A product plan template generation method and device

Abstract

The application discloses a product plan template generation method and device, and the method comprises the following steps: obtaining a concerned element in a historical plan; the concerned element comprises a concerned object, and the concerned object corresponds to a concerned index and a concerned time; determining a plan element according to the concerned element; the plan element comprises a plan object, and the plan object corresponds to a plan index and a plan time; matching a corresponding prediction algorithm and a data object for the plan index; the prediction algorithm is used for predicting the plan index, and the data object is used as input data of the prediction algorithm; and generating a plan template according to the plan object, the plan index corresponding to the plan object, the plan time corresponding to the plan object, the prediction algorithm corresponding to the plan index and the data object corresponding to the prediction algorithm. The application improves the processing efficiency and reliability of product demand planning of an enterprise.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of supply chain, and in particular to a product plan template generation method and device. BACKGROUND

[0002] The operation or operation plan of the supply chain operation in an enterprise is a very important output in the supply chain planning process, which can be used for subsequent specific work arrangement of supply chain execution. Usually, the planning department will prepare and output the following common plans: demand plan, inventory plan and replenishment plan; the demand plan is the estimation of the demand of the terminal or channel market for the products of the enterprise in the future period of time, and the specific demand prediction quantity of the goods in the specific channel / place is given in combination with different internal and external factors of the enterprise. The inventory plan is the inventory plan of the enterprise for different goods storage places (warehouses) in the future period of time, which includes the upper limit and lower limit of the safety inventory of the goods in the specific place, and the target inventory quantity expected to be achieved. The replenishment plan is the replenishment plan of the enterprise for different goods in the future period of time according to the output of the demand plan quantity and the inventory plan. In different enterprises, similar supply chain planning needs to be carried out, but the details of each enterprise for specific planning requirements are different. Even in an enterprise, different products have different detail requirements for different time periods; at present, most enterprises process these supply plans through excel tables, therefore, the planning version is multiple and complex, and the use efficiency and standardization degree are low. SUMMARY

[0003] In view of the above problems, the present application provides a product plan template generation method and device, which improves the processing efficiency and reliability of the product demand plan of the enterprise.

[0004] In a first aspect, the present application provides the following technical solutions through an embodiment:

[0005] A product plan template generation method, comprising:

[0006] obtaining a concerned element in a historical plan; wherein the concerned element comprises a concerned object, and a concerned index and a concerned time corresponding to the concerned object; determining a plan element according to the concerned element; the plan element comprises a plan object, and a plan index and a plan time corresponding to the plan object; matching a corresponding prediction algorithm and a data object for the plan index; wherein the prediction algorithm is used for predicting the plan index, and the data object is used as input data of the prediction algorithm; and generating the plan template according to the plan object, the plan index corresponding to the plan object, the plan time corresponding to the plan object, the prediction algorithm corresponding to the plan index, and the data object corresponding to the prediction algorithm.

[0007] Optionally, the generating the plan template according to the plan object, the plan index corresponding to the plan object, the plan time corresponding to the plan object, the prediction algorithm corresponding to the plan index and the data object corresponding to the prediction algorithm comprises:

[0008] determining the plan time granularity and the algorithm time granularity according to the plan time corresponding to the plan object; and generating the plan template according to the plan object, the plan index corresponding to the plan object, the plan time corresponding to the plan object, the prediction algorithm corresponding to the plan index, the data object corresponding to the prediction algorithm, the plan time granularity and the algorithm time granularity.

[0009] Optionally, after the generating the plan template according to the plan object, the plan index corresponding to the plan object, the plan time corresponding to the plan object, the prediction algorithm corresponding to the plan index and the data object corresponding to the prediction algorithm, the method further comprises:

[0010] acquiring a new element; the new element comprises a new object, a new index corresponding to the new object and a new time corresponding to the new object; updating the plan object according to the new object; updating the plan index corresponding to the plan object according to the new index corresponding to the new object; and updating the plan time corresponding to the plan object according to the new time corresponding to the new object.

[0011] Optionally, the plan object and the attention object each comprise one or more of a sales channel, a sales region and a sales store.

[0012] The plan index and the attention index each comprise one or more of product sales, a sales trend, product inventory and product yield.

