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

Abstract

The invention discloses an industrial system data verification method and system based on multi-source fusion, relates to the technical field of data processing, solves the problems of waste of computing power resources of data acquisition threads and poor visual data display mode, and provides an industrial system data verification method and system based on multi-source fusion by performing feature verification on the multi-source threads and confirming processing features of the threads in detail. According to the method, the influence condition of the data generation period of the data source end on the threads can be accurately identified, the threads are accurately calibrated and optimized, and then redundant computing power resources are reasonably calculated and integrated and are scientifically and equally distributed to the threads. By means of the process, computing power resources are fully and reasonably utilized, it is guaranteed that the data collection process is efficient and stable, each thread achieves the optimal effect and state in the aspect of comprehensive collection, the overall computing efficiency of an industrial system is remarkably improved, and resource waste is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and specifically to a method and system for industrial system data verification based on multi-source fusion. Background Art

[0002] Industrial systems are developing rapidly towards intelligence and digitization. During the industrial production process, a large amount of multi-source data of different types and formats are involved, such as equipment operation parameters, production process data, environmental monitoring data, etc. These data come from various data sources, such as sensors, intelligent meters, production management systems, etc.

[0003] Traditional industrial systems face many challenges in data analysis. On the one hand, due to the complex thread processing mechanisms associated with multi-source data sources, the data processing between different threads lacks effective overall planning. In the past, when facing multi-threaded data processing, it was impossible to accurately grasp the processing capabilities of each thread in different cycles, resulting in unreasonable allocation of computing power resources, with some threads having excessive computing power while some threads having insufficient computing power, greatly affecting the data processing efficiency and overall production efficiency.

[0004] On the other hand, data analysis in industrial systems has long relied on professional programmers. Ordinary industrial practitioners are difficult to conduct in-depth analysis of data due to the lack of programming skills. The complex data processing processes and professional programming requirements make a large amount of valuable data unable to be analyzed and utilized in a timely and effective manner, hindering enterprises from optimizing production and making decisions based on data-driven approaches. Moreover, traditional methods have limited means in data visualization presentation and cannot intuitively display the relationships and anomalies between data, resulting in difficulties for staff to quickly locate the data source of problems and delaying the solution of production problems. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a method and system for industrial system data verification based on multi-source fusion, which solves the problems of waste of computing power resources in data collection threads and weak intuitive display methods of data.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for industrial system data verification based on multi-source fusion, including the following steps:

[0007] Step1. Perform feature verification on multi-source threads associated with different multi-source data sources in the industrial system. First, perform data caching on a single thread, then process the cached data through the corresponding thread, and confirm the processing characteristics of the corresponding thread from the processing process. The specific sub-steps are as follows:

[0008] Step11. Based on different multi-source data ends associated with different processing threads, perform cache processing on the multi-source data generated by the specified multi-source data end. The cached data period T is determined according to the data generation period of the corresponding multi-source data end, and T = 3 × t, where t represents the data generation period of different multi-source data ends;

[0009] Step12. After completing the data caching work of the corresponding processing thread, process the cached data through the corresponding processing thread, and confirm the processing rate associated within a unit time to generate a processing rate change curve. Lock the processing characteristics from the generated processing rate change curve. The specific method is as follows:

[0010] Step121. Select the minimum value and the maximum value from the generated processing rate change curve, and construct two sets of measurement lines regarding the minimum value or the maximum value. 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:

[0011] The first processing process: Keep the lower measurement line and the upper measurement line unchanged, calibrate the numerical range between the two sets of measurement lines as F, and then confirm the total line length L between the two sets of measurement lines, which is actually the total line length of the processing rate change curve. Use F÷L = M1 to confirm the first set of process characteristics M1; move the upper measurement line downward by one unit rate, and confirm the second set of process characteristics M2, and so on, until the lower measurement line and the upper measurement line are only separated by one unit rate and then stop moving. Denote the several sets 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 upward by one unit rate and keep it unchanged, and then confirm the characteristic set of this processing process according to the same processing method as the first processing process;

[0013] When executing the third processing process, move the lower measurement line upward by one unit rate again, and synchronously confirm the characteristic set of the corresponding processing process, and so on, until the lower measurement line and the upper measurement line are only separated by one unit rate and then stop moving to complete the overall processing process;

