Group edge acquisition method, device, electronic device and readable storage medium

Abstract

This application discloses a grouped edge acquisition method, apparatus, electronic device, and readable storage medium. The grouped edge acquisition method includes: performing periodic measurements on each data point, grouping each data point to obtain each acquisition group; dividing the data points in each acquisition group into subsets, wherein each subset includes a first preset number of data points; determining the acquisition time of each acquisition group based on the configuration coefficient corresponding to each subset, the corresponding last acquisition time, and the configuration coefficient of the acquisition group corresponding to the subset; and performing edge acquisition on each data point in the acquisition group based on the acquisition time. This application solves the technical problem of low acquisition efficiency of edge acquisition systems.

Description

Technical Field

[0001] The present application relates to the field of data acquisition technology, and in particular to a group edge acquisition method, device, electronic device, and readable storage medium. Background Art

[0002] In modern industrial scenarios, data collection from a large number of industrial devices is often required. Currently, the predominant method for data point collection is single-point acquisition. This relatively simple method suffers from drawbacks such as relatively slow acquisition speed and long traversal times per acquisition cycle. This single-point acquisition method cannot meet the high-reality requirements of edge acquisition tasks. Edge acquisition strategies are also limited in their specificity for data points from industrial equipment with varying acquisition cycles, resulting in low real-time performance and resource utilization. Consequently, edge acquisition systems exhibit low efficiency. Summary of the Invention

[0003] The main purpose of this application is to provide a group edge acquisition method, device, electronic device and readable storage medium, aiming to solve the technical problem of low acquisition efficiency of the edge acquisition system.

[0004] To achieve the above objectives, the present application provides a group edge collection method, which includes:

[0005] By periodically measuring each data point, each data point is grouped to obtain each acquisition group set;

[0006] Dividing the data points in each of the collection sets into subsets, wherein the subsets include a first preset number of data points;

[0007] Determining the collection time of the collection group according to the configuration coefficient and the corresponding last collection time corresponding to each of the subsets and the configuration coefficient of the collection group corresponding to the subset;

[0008] Based on the acquisition time, edge acquisition is performed on each data point in the acquisition group.

[0009] Optionally, the step of performing periodic measurement on each data point and grouping each data point to obtain each acquisition group includes:

[0010] Traversing each of the data points and measuring the period of each of the data points to obtain the period, discrete distribution and delay corresponding to each of the data points;

[0011] The data point grouping model is used to group the data points according to their corresponding periods, discrete distributions and time delays to obtain the collection groups, wherein the data point grouping model is obtained by self-learning training of the data points with grouping labels.

[0012] Optionally, each of the collection sets includes a regular collection set, a burst collection set, and a high-latency collection set, and the step of grouping each of the data points according to the period, discrete distribution, and latency corresponding to each of the data points using a data point grouping model to obtain each of the collection sets includes:

[0013] dividing the data points with fixed periods among the data points into the regular collection group set;

[0014] Dividing the data points whose periods are not fixed and whose discrete distribution of collection time conforms to a preset distribution into the burst collection group set;

[0015] The data points whose response delay is greater than the preset delay are divided into the high-latency collection group set.

[0016] Optionally, the regular acquisition group includes a high-frequency acquisition group, a normal acquisition group, and a low-frequency acquisition group, and the step of dividing the data points with fixed periods among the data points into the regular acquisition group includes:

[0017] Obtaining period ranges corresponding to the high-frequency acquisition group set, the normal acquisition group set, and the low-frequency acquisition group set respectively;

[0018] The data points in the conventional acquisition group set are grouped based on each of the period ranges to obtain the high-frequency acquisition group set, the normal acquisition group set, and the low-frequency acquisition group set, wherein the period of the normal acquisition group set is smaller than the period of the low-frequency acquisition group set and larger than the period of the high-frequency acquisition group set.

[0019] Optionally, the configuration coefficient corresponding to the subset includes a subset weight coefficient and a priority threshold, and the configuration coefficient corresponding to the collection group includes a group weight coefficient. The step of determining the collection time of the collection group based on the configuration coefficient corresponding to each subset and the corresponding last collection time and the configuration coefficient of the collection group corresponding to the subset includes:

[0020] Determining the collection interval of each subset according to the time difference between the current time and the last collection time;

[0021] Determine a collection priority value corresponding to the subset based on the collection interval, the group weight coefficient, and the subset weight coefficient;

[0022] Determining whether there are a second preset number of subsets in the collection set whose collection priority values are all greater than the priority threshold;

[0023] If it exists, the current time is determined as the collection time of the collection set;

[0024] If not, determining that the number of subsets in the acquisition set is less than the second preset number and the acquisition priority values of the subsets in the acquisition set are all greater than the priority threshold;

[0025] If so, the current time is determined as the collection time of the collection set.

