Grain and oil reserve plan management system

Abstract

The invention provides a grain and oil reserve plan management system, which comprises the following steps of: analyzing a first association relationship between the change condition of grain and oil reserve multi-dimensional data of each subsidiary company and account authority grouping adjustment to obtain an association size; analyzing change characteristics including the estimated grain and oil reserve multi-dimensional data of the next month and the historical grain and oil reserve multi-dimensional data to obtain a grain and oil reserve multi-dimensional data prediction value of the next month; predicting the permission grouping adjustment condition of each account in combination with the relevance size and the predicted value of the grain and oil reserve multi-dimensional data to obtain the permission grouping adjustment probability of each account; and performing permission grouping adjustment reminding according to the permission grouping adjustment probability. According to the method, the possible permission grouping adjustment condition of each account in the next month can be predicted in advance, the optimal permission adjustment scheme is automatically recommended, and the timeliness and rationality of permission distribution are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of grain and oil reserve plan management, and in particular to a grain and oil reserve plan management system and a permission management method thereof. Background Art

[0004] As an important part of grain and oil reserve informatization, the intelligent grain and oil reserve management system has been widely used in various places. However, in the process of system construction, some problems and shortcomings have also been exposed, especially in terms of authority management. At present, most management modes are provincial-municipal-district three-level management. Considering that there are certain differences between the transaction volume and other data corresponding to different subsidiaries, and with the change of time, the scale of each subsidiary / storage unit will also change accordingly. In the grain and oil reserve management system, there will be changes in the access rights corresponding to multiple accounts in each subsidiary / storage unit, but due to the lack of a dynamic and flexible access rights grouping adjustment mechanism, it is difficult to adapt to the management demand adjustment brought about by the change in the scale of each subsidiary / storage unit, and there are certain management risks. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a grain and oil reserve plan management system and an authority management method thereof.

[0006] The grain and oil reserve plan management system includes a hardware environment and a software environment. The hardware environment includes a server, a storage hard disk and a display. The software environment includes a database, a server operating system, a client operating system and a browser. The server operating system includes a plan management system interface, an annual rotation plan application interface, a process management interface and an annual rotation plan entry interface. The plan management system interface and the annual rotation plan application interface are interconnected, the annual rotation plan application interface and the process management interface are interconnected, and the process management interface and the annual rotation plan entry interface are interconnected. The plan management system interface includes a plan application module, a plan entry module, a transaction application module, a transaction registration module, a transaction contract module, a warehouse notification module, a first data import module, a first accounting voucher module, a grain and oil settlement module, a quantity difference approval module, a warehouse application module, a warehouse issuance module, an empty warehouse verification module, a second data import module, a second accounting voucher module and a warehouse confirmation module.

[0007] A method for managing rights of a grain and oil reserve plan management system provided by the present invention comprises the following steps:

[0008] Obtain multi-dimensional data on grain and oil reserves and information on account authority groups of each subsidiary;

[0009] Analyze the first correlation between the change of the multi-dimensional data of grain and oil reserves of each subsidiary and the adjustment of the account authority grouping to obtain the correlation size;

[0010] Analyze the estimated multidimensional data of grain and oil reserves for the next month and the change characteristics of the historical multidimensional data of grain and oil reserves, predict the multidimensional data of grain and oil reserves for the next month, and obtain the predicted value of the multidimensional data of grain and oil reserves for the next month;

[0011] Combining the correlation size and the predicted value of the grain and oil reserve multidimensional data, predicting the authority grouping adjustment of each account, and obtaining the authority grouping adjustment probability of each account;

[0012] According to the permission grouping adjustment probability, a reminder for permission grouping adjustment is provided.

[0013] In some embodiments of the present invention, a first correlation relationship of a change sequence of the multidimensional data of grain and oil reserves in a time period between two authority grouping adjustments corresponding to the same account authority grouping between two different subsidiaries is analyzed to obtain the correlation size.

[0014] In some embodiments of the present invention, the first DTW distance relationship of the grain and oil reserve multidimensional data change sequence in the time period between the newly added permissions corresponding to the same account authority group between two different subsidiaries and the last permission group adjustment is analyzed to obtain the correlation between the newly added account authority group and the grain and oil reserve multidimensional data change.

[0015] In some embodiments of the present invention, the characteristics of the changes in the multidimensional data of grain and oil reserves of each subsidiary are analyzed, and the multidimensional data of grain and oil reserves in the subsequent time are predicted to obtain the predicted value of the multidimensional data of grain and oil reserves of each subsidiary, including:

[0016] The estimated multidimensional data of grain and oil reserves for the next month and the multidimensional data of grain and oil reserves for many years in history are grouped into one year's data to form a grain and oil reserve data sequence;

[0017] Analyze the correlation between each subsidiary's grain and oil reserve data series, including the estimated next month's grain and oil reserve multidimensional data, and other grain and oil reserve data series, predict each subsidiary's next month's grain and oil reserve multidimensional data, and obtain the predicted value of each subsidiary's grain and oil reserve multidimensional data.

