Industrial system data checking method and system based on multi-source fusion

Abstract

The application discloses an industrial system data checking method and system based on multi-source fusion, and relates to the technical field of data processing.The application solves the problems of waste of data acquisition thread computing resource and poor directivity of data display mode.The application can accurately identify the influence of data source end data generation period on threads, accurately calibrate and optimize threads, and then reasonably calculate and integrate redundant computing resource, and scientifically and evenly distribute the redundant computing resource to each thread.This process realizes full and reasonable use of computing resource, guarantees efficient and stable data acquisition process, and makes each thread achieve optimal effect and state in comprehensive acquisition, thereby significantly improving the overall operation efficiency of the industrial system and reducing resource waste.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to an industrial system data verification method and system based on multi-source fusion. BACKGROUND

[0002] Industrial systems are rapidly developing towards intelligence and digitization. A large number of multi-source data of different types and formats are involved in industrial production processes, such as equipment operation parameters, production process data, and environmental monitoring data. These data come from various data sources such as sensors, intelligent instruments, and production management systems.

[0003] Traditional industrial systems face many challenges in data analysis. On the one hand, due to the complex thread processing mechanism associated with multi-source data sources, there is a lack of effective overall planning for data processing between different threads. In the past, when facing multi-thread data processing, it was difficult to accurately grasp the processing capacity of each thread in different periods, resulting in unreasonable allocation of computing resources, with some threads having excess computing power and others having insufficient computing power, greatly affecting data processing efficiency and overall production performance.

[0004] On the other hand, data analysis in industrial systems has long relied on professional programmers. Ordinary industrial practitioners lack programming skills and are unable to conduct in-depth analysis of data. The complex data processing flow and professional programming requirements make it difficult for a large amount of valuable data to be analyzed and utilized in a timely and effective manner, hindering the enterprise's ability to make production optimization and decision-making based on data. Moreover, traditional methods have limited means for data visualization, and are unable to intuitively display the correlation and abnormal conditions between data, making it difficult for workers to quickly locate the problem data source and delaying the resolution of production problems. SUMMARY

[0005] To address the deficiencies of the prior art, the present application provides an industrial system data verification method and system based on multi-source fusion, which solves the problems of waste of data acquisition thread computing resources and lack of intuitive data display methods.

[0006] To achieve the above purpose, the present application is implemented by the following technical solution: an industrial system data verification method based on multi-source fusion, comprising the following steps:

[0007] Step 1, feature verification of multi-source threads associated with different multi-source data sources in the industrial system, preferentially caching data for a single thread, then processing the cached data through the corresponding thread, and confirming the processing characteristics of the corresponding thread from the processing process, with the following sub-steps:

[0008] Step 11, based on different multi-source data terminals associated with different processing threads, the multi-source data generated by the specified multi-source data terminal is cached, and the data period T is determined according to the data generation period of the corresponding multi-source data terminal, T=3xt, wherein t represents the data generation period of different multi-source data terminals;

[0009] Step 12, after completing the data caching work of the corresponding processing thread, the cached data is processed by the corresponding processing thread, and the processing rate associated with the unit time is confirmed, and a processing rate change curve is generated, and the processing characteristics are locked from the generated processing rate change curve, and the specific method is:

[0010] Step 121, select the minimum value and the maximum value from the generated processing rate change curve, and construct a set of two measurement lines about the minimum value or the maximum value, the measurement line associated with the minimum value is called the lower measurement line, and the measurement line associated with the maximum value is called the upper measurement line, execute multiple processing processes, and confirm the process characteristics associated with each processing process:

[0011] The first processing process: keep the lower measurement line and the upper measurement line unchanged, mark the value range between the two measurement lines as F, and confirm the total bus length L between the two measurement lines, that is, the total bus length of the processing rate change curve, confirm the first group of process characteristics M1 by F÷L=M1, move the upper measurement line down by one unit rate, and confirm the second group of process characteristics M2, and so on, stop moving when the lower measurement line and the upper measurement line are only separated by one unit rate, and record the several groups of process characteristics confirmed this time as the characteristic set of this processing process;

[0012] Then execute the second processing process: move the lower measurement line up by one unit rate and keep it unchanged, and confirm the characteristic set of this processing process according to the same processing method of the first processing process;

