A data management method and system for workshop production

Abstract

The present invention discloses a data management method and system for workshop production. The present invention relates to the technical field of data management, and solves the technical problems that the data volume is large and it is inconvenient to manage uniformly. Secondly, when storing data, different types of data are stored in a messy manner, which is inconvenient for subsequent data extraction. The present invention classifies data according to data types and generation time, and conducts unified classification of data, which is convenient for unified management of data. Secondly, in the process of storing data, unified format conversion is performed on the data. On the one hand, it can ensure the compatibility of different types of data, and on the other hand, it can achieve rapid extraction of different types of data. At the same time, by analyzing the duplicate data in the data, and extracting and analyzing the duplicate data, when storing data, storage analysis is carried out according to the characteristics of the data itself, and storage is carried out according to the correlation between the data, which is convenient for the overall extraction of different types of data.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a data management method and system for workshop production. Background Art

[0002] With the advent of the information age, intelligent, information-based and paperless factories have become the goal to be pursued, and the continuous development of industrial technology has also provided technical support for achieving this goal.

[0003] According to Chinese patent application number CN202410339303.X, a test data management method for industrial production is disclosed, including the following steps: S10: setting a product test database and a back-end program connected to the test database in a server; the test database is used to obtain the test data uploaded by each detection terminal and store the obtained test data; the back-end program is used to connect to each query terminal; S20: the back-end program obtains the query conditions input by the user from the query terminal; S30: the back-end program retrieves the test data that meets the query conditions from the test database and performs statistical analysis on the retrieved test data, and sends the test data and the corresponding statistical analysis results to the query terminal for display.

[0004] At present, drawings, processes and other technical documents have the following problems in actual production:

[0005] 1. Drawings, processes and other technical documents are delivered manually, which is time-consuming and easy to lose;

[0006] 2. Program documents are managed in a decentralized manner, making them difficult to share and track.

[0007] 3. Modifications to technical documents cannot be fed back to each workshop and production department in a timely manner, which is quite delayed.

[0008] 4. Drawing version management is difficult. The workshop can only see the latest version and cannot trace the old version.

[0009] At the same time, the large amount of data generated makes it difficult to manage the data in a unified manner. Secondly, when the data is subsequently extracted and utilized, the different types of data are stored in a messy manner, making extraction inconvenient and affecting the overall data management. Summary of the invention

[0010] In view of the shortcomings of the prior art, the present invention provides a data management method and system for workshop production, which solves the problem of large data volume, inconvenient unified management, and secondly, when storing data, different types of data are stored in a messy manner, which is inconvenient for subsequent data extraction.

[0011] To achieve the above objectives, the present invention is implemented through the following technical solutions: A workshop production data management method, comprising the following steps:

[0012] Step 1: Obtain all workshop production data;

[0013] Step 2: Classify the obtained workshop production data by data type and generation time to obtain classified data, and perform unified format conversion on the obtained classified data to obtain standard data;

[0014] Step 3: Store and analyze the obtained standard data. Judge the influence of duplicate data in the standard data, and eliminate or retain the duplicate data, and generate recombined data at the same time;

[0015] Step 4: Store and analyze the obtained recombined data. At the same time, divide the recombined data according to the data capacity of the recombined data to obtain segmented data packets, and analyze the relevance of the segmented data packets in combination with historical data to generate relevance information;

[0016] Step 5: Store and analyze the obtained associated data information and unassociated data respectively, and generate storage information.

[0017] As a further solution of the present invention: The specific method for obtaining the standard data in Step 2 is as follows:

[0018] Obtain all workshop production data, classify the workshop production data according to the data type to obtain same-type data, then obtain the generation time corresponding to the same-type data, and classify the same-type data with the time period t as the demarcation point to obtain classified data;

[0019] Then, for the data format corresponding to the obtained classified data, obtain the standard format, and the standard format is the data format with the fastest reading speed. At the same time, perform unified format conversion on the classified data according to the standard format to obtain standard data.

