WO2020062645A1 - 基于数据分析的定价方法、设备、存储介质及装置 - Google Patents

基于数据分析的定价方法、设备、存储介质及装置 Download PDF

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Publication number
WO2020062645A1
WO2020062645A1 PCT/CN2018/122830 CN2018122830W WO2020062645A1 WO 2020062645 A1 WO2020062645 A1 WO 2020062645A1 CN 2018122830 W CN2018122830 W CN 2018122830W WO 2020062645 A1 WO2020062645 A1 WO 2020062645A1
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pricing
cost
target product
unit
sales volume
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PCT/CN2018/122830
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English (en)
French (fr)
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马玉芳
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平安科技（深圳）有限公司
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Publication of WO2020062645A1 publication Critical patent/WO2020062645A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0206—Price or cost determination based on market factors

Definitions

  • the present application relates to the field of data processing technology, and in particular, to a method, a device, a storage medium, and a device for pricing based on data analysis.
  • the pricing of products is based on manual calculation of various types of cost data offline, which is a large amount of data and easy to calculate errors.
  • the company has a large amount of infrastructure resources and operating resources, and the cost of mutual calls cannot be counted.
  • the lack of data easily leads to pricing Not allowed; there are no fixed records of the types and forms of products sold, no complete records of sales lists, and scattered information, which leads to incomplete collection of reference data for pricing and affects pricing accuracy. Therefore, how to improve the accuracy of product pricing is an urgent technical problem to be solved.
  • the main purpose of this application is to provide a pricing method, equipment, storage medium and device based on data analysis, which aims to solve the technical problem of low product pricing accuracy in the prior art.
  • the present application provides a data analysis-based pricing method, and the data analysis-based pricing method includes the following steps:
  • the present application also proposes a data analysis-based pricing device.
  • the data analysis-based pricing device includes a memory, a processor, and a processor that is stored on the memory and can run on the processor.
  • Data analysis-based pricing readable instructions configured to implement the steps of the data analysis-based pricing method as described above.
  • the present application also proposes a storage medium.
  • the storage medium stores pricing-readable instructions based on data analysis, and the pricing-readable instructions based on data analysis are implemented as described above when executed by a processor. Steps of the data analysis-based pricing method.
  • the present application also proposes a pricing device based on data analysis, and the pricing device based on data analysis includes:
  • a calculation module for obtaining a cost factor of a single target product, and calculating a unit cost of the target product according to the cost factor
  • a search module configured to obtain a target product type of the target product, and search for a target pricing rule corresponding to the target product type;
  • the calculation module is further configured to calculate a unit price of the target product according to the target pricing rule and the unit cost.
  • the unit cost of a single target product is calculated according to the cost factor, without manually calculating various cost data, reducing labor costs; obtaining the target of the target product Product type, find the target pricing rule corresponding to the target product type, and calculate the unit pricing of the target product per unit according to the target pricing rule and the unit cost.
  • the product cost data is complete and clear, and according to the product category Refine pricing rules to improve pricing accuracy.
  • FIG. 1 is a schematic structural diagram of a data analysis-based pricing device in a hardware operating environment according to a solution of an embodiment of the present application;
  • FIG. 2 is a schematic flowchart of a first embodiment of a pricing method based on data analysis of the present application
  • FIG. 3 is a schematic flowchart of a second embodiment of a pricing method based on data analysis of the present application
  • FIG. 4 is a schematic flowchart of a third embodiment of a pricing method based on data analysis of the present application
  • FIG. 5 is a structural block diagram of a first embodiment of a pricing device based on data analysis of the present application.
  • FIG. 1 is a schematic structural diagram of a pricing device based on data analysis of a hardware operating environment according to an embodiment of the present application.
  • the data analysis-based pricing device may include: a processor 1001, such as a central processing unit (Central Processing Unit (CPU), communication bus 1002, user interface 1003, network interface 1004, and memory 1005.
  • the communication bus 1002 is used to implement connection and communication between these components.
  • the user interface 1003 may include a display screen.
  • the optional user interface 1003 may further include a standard wired interface and a wireless interface.
  • the wired interface of the user interface 1003 may be a USB interface in this application.
  • the network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WIreless-FIdelity (WI-FI) interface).
  • the memory 1005 may be a high-speed random access memory (Random Access Memory (RAM) memory, or non-volatile memory Memory (NVM), such as disk storage.
  • the memory 1005 may optionally be a storage device independent of the foregoing processor 1001.
  • RAM Random Access Memory
  • NVM non-volatile memory Memory
  • FIG. 1 does not constitute a limitation on the pricing equipment based on data analysis, and may include more or less components than shown in the figure, or combine some components, or different components. Layout.
  • the memory 1005 identified as a computer storage medium may include an operating system, a network communication module, a user interface module, and pricing-readable instructions based on data analysis.
  • the network interface 1004 is mainly used to connect to a background server and perform data communication with the background server;
  • the user interface 1003 is mainly used to connect to a user device; and the data analysis-based pricing
  • the device calls the data analysis-based pricing readable instruction stored in the memory 1005 through the processor 1001, and executes the data analysis-based pricing method provided in the embodiment of the present application.
  • FIG. 2 is a schematic flowchart of a first embodiment of a pricing method based on data analysis of the present application, and a first embodiment of a pricing method based on data analysis of the present application is proposed.
  • the data analysis-based pricing method includes the following steps:
  • Step S10 Obtain a cost factor of a single target product, and calculate a single cost of the target product according to the cost factor.
  • the execution subject of this embodiment is the data analysis-based pricing device, wherein the data analysis-based pricing device may be an electronic device such as a personal computer or a server.
  • the cost factor includes at least one of labor cost, purchase cost, and service call cost.
  • the labor cost, purchase cost, and service call cost required for the production process of the target product of a single piece can be calculated, and then based on Calculate the labor cost, purchase cost, and service call cost of the target product for a single piece, and calculate the single cost of the target product.
  • the amount of manpower required at different levels and the time spent in the production of the target product can be stored in advance of the unit time manpower cost corresponding to different levels of manpower, and according to the different levels of manpower used
  • the quantity, the labor time consumed, and the labor cost per unit of time corresponding to different levels of labor are calculated to obtain the labor cost of the single target product.
  • the production of a target product usually requires the purchase of raw materials. Some raw materials may already be in stock, and some raw materials may need to be purchased.
  • the purchase cost of the target product described in a single piece is calculated based on the available stock raw materials and newly purchased raw materials.
  • technical support or other services from other companies may be required to facilitate the smooth production of the target product.
  • the technical support or other services of other companies require a certain amount of financial support, it may be based on the The technical support of other companies required during the production process of the target product or the cost corresponding to the service is calculated to obtain the service invocation cost corresponding to the target product.
  • the cost of one target product is composed of labor cost, procurement cost, and service call cost
  • the labor cost, purchase cost, and service call cost of the target product in a single item can be obtained by adding up The unit cost of the target product per unit. It can also sort out the production process of the target product in advance, and calculate the cost factors involved in each production process.
  • the cost factors are the labor costs, procurement costs, and service call costs involved in each production process.
  • the labor cost, procurement cost and service call cost corresponding to the production process calculate the unit cost of the target product described in the unit.
  • the cost factors include labor costs, procurement costs, and service invocation costs
  • the step S10 includes: obtaining labor costs, procurement costs, and service invocation costs corresponding to each production process of a single target product; Calculate the process cost corresponding to each production process according to the labor cost, purchase cost and service call cost corresponding to each production process; add up the process cost corresponding to each production process to obtain the unit cost of the target product.
  • Step S20 Obtain a target product type of the target product, and find a target pricing rule corresponding to the target product type.
  • mapping relationship table can be established in advance according to the unit cost of each product type and the corresponding historical sales volume as reference data.
  • the mapping relationship table includes the correspondence relationship between the product type and the pricing rule, and the mapping relationship table can be obtained from the mapping relationship table. To find a target pricing rule corresponding to the target product type.