[0013] In the third processing process, the lower measurement line is moved up by one unit rate again, and the characteristic set of the corresponding processing process is confirmed synchronously, and so on, stop moving when the lower measurement line and the upper measurement line are only separated by one unit rate, complete the overall processing process;

[0014] Step 122, select the minimum value from the multiple characteristic sets confirmed, and record the two measurement lines associated with the minimum value as the standard line, confirm the value interval corresponding to the thread based on the two standard lines, and take this value interval as the processing characteristics of this thread;

[0015] Step2, according to the different processing characteristics confirmed by different threads and the associated computing power resources, the processing rate average of the corresponding thread in the actual processing process is confirmed, the processing rate average is checked with the processing characteristics, and whether the thread is labeled as an optimization thread is determined based on the checking result, and the redundant computing power resources are confirmed from the optimization thread, and the redundant computing power resources are divided into all threads, the specific method is:

[0016] Step21, the processing rate associated with the thread in the historical processing period is averaged to confirm the processing rate average, and the processing rate average is compared with the processing characteristics associated with the thread:

[0017] If the processing rate average is in the processing characteristics, there is no need to label the thread;

[0018] If Therefore, the thread is labeled as an optimization thread;

[0019] The specific method of dividing the redundant computing power resources into all threads is:

[0020] Step22, select the minimum value from the processing characteristics of the corresponding optimization thread, and label it as ZL i min, wherein i represents different optimization threads, and the processing rate average associated with the corresponding processing process is labeled as J i , using: J i ÷ZL i min=BF i Confirm the rate parameter proportion BF i , and label the computing power resources associated with the optimization thread as ZY i , using: ZY i -(ZY i ×BF i )=Yz i Confirm the redundant computing power resources Yz i , and confirm the different redundant computing power resources Yz i associated with each group of optimization threads;

[0021] Step23, and integrate the confirmed redundant computing power resources Yz i , confirm the integrated resources, and divide the integrated resources into different threads in turn, each group of division process is allocated one unit of computing power resources, after completing the computing power resource allocation, identify whether the processing rate of the corresponding thread after the computing power resource allocation is higher than the processing rate before the allocation, if higher, continue to allocate, if not higher, stop the computing power allocation of the thread;

[0022] Step3, quantize the data about different data sources processed by each different thread, confirm the quantization value associated with the data at the corresponding time, and then uniformly analyze the multiple sets of quantization values to confirm the quantization chart associated with the corresponding time and display it. The specific way is:

[0023] Step31, extract the data standard interval belonging to different data source ends, the endpoint value of which is a preset value, divide the data standard interval into six equal parts, and quantize the associated equal interval values from small to large as 1, 2, …, 5. Based on the quantization standard, the standard values associated with the corresponding data standard interval are sequentially quantized;

[0024] Step32, multiple data source ends associated with data are marked as the same type of source end, the total number G of the same type of source end is confirmed, and a polygon with the same number of edges is generated. Based on the center point of the polygon and the internal area of the polygon, the polygon is scaled into six equidistant associated regions. Each equidistant point from the inside to the outside corresponds to the quantization values 1-5 in turn;

[0025] Starting from the center point of the polygon, the connecting line with the polygon inflection point is confirmed, and different connecting lines are used as the reference line of different data source ends in the same type of source end;

[0026] Step33, the monitoring data about different data source ends monitored at the current time in the same type of source end is confirmed, the quantization value of the monitoring data is confirmed, and the corresponding quantization point position on the reference line is found. Based on the several quantization point positions associated with the polygon, it is identified whether the several quantization point positions confirmed at the current time are located in the same associated region. If not, connect several adjacent quantization points to obtain an adjusted polygon, and display the adjusted polygon. If yes, no further processing is needed.

[0027] Preferably, the industrial system data verification system based on multi-source fusion comprises:

[0028] The thread feature confirmation end verifies the features of the multi-source threads associated with different multi-source data ends in the industrial system. It preferentially stores data for a single thread, then processes the stored data through the corresponding thread, and confirms the processing features of the corresponding thread from the processing process;

[0029] The thread optimization processing end confirms the processing rate average of the corresponding thread in the actual processing process according to the different processing features confirmed by different threads and the associated computing power resources, checks the processing rate average and the processing features, determines whether to mark the thread as an optimized thread based on the checking result, confirms the redundant computing power resources from the optimized thread, and then divides the redundant computing power resources into all threads;

[0030] The quantification chart display end quantifies the data processed by each different thread about different data source ends, confirms the quantification value associated with the corresponding time data, and then uniformly analyzes multiple sets of quantification values to confirm the quantification chart associated with the corresponding time and display it.

