MES data intelligent management system and method based on data analysis

Abstract

The invention discloses an MES data intelligent management system and method based on data analysis, and relates to the technical field of data analysis. The system comprises a historical production data acquisition and processing module, a production efficiency evaluation and analysis module, a trend feature calculation and anomaly detection module and a real-time production data analysis and intelligent early warning module. The historical production data acquisition and processing module is responsible for acquiring data from the system, calculating inter-process waiting time and carrying out primary processing on the data; the production efficiency evaluation and analysis module extracts features, evaluates production efficiency and analyzes the trend of historical data; the trend feature calculation and anomaly detection module performs abnormal trend identification based on trend feature calculation and analysis of production efficiency; the real-time production data analysis and intelligent early warning module processes real-time data and carries out data comparison and early warning.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and particularly to an MES data intelligent management system and method based on data analysis. Background Art

[0002] With the continuous improvement of the informatization level of the manufacturing industry, the Manufacturing Execution System (MES), as an important part of enterprise informatization, is mainly used to manage production planning, scheduling, quality control and other links, helping enterprises to achieve real-time monitoring and refined management of the production process. Traditional MES systems mainly focus on the real-time monitoring and data collection of the production process, aiming to ensure that the production progress and quality of products from raw materials to finished products are effectively controlled.

[0003] However, most current MES systems on the market are limited to event tracking and work reporting records of the production process. Usually, work orders are registered by scanning barcodes or QR codes, etc., to record the completion of each production process. Such systems often focus on post-processing and passive data collection of the production process, lacking real-time intelligent analysis and active decision support functions for production data. Therefore, although existing MES systems can perform basic monitoring and data collection of the production process, their data analysis capabilities are relatively limited, lacking accurate prediction of production bottlenecks and potential risks. This makes it so that when facing production delays or bottlenecks, managers can only rely on manual experience to make adjustments, failing to timely discover potential problems and take preventive measures. Summary of the Invention

[0004] The purpose of the present invention is to provide an MES data intelligent management system and method based on data analysis to solve the problems raised in the above background art.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: An MES data intelligent management method based on data analysis, comprising the following steps: Step S100. Obtain the historical production records corresponding to all production processes in the production workshop within a selected period, and define each production process as a production node; according to the production process sequence, obtain the waiting time between every two adjacent production nodes, defined as the inter-process waiting time, summarize the inter-process waiting time within the selected period, and construct an inter-process waiting time set; Step S200. According to the historical production records, divide the historical production data according to the corresponding production nodes into several historical production data segments, and construct corresponding node data sets; extract the corresponding historical production data features from each node data set, and evaluate the production efficiency of the corresponding historical production data segments according to the historical production data features; Step S300. Correlate each element in the set of inter-process waiting times with the production efficiency evaluation results of the corresponding historical production data segments, and analyze the correlation between the inter-process waiting times and the production efficiency evaluation results; based on the correlation between the inter-process waiting times and the production efficiency evaluation results, obtain the historical production data characteristic trend curve; Step S400. Obtain the real-time production data corresponding to each production node, analyze the real-time production data to extract the real-time production data characteristics; analyze based on the real-time production data characteristics to obtain the real-time production data characteristic trend curve, and compare the real-time production data characteristic trend curve with the historical production data characteristic trend curve to generate the corresponding notification information.

[0006] Furthermore, step S100 includes: S101. Through the MES system, obtain the historical production records corresponding to all production processes in the production workshop within the selected period, and define each production process as a production node; the historical production records refer to the data information of each production process during the production process, including process name, start time, end time, historical event records, and historical production data; according to the production process sequence recorded in the MES system, sequentially obtain the start time Ts and end time Te of each production node, so as to obtain the time range of the corresponding production node as [ts, te]; according to the production process sequence, sequentially calculate the waiting time ti between every two adjacent production nodes, and ti = ts(i + 1) - te(i), where ts(i + 1) represents the start time of production node i + 1, and te(i) represents the end time of production node i; S102. Define the waiting time ti between every two adjacent production nodes as the inter-process waiting time, summarize the inter-process waiting time of each process within the selected period to construct the set of inter-process waiting times T, and T = {t1, t2,..., tn - 1}, where t1 represents the waiting time between production node 1 and production node 2, t2 represents the waiting time between production node 2 and production node 3, and so on, tn - 1 represents the waiting time between production node n - 1 and production node n, and n represents the total number of production nodes; each set of inter-process waiting times T corresponds to a production process sequence, and the number of sets of inter-process waiting times T is equal to the number of production process sequences within the selected period, and the production tasks corresponding to each production process sequence are the same.