[0014] Step122. Select the minimum value from the multiple confirmed characteristic sets, and denote the two sets of measurement lines associated with the minimum value as the standard lines. Based on the two sets of standard lines, confirm the numerical interval corresponding to the corresponding thread, and use this numerical interval as the processing characteristic of this thread;

[0015] Step 2. Based on the different processing characteristics confirmed by different threads and the associated computing power resources, confirm the average processing rate of the corresponding thread during the actual processing. Compare the average processing rate with the processing characteristics for verification. Based on the verification result, determine whether to label this thread as an optimized thread. Identify the redundant computing power resources from the optimized threads, and then evenly distribute the redundant computing power resources to all threads. The specific method is as follows:

[0016] Step 21. Calculate the average processing rate of the processing rates associated with this thread during the historical processing cycle to obtain the average processing rate, and compare this average processing rate with the processing characteristics associated with this thread:

[0017] If the average processing rate ∈ the processing characteristics, no calibration is required for this thread;

[0018] If Therefore, label this thread as an optimized thread;

[0019] The specific method for evenly distributing the redundant computing power resources to all threads is as follows:

[0020] 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 optimized threads, and label the average processing rate associated with the corresponding processing process as J i , and use: J i ÷ZL i min = BF i Confirm the rate participation ratio BF i , and then label the computing power resources associated with this optimized thread as ZY i , and use: 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 optimized threads;

[0021] Step 23. Integrate the confirmed multiple redundant computing power resources Yz i , confirm the integrated resources, and evenly distribute the integrated resources to different threads in sequence. Each group of distribution processes is allocated one unit of computing power resource. 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 that before the allocation. If it is higher, continue the allocation; if not, stop the computing power allocation for this thread;

[0022] Step 3. Quantify the data regarding different data source ends processed by each different thread, confirm the quantization values associated with the data at the corresponding moment, then perform unified analysis on multiple groups of quantization values, confirm the quantization chart associated with the corresponding moment and display it. The specific method is as follows:

[0023] Step 31. Extract the data standard intervals belonging to different data source ends. The endpoint values of the data standard intervals are all preset values. Divide the data standard intervals into six equal parts, and the associated equal values are sequentially quantified from small to large as 1, 2,..., 5. Based on the quantization standard, sequentially quantify the standard values associated within the corresponding data standard intervals;

[0024] Step 32. Label multiple data source ends with data associations as the same type of source ends, confirm the total number G of the same type of source ends, and generate a polygon with the same number of sides. Based on the center point of the polygon and the internal area of the polygon, scale this polygon into six equally spaced associated areas, and each equally spaced point corresponds to the quantization values 1 - 5 from the inside to the outside in sequence;

[0025] Starting from the center point of the polygon, confirm the connection lines with the inflection points of the polygon, and use the different connection lines as the reference lines for different data source ends within the same type of source ends;

[0026] Step 33. For the monitoring data regarding different data source ends monitored at the current moment within the same type of source ends, confirm the quantization value of this monitoring data, find the corresponding quantization point position on the reference line, and based on the several quantization point positions associated with this polygon, identify whether the several quantization point positions confirmed at the current moment are within the same associated area. If not, connect several adjacent quantization points to obtain an adjusted polygon, and display the adjusted polygon. If so, no processing is required.

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

[0028] Thread feature confirmation end, which performs feature verification on the multi - source threads associated with different multi - source data ends in the industrial system. First, perform data caching for a single thread, then process the cached data through the corresponding thread, and confirm the processing features of the corresponding thread from the processing process;

[0029] Thread optimization processing end, according to the different processing features confirmed by different threads and the associated computing power resources, confirm the average processing rate of the corresponding thread during the actual processing process, check and verify the average processing rate with the processing features, determine whether to label this thread as an optimized thread based on the verification result, confirm the redundant computing power resources from the optimized threads, and then evenly distribute the redundant computing power resources to all threads;

[0030] The quantification chart display end performs quantification processing on the data from different data source ends processed by each different thread, confirms the quantification values associated with the data at the corresponding moment, and then conducts unified analysis on multiple groups of quantification values to confirm and display the quantification chart associated with the corresponding moment.

