A data processing method, device and equipment

Abstract

The present application provides a data processing method, device and equipment. When the method specifies processing of high-frequency data and determines that the high-frequency data needs to be specified according to the target secondary processing level, for each time interval that meets the second time span, the target data corresponding to the upper processing level of the target secondary processing level that has been specified for processing within the time interval is specified for processing, and the second target data for monitoring the operating status of industrial equipment is obtained. It can be seen that the technical solution provided by the embodiment of the present application no longer independently processes the high-frequency data within the time span corresponding to different processing levels, but each secondary processing level can specify processing using the target data corresponding to the upper processing level that has been specified for processing when specifying processing of high-frequency data, which can greatly reduce the amount of data processed by the secondary processing level, thereby reducing the processing burden for edge computing devices.

Description

Technical Field

[0001] The present application relates to the field of industrial equipment monitoring technology, and in particular to a data processing method, device and equipment. Background Art

[0002] In practical applications, industrial equipment will generate a large amount of high-frequency data during operation, and this large amount of high-frequency data has important research value for monitoring the operating status and fault diagnosis of industrial equipment. Based on this, real-time monitoring of the high-frequency data generated by industrial equipment during operation is of great value for the status monitoring, online diagnosis and offline analysis of industrial equipment operation, such as the need to monitor various high-frequency data generated by wind turbines.

[0003] At present, when processing and analyzing the high-frequency data generated by the collected industrial equipment, a large amount of high-frequency data is generally processed according to time periods. If it is necessary to monitor second-level data and hierarchical data, then it is necessary to process the collected high-frequency data according to the time span corresponding to the second level, and to process the collected high-frequency data according to the time span corresponding to the hierarchical level, so as to determine the operating status of the industrial equipment in different time spans according to the processing results. It can be seen that no matter whether the time span is a time span of seconds or a hierarchical time span, the collected high-frequency data is processed accordingly independently. It is not difficult to see that the existing monitoring methods for high-frequency data are all processed independently according to different processing time periods, which will result in the higher the data collection frequency, the more the collected high-frequency data needs to be processed for each divided time span, which will make it difficult for edge computing devices with limited resources (CPU computing power and memory capacity) to complete the processing of large amounts of data in a timely manner. Summary of the invention

[0004] The present application provides a data processing method and apparatus to reduce the processing burden of edge computing devices.

[0005] The technical solutions provided in this application include:

[0006] In a first aspect, an embodiment of the present application provides a data processing method, which is applied to an edge computing device in an industrial device. The method includes:

[0007] When a preset data monitoring event is detected, initial high-frequency data is determined from all high-frequency data to be processed, and the initial high-frequency data is subjected to designated processing to obtain first target data for monitoring the operating status of industrial equipment, wherein the time difference of high-frequency data collected at adjacent time points in the initial high-frequency data meets the first time span of the preset primary processing level; wherein the first time span is an integer multiple of the interval between the corresponding time points of any two adjacent high-frequency data to be processed;

[0008] And when it is detected that the high-frequency data needs to be monitored according to the target secondary processing level, for each time interval that meets the second time span, the target data corresponding to the upper processing level of the target secondary processing level that has been specified in the time interval is processed in a specified manner to obtain the second target data for monitoring the operating status of the industrial equipment; the target secondary processing level is determined by dividing the time for collecting high-frequency data in advance according to the second time span, and the second time span is an integer multiple of the time span corresponding to the upper processing level.

[0009] In a second aspect, an embodiment of the present application provides a data processing device, which is applied to an edge computing device in an industrial device, and the device includes:

[0010] A primary processing level unit is used to determine initial high-frequency data from all high-frequency data to be processed when a preset data monitoring event is detected, and to perform specified processing on the initial high-frequency data to obtain first target data for monitoring the operating status of industrial equipment, wherein the time difference of high-frequency data collected at adjacent time points in the initial high-frequency data conforms to the first time span of the preset primary processing level; wherein the first time span is an integer multiple of the interval between the corresponding time points of any two adjacent high-frequency data to be processed;

[0011] The secondary processing level unit is used to, when it is detected that the high-frequency data needs to be monitored according to the target secondary processing level, perform specified processing on the target data corresponding to the upper processing level of the target secondary processing level that has been specified in the time interval to obtain the second target data for monitoring the operating status of the industrial equipment; the target secondary processing level is determined by dividing the time for collecting the high-frequency data in advance according to the second time span, and the second time span is an integer multiple of the time span corresponding to the upper processing level.