  • the product type is usually set according to the analysis of historical data and can be set according to the cost and sales volume of the product.
  • the product type includes high cost and low sales, low cost and high sales, and high cost and sales. High, low cost and low sales volume.
  • the pricing rules corresponding to each product type are: the unit price is the first proportion (such as 40%), and the unit price is the second proportion (such as 10%). ),
  • the single item pricing is the third percentage (such as 20%) of the single item cost rise, and the single item pricing is the fourth percentage (such as 30%) of the single item cost rise.
  • Each of the floating ratios can be set based on the analysis of historical sales price and historical sales volume to set a suitable ratio to achieve profit expectations.
  • the product type and corresponding pricing rules can also be set according to other factors in the product production process, which is not limited in this embodiment.
  • Step S30 Calculate a unit price of the target product according to the target pricing rule and the unit cost.
  • the corresponding target pricing rule found in the mapping relationship table is: the unit price is a 40% increase in unit cost, Then the unit pricing of the target product per unit is the unit cost plus 40% of the unit cost.
  • the pricing rules can also be implemented by related algorithms of machine learning, such as a convolutional neural network algorithm, which can use the historical sales of products, historical unit pricing, historical unit cost, and corresponding product types as sample data.
  • the preset pricing model is trained, and the product type of the target product and the unit cost of the target product are input into the preset pricing model, and the unit pricing of the target product can be output.
  • the cost factor of a single target product is obtained by obtaining the cost factor of a single target product, and the cost of a single target product is calculated according to the cost factor.
  • Target product type find the target pricing rule corresponding to the target product type, and calculate the unit pricing of the target product according to the target pricing rule and the unit cost.
  • the product cost data is complete and clear, and according to the product Categories refine pricing rules to improve pricing accuracy.
  • FIG. 3 is a schematic flowchart of a second embodiment of a pricing method based on data analysis of the present application. Based on the first embodiment shown in FIG. 2 described above, a second embodiment of a pricing method based on data analysis of the present application is proposed.
  • step S30 the method further includes:
  • Step S40 The unit price is estimated through a sales volume calculation model to obtain the estimated sales volume of the target product for a preset future period.
  • the preset future period refers to a period of one year, several years, months, or several quarters from the current moment, and the unit price is the preset future period.
  • the price at which the target product is marketed. Obtain the sample market price of the target product in multiple historical periods and the corresponding sample sales volume as sample data.
  • the historical period is calculated from the current moment and the past year, several years, months, or several quarters.
  • a basic measurement model may be established in advance, and the basic measurement model may be a convolutional neural network model, etc.
  • the basic measurement model is trained by using the sample data to obtain the sales measurement model.
  • the one-piece pricing may be input into the trained sales model, and the estimated sales of the target product in the preset future period may be output.
  • the method before step S40, further includes: establishing a basic measurement model; obtaining a sample market price and a corresponding sample sales price of the target product in multiple historical periods; and according to the sample market price and The sales volume of the sample is trained on the basic measurement model to obtain a sales volume measurement model.
  • step S40 the method includes: judging whether to adjust the unit pricing according to the estimated sales volume in the preset future period.
  • the sales volume expectation of the target product in the preset future period is generally set in advance, and the sales volume expectation is The sales volume of the target product is expected in the preset future period. Comparing the estimated sales volume with the sales volume expectations to determine whether the estimated sales volume meets the sales volume expectations, and if it is satisfied, there is no need to adjust the unit pricing, and if it is not satisfied, the sales volume needs to be adjusted Pricing adjustments.
  • the determining whether to adjust the unit pricing based on the estimated sales volume in the preset future period includes:
  • Step S50 Obtain sales expectations of the target product within the preset future period, and determine whether the estimated sales volume of the preset future period meets the sales expectation;
  • the preset future period refers to a period of one year, several years, months, or several quarters from the current moment, and the preset sales expectation for the period is based on Business or performance needs, set sales volume needs that need to be reached in the corresponding future time period. If the estimated sales volume is greater than or equal to the sales volume expectation, it indicates that the estimated sales volume of the preset future period meets the demand, and there is no need to adjust the single unit pricing. It is indicated that the estimated sales volume in the preset future period does not meet the demand, and the unit pricing needs to be adjusted.
  • Step S60 if the estimated sales volume in the preset future period does not meet the sales volume expectations, adjust the unit pricing.
  • the estimated sales volume is less than the sales volume expectations, indicating that the estimated sales volume in the preset future period does not meet the sales volume expectations, adjustments to the unit pricing may be required, and the The unit price is lowered to promote consumption and increase sales.
  • the one-piece pricing is used to estimate the sales volume through a sales calculation model, to obtain an estimated sales volume of the target product for a preset future period, and to determine whether to evaluate the sales volume based on the estimated sales volume for the preset future period.
  • the unit pricing is adjusted so that the unit pricing can meet the sales volume expectations required by the business, avoid excessive pricing and lead to unsalable products, thereby improving the rationality of unit pricing.
  • FIG. 4 is a schematic flowchart of a third embodiment of a pricing method based on data analysis of the present application. Based on the second embodiment shown in FIG. 3 described above, a third embodiment of the pricing method based on data analysis of the present application is proposed.
  • the method before step S60, the method further includes:
  • Step S501 Calculate an estimated profit of the target product in the preset future period according to the unit cost, the unit pricing, and the estimated sales.
  • the difference between the unit pricing and the unit cost may be calculated, and the difference between the unit pricing and the unit cost and the total cost may be calculated. Multiplying the estimated sales volume to obtain the estimated profit of the target product in the preset future period.
  • Step S502 Obtain the profit expectation of the target product in the preset future period, and determine whether the estimated profit of the preset future period meets the profit expectation.
  • the profit of the target product in the preset future period is generally required according to the operating needs.
  • the profit expectation of the target product in the preset future period is generally set in advance, and the profit expectation is It is expected that the profit amount of the target product in the preset future period. Comparing the estimated profit with the profit expectation to determine whether the estimated profit is greater than or equal to the profit expectation, and if the estimated profit is greater than or equal to the profit expectation, indicating the preset future period The estimated profit meets the profit expectation. If the estimated profit is less than the profit expectation, it indicates that the estimated profit of the preset future period does not meet the profit expectation.
  • step S60 includes:
  • Step S601 if the estimated sales volume in the preset future period does not satisfy the sales volume expectation, or the estimated profit in the preset future period does not satisfy the profit expectation, adjust the unit price.
  • the unit pricing needs to be adjusted, and the unit pricing may be reduced to promote Consumption, increase sales.
  • the unit price must be adjusted, and the unit price can be reduced to promote Consumption, increase sales, and thus increase profitability.
  • the unit cost, the unit pricing, the estimated sales volume, the estimated profit, and historical related data may be displayed, so that relevant personnel may decide whether to perform the unit pricing based on the displayed data. Adjustment.
  • step S601 the method further includes:
  • Step S70 Obtain the latest selling price of the target product in the most recent period.
  • the historical cost pricing of last year is usually a reference value for this year's cost pricing.
  • the cost of a product will not change much in the next year or two, so the cost pricing is not The change will be too great.
  • the most recent time period is the time period closest to the current moment, the past year or two, and so on.
  • the latest selling price is the selling price of the target product in the most recent period. If the target product has multiple selling prices in the most recent period, an average value calculation is performed on the multiple selling prices to calculate The average obtained is used as the recent selling price.
  • Step S80 Compare the recent selling price with the unit pricing to obtain a difference.
  • the recent selling price minus the unit pricing, and the obtained result is taken as an absolute value, and the absolute value is used as the difference. Comparing the recent selling price with the one-piece pricing to obtain a deviation between the one-piece pricing and the recent selling price, and if the difference is large, indicating the one-piece pricing and the recent selling price The deviation of the price is large. At this time, the cause of the deviation needs to be analyzed to avoid pricing errors. If the difference is small, it means that the deviation between the unit price and the recent selling price is not large. The unit pricing is more reasonable.