[0031] The application provides an industrial system data verification method and system based on multi-source fusion.

[0032] The application can accurately identify the influence of the data source end data generation cycle on the thread, accurately calibrate the optimized thread, and then reasonably calculate and integrate the redundant computing resources and scientifically divide them among the threads.

[0033] Quantifying the data processed by different threads, quantizing the data standard interval and displaying it through a polygon area and quantization points can intuitively present the data state. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 The figure is a schematic diagram of the method of the application;

[0035] Figure 2 The figure is a schematic diagram of the principle framework of the application. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0037] First embodiment

[0038] Please refer to Figure 1 The application provides an industrial system data verification method based on multi-source fusion, which includes the following steps:

[0039] Step1, the characteristics of the multi-source thread associated with different multi-source data terminals in the industrial system are checked, data caching is preferentially performed on a single thread, the cached data is processed through the corresponding thread, and the processing characteristics of the corresponding thread are confirmed from the processing process. Specifically, the maximum processing characteristic is to confirm the processing rate associated with the process under the total number of original computing resources, identify the strongest processing effect of the corresponding thread in the corresponding period, and facilitate subsequent allocation of computing resources to multiple threads;

[0040] The specific sub-step of confirming the processing characteristic is:

[0041] Step11, based on different multi-source data terminals associated with different processing threads (different data sources, different threads associated with each other, so that each thread does not interfere with each other when processing data, and the data processed depends on the relevant data generated by the corresponding data source in the period), the multi-source data generated by the specified multi-source data terminal is cached and processed, and the data caching period T is determined according to the data generation period of the corresponding multi-source data terminal, T = 3t, where t represents the data generation period of the different multi-source data terminals, that is, the data caching period is three data generation periods of the corresponding multi-source data terminal;

[0042] Step12, after completing the data caching work of the corresponding processing thread, the cached data is processed through the corresponding processing thread (including data denoising, formatting and other related data processing work), and the processing rate associated with the unit time is confirmed, a processing rate change curve is generated, the horizontal coordinate axis of the curve is the time line, and the vertical coordinate axis is the processing rate. Lock the processing characteristic from the generated processing rate change curve:

[0043] Step121, select the minimum value and the maximum value from the generated processing rate change curve, and construct a set of two measurement lines about the minimum value or the maximum value. The measurement line corresponds to the minimum value or the maximum value, and belongs to a group of horizontal lines associated with the corresponding value. The measurement line associated with the minimum value is recorded as the lower measurement line, and the measurement line associated with the maximum value is recorded as the upper measurement line. Execute multiple processing processes and confirm the process characteristics associated with each processing process:

[0044] The first processing procedure: keeping the lower and upper measuring lines unchanged, marking the numerical range between the two sets of measuring lines as F, and confirming the total bus length L between the two sets of measuring lines, which is actually the total bus length of the processing rate change curve, adopting F÷L=M1 to confirm the first set of processing features M1, moving the upper measuring line downward by one unit rate, and confirming the second set of processing features M2, and so on, until the lower and upper measuring lines are only separated by one unit rate, and stopping moving, and recording the several sets of processing features confirmed this time as the feature set of this processing procedure, and the unit rate is the preset rate which is determined by the operator according to the experience gradient, and is generally 10 bit / s;

[0045] Then, the second processing procedure is executed: moving the lower measuring line upward by one unit rate and keeping it unchanged, and then confirming the feature set of this processing procedure in the same way as the first processing procedure;

[0046] In the third processing procedure, the lower measuring line is moved upward by one unit rate again (relative to the second processing procedure), and the feature set of the corresponding processing procedure is confirmed synchronously, and so on, until the lower and upper measuring lines are only separated by one unit rate, and the overall processing procedure is completed;

[0047] Step 122, selecting the minimum value from the multiple confirmed feature sets, and recording the two sets of measuring lines associated with the minimum value as the standard lines, confirming the numerical interval corresponding to the thread based on the two sets of standard lines, and taking the numerical interval as the processing feature of the thread;

[0048] Specifically, by adjusting the relevant measuring lines up and down, confirming the features according to the preset adjustment program and adjustment logic, and confirming the standard from the specific features confirmed, the processing feature of the corresponding processing procedure is locked, which facilitates subsequent process adjustment and facilitates subsequent allocation of computing resources.