[0012] It can be seen from the above technical solutions that in this application, when the high-frequency data to be processed is designated for processing and it is determined that the high-frequency data needs to be designated for processing according to the target secondary processing level, then for each time interval that meets the second time span, the target data corresponding to the upper processing level of the target secondary processing level that has been designated for processing within the time interval is designated for processing, and the second target data for monitoring the operating status of industrial equipment is obtained. It can be seen that the technical solution provided by the embodiment of the present application no longer independently processes the high-frequency data within the time span corresponding to different processing levels, but each secondary processing level can use the target data corresponding to the upper processing level that has been designated for processing to designate the processing of high-frequency data, which can greatly reduce the amount of data processed by the secondary processing level, thereby reducing the processing burden for edge computing devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0014] Figure 1 A flowchart of a data processing method provided for this application;

[0015] Figure 2 A flowchart of another data processing method provided for this application;

[0016] Figure 3 A schematic diagram of the structure of a data monitoring device provided in this application;

[0017] Figure 4 A schematic diagram of the structure of an electronic device provided in this application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] See also Figure 1 , Figure 1 A flowchart of a data processing method provided in this application is applied to edge computing devices of industrial equipment.

[0020] The edge computing device is used to process the high-frequency data generated by industrial equipment, so as to analyze the processing results after processing the high-frequency data and then evaluate the operating status of the industrial equipment.

[0021] like Figure 1 As shown, the process may include the following steps:

[0022] Step 101, when a preset data monitoring event is detected, initial high-frequency data is determined from all high-frequency data to be processed, and the initial high-frequency data is processed in a specified manner to obtain first target data for monitoring the operating status of industrial equipment.

[0023] In this embodiment, the above-mentioned data monitoring event can be an event of monitoring the operating status of industrial equipment, or an event of monitoring the operating status of industrial equipment to determine whether there is a fault, or an event of monitoring the operating status of each industrial equipment to achieve fault diagnosis. This embodiment is not limited to this.

[0024] In this embodiment, the high-frequency data to be processed may be high-frequency data that has been processed. Exemplarily, the method for processing the high-frequency data may be the data processing method provided in steps 101 to 103 described in the application embodiment. Of course, it may also be other data processing methods, and this embodiment does not limit this. If the high-frequency data is processed using the data processing method provided in this embodiment, then the high-frequency data to be processed is the first target data obtained by the data processing method provided in the embodiment of the application or the second target data corresponding to each target secondary processing level. The high-frequency data to be processed may also be high-frequency data generated during the operation of collected industrial equipment. This embodiment does not limit the source of the high-frequency data to be processed.

[0025] The high-frequency data collected by this edge computing device may also be collected high-frequency data obtained from a device independent of the edge computing device, which is not limited in this embodiment. Based on this, as an embodiment, before step 101, the method further includes: collecting the high-frequency data generated by the industrial equipment during operation according to a preset sampling interval. The value of the above sampling interval is related to the actual application scenario and monitoring needs. The sampling interval can be 10ms or 20ms, which is not limited in this embodiment of the present application. As another embodiment, before step 101, the method further includes: obtaining a device independent of the edge computing device to collect the high-frequency data generated by the industrial equipment during operation according to a preset interval.

[0026] The time difference of the high-frequency data collected at adjacent time points in the above initial high-frequency data meets the first time span of the preset primary processing level.

[0027] The first time span is named only for the convenience of distinguishing it from the time spans in the following text, and is not used to limit a certain time span.

[0028] The first target data is named only for the convenience of distinguishing it from the target data in the following text, and is not used to limit a certain target data.

[0029] The primary processing level is determined by pre-dividing the duration of collecting high-frequency data according to the first duration span. It should be noted that if the duration of collecting high-frequency data is divided into multiple processing levels, the first duration span corresponding to the primary processing level is the smallest duration span.

[0030] In this step, the first time span is an integer multiple of the interval between any two adjacent time points of the high-frequency data to be processed. If the high-frequency data to be processed is the high-frequency data collected when the industrial equipment is running, then the first time span is an integer multiple of the sampling interval used when collecting the high-frequency data. If the sampling interval is 20ms and the first time span is 1 second, it can be seen that the first time span is 50 times the sampling interval, which means that the time interval between the 1st sampling interval and the 50th sampling interval is the first time span, and the time interval between the 50th sampling interval and the 100th sampling interval is the first time span. Accordingly, the time span between two adjacent time intervals in this step is the first time span.

[0031] Exemplarily, assuming that the first time span is 1 second and the sampling interval is 20ms, then a high-frequency data is collected every 20ms, and when the 50th high-frequency data is collected, the first time span of 1 second is reached. Continue collecting, and when the 50*2nd high-frequency data is collected, the second first time span of 1 second is reached. And so on, when the 50*Nth high-frequency data is collected, the Nth first time span of 1 second is reached, where N is a natural number. It can be seen that the above time interval is the first time span.

[0032] In this embodiment, the designated processing of the initial high-frequency data to obtain the first target data for monitoring the operating status of the industrial equipment is performed based on the collected high-frequency data.