[0018] In some embodiments of the present invention, the correlation between the grain and oil reserve data series including the estimated grain and oil reserve multidimensional data for the next month of each subsidiary and other grain and oil reserve data series is analyzed, and the grain and oil reserve multidimensional data for the next month of each subsidiary is predicted to obtain the predicted value of the grain and oil reserve multidimensional data of each subsidiary, including:

[0019] Analyze the Pearson correlation coefficient between each subsidiary's grain and oil reserve data series, including the estimated multidimensional data of grain and oil reserve for the next month, and other grain and oil reserve data series, and use the annual time series corresponding to the grain and oil reserve data series as the weight coefficient to predict the multidimensional data of grain and oil reserve for the next month of each subsidiary, and obtain the predicted value of the multidimensional data of grain and oil reserve for each subsidiary.

[0020] In some embodiments of the present invention, combining the correlation size and the predicted value of the grain and oil reserve multidimensional data, predicting the authority grouping adjustment of each account to obtain the authority grouping adjustment probability of each account includes:

[0021] Obtaining a first change sequence of the multidimensional data of grain and oil reserves corresponding to the month since the last authority grouping adjustment of each account;

[0022] Analyze the second correlation relationship between the first change sequence and the second change sequence of the multidimensional data of grain and oil reserves in the time period between the corresponding authority grouping adjustment of another subsidiary and the last authority grouping adjustment, and obtain the authority grouping adjustment probability of each account in combination with the size of the correlation.

[0023] In some embodiments of the present invention, analyzing the second association relationship between the first change sequence and the second change sequence of the multidimensional data of grain and oil reserves during the period between the corresponding authority grouping adjustment of another subsidiary and the last authority grouping adjustment, and combining the magnitude of the association, obtaining the authority grouping adjustment probability of each account includes:

[0024] Analyze the DTW distance relationship between the first change sequence and the second change sequence of the grain and oil reserve multidimensional data during the period from the corresponding authority grouping adjustment of another subsidiary to the last authority grouping adjustment, and traverse all dimensions of the grain and oil reserve multidimensional data and sum them up in combination with the correlation to obtain similarity;

[0025] For the similarity, all accounts of another subsidiary that have undergone corresponding authority grouping adjustment are traversed and averaged to obtain the authority grouping adjustment probability of each account.

[0026] In some embodiments of the present invention, prompting for permission group adjustment according to the permission group adjustment probability includes:

[0027] Set the probability threshold for permission group adjustment;

[0028] Determining whether the permission grouping adjustment probability is greater than the permission grouping adjustment probability threshold;

[0029] If so, a reminder message is sent to the system administrator to remind him or her whether to adjust the permission grouping.

[0030] In some embodiments of the present invention, the multi-dimensional data of grain and oil reserves include: transaction amount, number of employees, grain and oil sales volume, and surplus volume.

[0031] In some embodiments of the present invention, the account authority groups include: system administrator, financial personnel, warehouse supervisor, warehouse manager, and sales personnel.

[0032] It can be seen from the above embodiments that the grain and oil reserve plan management system and the authority management method provided by the embodiments of the present invention have the following beneficial effects:

[0033] 1. By analyzing the relationship between the multi-dimensional data of historical grain and oil reserves of different subsidiaries and the adjustment of authority grouping, the present invention can predict in advance the possible authority grouping adjustment of each account at the next moment, and realize the centralized management of the system. This forward-looking prediction capability enables the system to warn of potential authority changes in advance, so as to better respond to management needs.

[0034] 2. When conducting authority management, the historical multi-dimensional data of grain and oil reserves and the predicted multi-dimensional data of grain and oil reserves are comprehensively considered, and the management strategy can be adjusted dynamically. This data-based automated judgment mechanism reduces human intervention, improves the intelligence level of the system, and reduces human errors in the authority management process.

[0035] 3. Through in-depth analysis of the internal account authority grouping adjustments of each subsidiary, the correlation between different authority grouping adjustments and the multi-dimensional data of grain and oil reserves is identified, thereby optimizing the authority allocation strategy, so that the system can automatically recommend the optimal authority adjustment plan based on the changes in the multi-dimensional data of grain and oil reserves, making the authority allocation more reasonable and scientific.

[0036] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 A basic flow chart of a method for authority management of a grain and oil reserve plan management system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the authority management method of a grain and oil reserve plan management system proposed by the present invention, its specific implementation method, structure, features and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. Terms such as "comprises", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the article or device including the element.

[0041] In order to realize the intelligent, predictive and automated authority management in the grain and oil reserve planning management system, by analyzing the correlation between the historical grain and oil reserve data of different subsidiaries and the adjustment of authority grouping, and combining it with the prediction of future grain and oil reserve data, the system can automatically identify and warn accounts that may need to adjust their authority, thereby improving the efficiency, accuracy and security of authority management.

[0042] The following will introduce in detail the authority management method of a grain and oil reserve plan management system provided by this embodiment in conjunction with the accompanying drawings.