[0026] Optionally, the step of determining the acquisition priority value corresponding to the acquisition group based on the acquisition interval, the group weight coefficient, and the subset weight coefficient includes:

[0027] Determining a weight coefficient product based on the group weight coefficient and the subset weight coefficient;

[0028] The collection priority value is determined according to the ratio of the collection interval time to the product of the weight coefficient.

[0029] Optionally, the step of performing edge acquisition on each data point in the acquisition group based on the acquisition time includes:

[0030] determining a collection order for each subset according to a collection priority value of each subset in the collection set;

[0031] Based on the acquisition sequence and the acquisition time, edge acquisition is performed on the data points in each subset of the acquisition group in sequence.

[0032] The present application also provides a packet edge collection device, which is applied to a packet edge collection device, and includes:

[0033] An acquisition grouping module, configured to group the data points by periodically measuring the data points to obtain acquisition groups;

[0034] a subset division module, configured to divide the data points in each of the collection sets into subsets, wherein each subset includes a first preset number of data points;

[0035] a time determination module, configured to determine the collection time of the collection group according to the configuration coefficient and the corresponding last collection time corresponding to each of the subsets and the configuration coefficient of the collection group corresponding to the subset;

[0036] The edge acquisition module is used to perform edge acquisition on each data point in the acquisition group based on the acquisition time.

[0037] Optionally, the collection and grouping module is further configured to:

[0038] Traversing each of the data points and measuring the period of each of the data points to obtain the period, discrete distribution and delay corresponding to each of the data points;

[0039] The data point grouping model is used to group the data points according to their corresponding periods, discrete distributions and time delays to obtain the collection groups, wherein the data point grouping model is obtained by self-learning training of the data points with grouping labels.

[0040] Optionally, the collection and grouping module is further configured to:

[0041] dividing the data points with fixed periods among the data points into the regular collection group set;

[0042] Dividing the data points whose periods are not fixed and whose discrete distribution of collection time conforms to a preset distribution into the burst collection group set;

[0043] The data points whose response delay is greater than the preset delay are divided into the high-latency collection group set.

[0044] Optionally, the collection and grouping module is further configured to:

[0045] Obtaining period ranges corresponding to the high-frequency acquisition group set, the normal acquisition group set, and the low-frequency acquisition group set respectively;

[0046] The data points in the conventional acquisition group set are grouped based on each of the period ranges to obtain the high-frequency acquisition group set, the normal acquisition group set, and the low-frequency acquisition group set, wherein the period of the normal acquisition group set is smaller than the period of the low-frequency acquisition group set and larger than the period of the high-frequency acquisition group set.

[0047] Optionally, the time determination module is further configured to:

[0048] Determining the collection interval of each subset according to the time difference between the current time and the last collection time;

[0049] Determine a collection priority value corresponding to the subset based on the collection interval, the group weight coefficient, and the subset weight coefficient;

[0050] Determining whether there are a second preset number of subsets in the collection set whose collection priority values are all greater than the priority threshold;

[0051] If it exists, the current time is determined as the collection time of the collection set;

[0052] If not, determining that the number of subsets in the acquisition set is less than the second preset number and the acquisition priority values of the subsets in the acquisition set are all greater than the priority threshold;

[0053] If so, the current time is determined as the collection time of the collection set.

[0054] Optionally, the time determination module is further configured to:

[0055] Determining a weight coefficient product based on the group weight coefficient and the subset weight coefficient;

[0056] The collection priority value is determined according to the ratio of the collection interval time to the product of the weight coefficient.

[0057] Optionally, the edge acquisition module is further configured to:

[0058] determining a collection order for each subset according to a collection priority value of each subset in the collection set;

[0059] Based on the acquisition sequence and the acquisition time, edge acquisition is performed on the data points in each subset of the acquisition group in sequence.

[0060] The present application also provides an electronic device, which is a physical device and includes: a memory, a processor, and a program of the group edge collection method stored in the memory and executable on the processor. When the program of the group edge collection method is executed by the processor, the steps of the group edge collection method described above can be implemented.

