Methods for determining business plan data and related equipment

Abstract

This application provides a method and related equipment for determining business plan data. The main purpose is to address the problem that existing methods, such as using Excel spreadsheets or custom software development, are insufficient for enterprise supply chain planning. These methods have poor adaptability and require processing tens of millions of calculations in real-time, limiting computational speed and failing to provide page-level fast response. The method includes: obtaining target demand planning units and target planning indicator items; and calculating the planned indicator data of the target planning indicator items based on the current calculation processing logic of the target demand planning units and target planning indicator items. The planned indicator data is obtained through CPU calculation using an in-memory database.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method and related equipment for determining business plan data. Background Technology

[0002] With the comprehensive development of my country's industries, various plans across different fields require substantial operational data for analysis and decision-making by relevant personnel. During the process of enterprises implementing various supply chain plans, users primarily focus on the specific demand, supply, and replenishment quantities of specific products in specific locations and channels over a future period (monthly / weekly). Furthermore, when reviewing, examining, and adjusting plans, it's necessary to quickly and in real-time aggregate data up to product subcategories, product categories, and locations, which may also need to be aggregated up to regional or national levels. Similarly, demand and replenishment quantities set at higher levels need to be quickly broken down to the lowest product, location, and customer level. This also includes scenarios involving the aggregation and breakdown of daily, weekly, and monthly demand over time. Additionally, there's a need for rapid calculations using formulas between the same levels. Current methods typically use Excel spreadsheets or custom software development to meet these needs, but these solutions have poor adaptability, and real-time calculations require handling tens of millions of calculations through program code, limiting calculation speed and failing to provide page-level rapid response. Summary of the Invention

[0003] In view of the above problems, this patent proposes a method for determining business plan data. Using in-memory database technology, necessary information such as specific product / location / customer information (member information), measurement indicator information, and hierarchy information are read into the in-memory database, forming an Intersection (in-memory instance). The continuity of the in-memory data and the consistency of transaction processing are maintained through persistence. The main purpose is to significantly improve calculation speed when dealing with complex and massive amounts of data, achieving page-level computational responsiveness.

[0004] To address at least one of the aforementioned technical problems, in a first aspect, the present invention provides a method for determining business plan data, the method comprising:

[0005] Obtain target demand planning units and target planning indicator items;

[0006] Based on the target demand planning unit and the current calculation processing logic of the target planning indicator item, the planned indicator data of the target planning indicator item is calculated, wherein the planned indicator data is obtained by CPU calculation based on the in-memory database.

[0007] Optionally, the above methods include:

[0008] The in-memory database stores data in a columnar format data structure.

[0009] Optionally, the above methods also include:

[0010] Obtain the user's perspective selection instruction, wherein the perspective selection instruction includes the initial level information and initial planning period of the target demand planning unit. When the demand planning unit names are the same, the level information of the demand planning unit is used to distinguish different target demand planning units. The planning period is the time span for statistical analysis of the planning indicator data.

[0011] The target demand planning unit is determined based on the perspective selection instruction.

[0012] Optionally, the above methods also include:

[0013] Obtain the user's perspective switching command;

[0014] Based on the hierarchical perspective switching command, the target requirement planning unit is split or summarized;

[0015] Based on the merged or split target demand planning units, update the planning indicator data of the planning indicator items associated with the split or summarized target demand planning units.

[0016] Optionally, updating the planning indicator data of the planning indicator items associated with the split or summarized target demand planning units, based on the split or summarized target demand planning units, includes:

[0017] Obtain the update level information of the target demand planning unit to which the split or summarized unit belongs;

[0018] Obtain the preset plan indicator data update rules for the initial hierarchical information and the updated hierarchical information;

[0019] The plan indicator data of the plan indicator items associated with the split or summarized target demand plan units are updated based on the preset plan indicator data update rules.

[0020] Optionally, the above methods also include:

[0021] Obtain the user's perspective switching command;

[0022] Based on the time perspective switching command, the planned period is broken down or summarized;

[0023] Based on the merged or split planning cycle, update the planning indicator data of the planning indicator items associated with the split or summarized target demand planning units.

[0024] Optionally, the above methods also include:

[0025] In response to the user's viewing command, the current calculation and processing logic and current hierarchical relationship configuration information of the target plan indicator item are displayed;

[0026] Upon receiving a user's configuration request for the target plan indicator item, a list of functions associated with the calculation and processing logic and a hierarchical relationship configuration table are displayed, so that the user can configure the target calculation and processing logic and hierarchical relationship configuration information according to the function list.

