An industrial chain resource data reconstruction and integration system based on digital transformation enterprises

Abstract

The present invention relates to the field of data reconstruction technology, and discloses an industrial chain resource data reconstruction and integration system based on a digital transformation enterprise, comprising a data collection unit, a data processing unit, a reconstruction analysis unit, and a result display unit; by analyzing the difference between each link, it is possible to determine which links are in excess or insufficient, and then adjust the operating input parameters, such as reducing the amount of unnecessary processing equipment, the number of customer service personnel, etc., so as to reduce operating costs; in the production process, by comparing and analyzing the production volume, order volume and transportation volume, the production strategy is adjusted in time to ensure that the production matches the market demand and reduce inventory backlogs or out-of-stock situations; through the coordinated analysis of each link in the industrial chain, the enterprise can respond to market changes more quickly and adjust the product supply chain in time, and through the systematic analysis of the industrial chain data and the calculation of the reconstruction coefficient, the enterprise can better carry out long-term planning and strategic adjustments.

Description

Technical Field

[0001] The present invention relates to the field of data reconstruction technology, and in particular to an industrial chain resource data reconstruction and integration system based on digital transformation enterprises. Background Art

[0002] Resource integration refers to a complex dynamic process in which enterprises identify and select, absorb and configure, activate and organically integrate resources from different sources, levels, structures and contents, making them more flexible, organized, systematic and valuable, and creating new resources. In the operation and management of modern enterprises, the analysis and integration of industrial chain data plays a vital role in improving production efficiency, reducing costs and enhancing market competitiveness. With the advancement of digital transformation, enterprises can obtain a large amount of raw data, including data from various links such as production, sales and logistics. However, these data are often independent and scattered, and need to be integrated and analyzed through effective means to reveal the potential correlation and optimization potential.

[0003] Traditional methods may not be able to fully tap the value of data, or the processing is inefficient, making it difficult to meet the needs of rapid response to market changes. Therefore, an industrial chain resource data reconstruction and integration system based on digital transformation enterprises has emerged. The system aims to achieve in-depth mining and effective integration of data from all links of the industrial chain through advanced data processing and analysis technologies, thereby helping enterprises optimize resource allocation and improve overall operational efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide an industrial chain resource data reconstruction and integration system based on digital transformation enterprises, which solves the technical problems raised in the background technology.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] An industrial chain resource data reconstruction and integration system based on digital transformation enterprises, including:

[0007] The data collection unit is used to collect the original data of each link of the target enterprise's industrial chain in the historical standard period. The original data includes the production volume and production input parameters of the specified product in the production link, the order volume and operation input parameters of the specified product in the sales link, and the transportation volume and transportation input parameters of the specified product in the logistics link;

[0008] Among them, input parameters refer to the quantitative values ​​of personnel costs or equipment costs;

[0009] The data processing unit is used to pre-process the original data of each link of the industrial chain. According to the production volume, order volume and transportation volume of the specified products in each historical standard period, the total production volume, order volume and transportation volume of all the specified products in the specified historical period are counted, as well as the proportion of the production volume, order volume and transportation volume of the specified products in each historical standard period in the total production volume, order volume and transportation volume of all other specified products in the specified historical period except the corresponding historical standard period, and then the reconstruction coefficient of the industrial chain is calculated according to the corresponding proportion results;

[0010] The reconstruction analysis unit is used to compare the reconstruction coefficient with a preset reconstruction threshold. If the comparison result shows that the reconstruction coefficient is greater than the reconstruction threshold, an integration judgment analysis is performed on the product chain of the specified product. Based on the integration judgment analysis result, the operation input parameters that need to be increased or decreased in the sales link, the transportation input parameters that need to be increased or decreased in the logistics link, and the production input parameters that need to be increased or decreased in the sales link are obtained.

[0011] The result display unit is used to display the integrated judgment and analysis results to relevant personnel.

[0012] As a further solution of the present invention: the production input parameter refers to the amount of processing equipment or processing personnel for producing the designated products corresponding to the total production volume, the operation input parameter refers to the amount of operating cost or customer service personnel for selling the designated products corresponding to the total order volume, and the transportation input parameter.

