CN116582596B

CN116582596B - Data processing method and device and PLC data transmission system based on Internet of things

Info

Publication number: CN116582596B
Application number: CN202310812741.9A
Authority: CN
Inventors: 陈帼鸾
Original assignee: Foshan Interstellar Cloud Digital Technology Co ltd
Current assignee: Foshan Interstellar Cloud Digital Technology Co ltd
Priority date: 2023-07-05
Filing date: 2023-07-05
Publication date: 2023-11-10
Anticipated expiration: 2043-07-05
Also published as: CN116582596A

Abstract

The invention relates to the technical field of data processing, in particular to a data processing method and device and a PLC data transmission system based on the Internet of things, wherein the data processing method comprises the following steps: acquiring initial industrial data in a target time period, and dividing the target time period periodically; carrying out change division on the current time period and each historical time period; screening out matching subsections matched with each current subsection from each history subsection set; determining the joint matching degree between each current sub-segment and each matching sub-segment; determining a target local interval corresponding to each piece of initial industrial data in the current time period; carrying out distribution degree analysis processing, sensitivity degree analysis processing and self-adaptive adjustment on each initial industrial data in the current time period; and performing revolving door compression on the target industrial data set through a revolving door compression algorithm. The invention can improve the compression efficiency by processing the data before compressing the data, thereby improving the data transmission efficiency.

Description

Data processing method and device and PLC data transmission system based on Internet of things

Technical Field

The invention relates to the technical field of data processing, in particular to a data processing method and device and a PLC data transmission system based on the Internet of things.

Background

PLC (Programmable Logic Controller, editable logic controller) is one of the core components of modern industrial automation domains, commonly used in the field of production flows and control systems, which is programmed to meet different process flows and equipment requirements, ensuring stable and reliable performance in all cases. The combination of IoT (Internet of Things ) with PLC may enable information transfer and control between industrial automation devices.

In the process of data transmission, because the data is affected by network bandwidth and storage space of receiving end devices (such as an upper computer, an HMI (Human Machine Interface, a human-machine interface), an SCADA (Supervisory Control and Data Acquisition System, a monitoring and data acquisition system) and the like), the data is difficult to meet the requirement of an industrial control system with higher real-time requirements, and the resource utilization rate is low, so that effective data compression processing is usually performed in the transmission process, wherein the revolving door compression algorithm is a data compression algorithm widely applied to process control, and the transmission load can be reduced. However, the compression efficiency of the revolving door compression algorithm is affected by the data distribution characteristics, so that the fluctuation degree of the collected data is possibly larger, and the compression data which is supposed to be one section according to the set tolerance range in the revolving door compression algorithm is changed into multi-section compression data, so that the compression efficiency of the data is lower, and the transmission efficiency requirement in the transmission process cannot be met.

Disclosure of Invention

The summary of the invention is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. The summary of the invention is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

In order to solve the technical problem of low data compression efficiency, the invention provides a data processing method, a data processing device and a PLC data transmission system based on the Internet of things.

In a first aspect, the present invention provides a data processing method, the method comprising:

acquiring initial industrial data in a target time period, and periodically dividing the target time period to obtain a current time period and a historical time period set;

carrying out change division on each historical time period in a current time period and a historical time period set to obtain a current sub-period set corresponding to the current time period and a historical sub-period set corresponding to the historical time period;

screening matching subsections matched with each current subsection in the current subsection set from each history subsection set to obtain a matching subsection set corresponding to each current subsection;

Determining the joint matching degree between each current sub-segment and each matching sub-segment in the matching sub-segment set corresponding to the current sub-segment;

according to the joint matching degree, determining a target local interval corresponding to each initial industrial data in the current time period;

based on the historical time period set, carrying out distribution degree analysis processing on each initial industrial data in the current time period to obtain a target distribution degree corresponding to each initial industrial data in the current time period;

performing sensitivity degree analysis processing on each initial industrial data in a current time period to obtain a target sensitivity degree corresponding to each initial industrial data in the current time period;

based on a target local interval, a target distribution degree and a target sensitivity degree corresponding to initial industrial data in a current time period, carrying out self-adaptive adjustment on the initial industrial data in the current time period to obtain a target industrial data set;

and performing revolving door compression on the target industrial data set through a revolving door compression algorithm to obtain target compressed data.

Optionally, the periodically dividing the target time period to obtain a current time period and a historical time period set includes:

Determining target iteration times according to a preset time interval, a preset iteration step length, a preset termination value, and a starting time and an ending time included in the target time period;

determining an offset duration corresponding to each iteration in the target iteration times according to the preset time interval and the preset iteration step length, wherein the preset time interval and the preset iteration step length are positively correlated with the offset duration;

for each iteration in the target iteration times, moving the ending time included in the target time period to the starting time direction by the offset time corresponding to the iteration, determining the obtained time as the ending time included in the iteration time corresponding to the iteration, and determining the starting time included in the target time period as the starting time included in the iteration time corresponding to the iteration;

determining an autocorrelation function corresponding to each iteration time period according to the obtained initial industrial data in each iteration time period;

determining a time interval corresponding to the maximum function value in the autocorrelation function corresponding to each iteration time period as a target time interval corresponding to the iteration time period;

rounding the average value of the target time intervals corresponding to all the obtained iteration time periods to obtain a time period;

Dividing the target time period by taking the starting time included in the target time period as a starting point and the time period as a dividing step length to obtain a sub-time period set;

determining a sub-time period in which the ending time included in the target time period is located as a current time period;

and determining each sub-time period except the current time period in the sub-time period set as a historical time period to obtain a historical time period set.

Optionally, the performing a change division on each historical time period in the current time period and the historical time period set to obtain a current sub-period set corresponding to the current time period and a historical sub-period set corresponding to the historical time period includes:

taking time as an abscissa and industrial data as an ordinate, and making a current data distribution diagram corresponding to the current time period;

connecting the peak points in the current data distribution diagram to obtain a current peak change curve;

normalizing the absolute value of the difference value of the slope values corresponding to any two adjacent peak points on the current peak change curve to obtain a first change index between the two peak points;

when the first change index is larger than a preset change threshold, determining the latter peak point of the two peak points corresponding to the first change index as a mark point;

Connecting trough points in the current data distribution diagram to obtain a current trough change curve;

normalizing the absolute value of the difference value of the slope values corresponding to any two adjacent trough points on the current trough change curve to obtain a second change index between the two trough points;

when the second change index is larger than a preset change threshold, determining the latter trough point of the two trough points corresponding to the second change index as a mark point;

determining the time corresponding to all the marking points as marking time, and dividing the current time period by taking all the marking time as dividing time to obtain the current sub-segment set;

taking time as an abscissa and industrial data as an ordinate, and making a historical data distribution map corresponding to the historical time period;

and dividing the historical time period according to the historical data distribution diagram to obtain a historical sub-period set corresponding to the historical time period.

