Workshop production data management method and system

Abstract

The invention discloses a data management method and system for workshop production, relates to the technical field of data management, and solves the technical problems that the data volume is large, unified management is inconvenient, and when the data are stored, different types of data are stored disorderly, and subsequent data extraction is inconvenient. According to the method, the data are classified according to the data types and the generation time, the data are classified in a unified mode, unified management of the data is facilitated, in the process of storing the data, unified format conversion is conducted on the data, on one hand, compatibility of different types of data can be guaranteed, and the data storage efficiency is improved. On the other hand, rapid extraction of different types of data can be achieved, meanwhile, repeated data in the data are analyzed and extracted and analyzed, when the data are stored, storage analysis is conducted according to the characteristics of the data, storage is conducted according to the relevance between the data, and the data storage efficiency is improved. And overall extraction of different types of data is facilitated.

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: 1. Drawings, processes and other technical documents are delivered manually, which is time-consuming and easy to lose; 2. Program documents are managed in a decentralized manner, making them difficult to share and track.

[0005] 3. Modifications to technical documents cannot be fed back to each workshop and production department in a timely manner, which is quite delayed.

[0006] 4. Drawing version management is difficult. The workshop can only see the latest version and cannot trace the old version.

[0007] 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

[0008] 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.

[0009] 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: 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; 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; Step 4: storing and analyzing the acquired reorganized data, segmenting the reorganized data according to the data capacity of the reorganized data to obtain segmented data packets, and analyzing the relevance of the segmented data packets in combination with historical data to generate relevance information; Step 5: Perform storage analysis on the obtained associated data information and unassociated data respectively, and generate storage information.

[0010] As a further solution of the present invention: the specific method of obtaining the standard data in step 2 is: 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 classified 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 classified data is converted into a unified format according to the standard format to obtain the standard data.

[0011] As a further solution of the present invention: the specific method of generating the reorganized data in step 3 is: 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. At the same time, 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. At the same time, 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.

[0012] As a further solution of the present invention: the specific method of obtaining the segmented package in step 4 is: All reassembled data are obtained and labeled as n, where n=1, 2, ..., m, where m represents the number of reassembled data. The data capacity of the reassembled data n is obtained and recorded as Rn. Then the reassembled data n is equally divided according to the data capacity Rn to obtain two equally divided reassembled packets.

[0013] As a further solution of the present invention: the specific method of generating the relevance information in step 4 is: According to the analysis of historical usage records, the ten usage records before the current time point are obtained, and it is determined whether there is reorganized data used simultaneously based on 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.

[0014] As a further solution of the present invention: the specific method of analyzing the data to be analyzed in step 4 is: Get 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 related data information is generated. 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 unrelated data information is generated.

[0015] As a further solution of the present invention: the specific method of storing and analyzing the associated data information in step 5 to generate the storage information is: 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.

[0016] As a further solution of the present invention: the specific method of performing storage analysis on the unrelated data information in step 5 to generate storage information is: 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.

[0017] A workshop production data management system, comprising 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; 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.

[0018] Beneficial Effects The present invention provides a data management method and system for workshop production. Compared with the prior art, it has the following beneficial effects: The present invention classifies data according to data type and generation time, 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 converting the data into a unified format, on the one hand, the compatibility of different types of data can be guaranteed, and on the other hand, fast extraction of different types of data can be achieved. At the same time, repeated data in the data is analyzed, and repeated data is extracted and analyzed. When storing data, storage analysis is performed according to the characteristics of the data itself, and storage is performed according to the correlation between the data, which is convenient for the overall extraction of different types of data. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a method diagram of the present invention; Figure 2 This is a block diagram of the system principle of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] For example, see Figure 1 The present application provides a data management method for workshop production, which specifically includes the following steps: Step 1: Acquire all workshop production data, and the workshop production data acquired here includes current data and historical data.

[0022] 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 classification method is as follows: All workshop production data are obtained, and the workshop production data are classified according to the data type to obtain the same type of data, and then the generation time corresponding to the same type of data is obtained, and the same type of data is classified with time period t as the demarcation point to obtain classified data. Specifically, the obtained workshop production data are classified according to the corresponding data type. For example, the data type of the workshop production data includes table data, graphic data and text data, which are classified according to the corresponding type, and then classified according to the corresponding generation time. For example, if the value of time period t is one day, all types of workshop production data within one day are classified.

[0023] Then, the data format corresponding to the obtained classified data is obtained, and the standard format is obtained, and the classified data is converted into a unified format according to the standard format to obtain the standard data. The standard format here refers to the data format with the fastest reading speed. The reading speed of all data formats is analyzed, and the data format with the fastest reading speed is selected as the standard for unified format conversion.

[0024] Step 3: Store and analyze the acquired standard data, determine the impact of duplicate data in the standard data, and remove the duplicate data to generate reorganized data. The specific method of generating reorganized data is as follows: Obtain all standard data, and obtain standard data of any data type as the analysis object, and here the data type corresponding to any standard data is obtained, such as graphic data or table data before conversion, and then find the 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. Data analysis tools, such as Python, R or specific data cleaning tools, are used to process and analyze the duplicate data. The number of duplicate data is obtained and recorded as Li. The number of duplicate data here represents the number corresponding to the same type of duplicate data. For example, if there are three types of duplicate data in the analysis object, then i=3. The number of different types of duplicate data is further obtained, such as 3, 4 and 6 respectively. Then the number of duplicate data Li is judged. If the value of the number of duplicate data Li is equal to two, it means that the number of duplicate data of the corresponding type is not large, and the corresponding duplicate data is retained. At the same time, the duplicate data is bundled and labeled as a duplicate bundle package. At the same time, the label of the duplicate bundle is the same as the label of the corresponding analysis object. If the value of the number of duplicate data Li is greater than two, it means that the number of duplicate data of the corresponding type is large, and one duplicate data is retained. At the same time, the remaining duplicate data is deleted, and then the retained duplicate data is combined with the feature data to obtain the recombined data.