[0013] As a further solution of the present invention: the pretreatment method is as follows:

[0014] Extract the original data of each link in multiple historical standard periods within the specified historical period;

[0015] Take the production volume of a specified product in the production process as an example:

[0016] SS1. Mark the production volume of a specified product in each historical standard period as Si, where i = 1, 2, ... n, and n represents the number of multiple historical standard periods in the specified historical period;

[0017] At the same time, the order volume of the specified product in each historical standard period is marked as Di, and the transportation volume of the specified product in each historical standard period is marked as Yi;

[0018] SS2. According to the production volume, order volume and transportation volume of the specified product in each historical standard period, the total production volume, order volume and transportation volume of all specified products in the specified historical period are calculated and marked as SZ, DZ and YZ respectively;

[0019] SS3, through Calculate the proportion of the production volume, order volume, and transportation volume of the specified product in each historical standard period in the total production volume of all other specified products except the corresponding historical standard period in the specified historical period, BS i, BD i, and BYi, respectively;

[0020] SS4. Then, in the same historical standard period, the corresponding BS i, BD i, BYi are extracted, and the absolute values ​​of the differences between each other are calculated in BS i, BD i, BYi, and the absolute value of the difference between BS i and BD i is recorded as the difference analysis value SDci, the absolute value of the difference between BS i and BYi is recorded as the difference analysis value SYc i, and the absolute value of the difference between BD i and BYi is recorded as the difference analysis value DYc i;

[0021] By analogy, the difference analysis values ​​SDc i, SYc i, and DYc i in each historical standard period are calculated;

[0022] SS5. Use the variance calculation formula to calculate the discrete degree values ​​of the difference analysis values ​​SDc i, SYc i, and DYc i in each historical standard period;

[0023] Then, the discrete degree values ​​corresponding to SDc i, SYc i, and DYc i are compared with the corresponding preset discrete thresholds, and according to the comparison results, the corresponding SDc i, SYc i, and DYc i are screened and their average values ​​SDpp, SYpp, and DYpp are calculated;

[0024] SS6, using formula Calculate the reconstruction coefficient X of the industrial chain;

[0025] Among them, a1, a2, and a3 are all preset proportional coefficients, and μ is the error correction coefficient, and its value is 2.315.

[0026] As a further solution of the present invention, it is characterized in that the comparison method in step SS5 is as follows:

[0027] Taking SDc i as an example, if the discrete degree value corresponding to SDc i is greater than its corresponding discrete threshold, it means that the discrete degree value of the difference analysis value SDc i in each historical standard period is large, and then the corresponding SDc i values ​​are deleted in order from large to small according to |SDc i-SDcp| and the remaining discrete degree values ​​are calculated accordingly until the discrete degree value corresponding to SDc i is less than or equal to its corresponding discrete threshold;

[0028] Among them, SDcp represents the average value of all SDc i used in calculating the corresponding discrete degree value;

[0029] When the discrete degree value corresponding to SDc i is obtained to be less than or equal to the corresponding discrete threshold value, SDc i of the corresponding discrete degree value is calculated, and the average value SDpp of the corresponding SDc i involved in the calculation is obtained.

[0030] As a further solution of the present invention: if the reconstruction coefficient is less than or equal to the reconstruction threshold, no integration judgment analysis is performed.

[0031] As a further solution of the present invention: the integrated judgment and analysis method is as follows:

[0032] SA1. Obtain the original data within multiple specified periods, and respectively calculate the total production volume SZ, total order volume DZ, and total transportation volume YZ of all specified products within each specified historical period; wherein the time intervals between multiple specified periods are consistent;

[0033] SA2. Compare SZ, DZ, and YZ in each specified historical period with the preset minimum quantity thresholds Sy, Dy, and Yy respectively:

[0034] within the same specified historical period;

[0035] If SZ>Sy, DZ>Dy, YZ>Yy are all true, then the specified historical period is marked as the analysis period;

[0036] If one of SZ>Sy, DZ>Dy, YZ>Yy is not true, the original data corresponding to the specified historical period will be eliminated;

[0037] And so on, multiple analysis cycles are obtained;

[0038] SA3. Based on the total production, total order, and total transportation of all specified products in multiple analysis periods, calculate the corresponding mean of the total production, total order, and total transportation of all specified products in each analysis period, and record them as SZP, DZP, and YZP;

[0039] SA4. In the production link, sales link, and logistics link, select the value corresponding to one link as the standard value, and then conduct input increase and decrease analysis on each link based on the values ​​of other links and the standard values, and derive the operating input parameters that need to be increased or decreased in the sales link, the transportation input parameters that need to be increased or decreased in the logistics link, and the production input parameters that need to be increased or decreased in the sales link.

