odd number of sampled values ​​to calculate the median as the output. In this way, the median filter can be used to effectively remove the outliers in the target industrial data while retaining other features of the data, thereby improving the accuracy and reliability of the data. In addition, if the number of outliers or spike values ​​is large (for example, greater than a preset number threshold), the median filter can be combined with other smoothing filters (such as a mean filter, a Gaussian filter, etc.) to further smooth the data and reduce the noise in the data. In addition to the median filter, there are other smoothing filters such as, etc., which can be used to further smooth the data and reduce the noise.

[0038] In some embodiments of the present invention, the data features of the target industrial data may further include the noise level of the target industrial data. The noise level may be used to evaluate the intensity and characteristics of the noise in the target industrial data. The noise level may be determined based on the variance or noise coefficient of the target industrial data. The variance is a statistic used to measure the degree of fluctuation of the target industrial data. A high variance (e.g., greater than a preset variance threshold) means that there is a high noise level in the target industrial data; a low variance (e.g., less than a preset variance threshold) means that there is a low noise level in the target industrial data. In some embodiments, the noise coefficient may 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 may be determined based on the noise level. For example, if the noise level of the target industrial data is low and mainly high-frequency noise, a low-pass filter may be selected to filter out these high-frequency components; if the noise level of the target industrial data is low and mainly low-frequency noise, a high-pass filter may be selected to filter out these low-frequency components. When the noise level in the industrial data is high, a more complex filtering algorithm (e.g., a bandpass filter, a notch filter, or a combination filter) may be used to filter the target data, thereby more effectively removing the noise in the target data.

[0039] In some embodiments, multiple target filters and filtering parameters corresponding to the multiple target filters can be determined based on multiple data features. In other words, the filtering strategy can include multiple target filters, and the multiple target filters can work in series. For example, multiple target filters can be used to filter the target data in sequence to achieve the best filtering effect. For example, the target industrial data can be first filtered through one filter (such as a bandpass filter) among the multiple target filters, and then filtered through another filter (such as a simple moving average filter) among the multiple target filters. In this way, the advantages of different filters can be fully utilized to achieve more complex filtering requirements and improve the signal-to-noise ratio and clarity of the signal.

[0040] Figure 3 FIG. 4 is a schematic diagram showing a method for filtering data according to some embodiments of the present invention. Figure 3As shown, the target data 302 can be filtered four times, for example, four filters (such as filter 304-1, filter 304-2, filter 304-3 and filter 304-4) can be used to filter the target data 302. Among them, filter 304-1 can be a low-pass filter determined according to the frequency distribution of the target data 302, and the filtering parameters of the low-pass filter correspond to the frequency distribution of the target data 302. Filter 304-2 can be a simple moving average filter determined according to the change trend of the target data 302, and the filtering parameters of the simple moving average filter correspond to the change trend. In some embodiments, filter 304-3 can be a median filter determined according to the abnormal value of the target data 302, and the filtering parameters of the simple moving average filter correspond to the abnormal value. Filter 304-4 can be a Kalman filter determined according to the noise level of the target data 302, and the filtering parameters of the Kalman filter correspond to the noise level. After filtering four times, the 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.

[0041] In some embodiments of the present invention, after the target data is acquired, the data acquisition system needs to transmit the target data to the target location through a transmission path. There are generally multiple types of transmission paths (such as wireless transmission paths or wired transmission paths). In order to select the optimal target transmission path from multiple transmission paths to transmit the target data, the target transmission path can be determined based on the transmission parameters and path status of the transmission path. Among them, the transmission parameters of the transmission path can be used to characterize the transmission capacity of the transmission path, for example, including but not limited to signal strength (such as the quality of wireless communication between the currently connected device and the acquisition device), delay (such as transmission delay of the transmission path), bandwidth (such as maximum data transmission rate), transmission error parameters (such as transmission error rate), number of transmission failures, etc. The path status can be used to characterize whether the transmission path can transmit data normally, for example, the path status can include normal state, abnormal state or fault state. In some embodiments, the target transmission path can also be determined according to the priority of the transmission path. For example, the user can set priorities for multiple transmission paths according to actual transmission requirements, for example, the priority of the wired transmission path can be set to be greater than the priority of the wireless transmission path.