[0025] 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 method of generating the relevance information is as follows: Obtain all reassembled data and label them as n, where n=1, 2, ..., m, where m represents the number of reassembled data. 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. Here, the data is divided into two equally according to the data capacity. Obtain the historical usage record. 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 based on the usage records. If there is corresponding reorganized data used simultaneously, the corresponding reorganized data is obtained and marked as data to be analyzed. Otherwise, if there is no corresponding reorganized data used simultaneously, unrelated data information is generated. Specifically, simultaneous use here can be represented by the situation where two sets of data are used in combination. For example, when studying workshop production data, there are table-type reorganized data and graphic-type reorganized data used simultaneously for research, and the corresponding reorganized data is marked and recorded as data to be analyzed.

[0026] Then, the number of simultaneous uses corresponding to the data to be analyzed is obtained, and the corresponding usage ratio is calculated. The usage ratio here represents the ratio of the number of simultaneous uses of the data to be analyzed to ten usage records. At the same time, the usage ratio is compared with the 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 related data information is generated. 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 unrelated data information is generated.

[0027] Step 5: The obtained associated data information and unassociated data are stored and analyzed separately, and storage information is generated. The specific method of generating the storage information is: The specific method of analyzing and storing the associated data corresponding to the associated data information is: obtaining the associated data, and compressing the obtained associated data to obtain a compressed package, and 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, and then obtain the compression shortening Vo, compression error amount Ho and compression amount Ko corresponding to the compression method o, and the compression error amount here represents the capacity of erroneous data generated after compressing the data, and the compression amount Ko represents the overall data capacity after the data is compressed, 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 this 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.

[0028] The specific method of analyzing and storing the unrelated data corresponding to the unrelated data information is as follows: obtain the unrelated data, and label the unrelated data as a, where a=1, 2, ..., b, where b represents the number of unrelated data, and obtain the data capacity corresponding to the unrelated data a as Ra, and judge the value of the data capacity Ra. 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.

[0029] Example 2: As Example 2 of the present invention, please refer to Figure 2 The present 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 units are connected by unidirectional wires.

[0030] The workshop production data collection unit is used to collect the workshop production data and transmit the workshop production data to the production data classification unit.

[0031] A production data classification unit is used to classify the acquired workshop production data, by classifying the workshop production data according to the corresponding data type and generation time to obtain classified data, and at the same time converting the classified data into a unified format to obtain standard data, and then transmitting the standard data to the production data processing and analysis unit, and the analysis method here is the same as the analysis method of step two in embodiment one.

[0032] A production data processing and analysis unit 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 situation to eliminate the duplicate data, while generating reorganized data, and then transmitting the reorganized data to the production data storage and analysis unit. The processing method here is the same as the processing method of step three in embodiment one.

[0033] A production data storage and analysis unit is provided, 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, and analyze the correlation of the segmented data packets in combination with historical data to generate correlation information, and the correlation information includes associated data information and unassociated data information, and the associated data information and unassociated data information are analyzed separately to obtain storage information, and the storage information is transmitted to the management information output unit at the same time, and the specific method of generating the storage information is the same as the processing method of steps 4 and 5 in embodiment 1.

[0034] A management information output unit is used to store workshop production data according to the acquired storage information.

[0035] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0036] 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 skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents 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; 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; Step 4: storing and analyzing the acquired reorganized data, segmenting the reorganized data according to the data capacity of the reorganized data to obtain segmented data packets, and analyzing the relevance of the segmented data packets in combination with historical data to generate relevance information; Step 5: Perform storage analysis on the obtained associated data information and unassociated data respectively, and generate storage information.

2. A workshop production data management method according to claim 1, characterized in that: The specific method of obtaining the standard data in step 2 is: 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 classified 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 classified data is converted into a unified format according to the standard format to obtain the standard data.

3. A workshop production data management method according to claim 1, characterized in that: The specific method of generating the reorganized data in step 3 is: 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. At the same time, 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. At the same time, 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.

4. A workshop production data management method according to claim 1, characterized in that: The specific method of obtaining the split package in step 4 is: All reassembled data are obtained and labeled as n, where n=1, 2, ..., m, where m represents the number of reassembled data. The data capacity of the reassembled data n is obtained and recorded as Rn. Then the reassembled data n is equally divided according to the data capacity Rn to obtain two equally divided reassembled packets.

5. A workshop production data management method according to claim 1, characterized in that: The specific method of generating the relevance information in step 4 is: According to the analysis of historical usage records, the ten usage records before the current time point are obtained, and it is determined whether there is reorganized data used simultaneously based on 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.

6. A workshop production data management method according to claim 5, characterized in that: The specific method of analyzing the data to be analyzed in step 4 is: Get 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 related data information is generated. 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 unrelated data information is generated.

7. A workshop production data management method according to claim 1, characterized in that: The specific method of performing storage analysis on the associated data information to generate storage information in step 5 is: 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.

8. A workshop production data management method according to claim 1, characterized in that: The specific method of performing storage analysis on the unrelated data information to generate storage information in step 5 is: 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.

9. A workshop production data management system, used to execute a workshop production data management method according to any one of claims 1 to 8, 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.

Images