[0061] The present application also provides a computer-readable storage medium, on which is stored a program for implementing the grouped edge collection method. When the program for the grouped edge collection method is executed by a processor, the steps of the grouped edge collection method as described above are implemented.

[0062] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned group edge acquisition method when executed by a processor.

[0063] The present application provides a grouped edge acquisition method, device, electronic device and readable storage medium. First, by performing periodic measurement on each data point, each data point is grouped to obtain each acquisition group set, and then the data points in each acquisition group set are divided into subsets, wherein the subset includes a first preset number of data points. Then, based on the configuration coefficient corresponding to each subset and the corresponding last acquisition time and the configuration coefficient of the acquisition group set corresponding to the subset, the acquisition time of the acquisition group set is determined. Finally, based on the acquisition time, edge acquisition is performed on each data point in the acquisition group set. In the technical solution of the present application, by grouping each data point and then determining the corresponding acquisition time based on the configuration coefficient of each group, different acquisition strategies are implemented for different types of data points, and the data points in different acquisition cycles are targeted and real-time, thereby improving resource utilization and acquisition efficiency of the edge acquisition system. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0065] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0066] Figure 1 This is a flow chart of the first embodiment of the group edge acquisition method of the present application;

[0067] Figure 2 This is a flowchart of steps S121 to S123 in the first embodiment of the group edge acquisition method of the present application;

[0068] Figure 3 This is a flowchart of steps S31 to S36 in the first embodiment of the group edge acquisition method of the present application;

[0069] Figure 4 This is a schematic diagram of the structure of the group edge acquisition device of this application;

[0070] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the group edge acquisition method in the embodiment of the present application.

[0071] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0072] To make the above-mentioned purposes, features, and advantages of the present application more clearly understood, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of this application.

[0073] Example 1

[0074] In modern industrial scenarios, edge data collection is often required for a large number of industrial devices. Currently, the predominant data point collection method is single-point acquisition. While this method is relatively simple, it suffers from relatively slow acquisition speeds and long acquisition cycle times. Single-point acquisition methods cannot meet the requirements of real-time edge data collection tasks. Furthermore, edge data collection strategies are not well-targeted for data points collected at different acquisition cycles within industrial equipment. This results in low real-time performance and resource utilization, leading to low efficiency in edge data collection systems. Designing different edge data collection speeds based on the requirements of different data points can improve data collection efficiency.

[0075] The present application embodiment provides a group edge collection method. In the first embodiment of the group edge collection method of the present application, refer to Figure 1 , the group edge acquisition method includes:

[0076] Step S10, performing periodic measurement on each data point and grouping the data points to obtain collection groups;

[0077] Step S20, dividing the data points in each of the collection groups into subsets, wherein the subsets include a first preset number of data points;

[0078] Step S30, determining the collection time of the collection set according to the configuration coefficient and the corresponding last collection time corresponding to each subset and the configuration coefficient of the collection set corresponding to the subset;

[0079] Step S40: performing edge acquisition on each data point in the acquisition group based on the acquisition time.

[0080] In the embodiment of the present application, it should be noted that the grouping criteria of each of the data points include period, acquisition frequency and delay, and each of the acquisition groups is obtained. Each acquisition group has a specific acquisition strategy. The acquisition strategy includes the configuration coefficient of each acquisition group and the configuration coefficient of the subset of each acquisition group, so as to realize the execution of corresponding acquisition strategies according to different types of data points, improve the acquisition efficiency and improve the real-time performance and resource utilization of the entire edge acquisition system. Each of the configuration coefficients can be configured by the acquisition staff according to the specific work situation to reflect the differences between the acquisition strategies.

[0081] As an example, steps S10 to S40 include: traversing all data points to obtain the period, acquisition frequency and delay information of each data point; adding each data point to a regular acquisition group set, a burst acquisition group set and a high-latency acquisition group set according to the period, acquisition frequency and delay information of each data point; inserting each data point into each subset containing a first preset number of data points according to the data type, weight, device attribute and address range of the data point in each acquisition group set; obtaining the configuration coefficient of each acquisition group set and the corresponding subset, and determining the acquisition time interval according to the current time and the last acquisition time corresponding to the subset data point; calculating each configuration coefficient and the acquisition time interval to obtain the acquisition priority value corresponding to each subset; determining the acquisition time corresponding to the subset according to the relationship between the acquisition priority value corresponding to each subset and the priority threshold corresponding to each acquisition group set; and performing edge acquisition on the data points in each subset of the acquisition group set based on the acquisition time.