[0007] Furthermore, step S200 includes: S201. For each production process sequence, obtain the corresponding historical production records. Divide the historical production data corresponding to each production node in chronological order into several historical production data segments, and there is a one-to-one correspondence between each historical production data segment and the production node corresponding to the corresponding production process sequence. According to the production node number, summarize the historical production data segments corresponding to all the same production node numbers within the selected period, and sort the historical production data segments in chronological order to form the node data set Ai, and Ai = {ai1, ai2,..., aim}, where ai1 represents the historical production data segment corresponding to the first production process sequence of the i-th production node, ai2 represents the historical production data segment corresponding to the second production process sequence of the i-th production node, and so on. Aim represents the historical production data segment corresponding to the m-th production process sequence of the i-th production node, and m represents the number of production process sequences within the selected period. S202. For each element aij in the node data set Ai, extract the corresponding historical production data features, and the historical production data features include production time, number of abnormal events, production output, and unqualified rate. Perform normalization processing on the collected historical production data features to obtain the corresponding historical production data feature values, and evaluate the production efficiency of each element aij in the node data set Ai according to the historical production data feature values, so as to calculate the comprehensive production efficiency index Eij, and Eij = α×(Pij / Tij) - β×Nij - γ×Qij, where α, β, and γ all represent weight coefficients, and α + β + γ = 1; Tij represents the feature value of the production time corresponding to the element aij, Pij represents the feature value of the number of production products corresponding to the element aij, Nij represents the feature value of the number of abnormal events corresponding to the element aij, and Qij represents the feature value of the unqualified rate corresponding to the element aij.

[0008] Since step S200 analyzes each production process sequence, obtains the historical production records of each production process sequence, and divides the corresponding historical production data according to the corresponding production nodes, several historical production data segments are obtained, and each production node corresponds to a historical production data segment. And because there are multiple production process sequences within the selected period, for each production node, the number of corresponding historical production data segments is equal to the number of production process sequences within the selected period.

[0009] Further, step S300 includes: S301. For each node data set Ai, obtain the comprehensive production efficiency index Eij of all historical production data segments in the node data set Ai. According to the corresponding production process sequence number and the corresponding production node number, match the corresponding waiting time tij between processes, so as to form a number of associated data pairs G, and G = (Eij, tij); for each node data set Ai, summarize the corresponding associated data pairs G, and represent the associated data pairs G as a data point in the plane rectangular coordinate system. The horizontal axis of the plane rectangular coordinate system represents the comprehensive production efficiency index Eij, and the vertical axis represents the waiting time tij between processes. Connect the data points in sequence according to the order in the node data set Ai to obtain the curve Li; S302. For each curve Li corresponding to the node data set Ai, respectively obtain the values of all data points on the curve Li, and calculate the mean and standard deviation of the waiting time between processes and the comprehensive production efficiency index in sequence. Calculate the trend characteristic index Rij according to the mean and standard deviation of the waiting time between processes and the comprehensive production efficiency index. The specific calculation formula is: Rij = w1·[(Eij - μEi) / 3σEi] + w2·[(tij - μti) / 3σti]; Where Rij represents the trend characteristic index corresponding to the jth production process sequence of the ith production node, μEi and σEi represent the mean and standard deviation of the horizontal axis values of the data points of the curve Li corresponding to the node data set Ai, μti and σti represent the mean and standard deviation of the vertical axis values of the data points of the curve Li corresponding to the node data set Ai, w1 and w2 represent weight coefficients, and w1 + w2 = 1; S303. Summarize the trend characteristic index Rij corresponding to each element in the node data set Ai, and define the trend characteristic index threshold R corresponding to the node data set Ai in combination with the historical production records. Compare the trend characteristic index Rij corresponding to each element in the node data set Ai with the trend characteristic index threshold R. If Rij ≤ R, mark the data points on the corresponding curve Li as normal data points. If Rij > R, mark the data points on the corresponding curve Li as abnormal data points; according to the marked positions of the abnormal data points on the curve Li, divide the curve Li to obtain a number of continuous curve segments, and each continuous curve segment is composed of normal data points. Fit each continuous curve segment to obtain the curve equation of each continuous curve segment, and define the curve equation of each continuous curve segment as the historical production data characteristic trend curve Q.

[0010] Further, step S400 includes: S401. Obtain the real-time production data corresponding to each production node through the MES system, and analyze the real-time production data with reference to the analysis method of historical production data, so as to construct the corresponding node data set Bi for each production node; for each element in the node data set Bi of each production node, extract the corresponding real-time production data features with reference to the analysis method of the node data set Ai, and calculate the comprehensive production efficiency index E’ij corresponding to each element in the node data set Bi according to the real-time production data features; match the inter-operation waiting time t’ij of the corresponding element according to the production process sequence number and the corresponding production node number of each element in the node data set Bi; summarize the comprehensive production efficiency index E’ij and the inter-operation waiting time t’ij of each element in the node data set Bi to form a number of associated data pairs G’, and G’=(E’ij,t’ij); S402. For each associated data pair G’ corresponding to each production node, represent it as a data point in the plane rectangular coordinate system in sequence, and connect the data points in sequence according to the order of the node data set Bi, so as to obtain the real-time production data feature trend curve S; according to the production node number corresponding to the real-time production data feature trend curve S, match the historical production data feature trend curve Q with the same production node number, move the real-time production data feature trend curve S on the historical production data feature trend curve Q in sequence, select the one with the highest coincidence degree as the matching result, and mark the real-time production data feature trend curve S on the matched historical production data feature trend curve Q, and count the number of data points between the last data point of the real-time production data feature trend curve S and the last data point of the matched historical production data feature trend curve Q, and the last data point of the real-time production data feature trend curve S does not coincide with the last data point of the matched historical production data feature trend curve Q; when the number of data points is equal to 0, output the number of the current production node to the relevant personnel for corresponding processing by the relevant personnel.

