CN117056688A - New material production data management system and method based on data analysis - Google Patents
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Abstract
The invention discloses a new material production data management system and method based on data analysis, which relates to the technical field of production data management and comprises the following steps: s1: real-time monitoring is carried out on the new material production process, and a related data set of automatic production is acquired through a sensor or machine vision; s2: analyzing the production related data set collected by real-time monitoring according to the production history data, and identifying and screening abnormal data in the production related data set; s3: confirming that the abnormal data generates relevant elements by analyzing the generation paths and relevant characteristics of the abnormal data in the production process based on the abnormal data in the production related data set; s4: according to the analysis result of the abnormal data in the production process, a tracing result of the abnormal production state of the new material is obtained, and the production of the new material is correspondingly managed based on the tracing result; the invention improves the monitoring capability of the production process of new materials with different specifications and avoids potential risks.
Description
Technical Field
The invention relates to the technical field of production data management, in particular to a new material production data management system and method based on data analysis.
Background
With the development of technology, the current mainstream production materials are changed, and compared with the traditional materials, new materials with new composition structures and performances are more and more favored by various industries, so that new development and challenges are brought to various industries.
The new material recovery and reutilization can reduce resource waste from the source, and when new material products are recovered and reprocessed into new materials or products, proper treatment and processing are required to ensure that the quality and performance of the recovered materials meet the requirements. The new material production process involves the key steps of material mixing, molding processing, surface treatment, quality detection, packaging and storage and the like. Due to the self-characteristics of the new material, in the actual production process, the traditional production data management mode is insufficient for effectively processing abnormal data; the existing abnormal position query method cannot accurately judge the abnormal range of new material production, can not avoid production risks in time, and can even enlarge negative influences on new material production.
Accordingly, in order to solve the above-mentioned problems or some of the problems, the present invention provides a new material production data management system and method based on data analysis.
Disclosure of Invention
The invention aims to provide a new material production data management system and method based on data analysis, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a new material production data management method based on data analysis comprises the following steps:
s1: real-time monitoring is carried out on the new material production process, and a related data set of automatic production is acquired through a sensor or machine vision; for example, temperature, pressure, speed, etc.;
s2: analyzing the production related data set collected by real-time monitoring according to the production history data, and identifying and screening abnormal data in the production related data set;
s3: confirming that the abnormal data generates relevant elements by analyzing the generation paths and relevant characteristics of the abnormal data in the production process based on the abnormal data in the production related data set;
s4: and obtaining a tracing result of the abnormal production state of the new material according to the analysis result of the abnormal data in the production process, and correspondingly managing the production of the new material based on the tracing result.
Further, the step S1 includes:
step S1-1: collecting real-time monitoring data of new material production, preprocessing the collected real-time monitoring data, including data cleaning, missing value processing, abnormal value processing and the like, so as to ensure the consistency and accuracy of the data;
step S1-2: the monitoring data comprises one composition data set, and a new material production related data set S=S is constructed 1 ,S 2 ，...，S l Wherein S is 1 、S 2 、...、S l Each representing one of the production-related data sets;
step S1-3: setting m data detection points, adding identification to the production data passing through each detection point, and combining with a preset data detection standard to construct a standard data set Y=Y corresponding to each data detection point 1 ,Y 2 ，...，Y m Wherein Y is 1 、Y 2 、...、Y m Standard data sets corresponding to the 1 st, 2 nd, m detection points are respectively represented, and can be used for comparison and analysis with the actually collected data so as to evaluate the quality and stability of the production process. The standard dataset contains n production-related elements; the preset data detection standard is set according to the historical data average value of each detection point, and the data detection standard is set as the data in real time monitoring data of any detection point by adding an error factor alphaWhen the mapping value of each element is in the range of the historical data mean value with the up-down floating size alpha of the detection point, the current monitoring data of the detection point is considered to be normal.
For example, the criteria for a temperature detection point may be that the criteria for a humidity detection point is below a certain threshold, etc., within a certain range.