[0043] See also Figure 1 , which shows the basic process of a permission management method for a grain and oil reserve plan management system provided by an embodiment of the present invention.

[0044] like Figure 1 As shown, an embodiment of the present invention provides a method for managing rights of a grain and oil reserve plan management system, which specifically includes the following steps:

[0045] S100: Obtain the multi-dimensional data of grain and oil reserves and account authority grouping of each subsidiary.

[0046] Obtain the multi-dimensional data of grain and oil reserves such as transaction amount, number of employees, grain and oil sales volume, surplus volume, etc. corresponding to each subsidiary in the grain and oil reserve system. The following is recorded in months, and the multi-dimensional data of grain and oil reserves corresponding to the above average data of the ith subsidiary in each month are respectively X i,1 To X i,n, where i represents the i-th subsidiary and n represents the number of dimensions of the multidimensional data of grain and oil reserves.

[0047] In the traditional grain and oil management system, each subsidiary employee has his own account to log in to the system, report information, and audit, but due to differences in their functions, the authority information of their corresponding accounts will also be different. The system will group the accounts of employees with the same authority, such as system administrators, financial personnel, warehouse supervisors, warehouse managers, sales personnel, etc. Different employees have preset authority grouping information, and the authority grouping of different employees' corresponding IDs in the grain and oil reserve system will change over time. Therefore, the account authority grouping corresponding to different employees in each subsidiary is obtained, and it is synchronized with the multi-dimensional data of grain and oil reserves of each subsidiary.

[0048] At this point, we can obtain the time series data of the multidimensional data of grain and oil reserves of each subsidiary and the time series data of account authority grouping, which can provide basic data support for subsequent analysis.

[0049] S200: Analyze the first correlation between the change of the multi-dimensional data of grain and oil reserves of each subsidiary and the adjustment of the account authority grouping to obtain the correlation size.

[0050] When different accounts of different subsidiaries have a large correlation between the changes in the grain and oil reserve data of the corresponding dimensions under the same authority grouping adjustment, it means that the correlation between the grain and oil reserve data of the current dimension and the authority grouping of the corresponding account is high. For example, when the sales volume of grain and oil increases, it may correspond to the transfer of some sales personnel positions, and the authority grouping adjustments of multiple similar accounts will appear in the corresponding subsidiaries. Therefore, through the correlation between the multidimensional data of grain and oil reserves of different subsidiaries under the same authority grouping, the correlation between the grain and oil reserve data of each dimension in the grain and oil reserve multidimensional data and the authority grouping situation can be obtained.

[0051] Based on the above analysis, this embodiment obtains the correlation size by analyzing the first correlation relationship between the change of each subsidiary's grain and oil reserve multidimensional data and the account authority grouping adjustment. Further, by analyzing the first correlation relationship of the change sequence of grain and oil reserve multidimensional data in the time period between two authority grouping adjustments corresponding to the same account authority grouping between two different subsidiaries, the correlation size is obtained. Further, by analyzing the first DTW distance relationship of the change sequence of grain and oil reserve multidimensional data in the time period between the newly added authority corresponding to the same account authority grouping between two different subsidiaries and the last authority grouping adjustment, the correlation size between the newly added account authority grouping and the change of grain and oil reserve multidimensional data is obtained.

[0052] The specific operation is as follows: the number of initial permission groups is N, and the permissions corresponding to different permission groups are different. In order to analyze the relationship between the changes in permissions corresponding to different employee accounts and the changes in the multi-dimensional data of the subsidiary's grain and oil reserves, first obtain the time when the permission groups corresponding to different employee account IDs change, and record the time period corresponding to the rth account under the newly added pth permission group change as T r,p , that is, T r,p Represents the time period between the time when the rth account was added to the pth permission group and the time when the pth permission group was last updated.

[0053] Since the correlation between different authority groups and the size of subsidiaries is different, for example, inventory management and logistics coordination require the warehouse manager or warehouse supervisor to dispatch. When the corresponding grain and oil reserves of the enterprise increase, it may correspond to an increase in the number of employee accounts included in the warehouse supervisor authority group, that is, some employees will be promoted to warehouse supervisors, and the authority group corresponding to the employee account ID will also change accordingly. Therefore, it is necessary to first analyze the correlation between the changes in grain and oil reserve data in different dimensions and the changes in each authority group according to the changes in the same authority group.

[0054] When adding the pth permission group, the correlation between the uth dimension grain and oil reserve data and it for:

[0055]

[0056] in, Indicates the correlation between the grain and oil reserve data of the uth dimension and the newly added accounts of the pth authority group; represents the total number of newly added accounts in the pth permission group; i and j represent two different subsidiary numbers selected at random; r and l represent two account numbers belonging to subsidiaries i and j respectively; It represents the time period between the time when the rth account is added to the pth permission group and the time when the pth permission group was last updated; It represents the time period between the time when the lth account was added to the pth permission group and the time when the pth permission group was last updated; Indicates that the subsidiary to which the rth account belongs is The change sequence of grain and oil reserve data in the uth dimension within the time period; Indicates that the subsidiary to which the first account belongs is The change sequence of grain and oil reserve data in the uth dimension within the time period; D represents the DTW distance between the two sequences.

