Data management method and system for data conversion and quality control

Abstract

The invention relates to the technical field of big data management, in particular to a data management method and system for data conversion and quality control, and solves the problem of how to effectively convert multi-source data in an enterprise and improve the calculation accuracy of target data. The invention provides a data management method for data conversion and quality control, and the method comprises the steps: constructing a data management platform, and calculating a system return rate according to the target data of each business commodity; the target department inputs a target return rate of the business commodity, and when the target return rate is different from the system return rate, the business commodity is marked as a target commodity; comparing the target calculation formula with the system calculation formula, and determining distinguishing parameters; obtaining a corrected return rate according to the distinguishing parameters and the target return rate; determining a real return rate according to the corrected return rate and the system return rate; and adjusting the system calculation formula according to the difference between the real return rate and the system return rate in the target time period.

Description

Technical Field

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

[0002] In today's era of rapid development of informatization, data has become the core asset of enterprises and organizations, and an important basis for promoting business innovation, improving competitiveness, and optimizing operations. With the continuous advancement of technology and the popularization of various application systems, the massive data generated within enterprises has shown explosive growth, and the sources of data have become more and more diverse. In particular, the lack of unified standards and effective integration mechanisms between various departments and systems of enterprises has led to insufficient data integration, thus forming "information islands", that is, information between departments or systems cannot be shared and communicated, and ultimately makes enterprises only able to make decisions based on partial and isolated data for analysis, resulting in incomplete and inaccurate decision-making basis, thereby increasing the risk of wrong decisions. Therefore, how to effectively convert and manage data from different data sources and improve the calculation accuracy of target data has become a severe challenge facing enterprises. Summary of the invention

[0003] The problem solved by the present invention is: how to effectively convert multi-source data in an enterprise and improve the calculation accuracy of target data.

[0004] To solve the above problems, an embodiment of the present invention provides a data management method for data conversion and quality control, the data management method comprising: constructing a data management platform, the data management platform calculates the system yield rate according to the target data of each operating commodity at the target time point; the target department inputs the target yield rate of the operating commodity at the planned time point, when the target yield rate is the same as the system yield rate, the operating commodity is recorded as a standard commodity; when the target yield rate is different from the system yield rate, the operating commodity is recorded as a target commodity; the target calculation formula for calculating the target yield rate of the target commodity and the system calculation formula for calculating the system yield rate of the target commodity are compared to determine the distinguishing parameters; the target yield rate is corrected according to the distinguishing parameters to obtain the corrected yield rate; the real yield rate at the target time point is determined according to the corrected yield rate and the system yield rate; the system calculation formula is adjusted according to the difference between the real yield rate and the system yield rate within the target time period.

[0005] Compared with the existing technology, the technical effects achieved by adopting this technical solution are as follows: the data management platform can effectively centrally manage and analyze data, helping managers make more accurate decisions. The target data provides a data basis for the calculation of the system rate of return. The update and calibration of the target data improves the accuracy of the system rate of return prediction. The setting of the target department can analyze the target data from multiple angles, enhance the data quality and the traceability of the target data, thereby providing more reliable data support for the decision-making of the enterprise. By comparing the target rate of return with the system rate of return, it is judged whether the calculation of the target rate of return is accurate, which helps to identify the calculation errors in manual calculations. The difference parameter reflects the root cause of the difference between the target rate of return and the system rate of return, and provides a basis for the subsequent correction of the target rate of return. The corrected rate of return is used to reduce or eliminate the influence of the difference parameter on the target rate of return, thereby improving the calculation accuracy of the real rate of return. The calculation of the real rate of return fully considers the corrected rate of return and the system rate of return, so that the enterprise can more accurately evaluate the profitability of the operating commodities, thereby adjusting the operating strategy in time. The difference reflects the difference between the real rate of return and the system rate of return. The enterprise can train and optimize the data management platform according to the difference, thereby improving the adaptability of the data management platform and the calculation accuracy of the system rate of return.

[0006] In one embodiment of the present invention, a target calculation formula for calculating a target rate of return of a target commodity and a system calculation formula for calculating a system rate of return of a target commodity are compared to determine distinguishing parameters, specifically including: obtaining each calculation parameter in the target calculation formula to obtain a first parameter set, obtaining each calculation parameter in the system calculation formula to obtain a second parameter set; comparing the planned time point and the target time point for statistically calculating the target rate of return; when the planned time point and the target time point are the same, obtaining distinguishing parameters of the target calculation formula relative to the system calculation formula by comparing the first parameter set and the second parameter set; when the planned time point and the target time point are different, modifying the second parameter set according to the planned time point to obtain a modified parameter set, and comparing the modified parameter set with the first parameter set to obtain distinguishing parameters.

[0007] Compared with the existing technology, the technical effect achieved by adopting this technical solution is as follows: by comparing the target time point with the planned time point, it ensures that the system yield and the target yield are consistent in time when compared, and modifying the parameter set can reduce or eliminate the deviation of the calculation parameters caused by time differences. It fully considers the possible time differences when the staff of the target department uploads data, thereby making the identification of distinguishing parameters more accurate and effective, which helps enterprises to accurately understand the expected profitability of the operating commodities.

[0008] In one embodiment of the present invention, the target rate of return is modified according to the distinguishing parameters to obtain a modified rate of return, specifically including: the distinguishing parameters include newly added parameters, missing parameters and abnormal parameters; when there are missing parameters, the acquisition authority corresponding to the target department is obtained, and when the acquisition authority cannot obtain the missing parameters, the data management platform adjusts the target calculation formula according to the missing parameters to obtain an adjusted calculation formula; the target rate of return is modified according to the adjusted calculation formula, the newly added parameters and the abnormal parameters to obtain a modified rate of return.