Further, the step S2 includes:
step S2-1: constructing a probability distribution model according to the standard data set of each data detection point; for any detection point i and its corresponding standard data set Y i Assuming that the data conforms to a normal distribution, the probability density function of the normal distribution can be used for calculation:
wherein P (X) represents the probability density at a given data value X; sigma is the standard deviation, representing the degree of dispersion of the data; μ is a mean value representing an expected value of the data; during the calculation process, the element values in a specific production-related data set need to be brought into the formula to obtain the distribution probability of the corresponding data in the standard data range in the model.
Preferably, the standard data set of the data detection points may contain data of multiple dimensions, and probability density functions of the multiple dimensions can be jointly calculated according to actual conditions so as to obtain more comprehensive distribution probability information;
step S2-2: calculating the distribution probability of each element in the production related data set mapped in the standard data range in the model, screening the l composition data sets in the production related data set according to a preset probability threshold omega, and classifying each composition data set as conventional data or abnormal data;
step S2-3: when the data set forming the production related data set exceeds A percent and is judged to be abnormal data, first emergency feedback and emergency braking are needed, and secondary acquisition is carried out on the monitoring data so as to ensure the safety, stability and reliability of production.
Further, the step S3 includes:
step S3-1: extracting a corresponding data set in the production related data set which is judged to be the abnormal data, and obtaining a data identifier carried by the current first abnormal data and the first abnormal data to be analyzed;
step S3-2: extracting the production path L of the first abnormal data according to the arrangement sequence of the data detection points to obtain the production path L of the first abnormal data, wherein L=r 1 →...→r j →...→r z Wherein r is 1 、...、r j 、...、r z Production equipment respectively representing corresponding positions of the 1 st,..j,..z data detection points;
step S3-3: analyzing production equipment related to a production path of first abnormal data, extracting historical production related data of each related production equipment on the current material, and detecting production equipment r at a corresponding position of any data detection point j The production equipment r to which the first abnormal data belongs is subjected by a data fitting mode j Analyzing the change trend of the production related data of each production device to obtain a change trend function of each element in the production related data set of each production device in the production path to which the first abnormal data belongs;
step S3-4: interpolation is carried out on fitting results of all production equipment before a detection point to which first abnormal data belong, the interpolation represents that given variable values (the given variable values need to belong to the range of the existing data) are input on the basis of the existing historical data, and corresponding output variable values are calculated; according to the production path sequence, interpolation traversal is carried out on each front production device in the reverse sequence, and the production device r is obtained through simulation j The production device r when each element in the production related data set satisfies the limit in the limit condition of each element in the production related data set of the front production device j The corresponding first abnormal data are converted into normal data, and the influence range of the abnormal data is determined according to the reverse traversal cut-off information.
Further, the step S4 includes:
step S4-1: identifying and matching equipment or a position to which an abnormal source belongs according to the determined influence range of the abnormal data;
step S4-2: the new material production data management system is used for controlling and regulating production elements corresponding to the abnormal sources which are identified and matched;
step S4-3: and feeding back production element regulation operation in the production process, establishing a production parameter optimization system for new materials subjected to production and processing at present, and recording corresponding early warning thresholds of production related elements of new materials with different specifications.
A new material production data management system based on data analysis, the system comprising: the system comprises a production data monitoring module, a monitoring data analysis module, an abnormal condition analysis module and a production data management module;
the production data monitoring module is used for acquiring production data through a sensor or a machine vision interval at a first time unit, acquiring various data in the production process within a set time interval, and storing the various data into the database, wherein the time interval can be a constant preset in the database and is used for acquiring the data as the first unit time;
the monitoring data analysis module is used for analyzing the production data obtained by monitoring according to the production data in the historical data, calculating the deviation value of the production data obtained by monitoring through the server, and screening to obtain abnormal data in the production data;
the abnormal state analysis module is used for analyzing state information of new material production according to abnormal data in production data, and confirming sources of the abnormal data by analyzing production paths of the production abnormal data in a production process and relevant characteristics of the abnormal data; by analyzing the anomaly data, insight can be gained regarding the anomaly in the process and clues can be provided for problem investigation and solution.
