Method and electronic device for providing sales plan data

Abstract

The embodiment of the present application discloses a method for providing sales plan data and an electronic device, the method comprises: obtaining first sales plan data, the first sales plan data comprises sales plan data expressed by a dimension combination of a plurality of initial dimensions and corresponding numerical values; according to a preset dimension conversion scheme, the dimension combination of the plurality of initial dimensions is converted into a dimension combination of a plurality of target dimensions, and numerical value conversion is performed; the dimension combination of the plurality of target dimensions and the corresponding converted numerical values are saved as second sales plan data, so as to provide a plurality of sales plan data expressed by a plurality of dimension combinations for the user. Through the embodiment of the present application, the sales plan data expression demand of multiple scenes and multiple dimensions can be more effectively adapted.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to methods and electronic devices for providing sales plan data. Background Technology

[0002] Sales forecasting refers to using sales forecasting models provided by a commodity supply chain service system or customized by the merchant to predict future sales based on past sales data. Furthermore, based on sales forecasting, merchants can also develop sales plans, that is, they can define planned sales volumes for future periods, for example, for advance inventory preparation and warehousing resource planning.

[0003] Traditional sales planning schemes typically operate on a product + warehouse + daily / weekly basis, generating a sales plan data set daily or weekly to help merchants plan future replenishment quantities for specific products in specific warehouses. While this approach helps merchants generate sales plans, more complex scenarios may arise in practice. In these scenarios, product relationships can be intricate, especially with SKU-to-product relationships potentially being one-to-many or many-to-one. Product sales may also be strongly correlated with participation in specific marketing campaigns, which can be flexible and varied, with the same product participating in multiple different campaigns. Furthermore, a single product may correspond to multiple sales warehouses, requiring separate sales plans for each warehouse. Moreover, different users may have different needs for the sales plan data; some may need nationwide sales forecasts for operational planning, while others may need to replenish stock in advance based on sales forecasts for specific sales warehouses. In these complex scenarios, traditional sales planning schemes may become ineffective or fail to accurately represent the specific sales plan. However, if the algorithm is modified to adapt to various changing scenarios or data usage requirements, the system transformation cost will be relatively high, and multiple different algorithms need to be maintained for different scenarios and usage requirements, which will also result in greater overhead in terms of system computing resources and storage resources. Summary of the Invention

[0004] This application provides a method and electronic device for providing sales plan data, which can more effectively adapt to the needs of expressing sales plan data in multiple scenarios and dimensions.

[0005] This application provides the following solution:

[0006] A method for providing sales plan data includes:

[0007] Obtain the first sales plan data, which includes sales plan data expressed by a combination of multiple initial dimensions and their corresponding values;

[0008] According to the preset dimension transformation scheme, the dimension combination composed of multiple initial dimensions is transformed into a dimension combination composed of multiple target dimensions, and then numerical transformation is performed;

[0009] The combination of the multiple target dimensions and the corresponding converted values ​​are saved as the second sales plan data, so as to provide users with multiple sales plan data expressed through multiple combinations of dimensions.

[0010] The acquisition of data regarding the first sales plan includes:

[0011] Monitor events related to changes in sales plan data;

[0012] When the sales plan data change event is detected, the first sales plan data after the change is obtained.

[0013] The process of obtaining the revised first sales plan data includes:

[0014] Obtain the first sales plan data generated by the sales forecasting algorithm in the new forecast period.

[0015] The process of obtaining the revised first sales plan data includes:

[0016] When an event is detected in which a user modifies the sales plan data, the modified first sales plan data is retrieved.

[0017] The dimension transformation scheme includes: a dimension transformation path, and a numerical transformation strategy corresponding to the dimension transformation between adjacent nodes on the dimension transformation path;

[0018] The step of converting the combination of multiple initial dimensions into a combination of multiple target dimensions according to a preset dimension conversion scheme, and performing numerical conversion, includes:

[0019] The dimension combination consisting of the multiple initial dimensions is converted into a dimension combination consisting of multiple target dimensions according to the dimension transformation path.

[0020] According to the numerical transformation strategy, the sales plan data is determined by the combination of multiple target dimensions after transformation.

[0021] If there are multiple dimensions to be transformed, the dimension transformation path includes multiple nodes, and the multiple nodes correspond to different combinations of dimensions.

[0022] The step of converting the combination of multiple initial dimensions into a combination of multiple target dimensions according to a preset dimension conversion scheme, and performing numerical conversion, includes:

[0023] According to the dimensional transformation path and the numerical transformation strategy, multiple transformation steps are performed to transform the first sales plan data into the second sales plan data; wherein, each step transforms a single dimension.

[0024] The initial dimensions include: minimum sales unit (SKU) dimension, sales channel dimension, and first spatial dimension;

[0025] The multiple target dimensions include: the product dimension and the second spatial dimension, the second spatial dimension including the warehouse dimension;

[0026] The first intermediate node on the dimensional transformation path is a dimensional combination consisting of SKU dimension, sales channel dimension, and second space dimension, and the second intermediate node is a dimensional combination consisting of product dimension, sales channel dimension, and second space dimension;

[0027] The numerical conversion strategy from the initial node to the first intermediate node is used to: determine the sales plan values ​​corresponding to the current SKU, sales channel and each second space when the current first space is divided into multiple second spaces according to the pre-configured split ratio;

[0028] The numerical conversion strategy from the first intermediate node to the second intermediate node is used to: determine the sales plan values ​​corresponding to the current sales channel, the second space and each product when converting the SKU dimension to the product dimension, based on the pre-configured one-to-many and / or many-to-one correspondence between SKU and product;

[0029] The numerical conversion strategy from the second intermediate node to the target node is used to aggregate the sales plan values ​​of the same product and the same second space across multiple different sales channels to determine the sales plan value of the same product in the same second space.

[0030] This also includes:

[0031] The sales plan values ​​generated by the intermediate nodes on the dimensional transformation path, which are expressed by dimensional combinations composed of multiple intermediate dimensions, are saved to provide various sales plan data expressed by multiple dimensional combinations.

[0032] In this process, the nodes on the dimension transformation path perform dimension and numerical transformations through an event-driven streaming data processing method; the events include the event of the previous node completing its transformation.

[0033] The dimensions to be converted include those related to the planning entity, and the conversion requests corresponding to different planning entities are processed in a distributed and isolated manner by different queues.

[0034] This also includes:

[0035] The dimensional transformation path and numerical transformation strategy are configurable to meet the needs of transformation between different dimensional combinations in different scenarios.