[0033] The present application specifies processing of the first target data corresponding to the primary processing level once for each time interval that meets the first time span within the time period for collecting the high-frequency data. If the time period for collecting the high-frequency data is 10 minutes and the first time span is 1 second, then there are 600 time intervals in 10 minutes, which means that it is necessary to specify processing of the high-frequency data within the first time span corresponding to the primary processing level 600 times to obtain 600 first target data.

[0034] The above-mentioned designated processing is related to the preset data monitoring event. If the data monitoring event is to monitor the operating status of industrial equipment, the designated processing is to perform corresponding processing on the high-frequency data to obtain the target data used to characterize the operating status of the industrial equipment. If the data monitoring event is to monitor the operating status of industrial equipment to determine whether there is a fault, the designated processing is to perform corresponding processing on the high-frequency data to obtain the target data used to characterize whether the industrial equipment has a fault diagnosis.

[0035] Step 102, when it is detected that the high frequency data needs to be monitored according to the target secondary processing level, step 103 is executed.

[0036] In this step, the target sub-processing level may be a sub-processing level selected by the user, or may be a sub-processing level that has been selected in advance, and the sub-processing level to be processed is designated according to the processing sequence of the sub-processing levels.

[0037] As an embodiment, the processing level can be customized in advance, such as the target processing level can be a minute processing level, an hour processing level or a day processing level. The target processing level is a secondary processing level selected by the user from the pre-determined hierarchical processing level, hour processing level and day processing level. If the target processing level is the hour processing level, then after executing the primary processing level in step 102, the specified processing is directly performed according to the hour processing level. This will be described in detail later and will not be repeated here.

[0038] As another embodiment, the secondary processing levels may be divided in advance, such as the secondary processing levels consisting of the hierarchical processing level, the hourly processing level, and the daily processing level. When specifying the processing of high-frequency data, the specified processing is automatically performed in sequence according to the hierarchical processing level, the hourly processing level, and the daily processing level. This will be described in detail later and will not be repeated here.

[0039] Step 103, for each time interval that meets the second time span, designated processing is performed on the target data corresponding to the upper processing level of the target secondary processing level that has been designated processed within the time interval to obtain second target data for monitoring the operating status of the industrial equipment.

[0040] Here, the second time span is named for the convenience of description and is not used to limit a certain time span. The second target data is named for the convenience of description and is not used to limit a certain target data.

[0041] In the present application, the target secondary processing level is determined by pre-dividing the duration of collecting high-frequency data according to the second duration span, and the second duration span is an integer multiple of the duration span corresponding to the previous processing level.

[0042] The previous processing level can be a primary processing level, and accordingly, the target processing level is the secondary processing level of the next level of the primary processing level. The previous processing level can also be a secondary processing level, and accordingly, the target processing level is the next processing level of the secondary processing level.

[0043] As an embodiment, if the previous level processing level is the primary processing level, the implementation method of the above 103 is: specify the processing of the first target data corresponding to the primary processing level that has been specified for processing within the time interval. It can be seen that, compared with the prior art, when specifying the processing of the target secondary processing level, the high-frequency data to be processed is no longer specified directly separately, but the specified processing is performed based on the target data corresponding to the primary processing level, which can reduce the processing burden of the edge computing device. If the previous level processing level is not the primary processing level, the implementation method of the above 103 is: obtain the target data corresponding to the previous level processing level of the target secondary processing level within the time interval; specify the processing of the target data corresponding to the previous level processing level that has been obtained. It can be seen that, compared with the prior art, when specifying the processing of the target secondary processing level, the high-frequency data to be processed is no longer specified directly separately, but the target data corresponding to the previous level secondary processing level of the target secondary processing level is specified, which can also reduce the processing burden of the edge computing device.

[0044] So far, completed Figure 1 Description shown.

[0045] It can be seen that the technical solution provided by the embodiment of the present application specifies processing for the high-frequency data to be processed, and when it is determined that the high-frequency data needs to be specified according to the target secondary processing level, then for each time interval that meets the second time span, the target data corresponding to the previous level processing level of the target secondary processing level that has been specified for processing within the time interval is specified for processing, and the second target data for monitoring the operating status of industrial equipment is obtained. It can be seen that the technical solution provided by the embodiment of the present application no longer independently processes the high-frequency data within the time span corresponding to different processing levels, but each secondary processing level can specify processing the target data corresponding to the previous level processing level that has been specified for processing when specifying processing high-frequency data, which can greatly reduce the amount of data processed by the secondary processing level, thereby reducing the processing burden for edge computing devices.

[0046] In completion Figure 1 After the flowchart of, as an embodiment, the implementation method of the above step 101 may include: inputting the initial high-frequency data into the data processing model as an input parameter of a preset data processing model to obtain the first target data for monitoring the operating status of the industrial equipment.

[0047] The above data processing model may include at least one of the following functions, which is a denoising function, a maximum value finding function, a minimum value finding function, an average value calculation function, a peak-to-valley difference calculation function, a variance calculation function or a standard deviation calculation function.