[0042] In some embodiments of the present invention, if the path state of only one transmission path among multiple transmission paths is in a normal state, and the states of other transmission paths are in abnormal states, the transmission path can be used as the target transmission path. If the path states of multiple transmission paths among multiple transmission paths are all in a normal state, the target transmission path can be determined based on the transmission parameters of the transmission paths in the normal state. For example, the transmission path with the highest signal strength among multiple transmission paths can be used as the target transmission path. In other embodiments, the transmission capacity of the transmission path can also be comprehensively considered in combination with multiple transmission parameters, so as to determine the transmission path with stronger transmission capacity. For example, the total score of the transmission path can be determined based on multiple scores and values ​​determined by multiple transmission parameters. The transmission path with the highest score is selected from the total score as the target transmission path.

[0043] In some embodiments of the present invention, in order to determine a more accurate target transmission path, different weight values ​​may be assigned to multiple transmission parameters according to actual transmission requirements. The weight value may be used to characterize the degree of influence of the transmission parameter on the ability to evaluate the transmission path. For example, the transmission score of the transmission path may be determined according to the weighted sum values ​​corresponding to the multiple transmission parameters. In one example, the transmission score of the transmission path may be determined using the following formula: Path_Score=(Signal_Strength × Weight_Signal) + (1 / Latency × Weight_Latency) +(Bandwidth × Weight_Bandwidth)-(Error_Rate × Weight_Error). Path_Score may be the score of the transmission path, Signal_Strength may be the signal strength of the transmission path, and Weight_Signal may be the weight value of the signal strength. Latency may be the transmission delay of the transmission path, and Weight_Latency may be the weight value corresponding to the transmission delay. Bandwidth may be the bandwidth of the transmission path, and Weight_Bandwidth may be the weight value corresponding to the bandwidth. Error_Rate can be the transmission error rate of the transmission path (for example, it can be the ratio of the number of transmission errors to the total number of transmissions), and Weight_Error can be the weight value corresponding to the transmission error rate. After determining the transmission score corresponding to each transmission path using the above formula, the maximum transmission score can be determined from multiple transmission scores, and the transmission path corresponding to the maximum transmission score is used as the target transmission path.

[0044] In this way, the target transmission path can be determined simply and intuitively using the transmission parameters, thereby ensuring the accuracy and reliability of data transmission.

[0045] In some embodiments, the transmission state of the transmission path can be determined based on the signal strength and transmission error parameters of the transmission path. Among them, the signal strength can refer to the size of the signal power received by the receiving end such as a memory or a processor. In some embodiments, the signal strength of the transmission path can be obtained by a signal testing device or software. The transmission error parameter refers to parameters such as the error rate and packet loss rate that occur during data transmission. The transmission error parameter can reflect the reliability and stability of the data transmission of the transmission path. In some embodiments, the transmission error parameter can be obtained using a network monitoring tool (such as a network packet capture tool) or a protocol analysis tool.

[0046] In some embodiments of the present invention, different transmission scenarios have different requirements for transmission paths. For example, in high-quality transmission scenarios, the signal strength of the transmission path needs to be higher. On this basis, the state of the transmission path can be determined based on the comparison of the signal strength with the preset strength threshold. The preset strength threshold can be set by the user according to the transmission scenario or transmission requirements, and the number of preset signal strength thresholds can be one or more. In the case where there are multiple preset signal strength thresholds, the state of the transmission path can be divided into multiple states to meet the data transmission requirements in various situations. 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, and may not meet the transmission requirements or there are potential risks. On this basis, it can be considered that the transmission state of the transmission path is 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. On this basis, it can be considered that the transmission state of the transmission path is a normal state.

[0047] In other embodiments, the state of the transmission path may also be determined based on a comparison result between a transmission error parameter and a preset error parameter threshold. The comparison result may be used to characterize the reliability and stability of data transmission on the transmission path. For example, if the transmission error parameter is lower than or equal to the preset error parameter threshold, it may be determined that the reliability and stability of data transmission on the path are good and can meet business requirements. On this basis, it may be considered that the transmission state of the transmission path is normal.