[0082] The step of performing periodic measurement on each data point and grouping each data point to obtain each collection group includes:

[0083] Step S11, traversing each of the data points and measuring the period of each of the data points to obtain the period, discrete distribution and delay corresponding to each of the data points;

[0084] Step S12, grouping each of the data points according to the period, discrete distribution and delay corresponding to each of the data points through a data point grouping model to obtain each of the collection groups, wherein the data point grouping model is obtained by self-learning training of each data point with a grouping label.

[0085] In the embodiment of the present application, it should be noted that the data point grouping model is a self-learning model, which is trained by using data corresponding to a large number of data points and corresponding grouping labels. The training is successful after meeting the expected accuracy rate, and then the collected data points are periodically measured and grouped according to the data point grouping model to obtain each of the collection groups and the subsets corresponding to each collection group.

[0086] As an example, steps S11 to S12 include: traversing each of the data points based on the data point grouping model to obtain the period, discrete distribution and delay corresponding to each of the data points; determining the data points to be inserted into the regular collection group based on the period of each of the data points through the data point grouping model; inserting each of the data points that are centrally collected into the burst collection group based on the discrete distribution of each of the data points through the data point grouping model; inserting data points with a delay higher than a preset delay into the high-latency collection group based on the duration of each of the data points through the data point grouping model.

[0087] Among them, reference Figure 2 The step of grouping the data points according to the period, discrete distribution and delay corresponding to each data point by using the data point grouping model to obtain each collection group includes:

[0088] Step S121, dividing the data points with fixed periods among the data points into the regular collection group set;

[0089] Step S122, dividing the data points whose periods are not fixed and whose discrete distribution of collection time conforms to a preset distribution into the burst collection group set;

[0090] Step S123 , dividing the data points whose response delay is greater than the preset delay into the high-latency collection group.

[0091] In the embodiments of the present application, it should be noted that each of the collection groups includes a conventional collection group, a burst collection group and a high-latency collection group. In the embodiments of the present application, each collection group is grouped according to the period, discrete distribution and latency of the data points, so as to better formulate corresponding collection strategies for data points with different characteristics; wherein, there is no order between step S121, step S122 and step S123, and any technical solution including step S121, step S122 and step S123, regardless of the order, is within the scope of the embodiments of the present application.

[0092] As an example, steps S121 to S123 include: screening data points whose periodic fluctuation range is within a preset range from each of the data points; dividing the data points whose periodic fluctuation range is within the preset range into the regular collection group set; obtaining a preset distribution, wherein the preset distribution is that the collection time corresponding to the data point is distributed concentratedly on the time axis; screening data points whose periodic fluctuation range is outside the preset range and whose discrete distribution of the data points conforms to the preset distribution from each of the data points and divides them into the burst collection group set; obtaining a preset delay, and dividing the data points whose response delay is greater than the preset delay in each of the data points into the high-latency collection group set.

[0093] The step of dividing the data points with fixed periods among the data points into the regular collection group includes:

[0094] Step A10, obtaining period ranges corresponding to the high-frequency acquisition group set, the normal acquisition group set, and the low-frequency acquisition group set respectively;

[0095] Step A20: Grouping the data points in the conventional collection set based on the period ranges to obtain the high-frequency collection set, the normal collection set, and the low-frequency collection set, wherein the period of the normal collection set is smaller than the period of the low-frequency collection set and larger than the period of the high-frequency collection set.

[0096] In the embodiment of the present application, it should be noted that the conventional acquisition group set includes a high-frequency acquisition group set, a normal acquisition group set and a low-frequency acquisition group set. The difference between the high-frequency acquisition group set, the normal acquisition group set and the low-frequency acquisition group set is that the periods and acquisition frequencies are different. The higher the period, the lower the acquisition frequency.

[0097] As an example, steps A10 to A20 include: obtaining the period range of the high-frequency acquisition group set, the period range of the normal acquisition group set, and the period range of the low-frequency acquisition group set input by the user; and dividing the data points in the conventional acquisition group set into the high-frequency acquisition group set, the normal acquisition group set, and the low-frequency acquisition group set according to each of the period ranges and the period of each data point in the conventional acquisition group set.