[0013] In a second aspect, based on the same inventive concept, an embodiment of the present application provides the technical scheme as follows:

[0014] A product plan template generation device comprises:

[0015] The attention element acquisition module is configured to acquire an attention element in a historical plan, wherein the attention element comprises an attention object, and an attention index and an attention time corresponding to the attention object; the plan element determination module is configured to determine a plan element according to the attention element, wherein the plan element comprises a plan object, and a plan index and a plan time corresponding to the plan object; the matching module is configured to match a corresponding prediction algorithm and a data object for the plan index, wherein the prediction algorithm is used to predict the plan index, and the data object is used as input data of the prediction algorithm; and the template generation module is configured to generate the plan template according to the plan object, the plan index corresponding to the plan object, the plan time corresponding to the plan object, the prediction algorithm corresponding to the plan index, and the data object corresponding to the prediction algorithm.

[0016] Optionally, the template generation module is specifically configured to:

[0017] determine a plan time granularity and an algorithm time granularity according to the plan time corresponding to the plan object, and generate the plan template according to the plan object, the plan index corresponding to the plan object, the plan time corresponding to the plan object, the prediction algorithm corresponding to the plan index, the data object corresponding to the prediction algorithm, the plan time granularity, and the algorithm time granularity.

[0018] Optionally, the method further comprises an updating module configured to, after the plan template is generated according to the plan object, the plan index corresponding to the plan object, the plan time corresponding to the plan object, the prediction algorithm corresponding to the plan index, and the data object corresponding to the prediction algorithm:

[0019] acquire a new element, wherein the new element comprises a new object, a new index corresponding to the new object, and a new time corresponding to the new object; update the plan object according to the new object; update the plan index corresponding to the plan object according to the new index corresponding to the new object; and update the plan time corresponding to the plan object according to the new time corresponding to the new object.

[0020] Optionally, the attention object and the plan object each comprise one or more of a sales channel, a sales region, and a sales store; and the attention index and the plan index each comprise one or more of product sales, a sales trend, product inventory, and product yield.

[0021] In a third aspect, based on the same inventive concept, an embodiment of the present application provides the technical scheme as follows:

[0022] An electronic device comprising a processor and a memory coupled to the processor, the memory storing instructions that, when executed by the processor, cause the electronic device to perform the steps of any of the preceding methods.

[0023] In a fourth aspect, based on the same inventive concept, the application provides the following technical solution through an embodiment:

[0024] A readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the steps of any of the preceding methods.

[0025] The product planning template generation method and device provided by the embodiments of the application determine a focus element based on historical plans, and then determine a planning element based on the focus element. Since the focus element is obtained based on historical plans, the planning element obtained can more completely cover the supply planning required by an enterprise. Then, the planning object, the planning index corresponding to the planning object, and the planning time are included in the planning element; each planning index is matched with a corresponding prediction algorithm and data object; therefore, the corresponding planning template is finally generated. Since the planning template includes the planning element, and each planning index in the planning element is matched with a corresponding prediction algorithm and data object, the corresponding output can be completed only by calling and selecting when the planning template is used. In addition, the planning element, the prediction algorithm, and the data object in the planning template can be independently added and deleted in the management process, so that the planning template can be customized, and the processing efficiency and standardization degree of the supply planning are effectively improved.

[0026] The above description is only a summary of the technical solutions of the application. In order to more clearly understand the technical means of the application, the content of the specification can be implemented, and in order to make the above and other purposes, features and advantages of the application more obvious and easy to understand, the specific embodiments of the application are described below. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor. In the drawings:

[0028] Figure 1 A flowchart of a product planning template generation method provided by the embodiments of the application is shown;

[0029] Figure 2 And Figure 3 A visual interface schematic diagram of the planning template in the embodiments of the application is shown.

[0030] Figure 4 A structural schematic diagram of a product plan template generation device is shown. DETAILED DESCRIPTION

[0031] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly understood and so that the scope of the present disclosure can be conveyed to those skilled in the art.