[0020] As a further solution of the present invention: The specific method for generating recombined data in Step 3 is as follows:

[0021] Obtain all the standard data, and take the standard data of any one data type as the analysis object. Then, search for duplicate data in the analysis object, extract the duplicate data at the same time, record the remaining data in the analysis object as characteristic data, and then judge the influence of the extracted duplicate data;

[0022] Label the duplicate data as i, where i = 1, 2, …, j, and j represents the number of types of duplicate data. At the same time, obtain the number of duplicate data and denote it as Li. Then, judge the number Li of duplicate data. If the value of the number Li of duplicate data is equal to two, then retain the corresponding duplicate data, and bundle and label the duplicate data as a duplicate bundle. If the value of the number Li of duplicate data is greater than two, then retain one duplicate data, and delete the remaining duplicate data. Then, combine the retained duplicate data with the feature data to obtain the recombined data.

[0023] As a further solution of the present invention: The specific method for obtaining the split package in step four is:

[0024] Obtain all the recombined data and label it as n, where n = 1, 2, …, m, and m represents the number of recombined data. At the same time, obtain the data capacity Rn of the recombined data n, and then evenly divide the recombined data n according to the data capacity Rn into two evenly divided recombined packages.

[0025] As a further solution of the present invention: The specific method for generating the correlation information in step four is:

[0026] According to the analysis of historical usage records, obtain the ten usage records before the current time point, and judge whether there is recombined data used simultaneously according to the usage records. If there is corresponding recombined data used simultaneously, then obtain the corresponding recombined data and mark it as the data to be analyzed. At the same time, analyze the data to be analyzed. On the contrary, if there is no corresponding recombined data used simultaneously, then generate uncorrelated data information.

[0027] As a further solution of the present invention: The specific method for analyzing the data to be analyzed in step four is:

[0028] Obtain the corresponding number of times of simultaneous use of the data to be analyzed, calculate the corresponding usage ratio, and compare the usage ratio with a preset value. If the usage ratio is greater than the preset value, it means that there is a correlation between the data to be analyzed, and generate correlation data information. On the contrary, if the usage ratio is less than the preset value, it means that there is no correlation between the data to be analyzed, and generate uncorrelated data information.

[0029] As a further solution of the present invention: The specific method for storing and analyzing the correlation data information to generate storage information in step five is:

[0030] Obtain associated data, and perform compression processing on the obtained associated data to obtain a compressed package. Specifically, the compression method is as follows: Obtain all compression methods and label them as o, where o = 1, 2, …, p, and p represents the number of types of compression methods. Then, obtain the compression reduction Vo, compression error amount Ho, and compression amount Ko corresponding to the compression method o. At the same time, substitute the obtained parameters into the formula Calculate the compression value Qo corresponding to the compression method o, and select the compression method corresponding to the largest compression value Qo as the standard and denote it as the standard method. Then, compress the associated data in the standard method to obtain a compressed package. By analogy, analyze and process all the associated data to obtain a compressed package;

[0031] Obtain the capacity corresponding to the compressed package, and judge the capacity of the compressed package. If the value of the capacity of the compressed package is odd, then divide the compressed package into three equal parts, and store the divided compressed package to generate storage information. If the value of the capacity of the compressed package is even, then divide the compressed package into two equal parts, and store the divided compressed package to generate storage information.

[0032] As a further solution of the present invention: The specific method for storing and analyzing the unassociated data information in step five to generate storage information is as follows:

[0033] Obtain unassociated data, and at the same time label the unassociated data as a, where a = 1, 2, …, b, and b represents the number of unassociated data. At the same time, obtain the data capacity Ra corresponding to the unassociated data a, and judge the value of the data capacity Ra. If the data capacity Ra of the unassociated data a is odd, then divide the unassociated data into three equal parts, and store the divided unassociated data to generate storage information. If the data capacity Ra of the unassociated data a is even, then divide the unassociated data into two equal parts according to the data capacity, and store the divided unassociated data to generate storage information.