The production data management module is used for obtaining a tracing result of the abnormality of the new material production state according to the analysis result of the abnormality state analysis module on the current production abnormality data and managing the new material production according to the tracing result.
Further, the monitoring data analysis module comprises a data extraction unit, a monitoring data identification unit and a monitoring data analysis unit;
the data extraction unit is used for extracting monitoring data from the production related data collected by the production data monitoring module, converting the extracted monitoring data into structured data, and converting the structured data into a data format so as to facilitate subsequent data processing;
the monitoring data identification unit is used for identifying and classifying the extracted monitoring data into conventional data and abnormal data; the monitoring data can be identified and judged by machine learning or rule matching and other methods so as to facilitate rapid detection and distinguishing of abnormal conditions;
the monitoring data analysis unit is used for analyzing and processing the abnormal data after the identification and classification, and analyzing the characteristic node data in the monitoring data into characteristic elements so as to facilitate the related abnormal data analysis in the follow-up.
Further, the abnormal state analysis unit comprises an abnormal data extraction unit, an abnormal data analysis unit and an abnormal data tracing unit;
the abnormal data extraction unit is used for extracting the abnormal data identified and classified in the monitoring data identification unit;
the abnormal data analysis unit is used for analyzing the extracted abnormal data, and analyzing the associated path and the historical data of the abnormal data according to the characteristic elements of the abnormal data so as to facilitate relevant staff to obtain deeper insight on various different data according to the data analysis content;
the abnormal data tracing unit is used for screening the source of the abnormal data according to the analysis result of the abnormal data, locating to obtain related participation factors of the abnormal data, tracking the forming path of the abnormal data and determining the influence range of the abnormal data.
Further, the production data management module comprises a tracing result identification unit and a production related regulation and control unit;
the tracing result identification unit is used for identifying and matching the equipment or the position of the abnormal source according to the tracing result of the abnormal data tracing unit;
the production related regulation and control unit is used for controlling and regulating corresponding production elements according to the abnormal sources which are identified and matched by the traceability result identification unit, wherein the production elements comprise production equipment, production equipment positions, equipment related parameters, production materials and the like; the production-related regulation and control unit can interact with an automation system, production equipment and the like so as to conveniently adjust production parameters in real time, optimize a production flow or solve the problem in production, and realize real-time control and regulation and control of the production process.
Preferably, the system also comprises a material specification recording unit for recording key parameters and data of the new material produced with different circulation times in the processing process, and establishing a data tracking system, so that the system can perform problem investigation and quality tracking when required, ensure the usability of the product and improve the resource utilization rate.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, the production data is acquired through the sensor or the machine vision interval first time unit, and each item of data in the production process is acquired in the set time interval and stored in the database, so that the comprehensive monitoring of the production process is realized;
analyzing the production data obtained by monitoring according to the production data in the historical data through a monitoring data analysis module, calculating the deviation value of the production data obtained by monitoring through a server, and screening to obtain abnormal data in the production data; and analyzing the production related data collected by real-time monitoring by utilizing the production history data, and identifying and screening out abnormal data. This helps to quickly find out anomalies in the production process and to conduct targeted processing;
analyzing state information of new material production according to abnormal data in production data by an abnormal state analysis module, and confirming sources of the abnormal data by analyzing production paths of the production abnormal data in a production process and relevant characteristics of the abnormal data; this can help to further understand the source and cause of the anomaly data, providing an accurate basis for subsequent processing.
The production data management module is used for obtaining a tracing result of the abnormality of the production state of the new material according to the analysis result of the abnormality state analysis module on the current production abnormality data, and managing the production of the new material according to the tracing result; the source of the abnormal data can be tracked through the tracing result, and the abnormal data can be managed and improved in a targeted manner, so that the production quality and the production efficiency are improved.