[0098] Among them, reference Figure 3 The step of determining the collection time of the collection group according to the configuration coefficient and the corresponding last collection time corresponding to each subset and the configuration coefficient of the collection group corresponding to the subset includes:

[0099] Step S31, determining the collection interval time of each subset according to the time difference between the current time and the last collection time;

[0100] Step S32, determining a collection priority value corresponding to the subset based on the collection interval, the group weight coefficient, and the subset weight coefficient;

[0101] Step S33, determining whether there are a second preset number of subsets in the collection set whose collection priority values are all greater than the priority threshold;

[0102] Step S34: if it exists, the current time is determined as the collection time of the collection set;

[0103] Step S35: If not, determining that the number of subsets in the acquisition set is less than the second preset number and the acquisition priority values of the subsets in the acquisition set are all greater than the priority threshold;

[0104] Step S36: If yes, the current time is determined as the collection time of the collection set.

[0105] In the embodiment of the present application, it should be noted that the configuration coefficient corresponding to the subset includes a subset weight coefficient and a priority threshold, and the configuration coefficient corresponding to the acquisition group includes a group weight coefficient, wherein the smaller the group weight coefficient and the subset weight coefficient are, the higher the acquisition frequency of the corresponding acquisition group and the shorter the acquisition period, and the larger the priority threshold, the longer the acquisition period. The configuration coefficient can be set by the user to control the acquisition frequency and acquisition strategy of each acquisition group.

[0106] As an example, steps S31 to S34 include: calculating the time difference between the current time and the last acquisition time corresponding to the subset to obtain the acquisition interval time; calculating the product of the group weight coefficient of the acquisition group corresponding to the subset and the subset weight coefficient to obtain the weight coefficient product; calculating the ratio of the acquisition interval time to the weight coefficient product to obtain the acquisition priority value; obtaining a second preset number; if the acquisition group includes no less than the second preset number of subsets, when there is a second preset number of acquisition interval times and the weight coefficient whose ratio is greater than the priority threshold, the current time is determined as the acquisition time of the acquisition group corresponding to the subset; if the number of subsets included in the acquisition group is less than the second preset number, when the ratio of the acquisition interval time of all subsets of the acquisition subset to the weight coefficient is greater than the priority threshold, the current time is determined as the acquisition time of the acquisition group corresponding to the subset.

[0107] As an example, if the current time is Sc and the last collection time is Sh, the collection interval is expressed as:

[0108] T1=Sc-Sh

[0109] The collection set weight coefficient is M1, the subgroup weight coefficient is N1, and the priority threshold is Y1. Then, when there are a second preset number of subsets or all subsets satisfying:

[0110] When T1 / (M1*N1)>=Y1,

[0111] The current time Sc is determined as the collection time of the collection set.

[0112] The step of determining the acquisition priority value corresponding to the acquisition group based on the acquisition interval, the group weight coefficient, and the subset weight coefficient includes:

[0113] Step S321, determining a weight coefficient product according to the group weight coefficient and the subset weight coefficient;

[0114] Step S322: determining the collection priority value according to the ratio of the collection interval time to the product of the weight coefficient.

[0115] As an example, steps S321 to S322 include: calculating the product of the group weight coefficient and the subset weight coefficient to obtain the weight coefficient product; calculating the ratio of the collection interval time to the weight coefficient product to obtain the collection priority value, wherein the larger the collection priority value, the closer the time to the next collection.

[0116] The step of performing edge acquisition on each data point in the acquisition group based on the acquisition time includes:

[0117] Step S41, determining the collection order of each subset according to the collection priority value of each subset in the collection group;

[0118] Step S42 : performing edge acquisition on the data points in each subset of the acquisition group in sequence based on the acquisition sequence and the acquisition time.

[0119] In the embodiment of the present application, it should be noted that the data points in each subset of the collection group have different priorities and last collection times, wherein the priority is determined by the subset weight coefficient, the smaller the subset weight coefficient, the higher the corresponding priority, and the later the last collection time, the higher the corresponding priority. The collection priority value comprehensively considers the subset weight coefficient and the last collection time to determine the priority of the subset. For data points in the same subset, since the subset weight coefficients are consistent, the priority of each data point is determined by the last collection time.

[0120] As an example, steps S41 to S42 include: sorting the subsets in the collection group according to the collection priority corresponding to each subset to obtain the order of each subset; performing edge collection on the data points in each subset in the collection group in sequence according to the collection time and the order, wherein, in the process of edge collection of the data points in each subset, the edge collection order of each data point in the same subset is determined based on the last collection time of each data point.