[0009] Compared with the existing technology, the technical effects achieved by adopting this technical solution are as follows: the introduction of new parameters takes into account the various factors affecting the rate of return more comprehensively, effectively improving the prediction accuracy and rationality of the system rate of return; identifying and supplementing the missing parameters can make the target rate of return calculation more accurate, avoiding prediction bias caused by the lack of important information; identifying and processing abnormal parameters helps to improve data quality and ensure the stability and reliability of the calculation results; adjusting the calculation formula fully considers the impact of the missing parameters on the target rate of return, thereby improving the prediction accuracy of the target rate of return.

[0010] In one embodiment of the present invention, the target rate of return is corrected according to the adjustment calculation formula, the newly added parameters and the abnormal parameters to obtain a corrected rate of return, specifically including: when only the newly added parameters exist, obtaining the associated data corresponding to the newly added parameters, and calculating the associated results based on the associated data and the second parameter set; when only the abnormal parameters exist, obtaining the comparison data corresponding to the abnormal parameters and the changes in the comparison data within the target time period to obtain the abnormal results; when the newly added parameters and the abnormal parameters exist at the same time, converting the newly added parameters according to the associated results until all the newly added parameters are converted; according to the abnormal results, using the adjustment calculation formula to calculate the corrected rate of return.

[0011] Compared with the existing technology, the technical effects achieved by adopting this technical solution are as follows: the determination of associated data helps to judge whether the system calculation formula already contains data related to the newly added parameters, avoiding repeated calculation of associated data after the introduction of new parameters, and improving calculation efficiency and accuracy. By analyzing the associated results, it can be judged whether the new parameters can completely replace the associated data, thereby determining the necessity of the new parameters. The comparative data can help the target department find the cause of the abnormal parameters, and the modified rate of return is calculated through the abnormal results, which fully considers the changing trend of the comparative data and improves the accuracy of the calculation of the modified rate of return of the target product.

[0012] In one embodiment of the present invention, when only new parameters exist, the associated data corresponding to the new parameters are obtained, and the associated results are calculated based on the associated data and the second parameter set, and the revised rate of return is calculated based on the associated results and the adjusted calculation formula, specifically including: obtaining the association between the new parameters and each calculation parameter in the second parameter set, and recording the associated calculation parameters as associated parameters; calculating the weight ratio of the associated parameters in the system calculation formula, and judging whether the new parameters meet the consideration conditions based on the weight ratio; when the weight ratio is less than or equal to the weight threshold, modifying the new parameters based on the weight ratio to obtain new modified parameters, and calculating the revised rate of return based on the new modified parameters and the adjusted calculation formula; when the weight ratio is greater than the weight threshold, the new parameters do not meet the consideration conditions, deleting the new parameters and calculating the revised rate of return based on the adjusted calculation formula.

[0013] Compared with the existing technology, the technical effect achieved by adopting this technical solution is as follows: the determination of associated parameters helps to avoid duplication of new parameters and calculation parameters in the system calculation formula, and the calculation of the newly modified parameters effectively reduces the probability of duplication of new parameters and associated parameters, thereby improving the calculation accuracy of the data management platform. By comparing the weight ratio with the weight threshold, a scientific decision is made whether to introduce new parameters to avoid irrelevant or inefficient parameters reducing calculation efficiency and accuracy.

[0014] In one embodiment of the present invention, when only abnormal parameters exist, the comparison data corresponding to the abnormal parameters and the changes in the comparison data within the target time period are obtained to obtain the abnormal result, and the modified rate of return is calculated based on the abnormal result and the adjustment calculation formula, specifically including: obtaining the first value of the comparison data in the first parameter set and the second value in the second parameter set; obtaining the change trend of the comparison data in the target time period, when the change trends are different, manually calculating the modified rate of return; when the change trends are the same and the first value is different from the second value, recording the average of the first value and the second value as the standard value; calculating the normal parameters based on the standard value and the abnormal parameters, and calculating the modified rate of return based on the normal parameters and the adjustment calculation formula.

[0015] Compared with the existing technology, the technical effects achieved by adopting this technical solution are as follows: the determination and comparison of the first numerical value and the second numerical value are helpful to identify the specific deviations of abnormal parameters in the first parameter set and the second parameter set. By analyzing the change trend, it can be determined whether there are erroneous predictions in the calculation of abnormal parameters by manual and data management platforms. The calculation of normal parameters significantly improves the rationality and accuracy of the revised rate of return. The comparative analysis of the first numerical value and the second numerical value, the verification of the change trend and the calculation of normal parameters have constructed a closed-loop abnormal parameter detection and correction mechanism, which improves the rationality and effectiveness of corporate decision-making.

[0016] In one embodiment of the present invention, the real rate of return at a target time point is determined based on the modified rate of return and the system rate of return, specifically including: counting a first number in which the modified rate of return is the same as the system rate of return, and a second number in which the modified rate of return is different from the system rate of return; when the first number is greater than the second number, recording the system rate of return as the real rate of return; when the first number is less than or equal to the second number, calculating the weighted average of the modified rate of return and the system rate of return to obtain the real rate of return.

[0017] Compared with the existing technology, the technical effect achieved by adopting this technical solution is as follows: by comparing the first number with the second number, the accuracy of the system yield and the reliability of the data management platform can be effectively judged; when the first number is less than or equal to the second number, the weighted average can comprehensively consider the importance of the system yield and the modified yield to obtain a more reasonable real yield, thereby avoiding the final decision of the enterprise being affected by the deviation of the system yield data.

[0018] In one embodiment of the present invention, the system calculation formula is adjusted according to the difference between the real rate of return and the system rate of return within the target time period, specifically including: counting the number of times the system rate of return is replaced by the modified rate of return to obtain the number of replacements; calculating the rate of return difference corresponding to each number of replacements to obtain the unit difference; calculating the average deviation rate of the system rate of return within the target time period according to the unit difference; when the average deviation rate is greater than the deviation threshold, modifying the system calculation formula according to the abnormal parameters corresponding to the average deviation rate.