[0036] Specifically, a general data storage model is used to save sales plan data expressed in combination with multiple dimensions. The general data storage model includes the following fields: entity type, entity ID, spatial type, spatial identifier, time type, and planned time. This allows for the combination of multiple dimensions, such as entity dimensions, spatial dimensions, and / or time dimensions, to be used to express the sales plan data.

[0037] In this approach, sales channels and space are combined into a single dimension to express the sales plan data within the marketing campaign dimension.

[0038] A method for providing sales plan data includes:

[0039] A sales plan data query interface is provided. The query interface includes multiple tag options to correspond to multiple dimension combination methods. The multiple dimension combination methods correspond to multiple sales plan data. The multiple sales plan data includes: a first sales plan data expressed by a dimension combination composed of multiple initial dimensions, and at least one second sales plan data generated after performing dimension transformation and numerical transformation on the first sales plan data.

[0040] After receiving a selection operation for one of the target tag options, the query condition information is received;

[0041] Retrieve sales plan data that meets the query conditions and is expressed through the dimension combination method corresponding to the target label options, and display it in the query interface.

[0042] An apparatus for providing sales plan data includes:

[0043] The first sales plan data acquisition unit is used to acquire the first sales plan data, which includes sales plan data expressed by a combination of multiple initial dimensions and corresponding values.

[0044] The conversion unit is used to convert the combination of multiple initial dimensions into a combination of multiple target dimensions according to a preset dimension conversion scheme, and to perform numerical conversion.

[0045] The second sales plan data generation unit is used to save the dimension combination composed of the multiple target dimensions and the corresponding converted values ​​as the second sales plan data, so as to provide multiple sales plan data expressed through multiple dimension combinations.

[0046] An apparatus for providing sales plan data includes:

[0047] A tag option providing unit is used to provide a sales plan data query interface. The query interface includes multiple tag options to correspond to multiple dimension combination methods. The multiple dimension combination methods correspond to multiple sales plan data. The multiple sales plan data includes: a first sales plan data expressed by a dimension combination composed of multiple initial dimensions, and at least one second sales plan data generated after performing dimension transformation and numerical transformation on the first sales plan data.

[0048] The query condition receiving unit is used to receive query condition information after receiving a selection operation for one of the target label options;

[0049] The sales plan data display unit is used to obtain sales plan data that meets the query conditions and is expressed through the dimension combination method corresponding to the target label option, and to display it in the query interface.

[0050] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of any of the preceding methods.

[0051] An electronic device, comprising:

[0052] One or more processors; and

[0053] A memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method described in any of the preceding descriptions.

[0054] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0055] Through this embodiment, after obtaining the first sales plan data, a dimensional combination consisting of multiple initial dimensions can be converted into a dimensional combination consisting of multiple target dimensions according to a preset dimensional transformation scheme, and then numerical transformation can be performed to generate a dimensional combination consisting of multiple target dimensions and the corresponding transformed values. These dimensional combinations consisting of multiple target dimensions and the corresponding transformed values ​​can then be saved as the second sales plan data. This provides users with multiple sales plan data expressed through various dimensional combinations. In this way, in scenarios strongly related to front-end operations and complex product relationships, dimensional transformation can generate sales plan data expressed through more dimensional combinations, thereby meeting the needs of users with different identities for sales plan data without needing to modify the algorithm to adapt to changes in scenarios or changes in the needs of different users. Therefore, it is more efficient and helps save system computing and storage resources.

[0056] In addition, by using a universal storage model for sales plan details, sales plan data expressed in various combinations of dimensions can be saved through a unified storage model, without the need to design different data tables, thus further saving storage resources.

[0057] Furthermore, by employing an event-driven streaming process, the system maximizes automation through mechanisms such as automatic data recalculation triggered by data changes and a strategy center, minimizing manual intervention and effectively improving operational efficiency. In addition, the use of an abstract and extensible design ensures loose coupling, reusability, and rapid scalability of the overall system code modules, thereby enhancing development efficiency.

[0058] Of course, any product implementing this application does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a schematic diagram of the system architecture provided in the embodiments of this application;

[0061] Figure 2 This is a flowchart of the first method provided in the embodiments of this application;

[0062] Figure 3This is a schematic diagram of an event-triggered streaming process provided in an embodiment of this application;

[0063] Figure 4-1 , 4-2 This is a schematic diagram of the client interface provided in an embodiment of this application;

[0064] Figure 5 This is a flowchart of the second method provided in the embodiments of this application;

[0065] Figure 6 This is a schematic diagram of the first device provided in the embodiments of this application;

[0066] Figure 7 This is a schematic diagram of the second device provided in the embodiments of this application;

[0067] Figure 8 This is a schematic diagram of the electronic device provided in the embodiments of this application. Detailed Implementation

[0068] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0069] To facilitate understanding of the technical solutions provided in the embodiments of this application, it should first be noted that in some scenarios, the product supply chain may be strongly related to front-end operations. For example, a system may have frequently changing operational actions, including the frequent launch of various new marketing activities. Multiple different entities (products, SKUs (smallest sales units), etc.) may participate in the same marketing activity, and the same entity may participate in different marketing activities. Furthermore, different operational activities have different lifecycles, and an entity may withdraw midway through an operational activity, and so on. In this case, since the sales performance of a specific entity may differ under various marketing activities, specific sales plan data may need to be expressed separately for multiple different marketing activities to facilitate activity planning. Additionally, the same product (Item) may be associated with multiple SKUs (smallest sales units; the product details page usually corresponds to a specific Item, but when a user actually places an order, they need to select one or more SKUs to purchase. For example, an Item may be a certain model of a certain brand of mobile phone, but it can be combined with colors, memory, etc., to create four SKUs, etc.). In the aforementioned scenarios strongly related to front-end operations, specific marketing activities may be conducted at the SKU level. For example, only some SKUs of the same product might participate in a certain promotion. Therefore, for users such as operations planners, specific sales plan data can be expressed at the SKU level, but it is not necessary to specify each sales warehouse. It is only necessary to know the sales plan data for specific SKUs within the national or other regional scope.

[0070] In other words, based on the needs of users such as operations planners for sales plan data, specific sales forecasting algorithms can predict the sales volume that a specific SKU can achieve in a certain marketing campaign (also known as a channel). (At this time, the sales volume is usually not divided by warehouse; for example, it is assumed to be the national sales volume). This data is meaningful to users such as operations planners. However, for users such as inventory control managers, the specific need for sales plan data is to determine how to replenish stock to various sales warehouses, which sales warehouses have which products that need replenishment, and the replenishment quantity. In this case, the specific sales plan data needs to be specified down to the product and warehouse dimensions; that is, it is necessary to know the sales volume of a specific product in each warehouse in order to replenish stock in a timely manner, and so on.