[0027] Secondly, embodiments of the present invention also provide an apparatus for determining business plan data, comprising:

[0028] Acquisition unit: Users acquire target demand planning units and target planning indicator items;

[0029] The calculation unit is used to calculate the planned indicator data of the target planned indicator item based on the current calculation processing logic of the target demand planning unit and the target planned indicator item, wherein the planned indicator data is obtained by calculation using an in-memory database;

[0030] To achieve the above objectives, according to a third aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium comprising a stored program, wherein the method for determining the business plan data described above is implemented when the program is executed by a processor.

[0031] To achieve the above objectives, according to a fourth aspect of the present invention, an electronic device is provided, comprising at least one processor and at least one memory connected to the processor; wherein the processor is configured to invoke program instructions in the memory to execute the method for determining the business plan data.

[0032] By employing the above technical solutions, embodiments of the present invention provide a method for determining business plan data. Currently, in the process of various supply chain planning processes in enterprises, existing methods typically use Excel spreadsheets or custom software development to meet this requirement. These solutions have poor adaptability, and real-time calculations require processing tens of millions of calculations through program code. The present invention obtains target demand planning units and target planning indicator items, and then calculates the planned indicator data of the target planning indicator items based on the current calculation processing logic of the target demand planning units and target planning indicator items. In the above solution, even when facing complex and massive amounts of data, target demand planning units and target planning indicator items can be obtained, facilitating the calculation of the planned indicator data of the target planning indicator items based on the current calculation processing logic of the target demand planning units and target planning indicator items. Compared to the current conventional methods, which are limited in speed and cannot provide page-level fast response, the above method of the present invention can use in-memory database technology to read necessary content such as specific product / location / customer information (member information), measurement indicator information, and hierarchy information into an in-memory database, forming an Intersection (in-memory instance). This in-memory data continuity and transaction processing consistency are maintained through persistence. Performing the corresponding splitting / aggregation calculations within memory greatly improves calculation speed and achieves page-level calculation response. The above description is merely an overview of the technical solution of this invention. To better understand the technical means of this invention and to implement it according to the contents of the specification, and to make the above and other objects, features, and advantages of this invention more apparent, specific embodiments of this invention are described below. Attached Figure Description

[0033] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0034] Figure 1 A flowchart illustrating a method for determining business plan data according to an embodiment of the present invention is shown;

[0035] Figure 2 This diagram illustrates a schematic structural block diagram of a device for determining business plan data according to an embodiment of the present invention.

[0036] Figure 3 A schematic structural block diagram of an electronic device provided by an embodiment of the present invention is shown. Detailed Implementation

[0037] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0038] To address the issue that enterprises typically use Excel spreadsheets or custom software development to implement various supply chain plans, these solutions have poor adaptability. Furthermore, real-time calculations require processing tens of millions of calculations through program code, which limits the calculation speed and prevents the provision of fast page-level response.

[0039] This invention provides a method for determining business plan data, such as... Figure 1 As shown, the method includes:

[0040] S101. Obtain the target demand planning unit and target planning indicator items.

[0041] For example, in real-world project applications where companies balance potential demand and supply through supply chain planning to maximize efficiency and profitability. One example is retrieving the planned indicator for a specific product under the Oreo cookie category, Oreo Chocolate Knot (47g), with packaging code 4010503. Another example is retrieving the planned indicator for a specific product under the Coca-Cola category, Coca-Cola Zero (330ml), with packaging code 89757. These methods can be executed by a server, a computer terminal, or even other smart mobile terminals; no limitation is made here.

[0042] It should be noted that planned metrics include, but are not limited to: algorithmic GMV, final GMV, current month's inventory replenishment pace, historical inventory replenishment pace, current month's safety stock days, historical sales volume, demand, and replenishment volume. The numerical data types for planned metrics can be: integer, decimal (with specified decimal places), or string.

[0043] GMV: Gross Merchandise Volume, which is the total value of goods and services traded within a certain period of time. It is often used in the e-commerce industry and generally includes the amount of orders placed but not yet paid.

[0044] S102. Based on the target demand planning unit and the current calculation processing logic of the target planning indicator item, calculate the planning indicator data of the target planning indicator item, wherein the planning indicator data is obtained by CPU calculation based on the in-memory database.