[0048] In this embodiment, the input parameter of the above-mentioned data processing model is the high-frequency data within the first time span. For each first time span, the high-frequency data within the first time span is input into the data processing model, and the first target data corresponding to the first time span is output. Exemplarily, if the data processing model includes a denoising function, a maximum value function and an average value function, the high-frequency data within the first time span is input into the data processing model, so that the data processing model first denoises the high-frequency data within the first time span, and then processes the denoised data with the maximum value function and the average value function respectively, and outputs the maximum value and average value of the high-frequency data within the first time span.

[0049] As another embodiment, the implementation of the above step 102 includes:

[0050] The target data corresponding to the previous processing level of the target secondary processing level within the time interval is input into the data processing model as an input parameter of the preset data processing model to obtain the second target data corresponding to the target secondary processing level. The previous processing level of this embodiment is the primary processing level or the secondary processing level.

[0051] In some embodiments, after performing step 101, Figure 2 As shown, the method further includes steps 104 to 106:

[0052] Step 104, when it is necessary to perform designated processing according to the preset customized processing mode, the processing level selected by the user is obtained from the configured processing level array to form a customized processing level array.

[0053] The above-mentioned processing level array includes secondary processing levels with different time spans.

[0054] The above-mentioned customized processing mode can be understood as performing specified processing according to the secondary processing level in the customized processing level array.

[0055] The above processing level array is pre-composed according to the initial user-defined secondary processing levels that have been configured.

[0056] Exemplarily, if each element in the processing level array is sorted in order according to the length of the time span, the processing level array includes: hierarchical processing level, hourly processing level and daily processing level. The user selects the hourly processing level and the daily processing level from the processing level array to form a customized processing level array, which means that the customized processing level array includes the hourly processing level and the daily processing level.

[0057] Step 105, determine the processing order of each sub-processing level according to the size of the time span corresponding to each sub-processing level in the customized processing level array, and use the current processing level to be processed as the target sub-processing level according to the processing order, and execute step 103.

[0058] In this step, the time spans corresponding to each secondary processing level in the customized processing level array can be sorted in the order of time span from small to large, or can be sorted in the order of time span from large to small, and the specified processing is performed according to the secondary processing levels corresponding to the time spans from small to large in the customized processing level array.

[0059] Step 106, when processing in automatic processing mode is required, the current sub-processing level to be processed is determined as the target sub-processing level according to the processing order of each sub-processing level in the configured processing level array, and step 103 is executed.

[0060] Each sub-processing level in the above processing level array is determined according to the length of the time span corresponding to the sub-processing level in the above processing level array. Exemplarily, if the length of the time span corresponding to the sub-processing level is ranked second, then the sub-processing level is designated to be processed after the sub-processing level ranked first is designated to be processed.

[0061] Exemplarily, the processing order of each sub-processing level in the processing level array is a hierarchical processing level, an hourly processing level, and a daily processing level.

[0062] It can be seen that the technical solution provided in the embodiment of the present application can be flexibly configured for designated processing according to the secondary processing levels customized by different users, or it can be uniformly configured according to the pre-configured processing levels, which can not only meet the monitoring event requirements of different users, but also meet the processing efficiency.

[0063] When the target sub-processing level is the last sub-processing level to be processed, as an embodiment, the method further includes: sending the obtained second target data, and after determining that the sending is successful, deleting the second target data to release storage space of the edge computing device.

[0064] When this edge computing device processes high-frequency data, it is only related to the sampling interval used when collecting high-frequency data. When this edge computing device collects high-frequency data generated by industrial equipment during operation according to the preset sampling interval, when it is monitored that the sampling interval of the sampled high-frequency data has changed, the preset data monitoring event can be reset to re-execute steps 101 to 103, and finally realize the processing of data when collecting high-frequency data at different sampling intervals.

[0065] In the case where the edge computing device obtains high-frequency data generated by industrial equipment during operation collected by other devices according to a preset sampling interval, as an embodiment, determining the initial high-frequency data from the collected high-frequency data in step 101 includes:

[0066] Step A, determine whether the time difference between any two adjacent time points corresponding to the high-frequency data to be processed is equal; if yes, execute step B; if no, execute step C.

[0067] It should be noted that the high-frequency data to be processed in this step is the high-frequency data collected when the industrial equipment is running.

[0068] Step B: determining initial high-frequency data from the high-frequency data to be processed.

[0069] When it is determined that the time differences between the time points corresponding to the collected high-frequency data are all the same time difference, step 101 and step 103 are executed.

[0070] Step C: determining the adjacent high-frequency data with the same time difference as the initial high-frequency data.