[0011] A MES data intelligent management system based on data analysis, including: a historical production data collection and processing module, a feature extraction and efficiency evaluation module, a trend feature analysis and anomaly detection module, and a real-time production data analysis and intelligent warning module; The historical production data collection and processing module obtains the historical production records corresponding to all production processes in the production workshop within the selected period, and defines each production process as a production node; according to the production process sequence, obtain the waiting time between every two adjacent production nodes, which is defined as the inter-operation waiting time, summarize the inter-operation waiting time within the selected period, and construct an inter-operation waiting time set; The feature extraction and efficiency evaluation module divides the historical production data into corresponding production nodes according to the historical production records, divides it into several historical production data segments, and constructs corresponding node data sets; extracts the corresponding historical production data features from each node data set, and evaluates the production efficiency of the corresponding historical production data segments according to the historical production data features; The trend feature analysis and anomaly detection module corresponds each element in the set of waiting times between processes with the production efficiency evaluation results of the corresponding historical production data segments, and analyzes the correlation between the waiting times between processes and the production efficiency evaluation results; based on the correlation between the waiting times between processes and the production efficiency evaluation results, obtains the historical production data feature trend curve; The real-time production data analysis and intelligent warning module obtains the real-time production data corresponding to each production node, analyzes the real-time production data to extract real-time production data features; analyzes according to the real-time production data features to obtain the real-time production data feature trend curve, and compares the real-time production data feature trend curve with the historical production data feature trend curve to generate corresponding notification information.

[0012] Furthermore, the historical production data collection and processing module includes a data collection unit, a production node definition unit, and an inter-process waiting time calculation unit; The data collection unit obtains the historical production records corresponding to all production processes in the production workshop within the selected period from the MES system; the production node definition unit defines each production process as a production node and obtains the time range of each production node; the inter-process waiting time calculation unit calculates the waiting time between every two adjacent production nodes, summarizes the waiting time between each process within the selected period, and constructs a set of waiting times between processes.

[0013] Furthermore, the feature extraction and efficiency evaluation module includes a data division and feature extraction unit and an efficiency evaluation unit; The data division and feature extraction unit divides the historical production data according to the production nodes, forms several historical production data segments, and extracts the corresponding historical production data features; the efficiency evaluation unit evaluates the production efficiency of each node data set and calculates the comprehensive production efficiency index.

[0014] Furthermore, the trend feature analysis and anomaly detection module includes a trend feature analysis unit and an anomaly detection unit; The trend feature analysis unit analyzes the correlation between production efficiency and inter-process waiting time based on the historical production data features and calculates the trend feature index; the anomaly detection unit compares the trend feature index with the trend feature index threshold to mark normal data points and abnormal data points; based on the abnormal data points, obtains the historical production data feature trend curve.

[0015] Furthermore, the real-time production data analysis and intelligent warning module includes a real-time production data analysis unit and a trend comparison and warning unit; The real-time production data analysis unit obtains the real-time production data corresponding to each production node from the MES system, analyzes the real-time production data, extracts the real-time production data characteristics, and thus obtains the real-time production data characteristic trend curve; the trend comparison and warning unit compares the real-time production data characteristic trend curve with the historical production data characteristic trend curve, and thus generates corresponding notification information.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: by analyzing the historical production data, extracting characteristics such as production efficiency and waiting time between processes, and dynamically monitoring and analyzing the real-time production data through trend characteristic indicators, potential risks and bottlenecks in the production process can be identified in advance, so as to achieve active warning and decision support; compared with the ex post processing of the traditional MES system, the present invention provides real-time intelligent analysis and prediction functions, enhancing the active management ability of the production process. By associating the waiting time between processes with the production efficiency evaluation results, the present invention can identify potential bottlenecks and inefficient links in the production process, and then provide optimization suggestions for managers, so as to take effective measures before problems occur, reducing production delays and quality problems. By analyzing the historical production data and real-time production data, the present invention can evaluate the comprehensive efficiency of production nodes, provide specific optimization directions for managers, further improve production efficiency, reduce resource waste, optimize production scheduling, and improve the overall resource utilization rate. Different from the traditional method relying on manual experience and manual adjustment, the automatic evaluation and prediction ability based on data analysis of the present invention helps to reduce the decision-making pressure of managers, provides data-driven decision support, and reduces the risk of human errors. The present invention can timely discover abnormal data points in production through the comparison and analysis of the production data characteristic trend curve, mark them as abnormal and give warnings, effectively avoiding production losses caused by delayed problem discovery. The invention refines the data analysis of each production node, tailors corresponding production efficiency evaluation and process optimization strategies for each production node, thus ensuring the refined management of the entire production process, optimizing each process link, and improving the operation efficiency of the overall production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, and do not constitute a limitation to the present invention. In the drawings: Figure 1 is a module schematic diagram of an MES data intelligent management system based on data analysis according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with 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.