  • Step S90 Determine whether the spread is within a preset deviation range.
  • a pricing deviation range may be set in advance, that is, the preset deviation range. If the unit pricing is not within the preset deviation range, it means that the unit pricing may be wrong, and data verification is required to avoid pricing errors caused by errors in the manually entered basic data. There may also be a phenomenon that the deviation of the unit pricing is too large due to the rapid development of the market. At this time, there is no need to adjust the unit pricing.
  • the preset deviation range may be correspondingly set according to products in different environments, for example, the corresponding setting of products in environments such as development, operation, security, or data platform conforms to the preset deviation range of the corresponding environment.
  • Step S100 if the spread is not within the preset deviation range, an alarm is issued.
  • the price can be calculated based on the recent selling price and the unit.
  • the price difference generates alarm prompt information, and the alarm prompt information is sent to the mailbox of the relevant personnel by email to implement the alarm prompt.
  • the relevant personnel can view the alarm prompt information when checking the email. Know in time the difference between the recent selling price and the unit price, and check the reason for the difference in time to determine whether the unit price is reasonable.
  • an estimated profit of the target product in the preset future period is calculated according to the single-piece cost, the single-piece pricing, and the estimated sales volume, and the target product is obtained in the Profit expectations in a preset future period, and determine whether the estimated profit in the preset future period meets the profit expectation, if the estimated sales volume in the preset future period does not meet the sales volume expectation, or the forecast Assuming that the estimated profit in the future period does not meet the profit expectation, the unit pricing is adjusted so that the unit pricing meets the sales demand or profit demand to meet the business demand and improve the pricing rationality; Compare the recent selling price of the target product in the most recent period, compare the recent selling price with the unit pricing, obtain a difference, and determine whether the difference is within a preset deviation range; if the difference is not within the preset range If there is a deviation range, an alarm will be given to prevent pricing errors caused by errors in the manually entered basic data and improve the accuracy of pricing.
  • the aforementioned storage medium may be a read-only memory, a magnetic disk, or an optical disk.
  • an embodiment of the present application further provides a storage medium that stores data-readable pricing-readable instructions based on the data analysis.
  • the data-analysis-based pricing-readable instructions are executed by a processor, the implementation is implemented as described above. Steps of data analysis based pricing method.
  • an embodiment of the present application further proposes a data analysis-based pricing device.
  • the data analysis-based pricing device includes:
  • a calculation module 10 configured to obtain a cost factor of a single target product, and calculate a single cost of the target product according to the cost factor;
  • a search module 20 configured to obtain a target product type of the target product, and search for a target pricing rule corresponding to the target product type;
  • the calculation module 10 is further configured to calculate a unit price of the target product according to the target pricing rule and the unit cost.
  • the cost factor includes at least one of labor cost, procurement cost, and service call cost, and the labor cost, purchase cost, and service call cost that can be used in the production process of the target product for a single piece can be understood. Calculate, and then calculate the unit cost of the target product according to the labor cost, purchase cost, and service call cost of the target product.
  • the amount of manpower required at different levels and the time spent in the production of the target product can be stored in advance of the unit time manpower cost corresponding to different levels of manpower, and according to the different levels of manpower used
  • the quantity, the labor time consumed, and the labor cost per unit of time corresponding to different levels of labor are calculated to obtain the labor cost of the single target product.
  • the production of a target product usually requires the purchase of raw materials. Some raw materials may already be in stock, and some raw materials may need to be purchased.
  • the purchase cost of the target product described in a single piece is calculated based on the available stock raw materials and newly purchased raw materials.
  • technical support or other services from other companies may be required to facilitate the smooth production of the target product.
  • the technical support or other services of other companies require a certain amount of financial support, it may be based on the The technical support of other companies required during the production process of the target product or the cost corresponding to the service is calculated to obtain the service invocation cost corresponding to the target product.
  • the cost of one target product is composed of labor cost, procurement cost, and service call cost
  • the labor cost, purchase cost, and service call cost of the target product in a single item can be obtained by adding up The unit cost of the target product per unit. It can also sort out the production process of the target product in advance, and calculate the cost factors involved in each production process.
  • the cost factors are the labor costs, procurement costs, and service call costs involved in each production process.
  • the labor cost, procurement cost and service call cost corresponding to the production process calculate the unit cost of the target product described in the unit.
  • the cost factors include labor cost, procurement cost, and service call cost; the cost factor of obtaining a single target product, and calculating the single cost of the target product according to the cost factor, Including: Obtain the labor cost, procurement cost and service call cost corresponding to each production process of a single target product; calculate the process cost corresponding to each production process according to the labor cost, procurement cost and service call cost corresponding to each production process; The process costs corresponding to the processes are accumulated to obtain the unit cost of the target product for each unit.
  • mapping relationship table can be established in advance according to the unit cost of each product type and the corresponding historical sales volume as reference data.
  • the mapping relationship table includes the correspondence relationship between the product type and the pricing rule, and the mapping relationship table can be obtained from the mapping relationship table. To find a target pricing rule corresponding to the target product type.
  • the product type is usually set according to the analysis of historical data and can be set according to the cost and sales volume of the product.
  • the product type includes high cost and low sales, low cost and high sales, and high cost and sales. High, low cost and low sales volume.
  • the pricing rules corresponding to each product type are: the unit price is the first proportion (such as 40%), and the unit price is the second proportion (such as 10%). ),
  • the single item pricing is the third percentage (such as 20%) of the single item cost rise, and the single item pricing is the fourth percentage (such as 30%) of the single item cost rise.
  • Each of the floating ratios can be set based on the analysis of historical sales price and historical sales volume to set a suitable ratio to achieve profit expectations.
  • the product type can also be set according to other factors in the production process of the product, which is not limited in this embodiment.
  • the corresponding target pricing rule found in the mapping relationship table is: the unit price is a 40% increase in unit cost, Then the unit pricing of the target product per unit is the unit cost plus 40% of the unit cost.
  • the pricing rules can also be implemented by related algorithms of machine learning, such as a convolutional neural network algorithm, which can use the historical sales of products, historical unit pricing, historical unit cost, and corresponding product types as sample data.
  • the preset pricing model is trained, and the product type of the target product and the unit cost of the target product are input into the preset pricing model, and the unit pricing of the target product can be output.
  • the cost factor of a single target product is obtained by obtaining the cost factor of a single target product, and the cost of a single target product is calculated according to the cost factor.
  • Target product type find the target pricing rule corresponding to the target product type, and calculate the unit pricing of the target product according to the target pricing rule and the unit cost.
  • the product cost data is complete and clear, and according to the product Categories refine pricing rules to improve pricing accuracy.
  • the data analysis-based pricing device further includes: an estimation module, configured to estimate the sales volume of the single-item pricing through a sales model, and obtain an estimate of a preset future period of the target product. Sales
  • a judging module is configured to judge whether to adjust the pricing of the unit according to the estimated sales volume in the preset future period.
  • the data analysis-based pricing device further includes: a establishing module for establishing a basic measurement model;
  • An acquisition module configured to acquire a sample market price and a corresponding sample sales price of the target product in multiple historical periods
  • a training module is configured to train the basic measurement model according to the sales price of the sample market and the corresponding sales volume of the sample to obtain a sales measurement model.
  • the obtaining module is further configured to obtain sales expectations of the target product in the preset future period, and determine whether the estimated sales amount in the preset future period meets the sales expectation;
  • the data analysis-based pricing device further includes: an adjustment module for adjusting the unit pricing if the estimated sales volume in the preset future period does not meet the sales volume expectations.