[0040] As a further solution of the present invention: the standard value is expressed as taking the data of the corresponding link as the standard, and reconstructing and integrating the data of other links according to the data of the standard link.

[0041] As a further solution of the present invention: the method of input increase and decrease analysis is as follows:

[0042] SB1. Take the SZP of the selected production link as the standard value as an example;

[0043] DZP-SZP=K1, and the product difference K1 between the production link and the sales link is calculated;

[0044] If the value of K1 is negative, it means that the number of products sold in the sales phase is lower than the number of products produced in the production phase, and that the sales phase needs to increase the operating input parameters;

[0045] If the value of K1 is positive, it means that the number of products sold in the sales phase is higher than the number of products produced in the production phase, and that the sales phase can reduce the operating input parameters;

[0046] SB2, YZP-SZP=K2, calculate the product difference K2 in the production link and the logistics link;

[0047] If the value of K2 is negative, it means that the quantity of products transported in the logistics link is lower than the quantity of products produced in the production link, and it means that the logistics link needs to increase the transportation input parameters;

[0048] If the value of K2 is positive, it means that the quantity of products transported in the logistics link is higher than the quantity of products produced in the production link, and it means that the logistics link can reduce the transportation input parameters;

[0049] SB3, then obtain and extract the production input parameters, operation input parameters, and transportation input parameters of each specified historical period, and then calculate their averages, and mark the averages as ST, DT, and YT respectively;

[0050] Then, through the formula T1=(K1*DT) / DZP, T2=(K2*YT) / YZP), we can calculate the operational input parameter T1 that needs to be increased or decreased in the sales link and the transportation input parameter T2 that needs to be increased or decreased in the logistics link.

[0051] Beneficial effects of the present invention:

[0052] Data integration and analysis: By collecting the original data of each link in the enterprise's industrial chain during the historical standard period and pre-processing and analyzing these data, we can effectively grasp the operation of production, sales, logistics and other links, so as to better understand the operation status of the entire industrial chain.

[0053] Difference analysis and discrete degree calculation: By calculating the difference analysis values ​​between different links and further calculating the discrete degree values, the differences and fluctuations between the links can be identified, providing a basis for subsequent increases or decreases in investment.

[0054] Reconstruction coefficient calculation: The calculated discrete degree value is compared with the preset discrete threshold to obtain the reconstruction coefficient X of the industrial chain. This coefficient can be used as an important indicator to measure whether the industrial chain needs to be integrated.

[0055] Input increase and decrease analysis: By comparing the reconstruction coefficient with the preset reconstruction threshold, it is determined whether the industrial chain needs to be integrated and analyzed. If necessary, a more in-depth analysis will be conducted based on the data within multiple specified periods to determine whether each link needs to increase or decrease input parameters to optimize resource allocation.

[0056] Result presentation: The analysis results will eventually be presented to relevant personnel through the result presentation unit, so that decision makers can make corresponding strategic adjustments based on the analysis results.

[0057] In summary, the present invention can provide decision support for enterprises by accurately analyzing and reconstructing the resource data of the enterprise industrial chain, help enterprises to optimize resource allocation, improve the operating efficiency of the industrial chain, and enhance the market competitiveness of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The present invention will be further described below in conjunction with the accompanying drawings.

[0059] Figure 1 It is a system block diagram of an industrial chain resource data reconstruction and integration system based on a digital transformation enterprise of the present invention.

[0060] Figure 2 It is a flow chart of the integrated judgment and analysis of the industrial chain resource data reconstruction and integration system based on the digital transformation enterprise of the present invention. DETAILED DESCRIPTION

[0061] 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.