[0057] Similarly, the above process is the correlation between the grain and oil reserve data of each dimension when adding an account to the pth authority group, and when the account is reduced in the corresponding authority group, that is, when the account is removed from the pth authority group, the correlation between it and the grain and oil reserve data of the uth dimension is recorded as The calculation method is the same as Right now:

[0058]

[0059] in, Indicates the correlation between the grain and oil reserve data of the uth dimension and the account reduced by the pth authority group; represents the total number of accounts reduced in the pth permission group; i and j represent the numbers of different subsidiaries; r and l represent the account numbers of two subsidiaries i and j respectively; It represents the time period between the time when the rth account is reduced in the pth permission group and the time when the pth permission group was last updated; It represents the time period between the time when the account corresponding to the pth permission group is reduced and the time when the pth permission group is last updated; Indicates that the subsidiary to which the rth account belongs is The first-order difference change sequence of the grain and oil reserve data of the uth dimension within the time period; Indicates that the subsidiary to which the first account belongs is The first-order difference change sequence of the grain and oil reserve data of the uth dimension within the time period; D represents the DTW distance between the two sequences.

[0060] In the above correlation calculation process, the correlation of the changes in the multi-dimensional data of grain and oil reserves in the time period when the corresponding authority groups are changed in two different subsidiaries is used. When there is a large correlation between the grain and oil reserve data of the corresponding dimensions under the same authority grouping for different account IDs, it means that the correlation between the grain and oil reserve data of the current dimension and the corresponding authority grouping is high. For example, when the sales volume of grain and oil increases, it may correspond to the transfer of the positions of some sales personnel, and there will be multiple similar adjustments to the authority groups of account IDs in the corresponding subsidiaries. Therefore, through the correlation between the changes in the multi-dimensional data of grain and oil reserves of two subsidiaries under the same authority grouping, the correlation between the grain and oil reserve data of each dimension and the authority grouping situation is obtained.

[0061] S300: Analyze the estimated multidimensional data of grain and oil reserves for the next month and the change characteristics of the historical multidimensional data of grain and oil reserves, predict the multidimensional data of grain and oil reserves for the next month, and obtain the predicted value of the multidimensional data of grain and oil reserves for the next month.

[0062] Considering that when adjusting the authority groups of different accounts, the adjustment results of the authority groups corresponding to multiple historical accounts are analyzed, but for the changes in the multi-dimensional data of grain and oil reserves at the current moment, the adjustment of the authority groups of different accounts may lag behind the current actual needs due to the lag of the current multi-dimensional data of grain and oil reserves, and cannot adapt to the current changes in real time. For example, with the change of seasons, the amount of grain and oil reserves may surge. At this time, if the authority group is adjusted only based on the characteristics of the changes in the multi-dimensional data of grain and oil reserves in the previous month and before, it may lead to inaccurate results of the subsequent account authority group adjustment in the case of sudden changes in the multi-dimensional data of grain and oil reserves.

[0063] Based on the above analysis, this example predicts the multidimensional data of grain and oil reserves for the next month by analyzing the change characteristics of the multidimensional data of grain and oil reserves for the next month and the multidimensional data of grain and oil reserves for the history, and obtains the predicted value of the multidimensional data of grain and oil reserves for the next month. Further, firstly, the multidimensional data of grain and oil reserves for the next month and the multidimensional data of grain and oil reserves for many years in history are grouped into one year's data to form a grain and oil reserve data sequence; then, the correlation between the grain and oil reserve data sequence of each subsidiary including the multidimensional data of grain and oil reserves for the next month and other grain and oil reserve data sequences is analyzed, and the multidimensional data of grain and oil reserves for the next month of each subsidiary is predicted, and the predicted value of grain and oil reserves for the multidimensional data of grain and oil reserves for each subsidiary is obtained. Further, by analyzing the Pearson correlation coefficient between the grain and oil reserve data sequence of each subsidiary including the multidimensional data of grain and oil reserves for the next month and other grain and oil reserve data sequences, and taking the annual time series corresponding to the grain and oil reserve data sequence as the weight coefficient, the multidimensional data of grain and oil reserves for the next month of each subsidiary is predicted, and the predicted value of grain and oil reserves for the multidimensional data of grain and oil reserves for each subsidiary is obtained.

[0064] For a clearer description, the following are some specific examples:

[0065] Since there are certain seasonal changes in the multidimensional data of grain and oil reserves, for example, the sales volume of grain and oil may be affected by seasonal factors. For example, during the harvest season or specific holidays (such as the Spring Festival), the sales volume of grain and oil may increase, and during the harvest season, the increase in the supply of grain and oil may lead to an increase in surplus.