  • the calculation module 10 is further configured to calculate an estimated profit of the target product in the preset future period according to the unit cost, the unit pricing, and the estimated sales amount. ;
  • the obtaining module is further configured to obtain a profit expectation of the target product in the preset future period, and determine whether the estimated profit of the preset future period meets the profit expectation;
  • the adjustment module is further configured to: if the estimated sales volume of the preset future period does not meet the sales volume expectation, or if the estimated profit of the preset future period does not meet the profit expectation, Price adjustments.
  • the obtaining module is further configured to obtain a recent selling price of the target product in a recent period
  • the data analysis-based pricing device further includes: a comparison module, configured to compare the recent selling price with the unit price to obtain a difference;
  • the judging module is configured to judge whether the spread is within a preset deviation range
  • An alarm module is configured to perform an alarm prompt if the spread is not within the preset deviation range.
  • the cost factors include labor costs, procurement costs, and service call costs
  • the acquisition module is further configured to acquire labor costs, procurement costs, and service call costs corresponding to each production process of a single target product;
  • the calculation module 10 is further configured to calculate a process cost corresponding to each production process according to a labor cost, a procurement cost, and a service call cost corresponding to each production process;
  • the calculation module 10 is further configured to accumulate process costs corresponding to each production process to obtain a unit cost of the target product.
  • the method of the embodiment can be implemented by means of software plus a necessary universal hardware platform. Hardware, but in many cases the former is a better implementation.
  • the technical solution of the present application in essence or a part that contributes to the existing technology may be in the form of a software product.
  • the computer software product is stored in a storage medium (such as a Read Only Memory image (ROM) / Random Access Memory (Random Access Memory (RAM), magnetic disks, and optical disks) include a number of instructions for causing a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the embodiments of this application.
  • ROM Read Only Memory image
  • RAM Random Access Memory
  • magnetic disks magnetic disks
  • optical disks include a number of instructions for causing a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the embodiments of this application.

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Abstract

本申请公开了一种基于数据分析的定价方法、设备、存储介质及装置，该方法包括：获取单件目标产品的成本因素，根据所述成本因素计算单件所述目标产品的单件成本；获取所述目标产品的目标产品类型，查找与所述目标产品类型对应的目标定价规则；根据所述目标定价规则和所述单件成本计算单件所述目标产品的单件定价。本申请中，通过产品的成本因素计算产品的单件成本，再结合定价规则计算产品的单件定价，无需人工计算各类成本数据，减少人力成本，产品成本数据完整清晰，提高定价的准确度。

Description

基于数据分析的定价方法、设备、存储介质及装置
本申请要求于2018年09月25日提交中国专利局、申请号为2018111216065、发明名称为“基于数据分析的定价方法、设备、存储介质及装置”的中国专利申请的优先权，其全部内容通过引用结合在申请中。
技术领域
本申请涉及数据处理技术领域，尤其涉及一种基于数据分析的定价方法、设备、存储介质及装置。
背景技术
目前，对于产品的定价，是通过线下人工计算各类成本数据作为参考，数据量大且容易计算错误；公司基础架构资源和运营资源较多，相互调用成本无法统计，数据缺失，易导致定价不准；销售产品种类和形式无固定地方记载，销售清单无完整记录，信息零散，导致定价的参考数据收集不全，影响定价准确度。因此，如何提高产品定价的准确度是亟待解决的技术问题。
上述内容仅用于辅助理解本申请的技术方案，并不代表承认上述内容是现有技术。
发明内容
本申请的主要目的在于提供一种基于数据分析的定价方法、设备、存储介质及装置，旨在解决现有技术中产品定价准确度较低的技术问题。
为实现上述目的，本申请提供一种基于数据分析的定价方法，所述基于数据分析的定价方法包括以下步骤：
获取单件目标产品的成本因素，根据所述成本因素计算单件所述目标产品的单件成本；
获取所述目标产品的目标产品类型，查找与所述目标产品类型对应的目标定价规则；
根据所述目标定价规则和所述单件成本计算单件所述目标产品的单件定价。