[0062] Embodiment 1

[0063] See also Figure 1 and Figure 2 As shown, the present invention is a system for reconstructing and integrating industrial chain resource data based on digital transformation enterprises, including:

[0064] The data collection unit is used to collect the original data of each link of the target enterprise's industrial chain in the historical standard period. The original data includes the production volume and production input parameters of the specified product in the production link, the order volume and operation input parameters of the specified product in the sales link, and the transportation volume and transportation input parameters of the specified product in the logistics link;

[0065] Among them, the production input parameter refers to the amount of processing equipment or processing personnel required to produce the specified product corresponding to the total production volume, the operation input parameter refers to the amount of operating costs or customer service personnel required to sell the specified product corresponding to the total order volume, and the transportation input parameter;

[0066] The data processing unit is used to pre-process the raw data of each link of the industrial chain. The pre-processing method is as follows:

[0067] Extract the original data of each link in multiple historical standard periods within the specified historical period;

[0068] Take the production volume of a specified product in the production process as an example:

[0069] SS1. Mark the production volume of a specified product in each historical standard period as Si, where i = 1, 2, ... n, and n represents the number of multiple historical standard periods in the specified historical period;

[0070] At the same time, the order volume of the specified product in each historical standard period is marked as Di, and the transportation volume of the specified product in each historical standard period is marked as Yi;

[0071] SS2, through Calculate the total production SZ, total order DZ, and total transportation YZ of all specified products in the specified historical period;

[0072] SS3, through Calculate the proportion of the production volume, order volume, and transportation volume of the specified product in each historical standard period in the total production volume of all other specified products except the corresponding historical standard period in the specified historical period, BS i, BD i, and BYi, respectively;

[0073] SS4, then extract the corresponding BS i, BDi, BYi in the same historical standard period;

[0074] And through SDc i=|BSi-BDi|, SYc i=|BSi-BYi|, DYc i=|BDi-BYi|, the difference analysis value SDc i between BSi and BDi, the difference analysis value SYc i between BSi and BYi, and the difference analysis value DYc i between BD i and BYi are calculated respectively;

[0075] By analogy, the difference analysis values ​​SDc i, SYc i, and DYc i in each historical standard period are calculated;

[0076] SS5. Use the variance calculation formula to calculate the discrete degree values ​​of the difference analysis values ​​SDc i, SYc i, and DYc i in each historical standard period;

[0077] Then the discrete degree values ​​corresponding to SDc i, SYc i, and DYc i are compared with the corresponding preset discrete thresholds, and the comparison method is the same:

[0078] Taking SDc i as an example, if the discrete degree value corresponding to SDc i is greater than its corresponding discrete threshold, it means that the discrete degree value of the difference analysis value SDc i in each historical standard period is large, and then the corresponding SDc i values ​​are deleted in order from large to small according to |SDc i-SDcp| and the remaining discrete degree values ​​are calculated accordingly until the discrete degree value corresponding to SDc i is less than or equal to its corresponding discrete threshold;

[0079] Among them, SDcp represents the average value of all SDc i used in calculating the corresponding discrete degree value;

[0080] When the discrete degree value corresponding to SDc i is less than or equal to the discrete threshold value, the SDc i of the corresponding discrete degree value is calculated, and the average value SDpp of the SDc i involved in the calculation is obtained;

[0081] Similarly, the average value SYpp of the corresponding SYc i involved in the calculation and the average value DYpp of the corresponding DYc i involved in the calculation are obtained;

[0082] SS6, using formula Calculate the reconstruction coefficient X of the industrial chain;

[0083] Among them, a1, a2, and a3 are all preset proportional coefficients, μ is the error correction coefficient, and its value is 2.315;

[0084] By collecting the original data of enterprises in production, sales and logistics, and pre-processing and analyzing these data, it is possible to reconstruct and integrate the data of industrial chain resources more effectively. This helps enterprises identify the synergy and differences between various links, thereby optimizing resource allocation; in-depth analysis using historical data can reveal the differences in product volume between different links, and whether the corresponding input parameters need to be adjusted. Such analysis results can provide strong data support for management and improve decision-making efficiency; by monitoring and comparing data from different links, enterprises can discover potential risks earlier and take measures to manage and mitigate risks.