[0121] An embodiment of the present application provides a grouped edge acquisition method, which first performs periodic measurement on each data point, groups each data point to obtain each acquisition group set, and then divides the data points in each acquisition group set into subsets, wherein the subset includes a first preset number of data points, and then determines the acquisition time of the acquisition group set based on the configuration coefficient corresponding to each subset and the corresponding last acquisition time and the configuration coefficient of the acquisition group set corresponding to the subset. Finally, based on the acquisition time, edge acquisition is performed on each data point in the acquisition group set. In the technical solution of the embodiment of the present application, by grouping each data point and then determining the corresponding acquisition time based on the configuration coefficient of each group, different acquisition strategies are implemented for different types of data points, and the data points with different acquisition cycles are targeted and real-time, thereby improving resource utilization and acquisition efficiency of the edge acquisition system.

[0122] Example 2

[0123] The embodiment of the present application also provides a packet edge collection device, which is applied to the packet edge collection device, referring to Figure 4 , the packet edge collection device includes:

[0124] An acquisition grouping module, configured to group the data points by periodically measuring the data points to obtain acquisition groups;

[0125] a subset division module, configured to divide the data points in each of the collection sets into subsets, wherein each subset includes a first preset number of data points;

[0126] a time determination module, configured to determine the collection time of the collection group according to the configuration coefficient and the corresponding last collection time corresponding to each of the subsets and the configuration coefficient of the collection group corresponding to the subset;

[0127] The edge acquisition module is used to perform edge acquisition on each data point in the acquisition group based on the acquisition time.

[0128] Optionally, the collection and grouping module is further configured to:

[0129] Traversing each of the data points and measuring the period of each of the data points to obtain the period, discrete distribution and delay corresponding to each of the data points;

[0130] The data point grouping model is used to group the data points according to their corresponding periods, discrete distributions and time delays to obtain the collection groups, wherein the data point grouping model is obtained by self-learning training of the data points with grouping labels.

[0131] Optionally, the collection and grouping module is further configured to:

[0132] dividing the data points with fixed periods among the data points into the regular collection group set;

[0133] Dividing the data points whose periods are not fixed and whose discrete distribution of collection time conforms to a preset distribution into the burst collection group set;

[0134] The data points whose response delay is greater than the preset delay are divided into the high-latency collection group set.

[0135] Optionally, the collection and grouping module is further configured to:

[0136] Obtaining period ranges corresponding to the high-frequency acquisition group set, the normal acquisition group set, and the low-frequency acquisition group set respectively;

[0137] The data points in the conventional acquisition group set are grouped based on each of the period ranges to obtain the high-frequency acquisition group set, the normal acquisition group set, and the low-frequency acquisition group set, wherein the period of the normal acquisition group set is smaller than the period of the low-frequency acquisition group set and larger than the period of the high-frequency acquisition group set.

[0138] Optionally, the time determination module is further configured to:

[0139] Determining the collection interval of each subset according to the time difference between the current time and the last collection time;

[0140] Determine a collection priority value corresponding to the subset based on the collection interval, the group weight coefficient, and the subset weight coefficient;

[0141] Determining whether there are a second preset number of subsets in the collection set whose collection priority values are all greater than the priority threshold;

[0142] If it exists, the current time is determined as the collection time of the collection set;

[0143] If not, determining that the number of subsets in the acquisition set is less than the second preset number and the acquisition priority values of the subsets in the acquisition set are all greater than the priority threshold;

[0144] If so, the current time is determined as the collection time of the collection set.

[0145] Optionally, the time determination module is further configured to:

[0146] Determining a weight coefficient product based on the group weight coefficient and the subset weight coefficient;

[0147] The collection priority value is determined according to the ratio of the collection interval time to the product of the weight coefficient.

[0148] Optionally, the edge acquisition module is further configured to:

[0149] determining a collection order for each subset according to a collection priority value of each subset in the collection set;

[0150] Based on the acquisition sequence and the acquisition time, edge acquisition is performed on the data points in each subset of the acquisition group in sequence.