[0071] In traditional product information service systems, the relationship between SKUs and products is typically one-to-one. For example, a mobile phone product might have four SKUs, each corresponding to a different product. If sales forecasts are based on SKUs, these forecasts can be directly used as the sales forecasts for the corresponding products. In other words, SKUs and products belong to the same dimension. However, in scenarios strongly related to front-end operations, the relationship between SKUs and products can be more complex, exhibiting one-to-many or many-to-one relationships. For instance, during marketing campaigns, two or more products (e.g., two different specifications of the same brand) might be combined into a single SKU, resulting in a one-to-many relationship between SKUs and products. Alternatively, multiple different SKUs might be generated for the same product, participating in different promotions, leading to a many-to-one relationship between SKUs and products, and so on. In these cases, sales plan data based on SKUs cannot be directly used as sales plan data for the product dimension. In other words, the SKU dimension and the product dimension belong to different dimensions.

[0072] In summary, in scenarios strongly related to front-end operations or similar situations, sales forecasts are significantly influenced by frequent and variable operational actions. Complex relationships exist between SKUs and products, such as one-to-many or many-to-one relationships. In platform-operated scenarios, specific products may be stocked in multiple sales warehouses, and so on. Given these complexities, the expression of sales plan data may involve multiple dimensions and combinations thereof. Furthermore, different user roles may focus on different dimensions due to varying needs for sales plan data. For example, as mentioned earlier, since operational actions are typically SKU-based, those developing operational plans need to focus on operational activities & nationwide and SKU dimensions, without much concern for warehouse distribution. Conversely, those managing inventory need to reflect the final sales plan in terms of product and sales warehouse dimensions, and so on. In other words, compared to traditional methods of expressing sales plan data based on inventory + warehouse + daily / weekly dimensions, this embodiment requires expressing sales plan data from more dimensions. Furthermore, to meet the diverse needs of users with different roles for sales plan data, it may be necessary to provide ways to express the sales plan data through combinations of various dimensions. For example, based on the SKU dimension, the national sales volume that can be obtained when participating in different marketing activities can be predicted (and predictions can also be made separately for different time dimensions such as different dates). This data can be provided to users such as those who plan activities. When outputting sales plan data to users such as inventory control managers, sales plan data for specific products in specific sales warehouses can be provided, and so on.

[0073] To achieve the above objectives, one approach is to design different sales forecasting algorithms for various scenarios or different users' needs for sales plan data. These different algorithms can generate specific sales plan data based on various combinations of dimensions. However, as mentioned in the background section, this approach incurs high system modification costs and requires maintaining multiple different algorithms for various scenarios and usage requirements, resulting in significant overhead in terms of system computing and storage resources.

[0074] To address the above issues, this application provides a dimension transformation implementation scheme. Specifically, a specific sales forecasting algorithm can generate sales plan data for a single dimension combination. This data can then be transformed to represent the sales plan data using other dimension combinations, thereby obtaining multiple sets of sales plan data corresponding to various dimension combinations. This satisfies the sales plan data usage needs of users in different scenarios and with different identities. For example, a specific sales forecasting algorithm can generate sales plan data based on a certain dimension combination (e.g., SKU + marketing activities & nationwide). Then, through dimension transformation and data transformation, it can generate sales plan data expressed using other dimension combinations (e.g., product + sales location / warehouse). This allows the sales plan data to be expressed using multiple different dimension combinations, enabling users with different roles to obtain the required sales plan data from the desired dimension combinations.

[0075] In addition, it offers real-time sales plan data recalculation services. This means it not only generates sales plan data based on the periodic execution of sales forecasting algorithms, but also supports collaborative manual modification of the algorithm's prediction results. These modifications can be made whenever needed, allowing for a faster and better response to the changing impact of marketing campaigns, upgrading the original "day-level" planning capabilities to the "minute-level." Furthermore, each change in sales plan data triggers a specific dimension transformation process, producing sales plan data expressed through various combinations of dimensions to meet the needs of different user roles.

[0076] Furthermore, to achieve unified storage of sales plan data expressed through various dimensional combinations, a universal sales plan detail data storage model can be provided. This allows the same data storage model to be used when expressing sales plan data through different dimensional combinations, enabling storage in the same database table. Specifically, the dimensions related to sales plans can be divided into several major categories, such as entity dimensions, spatial dimensions, and time dimensions. Specific entities, spaces, and times can be further subdivided into various types of subdivided dimensions. For example, entities can be subdivided into product, SKU, and goods dimensions; spatial dimensions can be divided into sales location / warehouse, region, and nationwide dimensions according to different granularities; time dimensions can include daily, weekly, and monthly dimensions. The various combinations of dimensions are mainly obtained by selecting and combining either the entity subdivisions or the spatial subdivisions. For example, SKU + nationwide, SKU + sales location / warehouse, goods + sales location / warehouse, etc. Therefore, after classifying multiple dimensional categories, the sales plan data storage model can include fields such as entity type, entity ID, spatial type, spatial identifier, time type, and planned time. In this way, a unified expression of various combinations of dimensions can be achieved through this data storage model. Furthermore, in specific implementations, the data storage model can also include fields related to specific sales plan values, such as predicted values ​​(values ​​predicted by the algorithm), collaborative values ​​(values ​​modified through manual collaboration), and final values ​​(the final effective value selected from the predicted values ​​or collaborative values).

[0077] Regarding the sales channel (including various marketing activities) dimension, since some dimension combinations may not require information about this dimension (for example, the sales plan data ultimately provided to inventory control managers does not need to include the marketing activity / channel dimension, but only the product dimension and the sales location / warehouse dimension), to ensure the universality of the data storage model, for cases where sales channels need to be expressed, sales channels and spatially related dimensions can be merged into a single dimension. For example, "SKU + Sales Channel & Nationwide" expresses the predicted sales volume of a specific SKU participating in a certain sales channel nationwide. "SKU + Sales Channel & Sales Location / Warehouse" expresses the predicted sales volume of a specific SKU within the sales range corresponding to a specific sales location / warehouse under a certain sales channel. If the sales channel dimension is not needed, then the information related to that sales channel does not need to be reflected in the dimension combination, such as "Product + Sales Location / Warehouse," etc.