By combining the effects, the invention can improve the monitoring capability of the production process of new materials with different specifications, provides a more accurate monitoring method for the recovery and reprocessing of the products of the new materials, strengthens the identification and analysis of abnormal data, thereby improving the production quality, avoiding potential risks and optimizing the efficiency of the production process. Has important significance and practical application value for new material production enterprises.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic block diagram of a new material production data management system based on data analysis according to the present invention;
FIG. 2 is a flow chart of a new material production data management method based on data analysis.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention is further described with reference to fig. 1, 2 and embodiments.
Example 1: as shown in fig. 1, the present embodiment provides a new material production data management system based on data analysis, the system including: the system comprises a production data monitoring module, a monitoring data analysis module, an abnormal condition analysis module and a production data management module;
the production data monitoring module is used for acquiring production data through a sensor or a machine vision interval at a first time unit, acquiring various data in the production process within a set time interval, and storing the data into the database, wherein the time interval can be a constant preset in the database and is used for acquiring the data as the first unit time;
the monitoring data analysis module is used for analyzing the production data obtained by monitoring according to the production data in the historical data, calculating the deviation value of the production data obtained by monitoring through the server, and screening to obtain abnormal data in the production data; the monitoring data analysis module comprises a data extraction unit, a monitoring data identification unit and a monitoring data analysis unit;
the data extraction unit is used for extracting monitoring data from the production related data collected by the production data monitoring module, converting the extracted monitoring data into structured data, and converting the data format to facilitate the subsequent data processing;
the monitoring data identification unit is used for identifying and classifying the extracted monitoring data into conventional data and abnormal data; the monitoring data can be identified and judged by machine learning or rule matching and other methods so as to facilitate rapid detection and distinguishing of abnormal conditions;
the monitoring data analysis unit is used for analyzing and processing the abnormal data after the identification and classification, and analyzing the characteristic node data in the monitoring data into characteristic elements so as to facilitate the related abnormal data analysis in the follow-up.
The abnormal state analysis module is used for analyzing the state information of new material production according to the abnormal data in the production data, and confirming the source of the abnormal data by analyzing the production path of the production abnormal data in the production process and the related characteristics of the abnormal data; by analyzing the anomaly data, insight can be gained regarding the anomaly in the process and clues can be provided for problem investigation and solution. The abnormal state analysis unit comprises an abnormal data extraction unit, an abnormal data analysis unit and an abnormal data tracing unit;
the abnormal data extraction unit is used for extracting the abnormal data identified and classified in the monitoring data identification unit;
the abnormal data analysis unit is used for analyzing the extracted abnormal data, and analyzing the associated path and the historical data of the abnormal data according to the characteristic elements of the abnormal data so as to facilitate relevant staff to obtain deeper insight on various abnormal data according to the data analysis content;
the abnormal data tracing unit is used for screening the source of the abnormal data according to the analysis result of the abnormal data, locating to obtain related participation factors of the abnormal data, tracking the forming path of the abnormal data and determining the influence range of the abnormal data.
The production data management module is used for obtaining a tracing result of the abnormality of the production state of the new material according to the analysis result of the abnormality state analysis module on the current production abnormality data and managing the production of the new material according to the tracing result; the production data management module comprises a tracing result identification unit and a production related regulation and control unit;
the tracing result identification unit is used for identifying and matching the equipment or the position of the abnormal source according to the tracing result of the abnormal data tracing unit;
the production related regulation and control unit is used for identifying the matched abnormal sources according to the traceability result identification unit to control and regulate corresponding production elements, wherein the production elements comprise production equipment, production equipment positions, equipment related parameters, production materials and the like; the production-related regulation and control unit can interact with an automation system, production equipment and the like so as to conveniently adjust production parameters in real time, optimize a production flow or solve the problem in production, and realize real-time control and regulation and control of the production process.