[0048] In other embodiments of the present invention, in order to determine a more accurate transmission state, it is necessary to comprehensively consider multiple factors and weigh their effects. For example, even if the signal strength meets the requirements, if the transmission error parameter is too high, it may also cause the data transmission quality to decline or the transmission task to fail to be completed. In some embodiments, when the signal strength and the transmission error parameter meet the requirements at the same time, the path state of the transmission path can be determined to be a normal state. If any one of the signal strength and the transmission error parameter does not meet the requirements, it can be determined that the path state of the transmission path is an abnormal state. In other embodiments, different weight values ​​can be assigned to the signal strength and the transmission error parameter according to the importance of the signal strength and the transmission error parameter, and the path state of different transmission paths can be judged according to the weights and values ​​of the two. Alternatively, different priorities can be set for the signal strength and the transmission error parameter, and the transmission state of the transmission path can be determined to be a normal state if the parameter with a high priority meets the requirements.

[0049] Since the state of the transmission path may change at any time, on this basis, in order to ensure the stability of transmission, the state of the transmission path can be monitored in real time to see if it changes, and redundancy switching can be triggered when the state changes. For example, when 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 example, the transmission path with the highest transmission score among the remaining transmission paths can be used to transmit the remaining data.

[0050] Figure 4 FIG. 4 is a schematic diagram showing a method for switching a transmission path according to some embodiments of the present invention. Figure 4 As shown, according to the data characteristics of the data to be transmitted 402, it can be determined that the transmission path used to transmit the data is transmission path A, and transmission path A can transmit the data to be transmitted 402 to the memory 404. In the case where it is monitored that the state of transmission path A changes from a normal state to an abnormal state, the transmission path can be switched to transmission path B, and the state of transmission path B is a normal state. Transmission path B can continue to transmit the remaining data to the memory 404. After switching the transmission path, the number of transmission failures of transmission path A can be recorded in a log or other manner. In an example, a counter can be used to record the number of transmission failures of the transmission path. Each time the path is switched (due to a fault), the number of times of the counter will increase.

[0051] It can be understood that if the transmission state of transmission path B also becomes abnormal, the data transmission is suspended, and the transmission path A and the transmission path B are troubleshooted so that the transmission path A and the transmission path B can transmit data normally. In some embodiments, if the transmission state of transmission path A becomes normal again, when the main path troubleshoots and returns to normal, it can be determined whether the Path_Score of transmission path A exceeds the Path_Score of the currently used backup path, that is, transmission path B. If so, it automatically switches back to the main path, that is, transmission path A, and data transmission is restored to the main path.

[0052] 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.

[0053] In some embodiments of the present invention, in order to further improve the reliability of data transmission, data can be transmitted by dual-path synchronous transmission. For example, data can be transmitted simultaneously through a wired transmission path and a wireless transmission path, so that even if one transmission path fails, the other transmission path can continue to transmit data. In addition, ideally, the two transmission paths can work at the same time, thereby providing a higher total bandwidth than a single transmission path. It should be noted that the two transmission paths can be completely physically independent or logically independent (for example, using different network protocols or frequency bands).

[0054] Figure 5 FIG. 2 shows a schematic diagram of synchronous data transmission according to some embodiments of the present invention. Figure 5 As shown, the data to be transmitted 502 may be backed up to determine the backup data 504. The backup data 504 is completely the same as the data to be transmitted 502. The data to be transmitted 502 may be transmitted via transmission path A (wherein the transmission score of transmission path A may be the highest among multiple transmission paths), and the backup data 504 may be transmitted via transmission path B (wherein the transmission score of transmission path B may be the second highest among multiple transmission paths).

[0055] It should be noted that in dual-path transmission, since data is sent through two paths at the same time, the memory 506 may receive redundant data packets. In order to process these redundant data packets, the memory 506 can use timestamps to deduplicate the received data. Among them, the timestamp can be time information represented in digital form, which can provide accurate time proof for any electronic file or data packet. When the memory 506 receives different data packets from transmission path A and transmission path B at the same time or within a preset time interval, the timestamps of the same data packet can be compared. According to the order or consistency of the timestamps, the earliest or latest data packet is selected to be retained, and the redundant data packets are discarded.