[0032] Currently, when enterprises process product supply plans, they mostly use excel tables to process, but there are many versions of plans and they are complicated. Moreover, the plan display pages of most online software are hard-coded, and different plan scenarios require the development of a set of plan functions. Secondary development and customization are required for implementation in different enterprises, which is low in implementation efficiency and low in standardization. When the business scenarios of enterprises change, different plan functions can only be implemented by customizing the plan page. Based on this, a product plan template generation method is provided in the present application. The plan template obtained in the method can be used in different application scenarios, effectively improving the processing efficiency and standardization of the existing product supply plan. The specific implementation of the present application will be described in detail below through specific embodiments.

[0033] Please refer to Figure 1 In the embodiments of the present application, a product plan template generation method is provided, which comprises:

[0034] Step S10: obtaining a focus element in a historical plan; wherein the focus element comprises a focus object, and a focus index and a focus time corresponding to the focus object;

[0035] Step S20: determining a plan element according to the focus element; the plan element comprises a plan object, and a plan index and a plan time corresponding to the plan object;

[0036] Step S30: matching a corresponding prediction algorithm and a data object for the plan index; wherein the prediction algorithm is used to predict the plan index, and the data object is used as input data of the prediction algorithm;

[0037] Step S40: generating the plan template according to the plan object, the plan index corresponding to the plan object, the plan time corresponding to the plan object, the prediction algorithm corresponding to the plan index, and the data object corresponding to the prediction algorithm.

[0038] In the embodiment, through steps S10-S40, first, the attention elements are determined based on the historical plans, and then the plan elements are determined according to the attention elements. Since the attention elements are obtained based on the historical plans, the obtained plan elements can more completely cover the supply plans that the enterprise needs to make. Then, the plan objects are included in the plan elements, and the plan objects correspond to the plan indexes and the plan times; each plan index is also matched with the corresponding prediction algorithm and the data object; therefore, the corresponding plan template is finally generated. Since the plan template includes the plan elements, and each plan index in the plan elements is matched with the corresponding prediction algorithm and the data object, finally, when the plan template is used, only calling and selecting are needed to complete the corresponding output, and in the management process, the plan elements, the prediction algorithms and the data objects in the plan template can be independently added and deleted, so that the plan template can be customized to be generated, and the processing efficiency and the standardization degree of the supply plan are effectively improved. The implementation of each step in the embodiment will be described in detail below.

[0039] Step S10: obtaining attention elements in historical plans; wherein the attention elements include attention objects, and the attention objects correspond to attention indexes and attention times.

[0040] In step S10, the historical plans are some product supply plans that the enterprise has made in the past. The products can be a single product or multiple products, without limitation. That is, the plan template can be a plan template for a target product, or can be a template compatible with multiple different products.

[0041] The attention objects are objects for which the enterprise makes supply plans, for example, one or more of a product sales channel, a sales region, and a sales store. Each attention object has a corresponding attention index and an attention time. The attention index is a series of data indexes of the attention object, for example, one or more of product sales volume, sales trend, product inventory, and product yield. The attention time is a time period for which the attention object needs to be predicted (looked ahead) or counted, for example, 1 week, 1 month, 1 quarter, 6 months, 1 year, etc.; it can also be 10 days, 20 days, 30 days, etc.; or other custom values, without limitation.

[0042] Step S20: determining plan elements according to the attention elements; the plan elements include plan objects, and the plan objects correspond to plan indexes and plan times.

[0043] In step S20, all the attention elements can be determined as corresponding plan elements. That is, the plan object in the plan element can also be one or more of the product sales channel, the sales region, and the sales store. Each plan object has a corresponding plan index and a plan time, which are the outlook and prediction of the product in the future. Similarly, the plan index is a series of data indexes of the plan object, such as one or more of the product sales, the sales trend, the product inventory, and the product yield, and the plan index is the index that needs to be predicted (outlooked). The plan time is the time period in which the attention object needs to be predicted (outlooked), and the specific value of the plan time can refer to the specific value of the attention time, which will not be described here.

[0044] In some implementations, statistical analysis can also be performed on all the attention elements to remove noise in the attention elements. Specifically, after the attention elements in the historical plans of an enterprise in a certain period of time are counted, attention elements that only appear less than a preset number of times threshold can be removed; the attention element can be a supply plan suitable for a certain condition and does not have universality and can not appear in the future. Or, the attention element can be an inapplicable supply plan that appears in a certain period of time and does not appear again. Or, the attention element can be an erroneous supply plan that has only appeared once in history. Therefore, different number of times thresholds can be set, and then the number of times of the appearance of the attention element is compared with the number of times threshold; if the number of times of the appearance of the attention element is less than the number of times threshold, it means that the attention element is an attention element that needs to be discarded. In this way, the attention elements that are abnormal or belong to noise can be well filtered, so that the finally generated plan template is simple and reliable and has less redundant information.