[0151] The grouped edge acquisition device provided in this application utilizes the grouped edge acquisition method described in the aforementioned embodiment to address the technical issue of low acquisition efficiency in edge acquisition systems. Compared to the prior art, the grouped edge acquisition device provided in this embodiment achieves the same beneficial effects as the grouped edge acquisition method described in the aforementioned embodiment. Other technical features of this grouped edge acquisition device are the same as those disclosed in the aforementioned embodiment and are not further detailed here.

[0152] Example 3

[0153] An embodiment of the present application provides an electronic device, comprising: at least one processor; and a memory communicatively linked to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the grouped edge acquisition method of the above-mentioned embodiment 1.

[0154] Reference below Figure 5 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0155] like Figure 5 As shown, the electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage device into a random access memory (RAM). In the RAM, various programs and data required for the operation of the electronic device are also stored. The processing device, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0156] Typically, the following systems can be linked to the I / O interface: input devices including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices including, for example, magnetic tape, hard disk, etc.; and communication devices. The communication devices can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figures show electronic devices with various systems, it should be understood that not all of the illustrated systems are required to be implemented or present. More or fewer systems may be implemented or present instead.

[0157] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processing device, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0158] The electronic device provided in this application utilizes the grouped edge acquisition method of the aforementioned embodiment to resolve the technical issue of low acquisition efficiency in edge acquisition systems. Compared to the prior art, the beneficial effects of the electronic device provided in this embodiment are the same as those of the grouped edge acquisition method provided in the first embodiment above. Other technical features of this electronic device are the same as those disclosed in the method of the previous embodiment and are not further elaborated here.

[0159] It should be understood that various parts of the present disclosure can be implemented with hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in an appropriate manner.

[0160] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0161] Example 4

[0162] This embodiment provides a computer-readable storage medium having computer-readable program instructions stored thereon. The computer-readable program instructions are used to execute the method for grouping edge acquisition in the first embodiment.

[0163] The computer-readable storage medium provided in the embodiment of the present application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination thereof. A more specific example of a computer-readable storage medium can include, but is not limited to, an electrical link with one or more wires, 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 thereof. In the present embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a system or a device or used in combination therewith. The program code contained in the computer-readable storage medium can be transmitted with any appropriate medium, including but not limited to: an electric wire, an optical cable, RF (radio frequency), etc., or any suitable combination thereof.

[0164] The computer-readable storage medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0165] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by an electronic device, the electronic device: performs periodic measurement on each data point, groups each data point, and obtains each collection group; divides the data points in each collection group into subsets, wherein the subset includes a first preset number of data points; determines the collection time of the collection group based on the configuration coefficient corresponding to each subset and the corresponding last collection time and the configuration coefficient of the collection group corresponding to the subset; and performs edge collection on each data point in the collection group based on the collection time.

[0166] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be linked to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be linked to an external computer (e.g., through the Internet using an Internet service provider).

[0167] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0168] The modules involved in the embodiments described in this disclosure may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0169] The computer-readable storage medium provided in this application stores computer-readable program instructions for executing the aforementioned grouped edge acquisition method, thereby resolving the technical issue of low acquisition efficiency in edge acquisition systems. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this embodiment of the application are similar to those of the grouped edge acquisition method provided in the aforementioned embodiment, and are not further elaborated here.

[0170] Example 5

[0171] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned group edge acquisition method when executed by a processor.

[0172] The computer program product provided in this application solves the technical problem of low acquisition efficiency of edge acquisition systems. Compared with the prior art, the beneficial effects of the computer program product provided in the embodiments of this application are the same as the beneficial effects of the grouped edge acquisition method provided in the above embodiments, and will not be repeated here.

[0173] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent processing scope of the present application.

Claims

1. A group edge acquisition method, characterized in that: The group edge acquisition method includes: By periodically measuring each data point, each data point is grouped to obtain each acquisition group set; Dividing the data points in each of the collection sets into subsets, wherein the subsets include a first preset number of data points; Determining the collection time of the collection group according to the configuration coefficient and the corresponding last collection time corresponding to each of the subsets and the configuration coefficient of the collection group corresponding to the subset; Based on the acquisition time, edge acquisition is performed on each data point in the acquisition group; The configuration coefficient corresponding to the subset includes a subset weight coefficient and a priority threshold, the configuration coefficient corresponding to the collection group includes a group weight coefficient, and the step of determining the collection time of the collection group based on the configuration coefficient corresponding to each subset and the corresponding last collection time and the configuration coefficient of the collection group corresponding to the subset includes: Determining the collection interval of each subset according to the time difference between the current time and the last collection time; Determine a collection priority value corresponding to the subset based on the collection interval, the group weight coefficient, and the subset weight coefficient; Determining whether there are a second preset number of subsets in the collection set whose collection priority values are all greater than the priority threshold; If it exists, the current time is determined as the collection time of the collection set; If not, determining that the number of subsets in the acquisition set is less than the second preset number and the acquisition priority values of the subsets in the acquisition set are all greater than the priority threshold; If so, the current time is determined as the collection time of the collection set.