[0056] In this way, the dual-path data transmission method can improve data transmission reliability and fault tolerance. In addition, by deduplication, storage resources can be saved while ensuring data reliability.

[0057] It should be noted that the data transmission and data filtering can be recorded in the form of logs. For example, each time a transmission path is selected for data transmission, the content of the transmission can be recorded. For example, the selected transmission path, the selection time, the basis for selection (such as the transmission score, the various thresholds involved, etc.) and other relevant system status information can be recorded. Alternatively, when the transmission path is switched to another transmission path, the content related to the switching path can also be recorded, for example, the transmission path before and after the switch, the switching time, the reason for the switch, etc. can be recorded. In other embodiments, when a transmission path previously marked as "unavailable" is restored, the recovery information can also be recorded. For example, the restored path, the recovery time, the restored operation, and the system status after the recovery can be recorded.

[0058] In some embodiments of the present invention, logs can be collected regularly through log management tools and stored in a secure and accessible location. For example, by analyzing the logs, users (such as administrators) can find out which paths are more reliable and which paths are more prone to failure. Based on this information, users can adjust the weight values ​​and thresholds in the path selection algorithm to optimize the performance of the path selection algorithm. For example, if the number of transmission failures of a certain transmission path frequently exceeds a threshold, the user can reduce the weight value of the transmission path or increase its threshold to reduce dependence on the transmission path.

[0059] In this way, the process of data transmission and data filtering can be visualized by using logs, so that users can more easily and clearly identify problems in the process of data transmission and data filtering.

[0060] Figure 6 FIG. 6 is a block diagram of an apparatus 600 for processing data according to some embodiments of the present invention. Figure 6 As shown, the device 600 includes a target data acquisition unit 802, which is configured to acquire the target industrial data that has completed transmission in the target transmission cycle. The device 600 also includes a filtering strategy determination unit 604, which is configured to determine a filtering strategy corresponding to the data feature based on the data feature of the target industrial data, and the data feature at least includes one or more of frequency distribution information, trend, abnormal state, and noise parameter. The device 600 also includes a filtering unit 606, which is configured to filter the target data based on the filtering strategy.

[0061] It can be understood that by using the device 60 of the present invention, at least one of the advantages that can be achieved by the method or process described above can be achieved.

[0062] 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 a mapping relationship between the frequency distribution information and the filter type; determine filtering parameters of the target filter based on the frequency distribution information; and determine a corresponding filtering strategy based on the target filter and the filtering parameters.

[0063] In some embodiments, the filtering strategy determination unit 604 is also configured to: obtain the average value of the target data within a preset time period; based on the average value, determine the corresponding change trend of the target data; based on the trend, determine the target filter corresponding to the trend and the filtering parameters of the target filter; and based on the target filter and the filtering parameters, determine the corresponding filtering strategy.

[0064] In some embodiments, the filtering strategy determination unit 604 is also configured to: determine whether a specific value exists in the target data; determine the number of specific values ​​in response to the presence of a specific value in the target data; determine the corresponding target filter and filtering parameters of the target filter based on the specific value and the number of specific values; and determine the corresponding filtering strategy based on the target filter and the filtering parameters.

[0065] 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 a median filter as a target filter.

[0066] In some embodiments, the filtering strategy determination unit 604 is further configured to: determine the corresponding target filter and filtering parameters of the target filter based on the comparison result between the noise parameter and the preset noise threshold; and determine the corresponding filtering strategy based on the target filter and the filtering parameters.

[0067] 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.

[0068] In some embodiments, the transmission unit is also configured to: obtain the signal strength and / or transmission error parameter of the candidate transmission path; and determine the path state 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 parameter with a preset error parameter threshold.

[0069] 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 the target transmission path based on the first transmission score and the second transmission score.

[0070] In some embodiments, the target transmission path is a first transmission path, and the transmission unit is further configured to: in response to a path state of the first transmission path being an abnormal state, switch the target transmission path to a second transmission path and record switching information.