[0085] A reconstruction analysis unit, used to compare the reconstruction coefficient with a preset reconstruction threshold, and if the comparison result is that the reconstruction coefficient is greater than the reconstruction threshold, an integration judgment analysis is performed on the product chain of the designated product;

[0086] If the reconstruction coefficient is less than or equal to the reconstruction threshold, no integration judgment analysis is performed;

[0087] The integrated judgment analysis method is as follows:

[0088] SA1. Data collection and statistics

[0089] Obtain the original data within multiple specified periods, and respectively calculate the total production volume SZ, total order volume DZ, and total transportation volume YZ of all specified products within each specified historical period; wherein the time intervals between multiple specified periods are consistent;

[0090] SA2. Determine the analysis cycle

[0091] Compare SZ, DZ, and YZ in each specified historical period with the preset minimum quantity thresholds Sy, Dy, and Yy respectively:

[0092] within the same specified historical period;

[0093] If SZ>Sy, DZ>Dy, YZ>Yy are all true, then the specified historical period is marked as the analysis period;

[0094] If one of SZ>Sy, DZ>Dy, YZ>Yy is not true, the original data corresponding to the specified historical period will be eliminated;

[0095] And so on, multiple analysis cycles are obtained;

[0096] SA3, calculate the mean

[0097] In multiple analysis cycles, the total production, total order, and total transportation of all specified products in the analysis cycle are re-labeled as SZj, DZj, and YZj, respectively, where j = 1, 2, ... m, and m represents the number of multiple analysis cycles;

[0098] Then Calculate the mean values ​​SZP, DZP, and YZP corresponding to the total production, total orders, and total transportation of all specified products in each analysis period;

[0099] SA4. Analysis of increase and decrease of input

[0100] In the production link, sales link, and logistics link, select the value corresponding to one link as the standard value, and then conduct an input increase and decrease analysis on each link based on the values ​​of other links and the standard value;

[0101] The standard value is expressed as taking the data of the corresponding link as the standard, and reconstructing and integrating the data of other links according to the data of the standard link;

[0102] The specific method is as follows:

[0103] SB1. Take the SZP of the selected production link as the standard value as an example;

[0104] DZP-SZP=K1, and the product difference K1 between the production link and the sales link is calculated;

[0105] If the value of K1 is negative, it means that the number of products sold in the sales phase is lower than the number of products produced in the production phase, and that the sales phase needs to increase the operating input parameters;

[0106] If the value of K1 is positive, it means that the number of products sold in the sales phase is higher than the number of products produced in the production phase, and that the sales phase can reduce the operating input parameters;

[0107] SB2, YZP-SZP=K2, calculate the product difference K2 in the production link and the logistics link;

[0108] If the value of K2 is negative, it means that the quantity of products transported in the logistics link is lower than the quantity of products produced in the production link, and it means that the logistics link needs to increase the transportation input parameters;

[0109] If the value of K2 is positive, it means that the quantity of products transported in the logistics link is higher than the quantity of products produced in the production link, and it means that the logistics link can reduce the transportation input parameters;

[0110] SB3, then obtain and extract the production input parameters, operation input parameters, and transportation input parameters of each specified historical period, and then calculate their averages, and mark the averages as ST, DT, and YT respectively;

[0111] Then, through the formula T1=(K1*DT) / DZP, T2=(K2*YT) / YZP, we can calculate the operating input parameter T1 that needs to be increased or decreased in the sales link and the transportation input parameter T2 that needs to be increased or decreased in the logistics link;

[0112] The result display unit is used to display the results of the input increase and decrease analysis to relevant personnel;

[0113] By analyzing the differences in each link, we can determine which links have excess or shortages, and then adjust the operating input parameters, such as reducing unnecessary processing equipment and customer service personnel, thereby reducing operating costs; in the production process, through comparative analysis of production volume, order volume and transportation volume, we can adjust the production strategy in a timely manner to ensure that production matches market demand, reduce inventory backlogs or out-of-stock situations, and improve overall production efficiency.