[0071] In this step, for the case where the time difference between the time points corresponding to any two adjacent high-frequency data to be processed is not equal, there may be a phenomenon that the time difference between the time points corresponding to two non-adjacent high-frequency data may be equal. Exemplarily, assuming that there are 300 high-frequency data in total, the time difference between the time points corresponding to the first 100 high-frequency data is 1S, the time difference between the time points corresponding to the middle 100 high-frequency data is 2S, and the time difference between the time points corresponding to the last 100 high-frequency data is also 1S. It is not difficult to see that although the time difference between the time points corresponding to the first 100 high-frequency data and the time difference between the time points corresponding to the last 100 high-frequency data are both 1S, based on the description of determining the initial high-frequency data in step C, the first 100 high-frequency data and the last 100 high-frequency data are not adjacent. Based on this, the first 100 high-frequency data are determined as the initial high-frequency data, and step D is performed according to the same time difference. The middle 100 high-frequency data are determined as the initial high-frequency data, and step D is performed according to the same time difference. The last 100 high-frequency data are determined as the initial data, and step D is performed according to the same time difference.

[0072] Based on step C, the step 101 performs designated processing on the initial high-frequency data to obtain first target data for monitoring the operating status of industrial equipment, including:

[0073] Step D: for each initial high-frequency data corresponding to the same time difference, perform designated processing on the initial high-frequency data corresponding to the same time difference to obtain first target data for monitoring the operating status of the industrial equipment.

[0074] The time difference between the time points corresponding to the high-frequency data in this step includes at least two different time intervals. When the high-frequency data to be processed is the collected high-frequency data, step D indicates that the high-frequency data is collected at at least two different sampling intervals. Based on this, this embodiment uses the high-frequency data with the same time difference as the initial high-frequency data, and for the initial high-frequency data corresponding to the same time difference, performs the designated processing of the initial high-frequency data in step 101 to obtain the first target data for monitoring the operating status of the industrial equipment, and steps 102 to 103 to realize data processing.

[0075] This completes the description of the method embodiment.

[0076] In order to make the above embodiment easier to understand, a specific embodiment is now given. Assume that a certain model of wind turbine needs to collect 1000 high-frequency variables with a sampling interval of 20ms for edge computing. The requirement is to monitor the operating status of industrial equipment as the set data monitoring event. In order to improve performance, it is decided to perform relevant processing on the time series data of each high-frequency variable according to different time spans. The processing time span is divided into four processing levels: second processing level, minute processing level, hour processing level and day processing level. The second processing level is used as the primary processing level, and the remaining three processing levels constitute a processing level array. The time intervals corresponding to these four processing levels are 1 second, 1 minute, 1 hour and 1 day respectively. The data processing model includes five functions: finding the maximum value function, finding the minimum value function, calculating the average value function, calculating the peak-to-valley difference function and calculating the variance function. The specific processing method is:

[0077] The high-frequency data corresponding to different high-frequency variables are collected in real time at 20ms; the high-frequency variable is taken as an example of instant wind speed.

[0078] When the time interval for collecting the instantaneous wind speed meets 1 second, the instantaneous wind speed collected within 1 second is input into the above data processing model to obtain the target data corresponding to the 1-second processing level, that is, the maximum value max1 = the maximum value of 50 original wind speed values; the minimum value min1 = the minimum value of 50 original wind speed values; the average value avg1 = the average value of 50 original wind speed values; the peak-to-valley difference ptp1 = max1-min1; the variance dev1 = ∑(the sum of the squares of 50 original wind speed values) / 50-avg1 2 .

[0079] If the user selects a customized processing mode according to his own needs, and selects the hourly processing level and the daily processing level from the minute processing level, hourly processing level and daily processing level in the processing level array to form a customized processing level array, first use the hourly processing level as the target secondary processing level, and input the target data value corresponding to the second processing level that has been processed within the hour into the above data processing model, and output the following target data: maximum value max3 = the maximum value of 3600 second-level maximum values ​​max1; minimum value min3 = the minimum value of 3600 second-level minimum values ​​min1; average value avg3 = the average value of 3600 second-level average values ​​avg1; peak-to-valley difference ptp3 = max3–min3; variance dev3 = ∑(3600 (dev1+avg1 2 )) / 540000000–avg3 2 .

[0080] Then, the day-level processing level is used as the target secondary processing level, and the target data corresponding to the hour-level processing level that has been processed within the day is input into the above data processing model, and the following target data are output: maximum value max4 = the maximum value of the 24 hour-level maximum values ​​max3; minimum value min4 = the minimum value of the 24 hour-level minimum values ​​min3; average value avg4 = the average value of the 24 hour-level average values ​​avg3; peak-to-valley difference ptp4 = max4–min4; variance dev4 = ∑(24 (dev3+avg3 2 )) / 4320000–avg4 2 .