[0071] In some embodiments, the transmission unit is further configured to: in response to the path state of the first transmission path becoming normal again and the first transmission score of the first transmission path being greater than the second transmission score, switch the target transmission path to the second transmission path and record the switching information.

[0072] In some embodiments, the transmission parameters include at least one of the following parameters: signal strength, delay, bandwidth, transmission error parameter, priority, and number of transmission failures.

[0073] Figure 7 A schematic block diagram of an example device 700 that can be used to implement an embodiment of the present invention is shown. As shown, the device 700 includes a computing unit (i.e., CPU 701), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (i.e., ROM 702) or loaded from a storage unit 708 to a random access memory (i.e., RAM 703). In RAM 703, various programs and data required for the operation of the device 700 can also be stored. CPU 701, ROM 702, and RAM 703 are connected to each other via a bus 704. An input / output interface (i.e., I / O interface 705) is also connected to the bus 704.

[0074] A number of components in the device 700 are connected to the I / O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0075] CPU 701 may be a variety of general and / or special 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 dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, 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, which is tangibly contained in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on the 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 execute method 200 in any other appropriate manner (e.g., by means of firmware).

[0076] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip systems (SOCs), load programmable logic devices (CPLDs), and the like.

[0077] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0078] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In addition, although each operation is depicted in a specific order, this should be understood as requiring such operations to be performed in the specific order shown or in a sequential order, or requiring that all illustrated operations should be performed to obtain the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present invention. Certain features described in the context of a separate embodiment may also be implemented in a single implementation in combination. On the contrary, the various features described in the context of a single implementation may also be implemented in multiple implementations individually or in any suitable sub-combination.

[0079] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, 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. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A method for processing industrial data, It is characterized in that include: Acquire target industrial data that has completed transmission in a target transmission cycle; Based on data features of the target industrial data, determining a filtering strategy corresponding to the data features, the data features at least including one or more of frequency distribution information, trend, abnormal state, and noise parameter; as well as Based on the filtering strategy, the target industrial data is filtered.

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

3. The method according to claim 1, It is characterized in that Determining a filtering strategy corresponding to the data feature includes: Obtaining an average value of the target industrial data within a preset time period; Based on the average value, determining a change trend corresponding to the target industrial data; Based on the trend, determining a target filter corresponding to the trend and a filtering parameter of the target filter; and Based on the target filter and the filtering parameters, a corresponding filtering strategy is determined.

4. The method according to claim 1, It is characterized in that Determining a filtering strategy corresponding to the data feature includes: determining whether a specific value exists in the target industrial data; In response to the existence of a specific value in the target industrial data, determining the number of the specific value; Based on the specific value and the number of the specific values, determining a corresponding target filter and a filtering parameter of the target filter; and Based on the target filter and the filtering parameters, a corresponding filtering strategy is determined.

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

6. The method according to claim 1, It is characterized in that Determining a filtering strategy corresponding to the data feature includes: Based on the comparison result between the noise parameter and the preset noise threshold, determining the corresponding target filter and the filtering parameters of the target filter; and Based on the target filter and the filtering parameters, a corresponding filtering strategy is determined.

7. The method according to claim 1, It is characterized in that The method further comprises: Determining a target transmission path based on transmission parameters of a plurality of candidate transmission paths and corresponding path states; and Based on the target transmission path, the target industrial data is transmitted within the target transmission period.

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

9. The method according to claim 7, It is characterized in that The plurality of candidate transmission paths include a first transmission path and a second transmission path, and determining the target transmission path includes: Acquire a first transmission parameter corresponding to the first transmission path and a second transmission parameter corresponding to the second transmission path; determining a first transmission score for the first transmission path based on the first transmission parameter; determining a second transmission score for the second transmission path based on the second transmission parameter; A target transmission path is determined based on the first transmission score and the second transmission score.

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

11. The method according to claim 10, It is characterized in that The method further comprises: In response to the path state of the first transmission path becoming normal again 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 switching information is recorded.

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

13. A computing device, It is 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, the instructions, when executed by the at least one processor, causing the apparatus to perform the method according to any one of claims 1-12.

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

15. A computer program product, It is characterized in that The method comprises machine executable instructions which, when executed, cause a machine to perform the method according to any one of claims 1 to 12.

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