此外，为实现上述目的，本申请还提出一种基于数据分析的定价设备，所述基于数据分析的定价设备包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的基于数据分析的定价可读指令，所述基于数据分析的定价可读指令配置为实现如上文所述的基于数据分析的定价方法的步骤。
此外，为实现上述目的，本申请还提出一种存储介质，所述存储介质上存储有基于数据分析的定价可读指令，所述基于数据分析的定价可读指令被处理器执行时实现如上文所述的基于数据分析的定价方法的步骤。
此外，为实现上述目的，本申请还提出一种基于数据分析的定价装置，所述基于数据分析的定价装置包括：
计算模块，用于获取单件目标产品的成本因素，根据所述成本因素计算单件所述目标产品的单件成本；
查找模块，用于获取所述目标产品的目标产品类型，查找与所述目标产品类型对应的目标定价规则；
所述计算模块，还用于根据所述目标定价规则和所述单件成本计算单件所述目标产品的单件定价。
本申请中，通过获取单件目标产品的成本因素，根据所述成本因素计算单件所述目标产品的单件成本，无需人工计算各类成本数据，减少人力成本；获取所述目标产品的目标产品类型，查找与所述目标产品类型对应的目标定价规则，根据所述目标定价规则和所述单件成本计算单件所述目标产品的单件定价，产品成本数据完整清晰，并根据产品类别细化定价规则，从而提高定价的准确度。
附图说明
图1是本申请实施例方案涉及的硬件运行环境的基于数据分析的定价设备的结构示意图；
图2为本申请基于数据分析的定价方法第一实施例的流程示意图；
图3为本申请基于数据分析的定价方法第二实施例的流程示意图；
图4为本申请基于数据分析的定价方法第三实施例的流程示意图；
图5为本申请基于数据分析的定价装置第一实施例的结构框图。
本申请目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
具体实施方式
应当理解，此处所描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
参照图1，图1为本申请实施例方案涉及的硬件运行环境的基于数据分析的定价设备结构示意图。
如图1所示，该基于数据分析的定价设备可以包括：处理器1001，例如中央处理器（Central Processing Unit，CPU），通信总线1002、用户接口1003，网络接口1004，存储器1005。其中，通信总线1002用于实现这些组件之间的连接通信。用户接口1003可以包括显示屏（Display），可选用户接口1003还可以包括标准的有线接口、无线接口，对于用户接口1003的有线接口在本申请中可为USB接口。网络接口1004可选的可以包括标准的有线接口、无线接口（如无线保真（WIreless-FIdelity，WI-FI）接口）。存储器1005可以是高速的随机存取存储器（Random Access Memory，RAM）存储器，也可以是稳定的存储器（Non-volatile Memory，NVM），例如磁盘存储器。存储器1005可选的还可以是独立于前述处理器1001的存储装置。
本领域技术人员可以理解，图1中示出的结构并不构成对基于数据分析的定价设备的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置。
如图1所示，认定为一种计算机存储介质的存储器1005中可以包括操作系统、网络通信模块、用户接口模块以及基于数据分析的定价可读指令。
在图1所示的基于数据分析的定价设备中，网络接口1004主要用于连接后台服务器，与所述后台服务器进行数据通信；用户接口1003主要用于连接用户设备；所述基于数据分析的定价设备通过处理器1001调用存储器1005中存储的基于数据分析的定价可读指令，并执行本申请实施例提供的基于数据分析的定价方法。
基于上述硬件结构，提出本申请基于数据分析的定价方法的实施例。
参照图2，图2为本申请基于数据分析的定价方法第一实施例的流程示意图，提出本申请基于数据分析的定价方法第一实施例。
在第一实施例中，所述基于数据分析的定价方法包括以下步骤：
步骤S10：获取单件目标产品的成本因素，根据所述成本因素计算单件所述目标产品的单件成本。
应理解的是，本实施例的执行主体是所述基于数据分析的定价设备，其中，所述基于数据分析的定价设备可为个人电脑或服务器等电子设备。所述成本因素包括人力成本、采购成本和服务调用成本中的至少一项，可对单件所述目标产品的生产过程中需要用到的人力成本、采购成本和服务调用成本进行计算，再根据计算获得的单件所述目标产品的人力成本、采购成本和服务调用成本计算所述目标产品的单件成本。
需要说明的是，所述目标产品的生产过程中需要用到的不同级别的人力数量，耗费的人力时间，可预先存储不同级别的人力对应的单位时间人力成本，根据用到的不同级别的人力数量、耗费的人力时间和不同级别的人力对应的单位时间人力成本计算获得所述单件目标产品的人力成本。生产一件所述目标产品通常需要采购原材料，部分原材料可能库存中已有，部分原材料可能需要进行采购，根据可用库存原材料和新增采购原材料计算获得单件所述目标产品的采购成本。所述目标产品的生产过程中可能需要其他公司的技术支持或者其他服务，以助于所述目标产品的顺利生产，则其他公司的技术支持或者其他服务需要耗费一定资金支持，则可根据所述目标产品的生产过程中需要的其他公司的技术支持或者服务对应的费用计算获得单件所述目标产品对应的服务调用成本。
可理解的是，通常一件所述目标产品的成本由人力成本、采购成本和服务调用成本组成，则将单件所述目标产品的人力成本、采购成本和服务调用成本进行累加，即可获得单件所述目标产品的所述单件成本。还可预先对所述目标产品的生产流程进行梳理，并统计各生产流程涉及到的成本因素，所述成本因素为各生产流程涉及到的人力成本、采购成本和服务调用成本等，再根据各生产流程对应的人力成本、采购成本和服务调用成本计算单件所述目标产品的单件成本。本实施例中，所述所述成本因素包括人力成本、采购成本和服务调用成本；所述步骤S10，包括：获取单件目标产品的各生产流程对应的人力成本、采购成本和服务调用成本；根据各生产流程对应的人力成本、采购成本和服务调用成本计算各生产流程对应的流程成本；将各生产流程对应的流程成本进行累加，获得单件所述目标产品的单件成本。
步骤S20：获取所述目标产品的目标产品类型，查找与所述目标产品类型对应的目标定价规则。
在具体实现中，对于不同的产品类型，消费人群不同，需求量也不同，可预先对历史数据进行分析，获得历年不同类型产品的定价与成本之间的关系，建立所述定价规则，所述定价规则根据产品类型的不同而进行相应的设置，以提高定价的准确度。可预先根据各产品类型的单件成本和对应历史销量作为参考数据，预先建立映射关系表，所述映射关系表中包括产品类型与定价规则之间的对应关系，则可从所述映射关系表中查找与所述目标产品类型对应的目标定价规则。
应理解的是，所述产品类型通常根据对历史数据进行分析而进行相应的设置，可根据产品的成本和销量来设置，所述产品类型包括成本高销量低、成本低销量高、成本高销量高和成本低销量低，与各产品类型对应的定价规则分别为：单件定价为单件成本上浮第一比例（比如40%），单件定价为单件成本上浮第二比例（比如10%），单件定价为单件成本上浮第三比例（比如20%），单件定价为单件成本上浮第四比例（比如30%）。各上浮比例可根据对历史售价和历史销量进行分析，而设置合适的比例，以实现盈利期望。所述产品类型与对应的定价规则还可根据产品生产过程中的其他因素来进行设置，本实施例对此不加以限制。
步骤S30：根据所述目标定价规则和所述单件成本计算单件所述目标产品的单件定价。
应理解的是，在上述例子中，在所述目标产品类型为成本高销量低时，从所述映射关系表中查找到对应的目标定价规则为：单件定价为单件成本上浮40%，则单件所述目标产品的单件定价为所述单件成本加上所述单件成本的40%。所述定价规则也可通过机器学习的相关算法来实现，比如卷积神经网络算法，可将历年的产品的历史销量、历史单件定价、历史单件成本和对应的产品类型作为样本数据，对预设定价模型进行训练，则将所述目标产品的产品类型和目标产品的所述单件成本输入所述预设定价模型，则可输出所述目标产品的单件定价。
本实施例中，通过获取单件目标产品的成本因素，根据所述成本因素计算单件所述目标产品的单件成本，无需人工计算各类成本数据，减少人力成本；获取所述目标产品的目标产品类型，查找与所述目标产品类型对应的目标定价规则，根据所述目标定价规则和所述单件成本计算单件所述目标产品的单件定价，产品成本数据完整清晰，并根据产品类别细化定价规则，从而提高定价的准确度。
参照图3，图3为本申请基于数据分析的定价方法第二实施例的流程示意图，基于上述图2所示的第一实施例，提出本申请基于数据分析的定价方法的第二实施例。
在第二实施例中，所述步骤S30之后，还包括：
步骤S40：将所述单件定价通过销量测算模型进行销量预估，获得所述目标产品预设未来时段的预估销量。