[0019] Please refer to Figure 1 , the present invention provides a technical solution: An MES data intelligent management system based on data analysis, comprising: a historical production data collection and processing module, a feature extraction and efficiency evaluation module, a trend feature analysis and anomaly detection module, and a real-time production data analysis and intelligent warning module; The historical production data collection and processing module obtains the historical production records corresponding to all production processes in the production workshop within a selected period, and defines each production process as a production node; according to the production process sequence, obtains the waiting time between every two adjacent production nodes, which is defined as the inter-process waiting time, summarizes the inter-process waiting time within the selected period, and constructs an inter-process waiting time set; The feature extraction and efficiency evaluation module divides the historical production data according to the corresponding production nodes based on the historical production records, divides it into several historical production data segments, and constructs the corresponding node data sets; extracts the corresponding historical production data features from each node data set, and evaluates the production efficiency of the corresponding historical production data segments according to the historical production data features; The trend feature analysis and anomaly detection module corresponds each element in the inter-process waiting time set to the production efficiency evaluation result of the corresponding historical production data segment, and analyzes the correlation between the inter-process waiting time and the production efficiency evaluation result; based on the correlation between the inter-process waiting time and the production efficiency evaluation result, obtains the historical production data feature trend curve; The real-time production data analysis and intelligent warning module obtains the real-time production data corresponding to each production node, analyzes the real-time production data, and thus extracts the real-time production data features; analyzes according to the real-time production data features, and thus obtains the real-time production data feature trend curve, and compares the real-time production data feature trend curve with the historical production data feature trend curve, and thus generates the corresponding notification information.

[0020] The historical production data collection and processing module includes a data collection unit, a production node definition unit, and an inter-process waiting time calculation unit; The data acquisition unit obtains the historical production records corresponding to all production processes in the production workshop within the selected period from the MES system; the production node definition unit defines each production process as a production node and obtains the time range of each production node; the waiting time calculation unit between processes calculates the waiting time between every two adjacent production nodes, summarizes the waiting time between each process within the selected period, and constructs a set of waiting times between processes.

[0021] The feature extraction and efficiency evaluation module includes a data partitioning and feature extraction unit and an efficiency evaluation unit; The data partitioning and feature extraction unit partitions the historical production data according to production nodes, forms several historical production data segments, and extracts the corresponding historical production data features; the efficiency evaluation unit evaluates the production efficiency of each node data set and calculates the comprehensive production efficiency index.

[0022] The trend feature analysis and anomaly detection module includes a trend feature analysis unit and an anomaly detection unit; The trend feature analysis unit analyzes the correlation between production efficiency and the waiting time between processes based on the historical production data features and calculates the trend feature index; the anomaly detection unit compares the trend feature index with the trend feature index threshold to mark normal data points and abnormal data points; based on the abnormal data points, the trend curve of the historical production data features is obtained.

[0023] The real-time production data analysis and intelligent warning module includes a real-time production data analysis unit and a trend comparison and warning unit; The real-time production data analysis unit obtains the real-time production data corresponding to each production node from the MES system, analyzes the real-time production data, and extracts the real-time production data features, thereby obtaining the trend curve of the real-time production data features; the trend comparison and warning unit compares the trend curve of the real-time production data features with the trend curve of the historical production data features to generate the corresponding notification information.

[0024] A method for intelligent management of MES data based on data analysis includes the following steps: Step S100. Obtain the historical production records corresponding to all production processes in the production workshop within the selected period, and define each production process as a production node; according to the production process sequence, obtain the waiting time between every two adjacent production nodes, which is defined as the waiting time between processes, summarize the waiting time between each process within the selected period, and construct a set of waiting times between processes; Step S200. According to the historical production records, divide the historical production data according to the corresponding production nodes into several historical production data segments, and construct the corresponding node data sets; extract the corresponding historical production data features from each node data set, and evaluate the production efficiency of the corresponding historical production data segments according to the historical production data features; Step S300. Correlate each element in the set of waiting times between processes with the production efficiency evaluation results of the corresponding historical production data segments, and analyze the correlation between the waiting times between processes and the production efficiency evaluation results; based on the correlation between the waiting times between processes and the production efficiency evaluation results, obtain the historical production data feature trend curve; Step S400. Obtain the real-time production data corresponding to each production node, analyze the real-time production data to extract the real-time production data features; analyze according to the real-time production data features to obtain the real-time production data feature trend curve, and compare the real-time production data feature trend curve with the historical production data feature trend curve to generate the corresponding notification information.

[0025] Step S100 includes: S101. Through the MES system, obtain the historical production records corresponding to all production processes in the production workshop within the selected period, and define each production process as a production node; the historical production records refer to the data information of each production process in the production process, including process name, start time, end time, historical event records, and historical production data; according to the production process sequence recorded in the MES system, sequentially obtain the start time Ts and end time Te of each production node, so as to obtain the time range of the corresponding production node as [ts, te]; according to the production process sequence, sequentially calculate the waiting time ti between every two adjacent production nodes, and ti = ts(i + 1) - te(i), where ts(i + 1) represents the start time of production node i + 1, and te(i) represents the end time of production node i; S102. Define the waiting time ti between every two adjacent production nodes as the waiting time between processes, and summarize the waiting time between processes within the selected period to construct the set of waiting times between processes T, and T = {t1, t2,..., tn - 1}, where t1 represents the waiting time between production node 1 and production node 2, t2 represents the waiting time between production node 2 and production node 3, and so on, tn - 1 represents the waiting time between production node n - 1 and production node n, and n represents the total number of production nodes; where each set of waiting times between processes T corresponds to a production process sequence, and the number of sets of waiting times between processes T is equal to the number of production process sequences within the selected period, and the production tasks corresponding to each production process sequence are the same.