Optionally, the formula corresponding to the joint matching degree is:

;

wherein,the joint matching degree between the c-th matching sub-segment in the b-th current sub-segment in the current sub-segment set and the matching sub-segment set corresponding to the b-th current sub-segment; / >Is the start time of the b-th current sub-segment; />The starting time of the c-th matching sub-segment in the matching sub-segment set corresponding to the b-th current sub-segment; />Is the maximum value of the difference between the start time of all current sub-segments and the start time of each matching sub-segment in the corresponding set of matching sub-segments;the absolute value of the difference between the starting time of the b current sub-segment corresponding to the sequence number of the current time segment and the starting time of the c matching sub-segment corresponding to the sequence number of the historical time segment; />The absolute value of the difference between the sequence number of the b current sub-segment corresponding to the current time segment and the sequence number of the c matching sub-segment corresponding to the historical time segment; />Is the duration corresponding to the current time period; />Is the maximum value of the duration corresponding to all the historical time periods;is to take->And->Maximum value of (2); />Is the DTW distance between the initial industrial data in the b current sub-segment and the initial industrial data in the c matching sub-segment;is of natural constantA power of the second; b is the sequence number of the current sub-segment in the current sub-segment set; c is the sequence number of the matching sub-segment in the matching sub-segment set corresponding to the b current sub-segment.

Optionally, the determining, according to the joint matching degree, the target local interval corresponding to each initial industrial data in the current time period includes:

Screening a matching sub-segment with the largest joint matching degree from a matching sub-segment set corresponding to each current sub-segment to serve as an optimal matching sub-segment corresponding to the current sub-segment;

if the joint matching degree between the current sub-segment and the optimal matching sub-segment is larger than a preset matching threshold, respectively determining a sequence number of the starting time of the optimal matching sub-segment corresponding to the historical time period and a sequence number of the ending time of the optimal matching sub-segment corresponding to the historical time period as two endpoints included in the current interval corresponding to the current sub-segment;

if the joint matching degree between the current sub-segment and the optimal matching sub-segment is smaller than or equal to a preset matching threshold, respectively determining a sequence number of the current sub-segment corresponding to the current time segment and a sequence number of the current time segment corresponding to the ending time of the current sub-segment as two endpoints included in the current interval corresponding to the current sub-segment;

and determining the current interval corresponding to the current sub-section of the acquisition time corresponding to each initial industrial data in the current time period as the target local interval corresponding to each initial industrial data in the current time period.

Optionally, the analyzing the distribution degree of each piece of initial industrial data in the current time period to obtain a target distribution degree corresponding to each piece of initial industrial data in the current time period includes:

Determining each initial industrial data in the current time period as current industrial data;

screening initial industrial data which are the same as the current industrial data from the initial industrial data in the historical time period set, and obtaining a reference industrial data set corresponding to the current industrial data by using the initial industrial data as reference industrial data;

combining the initial industrial data in the historical time period set in pairs to obtain a first data vector set;

combining each reference industrial data in the reference industrial data set with the previous initial industrial data of the reference industrial data to form a second data vector corresponding to the reference industrial data, and obtaining a second data vector set corresponding to the current industrial data;

determining the frequency of each second data vector in the second data vector set in the first data vector set as a target frequency corresponding to the second data vector;

combining the reference industrial data with the same corresponding second data vector in the reference industrial data set into a candidate industrial data set corresponding to the second data vector;

determining variances of serial numbers of all candidate industrial data in the candidate industrial data set corresponding to the historical time period as target variances corresponding to the second data vector;

And determining the target distribution degree corresponding to the current industrial data according to the target frequency and the target variance corresponding to each second data vector in the second data vector set, wherein the target frequency and the target variance are positively correlated with the target distribution degree.

Optionally, the performing sensitivity degree analysis on each piece of initial industrial data in the current time period to obtain a target sensitivity degree corresponding to each piece of initial industrial data in the current time period includes:

determining a DTW distance between initial industrial data in each historical time period in the current time period and the historical time period set as a first difference indicator between the current time period and the historical time period;

screening a first candidate sub-segment corresponding to the current industrial data from the initial industrial data in the current time period;

screening out a second candidate sub-segment between the current industrial data and the historical time segment from the initial industrial data in each historical time segment in the set of historical time segments;

determining a DTW distance between the first candidate sub-segment and the second candidate sub-segment as a second difference indicator between the current industrial data and the historical time period;

Determining a third difference index between the current industrial data and the historical time period according to a first difference index between the current time period and each historical time period and a second difference index between the current industrial data and the historical time period;

and determining the target sensitivity degree corresponding to the current industrial data according to a third difference index between the current industrial data and each historical time period, wherein the third difference index is positively correlated with the target sensitivity degree.

Optionally, the adaptively adjusting the initial industrial data in the current time period based on the target local interval, the target distribution degree and the target sensitivity degree corresponding to the initial industrial data in the current time period to obtain the target industrial data set includes:

determining a normalized value of the target distribution degree corresponding to the initial industrial data as an abscissa included in the first position coordinate corresponding to the initial industrial data, and determining a normalized value of the target sensitivity degree corresponding to the initial industrial data as an ordinate included in the first position coordinate corresponding to the initial industrial data;

Determining local reachable density corresponding to the current industrial data according to the first position coordinates corresponding to the current industrial data;

determining local reachable densities corresponding to all initial industrial data in a target local interval corresponding to the current industrial data according to first position coordinates corresponding to all initial industrial data in the target local interval corresponding to the current industrial data;

determining an adjustment weight value corresponding to the current industrial data according to the local reachable density corresponding to the current industrial data and the local reachable density corresponding to each initial industrial data in the target local interval corresponding to the current industrial data;

when the adjustment weight value corresponding to the current industrial data is larger than a preset adjustment threshold value and the current industrial data is not in a preset tolerance range, determining the current industrial data as pending data;

when the adjustment weight value corresponding to the current industrial data is smaller than or equal to a preset adjustment threshold value and the current industrial data is not in a preset tolerance range, determining the current industrial data as reference data;

determining the acquisition time corresponding to the current industrial data as an abscissa included in a second position coordinate corresponding to the current industrial data, and determining the current industrial data as an ordinate included in the second position coordinate corresponding to the current industrial data;

Connecting second position coordinates corresponding to two reference data adjacent to the undetermined data to obtain an adjustment straight line corresponding to the undetermined data;

determining the abscissa on the adjustment straight line corresponding to the undetermined data as the ordinate of the abscissa included in the second position coordinate corresponding to the undetermined data as target industrial data corresponding to the undetermined data;

for the initial industrial data of which the corresponding adjustment straight line does not exist in the current time period, determining the initial industrial data as target industrial data;

and forming all the obtained target industrial data into a target industrial data set.