可理解的是，所述预设未来时段指的是从当前时刻起算，未来的一年、几年、几个月或者几个季度的时段，所述单件定价即为所述预设未来时段所述目标产品进行市场销售时的价格。获取所述目标产品在多个历史时段的样本市场售价以及对应的样本销量作为样本数据，所述历史时段为当前时刻起算，过去的一年、几年、几个月或者几个季度的时段，可预先建立基础测算模型，所述基础测算模型可以是卷积神经网络模型等，通过所述样本数据对所述基础测算模型进行训练，获得所述销量测算模型。则可将所述单件定价输入经过训练的所述销量测算模型，输出所述目标产品在所述预设未来时段的预估销量。在本实施例中，所述步骤S40之前，还包括：建立基础测算模型；获取所述目标产品在多个历史时段内的样本市场售价与对应的样本销量；根据所述样本市场售价与对应的所述样本销量对所述基础测算模型进行训练，获得销量测算模型。
在所述步骤S40之后，包括：根据所述预设未来时段的预估销量判断是否对所述单件定价进行调整。
应理解的是，通常根据经营需要，对所述目标产品在所述预设未来时段的销量有要求，通常预先设置所述目标产品在所述预设未来时段的销量期望，所述销量期望即期望在所述预设未来时段所述目标产品的销售量。将所述预估销量与所述销量期望进行比较，判断所述预估销量是否满足所述销量期望，若满足，则无需对所述单件定价进行调整，若不满足，则需要对所述单件定价进行调整。
进一步地，所述根据所述预设未来时段的预估销量判断是否对所述单件定价进行调整，包括：
步骤S50：获取所述目标产品在所述预设未来时段内的销量期望，判断所述预设未来时段的预估销量是否满足所述销量期望；
可理解的是，所述预设未来时段指的是从当前时刻起算，未来的一年、几年、几个月或者几个季度的时段，在所述预设为了时段内的销量期望为根据经营或业绩需要，设置的在相应的未来时段内需要达到的销售量需求。若所述预估销量大于或者等于所述销量期望，表明所述预设未来时段的预估销量满足需求，无需对所述单件定价进行调整，若所述预估销量小于所述销量期望，表明所述预设未来时段的预估销量不满足需求，则需要对所述单件定价进行调整。
步骤S60：若所述预设未来时段的预估销量不满足所述销量期望，则对所述单件定价进行调整。
需要说明的是，若所述预估销量小于所述销量期望，表明所述预设未来时段的预估销量不满足所述销量期望，则需要对所述单件定价进行调整，可将所述单件定价调低，以促进消费，提高销售量。
本实施例中，将所述单件定价通过销量测算模型进行销量预估，获得所述目标产品预设未来时段的预估销量，根据所述预设未来时段的预估销量判断是否对所述单件定价进行调整，以使所述单件定价能够满足经营需要的销售量期望，避免定价过高，导致产品滞销的情况发生，从而提高了单件定价的合理性。
参照图4，图4为本申请基于数据分析的定价方法第三实施例的流程示意图，基于上述图3所示的第二实施例，提出本申请基于数据分析的定价方法的第三实施例。
在第三实施例中，所述步骤S60之前，还包括：
步骤S501：根据所述单件成本、所述单件定价和所述预估销量计算所述目标产品在所述预设未来时段内的预估盈利。
应理解的是，在知晓所述预估销量，则可计算所述单件定价与所述单件成本之间的差价，将所述单件定价与所述单件成本之间的差价和所述预估销量进行相乘，获得所述目标产品在所述预设未来时段内的预估盈利。
步骤S502：获取所述目标产品在所述预设未来时段内的盈利期望，判断所述预设未来时段的预估盈利是否满足所述盈利期望。
可理解的是，通常根据经营需要，对所述目标产品在所述预设未来时段的盈利有要求，通常预先设置所述目标产品在所述预设未来时段的盈利期望，所述盈利期望即期望在所述预设未来时段所述目标产品的利润额度。将所述预估盈利与所述盈利期望进行比较，判断所述预估盈利是否大于或者等于所述盈利期望，若所述预估盈利大于或者等于所述盈利期望，表明所述预设未来时段的预估盈利满足所述盈利期望，若所述预估盈利小于所述盈利期望，表明所述预设未来时段的预估盈利不满足所述盈利期望。
本实施例中，所述步骤S60，包括：
步骤S601：若所述预设未来时段的预估销量不满足所述销量期望，或者，所述预设未来时段的预估盈利不满足所述盈利期望，则对所述单件定价进行调整。
需要说明的是，若所述预估销量大于或者等于所述销量期望，表明所述预设未来时段的预估销量满足所述销量期望，则无需对所述单件定价进行调整，若所述预估销量小于所述销量期望，表明所述预设未来时段的预估销量不满足所述销量期望，则需要对所述单件定价进行调整，可将所述单件定价调低，以促进消费，提高销售量。
在具体实现中，若所述预估盈利大于或者等于所述盈利期望，表明所述预设未来时段的预估盈利满足所述盈利期望，则无需对所述单件定价进行调整，若所述预估盈利小于所述盈利期望，表明所述预设未来时段的预估盈利不满足所述盈利期望，则需要对所述单件定价进行调整，可将所述单件定价调低，以促进消费，提高销售量，从而提高盈利。可将所述单件成本、所述单件定价、所述预估销量、所述预估盈利以及历史相关数据进行展示，以使相关人员根据展示的数据决定是否需要对所述单件定价进行调整。
本实施例中，所述步骤S601之后，还包括：
步骤S70：获取所述目标产品在最近时段的最近售价。
应理解的是，由于经济因素，通常去年的历史成本定价对于今年的成本定价具有参考价值，通常一件产品的成本在临近的一年或者两年中不会变动太大，因此成本定价也不会变动太大，考虑到市场波动等因素，所述最近时段为距离当前时刻最近的、过去的一年或者两年等时段。所述最近售价为所述最近时段内所述目标产品的售价，若在所述最近时段内所述目标产品有多个售价，则对该多个售价进行平均值计算，将计算获得的平均值作为所述最近售价。
步骤S80：将所述最近售价与所述单件定价进行比较，获得差价。
可理解的是，将所述最近售价减去所述单件定价，并将获得的结果取绝对值，将所述绝对值作为所述差价。将所述最近售价与所述单件定价进行比较，以获得所述单件定价与所述最近售价的偏差，若所述差价较大，说明所述单件定价与所述最近售价的偏差较大，此时需要对产生偏差的原因进行分析，以免出现定价失误，若所述差价较较小，说明所述单件定价与所述最近售价的偏差不大，可以认为所述单件定价比较合理。
步骤S90：判断所述差价是否处于预设偏差范围。
需要说明的是，为了避免出现定价的失误，可预先设置定价的偏差范围，即所述预设偏差范围。若所述单件定价不处于所述预设偏差范围，说明所述单件定价可能有误，需要进行数据核验，避免因为人工输入的基础数据的错误导致的定价错误。还可能因市场的快速发展而出现的所述单件定价的偏差过大的现象，此时，则无需对所述单件定价进行调整。所述预设偏差范围可根据不同环境的产品进行相应的设置，例如：开发、运营、安全或数据平台等环境的产品对应设置符合对应环境的所述预设偏差范围。
步骤S100：若所述差价不处于所述预设偏差范围，则进行告警提示。
在具体实现中，避免因为人工输入的基础数据错误等原因导致的定价错误的情况发生，可在所述差价不处于所述预设偏差范围时，可根据所述最近售价与所述单件定价之间的所述差价生成告警提示信息，将所述告警提示信息通过邮件发送至相关人员的邮箱中，以实现告警提示，相关人员在查看邮件时，能够对所述告警提示信息进行查看，及时了解所述最近售价与所述单件定价之间的所述差价，以及时对产生差价的原因进行排查，从而确定所述单件定价是否合理。
在本实施例中，根据所述单件成本、所述单件定价和所述预估销量计算所述目标产品在所述预设未来时段内的预估盈利，获取所述目标产品在所述预设未来时段内的盈利期望，判断所述预设未来时段的预估盈利是否满足所述盈利期望，若所述预设未来时段的预估销量不满足所述销量期望，或者，所述预设未来时段的预估盈利不满足所述盈利期望，则对所述单件定价进行调整，使得所述单件定价满足销量需求或者盈利需求，以满足营业需求，提高定价的合理性；获取所述目标产品在最近时段的最近售价，将所述最近售价与所述单件定价进行比较，获得差价，判断所述差价是否处于预设偏差范围，若所述差价不处于所述预设偏差范围，则进行告警提示，避免因为人工输入的基础数据错误等原因导致的定价错误的情况发生，提高定价的准确度。需要说明的是，本领域普通技术人员可以理解实现上述实施例的全部或部分步骤可以通过硬件来完成，也可以通过可读指令来指令相关的硬件完成，所述的可读指令可以存储于一种计算机可读存储介质中，上述提到的存储介质可以是只读存储器，磁盘或光盘等。
此外，本申请实施例还提出一种存储介质，所述存储介质上存储有基于数据分析的定价可读指令，所述基于数据分析的定价可读指令被处理器执行时实现如上文所述的基于数据分析的定价方法的步骤。
此外，参照图5，本申请实施例还提出一种基于数据分析的定价装置，所述基于数据分析的定价装置包括：
计算模块10，用于获取单件目标产品的成本因素，根据所述成本因素计算单件所述目标产品的单件成本；
查找模块20，用于获取所述目标产品的目标产品类型，查找与所述目标产品类型对应的目标定价规则；