[0026] In this embodiment, in the MES system, a period is selected, such as a certain day or week, and the historical records of all production processes within this period are obtained. Each production process includes the following information: process name, start time (ts), end time (te), historical event record: records events during the process (such as equipment failures, production interruptions, etc.), historical production data: such as production volume, pass rate, quality inspection results, etc.; Through the production process sequence of the MES system, calculate the waiting time between every two adjacent production nodes. For example, for production node 1 in the i-th production process sequence, the end time te1 = 10:00, and for production node 2, the start time ts2 = 10:05, then the corresponding waiting time: t1 = ts2 - te1 = 5 min; Summarize the waiting times between all adjacent production nodes in the i-th production process sequence to form the corresponding inter-process waiting time set T, and T = {t1, t2,..., tn - 1}.

[0027] Step S200 includes: S201. For each production process sequence, obtain the corresponding historical production records, divide the historical production data corresponding to each production node according to time order into several historical production data segments, and each historical production data segment has a one-to-one correspondence with the production node corresponding to the corresponding production process sequence; According to the production node number, summarize the historical production data segments corresponding to the same production node number within the selected period, and sort the historical production data segments in time order, thereby forming the node data set Ai, and Ai = {ai1, ai2,..., aim}, where ai1 represents the historical production data segment corresponding to the first production process sequence of the i-th production node, ai2 represents the historical production data segment corresponding to the second production process sequence of the i-th production node, and so on, aim represents the historical production data segment corresponding to the m-th production process sequence of the i-th production node, and m represents the number of production process sequences within the selected period; S202. For each element aij in the node dataset Ai, extract the corresponding historical production data features respectively, and the historical production data features include production time, number of abnormal events, production output, and unqualified rate; perform normalization processing on the collected historical production data features to obtain the corresponding historical production data feature values, and evaluate the production efficiency of each element aij in the node dataset Ai according to the historical production data feature values, so as to calculate the comprehensive production efficiency index Eij, and Eij = α×(Pij / Tij) - β×Nij - γ×Qij, where α, β, and γ all represent weight coefficients, and α + β + γ = 1; Tij represents the feature value of the production time corresponding to the element aij, Pij represents the feature value of the number of production products corresponding to the element aij, Nij represents the feature value of the number of abnormal events corresponding to the element aij, and Qij represents the feature value of the unqualified rate corresponding to the element aij.

[0028] Since step S200 analyzes each production process sequence, obtains the historical production records of each production process sequence, and divides the corresponding historical production data according to the corresponding production nodes, so as to obtain several historical production data segments, and each production node corresponds to a historical production data segment; and because there are multiple production process sequences within the selected period, for each production node, the number of corresponding historical production data segments is equal to the number of production process sequences within the selected period.

[0029] In this embodiment, assume that the historical production data features of node 1 of the i-th production process sequence are as follows: Node 1: Production time is 10 hours, output is 1000 pieces, number of abnormal events is 2 times, and unqualified rate is 5%.

[0030] Perform normalization processing on the collected historical production data features of node 1 to obtain the corresponding historical production data feature values; assume the preset: The maximum value of the production time (T) is 12 hours, and the minimum value is 8 hours.

[0031] The maximum value of the output (P) is 1200 pieces, and the minimum value is 800 pieces.

[0032] The maximum value of the number of abnormal events (N) is 3 times, and the minimum value is 0 times.

[0033] The maximum value of the unqualified rate (Q) is 5%, and the minimum value is 3%.

[0034] According to the formula of min-max normalization: X_norm = (X - X_min) / (X_max - X_min), where X is the original data, X_min is the minimum value of this feature, and X_max is the maximum value of this feature; then the normalized historical production data feature values obtained are: Ti1 = 0.5, Pi1 = 0.5, Ni1 = 0.667, Qi1 = 1; According to the formula: Eij = α × (Pij / Tij) - β × Nij - γ × Qij, calculate the corresponding comprehensive production efficiency index Ei1. Assume the weight coefficients are: α = 0.5, β = 0.3, γ = 0.2, then the corresponding Ei1 = 0.1.

[0035] Step S300 includes: S301. For each node dataset Ai, obtain the comprehensive production efficiency index Eij of all historical production data segments in the node dataset Ai, and match the corresponding waiting time tij between processes according to the corresponding production process sequence number and the corresponding production node number, so as to form a number of associated data pairs G, and G = (Eij, tij); for each node dataset Ai, summarize the corresponding associated data pairs G, and represent the associated data pairs G as a data point in the plane rectangular coordinate system. The horizontal axis of the plane rectangular coordinate system represents the comprehensive production efficiency index Eij, and the vertical axis represents the waiting time tij between processes. Connect the data points in sequence according to the order in the node dataset Ai to obtain the curve Li; S302. For each curve Li corresponding to the node dataset Ai, respectively obtain the values of all data points on the curve Li, calculate the mean and standard deviation of the waiting time between processes and the comprehensive production efficiency index in sequence, and calculate the trend feature index Rij according to the mean and standard deviation of the waiting time between processes and the comprehensive production efficiency index. The specific calculation formula is: Rij = w1 · [(Eij - μEi) / 3σEi] + w2 · [(tij - μti) / 3σti]; Among them, Rij represents the trend feature index corresponding to the jth production process sequence of the ith production node, μEi and σEi represent the mean and standard deviation of the horizontal axis values of the data points of the curve Li corresponding to the node dataset Ai, μti and σti represent the mean and standard deviation of the vertical axis values of the data points of the curve Li corresponding to the node dataset Ai, w1 and w2 represent the weight coefficients, and w1 + w2 = 1; S303. Aggregate the trend feature indicators Rij corresponding to each element in the node dataset Ai, define the threshold Rij of the trend feature indicator corresponding to the node dataset Ai in combination with the historical production records, compare the trend feature indicator Rij corresponding to each element in the node dataset Ai with the threshold Rij of the trend feature indicator. If Rij ≤ R, mark the data points on the corresponding curve Li as normal data points; if Rij > R, mark the data points on the corresponding curve Li as abnormal data points. Divide the curve Li according to the marked positions of the abnormal data points on the curve Li to obtain several continuous curve segments, and each continuous curve segment is composed of normal data points. Fit each continuous curve segment to obtain the curve equation of each continuous curve segment, and define the curve equation of each continuous curve segment as the historical production data characteristic trend curve Q.