[0081] If the user selects the automatic processing mode according to his own needs, the time span of each sub-processing level in the minute processing level, hourly processing level and daily processing level in the processing level array is processed in order from small to large. The minute processing level is first used as the target sub-processing level, and the target data corresponding to the second processing level that has been processed within 1 minute is input into the above data processing model, and the following target data is output: maximum value max2 = the maximum value of 60 second-level maximum values ​​max1; minimum value min2 = the minimum value of 60 second-level minimum values ​​min1; average value avg2 = the average value of 60 second-level average values ​​avg1; peak-to-valley difference ptp2 = max2-min2; variance dev2 = ∑(60 (dev1+avg1 2 )) / 3000–avg2 2 .

[0082] Then, the hourly processing level is used as the target secondary processing level, and the target data corresponding to the minute-level processing level that has been processed within the hour is input into the data processing model, and the following target data are output: maximum value max3 = the maximum value of 60 second-level maximum values ​​max2; minimum value min3 = the minimum value of 60 minute-level minimum values ​​min2; average value avg3 = the average value of 60 minute-level average values ​​avg2; peak-to-valley difference ptp3 = max3–min3; variance dev3 = ∑(60 (dev2+avg2 2 )) / 180000–avg3 2 .

[0083] Finally, the day-level processing level is used as the target secondary processing level, and the target data corresponding to the hour-level processing level that has been processed within the day is input into the above data processing model, and the following target data are output: maximum value max4 = the maximum value of the 24 hour-level maximum values ​​max3; minimum value min4 = the minimum value of the 24 hour-level minimum values ​​min3; average value avg4 = the average value of the 24 hour-level average values ​​avg3; peak-to-valley difference ptp4 = max4–min4; variance dev4 = ∑(24 (dev3+avg3 2 )) / 4320000–avg4 2 .

[0084] The above example is described.

[0085] The device provided by this application is described below:

[0086] See also Figure 3 , Figure 3 A data processing device 300 provided in the present application is applied to an edge computing device in an industrial device, and the device includes:

[0087] The primary processing level unit 301 is used to determine initial high-frequency data from the high-frequency data to be processed when a preset data monitoring event is detected, and to perform specified processing on the initial high-frequency data to obtain first target data for monitoring the operating status of industrial equipment, wherein the time difference of the high-frequency data collected at adjacent time points in the initial high-frequency data meets the first time span of the preset primary processing level; wherein the first time span is an integer multiple of the sampling interval between any two adjacent time points of the high-frequency data to be processed;

[0088] The secondary processing level unit 302 is used to, when it is detected that the collected high-frequency data needs to be monitored according to the target secondary processing level, perform specified processing on the target data corresponding to the upper processing level of the target secondary processing level that has been specified in the time interval to obtain the second target data for monitoring the operating status of the industrial equipment; the target secondary processing level is determined by dividing the time for collecting the high-frequency data in advance according to the second time span, and the second time span is an integer multiple of the time span corresponding to the upper processing level.

[0089] As an embodiment, the device may include: a data collection unit, configured to collect high-frequency data generated by the industrial equipment during operation according to a preset sampling interval.

[0090] As an embodiment, if the previous processing level is a primary processing level, the secondary processing level unit 302 is specifically used to:

[0091] Perform designated processing on the first target data corresponding to the primary processing level that has been designated to be processed within the time interval;

[0092] If the previous processing level is not the primary processing level, the secondary processing level unit 302 is specifically configured to:

[0093] Obtain target data corresponding to the upper processing level of the target secondary processing level within the time interval;

[0094] Perform designated processing on the target data corresponding to the previous processing level that has been acquired.

[0095] As an embodiment, the primary processing level unit 301 is specifically used for:

[0096] The initial high-frequency data is input into the data processing model as an input parameter of the preset data processing model to obtain first target data for monitoring the operating status of the industrial equipment.

[0097] As an embodiment, the secondary processing level unit 302 is specifically used for:

[0098] The target data corresponding to the previous processing level of the target secondary processing level within the time interval is input into the data processing model as an input parameter of the preset data processing model to obtain the second target data corresponding to the target secondary processing level.

[0099] As an embodiment, the device further includes:

[0100] A customized processing mode starting unit is used to obtain the processing level selected by the user from the configured processing level array to form a customized processing level array when a specified processing is required according to a preset customized processing mode; the processing level array includes secondary processing levels with different time spans;

[0101] A processing unit, used to determine the processing order of each sub-processing level according to the size of the time span corresponding to each sub-processing level in the customized processing level array, and to use the current processing level to be processed as the target sub-processing level according to the processing order to trigger the sub-processing level unit;

[0102] The automatic processing mode is used to determine the current sub-processing level to be processed as the target sub-processing level according to the processing order of each sub-processing level in the configured processing level array when processing is required according to the automatic processing mode, and trigger the sub-processing level unit.