Example 2: as shown in fig. 2, the present embodiment provides a new material production data management method based on data analysis, which is implemented based on a new material production data management system based on data analysis in the embodiment, and specifically includes the following steps:
s1: real-time monitoring is carried out on the new material production process, and a related data set of automatic production is acquired through a sensor or machine vision; for example, temperature, pressure, speed, etc.;
step S1-1: collecting real-time monitoring data of new material production, preprocessing the collected real-time monitoring data, including data cleaning, missing value processing, abnormal value processing and the like, so as to ensure the consistency and accuracy of the data;
step S1-2: the monitoring data comprises one composition data set, and a new material production related data set S=S is constructed 1 ,S 2 ，...，S l Wherein S is 1 、S 2 、...、S l Each representing one of the production-related data sets;
step S1-3: setting m data detection points, adding identification to the production data passing through each detection point, and combining with a preset data detection standard to construct a standard data set Y=Y corresponding to each data detection point 1 ,Y 2 ，...，Y m Wherein Y is 1 、Y 2 、...、Y m Standard data sets corresponding to the 1 st, 2 nd, m detection points are respectively represented, and can be used for comparison and analysis with the actually collected data so as to evaluate the quality and stability of the production process. The standard dataset contains n production-related elements; the preset data detection standard is set according to the historical data average value of each detection point, the data detection standard is set by adding an error factor alpha, and when the mapping value of each element in the real-time monitoring data of any detection point is in the range of the historical data average value with the floating up and down size alpha of the detection point, the current monitoring data of the detection point is considered to be normal.
For example, the criteria for a temperature detection point may be that the criteria for a humidity detection point is below a certain threshold, etc., within a certain range.
S2: analyzing the production related data set collected by real-time monitoring according to the production history data, and identifying and screening abnormal data in the production related data set;
step S2-1: constructing a probability distribution model according to the standard data set of each data detection point; for any detection point i and its corresponding standard data set Y i Assuming that the data conforms to a normal distribution, the probability density function of the normal distribution can be used for calculation:
wherein P (X) represents the probability density at a given data value X; sigma is the standard deviation, representing the degree of dispersion of the data; μ is a mean value representing an expected value of the data; during the calculation process, the element values in a specific production-related data set need to be brought into the formula to obtain the distribution probability of the corresponding data in the standard data range in the model.
Step S2-2: calculating the distribution probability of each element in the production related data set mapped in the standard data range in the model, screening the l composition data sets in the production related data set according to a preset probability threshold omega, and classifying each composition data set as conventional data or abnormal data;
step S2-3: when the proportion of the data sets forming the production related data set exceeds 10% and is judged to be abnormal data, first emergency feedback and emergency braking are needed, and secondary acquisition is conducted on the monitoring data so as to guarantee the safety, stability and reliability of production.
S3: confirming that the abnormal data generates relevant elements by analyzing the generation paths and relevant characteristics of the abnormal data in the production process based on the abnormal data in the production related data set;
step S3-1: extracting a corresponding data set in the production related data set which is judged to be the abnormal data, and obtaining a data identifier carried by the current first abnormal data and the first abnormal data to be analyzed;
step S3-2: extracting the production path L of the first abnormal data according to the arrangement sequence of the data detection points to obtain the production path L of the first abnormal data, wherein L=r 1 →...→r j →...→r z Wherein r is 1 、...、r j 、...、r z Production equipment respectively representing corresponding positions of the 1 st,..j,..z data detection points;
step S3-3: analyzing production equipment related to a production path of first abnormal data, extracting historical production related data of each related production equipment on the current material, and detecting production equipment r at a corresponding position of any data detection point j Using polynomial function to fit the data to the production equipment r to which the first abnormal data belongs j The previous change trend of the production related data of each production device is analyzed, and the coefficients of the polynomial function can be estimated by using a least square method and other methods to obtain the change trend function of each element in the production related data set of each production device in the production path to which the first abnormal data belongs;
step S3-4: interpolation is carried out on fitting results of all production equipment before a detection point to which first abnormal data belong, the interpolation represents that given variable values (the given variable values need to belong to the range of the existing data) are input on the basis of the existing historical data, and corresponding output variable values are calculated; according to the production path sequence, interpolation traversal is carried out on each front production device in the reverse sequence, and the production device r is obtained through simulation j The production device r when each element in the production related data set satisfies the limit in the limit condition of each element in the production related data set of the front production device j The corresponding first abnormal data are converted into normal data, and the influence range of the abnormal data is determined according to the reverse traversal cut-off information.