In a second aspect, the present invention provides a data processing apparatus comprising a processor and a memory, the processor being arranged to process instructions stored in the memory to implement a data processing method as described above.

In a third aspect, the present invention provides a PLC data transmission system based on the internet of things, the system comprising: the PLC equipment is used for controlling and monitoring the production process of the products on the production line; the data acquisition device is used for acquiring initial industrial data in the PLC equipment; the data processing device is used for compressing the initial industrial data; the communication device is used for transmitting the initial industrial data after compression processing to the cloud server through the Internet of things; the cloud server is used for storing and managing the initial industrial data after compression processing.

The invention has the following beneficial effects:

according to the data processing method, the data is processed before data compression, so that the compression efficiency can be improved, and the data transmission efficiency can be further improved. Firstly, the target time period is periodically divided, so that the distribution condition of the initial industrial data can be conveniently analyzed later. Then, each historical time period in the current time period and the historical time period set is divided in a changing mode, so that the matching sub-segment set corresponding to the current sub-segment can be conveniently determined later. The degree of joint matching between each current sub-segment and each matching sub-segment is then quantified. And continuing, based on the joint matching degree, the accuracy of determining the target local interval corresponding to each initial industrial data in the current time period can be improved. Furthermore, the accuracy of determining the target distribution degree corresponding to each initial industrial data in the current time period can be improved by comprehensively considering the historical time period set. And then, performing sensitivity degree analysis processing on each piece of initial industrial data in the current time period, so that the accuracy of determining the target sensitivity degree corresponding to each piece of initial industrial data in the current time period can be improved. Finally, the target local interval, the target distribution degree and the target sensitivity degree corresponding to the initial industrial data in the current time period are comprehensively considered, the initial industrial data in the current time period is adaptively adjusted, the adaptive adjustment of the data distribution characteristics can be realized, and the data compression efficiency is improved when the revolving door compression is carried out subsequently, so that the data transmission efficiency is improved.

Drawings

In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a data processing method of the present invention;

fig. 2 is a schematic structural diagram of a PLC data transmission system based on the internet of things according to the present invention.

Detailed Description

In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description is given below of the specific implementation, structure, features and effects of the technical solution according to the present invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

The invention provides a data processing method, which comprises the following steps:

carrying out change division on each historical time period in the current time period and the historical time period set to obtain a current sub-period set corresponding to the current time period and a historical sub-period set corresponding to the historical time period;

screening out matching subsections matched with each current subsection in the current subsection set from each history subsection set to obtain a matching subsection set corresponding to each current subsection;

Performing sensitivity degree analysis processing on each initial industrial data in the current time period to obtain a target sensitivity degree corresponding to each initial industrial data in the current time period;

based on a target local interval, a target distribution degree and a target sensitivity degree corresponding to the initial industrial data in the current time period, carrying out self-adaptive adjustment on the initial industrial data in the current time period to obtain a target industrial data set;

The following detailed development of each step is performed:

referring to FIG. 1, a flow of some embodiments of a data processing method according to the present invention is shown. The data processing method comprises the following steps:

step S1, obtaining initial industrial data in a target time period, and dividing the target time period periodically to obtain a current time period and a historical time period set.

In some embodiments, initial industrial data within a target time period may be acquired, and the target time period may be periodically partitioned to obtain a set of current time periods and historical time periods.

Wherein the target time period may be an operating time period of equipment on the production line. The equipment on the production line may be equipment for processing products. For example, the start time of the target time period may be a start time of a device on the production line. The end time of the target time period may be the current time. The current time period may be a time period including the current time. The historical time period in the set of historical time periods may be a time period prior to the current time period. The initial industrial data may be industrial data collected from a PLC (Programmable Logic Controller ) device. The initial industrial data may be data collected during processing of the same product by equipment on the production line. The PLC device can control and monitor parameters (initial industrial data) of the device on the production line. For example, the initial industrial data may be temperature data during product processing.

It should be noted that, for the collected initial industrial data, due to the distribution characteristics of the initial industrial data and the influence of noise, the compression rate of the collected data in the compression process of the revolving door is often smaller. According to the invention, the size of the initial industrial data which is possibly noise is adjusted through the self-adaptive adjustment processing, so that the distribution characteristics of the initial industrial data are changed, and the compression efficiency in the follow-up revolving door compression process is higher.

As an example, this step may include the steps of:

first, initial industrial data within a target time period is acquired.

For example, initial industrial data may be collected from the PLC device in real-time during a target time period.

Since the data is adaptively adjusted in real time according to the present embodiment, the initial industrial data in the target period may be industrial data after the adaptive adjustment in the target period or industrial data which is not yet adaptively adjusted. The industrial data that is not adaptive may be data that needs to be adaptive and compressed at the current time. The adaptive adjustment of the data in real time may be performed once every preset time. The preset time period may be a preset time period. The duration corresponding to the preset duration may be less than or equal to the production duration corresponding to the single product.

And secondly, determining the target iteration times according to a preset time interval, a preset iteration step length, a preset termination value, and the starting time and the ending time included in the target time period.

The preset time interval may be a preset time interval. For example, the preset time interval may be 1. The preset iteration step may be a step preset for iteration. For example, the preset iteration step may be 2. The preset termination value may be a preset termination value.

For example, the formula for determining the target number of iterations may be:

;

wherein,is the target number of iterations. />Is a value corresponding to a preset time interval. For example, a->May be 1.m is a value corresponding to a preset iteration number. The preset number of iterations may be a preset number of iterations. The value range of the preset iteration number may be greater than or equal to 1.k is a value corresponding to a preset iteration step. For example, k may be 2./>Is a preset termination value. />Is the start time included in the target period. />Is the end time included in the target period. a is a preset value greater than 1. For example, a may be 3./>Is to satisfy the formula conditionThe minimum of all m.

It should be noted that, determining the target iteration numberSubsequent iterations may be facilitated to determine the processing time for a single product.

And thirdly, determining the offset duration corresponding to each iteration in the target iteration times according to the preset time interval and the preset iteration step length.

The preset time interval and the preset iteration step length may both be positively correlated with the offset duration.