[0066] Therefore, through the multidimensional data of grain and oil reserves in previous years, we first estimate the multidimensional data of grain and oil reserves in the next month, and get the estimated value of the grain and oil reserves data of the uth dimension of the ith subsidiary in the next month as x. Then, taking the current moment as the end point, we obtain the multidimensional data of grain and oil reserves in the past five years, and record the grain and oil reserves data of the uth dimension of the ith subsidiary in the zth month as X. i,u,z, then the historical grain and oil reserve data of the u-th dimension of the i-th subsidiary is X i,u,1 To X i,u,N Here, N is recommended to be 59=5×12-1, where 5 represents 5 years, 12 represents 12 months, and 1 represents the next month. Due to the seasonal variation of the multidimensional data of grain and oil reserves, the multidimensional data of grain and oil reserves estimated for the next month and the multidimensional data of grain and oil reserves for many years in history are grouped into one year, forming a grain and oil reserve data sequence as follows:

[0067]

[0068] Among them, XI i,u,0 (x) represents the grain and oil reserve data sequence of the u-th dimension grain and oil reserve data of the i-th subsidiary in the most recent year (including the next month) when the estimated value of the u-th dimension grain and oil reserve data of the i-th subsidiary in the next month is x; XI i,u,1 represents the grain and oil reserve data sequence corresponding to the u-th dimension grain and oil reserve data of the i-th subsidiary in the second most recent year; XI i,u,2 , XI i,u,3 , XI i,u,4 Represents the grain and oil reserve data sequence corresponding to the u-th dimension grain and oil reserve data of the i-th subsidiary in the most recent third, fourth, and five years.

[0069] Formation of XI i,u,0 (u), XI i,u,1 , XI i,u,2 , XI i,u,3 , XI i,u,4 The grain and oil reserve data series contains estimated values ​​for the next month and also has the characteristics of corresponding to the same month every year.

[0070] Construct the predicted value X' of the u-th dimension of the grain and oil reserve data of the i-th subsidiary in the next month i,u,0 The calculation formula is:

[0071]

[0072] Among them, X' i,u,0 represents the predicted value of the u-th dimension grain and oil reserve data of the i-th subsidiary in the next month; argmax represents the maximum point function; norm represents the linear normalization function; ρ represents the Pearson correlation coefficient between two data series; XI i,u,0 (x) represents the grain and oil reserve data sequence of the u-th dimension of the i-th subsidiary in the most recent year (including the next month); XI i,u,v Represents the grain and oil reserve data sequence corresponding to the u-th dimension grain and oil reserve data of the i-th subsidiary in the most recent v+1 year.

[0073] ρ(XI i,u,0 (x),XI i,u,v ) represents the Pearson correlation coefficient between the grain and oil reserve data series of the u-th dimension of the i-th subsidiary and other grain and oil reserve data series in the most recent year (including the next month), [norm(-v)×ρ(XI i,u,0 (x),XI i,u,v )] means that the annual time series corresponding to the grain and oil reserve data series is used as the weight coefficient of the grain and oil reserve data series, and the earlier the grain and oil reserve data series, the smaller the corresponding weight, that is, the earlier the grain and oil reserve data series, the less reference it has; It means to traverse all other grain and oil reserve data series and sum the weighted Pearson correlation coefficients. i,u,0 In (x), the estimated value x is an unknown number, so is a function of x, so we can use the argmax function to The function finds the value of the independent variable x that achieves the maximum value, that is, the value of the independent variable x when the correlation coefficient between the predicted value and multiple historical data segments reaches the highest, thereby obtaining the predicted value X' for the next month i,u,0 .

[0074] S400: Based on the correlation size and the predicted value of the multi-dimensional data of grain and oil reserves, the authority grouping adjustment of each account is predicted to obtain the authority grouping adjustment probability of each account.

[0075] Step S200 and step S300 respectively obtain the correlation between the account authority grouping adjustment and the change of the grain and oil reserve multidimensional data and the predicted value of the grain and oil reserve multidimensional data of each subsidiary in the next month. This step predicts the authority grouping adjustment of each account in combination with the correlation size and the predicted value of the grain and oil reserve multidimensional data, and obtains the probability of authority grouping adjustment of each account. Further, by obtaining the first change sequence of the grain and oil reserve multidimensional data corresponding to the month since the last authority grouping adjustment of each account; then analyzing the second correlation relationship between the first change sequence and the second change sequence of the grain and oil reserve multidimensional data in the time period between the corresponding authority grouping adjustment of another subsidiary and the last authority grouping adjustment, the probability of authority grouping adjustment of each account is obtained in combination with the correlation size. Furthermore, by analyzing the DTW distance relationship between the first change sequence and the second change sequence of the grain and oil reserve multidimensional data in the time period between the corresponding authority grouping adjustment of another subsidiary and the last authority grouping adjustment, combined with the size of the correlation, all dimensions of the grain and oil reserve multidimensional data are traversed and summed to obtain the similarity; then, for the similarity, all accounts of the other subsidiary that have undergone the corresponding authority grouping adjustment are traversed and averaged to obtain the authority grouping adjustment probability of each account.