[0019] Compared with the existing technology, the technical effect achieved by adopting this technical solution is as follows: the number of replacements reflects the frequency of the difference between the system yield and the revised yield, the calculation of the unit difference and the average deviation rate helps to measure the degree of difference between the revised yield and the system yield, and by comparing the average deviation rate with the deviation threshold, it can be effectively determined whether the system calculation formula needs to be adjusted, thereby completing the closed-loop optimization mechanism of the data management platform and gradually reducing the calculation error of the system calculation formula.

[0020] In one embodiment of the present invention, when the average deviation rate is greater than the deviation threshold, the system calculation formula is modified according to the abnormal parameters corresponding to the average deviation rate, specifically including: obtaining comparison data corresponding to the abnormal parameters each time a unit difference is generated; filtering out the data to be adjusted from the comparison data according to the number of occurrences of the comparison data, modifying the parameter conversion method of the data to be adjusted according to the unit difference, and inputting the modified parameter conversion method into the system calculation formula.

[0021] Compared with the existing technology, the technical effect achieved by adopting this technical solution is as follows: the number of occurrences reflects the main source of the unit difference. By statistically comparing the number of occurrences of the data, the data to be adjusted that appears frequently and may affect the calculation results can be quickly determined. The correction of the data to be adjusted can effectively improve the accuracy and stability of the system yield calculation by the data management platform. By modifying the parameter conversion method of the data to be adjusted, the influence of abnormal parameters on the calculation results can be greatly reduced or eliminated, thereby improving the calculation accuracy of the system yield.

[0022] In one embodiment of the present invention, a data management system is also provided, in which the data management method recorded in the above embodiment is applied to the data management system, and the data management system includes: an input module, the input module is used to input the target rate of return of the operating commodities; a judgment module, the judgment module is used to judge whether the target rate of return is the same as the system rate of return; a comparison module, the comparison module is used to compare the target calculation formula and the system calculation formula to determine the distinguishing parameters; a calculation module, the calculation module is used to calculate the system rate of return. The data management system has all the technical features of the above data management method, which will not be repeated here one by one. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is one of the flow charts of the data management method for data conversion and quality control; Figure 2 The second flowchart of the data management method for data conversion and quality control; Figure 3 The third flowchart of the data management method for data conversion and quality control; Figure 4 The fourth flowchart of the data management method for data conversion and quality control; Figure 5 Flowchart 5 of the data management method for data conversion and quality control; Figure 6 System diagram of the data management system for data conversion and quality control; Description of reference numerals: 100 - data management system; 110 - input module; 120 - judgment module; 130 - comparison module; 140 - calculation module. DETAILED DESCRIPTION

[0024] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0025] [First embodiment] See also Figure 1 In a specific embodiment, the present invention provides a data management method for data conversion and quality control, the data management method comprising: S100, constructing a data management platform, which calculates the system rate of return based on the target data of each operating commodity at a target time point; S200, the target department inputs the target rate of return of the operating commodity at the planned time point. When the target rate of return is the same as the system rate of return, the operating commodity is recorded as a standard commodity; S300, when the target rate of return is different from the system rate of return, the operating commodity is recorded as the target commodity; S400, comparing the target calculation formula for calculating the target rate of return of the target commodity with the system calculation formula for calculating the system rate of return of the target commodity to determine a distinguishing parameter; S500, modifying the target rate of return according to the distinguishing parameter to obtain a modified rate of return; S600, determining the real rate of return at the target time point according to the modified rate of return and the system rate of return; S700. Adjust the system calculation formula according to the difference between the actual rate of return and the system rate of return within the target time period.

[0026] In step S100, the data management platform is an integrated system for collecting, processing and displaying data related to the business commodities. The target data refers to the key data related to the business commodities in the data management platform, such as cost and sales volume. The target data is usually selected based on the historical sales of the business commodities or market forecasts. The target time point refers to a fixed time node. The data management platform predicts the rate of return at this fixed time node to obtain the system rate of return. The target time point is usually set at the last day of each month, quarter or year. The system rate of return is the expected profit of the business commodities at the target time point calculated by the data management platform based on the target data.

[0027] For example, when the target time point is 23:59 on March 31, 2025, and the system yield rate in March needs to be predicted, the data management platform will calculate the system yield rate based on the target data before 23:59 on March 31, 2025.

[0028] In step S200 and step S300, the target department refers to a department that has a deep understanding of the operation and profit status of the operating commodity. The target department is usually related to the sales, marketing and operation of the operating commodity, including sales, production and finance departments. The target rate of return is the expected profit of the operating commodity at the planned time point calculated by each department based on multiple branch data. The branch data refers to the key data related to the operating commodity in each department, such as the order volume and return rate in the sales department, the production cost and inventory level in the production department. The target data includes the data of each branch. The planned time point refers to a random time node in the future. Each department calculates the rate of return at this time node to obtain the target rate of return. The planned time point is usually set at the end of the planned sales time of the operating commodity. For example, the operating commodity starts to be sold on January 1, 2025, and the planned sales time is 2 months. At this time, the planned time point is set to March 1, 2025. Standard commodities refer to operating commodities with the same target rate of return and system rate of return, and target commodities refer to operating commodities with different target rate of return and system rate of return.

[0029] For example, when the planned time point is the last day of March 2025, the planned time point can be any time point during normal working hours on March 31, 2025.

[0030] It should be noted that in the massive data environment of an enterprise, the prediction of the rate of return of operating products is usually calculated automatically based on the data management platform. However, due to the dynamic fluctuations of the market environment and the significant differences in the characteristics of operating product categories, the prediction models in the data management platform are often difficult to accurately capture the changing patterns of the returns of different products under different market conditions, resulting in a deviation between the calculated system rate of return and the target rate of return calculated manually by various departments. Therefore, it is necessary to dynamically optimize the calculation method of the data management platform based on the target rate of return, and through continuous iterative training, the data management platform can gradually enhance its dynamic adaptability to the characteristics of multiple categories of products and changes in the market environment.