[0114] Embodiment 2

[0115] As the second embodiment of the present invention, when the present application is implemented, compared with the first embodiment, the technical solution of the present embodiment is different from that of the first embodiment only in that:

[0116] The input increase and decrease analysis method in this embodiment is to select the DZP of the sales link as the standard value as an example;

[0117] SZP-DZP=K3, and the product difference K3 between the production link and the sales link is calculated;

[0118] If the value of K3 is negative, it means that the quantity of products produced in the production stage is lower than the quantity of products sold in the sales stage, and it means that the production stage needs to increase the production input parameters;

[0119] If the value of K3 is positive, it means that the quantity of products produced in the production stage is higher than the quantity of products sold in the sales stage, and that the sales stage can reduce the production input parameters;

[0120] SB2, YZP-DZP=K4, calculate the product difference K4 in the sales link and the logistics link;

[0121] If the value of K4 is negative, it means that the quantity of products transported in the logistics link is lower than the quantity of products sold in the sales link, and it means that the logistics link needs to increase the transportation input parameters;

[0122] If the value of K4 is positive, it means that the quantity of products transported in the logistics stage is higher than the quantity of products sold in the sales stage, and that the logistics stage can reduce the transportation input parameters;

[0123] SB3, then obtain and extract the production input parameters, operation input parameters, and transportation input parameters of each specified historical period, and then calculate their averages, and mark the averages as ST, DT, and YT respectively;

[0124] Then, by using the formula T3=(K3*ST) / SZP, T4=(K4*YT) / YZP, we can calculate the production input parameter T3 that needs to be increased or decreased in the production link and the transportation input parameter T4 that needs to be increased or decreased in the logistics link;

[0125] Embodiment 3

[0126] As the third embodiment of the present invention, when the present application is implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments, and the difference between the technical solution of this embodiment and the first and second embodiments is that:

[0127] The input increase and decrease analysis method in this embodiment is to select the YZP of the logistics link as the standard value as an example;

[0128] SZP-YZP=K5, and the product difference K5 between the production link and the logistics link is calculated;

[0129] If the value of K5 is negative, it means that the production quantity of products in the production stage is lower than the transportation quantity of products in the logistics stage, and it means that the production input parameters need to be increased in the production stage;

[0130] If the value of K5 is positive, it means that the production quantity of products in the production stage is higher than the transportation quantity of products in the logistics stage, and it means that the production input parameters can be reduced in the sales stage;

[0131] SB2, DZP-YZP=K5, calculate the product difference K6 in the sales link and logistics link;

[0132] If the value of K6 is negative, it means that the quantity of products sold in the sales phase is lower than the quantity of products transported in the logistics phase, and that the logistics phase needs to increase the operating input parameters;

[0133] If the value of K6 is positive, it means that the quantity of products sold in the sales phase is higher than the quantity of products transported in the logistics phase, and that the logistics phase can reduce the operating input parameters;

[0134] SB3, then obtain and extract the production input parameters, operation input parameters, and transportation input parameters of each specified historical period, and then calculate their averages, and mark the averages as ST, DT, and YT respectively;

[0135] Then, through the formula T5=(K5*ST) / SZP, T6=(K6*DT) / DZP), the production input parameter T5 that needs to be increased or decreased in the production link and the operation input parameter T6 that needs to be increased or decreased in the sales link are calculated;

[0136] Embodiment 4

[0137] As the fourth embodiment of the present invention, when the present application is specifically implemented, compared with the first, second and third embodiments, the technical solution of this embodiment is to combine and implement the solutions of the above-mentioned first, second and third embodiments.

[0138] Through coordinated analysis of each link in the industrial chain, enterprises can respond to market changes more quickly, adjust product supply chains in a timely manner, and meet market demand; the present invention provides a variety of implementation plans, and appropriate plans can be selected for application according to the specific circumstances of different enterprises, thereby increasing the flexibility and adaptability of the system; through systematic analysis of industrial chain data and calculation of reconstruction coefficients, enterprises can better carry out long-term planning and strategic adjustments; systematic data analysis provides enterprises with a tool for continuous improvement, which helps to continuously explore new business models and innovations.