[0031] The present invention provides an industrial system data verification method and system based on multi-source fusion. Compared with the prior art, it has the following beneficial effects:

[0032] Through feature verification of multi-source threads, the present invention details and confirms the processing features of each thread, can accurately identify the influence of the data generation cycle of the data source end on the thread, accurately calibrate and optimize the thread, and then reasonably calculate and integrate redundant computing power resources and scientifically distribute them equally to each thread. This process realizes the full and reasonable utilization of computing power resources, ensures the efficient and stable data acquisition process, enables each thread to achieve the optimal effect and state in comprehensive acquisition, significantly improves the overall operation efficiency of the industrial system, and reduces resource waste;

[0033] Quantification processing is performed on the data processed by different threads, and the data standard interval is quantified and displayed through a polygon area and quantification points, which can intuitively present the data state. External personnel can quickly confirm the abnormal data points by observing the polygon and lock the corresponding data source end. With the help of the graphical interface and drag-and-drop operation, non-programming professionals can also easily carry out complex industrial data analysis, greatly reducing the data analysis threshold, broadening the scope of data analysis participants, and enabling more people to make scientific decisions based on the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a schematic flowchart of the method of the present invention;

[0035] Figure 2 is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0037] The First Embodiment

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

[0039] Step 1. Perform feature verification on the multi-source threads associated with different multi-source data terminals within the industrial system. First, cache the data for a single thread, then process the cached data through the corresponding thread, and confirm the processing characteristics of the corresponding thread from the processing process. Specifically, the maximum processing characteristic is to confirm the processing rate associated with the process under the total state of the original computing power resources of the corresponding thread, and identify the strongest processing effect of the corresponding thread within the corresponding cycle, which facilitates the subsequent overall allocation of computing power resources for multiple threads.

[0040] Among them, the specific sub-steps for confirming the processing characteristics are as follows:

[0041] Step 11. Based on the different multi-source data terminals associated with different processing threads (since different data sources are associated with different threads, each thread will not interfere with each other during data processing, and the processed data depends on the relevant data generated by the corresponding data source within the cycle), perform cache processing on the multi-source data generated by the specified multi-source data terminal. The cached data cycle T is determined according to the data generation cycle of the corresponding multi-source data terminal, and T = 3 × t, where t represents the data generation cycle of different multi-source data terminals, that is, the data cache cycle is three data generation cycles of the corresponding multi-source data terminal.

[0042] Step 12. After completing the data caching work of the corresponding processing thread, process the cached data through the corresponding processing thread (including data denoising, formatting, and other related data processing work), and confirm the processing rate associated with the unit time, generate a processing rate change curve, where the horizontal axis of the curve is the time line and the vertical axis is the processing rate. Lock the processing characteristics from the generated processing rate change curve:

[0043] Step 121. Select the minimum value and the maximum value from the generated processing rate change curve, and construct two sets of measurement lines for the minimum value or the maximum value. The measurement lines correspond to the minimum value or the maximum value and belong to a set of horizontal lines associated with the corresponding value. 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:

[0044] First processing process: keep the lower and upper measuring lines unchanged, mark the value range between the two sets of measuring lines as F, and then confirm the total length L between the two sets of measuring lines, which is actually the total length of the processing rate change curve, and use; F÷L=M1 to confirm the first set of process characteristics M1; move the upper measuring line downward by one unit rate, and confirm the second set of process characteristics M2, and so on, until there is only one unit rate between the lower and upper measuring lines, stop moving, and record the several sets of process characteristics confirmed this time as the feature set of this processing process, and its unit rate is the preset rate set by the operator based on the experience gradient, generally 10bit / s;

[0045] Then execute the second processing process: move the lower measuring line upward by a unit rate and keep it constant, and then confirm the feature set of this processing process in the same processing method as the first processing process;

[0046] When executing the third processing process, the lower measuring line is moved upward again by one unit rate (for the second processing process), and the feature set of the corresponding processing process is simultaneously confirmed, and so on, until the lower measuring line and the upper measuring line are separated by only one unit rate, the movement is stopped, and the whole processing process is completed;

[0047] Step 122, from the confirmed multiple feature sets, select the minimum value, and record the two sets of measurement lines associated with the minimum value as standard lines, confirm the numerical range corresponding to the corresponding thread based on the two sets of standard lines, and use the numerical range as the processing feature of this thread;

[0048] Specifically, by adjusting the relevant measurement lines up and down, confirming the characteristics according to the preset adjustment procedures and adjustment logic, and making specific confirmation of the standards from the confirmed specific characteristics, the processing characteristics of the corresponding processing process are locked, which facilitates the subsequent process adjustment and the subsequent allocation of computing resources.