[0034] A data management system for workshop production, including a workshop production data acquisition unit, a production data classification unit, a production data processing and analysis unit, a production data storage and analysis unit, and a management information output unit;

[0035] The workshop production data acquisition unit is used to collect workshop production data and transmit the workshop production data to the production data classification unit;

[0036] The production data classification unit is used to classify the obtained workshop production data. Classify the workshop production data according to the corresponding data type and generation time to obtain classified data, and at the same time perform unified format conversion on the classified data to obtain standard data. Then, transmit the standard data to the production data processing and analysis unit;

[0037] A production data processing and analysis unit, which is used to analyze the acquired standard data, identify duplicate data in the standard data, judge the impact of the duplicate data on the whole to eliminate the duplicate data, generate restructured data, and then transmit the restructured data to the production data storage and analysis unit;

[0038] A production data storage and analysis unit, which is used to analyze the acquired restructured data, divide the restructured data according to the data capacity of the restructured data to obtain segmented data packets, analyze the relevance of the segmented data packets in combination with historical data to generate relevance information, and the relevance information includes associated data information and unassociated data information, and analyze the associated data information and unassociated data information separately to obtain storage information, and at the same time transmit the storage information to the management information output unit;

[0039] A management information output unit, which is used to store the workshop production data according to the acquired storage information.

[0040] Advantageous effects

[0041] The present invention provides a data management method and system for workshop production. Compared with the prior art, it has the following advantageous effects:

[0042] The present invention classifies data according to data types and generation times, so as to uniformly classify a large amount of data, which is convenient for unified management of data. Secondly, in the process of storing data, by performing unified format conversion on the data, on the one hand, it can ensure the compatibility of different types of data, on the other hand, it can achieve rapid extraction of different types of data. At the same time, by analyzing the duplicate data in the data, extracting and analyzing the duplicate data, when storing the data, perform storage analysis according to the characteristics of the data itself, and store according to the relevance between the data, which is convenient for overall extraction of different types of data. Description of the drawings

[0043] Figure 1 It is a diagram of the method of the present invention;

[0044] Figure 2 It is a block diagram of the system principle of the present invention. Detailed implementation manners

[0045] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the 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. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0046] Example 1, please refer to Figure 1 , this application provides a data management method for workshop production, and the method specifically includes the following steps:

[0047] Step 1: Obtain all workshop production data, and the obtained workshop production data here includes current data and historical data.

[0048] Step 2: Classify the obtained workshop production data by data type and generation time to obtain classified data, and perform unified format conversion on the obtained classified data to obtain standard data. The specific classification method is as follows:

[0049] Obtain all workshop production data, classify the workshop production data by data type to obtain same-type data, then obtain the generation time corresponding to the same-type data, and classify the same-type data with time period t as the demarcation point to obtain classified data. Specifically, classify the obtained workshop production data according to the corresponding data type. For example, the data types of workshop production data include tabular data, graphic data, and text data, and classify them according to the corresponding types. Then classify them according to the corresponding generation time. For example, if the value of time period t is one day, then classify all types of workshop production data within one day.

[0050] Then, obtain the data format corresponding to the obtained classified data, and obtain the standard format. At the same time, perform unified format conversion on the classified data according to the standard format to obtain standard data. And the standard format here refers to the data format with the fastest reading speed. By analyzing the reading speeds of all data formats, select the data format with the fastest reading speed as the standard for unified format conversion.

[0051] Step 3: Perform storage analysis on the obtained standard data. Judge the influence of duplicate data in the standard data, and eliminate the duplicate data to generate recombined data. The specific method for generating recombined data is as follows:

[0052] Obtain all the standard data, and take the standard data of any one data type as the analysis object. And the data type corresponding to the obtained standard data here is any one before conversion. For example, it can be graphic data or tabular data before conversion. Then search for duplicate data in the analysis object, extract the duplicate data at the same time, record the remaining data in the analysis object as characteristic data, and then judge the influence of the extracted duplicate data;