[0049] Step 2, confirming the processing rate average of the corresponding thread in the actual processing process according to the different processing features confirmed by different threads and the computing resources associated with them, checking the processing rate average and the processing feature, and determining whether to mark this thread as an optimized thread based on the checking result, and the specific sub-steps of marking are:

[0050] Step 21, confirming the processing rate average by averaging the processing rate associated with this thread in the historical processing period, and comparing the processing rate average with the processing feature associated with this thread:

[0051] If the processing rate average is in the processing feature, it means that the processing procedure can fully utilize the allocated computing resources and is not affected by the related influence of the corresponding data source end data generation period, and there is no need to mark this thread.

[0052] If represents that the processing process is affected by the data generation cycle of the corresponding data source end, that is, the corresponding processing process can reach a processing rate of 500 bits / s, but the corresponding data source end can only reach a generation rate of 300 bits / s, so the highest average processing rate of the corresponding processing process can only reach 300 bits / s, and the corresponding processing characteristics cannot be reached, so the thread is marked as an optimization thread;

[0053] Step 22, select the minimum value from the processing characteristics of the corresponding optimization thread, and mark it as ZL i min, wherein i represents different optimization threads, and the average processing rate associated with the corresponding processing process is marked as J i , using: J i ÷ZL i min=BF i Confirm the rate parameter proportion BF i , and mark the computing resource associated with this optimization thread as ZY i , using: ZY i -(ZY i ×BF i )=Yz i Confirm the excess computing resource Yz i , and confirm the different excess computing resources Yz i associated with each group of optimization threads;

[0054] Step 23, and integrate the confirmed several excess computing resources Yz i , confirm the integrated resources, and divide the integrated resources into different threads in turn, each group of divided processes is allocated one unit of computing resource (one unit is a preset value, generally 10 FLOPS), after completing the allocation of computing resources, identify whether the processing rate of the corresponding thread after the allocation of computing resources is higher than the processing rate before the allocation of computing resources, if it is higher, continue to allocate, if it is not higher, stop allocating computing resources to this thread. In this way, the excess computing resources of multiple different threads are allocated and planned, which can effectively and reasonably utilize the computing resources, effectively guarantee the related processes of data collection processes, and make each thread achieve the optimal effect and optimal state in comprehensive collection, and guarantee the overall optimization processing effect of each thread.

[0055] Step 3, quantitatively process the data about different data source ends processed by each different thread, confirm the quantization value associated with the corresponding time data, and uniformly analyze multiple groups of quantization values, confirm the quantization chart associated with the corresponding time and display, and the specific way of confirmation is:

[0056] Step 31, extract the data standard interval belonging to different data source ends, the endpoint value of which is a preset value, the specific value of which is determined by the operator according to experience, and the data standard interval is divided into six equal parts, and the associated equal interval value is quantified as 1, 2, …, 5 from small to large, and based on the quantification standard, the associated standard value in the corresponding data standard interval is quantified in turn;

[0057] Step 32, the multiple data source ends with data association are marked as the same type of source end, and the data association has been marked by the relevant personnel in advance, the total number G of the same type of source end is confirmed, and a polygon with the same number of edges is generated, based on the center point of the polygon and the internal area of the polygon, the polygon is scaled into six equidistant associated regions, and each equidistant point from inside to outside corresponds to the quantization value 1-5;

[0058] Starting from the center point of the polygon, the connecting line with the polygon inflection point is confirmed, and different connecting lines are used as the reference line of different data source ends in the same type of source end;

[0059] Step 33, the monitoring data about different data source ends monitored by the same type of source end at the current time is confirmed, the quantization value of the monitoring data is confirmed, and the corresponding quantization point position on the reference line is found, and based on the several quantization point positions associated with the polygon, it is identified whether the several quantization point positions confirmed at the current time (the data monitored by different data source ends has data display on different quantization points) are located in the same associated region, if yes, no further processing is needed, if not, several adjacent quantization points are connected to obtain an adjusted polygon, and the adjusted polygon is displayed for external personnel to view. External personnel can quickly confirm the corresponding point position with data anomaly by observing the polygon, quickly lock the corresponding data source end, and through the graphical interface and drag operation, non-programming professionals can also perform complex industrial data analysis, greatly reducing the threshold of data analysis.