[0045] It should be noted that this solution utilizes in-memory database technology to read the specific demand planning units and planning indicator items into an in-memory database, creating an in-memory instance. This in-memory data continuity and transaction processing consistency are then maintained through persistence. Finally, appropriate splitting / aggregation processing is performed in memory to transform the data into vectorized data, enabling faster data retrieval and computation by the CPU.

[0046] The above solution ensures that even when faced with complex and massive amounts of data, target demand planning units and target planning indicator items can be obtained. This allows for the calculation of planned indicator data for the target planning indicator items based on the current calculation processing logic of these units and indicators. Compared to conventional methods using Excel spreadsheets or custom software development, which have poor adaptability and require real-time calculations involving tens of millions of calculations, limiting computational speed and failing to provide page-level fast response, the method of this invention utilizes in-memory database technology. It reads necessary content such as specific product / location / customer information (member information), measurement indicator information, and hierarchy information into an in-memory database, creating an Intersection (in-memory instance). Persistence maintains the continuity of in-memory data and the consistency of transaction processing. Performing corresponding splitting / aggregation calculations in memory significantly improves computational speed, achieving page-level computational response.

[0047] In some embodiments, the above method includes the following when executed:

[0048] S201, The memory database stores data in a columnar format data structure.

[0049] It should be noted that the data storage method in the in-memory database of the above scheme adopts a columnar data structure, which defines a format for representing plans in memory. By storing data in a columnar data format and optimizing the structure, the advantages of using a columnar data structure are that it saves storage space and reduces I / O. Furthermore, relying on the columnar data structure allows for full utilization of the CPU's advantages in computation, enabling vectorized calculations that facilitate multi-dimensional calculations and modifications such as aggregation and splitting, thus simultaneously meeting the needs of both OLAP and OLTP.

[0050] Understandably, traditional row-based storage is natural for OLTP scenarios, where most operations are performed on an entity basis—that is, adding, deleting, modifying, and querying an entire row of records. Storing a row of data in physically adjacent locations is clearly a good choice. However, for OLAP scenarios, a typical query requires traversing the entire table, performing grouping, sorting, and aggregation operations. In this case, the advantages of row-based storage disappear. Furthermore, in analytical databases (SQL), not all columns are often used; only certain columns of interest are processed. Irrelevant columns in a row must also be scanned. Column-based storage is designed for these needs.

[0051] OLTP (On-Line Transaction Processing): This refers to online transaction processing.

[0052] OLAP (On-Line Analytical Processing): This refers to online analytical processing.

[0053] In some embodiments, the above method further includes, when executed:

[0054] S301. Obtain the user's perspective selection instruction, wherein the perspective selection instruction includes the initial level information and initial planning period of the target demand planning unit. When the demand planning unit names are the same, the level information of the demand planning unit is used to distinguish different target demand planning units, and the planning period is the time span for statistically analyzing the planning indicator data.

[0055] Understandably, the system acquires viewpoint selection commands from users via an external input device, or an indicator on a computer display system using coordinate positioning. By changing the viewpoint of the initial hierarchical information and initial planning period of the planning unit using these commands, users can view planning indicator data for different levels within the corresponding planning period—a multi-view mode. The advantage of this operation is that because each level of unit information has its corresponding planning indicator data for the planning period, a one-to-one correspondence between levels is achieved. Users can clearly and directly view the specific planning indicator data for their current target unit's needs by switching between levels.

[0056] S302. Determine the target demand planning unit based on the perspective selection instruction.

[0057] Understandably, users can switch between hierarchical information based on perspective selection commands to determine their preferred perspective. Furthermore, based on the selected perspective, they can clearly and directly view the specific planned indicator data for their current target unit's needs. Depending on the user's perspective selection, the server will display the corresponding target requirement planning unit. The advantage is that multiple perspectives allow for quick viewing and browsing of the planned indicator data corresponding to different target requirement planning units. At the hierarchical level, whether at a large or small granular level, there is corresponding planned indicator data, providing concrete control over operational plan data at both macro and micro levels.