2. The group edge collection method according to claim 1, characterized in that: The step of performing periodic measurement on each data point and grouping each data point to obtain each acquisition group set comprises: Traversing each of the data points and measuring the period of each of the data points to obtain the period, discrete distribution and delay corresponding to each of the data points; The data point grouping model is used to group the data points according to their corresponding periods, discrete distributions and time delays to obtain the collection groups, wherein the data point grouping model is obtained by self-learning training of the data points with grouping labels.

3. The group edge collection method according to claim 2, characterized in that: Each of the collection groups includes a regular collection group, a burst collection group, and a high-latency collection group. The step of grouping the data points according to the period, discrete distribution, and latency corresponding to each of the data points using the data point grouping model to obtain each of the collection groups includes: dividing the data points with fixed periods among the data points into the regular collection group set; Dividing the data points whose periods are not fixed and whose discrete distribution of collection time conforms to a preset distribution into the burst collection group set; The data points whose response delay is greater than the preset delay are divided into the high-latency collection group set.

4. The group edge collection method according to claim 3, wherein: The conventional collection group includes a high-frequency collection group, a normal collection group, and a low-frequency collection group. The step of dividing the data points with fixed periods into the conventional collection group includes: Obtaining period ranges corresponding to the high-frequency acquisition group set, the normal acquisition group set, and the low-frequency acquisition group set respectively; The data points in the conventional acquisition group set are grouped based on each of the period ranges to obtain the high-frequency acquisition group set, the normal acquisition group set, and the low-frequency acquisition group set, wherein the period of the normal acquisition group set is smaller than the period of the low-frequency acquisition group set and larger than the period of the high-frequency acquisition group set.

5. The group edge collection method according to claim 1, wherein: The step of determining the acquisition priority value corresponding to the acquisition group based on the acquisition interval, the group weight coefficient, and the subset weight coefficient comprises: Determining a weight coefficient product based on the group weight coefficient and the subset weight coefficient; The collection priority value is determined according to the ratio of the collection interval time to the product of the weight coefficient.

6. The group edge collection method according to claim 1, wherein: The step of performing edge acquisition on each data point in the acquisition group based on the acquisition time includes: determining a collection order for each subset according to a collection priority value of each subset in the collection set; Based on the acquisition sequence and the acquisition time, edge acquisition is performed on the data points in each subset of the acquisition group in sequence.

7. A group edge acquisition device, characterized in that: The packet edge collection device includes: An acquisition grouping module, configured to group the data points by periodically measuring the data points to obtain acquisition groups; a subset division module, configured to divide the data points in each of the collection sets into subsets, wherein each subset includes a first preset number of data points; a time determination module, configured to determine the collection time of the collection group according to the configuration coefficient and the corresponding last collection time corresponding to each of the subsets and the configuration coefficient of the collection group corresponding to the subset; An edge acquisition module, configured to perform edge acquisition on each data point in the acquisition group based on the acquisition time; Among them, the configuration coefficient corresponding to the subset includes a subset weight coefficient and a priority threshold, the configuration coefficient corresponding to the acquisition group includes a group weight coefficient, and the time determination module is also used to: determine the acquisition interval time of each subset based on the time difference between the current time and the last acquisition time; determine the acquisition priority value corresponding to the subset based on the acquisition interval time, the group weight coefficient and the subset weight coefficient; judge whether there are a second preset number of subsets in the acquisition group whose acquisition priority values are greater than the priority threshold; if so, determine the current time as the acquisition time of the acquisition group; if not, judge that the number of subsets in the acquisition group is less than the second preset number and the acquisition priority values of the subsets in the acquisition group are greater than the priority threshold; if so, determine the current time as the acquisition time of the acquisition group.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively linked to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the packet edge collection method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program for implementing the group edge collection method. The program for implementing the group edge collection method is executed by a processor to implement the steps of the group edge collection method according to any one of claims 1 to 6.

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