[0053] Label the duplicate data as i, where i = 1, 2, …, j, and j represents the number of types of duplicate data. Use data analysis tools such as Python, R, or specific data cleaning tools to process and analyze the duplicate data, and obtain the number of duplicate data, denoted as Li. Here, the number of duplicate data represents the number of corresponding duplicate data of the same type. For example, if there are 3 types of duplicate data in the analysis object, then i = 3. Further, obtain the number of duplicate data of different types, such as 3, 4, and 6 respectively. Then, judge the number Li of duplicate data. If the value of the number Li of duplicate data is equal to two, it means that the number of duplicate data of the corresponding type is small, and the corresponding duplicate data is retained. At the same time, bundle and label the duplicate data as a duplicate bundle, and the label of the duplicate bundle is the same as the label of the corresponding analysis object. If the value of the number Li of duplicate data is greater than two, it means that the number of duplicate data of the corresponding type is large, and one duplicate data is retained, and the remaining duplicate data is deleted. Then, combine the retained duplicate data with the feature data to obtain the recombined data.

[0054] Step 4: Store and analyze the obtained recombined data. At the same time, divide the recombined data according to the data capacity of the recombined data to obtain divided data packets, and analyze the relevance of the divided data packets in combination with historical data to generate relevance information. The specific method for generating relevance information is as follows:

[0055] Obtain all the recombined data and label them as n, where n = 1, 2, …, m, and m represents the number of recombined data. At the same time, obtain the data capacity Rn of the recombined data n, and then divide the recombined data n into two equal divided recombined packets according to the data capacity Rn. Here, it is divided into two equal parts according to the data capacity, and at the same time, obtain the historical usage records;

[0056] According to the analysis of the historical usage records, obtain the ten usage records before the current time point, and judge whether there are recombined data used simultaneously according to the usage records. If there are corresponding recombined data used simultaneously, obtain the corresponding recombined data and mark it as data to be analyzed. Otherwise, if there are no corresponding recombined data used simultaneously, generate non-associated data information. Specifically, the simultaneous use here can mean that there is a situation where two groups of data are used in combination. For example, when studying the workshop production data, there are recombined data in the form of tables and recombined data in the form of graphs used simultaneously for research, then the corresponding recombined data is marked and denoted as data to be analyzed.

[0057] Next, obtain the simultaneous usage times corresponding to the data to be analyzed, calculate the corresponding usage ratio, and here the usage ratio represents the ratio of the simultaneous usage times of the data to be analyzed to ten usage records. At the same time, compare the usage ratio with a preset value. If the usage ratio is greater than the preset value, it indicates that there is a correlation between the data to be analyzed, and generate associated data information. On the contrary, if the usage ratio is less than the preset value, it indicates that there is no correlation between the data to be analyzed, and generate unassociated data information.

[0058] Step Five: Analyze and store the obtained associated data information and unassociated data separately, and generate storage information. The specific method of generating storage information is as follows:

[0059] The specific method of analyzing and storing the associated data corresponding to the associated data information is as follows: Obtain the associated data, and perform compression processing on the obtained associated data to obtain a compressed package. The specific compression method is to obtain all compression methods and label them as o, and o = 1, 2,..., p, where p represents the number of types of compression methods. Then, obtain the compression reduction Vo, compression error amount Ho, and compression amount Ko corresponding to the compression method o. Here, the compression error amount represents the capacity of the error data generated after compressing the data, and the compression amount Ko represents the overall data capacity after compressing the data. At the same time, substitute the obtained parameters into the formula Calculate the compression value Qo corresponding to the compression method o, and select the compression method corresponding to the largest compression value Qo as the standard and record it as the standard method. Then, compress the associated data in the standard method to obtain a compressed package. And so on, analyze and process all the associated data to obtain compressed packages;

[0060] Obtain the capacity corresponding to the compressed package, and judge the capacity of the compressed package. If the value of the capacity of the compressed package is odd, divide the compressed package into three equal parts, and store the divided compressed packages to generate storage information. If the value of the capacity of the compressed package is even, divide the compressed package into two equal parts, and store the divided compressed packages to generate storage information.