[0139] In summary, this invention, by providing a data-based industrial chain resource integration system, helps enterprises to optimize resource allocation, improve operational efficiency, reduce costs and enhance market competitiveness in the process of digital transformation.

[0140] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0141] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A system for reconstructing and integrating industrial chain resource data based on digital transformation enterprises, characterized in that: include: The data collection unit is used to collect the original data of each link of the target enterprise's industrial chain in the historical standard period. The original data includes the production volume and production input parameters of the specified product in the production link, the order volume and operation input parameters of the specified product in the sales link, and the transportation volume and transportation input parameters of the specified product in the logistics link; Among them, input parameters refer to the quantitative values ​​of personnel costs or equipment costs; The data processing unit is used to pre-process the original data of each link of the industrial chain. According to the production volume, order volume and transportation volume of the specified products in each historical standard period, the total production volume, order volume and transportation volume of all specified products in the specified historical period are counted, as well as the proportion of the production volume, order volume and transportation volume of the specified products in each historical standard period in the total production volume, order volume and transportation volume of all other specified products except the corresponding historical standard period in the specified historical period, and then the reconstruction coefficient of the industrial chain is calculated according to the corresponding proportion results; The preprocessing method is as follows: Extract the original data of each link in multiple historical standard periods within the specified historical period; Production volume of specified products in the selected production stage: SS1. Mark the production volume of the specified product in each historical standard period as Si, where i=1, 2, ...n, and n represents the number of multiple historical standard periods in the specified historical period; At the same time, the order volume of the specified product in each historical standard period is marked as Di, and the transportation volume of the specified product in each historical standard period is marked as Yi; SS2. According to the production volume, order volume and transportation volume of the specified product in each historical standard period, the total production volume, order volume and transportation volume of all specified products in the specified historical period are calculated respectively, and marked as SZ, DZ and YZ respectively; SS3, through , , , calculate the production volume, order volume, and transportation volume of the specified product in each historical standard period, respectively, in the total production volume BSi, the total order volume BDi, and the total transportation volume BYi of all other specified products in the specified historical period except the corresponding historical standard period; SS4. Then, in the same historical standard period, the corresponding BSi, BDi, BYi are extracted, and the absolute values ​​of the differences between each other are calculated in BSi, BDi, BYi, and the absolute value of the difference between BSi and BDi is recorded as the difference analysis value SDci, the absolute value of the difference between BSi and BYi is recorded as the difference analysis value SYci, and the absolute value of the difference between BDi and BYi is recorded as the difference analysis value DYci; By analogy, the difference analysis values ​​SDci, SYci, and DYci in each historical standard period are calculated; SS5. Use the variance calculation formula to calculate the discrete degree values ​​of the difference analysis values ​​SDci, SYci, and DYci in each historical standard period; Then, the discrete degree values ​​corresponding to SDci, SYci, and DYci are compared with the corresponding preset discrete thresholds, and according to the comparison results, the corresponding SDci, SYci, and DYci are screened and their average values ​​SDpp, SYpp, and DYpp are calculated; The comparison is as follows: Select SDci for comparison. If the discrete degree value corresponding to SDci is greater than its corresponding discrete threshold, it means that the discrete degree value of the difference analysis value SDci in each historical standard period is large. Then, the corresponding SDci values ​​are deleted in order from large to small according to |SDci-SDcp| and the remaining discrete degree values ​​are calculated accordingly until the discrete degree value corresponding to SDci is less than or equal to its corresponding discrete threshold. Among them, SDcp represents the average value of all SDci used in calculating the corresponding discrete value; When the discrete degree value corresponding to SDci is less than or equal to the discrete threshold value, the SDci of the corresponding discrete degree value is calculated, and the average value SDpp of the SDci involved in the calculation is obtained; SS6, using formula , calculate the reconstruction coefficient X of the industrial chain; Among them, a1, a2, and a3 are all preset proportional coefficients, μ is the error correction coefficient, and its value is 2.315; The reconstruction analysis unit is used to compare the reconstruction coefficient with a preset reconstruction threshold. If the comparison result is that the reconstruction coefficient is greater than the reconstruction threshold, an integration judgment analysis is performed on the product chain of the specified product. Based on the integration judgment analysis results, the operating input parameters that need to be increased or decreased in the sales link, the transportation input parameters that need to be increased or decreased in the logistics link, and the production input parameters that need to be increased or decreased in the sales link are obtained.