[0031] In steps S400 to S600, the distinguishing parameter refers to the key variable that is identified as causing the difference between the target rate of return and the system rate of return when comparing the target calculation formula and the system calculation formula. The revised rate of return is the result of dynamically adjusting the target rate of return based on the distinguishing parameter. The real rate of return is the most accurate rate of return that the target product can achieve at the target time point, calculated based on the difference between the revised rate of return and the system rate of return.

[0032] In step S700, the time node for starting to calculate the system yield is recorded as the current time point, the time period from the current time point to the target time point is the target time period, and the difference refers to the degree of deviation between the actual yield and the system yield in the target time period.

[0033] For example, when the current time point is 0:00 on January 1, 2025, and the target time point is 23:59 on March 31, 2025, then the target time period is the time period between 0:00 on January 1, 2025 and 23:59 on March 31, 2025.

[0034] The data management platform can effectively manage and analyze data in a centralized manner, helping managers make more accurate decisions. The target data provides a data basis for the calculation of the system rate of return. The update and calibration of the target data improves the accuracy of the system rate of return forecast. The setting of the target department can analyze the target data from multiple angles, enhance the data quality and traceability of the target data, and thus provide more reliable data support for the company's decision-making. The comparison between the target rate of return and the system rate of return can be used to determine whether the calculation of the target rate of return is accurate, which helps to identify the calculation errors in manual calculations. The difference parameter reflects the root cause of the difference between the target rate of return and the system rate of return, and provides a basis for the subsequent correction of the target rate of return. The corrected rate of return is used to reduce or eliminate the impact of the difference parameter on the target rate of return, thereby improving the calculation accuracy of the real rate of return. The calculation of the real rate of return fully considers the corrected rate of return and the system rate of return, enabling enterprises to more accurately evaluate the profitability of operating commodities, thereby adjusting their business strategies in a timely manner. The difference reflects the difference between the real rate of return and the system rate of return. Enterprises can train and optimize the data management platform according to the difference, thereby improving the adaptability of the data management platform and the calculation accuracy of the system rate of return.

[0035] [Second embodiment] See also Figure 2 In a specific embodiment, the target calculation formula for calculating the target rate of return of the target commodity and the system calculation formula for calculating the system rate of return of the target commodity are compared to determine the distinguishing parameters, which specifically include: S410, obtaining each calculation parameter in the target calculation formula to obtain a first parameter set, and obtaining each calculation parameter in the system calculation formula to obtain a second parameter set; S420, comparing the planned time point and the target time point for calculating the target rate of return; S430, when the planned time point and the target time point are the same, obtaining the difference parameters of the target calculation formula relative to the system calculation formula by comparing the first parameter set with the second parameter set; S440. When the planned time point and the target time point are different, the second parameter set is modified according to the planned time point to obtain a modified parameter set, and the modified parameter set is compared with the first parameter set to obtain a distinguishing parameter.

[0036] In steps S410 to S440, the first parameter set refers to the set of all calculation parameters used in the target calculation formula when calculating the target rate of return, the second parameter set refers to the set of all calculation parameters used in the system calculation formula when calculating the system rate of return, and the modified parameter set refers to the set of calculation parameters required for the data management platform to recalculate the system rate of return at the planned time point when the planned time point and the target time point are different.

[0037] For example, when the target time point is 23:59 on March 31, 2025, the data management platform originally calculates the system yield at 23:59 on March 31, 2025, and records all the calculation parameters for calculating this system yield as the second parameter set. If the planned time point is 17:00 on March 31, 2025, the planned time point and the target time point are different at this time, then the data management platform needs to recalculate the system yield at 17:00 on March 31, 2025, and record all the calculation parameters for calculating this system yield as the modified parameter set.

[0038] It should be noted that, usually, the manual calculation of the target rate of return of each department depends on the time arrangements and workload of the business personnel, and the planned time points are distributed at any working time. Therefore, it is difficult to modify the first parameter set according to the target time points, and manual calculations need to comprehensively consider multiple calculation parameters. It is very difficult to modify and adjust the first parameter set. The data management platform can quickly re-extract the calculation parameters of the system rate of return before the planned time point. Therefore, the second parameter set can be modified according to the planned time point to obtain a modified parameter set.

[0039] By comparing the target time point with the planned time point, the system yield is ensured to be consistent with the target yield in time when compared. Modifying the parameter set can reduce or eliminate the deviation of the calculation parameters caused by time differences. It fully considers the possible time differences when the staff of the target department uploads data, thereby making the identification of distinguishing parameters more accurate and effective, which helps enterprises to accurately understand the expected profitability of the operating commodities.

[0040] [Third embodiment] See also Figure 3 In a specific embodiment, the target rate of return is modified according to the distinguishing parameter to obtain a modified rate of return, which specifically includes: S510, distinguishing parameters including newly added parameters, omitted parameters and abnormal parameters; S520, when there are missing parameters, obtaining the corresponding acquisition authority of the target department, and when the acquisition authority cannot obtain the missing parameters, the data management platform adjusts the target calculation formula according to the missing parameters to obtain an adjusted calculation formula; S530. Modify the target rate of return according to the adjustment calculation formula, the newly added parameters and the abnormal parameters to obtain a modified rate of return.

[0041] In steps S510 to S530, newly added parameters are parameters included in the target calculation formula but not in the system calculation formula. Missing parameters refer to parameters included in the system calculation formula but missing in the target calculation formula. Missing calculation parameters are usually caused by insufficient access permissions of various departments of the enterprise, resulting in the inability to comprehensively collect yield-related data. Abnormal parameters refer to parameters that exist in both the target calculation formula and the system calculation formula but have significantly unreasonable weight distribution or calculation logic. Access permissions refer to the permissions of the target department to access and obtain relevant data in the data management platform. Adjusting the calculation formula refers to when the target department cannot obtain the missing parameters, the data management platform automatically retrieves the missing parameters to adjust the target calculation formula.