S4: according to the analysis result of the abnormal data in the production process, a tracing result of the abnormal production state of the new material is obtained, and the production of the new material is correspondingly managed based on the tracing result;
step S4-1: identifying and matching equipment or a position to which an abnormal source belongs according to the determined influence range of the abnormal data;
step S4-2: the new material production data management system is used for controlling and regulating production elements corresponding to the abnormal sources which are identified and matched;
step S4-3: and feeding back production element regulation operation in the production process, establishing a production parameter optimization system for new materials subjected to production and processing at present, and recording corresponding early warning thresholds of production related elements of new materials with different specifications.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A new material production data management method based on data analysis is characterized in that: the method comprises the following steps:
s1: real-time monitoring is carried out on the new material production process, and a related data set of automatic production is acquired through a sensor or machine vision;
s2: analyzing the production related data set collected by real-time monitoring according to the production history data, and identifying and screening abnormal data in the production related data set;
s3: confirming that the abnormal data generates relevant elements by analyzing the generation paths and relevant characteristics of the abnormal data in the production process based on the abnormal data in the production related data set;
s4: and obtaining a tracing result of the abnormal production state of the new material according to the analysis result of the abnormal data in the production process, and correspondingly managing the production of the new material based on the tracing result.
2. The new material production data management method based on data analysis according to claim 1, wherein: the S1 comprises the following steps:
step S1-1: collecting real-time monitoring data of new material production, and preprocessing the collected real-time monitoring data;
step S1-2: the monitoring data comprises one composition data set, and a new material production related data set S=S is constructed 1 ,S 2 ，...，S l Wherein S is 1 、S 2 、...、S l Each representing one of the production-related data sets;
step S1-3: setting m data detection points, adding identification to the production data passing through each detection point, and combining with a preset data detection standard to construct a standard data set Y=Y corresponding to each data detection point 1 ,Y 2 ，...，Y m Wherein Y is 1 、Y 2 、...、Y m Standard data sets corresponding to the 1 st, 2 nd, m detection points, respectively, the standard data sets comprising n production-related elements; the preset data detection standard is set according to the historical data average value of each detection point, the data detection standard is set by adding an error factor alpha, and when the mapping value of each element in the real-time monitoring data of any detection point is in the range of the historical data average value with the floating up and down size alpha of the detection point, the current monitoring data of the detection point is considered to be normal.
3. The new material production data management method based on data analysis according to claim 1, wherein: the step S2 comprises the following steps:
step S2-1: constructing a probability distribution model according to the standard data set of each data detection point;
step S2-2: calculating the distribution probability of each element in the production related data set mapped in the standard data range in the model, screening the l composition data sets in the production related data set according to a preset probability threshold omega, and classifying each composition data set as conventional data or abnormal data;
step S2-3: when the proportion of the data sets forming the production related data set exceeds A percent, the data sets are judged to be abnormal data, first emergency feedback, emergency braking and secondary acquisition of monitoring data are needed.
4. The new material production data management method based on data analysis according to claim 1, wherein: the step S3 comprises the following steps:
step S3-1: extracting a corresponding data set in the production related data set which is judged to be the abnormal data, and obtaining a data identifier carried by the current first abnormal data and the first abnormal data to be analyzed;
step S3-2: extracting the production path L of the first abnormal data according to the arrangement sequence of the data detection points to obtain the production path L of the first abnormal data, wherein L=r 1 →...→r j →...→r z Wherein r is 1 、...、r j 、...、r z Production equipment respectively representing corresponding positions of the 1 st,..j,..z data detection points;
step S3-3: analyzing production equipment related to a production path of first abnormal data, extracting historical production related data of each related production equipment on the current material, and detecting production equipment r at a corresponding position of any data detection point j The production equipment r to which the first abnormal data belongs is subjected by a data fitting mode j The change of the production related data of each production facility beforeAnalyzing the potential to obtain a change trend function of each element in the production related data set of each production device in the production path to which the first abnormal data belong;
step S3-4: interpolation is carried out on fitting results of all production equipment before a detection point to which first abnormal data belong, the interpolation represents that given variable values are input on the basis of existing historical data, and corresponding output variable values are calculated; according to the production path sequence, interpolation traversal is carried out on each front production device in the reverse sequence, and the production device r is obtained through simulation j The production device r when each element in the production related data set satisfies the limit in the limit condition of each element in the production related data set of the front production device j The corresponding first abnormal data are converted into normal data, and the influence range of the abnormal data is determined according to the reverse traversal cut-off information.