[0078] Specifically, from a system architecture perspective, see Figure 1This application embodiment can provide sales planning-related functional modules through a supply chain system, etc. These functional modules can include a server and a client. On the server side, a sales forecasting algorithm can be provided, which can output sales plan data expressed based on a first dimension combination. Additionally, optionally, a user collaboration workbench can be provided, allowing users to modify the sales plan data output by the algorithm in real time according to actual needs. Furthermore, an event monitoring module can be provided to monitor changes to sales plan data (including events where the algorithm outputs new sales plan data, or events where manual collaboration modifies certain sales plan data, etc.), triggering specific dimension transformation processes. To complete the specific dimension and data transformations, specific dimension transformation paths and numerical transformation strategies can be pre-configured. The dimension transformation path specifies which initial dimensions will be transformed to which target dimensions. This path can also include intermediate nodes. When transformations between multiple dimensions are required, intermediate nodes can be used to complete the transformation step-by-step, with each step focusing on one dimension, etc. Numerical transformation strategies specifically determine how to convert sales plan data, expressed through a combination of multiple dimensions before transformation, into sales plan data expressed through a combination of multiple dimensions after transformation. In practical applications, the specific dimension transformation paths and numerical transformation strategies can be configurable. For example, a configuration center can be provided with a visual configuration interface, allowing users to quickly configure specific paths and strategies. This allows for the configuration of different dimension transformation paths and numerical transformation strategies according to different actual needs, ensuring the overall implementation adheres to a flexible and dynamic design philosophy of pluggable, configurable, and reusable features. It supports rapid expansion and development of new numerical transformation strategies and quick configuration of different transformation paths for different scenarios, meeting diverse needs for the use and analysis of sales plan data in various scenarios. On the client side, a sales plan data query interface can be provided. After a user initiates a query request, various sales plan data expressed through different combinations of dimensions can be displayed. For example, multiple tag options can be provided, each corresponding to a different dimension combination method, etc.

[0079] The specific implementation schemes provided in the embodiments of this application will be described in detail below.

[0080] Example 1

[0081] First, this first embodiment provides a method for providing sales plan data from the perspective of the aforementioned server, see [link to previous document]. Figure 2 The method may specifically include:

[0082] S201: Obtain first sales plan data, which includes sales plan data expressed by a combination of multiple initial dimensions and their corresponding values.

[0083] The first sales plan data can specifically be the sales plan data output by a sales prediction algorithm. Alternatively, since this embodiment supports collaboration between humans and algorithms, it can also include sales plan data after human intervention to collaboratively modify the predicted values ​​output by the algorithm. Specifically, the first sales plan data can include a sales plan value expressed through a combination of multiple initial dimensions. These initial dimensions can be pre-specified. For example, in a specific implementation scenario, the initial dimensions could include SKU dimension + marketing activity & nationwide. Of course, in other scenarios, other initial dimensions can also be specified, and the sales prediction algorithm can output specific sales plan values ​​according to the specified initial dimension combination. The sales plan value can specifically be the sales prediction result output by the algorithm, etc. For example, for a certain SKU, if it is predicted that the SKU can achieve sales of 1000 units nationwide on May 22nd through participation in a certain marketing activity, then the initial dimensions include SKU + marketing activity & nationwide, and the sales plan value is 1000, etc.

[0084] In specific implementation, the embodiments of this application can execute the specific dimension transformation process based on event triggering. Specifically, events related to changes in sales plan data can be monitored. When such a sales plan data change event is detected, the changed sales plan data can be obtained as the first sales plan data in the embodiments of this application.

[0085] Specifically, changes to sales plan data may occur under the following circumstances: First, since sales forecasting algorithms typically predict sales data on a daily basis, a change event occurs each time the algorithm generates new sales plan data. In this case, the first sales plan data generated by the sales forecasting algorithm in the new forecast period can be obtained.

[0086] Alternatively, in specific implementations, for scenarios strongly related to front-end operations, in order to better adapt to the flexible and ever-changing actions of front-end operations, it is also possible to support manual collaboration to modify the sales plan data output by the algorithm. In this way, it can support the automatic generation of near real-time multi-dimensional sales plan data at the minute level, meeting the real-time requirements of sales plan data.

[0087] In this case, such manual collaborative modification events can also be monitored. When an event is detected in which a user modifies a certain sales plan data, the modified sales plan data can be obtained as the first sales plan data in this application embodiment, and so on.

[0088] S202: According to the preset dimension transformation scheme, the dimension combination composed of the multiple initial dimensions is transformed into a dimension combination composed of multiple target dimensions, and numerical transformation is performed.

[0089] After obtaining the initial sales plan data, the combination of multiple initial dimensions can be converted into a combination of multiple target dimensions according to a preset dimension conversion scheme. Furthermore, numerical conversion can also be performed. That is, the specific dimension conversion scheme allows configuration of which dimensions to convert between and the corresponding numerical conversion methods. For example, if the initial dimensions include SKU and marketing activities & nationwide, and it needs to be converted to SKU + marketing activities & sales regions / warehouses, then during numerical conversion, the sales plan value corresponding to the marketing activities & nationwide is split into multiple sales regions / warehouses, obtaining the sales volume that the SKU can obtain in each sales region / warehouse when participating in the marketing activity. For example, if a certain SKU has a sales target of 1000 for a certain marketing campaign nationwide, and this SKU is associated with three sales warehouses, namely warehouse A, B, and C, then the split sales target data can include the sales target value for the SKU plus the marketing campaign and sales warehouse A, for example, it might be 400; the sales target value for the SKU plus the marketing campaign and sales warehouse B, for example, it might be 300; the sales target value for the SKU plus the marketing campaign and sales warehouse C, for example, it might be 300, and so on.

[0090] In one specific implementation, the dimension transformation scheme may include: a dimension transformation path, and a numerical transformation strategy corresponding to the dimension transformation between adjacent nodes on the dimension transformation path. Thus, during the transformation process, the dimension combination consisting of multiple initial dimensions can first be transformed into a dimension combination consisting of multiple target dimensions according to the dimension transformation path; then, the specific sales plan value can be numerically transformed according to the numerical transformation strategy.

[0091] Specifically, the dimensional transformation process may involve the transformation of multiple dimensions. For example, for entity-related dimensions, it needs to be transformed from SKU to product dimension; for spatial dimensions, it needs to be transformed from nationwide to sales location warehouse, and so on. To facilitate dimensional transformation, the dimensional transformation path can include multiple nodes, each corresponding to a different combination of dimensions. This allows for multiple steps of transformation according to the dimensional transformation path and the numerical transformation strategy, in order to transform the first sales plan data into the second sales plan data. Each step can perform a transformation on a single dimension.