[0042] For example, a newly added parameter may be the impact coefficient of new policies and regulations on the target product. When new policies and regulations have recently appeared in the industry where the target product is located, the impact coefficient generated by the new policies and regulations can be included as a new parameter in the calculation of the target rate of return. The calculation parameters of the system rate of return are determined based on the previous operating conditions of the target product, and therefore the impact of new policies and regulations has not been taken into account. Therefore, the new policies and regulations that have recently appeared are the newly added parameters. The omitted parameters may be the customer satisfaction data of the marketing department. When the customer satisfaction data of the marketing department has a certain impact on the calculation of the target rate of return, and the data belongs to the marketing department data, the finance department may not be able to consider the customer satisfaction data of the marketing department when calculating the target rate of return due to insufficient access rights, resulting in inaccurate prediction of the target rate of return. Therefore, the customer satisfaction data of the marketing department is the omitted parameter. The abnormal parameter may be the weight of the cost of the target product. When calculating the system rate of return and the target rate of return, the weight of the cost of the target product may be different. The data management platform calculates the cost weight of the target product as 0.4 based on historical conditions, and due to changes in logistics methods, the cost weight manually calculated by the target department is 0.3, so the cost weight of the target product is the abnormal parameter.

[0043] The introduction of new parameters has more comprehensively considered the various factors that affect the rate of return, effectively improving the prediction accuracy and rationality of the system rate of return. Identifying and supplementing missing parameters can make the target rate of return calculation more accurate and avoid prediction bias caused by missing important information. Identifying and processing abnormal parameters can help improve data quality and ensure the stability and reliability of the calculation results. Adjusting the calculation formula fully considers the impact of missing parameters on the target rate of return and improves the prediction accuracy of the target rate of return.

[0044] [Fourth embodiment] See also Figure 3In a specific embodiment, the target rate of return is modified according to the adjustment calculation formula, the newly added parameters and the abnormal parameters to obtain the modified rate of return, which specifically includes: S531. When only the newly added parameters exist, obtain the associated data corresponding to the newly added parameters, and calculate the associated result according to the associated data and the second parameter set; S532: when only abnormal parameters exist, obtain the comparison data corresponding to the abnormal parameters and the change of the comparison data within the target time period to obtain the abnormal result; S533. When the newly added parameters and the abnormal parameters exist at the same time, the newly added parameters are converted according to the association results until all the newly added parameters are converted; S534. Calculate the revised rate of return using the adjustment formula based on the abnormal result.

[0045] In step S531, the associated data refers to data that is logically associated or replaceable with the newly added parameters, and the association result is calculated based on the degree of association between the associated data and the newly added parameters.

[0046] It should be noted that by comparing the coverage and calculation logic of the newly added parameters and the associated data, it is determined whether the newly added parameters can completely replace the associated data. If so, it means that the system calculation formula is correct and there is no need to calculate the associated results. If not, it is further determined whether the newly added parameters cannot completely replace the associated data or cannot completely replace the associated data. When the newly added parameters cannot completely replace the associated data, it is necessary to calculate the degree of correlation between the associated data and the newly added parameters, and calculate the associated results based on the degree of correlation. When the newly added parameters cannot completely replace the associated data, it is necessary to adjust the system calculation formula based on the impact of the newly added parameters on the target rate of return.

[0047] For example, when the newly added parameter is monetary cost, and the monetary cost is calculated by the time cost and labor cost of the target product, if the associated data related to the monetary cost in the system calculation formula is time cost and labor cost, the time cost and labor cost in the system calculation formula can be completely converted into monetary cost, indicating that the system calculation formula is correct. If the associated data related to the monetary cost in the system calculation formula is time cost, labor cost and sunk cost, the newly added parameter cannot completely replace the associated data. At this time, it is necessary to calculate the degree of correlation between the sunk cost and the monetary cost, and calculate the correlation result based on the degree of correlation. When the newly added parameter is the impact coefficient of new policies and regulations on the target product, and the associated data in the system calculation formula is time cost and labor cost, the newly added parameter cannot completely replace the associated data, and the system calculation formula needs to be adjusted.

[0048] It should be noted that sunk costs will arise due to changes in business methods. For example, there is a backlog in the shipment of business goods. When the goods are not shipped, the business goods need to occupy a large area inside the factory. This occupied area will bring certain sunk costs. In the previous business model, this kind of business goods backlog will not occur, and the sunk costs will not be considered in the system calculation formula.

[0049] In step S532, the comparison data refers to the relevant data used to calculate the abnormal parameters, and the abnormal result is the final result of the abnormal parameters obtained by analyzing the change trend of the comparison data within the target time period.

[0050] For example, when the abnormal parameter is a sunk cost, the comparative data includes the initial investment cost, R&D cost, and inventory cost of the target product, and the abnormal result is the final result of the sunk cost calculated based on the change trend of these comparative data.

[0051] In step S533 and step S534, it should be noted that when new parameters and abnormal parameters exist in the adjustment calculation formula at the same time, all new parameters are first replaced according to the method in step S531. After the replacement is completed, the abnormal results of the abnormal parameters are calculated according to the method in step S532, and the abnormal results are input into the adjustment calculation formula to calculate the modified rate of return.

[0052] For example, the cost of the target product in the adjustment calculation formula includes sunk cost, labor cost and time cost, and the cost of the target product in the system calculation formula includes labor cost and time cost. At this time, the sunk cost is a newly added parameter. If the weight coefficient of the labor cost in the system calculation formula is 0.8, and the weight coefficient of the labor cost in the adjustment calculation formula is 1, then the labor cost is an abnormal parameter. When there is a new parameter, first replace the new parameter according to the corresponding weight coefficient. If the sunk cost with a weight coefficient of 1 can be replaced by a labor cost with a weight coefficient of 0.2 plus a time cost with a weight coefficient of 0.3, then correspondingly, the cost calculation method in the adjustment calculation formula can be replaced by 1.2 labor cost plus 1.3 time cost. At this time, both labor cost and time cost are abnormal parameters. The abnormal results of labor cost and time cost are calculated, and the adjusted rate of return is calculated based on the abnormal results.