所述计算模块10，还用于根据所述目标定价规则和所述单件成本计算单件所述目标产品的单件定价。
应理解的是，所述成本因素包括人力成本、采购成本和服务调用成本中的至少一项，可对单件所述目标产品的生产过程中需要用到的人力成本、采购成本和服务调用成本进行计算，再根据计算获得的单件所述目标产品的人力成本、采购成本和服务调用成本计算所述目标产品的单件成本。
需要说明的是，所述目标产品的生产过程中需要用到的不同级别的人力数量，耗费的人力时间，可预先存储不同级别的人力对应的单位时间人力成本，根据用到的不同级别的人力数量、耗费的人力时间和不同级别的人力对应的单位时间人力成本计算获得所述单件目标产品的人力成本。生产一件所述目标产品通常需要采购原材料，部分原材料可能库存中已有，部分原材料可能需要进行采购，根据可用库存原材料和新增采购原材料计算获得单件所述目标产品的采购成本。所述目标产品的生产过程中可能需要其他公司的技术支持或者其他服务，以助于所述目标产品的顺利生产，则其他公司的技术支持或者其他服务需要耗费一定资金支持，则可根据所述目标产品的生产过程中需要的其他公司的技术支持或者服务对应的费用计算获得单件所述目标产品对应的服务调用成本。
可理解的是，通常一件所述目标产品的成本由人力成本、采购成本和服务调用成本组成，则将单件所述目标产品的人力成本、采购成本和服务调用成本进行累加，即可获得单件所述目标产品的所述单件成本。还可预先对所述目标产品的生产流程进行梳理，并统计各生产流程涉及到的成本因素，所述成本因素为各生产流程涉及到的人力成本、采购成本和服务调用成本等，再根据各生产流程对应的人力成本、采购成本和服务调用成本计算单件所述目标产品的单件成本。本实施例中，所述所述成本因素包括人力成本、采购成本和服务调用成本；所述获取单件目标产品的成本因素，根据所述成本因素计算单件所述目标产品的单件成本，包括：获取单件目标产品的各生产流程对应的人力成本、采购成本和服务调用成本；根据各生产流程对应的人力成本、采购成本和服务调用成本计算各生产流程对应的流程成本；将各生产流程对应的流程成本进行累加，获得单件所述目标产品的单件成本。
在具体实现中，对于不同的产品类型，消费人群不同，需求量也不同，可预先对历史数据进行分析，获得历年不同类型产品的定价与成本之间的关系，建立所述定价规则，所述定价规则根据产品类型的不同而进行相应的设置，以提高定价的准确度。可预先根据各产品类型的单件成本和对应历史销量作为参考数据，预先建立映射关系表，所述映射关系表中包括产品类型与定价规则之间的对应关系，则可从所述映射关系表中查找与所述目标产品类型对应的目标定价规则。
应理解的是，所述产品类型通常根据对历史数据进行分析而进行相应的设置，可根据产品的成本和销量来设置，所述产品类型包括成本高销量低、成本低销量高、成本高销量高和成本低销量低，与各产品类型对应的定价规则分别为：单件定价为单件成本上浮第一比例（比如40%），单件定价为单件成本上浮第二比例（比如10%），单件定价为单件成本上浮第三比例（比如20%），单件定价为单件成本上浮第四比例（比如30%）。各上浮比例可根据对历史售价和历史销量进行分析，而设置合适的比例，以实现盈利期望。所述产品类型还可根据产品生产过程中的其他因素来进行设置，本实施例对此不加以限制。
应理解的是，在上述例子中，在所述目标产品类型为成本高销量低时，从所述映射关系表中查找到对应的目标定价规则为：单件定价为单件成本上浮40%，则单件所述目标产品的单件定价为所述单件成本加上所述单件成本的40%。所述定价规则也可通过机器学习的相关算法来实现，比如卷积神经网络算法，可将历年的产品的历史销量、历史单件定价、历史单件成本和对应的产品类型作为样本数据，对预设定价模型进行训练，则将所述目标产品的产品类型和目标产品的所述单件成本输入所述预设定价模型，则可输出所述目标产品的单件定价。
本实施例中，通过获取单件目标产品的成本因素，根据所述成本因素计算单件所述目标产品的单件成本，无需人工计算各类成本数据，减少人力成本；获取所述目标产品的目标产品类型，查找与所述目标产品类型对应的目标定价规则，根据所述目标定价规则和所述单件成本计算单件所述目标产品的单件定价，产品成本数据完整清晰，并根据产品类别细化定价规则，从而提高定价的准确度。
在一实施例中，所述基于数据分析的定价装置还包括：预估模块，用于将所述单件定价通过销量测算模型进行销量预估，获得所述目标产品预设未来时段的预估销量；
判断模块，用于根据所述预设未来时段的预估销量判断是否对所述单件定价进行调整。
在一实施例中，所述基于数据分析的定价装置还包括：建立模块，用于建立基础测算模型；
获取模块，用于获取所述目标产品在多个历史时段内的样本市场售价与对应的样本销量；
训练模块，用于根据所述样本市场售价与对应的所述样本销量对所述基础测算模型进行训练，获得销量测算模型。
在一实施例中，所述获取模块，还用于获取所述目标产品在所述预设未来时段内的销量期望，判断所述预设未来时段的预估销量是否满足所述销量期望；
所述基于数据分析的定价装置还包括：调整模块，用于若所述预设未来时段的预估销量不满足所述销量期望，则对所述单件定价进行调整。
在一实施例中，所述计算模块10，还用于根据所述单件成本、所述单件定价和所述预估销量计算所述目标产品在所述预设未来时段内的预估盈利；
所述获取模块，还用于获取所述目标产品在所述预设未来时段内的盈利期望，判断所述预设未来时段的预估盈利是否满足所述盈利期望；
所述调整模块，还用于若所述预设未来时段的预估销量不满足所述销量期望，或者，所述预设未来时段的预估盈利不满足所述盈利期望，则对所述单件定价进行调整。
在一实施例中，所述获取模块，还用于获取所述目标产品在最近时段的最近售价；
所述基于数据分析的定价装置还包括：比较模块，用于将所述最近售价与所述单件定价进行比较，获得差价；
所述判断模块，用于判断所述差价是否处于预设偏差范围；
告警模块，用于若所述差价不处于所述预设偏差范围，则进行告警提示。
在一实施例中，所述成本因素包括人力成本、采购成本和服务调用成本；
所述获取模块，还用于获取单件目标产品的各生产流程对应的人力成本、采购成本和服务调用成本；
所述计算模块10，还用于根据各生产流程对应的人力成本、采购成本和服务调用成本计算各生产流程对应的流程成本；
所述计算模块10，还用于将各生产流程对应的流程成本进行累加，获得单件所述目标产品的单件成本。
本申请所述基于数据分析的定价装置的其他实施例或具体实现方式可参照上述各方法实施例，此处不再赘述。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者系统不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者系统所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者系统中还存在另外的相同要素。
上述本申请实施例序号仅仅为了描述，不代表实施例的优劣。在列举了若干装置的单元权利要求中，这些装置中的若干个可以是通过同一个硬件项来具体体现。词语第一、第二、以及第三等的使用不表示任何顺序，可将这些词语解释为标识。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述 实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通 过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体 现出来，该计算机软件产品存储在一个存储介质(如只读存储器镜像（Read Only Memory image，ROM）/随机存取存储器（Random Access Memory，RAM）、磁碟、光盘)中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本申请各个实施例所述的方法。
以上仅为本申请的优选实施例，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内。

Claims (20)

  1. 一种基于数据分析的定价方法，其特征在于，所述基于数据分析的定价方法包括以下步骤：
    获取单件目标产品的成本因素，根据所述成本因素计算单件所述目标产品的单件成本；
    获取所述目标产品的目标产品类型，查找与所述目标产品类型对应的目标定价规则；
    根据所述目标定价规则和所述单件成本计算单件所述目标产品的单件定价。
  2. 如权利要求1所述的基于数据分析的定价方法，其特征在于，所述根据所述目标定价规则和所述单件成本计算单件所述目标产品的单件定价之后，所述基于数据分析的定价方法还包括：
    将所述单件定价通过销量测算模型进行销量预估，获得所述目标产品预设未来时段的预估销量；
    根据所述预设未来时段的预估销量判断是否对所述单件定价进行调整。