[0058] For example, if a user selects the perspective of "biscuits," the server confirms that "biscuits" is the broad category and uses it as the demand planning unit, displaying corresponding algorithms such as GMV, final GMV, current month's inventory replenishment schedule, historical inventory replenishment schedule, current month's safety stock days, historical sales, demand, and replenishment quantity. As another example, if a user switches from the perspective of "biscuits" to "beverages," the server confirms that "beverages" is the broad category and uses it as the new demand planning unit, displaying corresponding algorithms such as GMV, final GMV, current month's inventory replenishment schedule, historical inventory replenishment schedule, current month's safety stock days, historical sales, demand, and replenishment quantity.

[0059] In some embodiments, the above method, when executed, further includes:

[0060] S401, Obtain the user's perspective switching command.

[0061] Understandably, by obtaining perspective switching commands from users via an external input device, or an indicator for coordinate positioning on a computer display system, it is possible to flexibly switch between different perspectives and display the corresponding planned indicator data.

[0062] S402. Based on the hierarchical perspective switching instruction, split or summarize the target requirement planning unit.

[0063] It should be noted that when performing the above 401 steps, the corresponding target requirement planning unit will change continuously during the switching of different perspectives. Therefore, the target requirement planning unit must change with the switched perspective. The target requirement planning unit will be split into units when switching from a high-level perspective to a low-level perspective, and will be summarized when switching from a low-level perspective to a high-level perspective.

[0064] S403. Based on the merged or split target demand planning unit, update the planning indicator data of the planning indicator items associated with the split or summarized target demand planning unit.

[0065] Understandably, by changing the perspective of the initial hierarchical information and initial planning period of the planning unit using the above commands, users can view the planning indicator data within the planning period corresponding to different levels. The advantage of this operation is that because each level of unit information has its corresponding planning indicator data for the planning period, a one-to-one correspondence between levels is achieved. Users can clearly and directly view the specific planning indicator data for the user's current target unit requirements by switching the hierarchical information.

[0066] For example, the server has calculated through a multi-dimensional data processing engine that a discount promotion plan needs to be adopted and deployed in the next 3 months, and it is known that the plan will have an impact of +30% on the overall national sales plan. The server will split and allocate this 30% increment to different regions, different product categories and different time periods based on flexible allocation rules. Users can switch between different levels, such as the overall deployment and promotion plan for biscuit products at the national level, the local deployment and promotion plan for biscuits in a certain region, and the specific deployment and promotion plan for biscuits in a certain region in the most recent week.

[0067] In some embodiments, updating the planning indicator data of the planning indicator items associated with the split or summarized target demand planning units, based on the split or summarized target demand planning units, includes:

[0068] S501. Obtain the update level information of the target demand planning unit to which the split or summarized unit belongs.

[0069] Understandably, the hierarchy information is updated based on the user's understanding of the hierarchy of the target requirement planning units that are split or summarized.

[0070] For example, if a user switches their perspective on the target demand planning unit from the perspective of a product named Oreo to the perspective of a cookie, the hierarchical information will be updated to the higher level of the cookie.

[0071] For example, if a user switches the regional perspective of the target demand planning unit, such as from a regional perspective to a national perspective, the location level needs to be updated to the national level.

[0072] S502. Obtain the preset plan indicator data update rules for the initial hierarchical information and the updated hierarchical information.

[0073] It is understandable that, because the perspective is switched between different levels by splitting or summarizing, the planned indicator data corresponding to different levels will definitely be different after splitting or summarizing. Therefore, the planned indicator data will also be adjusted according to the corresponding splitting or summarizing. So, when obtaining the initial level information, it is also necessary to simultaneously obtain the preset planned indicator data update rules for updating the level information. Furthermore, according to the preset planned indicator data update rules, the corresponding data can be obtained when switching between different level perspectives.

[0074] S503. Update the plan indicator data of the plan indicator items associated with the split or summarized target demand plan units based on the preset plan indicator data update rules.

[0075] Based on the preset plan indicator data update rules obtained in step S502, the plan indicator data of the plan indicator items associated with the target plan requirement unit at the corresponding level of the current perspective are calculated from the split or summarized target plan requirement unit.

[0076] For example, aggregation calculations are first performed according to preset planned indicator data update rules. For instance, if the preset rule is summation, the result is 200. Simultaneously, the proportion of each subdivided planned indicator data is calculated. Based on specific business requirements, the aggregated value is modified; for example, if the aggregated value is 100, it is modified to 200. The modified value of 200 is then split according to the proportion of each subdivided planned indicator data calculated in the aggregation. After splitting, precision calculations are performed according to storage accuracy requirements. Finally, the split values ​​are summed, and the summed value is compared with the modified value. If there are differences, padding is performed to ensure value alignment.