[0103] As an embodiment, the primary processing level unit 301 determines initial high-frequency data from the high-frequency data to be processed, specifically for:

[0104] Determine whether the time differences between any two adjacent time points corresponding to the high-frequency data to be processed are equal;

[0105] If not, the adjacent high-frequency data with the same time difference is determined as the initial high-frequency data;

[0106] The step of performing designated processing on the initial high-frequency data to obtain first target data for monitoring the operating status of industrial equipment includes:

[0107] For each initial high-frequency data corresponding to the same time difference, performing designated processing on the initial high-frequency data corresponding to the same time difference to obtain first target data for monitoring the operating status of the industrial equipment;

[0108] If yes, initial high-frequency data is determined from the high-frequency data to be processed.

[0109] So far, completed Figure 3 Description shown.

[0110] It can be seen that the technical solution provided by the embodiment of the present application performs designated processing on the high-frequency data collected by the industrial equipment, and when it is determined that the high-frequency data needs to be designated for processing according to the target secondary processing level, then for each time interval that meets the second time span, the target data corresponding to the previous level processing level of the target secondary processing level that has been designated for processing within the time interval is designated for processing, and the second target data for monitoring the operating status of the industrial equipment is obtained. It can be seen that the technical solution provided by the embodiment of the present application no longer independently processes the high-frequency data within the time span corresponding to different processing levels, but each secondary processing level can use the target data corresponding to the previous level processing level that has been designated for processing to perform designated processing when designating the processing of high-frequency data, which can greatly reduce the amount of data processed by the secondary processing level, thereby reducing the processing burden for the edge computing device.

[0111] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0112] The electronic device provided in the embodiment of the present application, from the hardware level, can be seen in the hardware architecture diagram Figure 4 As shown. It includes: a machine-readable storage medium and a processor, wherein: the machine-readable storage medium stores machine-executable instructions that can be executed by the processor; the processor is used to execute the machine-executable instructions to implement the data processing operations disclosed in the above examples.

[0113] The machine-readable storage medium provided in an embodiment of the present application stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to implement the data processing operations disclosed in the above examples.

[0114] Here, the machine-readable storage medium may be any electronic, magnetic, optical or other physical storage device that may contain or store information, such as executable instructions, data, etc. For example, the machine-readable storage medium may be: RAM (RadomAccess Memory), volatile memory, non-volatile memory, flash memory, storage drive (such as hard disk drive), solid state drive, any type of storage disk (such as CD, DVD, etc.), or similar storage medium, or a combination thereof.

[0115] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which may be in the form of a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email transceiver, a game console, a tablet computer, a wearable device or a combination of any of these devices.

[0116] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0117] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the embodiments of the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0118] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0119] Moreover, these computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0121] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0122] So far, completed Figure 4 Description of the device shown.

[0123] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A data processing method, characterized in that: An edge computing device applied to an industrial device, the method comprising: When a preset data monitoring event is detected, initial high-frequency data is determined from the high-frequency data to be processed, and the initial high-frequency data is subjected to a specified process to obtain first target data for monitoring the operating status of industrial equipment; wherein the time difference of high-frequency data collected at adjacent time points in the initial high-frequency data meets the first time span of the preset primary processing level; the first time span is an integer multiple of the interval between corresponding time points of any two adjacent high-frequency data to be processed; And when it is detected that the high-frequency data needs to be monitored according to the target secondary processing level, for each time interval that meets the second time span, the target data corresponding to the previous processing level of the target secondary processing level that has been specified and processed within the time interval is specified and processed, wherein if the previous processing level is the primary processing level, the first target data corresponding to the primary processing level that has been specified and processed within the time interval is specified and processed; if the previous processing level is not the primary processing level, the target data corresponding to the previous processing level of the target secondary processing level within the time interval is obtained; the target data corresponding to the obtained previous processing level is specified and processed; and the second target data for monitoring the operating status of industrial equipment is obtained; the target secondary processing level is determined by dividing the time span of collecting high-frequency data in advance according to the second time span, and the second time span is an integer multiple of the time span corresponding to the previous processing level.

2. The method according to claim 1, characterized in that Before determining the initial high-frequency data from all the high-frequency data to be processed when the preset data monitoring event is detected, the method further includes: The high-frequency data generated by the industrial equipment during operation is collected according to a preset sampling interval, and the collected high-frequency data is determined as the high-frequency data to be processed.

3. The method according to claim 1, characterized in that The step of performing designated processing on the initial high-frequency data to obtain first target data for monitoring the operating status of the equipment includes: The initial high-frequency data is input into the data processing model as an input parameter of the preset data processing model to obtain first target data for monitoring the operating status of the industrial equipment.

4. The method according to claim 3, characterized in that The target data corresponding to the upper level processing level of the target secondary processing level obtained by the specified processing within the time interval is specified to be processed, including: The target data corresponding to the previous processing level of the target secondary processing level within the time interval is input into the data processing model as an input parameter of the preset data processing model to obtain the second target data corresponding to the target secondary processing level.