  3. 如权利要求2所述的基于数据分析的定价方法，其特征在于，所述将所述单件定价通过销量测算模型进行销量预估，获得所述目标产品预设未来时段的预估销量之前，所述基于数据分析的定价方法还包括：
    建立基础测算模型；
    获取所述目标产品在多个历史时段内的样本市场售价与对应的样本销量；
    根据所述样本市场售价与对应的所述样本销量对所述基础测算模型进行训练，获得销量测算模型。
  4. 如权利要求2所述的基于数据分析的定价方法，其特征在于，所述根据所述预设未来时段的预估销量判断是否对所述单件定价进行调整，包括：
    获取所述目标产品在所述预设未来时段内的销量期望，判断所述预设未来时段的预估销量是否满足所述销量期望；
    若所述预设未来时段的预估销量不满足所述销量期望，则对所述单件定价进行调整。
  5. 如权利要求4所述的基于数据分析的定价方法，其特征在于，所述若所述预设未来时段的预估销量不满足所述销量期望，则对所述单件定价进行调整之前，所述基于数据分析的定价方法还包括：
    根据所述单件成本、所述单件定价和所述预估销量计算所述目标产品在所述预设未来时段内的预估盈利；
    获取所述目标产品在所述预设未来时段内的盈利期望，判断所述预设未来时段的预估盈利是否满足所述盈利期望；
    所述若所述预设未来时段的预估销量不满足所述销量期望，则对所述单件定价进行调整，包括：
    若所述预设未来时段的预估销量不满足所述销量期望，或者，所述预设未来时段的预估盈利不满足所述盈利期望，则对所述单件定价进行调整。
  6. 如权利要求5所述的基于数据分析的定价方法，其特征在于，所述若所述预设未来时段的预估销量不满足所述销量期望，或者，所述预设未来时段的预估盈利不满足所述盈利期望，则对所述单件定价进行调整之后，所述基于数据分析的定价方法还包括：
    获取所述目标产品在最近时段的最近售价；
    将所述最近售价与所述单件定价进行比较，获得差价；
    判断所述差价是否处于预设偏差范围；
    若所述差价不处于所述预设偏差范围，则进行告警提示。
  7. 如权利要求1所述的基于数据分析的定价方法，其特征在于，所述成本因素包括人力成本、采购成本和服务调用成本；
    所述获取单件目标产品的成本因素，根据所述成本因素计算单件所述目标产品的单件成本，包括：
    获取单件目标产品的各生产流程对应的人力成本、采购成本和服务调用成本；
    根据各生产流程对应的人力成本、采购成本和服务调用成本计算各生产流程对应的流程成本；
    将各生产流程对应的流程成本进行累加，获得单件所述目标产品的单件成本。
  8. 一种基于数据分析的定价设备，其特征在于，所述基于数据分析的定价设备包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的基于数据分析的定价可读指令，所述基于数据分析的定价可读指令被所述处理器执行时实现以下步骤：
    获取单件目标产品的成本因素，根据所述成本因素计算单件所述目标产品的单件成本；
    获取所述目标产品的目标产品类型，查找与所述目标产品类型对应的目标定价规则；
    根据所述目标定价规则和所述单件成本计算单件所述目标产品的单件定价。
  9. 如权利要求8所述的基于数据分析的定价设备，其特征在于，所述基于数据分析的定价可读指令被所述处理器执行时还实现以下步骤：
    将所述单件定价通过销量测算模型进行销量预估，获得所述目标产品预设未来时段的预估销量；
    根据所述预设未来时段的预估销量判断是否对所述单件定价进行调整。
  10. 如权利要求9所述的基于数据分析的定价设备，其特征在于，基于数据分析的定价可读指令被所述处理器执行时还实现以下步骤：
    获取所述目标产品在所述预设未来时段内的销量期望，判断所述预设未来时段的预估销量是否满足所述销量期望；
    若所述预设未来时段的预估销量不满足所述销量期望，则对所述单件定价进行调整。
  11. 如权利要求10所述的基于数据分析的定价设备，其特征在于，基于数据分析的定价可读指令被所述处理器执行时还实现以下步骤：
    根据所述单件成本、所述单件定价和所述预估销量计算所述目标产品在所述预设未来时段内的预估盈利；
    获取所述目标产品在所述预设未来时段内的盈利期望，判断所述预设未来时段的预估盈利是否满足所述盈利期望；
    所述若所述预设未来时段的预估销量不满足所述销量期望，则对所述单件定价进行调整，包括：
    若所述预设未来时段的预估销量不满足所述销量期望，或者，所述预设未来时段的预估盈利不满足所述盈利期望，则对所述单件定价进行调整。
  12. 如权利要求11所述的基于数据分析的定价设备，其特征在于，所述基于数据分析的定价可读指令被所述处理器执行时还实现以下步骤：
    获取所述目标产品在最近时段的最近售价；
    将所述最近售价与所述单件定价进行比较，获得差价；
    判断所述差价是否处于预设偏差范围；
    若所述差价不处于所述预设偏差范围，则进行告警提示。
  13. 如权利要求8所述的基于数据分析的定价设备，其特征在于，所述成本因素包括人力成本、采购成本和服务调用成本；
    所述基于数据分析的定价可读指令被所述处理器执行时还实现以下步骤：
    获取单件目标产品的各生产流程对应的人力成本、采购成本和服务调用成本；
    根据各生产流程对应的人力成本、采购成本和服务调用成本计算各生产流程对应的流程成本；
    将各生产流程对应的流程成本进行累加，获得单件所述目标产品的单件成本。
  14. 一种存储介质，其特征在于，所述存储介质上存储有基于数据分析的定价可读指令，所述基于数据分析的定价可读指令被处理器执行时实现以下步骤：
    获取单件目标产品的成本因素，根据所述成本因素计算单件所述目标产品的单件成本；
    获取所述目标产品的目标产品类型，查找与所述目标产品类型对应的目标定价规则；
    根据所述目标定价规则和所述单件成本计算单件所述目标产品的单件定价。
  15. 如权利要求14所述的存储介质，其特征在于，所述基于数据分析的定价可读指令被处理器执行时还实现以下步骤：
    将所述单件定价通过销量测算模型进行销量预估，获得所述目标产品预设未来时段的预估销量；
    根据所述预设未来时段的预估销量判断是否对所述单件定价进行调整。
  16. 如权利要求15所述的存储介质，其特征在于，所述基于数据分析的定价可读指令被处理器执行时还实现以下步骤：
    获取所述目标产品在所述预设未来时段内的销量期望，判断所述预设未来时段的预估销量是否满足所述销量期望；
    若所述预设未来时段的预估销量不满足所述销量期望，则对所述单件定价进行调整。
  17. 如权利要求16所述的存储介质，其特征在于，所述基于数据分析的定价可读指令被处理器执行时还实现以下步骤：
    根据所述单件成本、所述单件定价和所述预估销量计算所述目标产品在所述预设未来时段内的预估盈利；
    获取所述目标产品在所述预设未来时段内的盈利期望，判断所述预设未来时段的预估盈利是否满足所述盈利期望；
    所述若所述预设未来时段的预估销量不满足所述销量期望，则对所述单件定价进行调整，包括：
    若所述预设未来时段的预估销量不满足所述销量期望，或者，所述预设未来时段的预估盈利不满足所述盈利期望，则对所述单件定价进行调整。
  18. 如权利要求17所述的存储介质，其特征在于，所述基于数据分析的定价可读指令被处理器执行时还实现以下步骤：
    获取所述目标产品在最近时段的最近售价；
    将所述最近售价与所述单件定价进行比较，获得差价；
    判断所述差价是否处于预设偏差范围；
    若所述差价不处于所述预设偏差范围，则进行告警提示。
  19. 如权利要求14所述的存储介质，其特征在于，所述成本因素包括人力成本、采购成本和服务调用成本；
    所述基于数据分析的定价可读指令被处理器执行时还实现以下步骤：
    获取单件目标产品的各生产流程对应的人力成本、采购成本和服务调用成本；
    根据各生产流程对应的人力成本、采购成本和服务调用成本计算各生产流程对应的流程成本；
    将各生产流程对应的流程成本进行累加，获得单件所述目标产品的单件成本。
  20. 一种基于数据分析的定价装置，其特征在于，所述基于数据分析的定价装置包括：
    计算模块，用于获取单件目标产品的成本因素，根据所述成本因素计算单件所述目标产品的单件成本；
    查找模块，用于获取所述目标产品的目标产品类型，查找与所述目标产品类型对应的目标定价规则；
    所述计算模块，还用于根据所述目标定价规则和所述单件成本计算单件所述目标产品的单件定价。
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