[0077] It is understandable that the above calculation uses the SIMD method to perform fast aggregation calculations and stores the calculation results in a new column storage memory as shown in step S201.

[0078] SIMD stands for Single Instruction Multiple Data, a set of instructions that can copy multiple operands and pack them into a large register. For example, taking the addition instruction, a single-instruction, single-data CPU, after decoding the addition instruction, first accesses memory to obtain the first operand; then it accesses memory again to obtain the second operand; only then can it perform the summation operation. In a SIMD CPU, however, after instruction decoding, several execution units access memory simultaneously, obtaining all operands at once for calculation.

[0079] In some embodiments, the above method further includes, when executed:

[0080] S601, Obtain the user's time perspective switching command.

[0081] Understandably, by obtaining perspective switching commands from users via an external input device, or an indicator of the computer display system's coordinate positioning, it is possible to flexibly switch perspectives at different times and display corresponding planned indicator data.

[0082] S602. Based on the time perspective switching command, split or summarize the planned period.

[0083] It's important to note that time perspectives can be further subdivided into weekly, monthly, quarterly, and yearly perspectives. The planning target data for the same target demand planning unit will differ depending on the planning period corresponding to its time perspective. When a user sends a time perspective switching command, the planning period will be split or aggregated into the user-selected time perspective. The advantage is that it allows viewing the planning target data for different planning periods, further refining the granularity. Even for a specific product, at the lowest level and within the smallest planning period, one can understand its corresponding algorithmic GMV, final GMV, current month's inventory replenishment rhythm, historical inventory replenishment rhythm, current month's safety stock days, historical sales, demand, replenishment volume, etc. This low-level granularity and accuracy ensures the accuracy of high-level planning target data even with massive datasets.

[0084] S603. Based on the merged or split planning cycle, update the planning indicator data of the planning indicator items associated with the split or summarized target demand planning units.

[0085] For example, if the packaging code for Oreo cookies (47g) under the cookie category is 4010503, and the planning period from June 1, 2022 to July 1, 2022 is broken down into a planning period from June 1, 2022 to June 6, 2022, then the planning indicator data for the corresponding planning period will also be broken down accordingly. Following the breakdown method in step S503, the algorithmic GMV, final GMV, current month's inventory replenishment rhythm, historical inventory replenishment rhythm, current month's safety stock days, historical sales, demand, and replenishment volume are broken down into data corresponding to the planning period from June 1, 2022 to June 6, 2022. The advantage of this method is that it can display the planning indicator data within the smallest planning period for fine-grained product categories, which is more conducive to user decision-making and makes the planning indicator data corresponding to higher-level data more accurate.

[0086] In some embodiments, the above method further includes, when executed:

[0087] S701. In response to the user's viewing command, display the current calculation and processing logic and current hierarchical relationship configuration information of the target plan indicator item.

[0088] It is understandable that by obtaining viewing configuration commands issued by the user through an external input device, or an indicator for positioning on the computer display system using the horizontal and vertical coordinates, it is possible to view the current calculation and processing logic and current hierarchical relationship configuration information of the target plan indicator items.

[0089] For example, a user can click "View Configuration" with a mouse or touch the screen to see Oreo's configuration under the Cookie category, which also includes other brands of cookies. Another example is the calculation formula for Oreo's algorithmic GMV and final GMV for a specific planning period. The advantage of this approach is that it allows users to easily view the configuration relationships between target planning metrics, current calculation logic, and current hierarchical relationships within a visualization module. This facilitates configuration on the front-end page with simple operations and allows for flexible configuration of relationship parameters based on updates to planning metrics.

[0090] S702. Upon receiving a user's configuration request for the target plan indicator item, display a list of functions associated with the calculation processing logic and a hierarchical relationship configuration table, so that the user can configure the target calculation processing logic and hierarchical relationship configuration information according to the function list.

[0091] Understandably, after following the steps in S701, the user can further click "Edit Configuration" via mouse or touch screen. Upon receiving the user's configuration requirements for the target plan indicator items, the server will display the function list and hierarchical relationship configuration table related to the calculation and processing logic as editable, allowing the user to edit them according to their actual needs.