[0061] The specific method of analyzing and storing the unassociated data corresponding to the unassociated data information is as follows: Obtain the unassociated data, and at the same time label the unassociated data as a, and a = 1, 2,..., b, where b represents the number of unassociated data. At the same time, obtain the data capacity Ra corresponding to the unassociated data a, and judge the value of the data capacity Ra. If the data capacity Ra of the unassociated data a is odd, divide the unassociated data into three equal parts, and at the same time store the divided unassociated data and generate storage information. If the data capacity Ra of the unassociated data a is even, divide the unassociated data into two equal parts according to the data capacity, and at the same time store the divided unassociated data and generate storage information.

[0062] Embodiment 2. As the second embodiment of the present invention, please refer to Figure 2 , this application provides a data management system for workshop production, including a workshop production data acquisition unit, a production data classification unit, a production data processing and analysis unit, a production data storage and analysis unit, and a management information output unit, and the above-mentioned each unit is connected by a one-way wire.

[0063] Workshop production data acquisition unit, this unit is used to collect workshop production data and transmit the workshop production data to the production data classification unit.

[0064] Production data classification unit, this unit is used to classify the obtained workshop production data. By classifying the workshop production data according to the corresponding data type and generation time, classified data is obtained. At the same time, the classified data is converted into a unified format to obtain standard data, and then the standard data is transmitted to the production data processing and analysis unit, and the analysis method here is the same as the analysis method in step two of Embodiment 1.

[0065] Production data processing and analysis unit, this unit is used to analyze the obtained standard data. By identifying the duplicate data in the standard data and judging the impact of the duplicate data on the whole, the duplicate data is eliminated, and at the same time, recombinant data is generated, and then the recombinant data is transmitted to the production data storage and analysis unit, and the processing method here is the same as the processing method in step three of Embodiment 1.

[0066] Production data storage and analysis unit, this unit is used to analyze the obtained recombinant data. According to the data capacity of the recombinant data, the recombinant data is segmented to obtain segmented data packets, and the relevance of the segmented data packets is analyzed in combination with historical data to generate relevance information, and the relevance information includes associated data information and unassociated data information, and the associated data information and unassociated data information are respectively analyzed to obtain storage information, and at the same time, the storage information is transmitted to the management information output unit, and the specific method of generating the storage information is the same as the processing methods in steps four and five of Embodiment 1.

[0067] Management information output unit, this unit is used to store the workshop production data according to the obtained storage information.

[0068] At the same time, the content not described in detail in this specification belongs to the prior art well known to those skilled in the art.