[0049] Step 2: According to the different processing characteristics confirmed by different threads and the associated computing resources, 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, and determine whether to calibrate this thread as an optimized thread based on the verification results. The specific sub-steps for calibration are:

[0050] Step 21. Perform average processing on the processing rate associated with this thread in the historical processing cycle to confirm the average processing rate, and compare this average processing rate with the processing characteristics associated with this thread:

[0051] If the processing rate mean ∈ processing feature, it means that the processing process can fully utilize the allocated computing resources and is not affected by the data generation cycle of the corresponding data source. There is no need to calibrate this thread;

[0052] If it means that this 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 bit / s, but the corresponding data source end can only reach a generation rate of 300 bit / s. Then the average processing rate can only reach 300 bit / s at most and cannot reach the corresponding processing characteristics. Therefore, this thread is marked as an optimized thread;

[0053] Step22. Select the minimum value from the processing characteristics of the corresponding optimized thread and mark it as ZL i min, where i represents different optimized threads, and mark the average processing rate associated with the corresponding processing process as J i , and use: J i ÷ZL i min = BF i Confirm the rate participation ratio BF i , and then mark the computing power resources associated with this optimized thread as ZY i , and use: ZY i -(ZY i ×BF i ) = Yz i Confirm the excess computing power resources Yz i , and confirm the different excess computing power resources Yz i associated with each group of optimized threads;

[0054] Step23. Integrate the confirmed several excess computing power resources Yz i , confirm the integrated resources, and evenly distribute the integrated resources into different threads in turn. Each group of evenly distributed processes is allocated one unit of computing power resources (one unit is a preset value, generally 10 FLOPS). After the computing power resources are allocated, identify whether the processing rate of the corresponding thread after the computing power resources are allocated is higher than that before the allocation. If it is higher, continue to allocate. If it is not higher, stop allocating the computing power to this thread. And so on, overall allocate the excess computing power resources for multiple different threads, and then the computing power resources can be effectively and reasonably utilized, so as to effectively guarantee the relevant processes of the data acquisition process, so that each thread can reach the optimal effect and state in comprehensive acquisition, and ensure the overall optimized processing effect of each thread;

[0055] Step3. Quantify the data processed by each different thread for different data source ends, confirm the quantization value associated with the data at the corresponding moment, and then conduct a unified analysis of multiple groups of quantization values, confirm the quantization chart associated with the corresponding moment and display it. The specific method for confirmation is as follows:

[0056] Step 31. Extract the data standard intervals belonging to different data source ends. The endpoint values of the data standard intervals are all preset values, and their specific values are determined by the operator according to experience. Divide the data standard intervals into six equal parts, and the associated equal values are quantified as 1, 2, ……, 5 in ascending order. Based on the quantification standard, quantify the standard values associated within the corresponding data standard intervals in sequence;

[0057] Step 32. Label multiple data source ends with data associations as the same type of source ends. Their data associations have been previously labeled by relevant personnel. Confirm the total number G of the same type of source ends and generate a polygon with the same number of corresponding sides. Based on the center point of the polygon and the internal area of the polygon, scale this polygon into six equally spaced associated areas, and each equally spaced point corresponds to the quantification values 1 - 5 from the inside to the outside in sequence;

[0058] Starting from the center point of the polygon, confirm the connection lines with the inflection points of the polygon, and use the different connection lines as the reference lines for different data source ends within the same type of source ends;

[0059] Step 33. For the monitoring data of different data source ends monitored at the current moment within the same type of source ends, confirm the quantification value of this monitoring data, find the corresponding quantification point position on the reference line, and based on the several quantification point positions associated with this polygon, identify whether the several quantification point positions confirmed at the current moment (for the data monitored by different data source ends, there are data displays at different quantification points) are located within the same associated area. If so, no processing is required. If not, connect several adjacent quantification points to obtain an adjusted polygon, and display the adjusted polygon for external personnel to view. By observing this polygon, external personnel can quickly confirm the corresponding points with data anomalies, quickly lock the corresponding data source ends, and through the graphical interface and drag - and - drop operations, non - programming professionals can also perform complex industrial data analysis, greatly reducing the threshold of data analysis.