[0036] Step S400 includes: S401. Through the MES system, obtain the real-time production data corresponding to each production node, and analyze the real-time production data with reference to the analysis method of historical production data, so as to construct the corresponding node dataset Bi for each production node. For each element in the node dataset Bi of each production node, extract the corresponding real-time production data characteristics with reference to the analysis method of the node dataset Ai, and calculate the comprehensive production efficiency index E’ij corresponding to each element in the node dataset Bi according to the real-time production data characteristics. Match the inter-process waiting time t’ij of the corresponding element according to the production process sequence number and the corresponding production node number of each element in the node dataset Bi. Aggregate the comprehensive production efficiency index E’ij and the inter-process waiting time t’ij of each element in the node dataset Bi to form several associated data pairs G’, and G’ = (E’ij, t’ij); S402. For each associated data pair G’ corresponding to a production node, it is successively represented as a data point in the plane rectangular coordinate system. According to the order of the node data set Bi, the data points are successively connected to obtain the real-time production data characteristic trend curve S. According to the production node number corresponding to the real-time production data characteristic trend curve S, the historical production data characteristic trend curve Q with the same production node number is matched. The real-time production data characteristic trend curve S is successively moved on the historical production data characteristic trend curve Q, and the one with the highest coincidence degree is selected as the matching result, and the real-time production data characteristic trend curve S is marked on the matched historical production data characteristic trend curve Q. The number of data points between the last data point of the real-time production data characteristic trend curve S and the last data point of the matched historical production data characteristic trend curve Q is counted, and the last data point of the real-time production data characteristic trend curve S does not coincide with the last data point of the matched historical production data characteristic trend curve Q. When the number of data points is equal to 0, the number of the current production node is output to the relevant personnel for corresponding processing by the relevant personnel.

[0037] In this embodiment, it is defaulted that there are no abnormal data points in the real-time production data characteristic trend curve S. Therefore, the number of data points of the real-time production data characteristic trend curve S is less than the number of data points of the historical production data characteristic trend curve Q with the same production node number. The specific operation process of successively moving the real-time production data characteristic trend curve S on the historical production data characteristic trend curve Q is as follows: The data points on the historical production data characteristic trend curve Q are successively defined as the starting points. The starting point of the real-time production data characteristic trend curve S is successively aligned with the starting points on the historical production data characteristic trend curve Q. The first data point of S is aligned with the first data point of Q, and the coincidence degree (such as correlation, similarity) between the data points is calculated. During the process of moving the curve, the most matching starting point position is found until the coincidence degree between the starting point of S and a certain point in Q reaches the highest. This process can be completed by calculating the correlation between the curves, the Euclidean distance, or other matching metrics. After the S curve and the Q curve are aligned, the number of data points between the last data point of the real-time production data characteristic trend curve S and the last data point of the matched historical production data characteristic trend curve Q is calculated. When the number of data points is 0, it indicates that there may be abnormal situations such as low production efficiency or long waiting time between processes in the next round of production of the current production node, so as to achieve accurate prediction of production bottlenecks and potential risks. Then, a notification message is generated through the MES system to inform the relevant personnel of the current production node number and the recommended measures (such as checking equipment, scheduling production, optimizing production processes, etc.).

[0038] It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or elements inherent to such process, method, article or device.

[0039] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for intelligent management of MES data based on data analysis, characterized in that: The method comprises the following steps: Step S100. Obtain the historical production records corresponding to all production processes of the production workshop within the selected period, and define each production process as a production node; according to the production process sequence, obtain the waiting time between each two adjacent production nodes, define it as the inter-process waiting time, summarize the inter-process waiting time within the selected period, and construct an inter-process waiting time set; Step S200. According to the historical production records, the historical production data is divided into several historical production data segments according to the corresponding production nodes, and a corresponding node data set is constructed; the corresponding historical production data features are extracted from each node data set, and the production efficiency of the corresponding historical production data segment is evaluated according to the historical production data features; Step S300. Correspond each element in the inter-process waiting time set to the production efficiency evaluation result of the corresponding historical production data segment, analyze the correlation between the inter-process waiting time and the production efficiency evaluation result; based on the correlation between the inter-process waiting time and the production efficiency evaluation result, obtain the characteristic trend curve of the historical production data; Step S400. Obtain the real-time production data corresponding to each production node, analyze the real-time production data, and extract the real-time production data characteristics; analyze according to the real-time production data characteristics to obtain the real-time production data characteristic trend curve, and compare the real-time production data characteristic trend curve with the historical production data characteristic trend curve to generate corresponding notification information.