[0069] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A data management method for workshop production, characterized in that: The following steps are involved: Step 1: Obtain all workshop production data; Step 2: Classify the acquired workshop production data by data type and generation time to obtain classified data, and convert the obtained classified data into a unified format to obtain standard data. The specific processing method is as follows: Obtain all workshop production data, and classify the workshop production data according to the data type to obtain data of the same type, then obtain the generation time corresponding to the data of the same type, and classify the data of the same type with time period t as the dividing point to obtain classified data; Then, the data format corresponding to the obtained classification data is obtained, and a standard format is obtained, and the standard format is the data format with the fastest reading speed. At the same time, the classification data is converted into a unified format according to the standard format to obtain the standard data; Step 3: Store and analyze the acquired standard data, determine the impact of duplicate data in the standard data, remove or retain the duplicate data, and generate reorganized data. The specific processing method is as follows: Obtain all standard data, and obtain standard data of any data type as the analysis object, then search for duplicate data in the analysis object, extract the duplicate data, record the remaining data in the analysis object as feature data, and then make an impact judgment on the extracted duplicate data; The duplicate data are labeled as i, and i=1, 2, ..., j, where j represents the number of types of duplicate data, and the number of duplicate data is obtained and recorded as Li. Then the number of duplicate data Li is judged. If the value of the number of duplicate data Li is equal to two, the corresponding duplicate data is retained, and the duplicate data is bundled and labeled as a duplicate bundle. If the value of the number of duplicate data Li is greater than two, one duplicate data is retained, and the remaining duplicate data is deleted. Then the retained duplicate data is combined with the characteristic data to obtain the recombined data; Step 4: Store and analyze the acquired reorganized data, and segment the reorganized data according to the data capacity of the reorganized data to obtain segmented data packets, and analyze the relevance of the segmented data packets in combination with historical data to generate relevance information. The specific processing method is as follows: According to the historical usage record analysis, the ten usage records before the current time point are obtained, and it is determined whether there is reorganized data used simultaneously according to the usage records. If there is corresponding reorganized data used simultaneously, the corresponding reorganized data is obtained and marked as data to be analyzed, and the data to be analyzed is analyzed at the same time. Otherwise, if there is no corresponding reorganized data used simultaneously, unrelated data information is generated; Obtain the number of simultaneous uses corresponding to the data to be analyzed, calculate the corresponding usage ratio, and compare the usage ratio with the preset value. If the usage ratio is greater than the preset value, it means that there is a correlation between the data to be analyzed, and generate related data information. Conversely, if the usage ratio is less than the preset value, it means that there is no correlation between the data to be analyzed, and generate unrelated data information; Obtain all reassembled data and label them as n, where n=1, 2, ..., m, where m represents the number of reassembled data, and obtain the data capacity of reassembled data n as Rn, then divide the reassembled data n equally according to the data capacity Rn to obtain two equally divided reassembled packets; Step 5: Store and analyze the associated data information and unassociated data separately, and generate storage information. The specific processing method is as follows: The specific method of performing storage analysis on the associated data information to generate storage information is as follows: Obtain the associated data, and compress the associated data to obtain a compressed package. The specific compression method is to obtain all compression methods and label them as o, and o=1, 2, ..., p, where p represents the number of types of compression methods. Then obtain the compression shortening Vo, compression error Ho and compression amount Ko corresponding to the compression method o, and substitute the obtained parameters into the formula Calculate the compression value Qo corresponding to the compression mode o, select the compression mode corresponding to the maximum compression value Qo as the standard and record it as the standard mode, then compress the associated data in the standard mode to obtain a compressed package, and analyze and process all the associated data in the same way to obtain a compressed package; Obtain the capacity corresponding to the compressed package and judge the capacity of the compressed package. If the value of the compressed capacity is an odd number, divide the compressed package into three equal parts, and store the divided compressed packages to generate storage information. If the value of the compressed capacity is an even number, divide the compressed package into two equal parts, and store the divided compressed packages to generate storage information. Unrelated data are obtained and labeled as a, where a=1, 2, ..., b, where b represents the number of unrelated data. The data capacity corresponding to the unrelated data a is obtained and recorded as Ra, and the value of the data capacity Ra is judged. If the data capacity Ra of the unrelated data a is an odd number, the unrelated data is divided into three equal parts, and the divided unrelated data is stored and storage information is generated. If the data capacity Ra of the unrelated data a is an even number, the unrelated data is divided into two equal parts according to the data capacity, and the divided unrelated data is stored and storage information is generated.

2. A workshop production data management system, used to execute a workshop production data management method according to claim 1, characterized in that: It includes workshop production data collection unit, production data classification unit, production data processing and analysis unit, production data storage and analysis unit and management information output unit; A workshop production data collection unit, which is used to collect workshop production data and transmit the workshop production data to a production data classification unit; A production data classification unit, which is used to classify the acquired workshop production data, obtain classified data by classifying the workshop production data according to the corresponding data type and generation time, and convert the classified data into a unified format to obtain standard data, and then transmit the standard data to the production data processing and analysis unit; A production data processing and analysis unit, which is used to analyze the acquired standard data, identify duplicate data in the standard data, and determine the impact of the duplicate data on the overall data to eliminate the duplicate data, and generate reorganized data, and then transmit the reorganized data to the production data storage and analysis unit; A production data storage and analysis unit, which is used to analyze the acquired reorganized data, segment the reorganized data according to the data capacity of the reorganized data to obtain segmented data packets, analyze the relevance of the segmented data packets in combination with historical data to generate relevance information, and the relevance information includes associated data information and unassociated data information, and analyze the associated data information and unassociated data information respectively to obtain storage information, and transmit the storage information to the management information output unit at the same time; A management information output unit is used to store workshop production data according to the acquired storage information.

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