[0076] For a clearer description, the following are some specific examples:

[0077] The time period from the last permission grouping adjustment of the rth account to the present is T' r , because there are some differences between the development of the subsidiary where the account is located and other subsidiaries, considering the multi-dimensional data results of grain and oil reserves of different subsidiaries in the next month, the changes in the authority grouping of different accounts in the next month are analyzed. r The u-th dimension grain and oil reserve data sequence is X' i,u (T' r ),have:

[0078]

[0079] Among them, X' i,u (T' r ) indicates that the rth account is located in the ith subsidiary in the time period T' r The u-th dimension grain and oil reserve data sequence in T' r Indicates the time period from the last permission grouping adjustment of the rth account to the present; n r Indicates the number of months since the last permission grouping adjustment of the rth account; Indicates the u-th dimension grain and oil reserve data of the i-th subsidiary in the n-th history r The value size under the month.

[0080] Then construct X' i,u (T' r ) and the similarity between the grain and oil reserve data of the uth dimension in the time period between the time when the new account was added and the time when the last permission group was updated. The calculation formula is:

[0081]

[0082] in, Represents X' i (T' r )and The similarity between data sequences, that is, the similarity between the multidimensional data of grain and oil reserves of two accounts and their respective subsidiaries in different time periods; n represents the number of data dimensions of the multidimensional data of grain and oil reserves; norm represents the linear normalization function; represents the correlation between the grain and oil reserve data of the uth dimension and the newly added accounts of the pth authority group; D represents the DTW distance between the two data sequences; ΔX' i,u (T' r) indicates that the rth account is located in the ith subsidiary in the time period T' r The first-order difference value sequence of the grain and oil reserve data in the uth dimension; T' r Indicates the time period from the last permission grouping adjustment of the rth account to the present; The jth subsidiary of the lth account in the time period The first-order difference value sequence of the grain and oil reserve data in the u-th dimension; Represents the time period between the time when the rth account was added to the pth permission group and the time when the pth permission group was last updated.

[0083] Denotes ΔX' i,u (T' r ) sequence and DTW distance between sequences. The smaller the value, the smaller the difference between the two sequences, and the greater the similarity between the two sequences; the size of the correlation The larger the value, the greater the similarity between the corresponding two sequences; then, all dimensions of the multidimensional data of grain and oil reserves are calculated separately. And using the correlation size The DTW distance is weighted to increase the credibility of the similarity, and finally the similarity calculation formula is obtained.

[0084] same The calculation logic is:

[0085]

[0086] in, Represents X' i (T' r )and The similarity between data sequences, that is, the similarity between the multidimensional data of grain and oil reserves of two accounts and their respective subsidiaries in different time periods; n represents the number of data dimensions of the multidimensional data of grain and oil reserves; norm represents the linear normalization function; represents the correlation between the u-th dimension grain and oil reserve data and the p-th authority grouping reduction account; D represents the DTW distance between the two data sequences; ΔX' i,u (T' r ) indicates that the rth account is located in the ith subsidiary in the time period T' r The first-order difference value sequence of the grain and oil reserve data in the uth dimension; T' r Indicates the time period from the last permission grouping adjustment of the rth account to the present; The jth subsidiary of the lth account in the time period The first-order difference value sequence of the grain and oil reserve data in the u-th dimension; Represents the time period between the time when the rth account is reduced in the pth permission group and the time when the pth permission group was last updated.

[0087] Then the probability of the rth account entering the pth permission group is recorded as for:

[0088]

[0089] in, Indicates the adjustment probability of the rth account newly entering the pth permission group; Indicates the total number of newly added accounts in the pth permission group; X' i (T' r ) indicates that the rth account is located in the ith subsidiary in the time period T' r Multi-dimensional data of grain and oil reserves in the country; Indicates that the jth subsidiary of the lth account is in the time period Multi-dimensional data of grain and oil reserves in the country; Represents the similarity between the multidimensional data of grain and oil reserves of the two accounts and their respective subsidiaries in different time periods.

[0090] represents T' corresponding to the rth account in the i-th subsidiary r The grain and oil reserve data series in the time period corresponds to all accounts in the j-th subsidiary The mean of the similarity between the dimensional grain and oil reserve data sequences within the time period. The larger the value, the higher the T' corresponding to the rth account in the i-th subsidiary. r The similarity between the grain and oil reserve data series in the time period and the changes in the p-th authority group in the j-th subsidiary. The greater the similarity, the greater the probability of the r-th account being newly added to the p-th authority group.

[0091] The probability of adjusting the rth account to the pth permission group is The calculation method is the same as Right now:

[0092]

[0093] in, Indicates the adjustment probability of the rth account calling out the pth permission group; Indicates the total number of accounts reduced in the pth permission group; X' i (T' r ) indicates that the rth account is located in the ith subsidiary in the time period T' r Multi-dimensional data of grain and oil reserves in the country; Indicates that the jth subsidiary of the lth account is in the time period Multi-dimensional data of grain and oil reserves in the country; Represents the similarity between the multidimensional data of grain and oil reserves of the two accounts and their respective subsidiaries in different time periods.