2. According to claim 1, a method for intelligent management of MES data based on data analysis is characterized in that: The step S100 includes: S101. Obtain the historical production records corresponding to all production processes of the production workshop within the selected period through the MES system, and define each production process as a production node; the historical production records refer to the data information of each production process in the production process, including the process name, start time, end time, historical event records and historical production data; according to the production process sequence recorded in the MES system, obtain the start time Ts and end time Te of each production node in turn, so that the time range of the corresponding production node is [ts, te]; according to the production process sequence, calculate the waiting time ti between each two adjacent production nodes in turn, and ti=ts(i+1)-te(i), where ts(i+1) represents the start time of production node i+1, and te(i) represents the end time of production node i; S102. Define the waiting time ti between each two adjacent production nodes as the inter-process waiting time, summarize the waiting time between each process in the selected period, and construct an inter-process waiting time set T, and T={t1,t2,...,tn-1}, where t1 represents the waiting time between production node 1 and production node 2, t2 represents the waiting time between production node 2 and production node 3, and so on, tn-1 represents the waiting time between production node n-1 and production node n, and n represents the total number of production nodes; each inter-process waiting time set T corresponds to a production process sequence, and the number of inter-process waiting time sets T is equal to the number of production process sequences in the selected period, and the production tasks corresponding to each production process sequence are the same.

3. The MES data intelligent management method based on data analysis according to claim 2 is characterized in that: The step S200 includes: S201. For each production process sequence, obtain the corresponding historical production record, divide the historical production data corresponding to each production node in chronological order, and divide it into several historical production data segments, and each historical production data segment has a one-to-one correspondence with the production node corresponding to the corresponding production process sequence; according to the production node number, summarize the historical production data segments corresponding to all the same production node numbers in the selected period, and sort the historical production data segments in chronological order, so as to form a node data set Ai, and Ai={ai1,ai2,...,aim}, where ai1 represents the historical production data segment corresponding to the first production process sequence of the i-th production node, ai2 represents the historical production data segment corresponding to the second production process sequence of the i-th production node, and so on, aim represents the historical production data segment corresponding to the m-th production process sequence of the i-th production node, and m represents the number of production process sequences in the selected period; S202. For each element aij in the node data set Ai, the corresponding historical production data features are extracted respectively, and the historical production data features include production time, number of abnormal events, production output and non-conforming rate; the collected historical production data features are normalized to obtain the corresponding historical production data characteristic values, and the production efficiency of each element aij in the node data set Ai is evaluated according to the historical production data characteristic values, so as to calculate the comprehensive production efficiency index Eij, and Eij=α×(Pij / Tij)-β×Nij-γ×Qij, wherein α, β and γ all represent weight coefficients, and α+β+γ=1; Tij represents the characteristic value of the production time corresponding to the element aij, Pij represents the characteristic value of the number of production products corresponding to the element aij, Nij represents the characteristic value of the number of abnormal events corresponding to the element aij, and Qij represents the characteristic value of the non-conforming rate corresponding to the element aij.

4. The MES data intelligent management method based on data analysis according to claim 3 is characterized in that: The step S300 includes: S301. For each node data set Ai, obtain the comprehensive production efficiency index Eij of all historical production data segments in the node data set Ai, match the corresponding inter-process waiting time tij according to the corresponding production process sequence number and the corresponding production node number, thereby forming a number of associated data pairs G, and G=(Eij,tij); for each node data set Ai, summarize the corresponding associated data pair G, and represent the associated data pair G as a data point in a plane rectangular coordinate system, and the horizontal axis of the plane rectangular coordinate system represents the comprehensive production efficiency index Eij, and the vertical axis represents the inter-process waiting time tij, and the data points are connected in sequence according to the order in the node data set Ai, thereby obtaining a curve Li; S302. For each curve Li corresponding to each node data set Ai, the values ​​of all data points on the curve Li are obtained respectively, and the mean and standard deviation of the waiting time between processes and the comprehensive production efficiency index are calculated in turn. The trend characteristic index Rij is calculated according to the mean and standard deviation of the waiting time between processes and the comprehensive production efficiency index, and the specific calculation formula is: Rij=w1·[(Eij-μEi) / 3σEi]+w2·[(tij-μti) / 3σti]; Among them, Rij represents the trend characteristic index corresponding to the j-th production process sequence of the i-th production node, μEi and σEi represent the mean and standard deviation of the horizontal axis values ​​of the data points of the curve Li corresponding to the node data set Ai, μti and σti represent the mean and standard deviation of the vertical axis values ​​of the data points of the curve Li corresponding to the node data set Ai, w1 and w2 represent the weight coefficients, and w1+w2=1; S303. Summarize the trend characteristic index Rij corresponding to each element in the node data set Ai, define the trend characteristic index threshold R corresponding to the node data set Ai in combination with the historical production records, compare the trend characteristic index Rij corresponding to each element in the node data set Ai with the trend characteristic index threshold R, if Rij≤R, then mark the data point on the corresponding curve Li as a normal data point, if Rij>R, then mark the data point on the corresponding curve Li as an abnormal data point; divide the curve Li according to the marked position of the abnormal data point on the curve Li to obtain a number of continuous curve segments, and each continuous curve segment is composed of normal data points, fit each continuous curve segment to obtain the curve equation of each continuous curve segment, and define the curve equation of each continuous curve segment as the historical production data characteristic trend curve Q.