[0060] Second embodiment

[0061] Combined Figure 2 , the industrial system data verification system based on multi-source fusion comprises:

[0062] The thread feature confirmation end performs feature verification on the multi-source threads associated with different multi-source data ends in the industrial system, preferentially stores data of a single thread, processes the stored data through the corresponding thread, and confirms the processing features of the corresponding thread from the processing process;

[0063] The thread optimization processing end confirms the processing rate average of the corresponding thread in the actual processing process according to different processing characteristics and associated computing power resources confirmed by different threads, checks the processing rate average and the processing characteristics, determines whether to mark this thread as an optimized thread based on the checking result, confirms the redundant computing power resources from the optimized thread, and then divides the redundant computing power resources into all threads.

[0064] The quantification chart display end quantifies the data about different data sources processed by each different thread, confirms the quantification value associated with the data at the corresponding time, and then uniformly analyzes a plurality of quantification values to confirm and display the quantification chart associated with the corresponding time.

[0065] Some data in the above formula are dimensionless numerical calculations, and the contents not described in detail in the specification all belong to the prior art known to those skilled in the art.

[0066] The above embodiments are only used to illustrate the technical method of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical method of the present application.

Claims

1. A data verification method for industrial systems based on multi-source fusion, characterized in that, Includes the following steps: Step 1: Perform feature verification on the multi-source threads associated with different multi-source data terminals within the industrial system. Prioritize data caching for individual threads, then process the cached data through the corresponding threads, and identify the processing characteristics of the corresponding threads from the processing flow. Specific sub-steps are as follows: Step 11: Based on the different multi-source data terminals associated with different processing threads, cache the multi-source data generated by the specified multi-source data terminal. The cached data period T depends on the data generation period of the corresponding multi-source data terminal. T = 3 × t, where t represents the data generation period of different multi-source data terminals. Step 12: After completing the data caching work for the corresponding processing thread, process the cached data through the corresponding processing thread, confirm the associated processing rate per unit time, generate a processing rate change curve, and lock the processing characteristics from the generated processing rate change curve. The specific method is as follows: Step 121: Select the minimum and maximum values ​​from the generated processing rate change curves, and construct two sets of measurement lines for the minimum or maximum values. Denote the measurement line associated with the minimum value as the lower measurement line, and the measurement line associated with the maximum value as the upper measurement line. Execute multiple processing processes and confirm the process characteristics associated with each processing process: First processing step: Keep the lower and upper measurement lines unchanged, define the numerical range between the two sets of measurement lines as F, and then confirm the bus length L between the two sets of measurement lines, which is actually the bus length of the processing rate change curve. Use F÷L=M1 to confirm the first process characteristic M1. Move the upper measurement line downward by one unit speed and confirm the second set of process features M2. Continue in this manner until there is only one unit speed interval between the lower measurement line and the upper measurement line. Stop moving when there is only one unit speed interval between them. Record the several sets of process features confirmed in this process as the feature set of this process. Then execute the second processing step: move the lower measurement line up by one unit rate and keep it constant, and then confirm the feature set of this processing step in the same way as the first processing step; When executing the third processing step, the lower measurement line is moved up by one unit speed again, and the feature set of the corresponding processing step is confirmed simultaneously. This process continues until the lower and upper measurement lines are only one unit speed apart, at which point the movement stops, completing the overall processing step. Step 122: Select the minimum value from the confirmed set of features, and record the two sets of measurement lines associated with the minimum value as standard lines. Based on the two sets of standard lines, confirm the numerical range corresponding to the thread, and use this numerical range as the processing feature of this thread. Step 2: Based on the different processing characteristics and associated computing resources of different threads, confirm the average processing rate of the corresponding thread in the actual processing process, check and verify the average processing rate with the processing characteristics, determine whether to mark this thread as an optimized thread based on the verification results, identify the excess computing resources from the optimized threads, and then distribute the excess computing resources evenly to all threads. Step 3: Quantify the data from different data sources processed by each thread, confirm the quantization value associated with the data at the corresponding time, and then perform a unified analysis on multiple sets of quantization values ​​to confirm and display the quantization chart associated with the corresponding time.