[0092] For example, in one specific implementation, the initial dimensions might include: a minimum sales unit (SKU) dimension, sales channels, and a first spatial dimension (e.g., nationwide or a specific province / city). The target dimensions might include: a product dimension and a second spatial dimension, where the second spatial dimension includes a warehouse dimension (i.e., a sales location warehouse, etc.). Since a conversion from the SKU dimension to the product dimension and from the sales channel & nationwide dimension to the sales location warehouse dimension is required, multiple dimension conversions are involved. Therefore, multiple intermediate nodes can be set in the dimension conversion path. This allows for multiple conversion steps to achieve the final conversion goal, and in each conversion step, only the same dimension needs to be converted.

[0093] For example, specifically, the dimension combinations consisting of multiple intermediate dimensions corresponding to intermediate nodes on the dimension transformation path include: a dimension combination consisting of SKU dimension, sales channel dimension and second space dimension (e.g., sales location warehouse), and a dimension combination consisting of product dimension, sales channel dimension and second space dimension.

[0094] That is, assuming the first spatial dimension is the whole country and the second spatial dimension is the sales location and warehouse, the nodes on the specific dimension transformation path can include:

[0095] Starting point: SKU + sales channels + nationwide;

[0096] First intermediate node: SKU + sales channel + sales location warehouse;

[0097] The second intermediate node: goods + sales channels + sales warehouses;

[0098] Target nodes: Goods + destination warehouses.

[0099] Additionally, it can provide corresponding numerical conversion strategies when converting between adjacent nodes. Specifically, the numerical conversion strategy from the initial node to the first intermediate node is used to determine, based on a pre-configured splitting ratio, the sales plan values ​​corresponding to the current SKU, sales channel, and each second space when the first space is split into multiple second spaces. The second space dimension can be a specific sales location / warehouse dimension, etc. The same SKU can correspond to multiple different sales locations / warehouses. Therefore, when converting from the first space dimension to the second space dimension, one sales plan data can be split into multiple data, each corresponding to a specific sales location / warehouse. Simultaneously, the specific sales plan value in each sales plan data can be determined according to the numerical conversion strategy. For this purpose, information such as the splitting ratio between each sales location / warehouse can be pre-configured. This allows for specific numerical splitting according to the specific splitting ratio, thereby determining the sales plan values ​​corresponding to each of the multiple split sales plan data.

[0100] The numerical conversion strategy from the first intermediate node to the second intermediate node is used to: determine the planned sales values ​​corresponding to the current sales channel, the second space, and each product when converting the SKU dimension to the product dimension, based on the pre-configured one-to-many and / or many-to-one correspondence between SKUs and products. Specifically, after converting the first space dimension to the second space dimension, the conversion from the SKU dimension to the product dimension can be performed. In this embodiment, the specific SKU and product relationship may be one-to-many or many-to-one; therefore, this correspondence can be pre-configured and saved in the specific numerical conversion strategy. Thus, when performing the dimension conversion from the first intermediate node to the second intermediate node, the specific planned sales values ​​can be converted based on this correspondence.

[0101] The numerical transformation strategy from the second intermediate node to the target node is used to aggregate the sales plan values ​​of the same product and the same second space across multiple different sales channels, thus determining the sales plan value of the same product within the same second space. Specifically, after transforming the first space dimension to the second space dimension and the SKU dimension to the product dimension, multiple sales plan data points are obtained. Each sales plan data point corresponds to the sales plan value of a specific product in a specific sales channel and at a specific sales location warehouse. Furthermore, the sales plan values ​​of the same product and the same second space across multiple different sales channels can be aggregated to obtain the final multiple sales plan data points, each corresponding to the sales plan value of a specific product in a specific sales location warehouse.

[0102] It's important to note that, as mentioned earlier, the specific conversion process can be executed based on event triggering. Furthermore, during the execution of this process, the conversion actions between various nodes can also be triggered by events. This event-driven streaming data processing approach allows recalculation requests from different planning entities (including products, SKUs, and goods) to be processed in a distributed manner by different queues, ensuring the overall timeliness of data computation. Additionally, recalculation requests from different planning entities are isolated at the queue level, preventing mutual interference in the event of data computation anomalies.

[0103] For example, a specific streaming data processing architecture can be as follows: Figure 3As shown, the specific conversion process can first be triggered in real time through data change events or by triggering a full offline task. Then, specific dimensional conversion calculations are performed on specific data sources, such as inbound and non-inbound products. Within the dimensional conversion process, event-triggered streaming processing can also be used; conversion actions between different nodes can be executed through event triggering. For example, the completion of a conversion at one node triggers the conversion processing at the next node, and so on. Furthermore, recalculation requests from different planning entities are processed in a distributed manner using different queues. Afterward, the sales plan data produced by each node can be saved in a persistent database. In addition, a stability assurance system built around the computational tasks can support stability foundation capabilities such as task monitoring, degradation retries, data reconciliation, and data white-boxing.

[0104] It should also be noted that in practical applications, the specific starting node and target node can vary depending on the specific application scenario. Therefore, in the preferred embodiment, the specific dimension transformation path and numerical transformation strategy can be configurable to meet the needs of transformation between different dimension combinations in different scenarios. For example, when it is necessary to generate sales plan data for other scenarios, other dimension transformation paths and numerical transformation strategies can be configured through the configuration platform, and so on.

[0105] In this way, the overall solution maximizes system automation through mechanisms such as automatic data recalculation triggered by system changes and a strategy center, avoiding manual intervention and effectively improving operational efficiency. Furthermore, the adoption of an abstract and extensible design ensures loose coupling, reusability, and rapid scalability of the system's overall code modules, thereby enhancing development efficiency.

[0106] S203: Save the dimension combination composed of the multiple target dimensions and the corresponding converted values ​​as the second sales plan data, so as to provide multiple sales plan data expressed through multiple dimension combinations.

[0107] After converting the data into a combination of multiple target dimensions and the resulting numerical values, this combination of target dimensions and its corresponding values ​​can be saved as the second sales plan data. This allows for multiple sales plan data sets expressed through various combinations of dimensions, catering to the needs of users with different identities.

[0108] In cases where the specific dimension transformation path includes multiple intermediate nodes, the sales plan data generated by the intermediate nodes on the dimension transformation path, which is expressed by a combination of multiple intermediate dimensions, can also be saved. This allows for the provision of sales plan data corresponding to more dimension combination methods.