5. The MES data intelligent management method based on data analysis according to claim 4 is characterized in that: The step S400 includes: S401. Obtain the real-time production data corresponding to each production node through the MES system, and analyze the real-time production data with reference to the analysis method of historical production data, so as to construct a corresponding node data set Bi for each production node; for each element in the node data set Bi of each production node, extract the corresponding real-time production data features with reference to the analysis method of the node data set Ai, and calculate the comprehensive production efficiency index E'ij corresponding to each element in the node data set Bi according to the real-time production data features; match the inter-process waiting time t'ij of the corresponding element according to the production process sequence number and the corresponding production node number corresponding to each element in the node data set Bi; summarize the comprehensive production efficiency index E'ij and the inter-process waiting time t'ij of each element in the node data set Bi to form a number of associated data pairs G', and G'=(E'ij,t'ij); S402. For each associated data pair G' corresponding to each production node, it is represented as a data point in the plane rectangular coordinate system in turn, and the data points are connected in turn according to the order of the node data set Bi, so as to obtain the real-time production data characteristic trend curve S; according to the production node number corresponding to the real-time production data characteristic trend curve S, the historical production data characteristic trend curve Q with the same production node number is matched, and the real-time production data characteristic trend curve S is moved on the historical production data characteristic trend curve Q in turn, and the one with the highest overlap is selected as the matching result, and the real-time production data characteristic trend curve S is marked on the matched historical production data characteristic trend curve Q, and the number of data points between the last data point of the real-time production data characteristic trend curve S and the last data point of the matched historical production data characteristic trend curve Q is counted, and the last data point of the real-time production data characteristic trend curve S does not overlap with the last data point of the matched historical production data characteristic trend curve Q; when the number of data points is equal to 0, the number of the current production node is output to the relevant personnel, and the relevant personnel perform corresponding processing.

6. An MES data intelligent management system based on data analysis, applied to an MES data intelligent management method based on data analysis as claimed in any one of claims 1 to 5, characterized in that: The system includes: a historical production data collection and processing module, a feature extraction and efficiency evaluation module, a trend feature analysis and anomaly detection module, and a real-time production data analysis and intelligent early warning module; The historical production data acquisition and processing module obtains the historical production records corresponding to all production processes of the production workshop within the selected period, and defines each production process as a production node; according to the production process sequence, the waiting time between each two adjacent production nodes is obtained, defined as the inter-process waiting time, the inter-process waiting time is summarized in the selected period, and the inter-process waiting time set is constructed; The feature extraction and efficiency evaluation module divides the historical production data into several historical production data segments according to the corresponding production nodes based on the historical production records, and constructs corresponding node data sets; extracts corresponding historical production data features from each node data set, and performs production efficiency evaluation on the corresponding historical production data segments based on the historical production data features; The trend feature analysis and anomaly detection module corresponds each element in the process waiting time set to the production efficiency evaluation result of the corresponding historical production data segment, analyzes the correlation between the process waiting time and the production efficiency evaluation result; based on the correlation between the process waiting time and the production efficiency evaluation result, obtains the historical production data characteristic trend curve; The real-time production data analysis and intelligent early warning module obtains the real-time production data corresponding to each production node, analyzes the real-time production data, and extracts the real-time production data characteristics; analyzes according to the real-time production data characteristics to obtain the real-time production data characteristic trend curve, and compares the real-time production data characteristic trend curve with the historical production data characteristic trend curve to generate corresponding notification information.

7. The MES data intelligent management system based on data analysis according to claim 6 is characterized in that: The historical production data collection and processing module includes a data collection unit, a production node definition unit and an inter-process waiting time calculation unit; The data acquisition unit obtains the historical production records corresponding to all production processes of the production workshop within the selected period from the MES system; the production node definition unit defines each production process as a production node and obtains the time range of each production node; the inter-process waiting time calculation unit calculates the waiting time between each two adjacent production nodes, summarizes the waiting time between each process in the selected period, and constructs an inter-process waiting time set.

8. The MES data intelligent management system based on data analysis according to claim 6 is characterized in that: The feature extraction and efficiency evaluation module includes a data partitioning and feature extraction unit and an efficiency evaluation unit; The data division and feature extraction unit divides the historical production data according to production nodes to form a number of historical production data segments, and extracts corresponding historical production data features; the efficiency evaluation unit evaluates the production efficiency of each node data set and calculates a comprehensive production efficiency index.

9. The MES data intelligent management system based on data analysis according to claim 6 is characterized in that: The trend feature analysis and anomaly detection module includes a trend feature analysis unit and an anomaly detection unit; The trend characteristic analysis unit analyzes the correlation between production efficiency and waiting time between processes based on historical production data characteristics, and calculates trend characteristic indicators; The anomaly detection unit compares the trend characteristic index with the trend characteristic index threshold, marks normal data points and abnormal data points; and obtains a historical production data characteristic trend curve based on the abnormal data points.

10. The MES data intelligent management system based on data analysis according to claim 6, characterized in that: The real-time production data analysis and intelligent early warning module includes a real-time production data analysis unit and a trend comparison and early warning unit; The real-time production data analysis unit obtains the real-time production data corresponding to each production node from the MES system, analyzes the real-time production data, extracts the real-time production data features, and thus obtains the real-time production data feature trend curve; The trend comparison and early warning unit compares the real-time production data characteristic trend curve with the historical production data characteristic trend curve to generate corresponding notification information.

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