[0094] At this point, the account authority grouping adjustment probability corresponding to each account at the current moment is obtained.

[0095] S500: Prompt for permission grouping adjustment according to the permission grouping adjustment probability.

[0096] According to the probability of permission group adjustment, reminders for permission group adjustment are made, including: setting a permission group adjustment probability threshold, where the value of the permission group adjustment probability threshold can be 0.8; judging whether the permission group adjustment probability is greater than the permission group adjustment probability threshold; if the permission group adjustment probability is greater than 0.8, it means that the multi-dimensional data of grain and oil reserves of the subsidiary to which the corresponding account belongs is highly similar to the multi-dimensional data of grain and oil reserves before the historical permission adjustment. At this time, reminder information can be sent to system managers to remind them to judge whether to adjust the permission group to meet the company's development needs. Otherwise, there is no need to send reminder information to system managers.

[0097] It should be noted that the sequence of the above embodiments of the present invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0098] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A grain and oil reserve plan management system, characterized by: It includes a hardware environment and a software environment. The hardware environment includes a server, a storage hard disk and a display. The software environment includes a database, a server operating system, a client operating system and a browser. The server operating system includes a plan management system interface, an annual rotation plan application interface, a process management interface and an annual rotation plan entry interface. The plan management system interface and the annual rotation plan application interface are interconnected, the annual rotation plan application interface and the process management interface are interconnected, and the process management interface and the annual rotation plan entry interface are interconnected. The plan management system interface includes a plan application module, a plan entry module, a transaction application module, a transaction registration module, a transaction contract module, a warehouse notification module, a first data import module, a first accounting voucher module, a grain and oil settlement module, a quantity difference approval module, a warehouse application module, a warehouse issuance module, an empty warehouse verification module, a second data import module, a second accounting voucher module and a warehouse confirmation module.

2. A method for managing the authority of a grain and oil reserve plan management system, characterized in that: The method comprises the following steps: Obtain multi-dimensional data on grain and oil reserves and information on account authority groups of each subsidiary; Analyze the first correlation between the change of the multi-dimensional data of grain and oil reserves of each subsidiary and the adjustment of the account authority grouping to obtain the correlation size; Analyze the estimated multidimensional data of grain and oil reserves for the next month and the change characteristics of the historical multidimensional data of grain and oil reserves, predict the multidimensional data of grain and oil reserves for the next month, and obtain the predicted value of the multidimensional data of grain and oil reserves for the next month; Combining the correlation size and the predicted value of the grain and oil reserve multidimensional data, predicting the authority grouping adjustment of each account, and obtaining the authority grouping adjustment probability of each account; According to the permission grouping adjustment probability, a reminder for permission grouping adjustment is provided.

3. The method according to claim 2, characterized in that The first correlation relationship of the change sequence of the multidimensional data of grain and oil reserves in the time period between two authority grouping adjustments corresponding to the same account authority grouping between two different subsidiaries is analyzed to obtain the correlation size.

4. The method according to claim 3, characterized in that Analyze the first DTW distance relationship of the grain and oil reserve multidimensional data change sequence in the time period between the newly added permissions corresponding to the same account permission group between two different subsidiaries and the last permission group adjustment, and obtain the correlation between the newly added account permission group and the grain and oil reserve multidimensional data change; The specific steps are as follows: the number of initial permission groups is N, and the permissions corresponding to different permission groups are different. In order to analyze the relationship between the changes in permissions corresponding to different employee accounts and the changes in the multi-dimensional data of the subsidiary's grain and oil reserves, first obtain the time when the permission groups corresponding to different employee account IDs change, and record the time period corresponding to the rth account under the newly added pth permission group change as T r,p , that is, T r,p It represents the time period between the time when the rth account is added to the pth permission group and the time when the pth permission group was last updated; Since the correlation between different authority groups and the size of subsidiaries is different, such as inventory management and logistics coordination require warehouse managers or warehouse supervisors to schedule, when the corresponding grain and oil reserves of the enterprise increase, it may correspond to an increase in the number of employee accounts included in the warehouse supervisor authority group, that is, some employees will be promoted to warehouse supervisors, and the authority group corresponding to the employee account ID will also change accordingly; therefore, it is necessary to first analyze the correlation between the changes in grain and oil reserve data in different dimensions and the changes in each authority group according to the changes in the same authority group; When adding the pth permission group, the correlation between the uth dimension grain and oil reserve data and it for: in, Indicates the correlation between the grain and oil reserve data of the uth dimension and the newly added accounts of the pth authority group; represents the total number of newly added accounts in the pth permission group; i and j represent two different subsidiary numbers selected at random; r and l represent two account numbers belonging to subsidiaries i and j respectively; It represents the time period between the time when the rth account is added to the pth permission group and the time when the pth permission group was last updated; It represents the time period between the time when the lth account was added to the pth permission group and the time when the pth permission group was last updated; Indicates that the subsidiary to which the rth account belongs is The change sequence of grain and oil reserve data in the uth dimension within the time period; Indicates that the subsidiary to which the first account belongs is The change sequence of grain and oil reserve data in the uth dimension within the time period; D represents the DTW distance between two sequences; The above process is the correlation between the grain and oil reserve data of each dimension when adding an account to the pth authority group. When the account is reduced in the corresponding authority group, that is, when the account is removed from the pth authority group, the correlation between it and the grain and oil reserve data of the uth dimension is recorded as The calculation method is the same as Right now: in, Indicates the correlation between the grain and oil reserve data of the uth dimension and the account reduced by the pth authority group; represents the total number of accounts reduced in the pth permission group; i and j represent the numbers of different subsidiaries; r and l represent the account numbers of two subsidiaries i and j respectively; It represents the time period between the time when the rth account is reduced in the pth permission group and the time when the pth permission group was last updated; It represents the time period between the time when the lth account is reduced in the pth permission group and the time when the pth permission group was last updated; Indicates that the subsidiary to which the rth account belongs is The first-order difference change sequence of the grain and oil reserve data of the uth dimension within the time period; Indicates that the subsidiary to which the first account belongs is The first-order difference change sequence of the grain and oil reserve data of the uth dimension within the time period; D represents the DTW distance between the two sequences.