[0092] For example, when a promotional shopping festival is interspersed within the original planned period, the planned performance data for a specific product during the promotional shopping festival period, such as algorithmic GMV, is updated and edited based on actual sales data. The effect is that regardless of whether the perspective is high-level or low-level, or whether the planned period is short or long, the overall planned data can be edited at any time based on actual data, improving overall data accuracy and facilitating more accurate acquisition of planned performance data for target projects.

[0093] It should be noted that, as a response to the above... Figure 1 In addition to the implementation of the methods shown in various related embodiments, this invention also provides a device for determining business plan data, used for the above-mentioned... Figure 1The device is implemented using the methods shown in the above embodiments. This device embodiment corresponds to the foregoing method embodiments. For ease of reading, this device embodiment will not repeat the details of the foregoing method embodiments one by one, but it should be clear that the device in this embodiment can implement all the contents of the foregoing method embodiments.

[0094] like Figure 2 As shown, the device includes:

[0095] Acquisition unit 21 is used to acquire target demand planning units and target planning indicator items;

[0096] Calculation unit 22 is used to calculate the planned indicator data of the target planned indicator item based on the current calculation processing logic of the target demand planning unit and the target planned indicator item, wherein the planned indicator data is obtained by CPU calculation based on the in-memory database;

[0097] By employing the above technical solutions, embodiments of the present invention provide a method for determining business plan data. Currently, in the process of various supply chain planning processes in enterprises, existing methods typically use Excel spreadsheets or custom software development to meet this requirement. These solutions have poor adaptability, and real-time calculations require processing tens of millions of calculations through program code, limiting calculation speed and failing to provide page-level fast response. The present invention obtains target demand planning units and target planning indicator items, and calculates the planning indicator data of the target planning indicator items based on the current calculation processing logic of the target demand planning units and the target planning indicator items. The planning indicator data is obtained through CPU calculation based on an in-memory database. In the above solution,

[0098] This invention can retrieve target demand planning units and target planning indicator items even when dealing with complex and massive datasets. Based on the current calculation processing logic of these target demand planning units and target planning indicator items, it can calculate the planned indicator data for each target planning indicator item. Compared to conventional methods, the method described above utilizes in-memory database technology to read necessary content such as specific product / location / customer information (member information), measurement indicator information, and hierarchy information into an in-memory database, creating an Intersection (in-memory instance). Persistence is used to maintain the continuity of in-memory data and the consistency of transaction processing. Performing corresponding splitting / aggregation calculations in memory significantly improves calculation speed, achieving page-level calculation responsiveness.

[0099] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and by adjusting kernel parameters, an automated call-based communication method can be implemented to address the problem that existing call-based communication methods cannot meet the requirements of forwarding functionality.

[0100] This invention provides a storage medium storing a program that, when executed by a processor, implements a method for determining the business plan data.

[0101] This invention provides a processor for running a program, wherein the program executes a method for determining business plan data during runtime.

[0102] This invention provides a device 30, such as... Figure 3 As shown, the device includes at least one processor 31, at least one memory 32 connected to the processor, and a bus 33; wherein the processor 31 and the memory 32 communicate with each other through the bus 33; the processor 31 is used to call program instructions in the memory to execute the above-mentioned method for determining business plan data.

[0103] The devices mentioned in this article can be servers, PCs, tablets, mobile phones, etc.

[0104] This application also provides a computer program product that, when executed on a data processing device, is suitable for executing an initialization program having the following method steps: obtaining target demand planning units and target planning indicator items;

[0105] Based on the target demand planning unit and the current calculation processing logic of the target planning indicator item, the planned indicator data of the target planning indicator item is calculated, wherein the planned indicator data is obtained by CPU calculation based on the in-memory database.

[0106] Furthermore, the above methods include:

[0107] The in-memory database stores data in a columnar format data structure.

[0108] Furthermore, the above methods also include:

[0109] Obtain the user's perspective selection instruction, wherein the perspective selection instruction includes the initial level information and initial planning period of the target demand planning unit. When the demand planning unit names are the same, the level information of the demand planning unit is used to distinguish different target demand planning units. The planning period is the time span for statistical analysis of the planning indicator data.

[0110] The target demand planning unit is determined based on the perspective selection instruction.

[0111] Furthermore, the above methods also include:

[0112] Obtain the user's perspective switching command;

[0113] Based on the hierarchical perspective switching command, the target requirement planning unit is split or summarized;

[0114] Based on the merged or split target demand planning units, update the planning indicator data of the planning indicator items associated with the split or summarized target demand planning units.