For example, the formula corresponding to determining the offset duration corresponding to each iteration in the target number of iterations may be:

;

wherein,is the offset time length corresponding to the ith iteration in the target iteration times. />Is a value corresponding to a preset time interval. i is the iteration number in the target number of iterations. k is a value corresponding to a preset iteration step. />And k are all equal to->And shows positive correlation.

It should be noted that, setting the offset duration corresponding to each iterationThe time periods when the iteration is carried out each time can be different, and the subsequent determination of the processing time length of the single product can be facilitated.

Fourth, for each iteration in the target iteration number, moving the end time included in the target time period to the start time direction by the offset time corresponding to the iteration, determining the obtained time as the end time included in the iteration time period corresponding to the iteration, and determining the start time included in the target time period as the start time included in the iteration time period corresponding to the iteration.

The start time of the iteration period may be a start time of a device on the production line. The end time of the iteration period corresponding to the ith iteration may be。/>Is the end time included in the target time period. />Is the offset time length corresponding to the ith iteration in the target iteration times.

And fifthly, determining an autocorrelation function corresponding to each iteration time period according to the obtained initial industrial data in each iteration time period.

And sixthly, determining the time interval corresponding to the maximum function value in the autocorrelation function corresponding to each iteration time period as the target time interval corresponding to the iteration time period.

And seventh, rounding the average value of the target time intervals corresponding to all the obtained iteration time periods to obtain the time period.

Wherein, the time period may be: rounding the average value of the target time intervals corresponding to all the iteration time periods.

It should be noted that, the target time interval corresponding to the iteration time period is often the processing duration of a single product determined by each iteration, so that the average value of the target time intervals corresponding to all obtained iteration time periods is rounded, and the obtained time period can more accurately represent the processing duration of the single product.

And eighth, dividing the target time period by taking the starting time included in the target time period as a starting point and the time period as a dividing step length to obtain a sub-time period set.

The sub-time period in the sub-time period set may be a time period obtained by dividing the target time period.

For example, if the start time of the target period is 2023, 06, 26, 09, 00 minutes, 00 seconds, and the end time of the target period is 2023, 06, 26, 09, 00 minutes, 05 seconds, and the time period is 2 seconds, the sub-period set may include 3 sub-periods, which may be a first sub-period, a second sub-period, and a third sub-period, respectively. The start time of the first sub-period may be 2023, 06, 26, 09, 00, minute, 00, and the end time of the first sub-period may be 2023, 06, 26, 09, 00, 02, second. The start time of the second sub-period may be 2023, 06, 26, 09, 00, 02, and the end time of the second sub-period may be 2023, 06, 26, 09, 00, 04, seconds. The start time of the third sub-period may be 2023, 06, 26, 09, 00, 04 seconds and the end time of the third sub-period may be 2023, 06, 26, 09, 00, 05 seconds.

And a ninth step of determining the sub-time period in which the ending time included in the target time period is located as the current time period.

And tenth, determining each sub-time period except the current time period in the sub-time period set as a historical time period to obtain a historical time period set.

It should be noted that, in the process of collecting initial industrial data (for example, temperature data), the initial industrial data is often determined along with the processing process of equipment on a production line, the equipment on the production line is often used for processing the same product, so that distribution characteristics of the initial industrial data corresponding to a plurality of products produced by the equipment on the production line are often similar, and the duration corresponding to each product produced by the equipment on the production line is often the same. The current time period may be a time period corresponding to one product being completely processed or a time period corresponding to one product being not completely processed. For example, if the current time is not the processing end time of a certain product, the current time period is a time period corresponding to a product that is not completely processed. If the current time is the processing end time of a certain product, the current time period is the time period corresponding to the whole processing of the product.

And S2, carrying out change division on each historical time period in the current time period and the historical time period set to obtain a current sub-period set corresponding to the current time period and a historical sub-period set corresponding to the historical time period.

In some embodiments, each historical time period in the current time period and the historical time period set may be divided in a changing manner to obtain a current sub-period set corresponding to the current time period and a historical sub-period set corresponding to the historical time period.

As an example, this step may include the steps of:

and the first step, taking time as an abscissa and industrial data as an ordinate, and making a current data distribution diagram corresponding to the current time period.

Wherein the current data profile may characterize a distribution of the initial industrial data over the current time period.

And step two, connecting the peak points in the current data distribution diagram to obtain a current peak change curve.

For example, the peak points in the current data distribution map may be sequentially connected, and the obtained connection line may be used as the current peak change curve.

And thirdly, normalizing the absolute value of the difference value of the slope values corresponding to any two adjacent peak points on the current peak change curve to obtain a first change index between the two peak points.

The slope value corresponding to the peak point may be a slope value of a line between the peak point and a previous peak point.

And fourthly, when the first change index is larger than a preset change threshold value, determining the latter peak point of the two peak points corresponding to the first change index as a mark point.

The preset change threshold may be a preset threshold. For example, the preset change threshold may be 0.48.

And fifthly, connecting trough points in the current data distribution diagram to obtain a current trough change curve.

For example, the trough points in the current data distribution diagram may be sequentially connected, and the obtained connection line may be used as the current trough change curve.

And sixthly, normalizing the absolute value of the difference value of the slope values corresponding to any two adjacent trough points on the current trough change curve to obtain a second change index between the two trough points.

The slope value corresponding to the trough point may be a slope value of a line between the trough point and a previous trough point.

And seventh, when the second change index is larger than the preset change threshold, determining the latter trough point of the two trough points corresponding to the second change index as a mark point.

And eighth, determining the time corresponding to all the marking points as marking time, and dividing the current time period by taking all the marking time as dividing time to obtain the current sub-segment set.

The time corresponding to the mark point may be an abscissa included in the coordinate corresponding to the mark point.

For example, a cut may be made at each marked time of the current time period, and the resulting time period is taken as the current sub-segment, resulting in a current sub-segment set.

And ninth, taking time as an abscissa and industrial data as an ordinate, and making a historical data distribution diagram corresponding to the historical time period.

And tenth, dividing the historical time period according to the historical data distribution diagram to obtain a historical sub-period set corresponding to the historical time period.

For example, the historical sub-segment set may be determined by referring to the manner in which the current sub-segment set is determined, and specifically may be: the historical data distribution diagram and the historical time period can be respectively used as the current data distribution diagram and the current time period, the second step to the eighth step included in the step S2 are executed, and the obtained current sub-section is the historical sub-section.

It should be noted that, the current time period and each historical time period in the historical time period set are divided in a changing manner, and the distribution change condition of the initial industrial data is considered, so that the distribution change of the initial industrial data in the obtained single current sub-period is similar, and the distribution change of the initial industrial data in the obtained single historical sub-period is similar.