5. The new material production data management method based on data analysis according to claim 1, wherein: the S4 comprises the following steps:
step S4-1: identifying and matching equipment or a position to which an abnormal source belongs according to the determined influence range of the abnormal data;
step S4-2: the new material production data management system is used for controlling and regulating production elements corresponding to the abnormal sources which are identified and matched;
step S4-3: and feeding back production element regulation operation in the production process, establishing a production parameter optimization system for new materials subjected to production and processing at present, and recording corresponding early warning thresholds of production related elements of new materials with different specifications.
6. A new material production data management system based on data analysis, which is characterized in that: the system comprises: the system comprises a production data monitoring module, a monitoring data analysis module, an abnormal condition analysis module and a production data management module;
the production data monitoring module is used for acquiring production data through a sensor or a machine vision interval at a first time unit, acquiring various data in the production process within a set time interval, and storing the various data into the database, wherein the time interval can be a constant preset in the database and is used for acquiring the data as the first unit time;
the monitoring data analysis module is used for analyzing the production data obtained by monitoring according to the production data in the historical data, calculating the deviation value of the production data obtained by monitoring through the server, and screening to obtain abnormal data in the production data;
the abnormal state analysis module is used for analyzing state information of new material production according to abnormal data in production data, and confirming sources of the abnormal data by analyzing production paths of the production abnormal data in a production process and relevant characteristics of the abnormal data;
the production data management module is used for obtaining a tracing result of the abnormality of the new material production state according to the analysis result of the abnormality state analysis module on the current production abnormality data and managing the new material production according to the tracing result.
7. The new material production data management system based on data analysis of claim 6, wherein: the monitoring data analysis module comprises a data extraction unit, a monitoring data identification unit and a monitoring data analysis unit;
the data extraction unit is used for extracting monitoring data from the production related data collected by the production data monitoring module and converting the extracted monitoring data into structured data;
the monitoring data identification unit is used for identifying and classifying the extracted monitoring data into conventional data and abnormal data;
the monitoring data analysis unit is used for analyzing and processing the abnormal data after the identification and classification, and analyzing the characteristic node data in the monitoring data into characteristic elements.
8. The new material production data management system based on data analysis of claim 6, wherein: the abnormal state analysis unit comprises an abnormal data extraction unit, an abnormal data analysis unit and an abnormal data tracing unit;
the abnormal data extraction unit is used for extracting the abnormal data identified and classified in the monitoring data identification unit;
the abnormal data analysis unit is used for analyzing the extracted abnormal data and analyzing the associated path and the historical data of the abnormal data according to the characteristic elements of the abnormal data;
the abnormal data tracing unit is used for screening the source of the abnormal data according to the analysis result of the abnormal data, locating to obtain related participation factors of the abnormal data, tracking the forming path of the abnormal data and determining the influence range of the abnormal data.
9. The new material production data management system based on data analysis of claim 6, wherein: the production data management module comprises a tracing result identification unit and a production related regulation and control unit;
the tracing result identification unit is used for identifying and matching the equipment or the position of the abnormal source according to the tracing result of the abnormal data tracing unit;
the production-related regulation and control unit is used for identifying the matched abnormal sources according to the tracing result identification unit to control and regulate the corresponding production elements.
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| CN117369426B (en) * | 2023-12-08 | 2024-03-01 | 台昌树脂(佛山)有限公司 | State monitoring method and system based on hot melt adhesive production control system |
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