[0053] Determining the associated data helps to determine whether the system calculation formula already contains data related to the newly added parameters, avoiding repeated calculation of the associated data after the introduction of the new parameters, and improving calculation efficiency and accuracy. By analyzing the associated results, it can be determined whether the new parameters can completely replace the associated data, thereby determining the necessity of the new parameters. Comparative data can help the target department find the cause of the abnormal parameters. The modified rate of return is calculated through the abnormal results, which fully considers the changing trend of the comparative data and improves the accuracy of the calculation of the modified rate of return of the target product.

[0054] [Fifth embodiment] See also Figure 3 In a specific embodiment, when only the newly added parameters exist, the associated data corresponding to the newly added parameters are obtained, and the associated results are calculated based on the associated data and the second parameter set, and the modified rate of return is calculated based on the associated results and the adjustment calculation formula, specifically including: S531a, obtaining the association between the newly added parameter and each calculation parameter in the second parameter set, and recording the associated calculation parameters as associated parameters; S531b, calculating the weight ratio of the associated parameters in the system calculation formula, and judging whether the newly added parameters meet the consideration conditions according to the weight ratio; S531c. When the weight ratio is less than or equal to the weight threshold, the newly added parameters are modified according to the weight ratio to obtain new modified parameters, and the revised rate of return is calculated according to the new modified parameters and the adjustment calculation formula; S531d. When the weight ratio is greater than the weight threshold, the newly added parameter does not meet the consideration conditions, the newly added parameter is deleted and the revised rate of return is calculated according to the adjustment calculation formula.

[0055] In steps S531a to S531d, the association situation refers to the relationship between the newly added parameters and the various calculation parameters in the second parameter set, the associated parameters refer to the calculation parameters in the second parameter set that are logically associated or substitutable with the newly added parameters, the weight ratio refers to the relative importance of the associated parameters in the system calculation formula, usually expressed as a percentage, the weight threshold is a preset standard used to determine whether it is necessary to introduce new parameters, and the consideration condition is that the weight ratio is less than or equal to the weight threshold, and the newly modified parameter refers to the parameter obtained after adjusting the new parameter based on the weight ratio when the weight ratio meets the consideration condition.

[0056] It should be noted that the larger the weight ratio is, the more it means that the system calculation formula has fully considered the impact of the associated parameters on the target product. Adding new parameters again may repeat the associated parameters, thereby affecting the calculation accuracy. Therefore, the smaller the weight ratio of the new parameters is, the greater the necessity of introducing new parameters.

[0057] For example, when the newly added parameter is a sunk cost, if the associated parameters in the second parameter set are time cost and labor cost, and the sum of the weight ratios of the associated parameters is 0.14, and the weight threshold is 0.2, then it means that the newly added parameter meets the consideration conditions. The sunk cost is modified according to the sum of the weight ratios to obtain the modified sunk cost. The modified sunk cost is input into the adjustment calculation formula to calculate the modified rate of return.

[0058] The determination of associated parameters helps to avoid duplication of new parameters and calculation parameters in the system calculation formula. The calculation of the newly modified parameters effectively reduces the probability of duplication of new parameters and associated parameters, thereby improving the calculation accuracy of the data management platform. By comparing the weight ratio with the weight threshold, a scientific decision is made whether to introduce new parameters to avoid irrelevant or inefficient parameters reducing calculation efficiency and accuracy.

[0059] [Sixth embodiment] See also Figure 3 In a specific embodiment, when only abnormal parameters exist, the comparison data corresponding to the abnormal parameters and the changes of the comparison data in the target time period are obtained to obtain the abnormal result, and the modified rate of return is calculated according to the abnormal result and the adjustment calculation formula, which specifically includes: S532a, obtaining a first value of the comparison data in the first parameter set and a second value of the comparison data in the second parameter set; S532b, obtaining the change trend of the comparison data within the target time period, and when the change trend is different, manually calculating the revised rate of return; S532c, when the change trends are the same and the first value is different from the second value, the average of the first value and the second value is recorded as the standard value; S532d. Calculate normal parameters based on standard values ​​and abnormal parameters, and calculate modified yield based on normal parameters and adjustment calculation formula.

[0060] In steps S532a to S532d, the first value refers to the specific value of the comparison data in the first parameter set, the second value refers to the specific value of the comparison data in the second parameter set, the change trend refers to the change pattern of the first value and the second value within the target time period, usually including rising, falling and maintaining stability, etc., and the normal parameter is the corrected value of the abnormal parameter calculated based on the change trend of the first value and the second value.

[0061] For example, when the abnormal parameter is production cost and the comparison data is market demand, the first value is the specific market demand predicted by the target department during the target time period, and the second value is the specific market demand predicted by the data management platform. If the first value and the second value are both on an upward trend during the target time period, and the first value is 1,000 pieces and the second value is 800 pieces, then the standard value is 900 pieces. The production cost is revised according to the standard value to obtain normal parameters. The abnormal parameters in the adjustment calculation formula are replaced with normal parameters to calculate the revised rate of return.

[0062] Determining and comparing the first and second values ​​helps to identify the specific deviations of abnormal parameters in the first parameter set and the second parameter set. By analyzing the changing trend, it can be determined whether there are erroneous predictions in the calculation of abnormal parameters by manual and data management platforms. The calculation of normal parameters significantly improves the rationality and accuracy of the revised rate of return. The comparative analysis of the first and second values, the verification of the changing trend and the calculation of normal parameters have established a closed-loop abnormal parameter detection and correction mechanism, which improves the rationality and effectiveness of corporate decision-making.

[0063] [Seventh embodiment] See also Figure 4 In a specific embodiment, determining the real rate of return at the target time point according to the modified rate of return and the system rate of return specifically includes: S610, counting the first quantity whose modified rate of return is the same as the system rate of return, and the second quantity whose modified rate of return is different from the system rate of return; S620, when the first amount is greater than the second amount, recording the system rate of return as the real rate of return; S630. When the first amount is less than or equal to the second amount, calculate the weighted average of the modified rate of return and the system rate of return to obtain the real rate of return.