[0045] In some implementations, each attention element can also be analyzed to find attention elements with missing elements. These attention elements with missing elements are removed. Specifically, the attention element includes the attention object, the attention index, and the attention time. When any one of the attention object, the attention index, and the attention time is missing in an attention element, the attention element can be determined as an attention element with missing elements, that is, the attention element is determined as noise and is removed. This implementation can better remove the attention elements with missing elements, and also can ensure that the generated plan template has less information redundancy, avoid errors in the operation of the user when applying the plan template, such as, after selecting the plan object, there can be no corresponding plan index and plan time, or the corresponding prediction algorithm cannot be established.

[0046] It can be understood that the two noise removal methods of the element of interest described above can be implemented simultaneously or only one of them can be implemented, without limitation. After the noise removal of the element of interest obtained in step S10, the remaining element of interest obtained can be determined as the planning element in step S20.

[0047] Step S30: matching a corresponding prediction algorithm and a data object for the planning indicator; wherein the prediction algorithm is used to predict the planning indicator, and the data object is used as input data of the prediction algorithm.

[0048] In step S30, the prediction algorithm corresponding to the planning indicator can be one or more, and each prediction algorithm is set in advance. The prediction algorithm can be a commonly used machine learning or deep learning algorithm. Each prediction algorithm has a corresponding input data, i.e., a data object. The data object includes but is not limited to: historical sales, historical sales targets, holiday types, holiday days, historical sales, historical prices, etc. When the planning indicator is matched with the prediction algorithm and the data object, when the generated planning template is used, a certain planning indicator is selected, and the prediction algorithm and the data object can only be determined in the prediction algorithm matched with the planning indicator, which can effectively avoid errors and improve the reliability and efficiency of supply planning processing.

[0049] In some implementations, a corresponding attention time can also be matched for a planning time, so that after selecting a corresponding planning time in the generated planning template, a corresponding attention time or multiple attention times can be automatically generated. For example, when the planning time is 1 month, the matched attention time is 1 month, 2 months, 3 months and 4 months. After determining the planning time, the user only needs to select any one of the above attention times.

[0050] Step S40: generating the planning template according to the planning object, the planning indicator corresponding to the planning object, the planning time corresponding to the planning object, the prediction algorithm corresponding to the planning indicator, and the data object corresponding to the prediction algorithm.

[0051] In step S40, the planning template generated includes the planning object, the planning indicator corresponding to the planning object, the planning time corresponding to the planning object, the prediction algorithm corresponding to the planning indicator, and the data object corresponding to the prediction algorithm. In addition, the planning template can also include a corresponding code and an enabled state.

[0052] Further, in some implementations, to more accurately adapt to supply plan production of different precision, the plan time granularity and the algorithm time granularity can also be determined according to the plan time corresponding to the plan object; then, the plan template is generated according to the plan object, the plan index corresponding to the plan object, the plan time corresponding to the plan object, the prediction algorithm corresponding to the plan index, the data object corresponding to the prediction algorithm, the plan time granularity and the algorithm time granularity.

[0053] The plan time granularity is the unit corresponding to the exhibition time. Through the plan time granularity, prediction and prospect can be performed on different time scales. For example, the plan time granularity can be day, week, month, quarter, year, etc. That is, the plan time in this implementation can be determined as a dimensionless value, and then through cooperation with the plan time granularity, more precise and concise prediction time determination is realized. For example, the prospect period is 6 months, the plan start date is set to May 1 when creating the plan, and the plan period is May 1-October 31. The plan time is 6, and the plan time granularity is month. The algorithm time granularity is the time granularity of the input data corresponding to the algorithm when the algorithm is calculated. Through the algorithm time granularity, the calculation precision can be flexibly adjusted based on different supply plans. The algorithm time granularity can be day, week, month, quarter, year, etc.