[0109] Specifically, as mentioned above, when saving sales plan data, since the embodiments of this application involve expressing sales plan data through a combination of different dimensions, a general data storage model can also be provided to avoid different dimension combinations being scattered across multiple different data tables.

[0110] Specifically, to achieve the above objectives, the dimensions related to sales plans can be categorized into several main types, such as entity dimensions, spatial dimensions, and time dimensions. Entities, spaces, and times can each be further subdivided into various types of sub-dimensions. For example, entities can be subdivided into product, SKU, and goods dimensions; spatial dimensions can be divided into sales location / warehouse, region, and nationwide dimensions according to different granularities; time dimensions can include daily, weekly, and monthly dimensions. Combinations of different dimensions are mainly achieved by selecting and combining either the entity sub-dimensions or the spatial sub-dimensions. For example, SKU + nationwide, SKU + sales location / warehouse, goods + sales location / warehouse, etc. Therefore, after classifying multiple dimensions, the sales plan data storage model can include fields such as entity type, entity ID, spatial type, spatial identifier, time type, and planned time. This data storage model allows for a unified expression of various combinations of dimensions. In addition, in specific implementation, the data storage model can also include fields related to specific sales plan values. Specifically, these can include predicted values ​​(values ​​predicted by the algorithm), collaborative values ​​(values ​​modified by manual collaboration), and final values ​​(the final effective value selected from the predicted values ​​or collaborative values).

[0111] Regarding the sales channel dimension (including various marketing activities), as mentioned earlier, to meet the requirement of universality, the sales channel and spatially related dimensions can be merged into a single dimension. Thus, a specific dimension combination could include "SKU + Sales Channel & Nationwide," expressing the predicted sales volume of a specific SKU participating in a particular sales channel nationwide. Alternatively, "SKU + Sales Channel & Sales Location Warehouse," expressing the predicted sales volume of a specific SKU within the sales area corresponding to a specific sales location warehouse under a particular sales channel. If the sales channel dimension is not required, then the information related to that sales channel need not be included in the dimension combination; for example, "Product + Sales Location Warehouse," etc.

[0112] Specifically, with "SKU + sales channel & nationwide" as the initial dimension combination, and "SKU + sales channel & sales warehouse" and "product + sales channel & sales warehouse" as intermediate nodes, and "product + sales warehouse" as the target dimension combination, the sales plan data generated by each node can be saved in the data storage structure shown in Table 1.

[0113] Table 1

[0114]

[0115] Having stored multiple sales plan data sets corresponding to various combinations of dimensions, the specific data can be made available to the relevant users. Specifically, users can initiate a query request for specific sales plan data through the client; for example, by entering a specific SKU ID, they can obtain the sales plan data corresponding to that SKU ID.

[0116] In this embodiment, sales plan data expressed through various combinations of dimensions can be stored. Therefore, in one optional implementation, multiple tab options can be provided in the query interface. Users can switch between these tabs to specify the desired dimension combination for the query. Then, specific query conditions, including SKU ID, product ID, etc., can be entered. Correspondingly, sales plan data that meets the query conditions and is expressed through the currently selected dimension combination can be returned.

[0117] For example, such as Figure 4-1 As shown at point 41, specific option labels can include: "Channel Activity National Sales Plan" (corresponding to the dimension combination of "SKU + Sales Channel & National"), "Channel Warehouse Sales Plan" (corresponding to the dimension combination of "SKU + Sales Channel & Sales Location Warehouse"), "Channel Activity Warehouse Product Sales Plan" (corresponding to the dimension combination of "Product + Sales Channel & Sales Location Warehouse"), and "Warehouse Product Sales Plan" (corresponding to the dimension combination of "Product + Sales Location Warehouse"). When users need to query sales plan data, they can first select these option labels to determine which dimension combination to query. Then, they can initiate a query by entering specific product names, SKU IDs, and other information as search criteria. Correspondingly, the sales plan data that meets the query criteria and is expressed through the currently selected dimension combination will be displayed.

[0118] For example, if the user selects Figure 4-1 The first option label shown at position 41 displays the following search results after entering search criteria (e.g., the ID of a SKU): Figure 4-1As shown at point 42, it displays information such as the specific SKU's name, packaging specifications, package type, industry, category, historical sales data, and the planned sales figures for specific dates. Since the current option label is related to "Channel Activity National Sales Plan," users can see the displayed figures, representing the potential sales volume of the current SKU nationwide within the current channel.

[0119] If the user selects the fourth option tab and enters a product ID or other search criteria, then... Figure 4-2 As shown, this can display the sales plan data for specific products in multiple different sales warehouses. For example, Figure 4-2 Position 43 shows the planned sales data for the current product in warehouse A, position 44 shows the planned sales data for the current product in warehouse B, and so on.

[0120] In summary, through the embodiments of this application, after obtaining the first sales plan data, a dimensional combination consisting of multiple initial dimensions can be converted into a dimensional combination consisting of multiple target dimensions according to a preset dimensional transformation scheme, and then numerical transformation can be performed to generate a dimensional combination consisting of multiple target dimensions and the corresponding transformed values. Furthermore, the dimensional combination consisting of multiple target dimensions and the corresponding transformed values ​​can be saved as the second sales plan data. This provides users with multiple sales plan data expressed through various dimensional combinations. In this way, in scenarios strongly related to front-end operations and complex product relationships, dimensional transformation can generate sales plan data expressed through more dimensional combinations, thereby meeting the needs of users with different identities for sales plan data without needing to modify the algorithm to adapt to changes in scenarios or changes in the needs of different users for sales plan data. Therefore, it achieves higher efficiency and helps save system computing and storage resources.

[0121] It should be noted that the embodiments of this application may involve the use of user data. In practical applications, user-specific personal data may be used in the scheme described herein within the scope permitted by applicable laws and regulations, provided that it complies with the applicable laws and regulations of the country (e.g., with the user's explicit consent, with the user being properly notified, etc.).

[0122] Example 2

[0123] This second embodiment corresponds to the first embodiment and provides a method for providing sales plan data from the client's perspective. See [link to relevant documentation]. Figure 5 The method may include:

[0124] S501: Provide a sales plan data query interface, which includes multiple tag options to correspond to multiple dimension combination methods. The multiple dimension combination methods correspond to multiple sales plan data. The multiple sales plan data include: a first sales plan data expressed by a dimension combination composed of multiple initial dimensions, and at least one second sales plan data generated after performing dimension transformation and numerical transformation on the first sales plan data.