And S3, screening out the matched subsections matched with each current subsection in the current subsection set from each history subsection set to obtain a matched subsection set corresponding to each current subsection.

In some embodiments, a matching sub-segment that matches each current sub-segment in the current sub-segment set may be selected from each historical sub-segment set, so as to obtain a matching sub-segment set corresponding to each current sub-segment.

As an example, a history sub-segment matching each current sub-segment may be screened out of each history sub-segment set as a matching sub-segment by a DTW (Dynamic Time Warping) algorithm. Wherein, the matching sub-segment set corresponding to the current sub-segment may include: each history sub-segment in the set of history sub-segments matches the current sub-segment.

It should be noted that, because the processing time of a plurality of products may be different in the production process due to the existence of noise, if the matching sub-segment is directly judged according to the time periods corresponding to the current sub-segment and the historical sub-segment, the determined matching sub-segment is often inaccurate. Therefore, the matching sub-segment can be accurately determined through the DTW algorithm. A set of historical subsections often corresponds to a complete process of a product, so a set of historical subsections often has a matching subsection that matches the current subsection. The product processing process of the current sub-segment and the matching sub-segment is always the same.

And S4, determining the joint matching degree between each current sub-segment and each matching sub-segment in the matching sub-segment set corresponding to the current sub-segment.

In some embodiments, a degree of joint matching between each current sub-segment and each matching sub-segment in the set of matching sub-segments corresponding to the current sub-segment may be determined.

As an example, the formula for determining the joint matching degree correspondence between each current sub-segment and each matching sub-segment in the matching sub-segment set corresponding to the current sub-segment may be:

;

wherein,the joint matching degree between the c-th matching sub-segment in the b-th current sub-segment in the current sub-segment set and the matching sub-segment set corresponding to the b-th current sub-segment. />Is the start time of the b-th current sub-segment. />The starting time of the c-th matching sub-segment in the matching sub-segment set corresponding to the b-th current sub-segment. />Is the maximum of the difference between the start times of all current sub-segments and the start times of the respective matching sub-segments in the corresponding set of matching sub-segments.Is the absolute value of the difference between the starting time of the b-th current sub-segment corresponding to the sequence number in the current time segment and the starting time of the c-th matching sub-segment corresponding to the sequence number in the history time segment. The starting time of the current sub-segment may correspond to the sequence number in the initial industrial data collected at the starting time and in the initial industrial data collected during the current time segment. For example, if the starting time of the current time period is 2023, 06, 26, 09 and 10 minutes and 01 seconds, and the initial industrial data is collected every 1 second, and the starting time of the current sub-period is 2023, 06, 26, 09 and 10 minutes and 10 seconds, then 9 pieces of initial industrial data have been collected during the current time period and before the starting time of the current sub-period, and the initial industrial data collected at the starting time of the current sub-period is the 10 th initial industrial data during the current time period, so the serial number of the current sub-period corresponding to the starting time of the current sub-period may be 10./ >Is the sequence of the ending time of the b current sub-segment corresponding to the current time segmentThe number and the end time of the c-th matching sub-segment correspond to the absolute value of the difference in sequence numbers of the historical time segments. />Is the duration corresponding to the current time period. />Is the maximum of the durations corresponding to all the historical time periods. />Is to take->And->Is the maximum value of (a). />Is the DTW distance between the initial industrial data in the b-th current sub-segment and the initial industrial data in the c-th matching sub-segment. />Is a natural constant +.>To the power. b is the sequence number of the current sub-segment in the current sub-segment set. c is the sequence number of the matching sub-segment in the matching sub-segment set corresponding to the b current sub-segment.

When the following is performedThe smaller the time span between the b current sub-segment and the c matching sub-segment is, the more adjacent the b current sub-segment and the c matching sub-segment are, and the more similar the aging degree of equipment on the production line is when the b current sub-segment and the c matching sub-segment are. Thus->Can be used asIs to be determined. When->The larger the distribution of the initial industrial data in the b current sub-segment and the c matching sub-segment in the corresponding time period is, the smaller the similarity between the initial industrial data in the b current sub-segment and the c matching sub-segment is. When- >The larger the similarity between the initial industrial data in the b-th current sub-segment and the c-th matching sub-segment is often explained to be smaller. Thus->The larger the similarity between the initial industrial data in the b-th current sub-segment and the c-th matching sub-segment is often explained.

And S5, determining a target local interval corresponding to each initial industrial data in the current time period according to the joint matching degree.

In some embodiments, a target local interval corresponding to each initial industrial data in the current time period can be determined according to the joint matching degree.

As an example, this step may include the steps of:

the first step, a matching sub-segment with the largest joint matching degree is screened out from the matching sub-segment set corresponding to each current sub-segment and is used as the optimal matching sub-segment corresponding to the current sub-segment.

And a second step of respectively determining the sequence number of the starting time corresponding to the historical time period and the sequence number of the ending time corresponding to the historical time period of the optimal matching sub-segment as two endpoints included in the current interval corresponding to the current sub-segment if the joint matching degree between the current sub-segment and the optimal matching sub-segment is larger than a preset matching threshold.

The preset matching threshold may be a preset threshold. The preset matching threshold may be 0.65.

For example, if the joint matching degree between the current sub-segment and the best matching sub-segment is greater than the preset matching threshold, and the time for acquiring the initial industrial data in the historical time period may include: the beginning time and the ending time of the optimal matching sub-segment are 2023, 07, 03, 14, 00 minutes, 02, 2023, 03, 14 minutes, 03, 14 minutes, 00 minutes, 04, 2023, 03, 14 minutes, 05, 2023, 03, 14 minutes, 06, and 2023, 03, 14 minutes, 00 minutes, 07, 14, 00, and end time of the optimal matching sub-segment are 2023, 07, 03, 14, 00 minutes, 03, and 2023, 03, 14 minutes, 00 minutes, 05 seconds, respectively, the beginning time of the optimal matching sub-segment corresponds to a number of 3 in a historical time period, the ending time of the optimal matching sub-segment corresponds to a number of 5 in the historical time period, and the current interval corresponding to the current sub-segment may be [3,5].

And thirdly, if the joint matching degree between the current sub-segment and the optimal matching sub-segment is smaller than or equal to a preset matching threshold, respectively determining the sequence number of the current sub-segment corresponding to the current time segment and the sequence number of the current time segment corresponding to the ending time of the current sub-segment as two endpoints included in the current interval corresponding to the current sub-segment.

For example, if the joint matching degree between the current sub-segment and the best matching sub-segment is less than or equal to the preset matching threshold, and the starting time of the current sub-segment corresponds to the sequence number 1 in the current time segment, and the ending time of the current sub-segment corresponds to the sequence number 4 in the current time segment, the current interval corresponding to the current sub-segment may be [1,4].