[0054] It should be noted that after the plan template is generated, the step of updating the plan template can also be included. For example, the plan template can be updated every predetermined time. Specifically, the new elements can be obtained. The new elements can be attention elements generated after the plan template is generated and not generated by the supply plan produced by the plan template. The new elements can be obtained every certain period of time, such as every 1 month, 1 quarter, half a year, etc. Then, the plan template is updated based on these new elements to ensure that the plan template can be compatible with more supply plans that the enterprise may encounter. Specifically, the new elements include new objects, new indexes corresponding to the new objects, and new times corresponding to the new objects; the plan object is updated according to the new object, and the plan index corresponding to the plan object is updated according to the new index corresponding to the new object.

[0055] For example, if the new object is not in the plan object of the current plan template, the new object is added as a plan object, and the associated new index is added as a plan index, and the associated new time is added as a plan time. If there is a target plan object that is the same as the new object in the current plan template, the target plan index corresponding to the target plan object is traversed, and it is determined whether the target plan index contains the new index. If the target plan index does not contain the new index, the new index is added as a target plan index. The target plan time corresponding to the target plan object is traversed, and it is determined whether the target plan time contains the new time. If the target plan time does not contain the new time, the new time is added as a target plan time.

[0056] Finally, after the plan template is generated, the demand information of the target product can be obtained. The demand information includes a prediction time and a prediction index. Then, the plan template is called, and the plan object corresponding to the target product, the plan index corresponding to the plan object, and the plan time corresponding to the plan object are set.

[0057] Please refer to Figure 2 and Figure 3 , Figure 2 and Figure 3 show a use example of the plan template made by the method in the embodiment. In the generated plan template, the code of the demand plan can be set through the code column, the plan object of the demand plan can be determined through the name column, and the plan time of the plan object in the demand plan can be set through the plan prospect column and the plan prospect period unit (plan time granularity). The corresponding review time can be set through the historical review period, which can be the attention time of the historical plan corresponding to the plan object. The parameters in the review time can be used as the input data corresponding to the prediction algorithm. The corresponding plan index can be selected through the plan index column, and the algorithm and algorithm input for predicting the plan index can be configured through the prediction algorithm column, the business data object column, and the algorithm time granularity. Further, the version corresponding to the current plan template can be consulted through the plan version number column, and the user can determine whether the plan template is the latest through the version number. Further, the start time of the prediction can be selected through the start time column.

[0058] The plan creation and processing of different plan objects of the product can be completed through the simple selection configuration, when the plan template selection is completed, the corresponding data index, plan period, algorithm scheme and the like are determined, and the overall demand plan will be displayed to the front end according to the setting in the plan template, and the user can modify and release the plan based on the plan. The whole processing process is high in operation efficiency, and does not need manual input of the user, and the operation process is only the simple selection of the existing options, so that the error input can be avoided, and the reliability is improved. Moreover, the plan template can be maintained through updating, is high in adaptability, can be continuously updated and iterated based on the business expansion of the enterprise, and can well adapt to the demand plan making and processing in different periods.

[0059] Please refer to Figure 4 , based on the same inventive concept, in an embodiment of the present application, a product plan template generation device is also provided, the product plan template generation device 300 comprises:

[0060] The attention element acquisition module 301 is configured to acquire an attention element in a historical plan, wherein the attention element comprises an attention object, and an attention index and an attention time corresponding to the attention object; the plan element determination module 302 is configured to determine a plan element according to the attention element; the plan element comprises a plan object, and a plan index and a plan time corresponding to the plan object; the matching module 303 is configured to match a corresponding prediction algorithm and a data object for the plan index; wherein the prediction algorithm is used for predicting the plan index, and the data object is used as input data of the prediction algorithm; and the template generation module 304 is configured to generate the plan template according to the plan object, the plan index corresponding to the plan object, the plan time corresponding to the plan object, the prediction algorithm corresponding to the plan index, and the data object corresponding to the prediction algorithm.

[0061] As an optional implementation, the template generation module 304 is specifically configured to:

[0062] determine a plan time granularity and an algorithm time granularity according to the plan time corresponding to the plan object;

[0063] generate the plan template according to the plan object, the plan index corresponding to the plan object, the plan time corresponding to the plan object, the prediction algorithm corresponding to the plan index, the data object corresponding to the prediction algorithm, the plan time granularity and the algorithm time granularity.