[0064] In step S610 to step S630, the first number refers to the number of modified yields that are the same as the system yield among the various modified yields calculated by the target department, the second number refers to the number of modified yields that are different from the system yield among the various modified yields calculated by the target department, the weighted average is the average value calculated based on the weight ratio of the modified yield and the system yield, and the real yield is the credible yield of the target product finally determined by comparing the first number with the second number. When the first number is less than or equal to the second number, the calculation formula of the real yield Rr is as follows: Rr=Rs×W1+Rc×W2.

[0065] Among them, Rs is the system yield, W1 is the weight ratio of the system yield, Rc is the mean of the modified yield, and W2 is the weight ratio of the modified yield.

[0066] For example, when the first quantity is 4, the second quantity is 6, the system yield is 20%, the weight of the system yield is 0.3, the average of the modified yield is 24%, and the weight of the modified yield is 0.7, the actual yield is 22.8%.

[0067] By comparing the first number with the second number, the accuracy of the system yield and the reliability of the data management platform can be effectively judged. When the first number is less than or equal to the second number, the weighted average can comprehensively consider the importance of the system yield and the revised yield to obtain a more reasonable real yield, thereby avoiding the impact of the deviation of the system yield data on the company's final decision.

[0068] [Eighth embodiment] See also Figure 5 In a specific embodiment, the system calculation formula is adjusted according to the difference between the real rate of return and the system rate of return in the target time period, specifically including: S710, counting the number of times the system yield is replaced by the modified yield, and obtaining the number of replacements; S720, calculating the rate of return difference corresponding to each replacement number to obtain a unit difference; S730, calculating the average deviation rate of the system yield within the target time period according to the unit difference; S740: When the average deviation rate is greater than the deviation threshold, the system calculation formula is modified according to the abnormal parameters corresponding to the average deviation rate.

[0069] In steps S710 to S740, the number of replacements refers to the number of times the system yield is replaced by the revised yield, the unit difference refers to the difference between the revised yield and the system yield at each replacement, the ratio of the sum of the unit differences in the target time period to the number of replacements is the average deviation rate, and the deviation threshold refers to a critical value set by the data management platform. When the average deviation rate exceeds the deviation threshold, it means that there is a large deviation in the system calculation formula and adjustment is required.

[0070] It should be noted that the unit difference is the difference between the modified rate of return and the system rate of return, which can be positive or negative. The absolute value of the unit difference cannot be used when calculating the average deviation rate.

[0071] The number of replacements reflects the frequency of the difference between the system yield and the revised yield. The calculation of the unit difference and the average deviation rate helps to measure the degree of difference between the revised yield and the system yield. By comparing the average deviation rate with the deviation threshold, it is possible to effectively determine whether the system calculation formula needs to be adjusted, thereby completing the closed-loop optimization mechanism of the data management platform and gradually reducing the calculation error of the system calculation formula.

[0072] [Ninth embodiment] See also Figure 5 In a specific embodiment, when the average deviation rate is greater than the deviation threshold, the system calculation formula is modified according to the abnormal parameters corresponding to the average deviation rate, specifically including: S741, obtaining comparison data corresponding to abnormal parameters each time a unit difference is generated; S742. Filter out the data to be adjusted from the comparison data according to the number of occurrences of the comparison data, modify the parameter conversion method of the data to be adjusted according to the unit difference, and input the modified parameter conversion method into the system calculation formula.

[0073] In step S741 and step S742, the number of occurrences refers to the number of times that the comparison data within the target time period causes a unit difference between the system yield and the modified yield. The data to be adjusted refers to the comparison data that is screened out based on the number of occurrences and needs to be adjusted first. Usually, the comparison data with the largest number of occurrences is selected as the data to be adjusted. The parameter conversion method refers to the calculation method or formula for converting the data to be adjusted into new calculation parameters.

[0074] For example, when the abnormal parameter is the production cost weight, and the comparison data is the actual value of production cost, raw material price volatility, production efficiency index, etc. in the target time period, if the raw material price volatility occurs 10 times, the actual value of production cost occurs 7 times, and the production efficiency index occurs 5 times, then the raw material price volatility is determined as the data to be adjusted. When the unit difference between the corrected rate of return and the system rate of return is 2%, it means that the predicted result of the system rate of return is too low. At this time, it is necessary to recalculate the raw material price volatility in the system calculation formula based on the unit difference and the raw material price volatility in the adjustment calculation formula, and adjust the system calculation formula.

[0075] The number of occurrences reflects the main source of the unit difference. By statistically comparing the number of occurrences of the data, we can quickly determine the data to be adjusted that appears frequently and may affect the calculation results. The correction of the data to be adjusted can effectively improve the accuracy and stability of the system yield calculation of the data management platform. By modifying the parameter conversion method of the data to be adjusted, the impact of abnormal parameters on the calculation results can be greatly reduced or eliminated, thereby improving the calculation accuracy of the system yield.

[0076] [Tenth embodiment] See also Figure 6 In one embodiment of the present invention, a data management system 100 is further provided. The data management method described in the above embodiment is applied to the data management system 100. The data management system 100 includes: an input module 110, the input module 110 is used to input the target rate of return of the operating commodities; a judgment module 120, the judgment module 120 is used to judge whether the target rate of return is the same as the system rate of return; a comparison module 130, the comparison module 130 is used to compare the target calculation formula and the system calculation formula to determine the distinguishing parameters; a calculation module 140, the calculation module 140 is used to calculate the system rate of return. The data management system has all the technical features of the above data management method, which will not be described one by one here.

[0077] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the scope defined by the claims.

Claims

1. A data management method for data conversion and quality control, characterized in that: The data management method comprises: Building a data management platform, which calculates the system rate of return based on target data of each operating commodity at a target time point; The target department inputs the target rate of return of the operating commodity at the planned time point, and when the target rate of return is the same as the system rate of return, the operating commodity is recorded as a standard commodity; When the target rate of return is different from the system rate of return, the operating commodity is recorded as the target commodity; Compare the target calculation formula for calculating the target rate of return of the target commodity with the system calculation formula for calculating the system rate of return of the target commodity to determine a distinguishing parameter; Modifying the target rate of return according to the distinguishing parameter to obtain a modified rate of return; Determine the real rate of return at the target time point according to the modified rate of return and the system rate of return; The system calculation formula is adjusted according to the difference between the actual rate of return and the system rate of return within the target time period.