5. The method according to claim 2, characterized in that Analyze the characteristics of the changes in the multidimensional data of grain and oil reserves of each subsidiary, predict the multidimensional data of grain and oil reserves in the subsequent time, and obtain the predicted value of the multidimensional data of grain and oil reserves of each subsidiary, including: The estimated multidimensional data of grain and oil reserves for the next month and the multidimensional data of grain and oil reserves for many years in history are grouped into one year's data to form a grain and oil reserve data sequence; Analyze the correlation between each subsidiary's grain and oil reserve data series, including the estimated next month's grain and oil reserve multidimensional data, and other grain and oil reserve data series, predict each subsidiary's next month's grain and oil reserve multidimensional data, and obtain the predicted value of each subsidiary's grain and oil reserve multidimensional data.

6. The method according to claim 5, characterized in that Analyze the correlation between the grain and oil reserve data series of each subsidiary, including the estimated grain and oil reserve multidimensional data for the next month, and other grain and oil reserve data series, and predict the grain and oil reserve multidimensional data of each subsidiary for the next month, and obtain the predicted value of the grain and oil reserve multidimensional data of each subsidiary, including: Analyze the Pearson correlation coefficient between each subsidiary's grain and oil reserve data series, including the estimated multidimensional data of grain and oil reserve for the next month, and other grain and oil reserve data series, and use the annual time series corresponding to the grain and oil reserve data series as the weight coefficient to predict the multidimensional data of grain and oil reserve for the next month of each subsidiary, and obtain the predicted value of the multidimensional data of grain and oil reserve for each subsidiary.

7. The method according to claim 2, characterized in that Combining the correlation size and the predicted value of the grain and oil reserve multidimensional data, the authority grouping adjustment of each account is predicted to obtain the authority grouping adjustment probability of each account, including: Obtaining a first change sequence of the multidimensional data of grain and oil reserves corresponding to the month since the last authority grouping adjustment of each account; Analyze the second correlation relationship between the first change sequence and the second change sequence of the multidimensional data of grain and oil reserves in the time period between the corresponding authority grouping adjustment of another subsidiary and the last authority grouping adjustment, and obtain the authority grouping adjustment probability of each account in combination with the size of the correlation.

8. The method according to claim 7, characterized in that Analyze the second correlation relationship between the first change sequence and the second change sequence of the multidimensional data of grain and oil reserves during the period from the corresponding authority grouping adjustment of another subsidiary to the last authority grouping adjustment, and obtain the authority grouping adjustment probability of each account in combination with the magnitude of the correlation, including: Analyze the DTW distance relationship between the first change sequence and the second change sequence of the grain and oil reserve multidimensional data during the period from the corresponding authority grouping adjustment of another subsidiary to the last authority grouping adjustment, and traverse all dimensions of the grain and oil reserve multidimensional data and sum them up in combination with the correlation to obtain similarity; For the similarity, all accounts of another subsidiary that have undergone corresponding authority grouping adjustment are traversed and averaged to obtain the authority grouping adjustment probability of each account.

9. The method according to claim 2, characterized in that Prompting the adjustment of the permission grouping according to the permission grouping adjustment probability includes: Set the probability threshold for permission group adjustment; Determining whether the permission grouping adjustment probability is greater than the permission grouping adjustment probability threshold; If so, a reminder message is sent to the system administrator to remind him or her whether to adjust the permission grouping.

10. The method according to claim 2, characterized in that The multi-dimensional data of grain and oil reserves include: transaction amount, number of employees, grain and oil sales volume, and surplus volume; the account authority groups include: system administrator, financial personnel, warehouse supervisor, warehouse manager, and sales personnel.

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