And fourthly, determining the current interval corresponding to the current sub-section where the acquisition time corresponding to each piece of initial industrial data in the current time section is located as the target local interval corresponding to each piece of initial industrial data in the current time section.

For example, if the current interval corresponding to the current sub-segment where the acquisition time corresponding to a certain initial industrial data in the current time segment is located is [1,4], the target local interval corresponding to the initial industrial data may be [1,4].

And S6, carrying out distribution degree analysis processing on each piece of initial industrial data in the current time period based on the historical time period set to obtain a target distribution degree corresponding to each piece of initial industrial data in the current time period.

In some embodiments, the distribution degree analysis processing may be performed on each initial industrial data in the current time period based on the historical time period set, so as to obtain the target distribution degree corresponding to each initial industrial data in the current time period.

As an example, this step may include the steps of:

first, each piece of initial industrial data in the current time period is determined as current industrial data.

And secondly, screening out initial industrial data which is the same as the current industrial data from the initial industrial data in the historical time period set, and obtaining a reference industrial data set corresponding to the current industrial data by using the initial industrial data as the reference industrial data.

For example, if a current industrial data is 30 °, the reference industrial data set corresponding to the current industrial data may include: all the initial industrial data collected during the set of historical time periods at a temperature equal to 30 deg..

And thirdly, combining the initial industrial data in the historical time period set in pairs to obtain a first data vector set.

Wherein a first data vector in the first set of data vectors may consist of two adjacent initial industrial data.

For example, the initial industrial data in each historical time period may be combined two by two to obtain a plurality of first data vectors, forming a first data vector group, and forming a first data vector set for the first data vectors in all the first data vector groups. Wherein the first set of data vectors may comprise: and combining the initial industrial data in a certain historical time period in pairs to obtain a plurality of first data vectors. The first set of data vectors may include: all first data vectors in the first data vector group.

For example, if the initial industrial data in a certain historical period is: the first initial industrial data, the second initial industrial data, the third initial industrial data and the fourth initial industrial data are combined in pairs, and the obtained plurality of first data vectors may include: (first initial industrial data, second initial industrial data), (second initial industrial data, third initial industrial data) and (third initial industrial data, fourth initial industrial data).

Fourth, combining each reference industrial data in the reference industrial data set with the previous initial industrial data of the reference industrial data to obtain a second data vector corresponding to the reference industrial data, and obtaining a second data vector set corresponding to the current industrial data.

Wherein the second data vector corresponding to the reference industrial data may be a vector composed of the reference industrial data and the previous initial industrial data of the reference industrial data. For example, the second data vector corresponding to a certain reference industrial data may be (the reference industrial data, the previous initial industrial data of the reference industrial data). The second set of data vectors may be a set of different second data vectors.

For example, if a certain current industrial data is 30 °, the reference industrial data in the reference industrial data set is 30 °, the second data vector in the second data vector set corresponding to the current industrial data may be a vector composed of the previous initial industrial data of 30 ° and 30 °.

And fifthly, determining the frequency of each second data vector in the second data vector set in the first data vector set as the target frequency corresponding to the second data vector.

And sixthly, combining the reference industrial data with the same corresponding second data vector in the reference industrial data set into a candidate industrial data set corresponding to the second data vector.

For example, if the second data vector is (30 °,35 °), the candidate industrial data set corresponding to the second data vector may include: the corresponding second data vector in the reference industrial data set is (30 °,35 °) reference industrial data.

And seventh, determining the variances of the serial numbers of all candidate industrial data in the candidate industrial data set corresponding to the historical time period as target variances corresponding to the second data vector.

Wherein the sequence number of the candidate industrial data corresponding to the historical period may be a sequence number in the initial industrial data collected by the candidate industrial data during the historical period.

And eighth, determining the target distribution degree corresponding to the current industrial data according to the target frequency and the target variance corresponding to each second data vector in the second data vector set.

Wherein, the target frequency and the target variance can be positively correlated with the target distribution degree.

For example, the formula for determining the target distribution degree corresponding to each initial industrial data in the current time period may be:

;

wherein,is the target distribution degree corresponding to the h initial industrial data in the current time period. />Is the number of second data vectors in the second set of data vectors corresponding to the h-th initial industrial data in the current time period. />Is the second data vector set corresponding to the h th initial industrial data in the current time period>The target frequencies corresponding to the second data vectors. />Is the second data vector set corresponding to the h th initial industrial data in the current time period>The target variances corresponding to the second data vectors. />And->All are in charge of>And shows positive correlation. h is the sequence number of the initial industrial data in the current time period. />Is the sequence number of the second data vector in the second data vector set corresponding to the h th initial industrial data.

When the following is performedThe larger the value, the more ++the h initial industrial data corresponds to>The larger the variation in sequence number of the second data vector occurring during the product processing, the more frequent the variation is often indicated, the more random the distribution of the h-th initial industrial data is often indicated, and the greater the degree of the distribution of the h-th initial industrial data is often indicated. When->The greater the case, the more +.>The greater the reference of the second data vector, the greater the ability to characterize the degree of distribution of the h-th initial industrial data. Thus, when->The larger the distribution of the h-th initial industrial data, the more random the distribution tends to be.

And S7, performing sensitivity degree analysis processing on each piece of initial industrial data in the current time period to obtain a target sensitivity degree corresponding to each piece of initial industrial data in the current time period.

In some embodiments, the sensitivity level analysis process may be performed on each initial industrial data in the current time period, so as to obtain the target sensitivity level corresponding to each initial industrial data in the current time period.

As an example, this step may include the steps of:

And a second step of determining a DTW distance between the initial industrial data in each of the current time period and the set of historical time periods as a first difference index between the current time period and the historical time period.

And thirdly, screening a first candidate sub-segment corresponding to the current industrial data from the initial industrial data in the current time period.

Wherein the first candidate sub-segment corresponding to the current industrial data may include: initial industrial data other than the current industrial data in the initial industrial data in the current period.

For example, if the initial industrial data in the current time period sequentially includes: 30 °,31 °, 32 °,33 °,34 °,35 °, and 36 °, then the first candidate sub-segment corresponding to 32 ° may be {30 °,31 °,33 °,34 °,35 °,36 °.

And step four, screening out a second candidate sub-segment between the current industrial data and the historical time segment from the initial industrial data in each historical time segment in the historical time segment set.

Wherein the second candidate sub-segment between the current industrial data and the historical time segment may include: initial industrial data in the historical period of time except initial industrial data that matches the current industrial data.