5. The method according to claim 1, characterized in that: After the initial high-frequency data is processed in a designated manner to obtain first target data for monitoring the operating status of industrial equipment, the method further includes: When it is necessary to perform designated processing according to a preset customized processing mode, the processing level selected by the user is obtained from the configured processing level array to form a customized processing level array; the processing level array includes secondary processing levels with different time spans; According to the size of the time span corresponding to each sub-processing level in the customized processing level array, the processing order of each sub-processing level is determined, and according to the processing order, the current processing level to be processed is used as the target sub-processing level, and the step of performing specified processing on the target data corresponding to the previous level processing level of the target sub-processing level obtained by the specified processing within each time interval that meets the second time span is performed; When it is necessary to process in automatic processing mode, the current secondary processing level to be processed is determined as the target secondary processing level according to the processing order of each secondary processing level in the configured processing level array, and the step of performing specified processing on the target data corresponding to the previous level processing level of the target secondary processing level obtained by the specified processing within each time interval that meets the second time span is executed.

6. The method according to any one of claims 1 to 5, characterized in that: The step of determining initial high-frequency data from the high-frequency data to be processed includes: Determine whether the time differences between any two adjacent time points corresponding to the high-frequency data to be processed are equal; If not, the adjacent high-frequency data with the same time difference is determined as the initial high-frequency data; The step of performing designated processing on the initial high-frequency data to obtain first target data for monitoring the operating status of industrial equipment includes: For each initial high-frequency data corresponding to the same time difference, performing designated processing on the initial high-frequency data corresponding to the same time difference to obtain first target data for monitoring the operating status of the industrial equipment; If yes, initial high-frequency data is determined from the high-frequency data to be processed.

7. A data processing device, characterized in that: Edge computing equipment used in industrial equipment, the device includes: A primary processing level unit is used to determine initial high-frequency data from the high-frequency data to be processed when a preset data monitoring event is detected, and to perform specified processing on the initial high-frequency data to obtain first target data for monitoring the operating status of industrial equipment, wherein the time difference of the high-frequency data collected at adjacent time points in the initial high-frequency data meets the first time span of the preset primary processing level; wherein the first time span is an integer multiple of the interval between the corresponding time points of any two adjacent high-frequency data to be processed; The secondary processing level unit is used for, when it is detected that the high-frequency data needs to be monitored according to the target secondary processing level, performing designated processing on the target data corresponding to the previous processing level of the target secondary processing level that has been designated to be processed within the time interval for each time interval that meets the second time span, wherein if the previous processing level is the primary processing level, the first target data corresponding to the primary processing level that has been designated to be processed within the time interval is designated to be processed; if the previous processing level is not the primary processing level, the target data corresponding to the previous processing level of the target secondary processing level within the time interval is obtained; the target data corresponding to the acquired previous processing level is designated to be processed; and the second target data for monitoring the operating status of industrial equipment is obtained; the target secondary processing level is determined by dividing the time span of collecting high-frequency data in advance according to the second time span, and the second time span is an integer multiple of the time span corresponding to the previous processing level.

8. The device according to claim 7, characterized in that The device also includes: The collecting unit is used to collect the high-frequency data generated by the industrial equipment during operation according to a preset sampling interval, and determine the collected high-frequency data as the high-frequency data to be processed.

9. The device according to claim 7, characterized in that The device also includes: A customized processing mode starting unit is used to obtain the processing level selected by the user from the configured processing level array to form a customized processing level array when a specified processing is required according to a preset customized processing mode; the processing level array includes secondary processing levels with different time spans; A processing unit, used to determine the processing order of each sub-processing level according to the size of the time span corresponding to each sub-processing level in the customized processing level array, and to use the current processing level to be processed as the target sub-processing level according to the processing order to trigger the sub-processing level unit; The automatic processing mode is used to determine the current sub-processing level to be processed as the target sub-processing level according to the processing order of each sub-processing level in the configured processing level array when processing is required according to the automatic processing mode, and trigger the sub-processing level unit.

10. The device according to any one of claims 7 to 9, characterized in that: The primary processing level unit determines initial high-frequency data from the high-frequency data to be processed, specifically for: Determine whether the time differences between any two adjacent time points corresponding to the high-frequency data to be processed are equal; If not, the adjacent high-frequency data with the same time difference is determined as the initial high-frequency data; The step of performing designated processing on the initial high-frequency data to obtain first target data for monitoring the operating status of industrial equipment includes: For each initial high-frequency data corresponding to the same time difference, performing designated processing on the initial high-frequency data corresponding to the same time difference to obtain first target data for monitoring the operating status of the industrial equipment; If yes, initial high-frequency data is determined from the high-frequency data to be processed.

11. An electronic device, characterized in that: It comprises a processor and a machine-readable storage medium, wherein the machine-readable storage medium stores machine-executable instructions that can be executed by the processor; the processor is used to execute the machine-executable instructions to implement the method steps described in any one of claims 1-6.

Images