[0125] S502: After receiving the selection operation for one of the target label options, receive the query condition information;

[0126] S503: Obtain sales plan data that meets the query conditions and is expressed through the dimension combination method corresponding to the target label option, and display it in the query interface.

[0127] For the parts of this embodiment that are not described in detail, please refer to the description in embodiment one and other parts of this application specification, which will not be repeated here.

[0128] Corresponding to Embodiment 1, this application also provides an apparatus for providing sales plan data, see [link to embodiment]. Figure 6 The device may include:

[0129] The first sales plan data acquisition unit 601 is used to acquire first sales plan data, which includes sales plan data expressed by a combination of multiple initial dimensions and corresponding values.

[0130] The conversion unit 602 is used to convert the combination of multiple initial dimensions into a combination of multiple target dimensions according to a preset dimension conversion scheme, and to perform numerical conversion.

[0131] The second sales plan data generation unit 603 is used to save the dimension combination composed of the multiple target dimensions and the corresponding converted values ​​as the second sales plan data, so as to provide multiple sales plan data expressed through multiple dimension combinations.

[0132] Specifically, the first sales plan data acquisition unit can be used for:

[0133] The event monitoring subunit is used to monitor events related to changes in sales plan data;

[0134] The triggering subunit is used to obtain the changed first sales plan data when the sales plan data change event is detected.

[0135] Specifically, the event monitoring subunit can be used for:

[0136] Obtain the first sales plan data generated by the sales forecasting algorithm in the new forecast period.

[0137] Alternatively, the event monitoring subunit can be specifically used for:

[0138] When an event is detected in which a user modifies the sales plan data, the modified first sales plan data is retrieved.

[0139] Specifically, the dimension transformation scheme includes: a dimension transformation path, and a numerical transformation strategy corresponding to the dimension transformation between adjacent nodes on the dimension transformation path;

[0140] At this time, the conversion unit can specifically be used for:

[0141] The dimension combination consisting of the multiple initial dimensions is converted into a dimension combination consisting of multiple target dimensions according to the dimension transformation path.

[0142] According to the numerical transformation strategy, the sales plan data is determined by the combination of multiple target dimensions after transformation.

[0143] If there are multiple dimensions to be transformed, the dimension transformation path includes multiple nodes, and the multiple nodes correspond to different combinations of dimensions.

[0144] The conversion unit can specifically be used for:

[0145] According to the dimensional transformation path and the numerical transformation strategy, multiple transformation steps are performed to transform the first sales plan data into the second sales plan data; wherein, each step transforms a single dimension.

[0146] Specifically, the multiple initial dimensions include: the smallest sales unit (SKU) dimension, the sales channel dimension, and the first spatial dimension;

[0147] The multiple target dimensions include: the product dimension and the second spatial dimension, the second spatial dimension including the warehouse dimension;

[0148] The first intermediate node on the dimensional transformation path is a dimensional combination consisting of SKU dimension, sales channel dimension, and second space dimension, and the second intermediate node is a dimensional combination consisting of product dimension, sales channel dimension, and second space dimension;

[0149] The numerical conversion strategy from the initial node to the first intermediate node is used to: determine the sales plan values ​​corresponding to the current SKU, sales channel and each second space when the current first space is divided into multiple second spaces according to the pre-configured split ratio;

[0150] The numerical conversion strategy from the first intermediate node to the second intermediate node is used to: determine the sales plan values ​​corresponding to the current sales channel, the second space and each product when converting the SKU dimension to the product dimension, based on the pre-configured one-to-many and / or many-to-one correspondence between SKU and product;

[0151] The numerical conversion strategy from the second intermediate node to the target node is used to aggregate the sales plan values ​​of the same product and the same second space across multiple different sales channels to determine the sales plan value of the same product in the same second space.

[0152] In this process, the nodes on the dimension transformation path perform dimension and numerical transformations through an event-driven streaming data processing method; the events include the event of the previous node completing its transformation.

[0153] Specifically, the dimensions to be converted include dimensions related to the planning entity, wherein conversion requests corresponding to different planning entities are processed in a distributed and isolated manner by different queues.

[0154] Additionally, the device may also include:

[0155] The intermediate result storage unit is used to store the sales plan values ​​generated by the intermediate nodes on the dimension transformation path, which are expressed by a combination of multiple intermediate dimensions, so as to provide multiple sales plan data expressed by multiple combinations of dimensions.

[0156] The dimensional transformation path and numerical transformation strategy can be configurable to meet the needs of transformation between different dimensional combinations in different scenarios.

[0157] Corresponding to Embodiment 2, this application also provides an apparatus for providing sales plan data, see [link to embodiment]. Figure 7 The device may include:

[0158] The tag option providing unit 701 is used to provide a sales plan data query interface. The query interface includes multiple tag options, which correspond to multiple dimension combination methods. The multiple dimension combination methods correspond to multiple sales plan data. The multiple sales plan data include: a first sales plan data expressed by a dimension combination composed of multiple initial dimensions, and at least one second sales plan data generated after performing dimension transformation and numerical transformation on the first sales plan data.

[0159] The query condition receiving unit 702 is used to receive query condition information after receiving a selection operation for one of the target label options;

[0160] The sales plan data display unit 703 is used to obtain sales plan data that meets the query conditions and is expressed through the dimension combination method corresponding to the target label option, and to display it in the query interface.

[0161] In addition, embodiments of this application also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described in any of the foregoing method embodiments.

[0162] And an electronic device, comprising:

[0163] One or more processors; and

[0164] A memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method described in any of the foregoing method embodiments.

[0165] in, Figure 8 An exemplary architecture of an electronic device is shown, which may include a processor 810, a video display adapter 811, a disk drive 812, an input / output interface 813, a network interface 814, and a memory 820. The processor 810, video display adapter 811, disk drive 812, input / output interface 813, network interface 814, and memory 820 can communicate with each other via a communication bus 830.

[0166] The processor 810 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to achieve the technical solution provided in this application.

[0167] The memory 820 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 820 can store the operating system 821 for controlling the operation of the electronic device 800, and the basic input / output system (BIOS) for controlling the low-level operations of the electronic device 800. Additionally, it can store a web browser 823, a data storage management system 824, and a sales planning processing system 825, etc. The aforementioned sales planning processing system 825 can be the application program that specifically implements the aforementioned steps in this embodiment. In summary, when implementing the technical solution provided in this application through software or firmware, the relevant program code is stored in the memory 820 and executed by the processor 810.