For example, the initial industrial data matching the current industrial data may be screened out from the historical time period by a DTW (Dynamic Time Warping) algorithm, recorded as the matching data of the current industrial data, and the initial industrial data except the matching data in the initial industrial data in the historical time period is formed into a second candidate sub-period between the current industrial data and the historical time period. There may be a plurality of matching data of the current industrial data.

As another example, if the initial industrial data in the current time period sequentially includes: the initial industrial data over the historical time period, 31 °, 32 ° and 33 °, in order, includes: 31 °, 32 °, and 33 °, then 31 ° in the history period may be matching data of 31 ° in the current period, and the second candidate sub-period between 31 ° in the current period and the history period may include: the initial industrial data other than the matching data in the initial industrial data in the history period, that is, the second candidate sub-period between 31 ° in the current period and the history period may be {32 °,33 ° }.

And fifthly, determining the DTW distance between the first candidate sub-segment and the second candidate sub-segment as a second difference index between the current industrial data and the historical time segment.

And a sixth step of determining a third difference index between the current industrial data and the historical time period according to the first difference index between the current time period and each historical time period and the second difference index between the current industrial data and the historical time period.

And seventh, determining the target sensitivity corresponding to the current industrial data according to a third difference index between the current industrial data and each historical time period.

Wherein, the third difference index may be positively correlated with the target sensitivity level.

For example, the formula for determining the target sensitivity level for each initial industrial data in the current time period may be:

;

wherein,is the target sensitivity corresponding to the h initial industrial data in the current time period. M is the number of historical time periods in the set of historical time periods. />Is a third difference indicator between the h initial industrial data in the current time period and the j historical time period in the set of historical time periods. />Is a first difference indicator between the current time period and the jth historical time period, that is, the DTW distance between the initial industrial data in the current time period and the initial industrial data in the jth historical time period. / >Is a second difference indicator between the h initial industrial data and the j historical time period in the current time period; i.e., the DTW distance between the first candidate sub-segment corresponding to the h th initial industrial data and the second candidate sub-segment between the h th initial industrial data and the j th historical period. />Is a factor greater than 0 preset, mainly for preventing denominator from being 0, such as ++>0.01 may be taken. />Is->Is the absolute value of (c). h is the sequence number of the initial industrial data in the current time period. j is the sequence number of the history period in the history period set。

When the following is performedThe larger the case, the more ∈ ->And->The lower the similarity, the greater the change after the h-th initial industrial data and the matching data corresponding to the h-th initial industrial data are removed, the greater the trend distribution information expressive ability of the h-th initial industrial data is, and the more sensitive the h-th initial industrial data is.

Step S8, based on the target local interval, the target distribution degree and the target sensitivity degree corresponding to the initial industrial data in the current time period, the initial industrial data in the current time period is subjected to self-adaptive adjustment, and a target industrial data set is obtained.

In some embodiments, the initial industrial data in the current time period may be adaptively adjusted based on a target local interval, a target distribution degree, and a target sensitivity degree corresponding to the initial industrial data in the current time period, so as to obtain a target industrial data set.

The target industrial data may be initial industrial data after adaptive adjustment.

As an example, this step may include the steps of:

the method comprises the steps of firstly, determining a normalized value of target distribution degree corresponding to initial industrial data as an abscissa included in a first position coordinate corresponding to the initial industrial data, and determining a normalized value of target sensitivity degree corresponding to the initial industrial data as an ordinate included in the first position coordinate corresponding to the initial industrial data.

And secondly, determining each piece of initial industrial data in the current time period as current industrial data.

And thirdly, determining the local reachable density corresponding to the current industrial data according to the first position coordinates corresponding to the current industrial data.

For example, the local reachable density corresponding to the current industrial data may be determined by a LOF (Local Outlier Factor ) algorithm according to the first position coordinate corresponding to the current industrial data. The K in the kth distance neighborhood in the LOF algorithm may be 5, and the local reachable density corresponding to the current industrial data may be the local reachable density of the data in the kth distance neighborhood of the current industrial data.

And fourthly, determining local reachable densities corresponding to the initial industrial data in the target local interval corresponding to the current industrial data according to the first position coordinates corresponding to the initial industrial data in the target local interval corresponding to the current industrial data.

For example, the local reachable density corresponding to the initial industrial data may be determined by a LOF algorithm according to the first position coordinate corresponding to the initial industrial data. The K in the kth distance neighborhood in the LOF algorithm may be 5, and the local reachable density corresponding to the initial industrial data may be the local reachable density of the data in the kth distance neighborhood of the initial industrial data.

And fifthly, determining an adjustment weight value corresponding to the current industrial data according to the local reachable density corresponding to the current industrial data and the local reachable density corresponding to each initial industrial data in the target local interval corresponding to the current industrial data.

For example, the formula for determining the adjustment weight value for each initial industrial data in the current time period may be:

;

wherein,is the adjustment weight value corresponding to the h initial industrial data in the current time period. />Is the h initial worker in the current time period The number of initial industrial data within the target local interval corresponding to the industrial data. />Is the local reachable density corresponding to the h initial industrial data in the current time period. />Is the local reachable density corresponding to the v initial industrial data in the target local interval corresponding to the h initial industrial data in the current time period. h is the sequence number of the initial industrial data in the current time period. v is the serial number of the initial industrial data in the target local interval corresponding to the h initial industrial data.Is a normalization function, and normalization can be achieved. />Is a factor greater than 0 preset, mainly for preventing denominator from being 0, such as ++>0.01 may be taken. />Is thatNormalized values. />Is a normalization function, and normalization can be achieved.

When the following is performedThe larger the case, the more ∈ ->And->The lower the similarity of (2), the more often saidThe more likely the h-th initial industrial data is to be data that is affected by noise, the more often the h-th initial industrial data needs to be adjusted.

And sixthly, determining the current industrial data as undetermined data when the adjustment weight value corresponding to the current industrial data is larger than a preset adjustment threshold value and the current industrial data is not in a preset tolerance range.

The preset adjustment threshold may be a preset threshold. For example, the preset adjustment threshold may be 0.73. The preset tolerance range may be a preset tolerance range of the revolving door compression algorithm.

It should be noted that, the current industrial data with the adjustment weight value greater than the preset adjustment threshold value is often data affected by noise, and adjustment is often required.

Seventh, when the adjustment weight value corresponding to the current industrial data is smaller than or equal to a preset adjustment threshold value and the current industrial data is not within a preset tolerance range, determining the current industrial data as reference data.

It should be noted that, the current industrial data with the adjustment weight value smaller than or equal to the preset adjustment threshold value is often data not affected by noise, and no adjustment is often needed.