2. According to claim 1, the industrial chain resource data reconstruction and integration system based on digital transformation enterprises is characterized in that: The production input parameters refer to the amount of processing equipment or processing personnel required to produce the specified products corresponding to the total production volume; the operation input parameters refer to the amount of operating costs or customer service personnel required to sell the specified products corresponding to the total order volume; and the transportation input parameters.

3. According to claim 1, the industrial chain resource data reconstruction and integration system based on digital transformation enterprises is characterized in that: If the reconstruction coefficient is less than or equal to the reconstruction threshold, no integration judgment analysis is performed.

4. According to claim 1, the industrial chain resource data reconstruction and integration system based on digital transformation enterprises is characterized in that: The integrated judgment analysis method is as follows: SA1. Obtain the original data within multiple specified periods, and respectively calculate the total production volume SZ, total order volume DZ, and total transportation volume YZ of all specified products within each specified historical period; wherein the time intervals between multiple specified periods are consistent; SA2. Compare SZ, DZ, and YZ in each specified historical period with the preset minimum quantity thresholds Sy, Dy, and Yy respectively: within the same specified historical period; If SZ>Sy, DZ>Dy, YZ>Yy are all true, then the specified historical period is marked as the analysis period; If one of SZ>Sy, DZ>Dy, YZ>Yy is not true, the original data corresponding to the specified historical period will be eliminated; And so on, multiple analysis cycles are obtained; SA3. Based on the total production, total order, and total transportation of all specified products in multiple analysis periods, calculate the corresponding mean of the total production, total order, and total transportation of all specified products in each analysis period, and record them as SZP, DZP, and YZP; SA4. In the production link, sales link, and logistics link, select the value corresponding to one link as the standard value, and then conduct input increase and decrease analysis on each link based on the values ​​of other links and the standard values, and derive the operating input parameters that need to be increased or decreased in the sales link, the transportation input parameters that need to be increased or decreased in the logistics link, and the production input parameters that need to be increased or decreased in the sales link.

5. According to claim 4, the industrial chain resource data reconstruction and integration system based on digital transformation enterprises is characterized in that: The standard value is expressed as taking the data of the corresponding link as the standard, and reconstructing and integrating the data of other links according to the data of the standard link.

6. The industrial chain resource data reconstruction and integration system based on digital transformation enterprises according to claim 4 is characterized in that: The method of input increase and decrease analysis is as follows: SB1. Take the SZP of the selected production link as the standard value; DZP-SZP=K1, and the product difference K1 between the production link and the sales link is calculated; If the value of K1 is negative, it means that the number of products sold in the sales phase is lower than the number of products produced in the production phase, and that the sales phase needs to increase the operating input parameters; If the value of K1 is positive, it means that the number of products sold in the sales phase is higher than the number of products produced in the production phase, and that the sales phase can reduce the operating input parameters; SB2, YZP-SZP=K2, calculate the product difference K2 in the production link and the logistics link; If the value of K2 is negative, it means that the quantity of products transported in the logistics link is lower than the quantity of products produced in the production link, and it means that the logistics link needs to increase the transportation input parameters; If the value of K2 is positive, it means that the quantity of products transported in the logistics link is higher than the quantity of products produced in the production link, and it means that the logistics link can reduce the transportation input parameters; SB3, then obtain and extract the production input parameters, operation input parameters, and transportation input parameters of each specified historical period, and then calculate their averages, and mark the averages as ST, DT, and YT respectively; Then, through the formula T1=(K1*DT) / DZP, T2=(K2*YT) / YZP, we can calculate the operational input parameter T1 that needs to be increased or decreased in the sales link and the transportation input parameter T2 that needs to be increased or decreased in the logistics link.

7. The industrial chain resource data reconstruction and integration system based on digital transformation enterprises according to claim 1 is characterized in that: Also includes: The result display unit is used to display the integrated judgment and analysis results to relevant personnel.

Images