[0060] Second Embodiment

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

[0062] Thread feature confirmation end, which performs feature verification on the multi - source threads associated with different multi - source data ends within the industrial system. First, perform data caching on a single thread, then process the cached data through the corresponding thread, and confirm the processing features of the corresponding thread from the processing process;

[0063] The thread optimization processing end determines the average processing rate of the corresponding thread during the actual processing based on the different processing characteristics confirmed by different threads and the associated computing power resources, checks and verifies the average processing rate with the processing characteristics, determines whether to label this thread as an optimized thread based on the verification result, identifies the redundant computing power resources from the optimized threads, and then evenly distributes the redundant computing power resources to all threads;

[0064] The quantization chart display end performs quantization processing on the data related to different data source ends processed by each different thread, determines the quantization values associated with the data at the corresponding moment, and then conducts a unified analysis of multiple groups of quantization values to determine and display the quantization chart associated with the corresponding moment.

[0065] Some of the data in the above formula are used for numerical calculations after removing their dimensions, and the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

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

Claims

1. An industrial system data verification method based on multi-source fusion, characterized in that It includes the following steps: Step 1: Perform feature verification on the multi-source threads associated with different multi-source data ends in the industrial system. First, perform data caching on a single thread, then process the cached data through the corresponding thread, and confirm the processing characteristics of the corresponding thread from the processing process; Step 2: According to the different processing characteristics confirmed by different threads and the associated computing power resources, confirm the average processing rate of the corresponding thread during the actual processing process. Compare the average processing rate with the processing characteristics, and based on the verification result, determine whether to label this thread as an optimized thread. Then, identify the excess computing power resources from the optimized threads, and evenly distribute the excess computing power resources to all threads; Step 3: Perform quantization processing on the data of different data source ends processed by each different thread, confirm the quantization values associated with the data at the corresponding moment, and then perform unified analysis on multiple groups of quantization values to confirm and display the quantization chart associated with the corresponding moment.

2. The method for verifying industrial system data based on multi-source fusion according to claim 1, wherein In the above Step 1, the specific sub-steps for confirming the processing characteristics of the corresponding thread are: Step 11: Based on the different multi-source data ends associated with different processing threads, perform caching processing on the multi-source data generated by the specified multi-source data end. The cached data period T is determined according to the data generation period of the corresponding multi-source data end, and T = 3×t, where t represents the data generation period of different multi-source data ends; Step 12: After completing the data caching work of the corresponding processing thread, process the cached data through the corresponding processing thread, and confirm the processing rate associated per unit time, generate a processing rate change curve, and lock the processing characteristics from the generated processing rate change curve.

3. The industrial system data verification method based on multi-source fusion according to claim 2, characterized in that In the above Step 12, the specific method for locking the processing characteristics is: Step 121: Select the minimum value and the maximum value from the generated processing rate change curve, and construct two sets of measurement lines for the minimum value or the maximum value. 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: The first processing process: Keep the lower measurement line and the upper measurement line unchanged, label the numerical range between the two sets of measurement lines as F, and then confirm the total line length L between the two sets of measurement lines, which is actually the total line length of the processing rate change curve. Use F÷L = M1 to confirm the first set of process characteristics M1; move the upper measurement line downward by one unit rate, and confirm the second set of process characteristics M2, and so on, until the lower measurement line and the upper measurement line are only one unit rate apart and then stop moving. Denote the several sets of process characteristics confirmed this time as the characteristic set of this processing process; Then execute the second processing process: Move the lower measurement line upward by one unit rate and keep it unchanged, and then confirm the characteristic set of this processing process according to the same processing method as the first processing process; When executing the third processing process, move the lower measurement line up by one unit rate again, and synchronously confirm the feature set of the corresponding processing process, and so on, until the movement stops when there is only one unit rate between the lower measurement line and the upper measurement line, and the overall processing process is completed; Step122. Select the minimum value from the confirmed multiple feature sets, and record the two sets of measurement lines associated with the minimum value as the standard lines. Based on the two sets of standard lines, confirm the numerical interval corresponding to the corresponding thread, and use this numerical interval as the processing feature of this thread.