2. The data management method according to claim 1, characterized in that: The step of comparing the target calculation formula for calculating the target rate of return of the target commodity with the system calculation formula for calculating the system rate of return of the target commodity to determine the distinguishing parameter specifically includes: Obtaining each calculation parameter in the target calculation formula to obtain a first parameter set, and obtaining each calculation parameter in the system calculation formula to obtain a second parameter set; Comparing the planned time point for counting the target rate of return with the target time point; When the planned time point and the target time point are the same, the first parameter set and the second parameter set are compared to obtain the difference parameter of the target calculation formula relative to the system calculation formula; When the planned time point and the target time point are different, the second parameter set is modified according to the planned time point to obtain a modified parameter set, and the modified parameter set is compared with the first parameter set to obtain the difference parameter.

3. The data management method according to claim 2, characterized in that: The modifying the target rate of return according to the distinguishing parameter to obtain a modified rate of return specifically includes: The distinguishing parameters include newly added parameters, omitted parameters and abnormal parameters; When the missing parameter exists, obtaining the acquisition authority corresponding to the target department, and when the acquisition authority cannot obtain the missing parameter, the data management platform adjusts the target calculation formula according to the missing parameter to obtain an adjusted calculation formula; The target rate of return is modified according to the adjustment calculation formula, the newly added parameters and the abnormal parameters to obtain a modified rate of return.

4. The data management method according to claim 3, characterized in that: The target rate of return is modified according to the adjustment calculation formula, the newly added parameters and the abnormal parameters to obtain a modified rate of return, specifically including: When only the newly added parameter exists, obtaining association data corresponding to the newly added parameter, and calculating an association result according to the association data and the second parameter set; When only the abnormal parameter exists, the comparison data corresponding to the abnormal parameter and the change of the comparison data within the target time period are obtained to obtain the abnormal result; When the newly added parameters and the abnormal parameters exist at the same time, the newly added parameters are converted according to the association result until all the newly added parameters are converted; The modified rate of return is calculated using the adjustment formula according to the abnormal change result.

5. The data management method according to claim 4, characterized in that: When only the newly added parameter exists, obtaining the associated data corresponding to the newly added parameter, calculating the associated result according to the associated data and the second parameter set, and calculating the modified rate of return according to the associated result and the adjustment calculation formula, specifically includes: Obtaining the association between the newly added parameter and each of the calculation parameters in the second parameter set, and recording the associated calculation parameters as associated parameters; Calculate the weight ratio of the associated parameters in the system calculation formula, and determine whether the newly added parameters meet the consideration conditions according to the weight ratio; When the weight ratio is less than or equal to the weight threshold, the newly added parameter is modified according to the weight ratio to obtain a new modified parameter, and the modified rate of return is calculated according to the new modified parameter and the adjustment calculation formula; When the weight ratio is greater than the weight threshold, the newly added parameter does not meet the consideration condition, the newly added parameter is deleted and the modified rate of return is calculated according to the adjustment calculation formula.

6. The data management method according to claim 5, characterized in that: When only the abnormal parameter exists, obtaining the comparison data corresponding to the abnormal parameter and the change of the comparison data within the target time period to obtain the abnormal result, and calculating the modified rate of return according to the abnormal result and the adjustment calculation formula specifically includes: Acquire a first value of the comparison data in the first parameter set and a second value of the comparison data in the second parameter set; Obtaining the change trend of the comparison data within the target time period, and when the change trend is different, manually calculating the modified rate of return; When the change trends are the same and the first value is different from the second value, the average of the first value and the second value is recorded as the standard value; Normal parameters are calculated according to the standard values ​​and the abnormal parameters, and the modified rate of return is calculated according to the normal parameters and the adjustment calculation formula.

7. The data management method according to claim 6, characterized in that: Determining the real rate of return at the target time point according to the modified rate of return and the system rate of return specifically includes: Counting a first number of which the modified rate of return is the same as the system rate of return, and a second number of which the modified rate of return is different from the system rate of return; When the first amount is greater than the second amount, recording the system rate of return as the real rate of return; When the first amount is less than or equal to the second amount, the weighted average of the modified rate of return and the system rate of return is calculated to obtain the real rate of return.

8. The data management method according to claim 7, characterized in that: The adjusting of the system calculation formula according to the difference between the real rate of return and the system rate of return within the target time period specifically includes: Counting the number of times the system rate of return is replaced by the modified rate of return to obtain the number of replacements; Calculate the yield difference corresponding to each replacement number to obtain a unit difference; Calculate the average deviation rate of the system yield within the target time period according to the unit difference; When the average deviation rate is greater than a deviation threshold, the system calculation formula is modified according to the abnormal parameter corresponding to the average deviation rate.

9. The data management method according to claim 8, characterized in that: When the average deviation rate is greater than the deviation threshold, the system calculation formula is modified according to the abnormal parameter corresponding to the average deviation rate, specifically including: Acquire the comparison data corresponding to the abnormal parameter each time the unit difference is generated; The data to be adjusted are screened out from the comparison data according to the number of occurrences of the comparison data, the parameter conversion method of the data to be adjusted is modified according to the unit difference, and the modified parameter conversion method is input into the system calculation formula.

10. A data management system, characterized in that: The data management method according to any one of claims 1 to 9 is applied to the data management system, and the data management system comprises: An input module, the input module is used to input the target rate of return of the operating commodity; A judgment module, the judgment module is used to judge whether the target rate of return is the same as the system rate of return; A comparison module, the comparison module is used to compare the target calculation formula and the system calculation formula to determine the difference parameter; A calculation module is used to calculate the system yield.

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