2. The industrial system data verification method based on multi-source fusion according to claim 1, characterized in that, In Step 2, the specific method for labeling the optimization thread is as follows: Step 21: Calculate the average processing rate associated with this thread over historical processing cycles to confirm the average processing rate, and compare this average processing rate with the processing characteristics associated with this thread: If the average processing rate is greater than or equal to the processing characteristics, no calibration is required for this thread. If the average processing rate is greater than or equal to the processing characteristics, this thread is designated as an optimized thread.

3. The industrial system data verification method based on multi-source fusion according to claim 2, characterized in that, In Step 2, the specific method for distributing excess computing resources evenly across all threads is as follows: Step 22: Select the minimum value from the processing characteristics of the corresponding optimized thread and label it as ZL. i min, where i represents different optimization threads, and the average processing rate associated with the corresponding processing process is denoted as J. i , using: J i ÷ZL i min=BF i Confirmation rate parameter percentage BF i Then, the computing resources associated with this optimization thread are labeled as ZY. i Using: ZY i -(ZY i ×BF i =Yz i Confirm excess computing power resources Yz i And for each group of optimization threads, the different redundant computing resources Yz associated with them. i All were confirmed; Step 23, and then identify the several surplus computing resources Yz i The process involves integration, confirmation of integrated resources, and equal distribution of these resources to different threads. Each group of threads is allocated one unit of computing power. After the allocation of computing power, the process checks whether the processing rate of the corresponding thread after the allocation is higher than the processing rate before the allocation. If it is higher, the allocation continues; otherwise, the allocation of computing power to this thread is stopped.

4. The industrial system data verification method based on multi-source fusion according to claim 1, characterized in that, Step 3, confirming and displaying the quantitative chart, is as follows: Step 31: Extract the data standard intervals belonging to different data sources. The endpoint values ​​of the data standard intervals are all preset values. Divide the data standard intervals into six equal parts. The associated equal parts are quantized from small to large as 1, 2, ..., 5. Based on the quantization standard, the associated standard values ​​in the corresponding data standard intervals are quantized sequentially. Step 32: Mark multiple data sources with data association as similar sources, confirm the total number of similar sources G, and generate polygons with the same number of sides. Based on the center point of the polygon and the internal area of ​​the polygon, scale the polygon into six equidistant associated areas. Each equidistant point corresponds to a quantization value of 1-5 from the inside to the outside. Starting from the center point of the polygon, identify the lines connecting to the inflection points of the polygon, and use the different lines as the baselines for different data sources within the same source end; Step 33: For the monitoring data of different data sources monitored at the current time within the same source end, confirm the quantization value of this monitoring data, find the corresponding quantization point position on the baseline, and based on the positions of several quantization points associated with this polygon, identify whether the positions of several quantization points confirmed at the current time are located in the same associated area. If not, connect several adjacent quantization points to obtain the adjusted polygon, and display the adjusted polygon.

5. The industrial system data verification method based on multi-source fusion according to claim 4, characterized in that, If the confirmed locations of several quantization points are within the same associated region, no processing is required.

6. A data verification system for industrial systems based on multi-source fusion, wherein the data verification system operates according to the data verification method for industrial systems based on multi-source fusion as described in any one of claims 1-5, characterized in that, include: The thread feature verification end performs feature verification on the multi-source threads associated with different multi-source data terminals within the industrial system. It prioritizes data caching for individual threads, then processes the cached data through the corresponding thread, and confirms the processing characteristics of the corresponding thread from the processing process. The thread optimization processing end determines the average processing rate of the corresponding thread in the actual processing process based on the different processing characteristics and associated computing resources of different threads. It then checks and verifies the average processing rate against the processing characteristics. Based on the verification results, it determines whether to mark the thread as an optimization thread, identifies the excess computing resources from the optimization threads, and then distributes the excess computing resources evenly among all threads. The quantitative chart display end performs quantitative processing on data from different data sources processed by different threads, confirms the quantitative value associated with the data at the corresponding time, and then performs unified analysis on multiple sets of quantitative values ​​to confirm and display the quantitative chart associated with the corresponding time.

Images