4. The method for validating industrial system data based on multi-source fusion according to claim 1, wherein, In the said Step2, the specific method for calibrating the optimization thread is: Step21. Perform mean processing on the processing rates associated with this thread in the historical processing cycle to confirm the mean processing rate, and compare this mean processing rate with the processing features associated with this thread: If the mean processing rate ∈ the processing feature, there is no need to perform any calibration on this thread; If the average processing rate has processing characteristics, so this thread is labeled as an optimized thread.

5. The industrial system data verification method based on multi-source fusion according to claim 4, wherein In the said Step2, the specific method for evenly distributing the excess computing resources to all threads is: Step 22: Select the minimum value from the processing characteristics corresponding to the optimization threads and label it as ZL i min, where i represents different optimization threads, and label the average processing rate associated with the corresponding processing process as J i , and use: J i ÷ZL i min = BF i Confirm the rate participation ratio BF i , and then label the computing power resources associated with this optimization thread as ZY i , and use: ZY i -(ZY i ×BF i ) = Yz i Confirm the excess computing power resources Yz i , and confirm the different excess computing power resources Yz i associated with each group of optimization threads; Step 23. Combine the identified multiple redundant computing power resources Yz i Integrate them to confirm the integrated resources, and evenly distribute the integrated resources to different threads in sequence. Each group of even distribution processes is allocated one unit of computing power resource. After the computing power resource allocation is completed, identify whether the processing rate of the corresponding thread after the computing power resource allocation is higher than that before the allocation. If it is higher, continue the allocation; if not, stop the computing power allocation for this thread.

6. The method for validating industrial system data based on multi-source fusion according to claim 1, wherein In the said Step3, the specific method for confirming and displaying the quantization chart is: Step31. Extract the data standard intervals belonging to different data source ends. The endpoint values of the data standard intervals are all preset values. Divide the data standard intervals into six equal parts, and the associated equal values are sequentially quantified as 1, 2,..., 5 from small to large. Based on the quantization standard, sequentially quantify the standard values associated within the corresponding data standard intervals; Step32. Label multiple data source ends with data associations as the same type of source ends, confirm the total number G of the same type of source ends, and generate a polygon with the same number of sides. Based on the center point of the polygon and the internal area of the polygon, scale this polygon into six equally spaced associated areas, and each equally spaced point corresponds to the quantization values 1 - 5 from the inside to the outside in sequence; Starting from the center point of the polygon, confirm the connection lines with the inflection points of the polygon, and use the different connection lines as the reference lines for different data source ends within the same type of source end; Step33. Confirm the quantization value of the monitoring data regarding different data source ends monitored at the current moment within the same type of source end, find the corresponding quantization point position on the reference line, and based on the several quantization point positions associated with this polygon, identify whether the several confirmed quantization point positions at the current moment are located within the same associated area. If not, connect several adjacent quantization points to obtain an adjusted polygon, and display the adjusted polygon.

7. The method for verifying industrial system data based on multi-source fusion according to claim 6, wherein If the several confirmed quantization point positions are located within the same associated area, no processing is required.

8. An industrial system data verification system based on multi-source fusion, which operates according to the industrial system data verification method based on multi-source fusion described in any one of claims 1-7, characterized in that Including: A thread feature confirmation end, which performs feature verification on the multi-source threads associated with different multi-source data ends in the industrial system, preferentially caches data for a single thread, then processes the cached data through the corresponding thread, and confirms the processing features of the corresponding thread from the processing process; The thread optimization processing end determines the average processing rate of the corresponding thread during the actual processing based on the different processing characteristics confirmed by different threads and the associated computing power resources, checks and verifies the average processing rate with the processing characteristics, determines whether to label this thread as an optimized thread based on the verification result, identifies the redundant computing power resources from the optimized threads, and then evenly distributes the redundant computing power resources to all threads; The quantization chart display end performs quantization processing on the data of different data source ends processed by each different thread, determines the quantization values associated with the data at the corresponding moment, then conducts a unified analysis of multiple groups of quantization values, and determines and displays the quantization chart associated with the corresponding moment.

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