And eighth, determining the acquisition time corresponding to the current industrial data as an abscissa included in the second position coordinate corresponding to the current industrial data, and determining the current industrial data as an ordinate included in the second position coordinate corresponding to the current industrial data.

And a ninth step of connecting second position coordinates corresponding to two reference data adjacent to the undetermined data to obtain an adjustment straight line corresponding to the undetermined data.

The adjustment straight line may be a straight line obtained by connecting second position coordinates corresponding to two reference data adjacent to the data to be determined. The two reference data adjacent to the pending data may include: on the left side of the abscissa included in the second position coordinate corresponding to the pending data, reference data closest to the abscissa included in the second position coordinate corresponding to the pending data; and on the right side of the abscissa corresponding to the pending data, the reference data closest to the abscissa corresponding to the pending data.

For example, if the abscissa included in the second position coordinate corresponding to a certain to-be-determined data is 8, the abscissa included in the second position coordinate corresponding to the first reference data is 7, the abscissa included in the second position coordinate corresponding to the second reference data is 6, the abscissa included in the second position coordinate corresponding to the third reference data is 10, and the abscissa included in the second position coordinate corresponding to the fourth reference data is 15, the adjustment straight line corresponding to the to-be-determined data may be a line connecting the second position coordinates corresponding to the first reference data and the third reference data.

And tenth, determining the ordinate of the abscissa on the adjustment straight line corresponding to the undetermined data, which is equal to the abscissa included in the second position coordinate corresponding to the undetermined data, as the target industrial data corresponding to the undetermined data.

Eleventh, for the initial industrial data that does not have a corresponding adjustment line in the current time period, determining the initial industrial data as target industrial data.

And twelfth, forming a target industrial data set by all the obtained target industrial data.

And S9, performing revolving door compression on the target industrial data set through a revolving door compression algorithm to obtain target compressed data.

In some embodiments, the target industrial data set may be subjected to a revolving door compression algorithm to obtain target compressed data.

The target compressed data may be data obtained by performing revolving door compression on the target industrial data set.

As an example, the target industrial data set may be subjected to a rotation gate compression by a rotation gate compression algorithm, and the compressed target industrial data set is taken as target compressed data.

Based on the same inventive concept as the above-described method embodiments, the present invention provides a data processing apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, which computer program, when executed by the processor, implements the steps of a data processing method.

Referring to fig. 2, a schematic structural diagram of a PLC data transmission system based on the internet of things according to the present invention is shown. This PLC data transmission system based on thing networking includes:

the PLC device 201 is used for controlling and monitoring the production process of the product on the production line.

The data acquisition device 202 is used for acquiring initial industrial data in the PLC equipment.

And the data processing device 203 is used for compressing the initial industrial data.

The communication device 204 is configured to transmit the compressed initial industrial data to the cloud server through the internet of things.

The cloud server 205 is configured to store and manage the initial industrial data after the compression processing.

In summary, due to the distribution characteristics of the initial industrial data and the influence of noise, the compression rate of the collected data in the compression process of the revolving door is often smaller. According to the invention, the size of the initial industrial data which is possibly noise is adjusted through the self-adaptive adjustment processing, so that the distribution characteristics of the initial industrial data are changed, the compression efficiency in the follow-up revolving door compression process can be improved, and the data transmission efficiency is improved.

The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention and are intended to be included within the scope of the invention.

Claims

1. A method of data processing comprising the steps of:

performing revolving door compression on the target industrial data set through a revolving door compression algorithm to obtain target compressed data;

the formula corresponding to the joint matching degree is as follows:

;

wherein,is the first in the current sub-segment setbCurrent sub-segment, andbthe first sub-segment in the matched sub-segment set corresponding to the current sub-segmentcThe degree of joint matching between the individual matching subsections; />Is the firstbThe start time of the current sub-segment; />Is the firstbThe first sub-segment in the matched sub-segment set corresponding to the current sub-segmentcStart times of the individual matching subsections; />Is the maximum value of the difference between the start time of all current sub-segments and the start time of each matching sub-segment in the corresponding set of matching sub-segments; />Is the firstbThe start time of the current sub-segment corresponds to the sequence number in the current time segment, andcthe start time of each matching sub-segment corresponds to the absolute value of the difference in sequence numbers of the historical time segments; />Is the firstbThe end time of the current sub-segment corresponds to the sequence number in the current time segment, and cThe end time of each matching sub-segment corresponds to the absolute value of the difference of the sequence numbers in the historical time segment; />Is the duration corresponding to the current time period; />Is the maximum value of the duration corresponding to all the historical time periods; />Is to takeAnd->Maximum value of (2); />Is the firstbInitial industrial data within the current subsection, andcDTW distances between the initial industrial data within the individual matching subsections; />Is of natural constantA power of the second;bis the sequence number of the current sub-segment in the current sub-segment set;cis the firstbSequence numbers of the matched subsections in the matched subsection set corresponding to the current subsection;

according to the joint matching degree, determining a target local interval corresponding to each initial industrial data in the current time period comprises the following steps:

2. The method of claim 1, wherein the periodically dividing the target time period to obtain a set of current time period and historical time period includes:

3. The method of claim 1, wherein the performing the variable division on each historical time segment in the current time segment and the historical time segment set to obtain a current sub-segment set corresponding to the current time segment and a historical sub-segment set corresponding to the historical time segment includes:

4. The method for processing data according to claim 1, wherein the performing the distribution degree analysis processing on each initial industrial data in the current time period to obtain the target distribution degree corresponding to each initial industrial data in the current time period includes:

5. The data processing method according to claim 1, wherein the performing the sensitivity level analysis on each initial industrial data in the current time period to obtain the target sensitivity level corresponding to each initial industrial data in the current time period includes:

6. The data processing method according to claim 1, wherein the adaptively adjusting the initial industrial data in the current time period based on the target local interval, the target distribution degree and the target sensitivity degree corresponding to the initial industrial data in the current time period to obtain the target industrial data set includes:

7. A data processing apparatus comprising a processor and a memory, the processor being arranged to process instructions stored in the memory to implement a data processing method as claimed in any one of claims 1 to 6.

8. PLC data transmission system based on thing networking, characterized in that, the system includes:

the PLC equipment is used for controlling and monitoring the production process of the products on the production line;

the data acquisition device is used for acquiring initial industrial data in the PLC equipment;

a data processing device, which is used for compressing the initial industrial data according to the claim 7;

the communication device is used for transmitting the initial industrial data after compression processing to the cloud server through the Internet of things;

and the cloud server is used for storing and managing the initial industrial data after the compression processing.