[0064] As an optional implementation, the method further comprises an updating module, configured to, after the plan template is generated according to the plan object, the plan object corresponding plan index, the plan object corresponding plan time, the plan index corresponding prediction algorithm and the prediction algorithm corresponding data object:

[0065] An added element is acquired, the added element comprising an added object, an added index corresponding to the added object and an added time corresponding to the added object; the plan object is updated according to the added object; the plan index corresponding to the plan object is updated according to the added index corresponding to the added object; and the plan time corresponding to the plan object is updated according to the added time corresponding to the added object.

[0066] As an optional implementation, the concerned object and the plan object each comprise one or more of a sales channel, a sales region and a sales store; and the concerned index and the plan index each comprise one or more of product sales, a sales trend, product inventory and product yield.

[0067] It should be noted that the product plan template generation device 300 provided by the embodiments of the present application has the same specific implementation and technical effects as the foregoing method embodiments, and for brief description, the part not mentioned in the device embodiment can be referred to the corresponding content in the foregoing method embodiments.

[0068] Based on the same inventive concept, another embodiment of the present application further provides an electronic device comprising a processor and a memory, the memory being coupled to the processor, the memory storing instructions which, when executed by the processor, cause the electronic device to perform the steps of the method of any one of the foregoing embodiments. It should be noted that the electronic device provided by the embodiments of the present application, when the instructions are executed by the processor, has the same specific implementation and technical effects as the foregoing method embodiments, and for brief description, the part not mentioned in this embodiment can be referred to the corresponding content in the foregoing method embodiments.

[0069] Based on the same inventive concept, another embodiment of the present application further provides a readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the steps of the method of any one of the foregoing method embodiments. It should be noted that the readable storage medium provided by the embodiments of the present application, when the program is executed by the processor, has the same specific implementation and technical effects as the foregoing method embodiments, and for brief description, the part not mentioned in this embodiment can be referred to the corresponding content in the foregoing method embodiments.

[0070] The term "and / or", occurring in this text, is merely used to describe associated objects, and means that three cases can exist, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. In addition, the character " / " in this text generally means that the associated objects before and after are in an "or" relationship; the word "comprising" does not exclude the existence of elements or steps not listed in the claims. The word "one" or "an" before an element does not exclude the existence of multiple such elements. The present application can be implemented by means of hardware including a plurality of different elements and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means can be embodied by one and the same item of hardware. The use of the words "first", "second", and "third", etc. does not indicate any order. These words can be interpreted as names.

[0071] Those skilled in the art should understand that embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer usable program code.

[0072] The present application is described with reference to the flowcharts and / or block diagrams of the methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce an apparatus that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The flow or flows and / or blocks in a flowchart and / or a combination of flows and / or blocks in a flowchart can be implemented by computer program instructions. Figure 1 The means for performing the functions specified in the flow or flows and / or blocks in a flowchart and / or a combination of flows and / or blocks in a flowchart.

[0073] These computer program instructions can also be stored in a computer readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The flow or flows and / or blocks in a flowchart and / or a combination of flows and / or blocks in a flowchart can be implemented by computer program instructions. Figure 1 The means for performing the functions specified in the flow or flows and / or blocks in a flowchart and / or a combination of flows and / or blocks in a flowchart.

[0074] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 Figure 1

[0075] Although preferred embodiments of the application have been described herein, it will be apparent to those skilled in the art that various modifications can be made within the scope of the application without departing from the spirit of the application. Accordingly, it is intended that all such possible modifications be included within the scope of the application as defined in the following claims in which the use of the singular is deemed to include the plural, unless specifically stated otherwise.

[0076] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described herein.​​

Claims

1. A product plan template generation method characterized by comprising: The method comprises the following steps: acquiring a focus element in a historical plan; wherein the focus element comprises a focus object, a focus index corresponding to the focus object, and a focus time corresponding to the focus object; determining a plan element according to the focus element; the plan element comprises a plan object, a plan index corresponding to the plan object, and a plan time corresponding to the plan object; matching a corresponding prediction algorithm and a data object for the plan index; wherein the prediction algorithm is used to predict the plan index, and the data object is used as input data of the prediction algorithm; generating a plan template according to the plan object, the plan index corresponding to the plan object, the plan time corresponding to the plan object, the prediction algorithm corresponding to the plan index, and the data object corresponding to the prediction algorithm; the plan template is used for: when the plan template is used, only calling and selecting are needed to complete the corresponding output, and in the management process, the plan element, the prediction algorithm, and the data object in the plan template can be independently added and deleted, so that the plan template is customized and generated.