[0115] Furthermore, the step of updating the planning indicator data of the planning indicator items associated with the split or summarized target demand planning units, based on the split or summarized target demand planning units, includes:

[0116] Obtain the update level information of the target demand planning unit to which the split or summarized unit belongs;

[0117] Obtain the preset plan indicator data update rules for the initial product information and the updated level information;

[0118] The plan indicator data of the plan indicator items associated with the split or summarized target demand plan units are updated based on the preset plan indicator data update rules.

[0119] Furthermore, the above methods also include:

[0120] Obtain the user's perspective switching command;

[0121] Based on the time perspective switching command, the planned period is broken down or summarized;

[0122] Based on the merged or split planning cycle, update the planning indicator data of the planning indicator items associated with the split or summarized target demand planning units.

[0123] Furthermore, the above methods also include:

[0124] In response to the user's viewing command, the current calculation and processing logic and current hierarchical relationship configuration information of the target plan indicator item are displayed;

[0125] Upon receiving a user's configuration request for the target plan indicator item, a list of functions associated with the calculation processing logic and a hierarchical relationship configuration table are displayed, allowing the user to configure the target calculation processing logic and hierarchical relationship configuration information according to the function list. This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the process... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0126] In a typical configuration, the device includes one or more processors (CPUs), memory, and a bus. The device may also include input / output interfaces, network interfaces, etc.

[0127] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM, and memory includes at least one memory chip. Memory is an example of computer-readable media.

[0128] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0129] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0130] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0131] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for determining business plan data, comprising: obtaining a target demand plan unit and a target plan index item; calculating plan index data of the target plan index item based on a current calculation processing logic of the target demand plan unit and the target plan index item, wherein the plan index data is obtained by CPU calculation based on an in-memory database; further comprising: obtaining a view selection instruction of a user, wherein the view selection instruction comprises initial hierarchical information and an initial plan period to which the target demand plan unit belongs, and in the case of the same demand plan unit name, the hierarchical information is used to distinguish different target demand plan units, and the plan period is a time span for calculating the plan index data; determining the target demand plan unit based on the view selection instruction; further comprising: obtaining a view switching instruction of the user; splitting or aggregating the target demand plan unit based on the hierarchical view switching instruction; updating plan index data of a plan index item associated with the split or aggregated target demand plan unit based on the split or aggregated target demand plan unit; the target demand plan unit is split when switching from a high level to a low level, and is aggregated when switching from a low level to a high level; the updating of the plan index data of the plan index item associated with the split or aggregated target demand plan unit based on the split or aggregated target demand plan unit comprises: obtaining updated hierarchical information to which the split or aggregated target demand plan unit belongs; obtaining a pre-designed plan index data updating rule of the initial hierarchical information and the updated hierarchical information; updating the plan index data of the plan index item associated with the split or aggregated target demand plan unit based on the pre-designed plan index data updating rule; further comprising: obtaining a view switching instruction of the user; splitting or aggregating the plan period based on a time view switching instruction; updating the plan index data of the plan index item associated with the split or aggregated target demand plan unit based on the split or aggregated plan period; further comprising: displaying current calculation processing logic and current hierarchical relationship configuration information of the target plan index item in response to a viewing instruction of the user; in the case of receiving a configuration demand of the target plan index item from the user, displaying a function list associated with the calculation processing logic and a hierarchical relationship configuration table, so that the user configures target calculation processing logic and hierarchical relationship configuration information according to the function list. 2.The method of claim 1, wherein: the in-memory database stores data in a columnar format data structure. 3.An apparatus based on the method for determining business plan data according to any one of claims 1-2, comprising: an obtaining unit configured to obtain a target demand plan unit and a target plan index item. ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ A computing unit is configured to calculate the plan index data of the target plan index item based on a current calculation processing logic of the target demand plan unit and the target plan index item, wherein the plan index data is obtained by using an in-memory database calculation.

4. A computer readable storage medium, comprising a stored program, wherein the program, when executed by a processor, implements the method for determining business plan data according to any one of claims 1 to 2.

5. An electronic device, comprising at least one processor and at least one memory connected to the processor, wherein the processor is configured to invoke program instructions in the memory to execute the method for determining business plan data according to any one of claims 1 to 2.

6. A computer readable storage medium, comprising a stored program, wherein the program, when executed by a processor, implements the method for determining business plan data according to any one of claims 1 to 2. ​

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