[0168] The input / output interface 813 is used to connect input / output modules to enable information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0169] Network interface 814 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0170] Bus 830 includes a pathway for transmitting information between various components of the device, such as processor 810, video display adapter 811, disk drive 812, input / output interface 813, network interface 814, and memory 820.

[0171] It should be noted that although the above-described device only shows the processor 810, video display adapter 811, disk drive 812, input / output interface 813, network interface 814, memory 820, bus 830, etc., in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the solution of this application, and does not necessarily include all the components shown in the figures.

[0172] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0173] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0174] The method and electronic device for providing sales plan data provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and its core ideas. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for providing sales plan data, characterized in that, include: Obtain the first sales plan data, which includes sales plan data expressed by a combination of multiple initial dimensions and their corresponding values; The initial dimensions include: the smallest sales unit (SKU) dimension, the sales channel dimension, and the first spatial dimension; According to a preset dimension transformation path, the dimension combination composed of multiple initial dimensions is transformed into a dimension combination composed of multiple target dimensions. Following the numerical transformation strategy used when performing dimension transformation between adjacent nodes on the dimension transformation path, the sales plan data expressed by the dimension combination composed of multiple initial dimensions and its corresponding values ​​is transformed into sales plan data expressed by the transformed dimension combination composed of multiple target dimensions and its corresponding values. The multiple target dimensions include: a product dimension and a second spatial dimension, where the second spatial dimension includes a warehouse dimension. The dimension transformation path includes multiple nodes, each corresponding to a different dimension combination. These multiple nodes include: a first intermediate node: a dimension combination composed of SKU dimension, sales channel dimension, and second spatial dimension; and a second intermediate node: a dimension combination composed of product dimension, sales channel dimension, and second spatial dimension. The sales plan data expressed by the combination of the multiple target dimensions and the corresponding values ​​is saved as the second sales plan data, so as to provide users with multiple sales plan data expressed through multiple combinations of dimensions.

2. The method according to claim 1, characterized in that, The acquisition of the first sales plan data includes: Monitor events that change sales plan data; When a change event is detected in the sales plan data, the first sales plan data after the change is obtained.

3. The method according to claim 2, characterized in that, The process of obtaining the revised first sales plan data includes: Obtain the first sales plan data generated by the sales forecasting algorithm in the new forecast period.

4. The method according to claim 2, characterized in that, The process of obtaining the revised first sales plan data includes: When an event is detected in which a user modifies the sales plan data, the modified first sales plan data is retrieved.

5. The method according to claim 1, characterized in that, The process of converting the dimension combination composed of multiple initial dimensions into a dimension combination composed of multiple target dimensions according to a preset dimension conversion path, and converting the sales plan data expressed by the dimension combination composed of multiple initial dimensions and its corresponding values ​​into the sales plan data expressed by the dimension combination composed of multiple target dimensions and its corresponding values, according to the numerical conversion strategy corresponding to the dimension conversion between adjacent nodes on the dimension conversion path, includes: According to the dimensional transformation path and the numerical transformation strategy, multiple transformation steps are performed to transform the first sales plan data into the second sales plan data; wherein, each step transforms a single dimension.

6. The method according to claim 5, characterized in that, The numerical conversion strategy from the initial node to the first intermediate node is used to: determine the sales plan values ​​corresponding to the current SKU, sales channel and each second space when the current first space is divided into multiple second spaces according to the pre-configured split ratio; The numerical conversion strategy from the first intermediate node to the second intermediate node is used to: determine the sales plan values ​​corresponding to the current sales channel, the second space and each product when converting the SKU dimension to the product dimension, based on the pre-configured one-to-many and / or many-to-one correspondence between SKU and product; The numerical conversion strategy from the second intermediate node to the target node is used to aggregate the sales plan values ​​of the same product and the same second space across multiple different sales channels to determine the sales plan value of the same product in the same second space.

7. The method according to claim 5 or 6, characterized in that, Also includes: The sales plan values ​​generated by the intermediate nodes on the dimensional transformation path, which are expressed by dimensional combinations composed of multiple intermediate dimensions, are saved to provide various sales plan data expressed by multiple dimensional combinations.

8. The method according to claim 5 or 6, characterized in that, The nodes on the dimension transformation path perform dimension and numerical transformations through an event-driven streaming data processing method; the events include the event of the previous node completing its transformation.

9. The method according to any one of claims 1 to 6, characterized in that, A general data storage model is used to save sales plan data expressed by multiple dimensions. The general data storage model includes the following fields: entity type, entity ID, spatial type, spatial identifier, time type, and planned time. This allows for the combination of multiple dimensions, such as entity dimensions, spatial dimensions, and / or time dimensions, to be used to express the sales plan data.

10. The method according to claim 9, characterized in that, By merging sales channels and space into a single dimension, the sales plan data can be expressed in terms of marketing campaign dimensions.

11. A method for providing sales plan data, characterized in that, include: The system provides a sales plan data query interface, which includes multiple tag options corresponding to various dimension combination methods. These multiple dimension combination methods correspond to multiple sales plan data sets. The multiple sales plan data sets include: first sales plan data expressed as a combination of multiple initial dimensions and their corresponding values; and third, sales plan data expressed as a combination of multiple initial dimensions converted into a combination of multiple target dimensions according to a preset dimension transformation path. Furthermore, the system uses a numerical transformation strategy corresponding to the dimension transformation between adjacent nodes on the dimension transformation path to convert the sales plan data expressed as a combination of multiple initial dimensions and their corresponding values ​​into a transformed data set. The system comprises multiple target dimensions and corresponding numerical representations of sales plan data, which are then saved as at least one second sales plan data set. The initial dimensions include: the smallest sales unit (SKU) dimension, the sales channel dimension, and a first spatial dimension. The target dimensions include: the product dimension and a second spatial dimension, where the second spatial dimension includes the warehouse dimension. The dimension transformation path includes multiple nodes, each corresponding to a different dimension combination. These nodes include: a first intermediate node (a dimension combination consisting of the SKU dimension, sales channel dimension, and second spatial dimension) and a second intermediate node (a dimension combination consisting of the product dimension, sales channel dimension, and second spatial dimension). After receiving a selection operation for one of the target tag options, the query condition information is received; Retrieve sales plan data that meets the query conditions and is expressed through the dimension combination method corresponding to the target label options, and display it in the query interface.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program performs the steps of the method described in any one of claims 1 to 11.

13. An electronic device, characterized in that, include: One or more processors; as well as A memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method according to any one of claims 1 to 11.

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