2. The method of claim 1, wherein, The method further comprises the following steps: determining a plan time granularity and an algorithm time granularity according to the plan time corresponding to the plan object; generating the plan template according to the plan object, the plan index corresponding to the plan object, the plan time corresponding to the plan object, the prediction algorithm corresponding to the plan index, the data object corresponding to the prediction algorithm, the plan time granularity, and the algorithm time granularity.

3. The method of claim 1, wherein, The method further comprises the following steps after the step of generating the plan template according to the plan object, the plan index corresponding to the plan object, the plan time corresponding to the plan object, the prediction algorithm corresponding to the plan index, and the data object corresponding to the prediction algorithm: acquiring a new element; the new element comprises a new object, a new index corresponding to the new object, and a new time corresponding to the new object; updating the plan object according to the new object, updating the plan index corresponding to the plan object according to the new index corresponding to the new object, and updating the plan time corresponding to the plan object according to the new time corresponding to the new object.

4. The method of claim 1, wherein, The focus object and the plan object each comprise one or more of a sales channel, a sales region, and a sales store. The focus index and the plan index each comprise one or more of product sales, a sales trend, product inventory, and product yield.

5. A product plan template generation device characterized by comprising: The method comprises the following steps: a focus element acquisition module is configured to acquire a focus element in a historical plan; wherein the focus element comprises a focus object, a focus index corresponding to the focus object, and a focus time corresponding to the focus object; a plan element determination module is configured to determine a plan element according to the focus element; the plan element comprises a plan object, a plan index corresponding to the plan object, and a plan time corresponding to the plan object; A matching module is configured to match a corresponding prediction algorithm and a data object for the planning index, wherein the prediction algorithm is configured to predict the planning index, and the data object is configured to serve as input data of the prediction algorithm; A template generation module is configured to generate the planning template according to the planning object, the planning index corresponding to the planning object, the planning time corresponding to the planning object, the prediction algorithm corresponding to the planning index, and the data object corresponding to the prediction algorithm; The planning template is configured to: when the planning template is used, only calling and selecting are needed to complete corresponding output, and in the management process, the planning elements, the prediction algorithm, and the data object in the planning template can be independently added and deleted, so that the planning template is customized and generated.

6. The apparatus of claim 5, wherein, The template generation module is specifically configured to: determine the planning time granularity and the algorithm time granularity according to the planning time corresponding to the planning object; generate the planning template according to the planning object, the planning index corresponding to the planning object, the planning time corresponding to the planning object, the prediction algorithm corresponding to the planning index, the data object corresponding to the prediction algorithm, the planning time granularity, and the algorithm time granularity.

7. The apparatus of claim 5, wherein, Further comprising an updating module configured to, after the planning template is generated according to the planning object, the planning index corresponding to the planning object, the planning time corresponding to the planning object, the prediction algorithm corresponding to the planning index, and the data object corresponding to the prediction algorithm: obtain an added element, wherein the added element includes an added object, an added index corresponding to the added object, and an added time corresponding to the added object; update the planning object according to the added object, update the planning index corresponding to the planning object according to the added index corresponding to the added object, and update the planning time corresponding to the planning object according to the added time corresponding to the added object.

8. The apparatus of claim 5, wherein, The attention object and the planning object each include one or more of a sales channel, a sales region, and a sales store. The attention index and the planning index each include one or more of product sales, a sales trend, product inventory, and product yield.

9. An electronic device, comprising: The electronic device includes a processor and a memory coupled to the processor, and the memory stores instructions that, when executed by the processor, cause the electronic device to perform the steps of the method of any one of claims 1-4.

10. A readable storage medium, having stored thereon a computer program, characterized in that, The program, when executed by the processor, implements the steps of the method of any one of claims 1-4.

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